Current use of existing antiviral drugs for COVID-19 [56].
\r\n\tThe fifth topic is “complications and drug side effects in the treatment of pigmentation disorders”. These include drug allergies, hyper- and hypopigmentation, persistent skin depigmentation, scars, skin burns, and the potential for skin cancer and skin lymphoma. The last topic is called “coping and support along with skin pigmentation diseases”. Increase the quality of life, psychotherapy, team therapy, and asking for understanding and support from family members.
",isbn:"978-1-80356-900-0",printIsbn:"978-1-80356-899-7",pdfIsbn:"978-1-80356-901-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"2ac6c9f424eec37ed85232c2c97ef6f6",bookSignature:"Associate Prof. Shahin Aghaei",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11698.jpg",keywords:"Melanoma, Post-inflammatory Hyperpigmentation, Albinism, Piebaldism, Vitiligo, Pityriasis Alba, Laser Therapy, Cosmetic Coverage, Drug Reactions, Skin Sensitivity, Quality of Life, Team Therapy",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 5th 2022",dateEndSecondStepPublish:"June 14th 2022",dateEndThirdStepPublish:"August 13th 2022",dateEndFourthStepPublish:"November 1st 2022",dateEndFifthStepPublish:"December 31st 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Associate Professor of Clinical Dermatology and Dermatologic Surgery, lead author or contributor to nearly 60 articles published in international dermatology journals, and editor-in-chief of the Journal of Surgical Dermatology in Singapore.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"64024",title:"Associate Prof.",name:"Shahin",middleName:null,surname:"Aghaei",slug:"shahin-aghaei",fullName:"Shahin Aghaei",profilePictureURL:"https://mts.intechopen.com/storage/users/64024/images/system/64024.jpg",biography:"Shahin Aghaei, MD, graduated from Shiraz University of Medical Sciences, Iran, in 2004. He was awarded a fellowship from the International Society of Dermatopathology (ISD) from Charles University, Czech Republic, in 2008 and a fellowship in Dermatologic Surgery from the Medical University of Graz, Austria, in 2010. He is currently editor in chief of the Journal of Surgical Dermatology in Singapore and Associate Professor of Dermatology and Dermatologic Surgery at Iran University of Medical Sciences, School of Medicine. He is also a member of the American Academy of Dermatology, European Academy of Dermatology and Venereology, American Society for Laser Medicine and Surgery, International Society of Dermatology, International Hyperhidrosis Society, and Iranian Society of Dermatology.",institutionString:"Iran University of Medical Sciences",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Iran University of Medical Sciences",institutionURL:null,country:{name:"Iran"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"225753",firstName:"Marina",lastName:"Dusevic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/225753/images/7224_n.png",email:"marina.d@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Nursing is a very busy job, and up to now, much attention has been paid to problems arising from the physical and psychological effects of busyness on nurses, and to consequent problems relating to patient safety and quality of care. Because a large part of any estimates of whether a nurse is busy or not depends on subjective judgment, however, it is difficult both to define busyness and to formulate methods of measuring and assessing busyness. It is also true that merely demonstrating busyness will not have a significant effect in solving problems. Consequently, ways of estimating nursing care quantity itself have been sought. If it were possible to make quantitative estimates of necessary nursing care, we could expect to improve patient safety and achieve a better quality of patient care through such elements of nursing care management as appropriate allocation of nursing staff and effective distribution of tasks.
\n\t\t\tIn studies of the measurement of work quantity for the purposes of nursing care management, a typical approach has been to conduct work quantity surveys based on time study (Meyers & Stewart, 2002). Various techniques for calculating work quantity have been used, some of which focus on patient condition (Fagerstom & Rainio, 1999) and others on patient outcomes (Hall et al., 2004), but methods based on time study have the particular advantage that they make it possible to obtain clear quantitative results in the form of work times. Time studies quantitatively examine how much time is spent on what sort of work activities, and yield highly reliable results concerning the amounts of work time expended. They are therefore widely used not only in fields related to nursing but also in clinical locations where doctors and other co-medicals work (Vinson et al., 1996; Langlois et al., 1999; Magnusson et al., 1998). They are carried out in various forms (Thomas et al., 2000; Caughey & Chang, 1998) and in the course of this study also we have used time study to elucidate the actual state of ward nursing care from a variety of perspectives.
\n\t\t\tIn most such studies, however, the analyses of the time study data do not go beyond factual descriptions of the actual state of affairs. So far, almost no methodology has been established for the purpose of linking the data to the calculation of quantities of nursing care required or to nursing care management. The following points may be cited as contributing factors:
\n\t\t\tIt is difficult to carry out long-term time studies
It is difficult to obtain an overall picture of tasks in hospital wards
There is no place for trial and error in the actual execution of the plan devised.
With regard to point 1)→1, for example, researcher-administered time studies (see 2.2.1.1. below) produce what are regarded as the most reliable data, but the outlay in terms of staffing and financial costs, from the pre-survey preparation stage to the results analysis, as well as the high burden on the clinical location concerned, make it difficult to carry out such studies with any great frequency, and the survey periods must also be kept short.
\n\t\t\tWith regard to point 2)→2, where nursing care management matters such as appropriate staff allocation are concerned, inconsistencies in shift conditions will arise (there will be days when shifts have crowded schedules and days when they do not), so it will be necessary to obtain an over-all picture of tasks on the ward based on the evidence of frequent or long-term surveys. For the reason given above, however, time studies are restricted, in almost all cases, to short survey periods. The results obtained therefore provide an interpretation only of the period when the survey was conducted and are confined to the realm of factual description.
\n\t\t\tWith regard to point 3)→3, having obtained an over-all picture of the tasks on the ward, the next step in nursing care management is to formulate a concrete plan that takes into consideration changes in working hours when there is a shortage of nursing staff or when there is an increase in the number of patients admitted. In practice, however, it is difficult to carry out the formulated plan in the actual ward environment because such plans are accompanied by risks and involve many ethical problems. This means that an investigation of a new method of work management is in fact impossible. This has been a major barrier.
\n\t\t\tConsidering the above adverse factors, it would be effective, for the purposes of time-study based management of ward tasks, to establish a methodology of the following kind:
\n\t\t\tEstimates of ward task times based on time study data
Creation of a computer-based virtual ward environment using the estimated values
Test experiment on a plan for work management using the virtual ward environment
The goal of this study is therefore the formulation of a methodology, based on data from a short-term time study, for estimating ward task times and for creating a virtual ward environment relating to job times.
\n\t\tThe procedure followed was:
\n\t\t\tFraming a plan for the creation of a virtual environment
Computation of basic data required for a virtual environment based on short-term research and long-term cumulative information
Construction of a procedure for the creation of a virtual ward environment
Trial experiment using the virtual ward environment
First of all, in order to establish a way of thinking about how to simulate an actual ward environment, we drew up a diagram showing what kinds of factors would have a bearing on the time devoted to nursing tasks (Fig. 1).
\n\t\t\t\tConstituent factors in nursing tasks.
We assumed that the tasks carried out by a given nurse during one shift would comprise (i) tasks relating to patients for whom the nurse is responsible, (ii) tasks relating to other patients, (iii) other tasks, such as those relating to the running of the ward, and (iv) rest time. Task times devoted to these four items would be interdependent and would vary, but we thought that ‘task times devoted to patients for whom the nurse is responsible’ would have particularly high priority, and would affect ‘time devoted to other patients,’ ‘time devoted to other tasks’ and ‘rest time.’ We also assumed, first, that the number of patients for whom a given nurse is responsible, and the severity of their conditions, would affect ‘task times devoted to patients for whom the nurse is responsible’; second, that ‘number of patients’ in the nurse’s charge and ‘severity of their conditions’ would be affected by ‘number of patients by severity of condition’ who were on the ward at a given time and ‘number of nurses’ actually available to carry out patient care; and third, that ‘number of patients by intensity of nursing care’ would be affected by ‘patient outcomes’ and ‘number of patients admitted.
\n\t\t\t\t\n\t\t\tA continuous 24-hour researcher-administered time study was conducted over a total of fifteen days during 1999 and 2000 in a gastrointestinal surgical ward in a university hospital. Of the various forms of time study techniques we adopted the researcher-administered method for the present study because on the basis of the characteristics of the ward studied, we judged that there were limits to the extent to which nurses on duty would themselves be able to keep a record of the content of the tasks in the intervals between the tasks they performed. The survey was conducted in relation to all three work shifts: ‘night shift,’ ‘day shift’ and ‘evening shift.’ The total number of nurses observed was 69 (Table 1).
\n\t\t\t\t\t\tTime study period and subjects.
The ward studied was a fifty-bed ward with a staff of 23 nurses, including the head nurse. The average number of staff actually on duty on weekdays was 8.6. The ward’s nursing system combined a three-module organization, under which ward nursing staff were divided into three groups, A, B and C, and a ‘primary nursing’ model, under which the same nurse was responsible for a given patient throughout, from admission to discharge (Fig. 2). When the primary nurse was not on duty, a nurse from the same group took responsibility for the patient. Information was gathered during the period of the survey on both the nurses being surveyed and the patients for whom they were responsible.
\n\t\t\t\t\t\t\n\t\t\t\t\t\tAfter completion of the time study, the task content recorded was coded in accordance with a specially created system of task classification and entered into a database. The nursing task classification was based on the Public Health Nurse, Midwife and Nurse Law [1948]. The four principal categories were ‘clinical nursing,’ ’consulting support nursing,’ ‘other nursing,’ and ‘non-nursing tasks.’ At the most detailed level, there were 92 headings altogether. The overall number of individual task action-units recorded was 46,775.
\n\t\t\t\t\tModule system and primary nursing model.
Patient condition information for each patient on the ward was collected and recorded daily throughout the fifteen days of the time study period. ‘Patient condition information’ means information that indicates a hospital patient’s condition, such as how many times in the course of the day vital signs are checked, whether an artificial respirator is in use, or whether there is any fever or bleeding. About 70 items are covered. Information collected during the day shift, at about 10 a.m., served as the base, and was incrementally updated for any patient who underwent an operation or other invasive procedure during the day shift and whose nursing intensity changed. The information recorded was entered into a database. Ultimately, the overall number of patient-shift units recorded was 2,015.
\n\t\t\t\t\t\tNursing intensity, assessed daily by an experienced nurse, was included in patient condition information. Nursing intensity is a method of classifying patient severity from two points of view – ‘level of observation’ and ‘freedom of life’ – that was proposed in 1984 in a report by the Study Group on Nursing Systems set up by the Ministry of Health, Labor and Welfare (formerly the Ministry of Health and Welfare) (Table 2). In the process of the present study, it was suggested that patient severity observations collected on the ward being studied could be regarded as ‘level of observation’ for the purpose of assessing nursing intensity and these were used in carrying out our analysis.
\n\t\t\t\t\t\t\n\t\t\t\t\t\tBy integrating the time study database and the patient condition information database on the basis of ‘day of survey,’ ‘shift,’ ‘nurse ID’ and ‘patient ID,’ we created a data set that made it possible to tell which nurse had spent how much time performing what tasks for patients in what condition. We assumed that among the task actions, subject patients would be available for nursing task classifications from 10101 to 301T1.
\n\t\t\t\t\t\tThe names of nurses and patients included in the survey records, as well as any other items of information from which it would be possible to identify individuals, were all coded and only if this used for analysis after the information had been made secure.
\n\t\t\t\t\tNursing intensity.
In order to understand over-all patterns of change in nursing intensity for the patients on the ward being studied, we obtained information from the HIS (Hospital Information System) under the headings ‘date of admission,’ ‘date of discharge,’ ‘date of update of nursing intensity,’ and ‘nursing intensity’ covering the period from January 1, 2000, to December 31, 2000. This information was obtained in addition to the patient condition information gathered during the time study period.
\n\t\t\t\t\t\t‘Pattern of change in nursing intensity’ shows the outcome for a given patient. We defined it in terms of the number of days for which the patient was hospitalized and any changes in nursing intensity during that period. Pattern of change in nursing intensity varies according to individual factors, such as the disease from which the patient is suffering, surgical procedures undergone, and medical treatment. For example, patient J is in hospital for 3 days. On the first day nursing intensity is B, on the second day C, and again on the third day C. The pattern of change in nursing intensity for this patient is ‘BCC.’ Patient S is in hospital for four days. On the first two days nursing intensity is A, and on the remaining two days B. The pattern of change of nursing intensity for this patient is ‘AABB.’ It is no exaggeration to say that, except in the cases of patients where there is no clinical pathway variance, each individual patient exhibits a unique pattern of change in nursing intensity during the period of hospitalization.
\n\t\t\t\t\tBasic data required for the creation of a virtual environment was calculated from a short-term survey and a long-term cumulative information survey.
\n\t\t\t\tOn the basis of time study data and patient condition information, we found recorded statistical values relating to the number of patients admitted to the ward studied. The results were as follows. Average daily number of patients on the ward was 44.2, and the standard deviation (SD) was 2.3. The greatest number of patients on the ward at one time was 49, the smallest 42. The average number of patients for whom one nurse was responsible was 4.9. The largest number was 7, the smallest 3. A total of 281 patterns of change in nursing intensity was abstracted from the HIS information in relation to admissions to the ward in question in the year 2000.
\n\t\t\t\tFrom the data set obtained by integrating time study data and patient condition information, we calculated, for individual nurses on the day shift, time spent on ‘patients for whom the nurse is responsible,’ time spent on ‘other patients,’ time spent on ‘other duties’ and ‘rest time.’ Results showed that the greatest amount of task time was spent on ‘patients for whom the nurse is responsible.’ Next came ‘other patients’ and ‘other duties,’ almost the same amount of time being spent on each. Average rest time was less than the 60-minute rest period stipulated by law (Table 3).
\n\t\t\t\t\tRecorded statistical quantities for task times by purpose.
Correlations between task times by purpose.
\n\t\t\t\t\t\tFig. 3 shows that there was a strong negative correlation between time spent on ‘tasks relating to patients for whom the nurse is responsible’ and time spent on ‘tasks relating to other patients’ and ‘other tasks,’ and that the correlation of ‘rest time’ with other task items was low. It appears at first glance that the correlation coefficient between ‘tasks relating to other patients’ and ‘other tasks’ is high at 0.727, but the partial correlation coefficient of the two is 0.019 and almost no direct correlation was observed. We were therefore able to judge that this was a spurious correlation influenced by ‘tasks relating to patients for whom the nurse is responsible.’ In other words, what this shows is that the relationship between the two is not such that when one increases the other decreases, but such that when time spent on ‘patients for whom the nurse is responsible’ increases, the two decrease together.
\n\t\t\t\t\t\n\t\t\t\tIn accordance with the plan formulated under 2.1, a virtual ward environment was created according to the following procedure:
\n\t\t\t\tConstruction of a model for estimation of task time devoted to patients for whom the nurse is responsible.
Construct a model to estimate the kinds of factors that influence care time devoted to a given patient.
Calculation of number of patients for whom one nurse is responsible and of care time
Determine which patients a given nurse is responsible for and find the total task time spent by that nurse on patients for whom she is responsible.
Estimation of task time by purpose
On the basis of the total task time devoted to ‘patients for whom the nurse is responsible’ calculated under 2., estimate time spent on ‘patients for whom the nurse is not responsible,’ ‘other tasks,’ and ‘rest time,’ all of which are correlated.
In the virtual environment we had created, we carried out the following test experiments and investigated the difference from actual data.
\n\t\t\t\tEstimation of number of patients on the ward by nursing intensity
Estimation of task times by purpose
Effect of increase or decrease in number of nurses on each task time
Our ‘model for estimation of task time devoted to patients for whom nurse is responsible’ is a regression analysis model using ‘care time devoted to a given patient for whom a given nurse is responsible’ as the dependent variable. When formulating a plan for the creation of a virtual environment, we entered the factor ‘nursing intensity’ as an independent variable on the assumption that task time devoted to patients for whom a nurse is responsible would be affected by patient severity. Bearing in mind that changes in shift time and in the number of nurses actually on duty on a shift would also have a great effect on care time devoted to patients, we included the factor ‘shift’ as well.
\n\t\t\t\tThe subject of analysis consisted of records, extracted from the data set created by integrating time study data and patient condition information, which revealed responsibility relationships between nurses and patients. The data extracted related to a total of 425 patients (a total of 57 nurses on 57 shifts).
\n\t\t\t\tA multiple regression analysis model is generally formulated as
\n\t\t\t\t\twhere
Because the time study survey periods were continuous, the same patients were included in different shifts in the data set relating to 425 patients subject to analysis. This means that there were elements that affected the care time, which is a variable dependent on patient identity (i.e., there were internal correlations in the data), although it was difficult to treat these elements as regular fixed effects, as in the case of the patient’s bodily strength or personality.
\n\t\t\t\t\tIn order to estimate parameters having variable effects that explained these internal correlations, we constructed, for the purposes of this study, a model for the estimation of task time devoted to patients for whom the nurse was responsible using Multilevel Analysis, introducing randomness for each ‘patient’ with respect to the intercept.
\n\t\t\t\tRather than a model in which the actual care times are regressed in their original dimensions, we judged that the optimal model was one in which logistic conversion was performed with regard to the dependent variable.
\n\t\t\t\t\tOur reasons were as follows.
\n\t\t\t\t\tBecause it is a mathematical model for explaining task times, when the estimated expected value
In the later creation of the virtual ward environment, we used this model for estimation of task time devoted to ‘patients for whom the nurse is responsible,’ which used normal random numbers (see 3.2.4. below). Because the distribution of the raw data values (here, the distribution of the raw care times) was reproduced using normal random numbers, it was necessary at the point where random number values were generated to convert the raw data so that it showed a data distribution close to a normal distribution (see 3.3.1. for details) (Fig. 4).
\n\t\t\t\t\tConversions for the purpose of recreating actuality using normal random numbers.
We found parameters for a logistic conversion that would give real upper and lower limits by minimizing AIC (Akaike’s Information Criterion) after performing logistic conversion. The data set used for this analysis included some extremely small time values, so we fixed the lower limit at zero. AIC for the case where the upper limit was \n\t\t\t\t\t\t\t
Here, \n\t\t\t\t\t\t\t
This means that the smaller the AIC value the closer to a normal distribution; we used MLwiN ver. 1.1 for model analysis.
\n\t\t\t\t\tThe constant \n\t\t\t\t\t\t\t
The result was that a data conversion close to normal distribution became possible, as seen in Fig. 5.
\n\t\t\t\t\tWith regard to the method of entering independent variables, we found as a result of repeated investigation that a model using 3x3 items in which ‘shift’ and ‘nursing intensity’ were confounded, as in ‘night shift, nursing intensity A,’ was optimal (equation 0.4→4). This numerical formula is a model for the estimation of nursing time devoted to one patient by the nurse responsible. Here, \n\t\t\t\t\t\t\t
Data distributions after conversions.
According to the results of estimation using this model, given a patient with ‘day shift, nursing intensity A,’ adding the parameter value 2.695 of ‘day shift, nursing intensity A’ to the intercept -3.834, then adding variability due to the individual patient and margin of error, gives the care time for this patient. Returning to the time dimension by using the reverse logistic conversion shows it to be about 72.8 minutes. In the same way, in the case of ‘day shift, nursing intensity B’ the time is about 39.4 minutes, and in the case of ‘day shift, nursing intensity C’ about 20 minutes.
\n\t\t\t\tNext, we determined the number of patients for whom one nurse was responsible and estimated the total time spent on those patients. At this point we embarked on the construction of an algorithm using a Monte Carlo Simulation.
\n\t\t\t\tA Monte Carlo Simulation is a method of obtaining approximate solutions to problems when simulating the processes of chance phenomena by carrying out numerical value calculations using random numbers. In this study, we used normal random numbers and created algorithms for them using MATLABR2012a. This simulation was conducted with respect to the day shift.
\n\t\t\t\tWe went through the process of recreating the actual bed occupancy status, which changes daily as a result of the admission and discharge of patients.
\n\t\t\t\t\tSince the ward studied was a 50-bed ward, we created a matrix for use in the simulation (hereafter ‘bed matrix’) consisting of vertical columns of 50 cells representing the beds, and on the horizontal time axis (representing days elapsed) we used rows containing enough cells to cover a long time period (for reasons explained later, we used rows of 1,000 cells in this study). Each cell in the bed matrix represents one bed-day.
\n\t\t\t\tBy randomly determining the daily number of patients on the ward from the average number of patients on the ward already calculated and its standard deviation, we recreated the changes in the actual number of patients on the ward. In the case of Fig. 6, for example, the number of patients on the ward over a seven-day period is randomly divided up and shaded cells show patients on the ward.
\n\t\t\t\tHaving determined the number of patients on the ward each day, we simulated the state of affairs relating to patients on the ward whose condition underwent change by inserting the patterns of change in nursing intensity by number of patients on the ward. For this purpose we apportioned patterns randomly chosen from among the total of 281 patterns of change in nursing intensity found under 2.3.1. At this time, the patients on the ward were present for the number of days shown by the patterns of change. The patterns were inserted in column direction.
\n\t\t\t\t\tDetermination of number of patients on ward.
Layout of patterns of change in nursing intensity.
For example, in Fig. 7, pattern No. 5 was chosen and ‘CCCBBBA’ was inserted horizontally, one letter per cell, starting in bed number 1 on day number 1. At the beginning of the simulation there were no patients at all, and patterns of nursing intensity were allocated for the number of allocated patients on the ward. The pattern of nursing intensity differed depending on the number of days for which a patient was on the ward, so as days elapsed, patients began to be discharged. In cases where the total number of patients given by change in patterns of nursing intensity for a given day fell below the specified minimum number of patients for the ward, we apportioned new patients randomly from the patterns of change in nursing intensity. At this point, we had reached the stage where the set number of days per patient and patients on the ward were shown by nursing intensity in the bed matrix.
\n\t\t\t\t\n\t\t\t\tOn the basis of this nursing intensity, we calculated the care time believed necessary using the previously constructed model for estimating task time devoted to patients for whom the nurse is responsible. From equation (4) we saw that care time is not simply a function of nursing intensity but is the sum of (i) a quantity depending on nursing intensity, (ii) variability depending on the individual patient, and (iii) other chance variability. The average of each of the latter two items was 0, and they were the parts that varied according to a normal distribution with certain variances. For the calculation of care time, first we took as the basis an estimated value for care time corresponding to nursing intensity, then generated a normal random number with a certain estimated variance and an average of 0 as the common value for all the days the patient spent in hospital and added that number. We then added a normal random number with another estimated variance and an average of 0 as chance variability.
\n\t\t\t\t\tIn Fig. 8, the right-hand side is an example of the bed matrix when there are patients on the ward. The figure shows the method of calculating care time for a given patient
On the basis of the statistical values already found for numbers of patients for whom nurses are responsible, we simulated the allocation of responsibility for these ward patients to individual nurses. The following two points suggested themselves as factors in determining the patients for whom nurses are responsible in the actual ward environment:
\n\t\t\t\t\tNumber of patients for whom the nurse is responsible
Severity of the conditions of those patients
Method of calculating care time for each patient.
Task times devoted to patients for whom nurse is responsible.
When the number of patients for whom a given nurse is responsible is large, or when they include patients whose condition is very severe, no further patients can be assigned to that nurse, and the quantity of tasks is distributed so that, for example, new patients are apportioned among other nurses with a relatively small number of patients in their charge or nurses whose patients have relatively mild conditions. Consequently, in the virtual environment, first, at the point where patients were randomly admitted to the ward, we totaled the number of patients for whom each nurse was responsible and specified that if the number was 0, priority would be given to the assignment of patients to that nurse. We also controlled assignment of patients so that, as far as possible, each nurse was responsible for no fewer than 4 and no more than 7 patients. In addition, we carried out weighting such that extra patient responsibility was first given to nurses who were devoting a relatively small amount of task time to the patients for whom they had already been assigned responsibility.
\n\t\t\t\t\tIn this way, we determined which patients a given nurse was responsible for. The shaded cells in the example shown in Fig. 9 show the task times devoted by one nurse to the patients in her charge. The total, 165 minutes, is the ‘task time devoted to patients’ by that nurse. At this stage, we have created a virtual simulation of the approximate amount of time a nurse devotes in reality to all the patients for whom she is responsible.
\n\t\t\t\t\t\n\t\t\t\tNext, on the basis of ‘care time devoted to all patients for whom the nurse is responsible’ by a single nurse found in 3.2.5, we randomly generated ‘task time devoted to other patients,’ ‘time devoted to other tasks’ and ‘rest time’ for the day shift and proceeded to simulate actual task times by purpose. As mentioned in 3.1.2, it is necessary to bear the following points in mind when carrying out these simulations.
\n\t\t\t\tIt is a prerequisite that the random numbers generated should follow a normal distribution
In this study we have employed a Monte Carlo simulation using normal random numbers, and it is a prerequisite that the distribution for the generation of random numbers should be a normal distribution.
\n\t\t\t\tNegative values are to be avoided among randomly generated values
Since the units in the results obtained are times, task times that have negative values are not realistic and will prevent the simulation results from conforming to reality.
\n\t\t\t\tCovariance among the four task times by purpose is to be maintained
The generation of random values in which covariance among the variates is not taken into consideration makes it impossible to recreate the characteristic feature that they vary in relation to one another.
\n\t\t\t\tBearing these points in mind, we carried out the following operations.
\n\t\t\t\tAs Fig. 3 shows, a certain amount of skew with regard to all four of the items ‘tasks performed for patients for whom the nurse is responsible,’ ‘tasks performed for other patients,’ ‘other tasks,’ and ‘rest time’ and a degree of unevenness (probably attributable to sampling limitations) were observed. It is not possible to recreate the distribution exhibited by these original data even if one generates normal random numbers using only the average values and variance of the original data. In order to recreate the distribution of the original data using normal random numbers, it is necessary to think of a conversion in which a distribution based on the conversion of the original is as close as possible to a normal distribution and, after having generated random numbers that have the post-conversion average and variance, it is then necessary to perform reverse conversion to obtain a value. At the same time, it is necessary to ensure that the randomly created values do not have a negative value. For these reasons, we decided in this study to perform the following logistic conversion with respect to task times by purpose
We used AIC (Akaike’s Information Criteria) to evaluate whether distribution of the post-conversion values was close to a normal distribution. We looked for an upper limit value \n\t\t\t\t\t\t\t
As in formula (2), \n\t\t\t\t\t\t\t
Conversion data plots.
The upper limit \n\t\t\t\t\t\t\t
It will be seen from scrutiny of the post-conversion data plots (Fig. 10) that in each of the variables, data distribution is closer to a normal distribution than before conversion (Fig. 3).
\n\t\t\t\t\tUpper and lower limits of conversion functions.
Using the above conversion data, we generated random numbers that maintained covariance among the 4 variables (the third point to be borne in mind). Here,
If the equation
\n\t\t\t\t\tis used when generating
and the unknown quantities \n\t\t\t\t\t\t\t
If coefficient values with regard to
In this way, it is possible to maintain covariance between (
and generate 3-dimensional normal random numbers with this covariance.
\n\t\t\t\t\tSubstituting the various coefficients calculated as a result of the above into formula (10) gives the result
\n\t\t\t\t\tCovariance structure of \n\t\t\t\t\t\t\t
Covariance structure of residuals.
Using residuals with these regression coefficients and covariances, we generated random numbers. Fig. 11 shows on the same plots the values obtained after logistic conversion of the original data (red dots) and the random numbers generated (blue dots). It can be seen that they have almost identical distributions. This shows that it is possible to generate random numbers that maintain the correlation structure between the variates (covariance).
\n\t\t\t\t\tRandom number values and logistic conversion values.
Time dimensions and random number values.
\n\t\t\t\t\t\tFig. 12 shows together the original data (red dots) and the values obtained after performing reverse conversion on the logistic conversions of the random numbers generated (blue dots). It can be seen that the distribution of the original data relating to time dimensions has been almost exactly recreated. The black dots represent what we judged to be anomalies in the original data. These data relate to anomalous tasks, such as receiving training or attending meetings, that were performed in the afternoons, with only the mornings being spent on ward duties. We therefore decided to exclude them from the analysis.
\n\t\t\t\t\tFollowing the above procedure, we randomly generated individual task times while maintaining associations between ‘tasks performed for patients for whom the nurse is responsible,’ ‘tasks performed for other patients,’ ‘other tasks,’ and ‘rest.’ At this point, we had completed construction of a virtual ward environment for the purpose of simulating actual ‘time devoted to tasks performed for patients for whom the nurse is responsible,’ ‘time devoted to tasks performed for other patients,’ ‘time devoted to other tasks,’ and ‘rest time’ for one nurse, and for conducting test experiments.
\n\t\t\t\t\t\n\t\t\t\tUsing the virtual ward environment we had constructed, we simulated long-term ward task times. On the first day of simulation the situation was that all patients were admitted to the ward at once, so none would be discharged for some time. As days passed in the simulation, gradually some patients began to be discharged. We disregarded simulation results obtained up to the point where it seemed that a stable situation had eventually been reached, with a balance between admissions and discharges. From that point, we specified 1,000 days of simulation. For the purposes of the simulation, we also specified that from the point of view of the work system, every day was a weekday.
\n\t\t\t\tThe result of the simulation was as follows: the total number of patients was 44,846; totals by nursing intensity were: A=11,193 (25.0%), B=26,469 (59.0%), and C=7,184 (16.0%). The largest cohort of patients comprised those subject to nursing intensity B, the next largest those subject to nursing intensity A, and the smallest those subject to nursing intensity C. Unsurprisingly, this trend reflected almost exactly the trend in the 281 nursing intensity patterns we had established, where the frequency of nursing intensity A was 3,287 (24.2%), that of B 8,010 (59.1%), and that of C 2,265 (16.7%).
\n\t\t\t\t\t\n\t\t\t\t\t\tFig. 13 is a graph showing changes in number of patients by nursing intensity. The vertical axis shows number of patients and the horizontal axis days elapsed. The upper panel is a graph showing nursing intensity. The daily number of patients at nursing intensity C, the lowest level of severity, is smallest, and the number at B, the intermediate level, is highest. The lower panel shows cumulative totals by nursing intensity. It will be seen, first, that almost all patients on the ward are accounted for by nursing intensity A and B, and, second, that once the number of patients has risen, it remains for some time at the higher level.
\n\t\t\t\tThe quantity of care time per patient necessary when patients for whom the nurse was responsible were at nursing intensity A was on average 93.5 minutes. For patients on nursing intensity B the average time was 57.1 minutes, and for those on nursing intensity C the average time was 31.6 minutes.
\n\t\t\t\tThe upper panel in Fig. 14 shows changes in task times by purpose for the ward as a whole. The horizontal axis shows number of days elapsed and the vertical axis shows times. ‘Task times devoted to patients for whom the nurse is responsible’ show large fluctuations, while ‘rest times’ display nowhere near as large a range of variation. Further, it can be seen that when ‘task times devoted to patients for whom the nurse is responsible’ decrease, ‘task times devoted to other patients’ and ‘times devoted to other tasks’ increase; and when ‘task times devoted to patients for whom the nurse is responsible’ increase, ‘task times devoted to
\n\t\t\t\t\tChanges in patient numbers by nursing intensity.
other patients’ and ‘times devoted to other tasks’ decrease.
\n\t\t\t\t\tThe lower panel in Fig. 14 shows cumulative totals of task times by purpose. In spite of the fact that ‘task times devoted to patients for whom the nurse is responsible’ and ‘task times devoted to other patients’ fluctuate widely, total task times as a whole are kept to an almost uniform level.
\n\t\t\t\tWe simulated changes in task times by purpose for an individual nurse when the number of nurses on a single day shift was increased gradually from 8 to 15. Assuming that the number of nurses actually on duty on a single day shift is 8, a 1,000 day simulation is equivalent to 8,000 nurse-shifts; assuming that the number is 9, a 1,000 day simulation is equivalent to 9,000 nurse-shifts, and so on. Figures 15-18 show the distribution of the number of patients for whom nurses are responsible when the total task times for the entire ward are shared by 8 to 15 nurses, and the frequency distribution of task times per nurse in each of those cases (Horizontal axis: minutes. Vertical axis: number of persons).
\n\t\t\t\t\tLet us look first at the situation where the number of nurses specified is lowest. Almost every nurse is responsible for between 5 and 7 patients. The largest number of nurses has overall task times of between 560 minutes (9.3 hours) and 570 minutes (9.5 hours). The average time for the whole group is 530 minutes. It can be seen that there are some nurses whose overall task time exceeds 10 hours (over 600 minutes). Next, with regard to ‘patients
\n\t\t\t\t\tChanges in task times by purpose.
for whom the nurse is responsible,’ the largest number of nurses have times corresponding to the median of 312 minutes, or about 5 hours. Time spent on ‘tasks performed for other patients’ is about 30 minutes, and the number of nurses who spend about 60 minutes on ‘other tasks’ stands out. With regard to ‘rest time,’ it will be seen that almost no nurses were able to take the 60 minutes of rest prescribed by law.
\n\t\t\t\t\tAs the number of nurses on duty progressively increases, the number of patients for whom each nurse is responsible gradually decreases, until a situation is reached in which some nurses are responsible for 0 patients, and where the ward’s nurse requirement can be said to be satisfied.
\n\t\t\t\t\tTasks times when number of nurses is 8.
Tasks times when number of nurses is 11.
In addition, while time spent on ‘tasks performed for patients for whom the nurse is responsible’ decreases along with this variation in the number of patients for whom a nurse is responsible, more nurses are able to increase the time they spend on ‘task times devoted to other patients’ and ‘time spent on other tasks,’ while the number able to take a rest period approaching 60 minutes will be seen to have increased.
\n\t\t\t\t\t\n\t\t\t\t\tTasks times when number of nurses is 13.
Tasks times when number of nurses is 15.
We carried out an evaluation of whether the simulation algorithms we constructed might be incompatible with reality.
\n\t\t\tWe were able to judge whether the cohort of patients on the ward reflected the real-world situation, in which patients still requiring assistance are in a majority. The reasons are as follows.
\n\t\t\t\tThe ward studied was a surgical ward, so for almost all patients the period immediately following surgery or related tests was when their condition was at its severest. After a few days they would emerge from the acute stage (when nursing intensity was A) and enter a period during which they received intravenous drugs or other active treatments as their wounds followed the healing process (the period of nursing intensity B). This follow-up period was the longest. As soon as the outlook was such that the patient could be sent home or return to work (the period of nursing intensity C), discharge followed within 1 to 2 days. In our simulation results also, almost all the patients on the ward were at nursing intensity A or B.
\n\t\t\t\tIn addition, it seemed that not only nurses but also all medical professionals agreed that they had a sense that on occasion, after the number of patients in a severe condition increased, that state of affairs would continue for some time, and then the patients would all recover at once.
\n\t\t\tIt is, of course, entirely natural that care time required increases with severity of nursing intensity. The results we obtained conformed to this observation and thus seemed to reflect reality.
\n\t\t\tThere are some tasks that need to be performed when spare time becomes available, but, because there is a fixed limit on task time, tasks are in fact omitted. Our simulation appeared to reflect this reality. The reasons are as follows.
\n\t\t\t\tAn increase in the amount of care time devoted to the patients for whom a nurse is responsible means that the quantity of care, in the form of treatment and observation of the patient, is greater. But on closer examination it appears that the number of drugs prescribed increases, many tests have to be carried out, extra treatments and prescriptions are added, tasks such as changing dressings increase in number, or the procedures involved become more complicated. This affects the usage quantities of documents, medicines, and other materials managed by the ward. The result is that management task time also expands. In theory, therefore, it seems that if ‘task time devoted to patients for whom the nurse is responsible’ increases, ‘time devoted to other tasks’ that have no direct connection with patients should also increase, and as a consequence overall task time (shift time) ought to increase. The results of our simulation show, however, that when ‘task time devoted to patients for whom the nurse is responsible’ increases ‘task time devoted to other tasks’ is reduced and overall task time does not increase very much. No extension of time devoted to ‘other tasks’ or of overall task time was observed.
\n\t\t\tThe 9-hour work shift prescribed by law comprises 8 hours of working time and 1 hour of rest time. There was no marked deviation from this time in our simulation results.
\n\t\t\tWhen task time available for completion of ‘tasks performed for patients for whom the nurse is responsible’ is insufficient, the nurse is unable to carry out ‘tasks for other patients’ and ‘other tasks.’ But when the number of nurses increases and adequate care time can be devoted to patients for whom a nurse is responsible, time can be found to spend on aspects of nursing care such as tasks performed for ‘other patients’ and ‘other tasks’ that have had to be neglected before the increase in nursing staff. Our simulation appeared to reflect this reality. This is also a reflection of the fact that, as noted under 4.3, there are tasks that are omitted because working time is limited. Specific examples are given below. There are occasions when a nurse is so busy providing care to patients for whom she is responsible that she is unable to respond to a call from another patient, even one she is responsible for. On occasion, under these conditions, if a nurse passing along a corridor discovers a patient whose intravenous drip is leaking, if that patient is not one for whom she is responsible the series of tasks involved in dealing with a leaking intravenous drip assume a low priority for her and she must call the nurse who is responsible for the patient in question. If the nurses continue to be fully occupied with patient care, they are unable to tidy up the ward or put things in order. As a result, the ward declines into a state where in an emergency staff must look for a wheelchair that is not in its proper place, or they trip over or bump into things that are in places that should be empty, or they find that when they need to fix a drip in place quickly the tape they need has run out, or that there are not enough specimen containers when specimens are needed for urgent tests, or when pressure of work slackens a little and they set out to update their records they find that the necessary forms have run out. However, when the patients on the ward are in a relatively settled state and care time requirements are met, if a nurse discovers a patient with a leaking intravenous drip in a corridor she will undertake the series of measures necessary to replace it, even if the patient is not one for whom she is responsible, and will fully carry out administrative and management tasks such as tidying the ward and putting things in order.
\n\t\t\t\tWe observed in our simulation results also that when the number of nurses on a shift was increased, there was a decrease in the number of nurses who spent a very large amount of time on ‘tasks performed for patients for whom the nurse is responsible’ (unbalanced workloads were resolved) and at the same time there was an increase in the number of nurses who spent a large amount of time on ‘tasks performed for other patients’ and ‘other tasks.’
\n\t\t\t\tWe concluded from the above that the simulation algorithms constructed in this study conformed to reality.
\n\t\t\tWe created a formula for the nursing times provided on the basis of time study data obtained through a short-term survey and patient condition information, and quantified factors governing tasks.
\n\t\t\t2) We constructed simulation algorithms combining the results under 1) with information accumulated over an extended period on the length of hospitalization and patient condition (nursing intensity).
\n\t\tWe believe that there is scope for further investigation of the points below to enable the algorithms constructed for this study to reflect reality more accurately.
\n\t\t\tGiven that it is based on subjective observation, the concept of nursing intensity is lacking in objectivity. Evaluation of patient nursing intensity was carried out on the ward studied by highly experienced nurses. Fixed evaluation standards exist on certain wards and confidence is high with respect to the replicability of judgments on those wards, but it is clear that these standards differ from one ward to the next. In order to make clear what factors enter into evaluations relating to nursing intensity, it is necessary to secure methods of evaluation of patient condition that use phenomena observable by anyone, with objective indicators such as ‘how many drains have been inserted.’ We believe it is necessary to investigate objective indicators to replace nursing intensity, or to attempt to effectively quantify nursing intensity.
\n\t\t\tSince there is a time-lag between actual patient condition and the collection of patient condition information, there may be some margin of error in estimated care times. The patient condition information used in this study was based on information gathered at about 10:00 a.m. during the day shift. Information on patients who underwent surgery or other invasive procedures during the day shift and whose nursing intensity changed was incrementally updated and adjusted appropriately. The reason for carrying out the evaluation at 10:00 a.m. was simply that this was a convenient time from the point of view of the running of the ward, and in spite of the fact that patients’ conditions were actually changing hour by hour, care time was only estimated for one shift at a time. In the present study, we regarded this as a limitation about which nothing could be done, but we believe that it will be necessary to carry out further investigations in the future, as developments in IT systems within institutions make it possible to accumulate information concerning changes in patient condition in real time.
\n\t\t\tThere is a need to calculate averages and variances for changes in number of patients on the ward over a relatively long period. For this study, averages and standard deviations for numbers of patients on the ward were calculated using data from a short-term time study and cannot be used as population means with any confidence. But when long-term changes in numbers of patients on the ward are used, it has to be borne in mind that numbers of patients on the ward fluctuate markedly during holiday periods such as New Year and the summer O-bon Festival, on weekends, and at times when conferences attended by large numbers of doctors are held.
\n\t\t\tAs mentioned in 3.3.2, data relating to anomalous tasks deviated from normal distribution and was therefore excluded from the present analysis. However, it is a fact that nurses may carry out duties on the ward in the morning and undertake anomalous tasks such as attending meetings in the afternoon. Such anomalous tasks occur in a certain proportion throughout the year and a special distribution, different from those of ordinary tasks, must be assumed for them. We believe that we need to improve the accuracy of our simulation by actively seeking to include data concerning unusual phenomena as variables.
\n\t\t\tIn this study, as we explained under ‘Method,’ only simulations of day-shifts on weekdays were carried out and we were unable to accommodate the special systems in force on weekends and at the holiday times mentioned above. Under these special systems, the numbers of nurses on duty and of patients on the ward fluctuate considerably. Because this greatly affects task times, we believe that there is room here for future investigation.
\n\t\t\tWe have not incorporated into our simulation the difference in function of nurses such as team leaders, who head and support a team rather than taking responsibility for patients, or nurses that have responsibility for a small number of patients and carry out management tasks alongside these duties, as is very often the case with ward supervisors. We assumed for the purpose of the present simulation that all nurses were nurses whose actual work involved being responsible for patients, but in fact there are nurses who perform their roles in different ways. In addition, each year there are new recruits who need constant guidance from experienced nurses. They may, after some months, be able to cope with basic tasks, but they still have limitations, such as not being able to take responsibility for patients whose condition is severe. Further investigation of a methodology that will reflect this state of affairs is needed.
\n\t\t\tIt is not possible at this stage, but an evaluation that compared simulation results with reality would be the most reliable form of evaluation. In recent years, computer systems such as ordering systems, distribution systems, and electronic patient charts have been actively adopted as hospital information systems, and even more widespread use of IT→it can be expected in the future. We believe that if it becomes possible to collect task time data without committing large amounts of effort and funding, as required for time studies at present, this is an approach that must be investigated.
\n\t\t\tWe believe that it would be useful to standardize the methodology for carrying out the series of operations that was constructed for this study. Some reasons are suggested below.
\n\t\t\t\tBecause each institution and each ward has different attitudes towards individual patient characteristics and tasks, and different methods of executing tasks, it is difficult to calculate universal quantities for essential nursing tasks that can be applied in any institution. In addition, there are cases in which it would be dangerous, or lead to the loss of desirable qualities, if a fixed value were applied to all institutions. It is desirable to go through the following series of operations. Having considered the task management appropriate to the ward, while preserving the ward’s characteristics, a time study of the ward should first be carried out, then a virtual environment simulating the actual ward should be created, making use of existing cumulative information, and test experiments should be conducted using that virtual environment.
\n\t\t\tWe believe that it is possible, on the basis of information derived from incident and accident reports, to explore the relationship between medical errors and task times from a number of viewpoints. As medical malpractice suits have increased in recent years, consciousness of medical errors by nurses has increased and the number of nursing departments that make it a requirement to write near-miss incident reports has grown. Protection of patient safety requires maintenance of minimum standards in all medical jobs, including nursing, and is of the utmost importance. Fujita et.al. have pointed out that there are errors that are related to busyness and errors that are not related to busyness. It is possible to extract from the analysis results the answers to such questions as: ‘What kinds of incidents and accidents increase with an increase in task time?’ ‘What amount of task time elapses before the number of cases reported begins to increase?’ and ‘After how many hours of overtime work over how many days in a row does the number of cases reported begin to increase?’ These analysis results will also provide important material for the investigation of task allocation and assignment of nursing staff with a view to minimizing medical errors.
\n\t\t\t\tIt is possible to explore the relationship between nurse’s task time, particularly ‘time devoted to patients for whom the nurse is responsible,’ and patient satisfaction. We believe that this has great significance for the improvement of nursing care. We are entering an era when patients are expected to draw sharp distinctions among hospitals. As a result, more and more hospitals are increasing the number of their private rooms, where patients can spend their hospital stay in privacy, and are giving thought to the appearance of the hospital’s interior and the richness of its amenities. But we believe that what is more important to patients than the physical elements of the institution is that they should be able to receive care that they are satisfied with in an atmosphere based on a relationship of trust with the medical personnel. Sickness is a special condition, and patients need warm-hearted support at all times. The nurses, who spend more time in contact with the patient than any other medical personnel, have a particularly large role to play, and are at the forefront of ensuring customer satisfaction.
\n\t\t\t\tIn the present study we analyzed only the day shift, but we believe that by constructing a virtual ward environment that takes other shifts into account and carrying out simulations, it would be possible to show the relationship between task time and nurses’ fatigue. It has been pointed out that symptoms of fatigue among nurses are greatest after the evening shift and that where the night shift is concerned there is considerable fatigue before the shift begins. There is concern that the physical and mental fatigue of nurses on the night shift has a negative effect on their work. Attempts have long been made to reduce the burden on nurses and to establish an efficient nursing system. One notable example was the introduction of the two-shift system, but no reference has been made to investigation of specific aspects of this working system, such as how its merits and demerits are related to the characteristics of the ward. It is important to re-investigate nurses’ work systems, including conditions such as these.
\n\t\t\t\tIn this study we chose pattern of nursing intensity as the clinical path and, having fixed patient severity as a definite condition, it was possible to make a preliminary calculation of actual nursing task times. In recent years, much has been made of efficiency of treatment, and an increasing number of institutions have introduced the clinical path as a specific methodology. Among city hospitals and privately run general hospitals, there are institutions and wards that have almost completely adopted clinical paths, and that have been successful in the management of planned admission with almost no variance. We believe that in hospital institutions like this, it will be possible to effectively apply patterns of change in nursing intensity to items such as preliminary calculations of nursing personnel costs, which have a great influence on hospital management.
\n\t\t\t\t\tWe feel that a combination of the experimental results derived from virtual environments as described in this study and other information will be helpful in the management of nursing tasks suited to various goals.
\n\t\t\t\tThis study was supported in part by research grants of 22792142 Grant-in-Aid for Young Scientists (B) from the Ministry of Education, Culture, Sports, Science and Technology of Japan and in part by the Osaka University Program for the Support of Networking among Present and Future Researchers.
\n\t\tCoronaviruses (CoVs) belong to the subfamily Orthocoronavirinae in the family of Coronaviridae. In this family, there are four types of viruses: α-coronavirus, β-coronavirus, γ-coronavirus, δ-coronavirus [1]. The CoV genome is an enveloped, positive-sense, and single-stranded RNA, and it has the largest genome of known RNA viruses. It is known that α- and β-CoV types cause infections in mammals as δ- and γ-CoVs infect birds [2]. Severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS) belonging to β-CoVs are the most aggressive strains of coronaviruses and cause viral pneumonia outbreaks [3, 4]. SARS-CoV disease is a kind of pneumonia and caused by novel CoV whose genome structure was more than 82% identical to those of SARS-CoV, named coronavirus disease 2019 (COVID-19) [5, 6]. SARS-Cov-2 is a beta gene virus genetically very close to bat-CoVRaTG13, and bat-SL-CoVZC45 Covs can cause severe illness. As the COVID-19 outbreak turned into a global threat, the World Health Organization (WHO) announced it as a global pandemic on 12 March 2020. The COVID-19 pandemic has changed the scenario of the entire world. COVID-19 outbreak started in Wuhan, China, has globally spread to 219 countries and territories [7]. Currently, there are few vaccines for COVID-19. Their acceptance and efficacy are an issue of debate across the whole world. Therefore, there is an urgent need to find drugs or vaccines for the treatment of COVID-19 infections effectively. However, there are some studies related to the use of known drugs such as remdesivir and chloroquine that have proved efficacy on COVID-19 infection. We summarize some antiviral drugs as therapeutic options for the treatment of COVID-19 [8].
COVID-19 mainly attacks the respiratory-tract-associated organs. Additionally, the virus has shown impact various to other organs or systems such as the gastrointestinal system, nervous system, etc. [9]. The most common symptoms in COVID-19 patients are fever, dry cough, loss of taste, lethargy, shortness of breath, dyspnea, chest pain, fatigue, myalgia, whereas headache, dizziness, abdominal pain, diarrhea, nausea, and vomiting are less commonly observed [10, 11]. Anosmia is also one of the most critical symptoms in COVID-19 patients [12]. COVID-19 is more contagious than other coronaviruses, and its transmission rate is higher than the closely related strain, SARS-CoV-10 [13]. Currently, new variants of COVID-19 are reported from different regions of the world. Coronavirus interacts with cell surface receptors such as angiotensin-converting enzyme-2 (ACE-2) and neuropilin to gain entry inside the cell. The receptor-binding domain of viral spike protein is essential in SARS-CoV-2 entry into the host cell via surface ACE-2 [14]. Recently, another cell receptor Neuropilin-1 was found to be involved in SARS-CoV-2 entry. After binding to the receptor, the conformational change in the spike protein leads to virus fusion with the host cell membrane. The virus may transfer the RNA directly inside the cells or may proceed through the endosomal pathway [15]. Upon translation of viral RNA, the viral replicase polyprotein PP1a and PP1ab are produced and cleaved into small products by viral endopeptidase [16]. RNA-dependent RNA polymerase (RdRp) produces subgenomic RNAs by discontinuous transcription [16, 17]. This further gets translated into respective viral proteins. After processing through the endoplasmic reticulum (ER), ER-Golgi intermediate compartment (ERGIC), and Golgi complex, the viral RNA and proteins are assembled into virions. These virions are transported through vesicles and exocytosed for transmission. These steps of the viral life cycle are beneficial virus inhibition targets for different drugs. The coronaviruses are ribonucleic acid (RNA) viruses, which have a positive single-strand RNA [14, 18]. When SARS-CoV-2 enters the body and comes in contact with the host cell membrane, some changes occur in the structure of the virus. The human TMPRSS2 protein alters the conformation of the spike glycoprotein in the virus. Two substantial protease enzymes, 3-chymotrypsin-like protease (3CLpro) and papain-like protease (PLPro), have essential roles in its viral replication process after it enters the host cell via ACE2 receptors [19]. The expression of several genes, such as AHCYL2, ZNF385B, etc., appears to have a strong correlation with the expression of ACE2 and TMPRSS2 protein receptors in human healthy and normal lung cells [20].
However, repurposing drugs could prove to be beneficial tactics for finding COVID-19 treatment, including cost-effectiveness, elimination of some clinical trial steps, faster on-field availability, combining the drugs with other possible drugs, and the invention of information about the mechanisms of the existing drug. Researchers were able to develop the possible COVID-19 medications using information from previous CoVs therapies, genetic sequences, and protein modeling studies. Antimalarials, antivirals, antibiotics, and corticosteroids are among the most often studied medications, and they have been repurposed based on their ability to neutralize viruses, reduce lung inflammation, or alleviate other illness symptoms. Chloroquine (CQ), hydroxychloroquine (HCQ), and azithromycin (AZM) are the most often utilized antiviral drugs against COVID-19, since they have already demonstrated reasonable antiviral efficacy against SARS-CoV, MERS-CoV, and SARS-CoV-2. Anti-HIV medications lopinavir/ritonavir (LPV/RTV) are being studied for COVID-19 since they were successful in previous CoV epidemics. Furthermore, the anti-Ebola medicine remdesivir (RDV) was evaluated for COVID-19 and garnered further attention.
Similarly, favipiravir (FPV), ribavirin (RBV), umifenovir (UFV), and oseltamivir (OTV) have broad-spectrum antiviral activities and clinically tested against COVID-19. The effective uses of HCQ, RDV, LPV/RTV, or LPV/RTV in combination with Interferon (IFN) β-1a against COVID-19 [21], all these drugs had little or no effect on overall mortality, initiation of ventilation, and duration of hospital stay in hospitalized patients. So far, to treat severe and critical COVID-19, only corticosteroids have proven effective [21]. Other drugs, such as Angiotensin-Converting-Enzyme inhibitors (ACEi), have also been used to treat COVID-19. However, no clear correlation was reported between mortality rate and ACEi drugs in hypertension patients with COVID-19 [22]. Due to the possibility of secondary infection in these patients, antibiotics have been used as various protocols [23].
Umifenovir (UFV) may interact with SARS-CoV-2 surface glycoproteins and lipids and obstruct the interaction with the entry receptor ACE-2. Antibodies against SARS-CoV-2 may prevent the virus from entering the body and causing illness. Chloroquine (CQ), hydroxychloroquine (HCQ), and azithromycin (AZM) can raise endosomal pH, making viral entrance and RNA release more difficult. CQ, HCQ, and AZM all have immunomodulatory properties. RDV, FPV, and RBV are nucleoside inhibitors that impede RNA replication and reduce RNA-dependent RNA polymerase activity. Fraternization of LPV with viral protease may change proteolysis. OTV may interact with components involved in exocytosis, preventing the virus from leaving the cell. Antibodies against cytokine receptors and corticosteroids have been shown to have anti-inflammatory properties in the face of excessive immune responses. Drugs such as CQ are wide-spectrum inhibitors of viral cell entry, and RDV is a wide-spectrum RNA polymerase inhibitor. SARS-CoV-2 infection concurrently triggers the host immune system and an inflammatory cascade response (cytokine storm). These are being targeted in the treatment of COVID-19 patients [23].
So far, no fully effective drug has been discovered against this virus. The antiviral drugs, usually nucleoside analogs or intracellular proteases, block the virus by preventing its entry into the cell or by interfering with its replication inside the cell. Protease inhibitors target certain proteases, whereas fusion inhibitors block the fusion phase of viral entrance. Transcription inhibitors impede viral replication by inhibiting RNA-dependent RNA polymerase during the reverse transcription process. Nucleoside reverse transcriptases are some of the transcriptase inhibitors. M2 channel protein is a target for certain antivirals. In this chapter, we have provided information about repurposed drugs that are used against COVID-19, the mechanism of activity, therapeutic regimens, pharmacokinetics, and drug-drug interactions [7, 8].
The rationale major biochemical events and components in the replication cycle of coronavirus are considered as targets for currently developed drugs. These include the spike protein, proteolytic enzymes, and RNA-dependent RNA polymerase [24]. SARS-CoV-2 is transmitted mainly via respiratory droplets. The virus enters the host cells through two pathways, either via endosomes or plasma membrane fusion. In both mechanisms, the viral S protein mediates attachment to the membrane of the host cell and engages ACE2 as the entry receptor [25]. A host protease termed transmembrane serine protease 2 (TMPRSS2) activates the connection between S protein and ACE-2 [26]. S protein is used by the virus to destroy antibodies and make it simpler for it to attach to host receptors [27]. Beta-coronaviruses generally employ hemagglutinin-esterase (HE) to bind to sialic acid on the glycoprotein surface, despite the fact that the fusion machinery of SARS-CoV-2 remains unknown [28]. Fusion inhibitors might be used to prevent these fusion stages.
The envelope is peeled off when fusion is complete, and the SARS-CoV-2 genome, together with its nucleocapsid, penetrates the cytoplasm of the host cell. Its genome comprises the open reading frames 1a and 1b (ORF1a and ORF1b) genes, which create two polyproteins (pp) named pp1a and pp1b, which aid in the viral translation process by hijacking host ribosomes [29]. Main protease (Mpro) and papain-like protease (Ppro) break these polyproteins to create multiple non-structural proteins [30]. Aside from Mpro and Ppro, SARS-CoV-2 has 3C-like cysteine protease (3CLPro), which has a 96% resemblance to SARS-CoV. These proteases are essential for viral replication and transcription, and protease inhibitors inhibiting these proteases are potential antivirals for SARS-CoV-2. The promising clinical outcomes for COVID-19 patients should be obtained by using alpha-interferon, chloroquine phosphate, arabinol, remdesivir, lopinavir/ritonavir, and anti-inflammatory drugs [31, 32, 33, 34]. Moreover, clinical trials with these drugs should be performed on COVID-19 patients to prove their efficacy and safety as proposed for tocilizumab (Figure 1) [35].
Schematic diagram of the life cycle of SARS-CoV-2.
Highest sequence similarity (~96%) was observed for the bat Coronavirus. So, it has been speculated that COVID-19 was transmitted from bats to humans. The intermediary animal host could be a pangolin or dog. COVID-19 illness is spread via intimate contact with an infected individual, as well as minute respiratory droplets emitted during coughing, sneezing, or talking [36]. Small droplets of saliva or sputum emitted from the mouth might carry large amounts of viruses that can linger in the air for lengthy periods of time and function as infection carriers. Even when a person is not in direct physical touch with the infected individual, inhaling these minute droplets causes viral infection to move from the sick to the healthy. The virus enters the human body via the eyes, nose, and mouth and spreads by encountering the virus on infected surfaces and then touching these bodily areas [37]. Environmental factors such as temperature and humidity influence viral propagation across infected surfaces [38]. The binding of homotrimer spike protein (S) on the virus’s surface to ACE2 on the host’s cell membrane facilitates SARS-CoV-2 entry into host cells [16]. The host cell receptor’s credit is a critical predictor of the virus’s tissue tropism and pathogenicity. The life cycle of SARS-CoV-2 is similar to the SARS-CoV and MERS-CoV [39]. Different strategies have been adopted to fight COVID-19.
COVID-19 diagnosis is a crucial step in tracking the virus and understanding its spread. This aids in the prevention of transmission as well as adequate patient care. COVID-19 is diagnosed in the first instance by observing signs and symptoms such as first loss of smell or taste or both, cough, mild to high fever, myalgia or weariness, and so on [40]. In addition, some people experience gastrointestinal problems such as vomiting, diarrhea, and nausea [41]. However, variations in the development of symptoms ranging from asymptomatic to severe instances, such as septic shock, metabolic acidosis, coagulation malfunction, and acute respiratory pneumonia-like syndrome, have been recorded often [17]. These indications and symptoms should only be used as a starting point for additional testing, not as a diagnostic tool. The recognition of symptoms in clinical conditions is the most important factor in diagnosis. Swabs are used to obtain pathological samples from the upper and lower respiratory areas (throat, oropharyngeal, nasopharyngeal, broncho-alveolar fluid, and sputum). The virus is still absent in the blood and urine of infected people, hence they are not regarded valid clinical specimens. The interlink between the temporal surge of viral load and its bio-distribution in different tissues of the body has a critical implication on the accuracy of various tests for diagnosis, according to reports of inconsistency in RTPCR test results for CoV-SARS-2 in various tissues [42] and temporal variation of test results from the same tissues [43]. SARS-spike CoV-2’s surface glycoprotein binds to the ACE2 receptor and then enters the host cell. Viral particles release their DNA after entering the host cell, which is then translated into protein, and additional viral particles are created, which are then released to infect the next cells. Many assays (molecular and immunological assays) or tools have been used for the diagnosis of COVID-19 and many more are currently in development.
SARS-Cov-2 infections currently have no vaccinations or antiviral therapies available [44]. Because developing safe and stable vaccines takes time and the pandemic is still going on, it’s critical to test and discover current medications that are already effective against SARS and MERS to determine whether they can be effectively applied to SARSCov-2. Various preclinical studies on other CoVs genetically very close to SARS-Cov-2 suggested that promising clinical outcomes for COVID-19 patients should be obtained by using several drugs including alpha-interferon, chloroquine phosphate, arabinol, remdesivir, lopinavir/ritonavir, and anti-inflammatory drugs. In a large-scale drug screening, nelfinavir has potent antiviral activity against SARS-Cov-2 [45]. Besides, praziquantel, pitavastatin, and perampanel might be effective against SARS-CoV-2. The outbreak of COVID-19 infection is related to the unavailability of specific drugs to combat this viral infection. Despite the challenges related to COVID-19 therapy, there are still several approaches being undertaken that show significant outcomes [5]. Discuss the positive impacts of some of the clinically used drugs for the COVID-19. Some drugs are in clinical trials, and some have shown significant promise in COVID-19 patients [46]. To find the solutions for COVID-19, great efforts have been made and are continued to develop vaccines, small-molecule drugs, or monoclonal antibodies that can prevent the infection [47]. In addition to drugs under clinical trials, some vaccines are expected to play a significant role in controlling the COVID-19 pandemic (Figure 2).
Common inhibitory action of antiviral drugs.
In 2009, Gilead Sciences, Inc. (USA) developed an antiviral drug called Remdesivir (RDV) to treat hepatitis B [48]. It did not indicate a desirable act against hepatitis. However, it is effective against other viruses, such as the Nipah virus, hepatitis C, and Marburg [49]. RDV is a broad-spectrum antiviral nucleoside analog, and now it is used as a treatment option for COVID-19 [50]. It is the class of polymerase inhibitors and showed activity against different RNA viruses, including SARS-CoV, MERS-CoV, Lassa fever virus, Junin virus, respiratory syncytial virus, Nipah virus, Hendra viruses, filoviruses, and Ebola viruses. RDV is a prodrug of its parent adenosine triphosphate analog, (2R,3R,4S,5R)-2-(4-aminopyrrolo(2,1-f)(1,2,4)triazin-7-yl)-3,4-dihydroxy-5-(hydroxymethyl)oxolane-2-carbonitrile (GS-441524), and has similarity to the adenine nucleic acid structurally. Both of these drugs are metabolized into the active component as nucleoside triphosphate (GS-443902) after ingestion and show antiviral activity against SARS-CoV [51]. RDV targets the viral genome replication process by acting as an RdRp inhibitor [52], RDV was used to block the RNA-dependent RNA polymerase of SARS-CoV-2. On metabolization of RDV into active nucleoside triphosphate (NTP), which competes with ATP for incorporation into nascent RNA strands, premature RNA synthesis occurs, resulting in RNA strand termination and cessation of growth [51]. RDV when tested through in vitro studies using the Vero E6 cells showed an EC50 value of 1.76 μM that showed its activity against SARS-CoV-2 [53]. Intravenous remdesivir treatment showed significant improvement for COVID-19. RDV and chloroquine are highly effective in the control of SARS-CoV-2 infection. In severe COVID-19 treated with RDV, improvements in the clinical finding were observed in 68% of patients [54]. However, in October 2020, the WHO removed it from the list of effective drugs in the treatment procedure of COVID-19 patients because it failed in the first trials for the treatment of COVID-19 [42]. There are still controversies regarding the results, no benefit in COVID-19 treatment using RDV; whereas, the company claims it as a promising drug for the same. After penetrating the cell, RDV as a prodrug (GS-5734) and like Favipiravir, binds to the triphosphate group under esterase, kinase, and phosphatase enzymatic reactions. These enzymes modify the structure of RDV and convert it to the active form, RDV-triphosphate (RDV-TP or GS-441524) [55]. After virus entry into the cell cytoplasm, this prodrug gets activated and loses its ability to diffuse to the intercellular space [53]. However, the primary mechanism of action of RDV against SARS-CoV-2 is unclear, and more research is necessary to understand it [56]. In an in vitro study, the combination of RDV and chloroquine (antimalarial drug) effectively inhibited SARS-CoV-2 growth in Vero E6 cells [19]. RDV is used to treat COVID-19 cases.
The combined use of RDV and IFN-β created a higher antiviral activity compared with the lopinavir/ritonavir-IFN-β combination against the MERS-CoV virus. Additionally, RDV could be better pulmonary function, cause fall lung viral loads and severe lung pathology in mice; on the contrary, lopinavir/ritonavir-IFN-β could not [57]. In two clinical studies, the use of RDV has been carried out against severe or mild respiratory infections caused by COVID-19. Recently, RDV for emergency use to treat COVID-19, including five antiviral drugs, ribavirin, RDV, sofosbuvir, galidesivir, and tenofovir, was conducted against SARS-CoV-2 RNA-dependent RNA polymerase (RdRp); these drugs showed promising results against COVID-19. Prominent adverse reactions were an acute respiratory failure, decreased glomerular filtration rate, lymphocytopenia, pyrexia, hyperglycemia, increased anemia, increased creatine, and liver transaminases. RDV given in combination with baricitinib (Janus kinase inhibitor used to hinder intracellular signaling of cytokines) was effective compared with RDV alone in terms of reducing recovery time additionally speeding improvement. RDV’s parent nucleotide GS-441524 is superior and less toxic than its prodrug form and has shown efficacy [58].
Favipiravir (Avigan or T705) is a synthetic antiviral agent that was first marketed as an anti-influenza drug in Japan. It is a derivative of pyrazine carboxamide (6-fluoro-3-hydroxy-2-pyrazine carboxamide) [59]. Due to its similarity to the purine (guanine) nucleotide, it is a type of RNA-dependent RNA-polymerase (RdRp) inhibitor. RdRp uses Favipiravir-RTP in the synthesis of mRNA strands, which can consequently stop viral protein synthesis via suppressing the translation process. Activated Favipiravir-RTP could suppress the SARS-CoV-2 RdRp enzyme and inhibit viral mRNA elongation and protein synthesis [60]. Favipiravir acts against RNA viruses by working on viral genetic copying to prevent its reproduction. A phase 3 clinical trial was involved for the treatment of COVID-19 disease using Favipiravir. For the first day, take 1800 mg twice a day, then 600 mg three times a day from the second day onward for a total of 14 days. Normalization of pyrexia, respiratory rate, and cough alleviation for at least 72 h are the key objectives [61]. The precursor of this drug known as T1105 has anti-influenza effects [62]. Drug excretion is through renal elimination and is mainly impacted by aldehyde oxidase and xanthine oxidase [62]. Favipiravir is a prodrug that is phosphorylated upon its entry into the cell and converted to an active antiviral form, favipiravir ibufuranosyl-5′-triphosphate (T-705-RTP). Favipiravir was first prescribed in Wuhan, to treat patients with SARS-CoV-2 infection. In June 2020, it was approved for mild-to-moderate COVID-19 cases in India. Favipiravir has been consumed to cure distinct viral diseases. Favipiravir was effective against some RNA viruses, such as yellow fever virus, Lisa virus, West Nile virus, Bunyavirus, arenavirus, flavivirus, filoviruses, and Ebola virus [63]. The exact mechanism of action is not clear against SARS-CoV-2. Favipiravir is considered a potential drug for COVID-19 and is currently used for COVID-19 treatment in Japan and Indonesia. Besides, its anti-influenza virus action, it stops the replication of RNA viruses such as flavi-, alpha-, filo-, bunya-, arena-, noroviruses [64]. Favipiravir showed a more powerful antiviral activity than lopinavir/ritonavir. Adverse reactions are not observed in a favipiravir therapy group. Compared with the lopinavir/ritonavir group, it had considerably fewer adverse effects. In a Japanese study, FPV was also shown to control inflammatory mediators and pneumonia progression in COVID-19 patients [65]. Severe or critical COVID-19 patients showed improvements after treating with FPV and FPV also led to improved lung histology [66].
Lopinavir is an antiviral drug belonging to the family of protease inhibitors. It is commonly used to treat Acquired Immunodeficiency Syndrome (AIDS) and prevent HIV from spreading inside the body. Lopinavir/ritonavir (LPV/RTV) is used in combination with other antiretroviral drugs for the treatment of HIV-1 infection. In the coronavirus pandemic, when no definitive drug was proposed to treat patients, it was used in combination with Ritonavir. This LPV/RTV is branded as Kaletra. Lopinavir has a relatively short half-life in the blood and is affected by the cytochrome p450 enzyme, while Ritonavir is a protease inhibitor and reduces the Lopinavir metabolism by suppressing the function of cytochrome p450. The half-life of Lopinavir is improved, and its circulation period is increased. LPV/RTV acts as a protease inhibitor drug and inhibits the action of 3-CLpro, a chymotrypsin-like protease enzyme, that plays a vital role in the processing and interferes with the process of viral replication and its release from host cells [67, 68, 69]. LPV/RTV use is related to diverse side effects, mainly in the gastrointestinal tract. Diarrhea, impaired hepatic cell function, and pancreatitis are some of these crucial side effects.
The use of lopinavir as an emergency drug in China increased the eosinophil count among COVID-19 patients [70]. In an in silico study, LPV/RTV used as HIV protease inhibitors inhibited the main protease (MPro) of SARS-CoV-2 [71]. The LPV/RTV is being used as an emergency treatment for COVID-19 patients in some countries [72]. LPV/RTV alone or in combination with interferon (INF)-β, an inflammation regulator, has been listed by WHO as options for “solidarity” clinical trial for COVID-19. COVID-19 might benefit from LPV/RTV since it reduces viral load and improves clinical symptoms. Lung damage was also significantly reduced when LPV/RTV and umifenovir were used together [73]. A research found that while LPV/RTV therapy was associated with a better result, it did not significantly speed up the clinical progression of severe COVID-19 infection. Although the efficacy of lopinavir for COVID-19 has yet to be determined, LPV/RTV has been employed in the treatment of COVID-19 patients [57]. Now, LPV/RTV and IFN-β1b are in phase 2 for the MERS therapy. Despite the positive findings, in a recent study performed on patients with SARS-CoV-2 infection, the LPV/RTV did not provide clinical improvement compared with standard care processes [72]. Findings of LPV/RTV clinical efficacy remain limited and primarily anecdotal cases. LPV/RTV in the therapy of COVID-19 is needed as current results contradict. LPV/RTV can ameliorate the outcome of MERS-CoV infection [74]. Moreover, LPV/RTV is assumed as a therapeutic option for COVID-19 pneumonia [72]. Thus, more well-designed clinical studies are necessary to identify their efficacy as therapeutic agents for COVID-19.
Novaferon has potential as an antiviral drug against COVID-19. It is a synthesized protein consisting of 167 amino acids, designed on the technical basis of DNA shuffling technology. The antiviral effects of novaferon are shown alone and in combination with lopinavir/ritonavir (LPV/RTV) for COVID-19 treatment. Novaferon inhibited the viral replication in infected cells (EC50 = 1.02 ng/ml) and protected healthy cells from SARS-CoV-2 infection (EC50 = 0.1 ng/ml). Both novaferon and novaferon plus LPV/RTV groups had significantly higher SARS-CoV-2 clearance rates on day 6 than the LPV/RTV group [8].
Ribavirin (Virazole) is an antiviral drug belonging to the nucleoside analogues, (1-beta-d-ribofuranosyl-1,2,4-triazole-3-carboxamide). It is a synthetic nucleoside analog with a guanosine-like structure. Ribavirin disrupts viral DNA and RNA replication, thereby inhibiting virus proliferation in the cell. Although Ribavirin’s primary mechanism of action is suppressing the virus replication, and can also interfere with viral RNA capping, which depends on the presence of natural guanosine in the RNA structure. The natural guanosine in the viral RNA structure prevents the breakdown of RNA strands. Ribavirin reduces the guanosine synthesis in the cell by inhibiting the activity of the inosine monophosphate dehydrogenase enzyme, which negatively impacts virus replication [75]. Although Virazole does not entirely inhibit viral RNA synthesis, the synthesis of the viral genetic material is severely impaired. It results in significant and persistent mutations in viral RNA, which reduce the viability of the virus in host cells [76]. Besides, the presence of Ribavirin in the patient’s body can reduce viral immune evasion and boost immune maintenance [77]. It is the first broad-spectrum antiviral drug against DNA and RNA viruses [75]. It is used clinically to treat HIV and hepatitis C virus (HCV) patients.
Ribavirin, which has been studied for its antiviral effectiveness against SARS-CoV-2, is used to inhibit viral RNA production and viral mRNA capping with a broad range of antiviral activity. It’s a prodrug that, when metabolized, looks like purine RNA nucleotide, which prevents viral multiplication by interfering with RNA metabolism. It was discovered in a comparison study of SARS-CoV-2 patients treated with lopinavir/ritonavir (LPV/RTV) and ribavirin combination treatment [77]. Ribavirin is one of the medications used to treat COVID-19 in conjunction with either IFN alpha or LPV/RTV [46]. Using ribavirin in combination with sofosbuvir and remdesivir, docking and modeling studies revealed that ribavirin is a viable candidate medication for COVID-19 therapy [78]. Ribavirin and sofosbuvir are currently part of the therapeutic regimen to treat COVID-19 in some countries.
Ribavirin inhibits the function of inosine monophosphate dehydrogenase, which affects the formation of guanosine triphosphate (GTP), preventing RNA and DNA viral replication. During the SARS outbreak in Hong Kong, ribavirin was utilized. With or without steroids, it was occasionally chosen. The combination of ribavirin and interferon-β, which appears to inhibit SARS-CoV replication, has shown significant efficacy in the inhibition of SARS-CoV [79]. The ribavirin triple antiviral treatment was safe and superior compared with lopinavir-ritonavir combined therapy.
The drug showed antiviral efficacy against canine distemper virus, hepatitis C virus, Enterovirus, Chikungunya virus, and Semliki Forest virus, orthopoxvirus, influenza virus, flavi- and paramyxoviruses [80]. A study observed reduced replication of the MERS-CoV in rhesus macaques upon treatment with IFN-α2b and RBV [81]. RBV in combination with LPV/RTV was used in SARS-CoV and MERS-CoV trials [82]. In the case of SARS-CoV-2 infection, an in vitro study showed the EC50 of RBV as 109.50 uM [31]. A study included RBV along with LPV/RTV and IFN-α in the treatment of hospitalized COVID-19 patients. When compared with those that only received LPV-RTV, the triple treatment was found to be effective in reducing illness symptoms and viral shedding. The RBV dosage was 400 mg bid for 14 days, paired with 400 mg/100 mg of LPV/RTV + IFN-β. A research examined the effectiveness of antivirals sofosbuvir/daclatasvir and RBV in the treatment of COVID-19 patients. COVID-19 patients treated with RBV had a greater death rate (33%) than those treated with sofosbuvir/daclatasvir. A cohort study comparing RBV vs. supportive therapy stated that RBV did not help in reducing the mortality rate in COVID-19 patients [83].
It is an antiviral widely used to treat the influenza virus. Arbidol can prevent SARS-CoV-2 infection
Darunavir, an anti-HIV drug, is recommended for COVID-19 treatment in Italy. It is used in a combined regimen along with cytochrome P-450 inhibitors such as ritonavir or cobicistat and confirmed their replication inhibitory effect against SARS-CoV-2. A clinical trial assessed the effectiveness of darunavir combination with other antivirals and hydroxychloroquine for COVID-19 patients. A combination of darunavir and cobicistat is also being tested [93]. PREZCOBIX®, a fixed-dose combination of darunavir and cobicistat, is also used to treat COVID-19. COVID-19 infection was recently discovered in HIV-positive individuals who were already taking darunavir, raising questions about the effectiveness of this HIV protease inhibitor. The darunavir might not be effective in preventing SARS-CoV-2 infection at the dosage of 800 mg [94]. Darunavir is a second generation of HIV-1 protease inhibitors used to prevent SARS-CoV-2 infection in vitro [17] by inhibiting viral replication at 300 μM, and this inhibition efficiency was 280-fold compared with the untreated groups. Darunavir boosted with ritonavir or cobicistat is used in HIV/AIDS treatment. The efficacy of darunavir or ritonavir is enhanced by cytochrome p450 (CYP3A) inhibition [95]. Cell experiments with darunavir showed that the drug inhibited viral replication of COVID-19 in vitro. The lopinavir/ritonavir used in the treatment of HIV/AIDS has more efficacy and tolerability than darunavir, its use in COVID-19 is limited.
Oseltamivir (Tamiflu) is an antiviral agent that is used for patients with influenza A and B. It is a protease inhibitor, which specifically inhibits the neuraminidase enzyme in the influenza virus. This enzyme has a key role in the binding of the influenza virus to the cell membrane and spread throughout the body. Therefore, Oseltamivir, by targeting neuraminidase, prevents the spread of the influenza virus and its progression inside the body [96]. This drug was used in the treatment of COVID-19 infection, which showed an appropriate effect on patients [41]. Oseltamivir has been applied in concomitant regimens with other drugs such as Hydroxychloroquine or Favipiravirs [97]. In addition to treating influenza A and B patients, this drug may also be used in severe cases. For the treatment of flu patients, Tamiflu is prescribed in a 75 mg dosage twice a day and once a day as prophylaxis. The main side effects of this drug can be nausea and headache [98]. Neuraminidase inhibitors seem beneficial for COVID-19 patients and can reduce their ventilator requirements [99]. The precise mechanism of action of Oseltamivir against COVID-19 infection is still unclear. Oseltamivir is a synthetic derivative prodrug of ethyl ester [100]. It acts as a neuraminidase inhibitor against the influenza virus and is also effective for various avian influenza virus strains [101]. An in vitro oseltamivir study on H5N1 influenza showed that the IC50 was 0.1–4.9 nM [102]. In vivo study involving H5N1 infection required a longer course and higher dosage of Oseltamivir. The COVID-19 originated in China during flu season, and hence earlier, many patients received oseltamivir treatment until the causative agent SARSCoV-2 was discovered. Some current clinical trials have used oseltamivir in combination with other major therapeutic drugs [31, 41].
Sofosbuvir is an antiviral drug and RdRp inhibitor that exerts its effect by suppressing RdRp enzyme activity. A combination of Sofosbuvir with Ledipasvir is used for treating patients with genotype 1 of HCV67. Because of the similarity in the transcription and replication mechanism of the SARS-CoV-2 with HCV in host cells, physicians speculate that this drug may help treat COVID-19 patients [103]. This drug disrupts the activity of RdRp by acting like free nucleotides that are essential for viral mRNA synthesis [104]. Sofosbuvir is a potential option for COVID-19 treatment [105], and extensive clinical studies should be performed to verify the effectiveness of this drug.
Danoprevir, an HCV N53 protease inhibitor, is authorized in China for the treatment of noncirrhotic genotype 1b chronic hepatitis C in combination with other medications. In China, only two clinical studies of danoprevir coupled with ritonavir in the treatment of SARS-CoV-2 infection were completed [8].
In a computer simulation, atazanavir bonded more firmly to the active site of SARS-CoV-2 MPro than lopinavir, and atazanavir suppressed SARS-CoV-2 replication in a test tube. A prior trial on HIV-positive individuals found that combining atazanavir with ritonavir enhanced glucose uptake and lipid parameters while also lowering fasting glucose levels more efficiently than lopinavir-ritonavir. The atazanavir might be an alternative for lopinavir when combined with ritonavir for COVID-19 treatment. This antiviral drug is an option for COVID-19 treatment [8].
SARS-CoV-2 penetrates host cells by receptor-mediated endocytosis, just as other viruses. AP2-related protein kinase 1 controls the process of endocytosis (AAK1). As a result, disrupting AAK1 will prevent not just viral entrance but also intracellular viral assembly. Baricitinib is a Janus kinase (JAK) inhibitor that has a high affinity for AAK1 and can inhibit it. SARS-CoV-2 infection can be treated with baricitinib, which inhibits both viral entry and the inflammatory response [106]. JAK inhibitors such as ruxolitinib and fedratinib, which are linked to baricitinib, decreased clathrin-mediated endocytosis at higher dosages, suggesting that they may not be effective at acceptable concentrations in lowering viral infectivity. Neutropenia, lymphocytopenia, and viral reactivation have all been linked to the use of baricitinib for therapeutic purposes. Because individuals infected with SARSCoV-2 had a lower absolute lymphocyte count, baricitinib may increase the risk of co-infection [107].
Blocking virus-host fusion is a promising target for the novel antiviral agents that inhibit the Abl kinase pathway [41]. In a study, imatinib, an Abl kinase inhibitor, was observed to block the replication of SARS and MERS viruses by blocking viral fusion in 2016 [108]. COVID-19 utilized the SARS-coronavirus receptor ACE2 as well as the cellular protease TMPRSS2 to get access to target cells; therefore, TMPRSS2, transmembrane serine protease 2, inhibiting medicines such imatinib might be evaluated as COVID-19 disease treatment alternatives [37].
Another possible medicine that targets the fusion stage in viruses is camostat mesylate, a serine protease inhibitor. SARS-CoV-2 enters target host cells via ACE-2 receptors and/or TMPRSS2 receptors, with camostat mesylate acting as a TMPRSS2 inhibitor. It inhibits the virus’s cellular entrance by downregulating the production of the SARS-CoV-2 spike (S) protein, which prevents surface fusion. SARS-CoV infection in human bronchial epithelial cells was inhibited by camostat mesylate [109]. In vitro testing revealed that camostat mesylate and E-64d (a cysteine protease inhibitor) effectively blocked SARS-CoV-2 TMPRSS2 binding. Clinical studies are now underway to compare the efficacy of hydroxychloroquine and camostat mesylate vs. hydroxychloroquine alone. Another serine protease inhibitor, nafamostat mesylate, was shown to be 15 times more effective in preventing the SARS-CoV-2 virus from infecting host cells. As a result, nafamostat mesylate can be regarded a preferable option to camostat mesylate due to its more robust antiviral activity and acceptable safety profile [37]. Disseminated intravascular coagulation is also treated with nafamostat mesylate (DIC). It will aid in the management of DIC, as seen by increased fibrinolysis in COVID-19 patients [110].
In an in vitro research utilizing Vero E6 cells, nitazoxanide and its active component, tizoxanide, showed promise against MERS CoV and SARS CoV-2, with EC50 values of 0.92 and 2.12 μM, respectively [111]. In addition to coronaviruses, it exhibited action against norovirus, rotavirus, parainfluenza, respiratory syncytial virus, and influenza virus. This antiviral efficacy is due to the fact that the action mechanism is based on interfering with the virus’s host-regulated reproduction pathways rather than the virus’s particular pathways [112]. Nitazoxanide stimulates innate antiviral systems through amplification of cytoplasmic RNA sensing and type 1 IFN pathways. Nitazoxanide increases the expression of certain host systems that interfere with viral infection, allowing viruses to evade the host’s cellular defenses [113]. The nitazoxanide used against influenza viruses blocks the maturation of viral hemagglutinin at the post-translational stage [112]. Even if the findings aren’t promising, this medicine is used to treat some acute respiratory infections such as influenza. Although the in vitro activity of nitazoxanide against SARS-CoV-2 is promising, additional research is needed to understand its function in the management of COVID-19 (Figure 3).
Chemical structure of antiviral drugs.
Other various antiviral agents have been utilized to determine their impacts against SARS-CoV-2. Galidesivir is a nucleoside analog and a protease inhibitor [114]. This drug mechanism on COVID-19 is hypothesized to be similar to other antivirals, although its exact action mechanism is unknown. Another antiviral agent for COVID-19 is Tenofovir, which is known as an anti-influenza drug. It is an antiretroviral agent that targets DNA polymerase and inhibits virus replication [115, 116]. The action mechanism of this substance against COVID-19 requires further studies.
A fusion inhibitor is a group of antivirals that inhibit the fusion process during viral entry into the host cells. Some drugs are available with umifenovir and camostat mesylate representing antiviral activity against SARS-CoV-2 [117].
Some protease inhibitors such as lopinavir, darunavir, and atazanavir are used against COVID-19 [118]. In a computational study, drugs such as carfilzomib, valrubicin, eravacycline, lopinavir, and elbasvir inhibited the main protease in SARS-CoV-2. Further studies are required to confirm the efficacy of these drugs. Saquinavir and other protease inhibitors such as indinavir, amprenavir, and nelfinavir might also show the same effects against COVID-19 like protease inhibitors, due to resemblance between the structures. In a computer simulation, saquinavir and indinavir were found to suppress 3CLPro activity in SARS-CoV-2 [119]. In vitro inhibition of SARS-CoV-2 was shown to be inhibited by saquinavir, indinavir, amprenavir, and nelfinavir, with nelfinavir demonstrating the greatest suppression when compared with the others. In Singapore, saquinavir has been used to treat COVID-19 patients. Two other medications, raltegravir and paritaprevir, were shown to have the ability to block 3CLPro activity in SARS-CoV-2 in a computational investigation (Tables 1 and 2) [120].
Class | Drugs | Application | Emergency use for COVID-19 |
---|---|---|---|
Fusion inhibitor | Umifenovir (Arbidol) | Influenza | Singapore, China |
Protease Inhibitor | Lopinavir | HIV | USA, Japan, Singapore, Italy, China, IPC (Lopinavir-Ritonavir fix dose) |
Darunavir | HIV-1 | Italy (Darunavir-Ritonavir fix dose) | |
Atazanavir | HIV-1 | Singapore | |
Saquinavir | HIV-1 | Singapore | |
Nucleoside reverse transcriptase inhibitor | Emtricitabine | HIV-1 | Singapore (Emtricitabine-Tenofovir fix dose) |
Azvudine | HIV-1 | Singapore | |
Nucleotide reverse transcriptase inhibitor | Remdesivir | Ebola | WHO, IPC, USA, Singapore, Italy |
Favipiravir | (Avigan) Influenza | Singapore, Japan, Indonesia | |
Ribavirin | HCV | Singapore, IPC | |
Sofosbuvir | HCV | Singapore | |
Neuraminidase inhibitor (Virus release inhibitor) | Oseltamivir (Tamiflu) | Influenza A & B | IPC, Singapore, Indonesia |
Current use of existing antiviral drugs for COVID-19 [56].
International Pulmonologists’ Consensus includes the USA, India, Iran, China, Italy, Great Britain, EUA, Colombia, Egypt, Singapore, Romania, Ireland, Malaysia, Saudi Arabia, Sudan, Greece, and Bolivia.
Group | Drugs | Mechanism of action |
---|---|---|
Viral RNA polymerase inhibitors | Remdesivir (GS-5734) | RdRp inhibitor, prodrug, the analog of adenosine nucleotide |
Favipiravir | RdRp inhibitor, prodrug, the analog of guanosine nucleotide | |
Viral protein synthesis inhibitors | Ritonavir/Lopinavir | Inhibitor of protease |
Inhibitors of viral entry | Hydroxychloroquine Chloroquine | Increase in endosomal pH needed for the virus/cell fusion. Interfere with cellular receptor glycosylation of SARS CoV (ACE-2) |
Immunomodulators | Nitazoxanide | Interfere with host regulated pathways of virus replication, amplification of type 1 IFN pathways, and cytoplasmic RNA sensing |
Ivermectin | Inhibition of importin 1 heterodimer to inhibit the nuclear import of host and viral proteins |
Mechanism of action of antiviral drugs used for the treatment of COVID-19.
Another technique for combating SARS-CoV-2 infection is to inhibit RdRp and impede viral replication by targeting the reverse transcription process. Nucleoside reverse transcriptase inhibitors (NRTIs), nucleotide reverse transcriptase inhibitors (NtRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), and nucleoside reverse transcriptase translocation inhibitors are a few examples of possible inhibitors (NRTTIs).
Other NtRTIs with comparable structural properties to remdesivir or ribavirin, such as adefovir, tenofovir alafenamide, tenofovir disoproxil, abacavir, ganciclovir, and didanosine, exhibit antiviral effectiveness against SARS-CoV-2. NRTIs (lamivudine, stavudine, zidovudine, emtricitabine, zalcitabine, and azvudine) and NNRTIs (efavirenz, nevirapine, delavirdine, and rilpivirine) may also have antiviral activity against SARS-CoV-2 [56].
Oseltamivir is a neuraminidase inhibitor used in preventing influenza Neuraminidase inhibitor drugs such as oseltamivir, zanamivir, and peramivir are antiviral drugs that inhibit the viral neuraminidase enzyme and are recommended for influenza and to block the release of viral particles out of host cells. Neuraminidase inhibitors are also used as empirical treatment in MERS-CoV infection [121, 122]. However, a combination of oseltamivir with ganciclovir and lopinavir/ritonavir is used to treat COVID-19 patients [40]. A computational study also supported synergistic effects of oseltamivir-lopinavir-ritonavir combination against SARS-CoV-2 [123]. Oseltamivir is used with ceftriaxone and terbutaline to treat COVID-19 [124]. A study showed that the CT scan of the lungs of a COVID-19 patient showed significant improvement after a three-day course of oseltamivir [19]. Oseltamivir has been used either with or without antibiotics and corticosteroids against COVID-19. In a clinical trial, oseltamivir is tested with chloroquine and favipiravir [93, 125].
Nowadays, the rising SARS-CoV-2 turned into a global threat. COVID-19 targets lung cells by connecting to ACE2 protein. This protein is largely produced in some tissues such as the bile duct, liver, gastrointestinal organs, esophagus, testis, and kidney as well as lung tissue. Thus, COVID-19 may damage these organs and tissues. With the global threatening caused by COVID-19, efficient therapy against COVID-19 is quickly necessary. Nevertheless, the development of new drugs for this disease is still a huge problem for people in the world, and we have none formally approved drugs against COVID-19 now. It is very crucial to cut off the extending of this virus owing to epidemic avoidance and checking techniques. We need to develop novel drugs and to find new therapy methods to prevent this outbreak and to treat COVID-19. The extent of the current pandemic, along with other factors, such as the lack of time to develop novel and effective agents against COVID-19, the high mortality rate, possible mutations in its genetic material and severe economic shocks to societies highlight the value of testing antiviral drugs present in our drug arsenal. Some drugs that have already started with repositioning may be effective against COVId-19 as well. It is essential to address the drug-drug interaction of the drugs in COVID-19 patients with comorbidities. We hope that the continuing studies may provide solutions for the prevention and therapy against the COVID-19.
Despite the fact that specific antiviral medications for COVID-19 have yet to be identified or authorized by the FDA, the usage of some currently existing antiviral agents that target various phases in COVID-19’s life cycle might be an alternate therapeutic strategy for combating the pandemic. Fusion inhibitors, protease inhibitors, and transcription inhibitors are just a few of the interesting antiviral medication classes to investigate. Apart from antiviral medicines, various interesting techniques to treating COVID-19 are being employed, such as convalescent plasma, which has been found to reduce viral load and patient morbidity. The effects of interferon (IFN)-α/β and IL-6R inhibitor1 have also been encouraging [126, 127, 128]. The introduction of several new technologies is likely to yield good benefits. The safety of patients should be prioritized while evaluating new SARS-CoV-2 vaccinations. Nanotechnology offers an effective new route for diagnostics and treatment techniques. The more distinctive nanoparticles operate as excellent antiviral medication delivery vehicles, increasing the procedure’s effectiveness. Finding appropriate diagnostic and therapeutic strategies for the fast and efficient care of severe COVID-19 patients is urgently needed [129, 130]. Different research on different CoV-induced diseases shows that using α-interferon, chloroquine phosphate, arabinol, remdesivir, lopinavir/ritonavir, and anti-inflammatory medications might result in encouraging clinical results for SARS-Cov-2 patients. Tocilizumab should be used as a therapy approach for severe COVID-19 pneumonia to achieve favorable results. Furthermore, further clinical studies with appropriate medications should be conducted on SARS-CoV-2 patients to demonstrate effectiveness and safety.
We are thankful to the Laboratory Medicine Department, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, 21955, Saudi Arabia, and Glocal School of Pharmacy, Glocal University, Mirzapur Pole, 247121, Saharanpur, Uttar Pradesh, India for providing necessary facilities.
The authors declare no conflicts of interest.
3CLpro | 3-chymotrypsin-like protease |
AAK1 | AP2-associated protein kinase 1 |
ACE2 | angiotensin-converting enzyme 2 |
ACEi | angiotensin-converting-enzyme inhibitors |
ADR | acute respiratory distress syndrome |
AIDS | acquired immunodeficiency syndrome |
Cm | maximum concentration |
CMV | cytomegalovirus |
COVID-19 | coronavirus disease 2019 |
DCGI | drug controller general of India |
DR | drug repurposing |
E | envelope protein |
ER | endoplasmic reticulum |
ERGIC | endoplasmic reticulum-Golgi apparatus compartment |
EVD | Ebola virus disease |
HA | hemagglutinin envelope glycoprotein |
HAV | hepatitis A virus |
HCMV | human cytomegalovirus |
HCV | hepatitis-C virus |
HE | hemagglutinin esterase |
HSV | herpes simplex virus |
ICU | intensive care unit |
INF-β | interferon |
Kb | kilo base pairs |
M | membrane protein |
MERS | Middle-East Respiratory Syndrome |
Mpro | main protease |
N | nucleocapsid |
NNRTI | non-nucleoside reverse-transcriptase inhibitors |
NRTTI | nucleoside reverse transcriptase translocation inhibitors |
NtRTI | nucleotide reverse-transcriptase inhibitor |
PLPro | Papain-like protease |
qRT-PCR | quantitative real-time polymerase chain reaction |
R0 | reproductive number |
RdRp | RNA-dependent RNA polymerase |
RNA | ribonucleic acid |
RVFV | Rift Valley fever virus |
S(P) | Spike protein |
S | glycoprotein spike |
SARS | severe acute respiratory syndrome |
SARS-CoV-2 | Severe acute respiratory syndrome coronavirus 2 |
SPs | spike proteins |
TMPRSS2 | transmembrane serine protease 2 |
US FDA | United States Food and Drug Administration |
WHO | World Health Organization |
IntechOpen has always supported new and evolving ideas in scholarly publishing. We understand the community we serve, but to provide an even better service for our IntechOpen Authors and Academic Editors, we have partnered with leading companies and associations in the scientific field and beyond.
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Al-Megren and Rashid H. Altamimi",coverURL:"https://cdn.intechopen.com/books/images_new/5135.jpg",editedByType:"Edited by",editors:[{id:"104672",title:"Dr.",name:"Hamid",middleName:"Audah",surname:"Al-Megren",slug:"hamid-al-megren",fullName:"Hamid Al-Megren"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:1,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"53965",doi:"10.5772/67301",title:"Shale Gas in Poland",slug:"shale-gas-in-poland",totalDownloads:1521,totalCrossrefCites:7,totalDimensionsCites:10,abstract:"An example of interpretation of the Silurian and Ordovician shale formations in the Baltic Basin in Poland regarding determination of potential sweet spots is presented. Short geological information shows the position of shale gas play. Description of the data—laboratory measurement outcomes (petrophysical and geochemical) and well logging—presents results available for analyses. Detailed elemental analyses and various statistical classifications show the differentiation between sweet spots and adjacent formations. Elastic property modelling based on the known theoretical models and results of comprehensive interpretation of well logs is a good tool to complete information, especially in old wells. Acoustic emission investigations show additional characteristic features of shale gas rock and reveal that acoustic emission and volumetric strain of a shale sample induced by the sorption processes are lower for shale than for coals.",book:{id:"5135",slug:"advances-in-natural-gas-emerging-technologies",title:"Advances in Natural Gas Emerging Technologies",fullTitle:"Advances in Natural Gas Emerging Technologies"},signatures:"Jadwiga A. Jarzyna, Maria Bała, Paulina I. Krakowska, Edyta\nPuskarczyk, Anna Strzępowicz, Kamila Wawrzyniak-Guz, Dariusz\nWięcław and Jerzy Ziętek",authors:[{id:"179925",title:"Prof.",name:"Jadwiga",middleName:null,surname:"Jarzyna",slug:"jadwiga-jarzyna",fullName:"Jadwiga Jarzyna"},{id:"186797",title:"Prof.",name:"Maria",middleName:null,surname:"Bała",slug:"maria-bala",fullName:"Maria Bała"}]},{id:"53819",doi:"10.5772/67022",title:"Improvement of Hydraulic Fracture Conductivity Using Nanoparticles",slug:"improvement-of-hydraulic-fracture-conductivity-using-nanoparticles",totalDownloads:1477,totalCrossrefCites:3,totalDimensionsCites:7,abstract:"Hydraulic fracturing is a commonly used practice in the oil industry for well stimulation and production enhancement. With the general theme of the oil and gas industry moving toward systems with nano-sized pores, nanoparticles have gained a significant amount of attention especially in the field of hydraulic fracturing. Several groups have developed different nanoparticle systems that improve hydraulic fracture conductivity. This paper is a review of the highlighted work published in the area of application of nanoparticles to improve fracture conductivity. Nanotechnology can be used to improve the efficiency of hydraulic fracturing process. Four major production challenges faced by the oil and gas industry including incomplete filter cake cleanup, proppant pack damage, formation damage, and having micro-fractures that are not packed with proppants and will close under closure stress are introduced in this work. Solutions have also been reported using the advances in nanotechnology to address some of these challenges.",book:{id:"5135",slug:"advances-in-natural-gas-emerging-technologies",title:"Advances in Natural Gas Emerging Technologies",fullTitle:"Advances in Natural Gas Emerging Technologies"},signatures:"Reza Barati and Charles Chempakathinal Bose",authors:[{id:"194161",title:"Prof.",name:"Reza",middleName:null,surname:"Barati",slug:"reza-barati",fullName:"Reza Barati"}]},{id:"54180",doi:"10.5772/67336",title:"Compressed Natural Gas Direct Injection: Comparison Between Homogeneous and Stratified Combustion",slug:"compressed-natural-gas-direct-injection-comparison-between-homogeneous-and-stratified-combustion",totalDownloads:1668,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Due to abundance of natural gas, the use of natural gas for automotive use, particularly for internal combustion engine (ICE), is more practical and cheaper than their future successors. Even though natural gas is a cleaner fuel than other fossil fuels and has a higher octane number and can lead to higher thermal efficiency, its low carbon number makes it less attractive as compared to gasoline and diesel. Based on its potential, an engine referred to as compressed natural gas direct injection engine (CNGDI) was designed, developed and tested to operate on compressed natural gas (CNG) as monofuel directly and centrally injected into the engine. Computational and experimental works have been performed to investigate the viability of the design. Computational fluid dynamics (CFD) simulations and experimental works with homogenous combustion showed that the results were in good agreement. From experimental works, it is found that combustion characteristics could be improved by using a stratified charge piston configuration with some drawback on performance. In terms of exhaust emissions, stratified configuration causes slight increase in the emission of CO, CO2 and NOx, which highlight a need for further study on this issue.",book:{id:"5135",slug:"advances-in-natural-gas-emerging-technologies",title:"Advances in Natural Gas Emerging Technologies",fullTitle:"Advances in Natural Gas Emerging Technologies"},signatures:"Shahrir Abdullah, Wan Mohd Faizal Wan Mahmood, Saad Aljamali\nand Azhari Shamsudeen",authors:[{id:"194152",title:"Prof.",name:"Shahrir",middleName:null,surname:"Abdullah",slug:"shahrir-abdullah",fullName:"Shahrir Abdullah"},{id:"194227",title:"Prof.",name:"Wan Mohd Faizal",middleName:null,surname:"Wan Mahmood",slug:"wan-mohd-faizal-wan-mahmood",fullName:"Wan Mohd Faizal Wan Mahmood"},{id:"194228",title:"Mr.",name:"Saad",middleName:null,surname:"Aljamali",slug:"saad-aljamali",fullName:"Saad Aljamali"},{id:"194229",title:"Mr.",name:"Azhari",middleName:null,surname:"Shamsudeen",slug:"azhari-shamsudeen",fullName:"Azhari Shamsudeen"}]},{id:"54526",doi:"10.5772/67771",title:"Energy Recovery from the LNG Regasification Process",slug:"energy-recovery-from-the-lng-regasification-process",totalDownloads:2758,totalCrossrefCites:0,totalDimensionsCites:2,abstract:"The global request of natural gas (NG) is continuously increasing, consequently also the regasification of liquefied natural gas (LNG) is becoming a process largely employed. Liquefied natural gas at a temperature of around 113 K at atmospheric pressure has to be regasified for its transportation by pipeline. The regasification process makes the LNG exergy available for various applications, particularly for the production of electrical energy. Different possibilities to exploit the thermal energy released during regasification are available. New plant configurations whose functioning does not constrain the processes of the regasification terminal are proposed. A possible solution is LNG exploitation as a cold source for ocean thermal energy conversion (OTEC) power plants. Electric energy can be produced also by the exploitation of heat released from hot sources, for instance, the condensation heat of power plants by means of consecutive thermodynamic cycles. The rational use of the cold source (LNG) allows the increment of electrical production and growth of the thermodynamic efficiency, with corresponding environmental benefits.",book:{id:"5135",slug:"advances-in-natural-gas-emerging-technologies",title:"Advances in Natural Gas Emerging Technologies",fullTitle:"Advances in Natural Gas Emerging Technologies"},signatures:"Roberto Bruno, Piero Bevilacqua and Natale Arcuri",authors:[{id:"178283",title:"Ph.D.",name:"Roberto",middleName:null,surname:"Bruno",slug:"roberto-bruno",fullName:"Roberto Bruno"},{id:"178285",title:"Prof.",name:"Natale",middleName:null,surname:"Arcuri",slug:"natale-arcuri",fullName:"Natale Arcuri"},{id:"178286",title:"Dr.",name:"Piero",middleName:null,surname:"Bevilacqua",slug:"piero-bevilacqua",fullName:"Piero Bevilacqua"}]},{id:"54923",doi:"10.5772/67952",title:"Biomass as an Alternative for Gas Production",slug:"biomass-as-an-alternative-for-gas-production",totalDownloads:1813,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Natural gas comes from the decomposition of organic material under anaerobic conditions in a process that occurred around 150 million years ago, which allows the gas trapping between rock pore spaces (porous system). Even though natural gas has become one of the most used fuels around the world, there are other spontaneous, continuous, ongoing, or inducing processes that can produce a similar gas in a short time (considering human scale); we refer to biogas. The aim of this chapter is to describe the biomass potential from organic residues for biogas production. The first part explains the biomass as an energy source, a comparison between natural gas reserves and sources of biogas with a global perspective of their energy contribution. The main biomass conversion technologies followed by case studies are shown in the second part. Finally, the biomethanization process is covered as a promising way to valorize some biomass residues into natural gas. Information about where and how the biogas can be contained, controlled, and distributed is provided. This chapter focuses in considering biogas as an alternative in the fuel demand with the advantage of coming from a renewable source, providing electricity, heat, or transport, and the generation of by-products.",book:{id:"5135",slug:"advances-in-natural-gas-emerging-technologies",title:"Advances in Natural Gas Emerging Technologies",fullTitle:"Advances in Natural Gas Emerging Technologies"},signatures:"Liliana Pampillón-González and José Ramón Laines Canepa",authors:[{id:"178478",title:"Dr.",name:"Liliana",middleName:null,surname:"Pampillón-González",slug:"liliana-pampillon-gonzalez",fullName:"Liliana Pampillón-González"},{id:"200100",title:"Dr.",name:"José Ramón",middleName:null,surname:"Laines Canepa",slug:"jose-ramon-laines-canepa",fullName:"José Ramón Laines Canepa"}]}],mostDownloadedChaptersLast30Days:[{id:"54526",title:"Energy Recovery from the LNG Regasification Process",slug:"energy-recovery-from-the-lng-regasification-process",totalDownloads:2757,totalCrossrefCites:0,totalDimensionsCites:2,abstract:"The global request of natural gas (NG) is continuously increasing, consequently also the regasification of liquefied natural gas (LNG) is becoming a process largely employed. Liquefied natural gas at a temperature of around 113 K at atmospheric pressure has to be regasified for its transportation by pipeline. The regasification process makes the LNG exergy available for various applications, particularly for the production of electrical energy. Different possibilities to exploit the thermal energy released during regasification are available. New plant configurations whose functioning does not constrain the processes of the regasification terminal are proposed. A possible solution is LNG exploitation as a cold source for ocean thermal energy conversion (OTEC) power plants. Electric energy can be produced also by the exploitation of heat released from hot sources, for instance, the condensation heat of power plants by means of consecutive thermodynamic cycles. The rational use of the cold source (LNG) allows the increment of electrical production and growth of the thermodynamic efficiency, with corresponding environmental benefits.",book:{id:"5135",slug:"advances-in-natural-gas-emerging-technologies",title:"Advances in Natural Gas Emerging Technologies",fullTitle:"Advances in Natural Gas Emerging Technologies"},signatures:"Roberto Bruno, Piero Bevilacqua and Natale Arcuri",authors:[{id:"178283",title:"Ph.D.",name:"Roberto",middleName:null,surname:"Bruno",slug:"roberto-bruno",fullName:"Roberto Bruno"},{id:"178285",title:"Prof.",name:"Natale",middleName:null,surname:"Arcuri",slug:"natale-arcuri",fullName:"Natale Arcuri"},{id:"178286",title:"Dr.",name:"Piero",middleName:null,surname:"Bevilacqua",slug:"piero-bevilacqua",fullName:"Piero Bevilacqua"}]},{id:"54370",title:"Gas Well Testing",slug:"gas-well-testing",totalDownloads:1669,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Modeling liquid flow for well test interpretation considers constant values of both density and compressibility within the range of dealt pressures. This assumption does not apply for gas flow case in which the gas compressibility factor is also included for a better mathematical representation. The gas flow equation is normally linearized to allow the liquid diffusivity solution to satisfy gas flow behavior. Depending upon the viscosity-compressibility product, three treatments are considered for the linearization: square of pressure squared, pseudopressure, or linear pressure. When wellbore storage conditions are insignificant, drawdown tests are best analyzed using the pseudopressure function. Besides, since the viscosity-compressibility product is highly sensitive in gas flow; then, pseudotime best captures the gas thermodynamics. Buildup pressure tests, for example, require linearization of both pseudotime and pseudopressure. The conventional straight-line method has been customarily used for well test interpretation. Its disadvantages are the accuracy in determining of the starting and ending of a given flow regime and the lack of verification. This is not the case of the Tiab’s Direct Synthesis technique (TDS) which is indifferently applied to either drawdown or buildup tests and is based on features and intersection points found of the pressure and pressure derivative log-log plot.",book:{id:"5135",slug:"advances-in-natural-gas-emerging-technologies",title:"Advances in Natural Gas Emerging Technologies",fullTitle:"Advances in Natural Gas Emerging Technologies"},signatures:"Freddy Humberto Escobar",authors:[{id:"142270",title:"Dr.",name:"Freddy",middleName:"Humberto",surname:"Escobar",slug:"freddy-escobar",fullName:"Freddy Escobar"}]},{id:"56468",title:"Concepts for Regasification of LNG in Industrial Parks",slug:"concepts-for-regasification-of-lng-in-industrial-parks",totalDownloads:1974,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"The exponentially growing markets of liquefied natural gas (LNG) require efficient processes for LNG regasification within import terminals. Usually, the regasification of LNG is accomplished by direct or indirect heating. However, integrating LNG regasification into different processes within industrial parks (mainly processes involving low temperatures) is an efficient approach because of the utilization of the low-temperature energy. In some LNG import terminals, integration technologies are already being used. Previous publications showed an increase in the thermodynamic efficiency for systems combining air separation (as an example) and LNG regasification. In addition, the variation in the efficiency as well as the capital investment depends on the schematic and operation conditions. This fact creates great potential for improving the systems. In this chapter, different schematics are evaluated using exergy-based methods in order to improve the effectiveness of complex industrial processes that can involve LNG regasification.",book:{id:"5135",slug:"advances-in-natural-gas-emerging-technologies",title:"Advances in Natural Gas Emerging Technologies",fullTitle:"Advances in Natural Gas Emerging Technologies"},signatures:"Tatiana Morosuk, Stefanie Tesch and George Tsatsaronis",authors:[{id:"193888",title:"Prof.",name:"Tatiana",middleName:null,surname:"Morosuk",slug:"tatiana-morosuk",fullName:"Tatiana Morosuk"},{id:"194210",title:"MSc.",name:"Stefanie",middleName:null,surname:"Tesch",slug:"stefanie-tesch",fullName:"Stefanie Tesch"},{id:"194211",title:"Prof.",name:"George",middleName:null,surname:"Tsatsaronis",slug:"george-tsatsaronis",fullName:"George Tsatsaronis"}]},{id:"55066",title:"Experiment and Evaluation of Natural Gas Hydration in a Spraying Reactor",slug:"experiment-and-evaluation-of-natural-gas-hydration-in-a-spraying-reactor",totalDownloads:1262,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"1L spraying reactor with a heat exchanger outside was used to investigate the effect of spraying hydration process on storage capacity of methane in hydrate and on a methane storage rate in hydrate to solve a problem of lower gas molecular transfer rate and worse heat transfer rate. Some results showed that ethanol as a promoter had better spraying hydration rate under the liquid spraying pressure 4–5 MPa, 0.46Vg VH‐1 min‐1, which had been approximately 10 times when conventional additive, sodium dodecyl sulfate, was added to reaction system. Others showed that the spraying hydration reactor in advantage had lain in achieving higher hydration rate at lower operational pressure of gas phase compared with semi‐continuous stirred tank reactor. Furthermore, evaluation investigation on spraying hydration reaction showed that energy consumption had been 0.41kJ, while methane hydrates containing 1kJ heat were produced, and that the capital efficiency in economy for the hydration process had been 0.41 under perfect competition. Finally, the process evaluation parameter used had become a measure instrument for the prospect of resource utilization efficiency or for venture forecasting of capital investment.",book:{id:"5135",slug:"advances-in-natural-gas-emerging-technologies",title:"Advances in Natural Gas Emerging Technologies",fullTitle:"Advances in Natural Gas Emerging Technologies"},signatures:"Wenfeng Hao",authors:[{id:"10087",title:"Dr.",name:"Wenfeng",middleName:null,surname:"Hao",slug:"wenfeng-hao",fullName:"Wenfeng Hao"}]},{id:"54923",title:"Biomass as an Alternative for Gas Production",slug:"biomass-as-an-alternative-for-gas-production",totalDownloads:1812,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Natural gas comes from the decomposition of organic material under anaerobic conditions in a process that occurred around 150 million years ago, which allows the gas trapping between rock pore spaces (porous system). Even though natural gas has become one of the most used fuels around the world, there are other spontaneous, continuous, ongoing, or inducing processes that can produce a similar gas in a short time (considering human scale); we refer to biogas. The aim of this chapter is to describe the biomass potential from organic residues for biogas production. The first part explains the biomass as an energy source, a comparison between natural gas reserves and sources of biogas with a global perspective of their energy contribution. The main biomass conversion technologies followed by case studies are shown in the second part. Finally, the biomethanization process is covered as a promising way to valorize some biomass residues into natural gas. Information about where and how the biogas can be contained, controlled, and distributed is provided. This chapter focuses in considering biogas as an alternative in the fuel demand with the advantage of coming from a renewable source, providing electricity, heat, or transport, and the generation of by-products.",book:{id:"5135",slug:"advances-in-natural-gas-emerging-technologies",title:"Advances in Natural Gas Emerging Technologies",fullTitle:"Advances in Natural Gas Emerging Technologies"},signatures:"Liliana Pampillón-González and José Ramón Laines Canepa",authors:[{id:"178478",title:"Dr.",name:"Liliana",middleName:null,surname:"Pampillón-González",slug:"liliana-pampillon-gonzalez",fullName:"Liliana Pampillón-González"},{id:"200100",title:"Dr.",name:"José Ramón",middleName:null,surname:"Laines Canepa",slug:"jose-ramon-laines-canepa",fullName:"José Ramón Laines Canepa"}]}],onlineFirstChaptersFilter:{topicId:"947",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:33,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:42,paginationItems:[{id:"82914",title:"Glance on the Critical Role of IL-23 Receptor Gene Variations in Inflammation-Induced Carcinogenesis",doi:"10.5772/intechopen.105049",signatures:"Mohammed El-Gedamy",slug:"glance-on-the-critical-role-of-il-23-receptor-gene-variations-in-inflammation-induced-carcinogenesis",totalDownloads:11,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Chemokines Updates",coverURL:"https://cdn.intechopen.com/books/images_new/11672.jpg",subseries:{id:"18",title:"Proteomics"}}},{id:"82875",title:"Lipidomics as a Tool in the Diagnosis and Clinical Therapy",doi:"10.5772/intechopen.105857",signatures:"María Elizbeth Alvarez Sánchez, Erick Nolasco Ontiveros, Rodrigo Arreola, Adriana Montserrat Espinosa González, Ana María García Bores, Roberto Eduardo López Urrutia, Ignacio Peñalosa Castro, María del Socorro Sánchez Correa and Edgar Antonio Estrella Parra",slug:"lipidomics-as-a-tool-in-the-diagnosis-and-clinical-therapy",totalDownloads:7,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Fatty Acids - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11669.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82440",title:"Lipid Metabolism and Associated Molecular Signaling Events in Autoimmune Disease",doi:"10.5772/intechopen.105746",signatures:"Mohan Vanditha, Sonu Das and Mathew John",slug:"lipid-metabolism-and-associated-molecular-signaling-events-in-autoimmune-disease",totalDownloads:17,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Fatty Acids - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11669.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82483",title:"Oxidative Stress in Cardiovascular Diseases",doi:"10.5772/intechopen.105891",signatures:"Laura Mourino-Alvarez, Tamara Sastre-Oliva, Nerea Corbacho-Alonso and Maria G. Barderas",slug:"oxidative-stress-in-cardiovascular-diseases",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Importance of Oxidative Stress and Antioxidant System in Health and Disease",coverURL:"https://cdn.intechopen.com/books/images_new/11671.jpg",subseries:{id:"15",title:"Chemical Biology"}}}]},overviewPagePublishedBooks:{paginationCount:33,paginationItems:[{type:"book",id:"7006",title:"Biochemistry and Health Benefits of Fatty Acids",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7006.jpg",slug:"biochemistry-and-health-benefits-of-fatty-acids",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Viduranga Waisundara",hash:"c93a00abd68b5eba67e5e719f67fd20b",volumeInSeries:1,fullTitle:"Biochemistry and Health Benefits of Fatty Acids",editors:[{id:"194281",title:"Dr.",name:"Viduranga Y.",middleName:null,surname:"Waisundara",slug:"viduranga-y.-waisundara",fullName:"Viduranga Y. Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science\nand Technology from the Department of Chemistry, National\nUniversity of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013.\nShe relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the\nNational Institute of Fundamental Studies from April 2013 to\nOctober 2016. She was a senior lecturer on a temporary basis at the Department of\nFood Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is\ncurrently Deputy Principal of the Australian College of Business and Technology –\nKandy Campus, Sri Lanka. She is also the Global Harmonization Initiative (GHI)",institutionString:"Australian College of Business & Technology",institution:{name:"Kobe College",institutionURL:null,country:{name:"Japan"}}}]},{type:"book",id:"6820",title:"Keratin",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6820.jpg",slug:"keratin",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Miroslav Blumenberg",hash:"6def75cd4b6b5324a02b6dc0359896d0",volumeInSeries:2,fullTitle:"Keratin",editors:[{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}}]},{type:"book",id:"7978",title:"Vitamin A",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7978.jpg",slug:"vitamin-a",publishedDate:"May 15th 2019",editedByType:"Edited by",bookSignature:"Leila Queiroz Zepka, Veridiana Vera de Rosso and Eduardo Jacob-Lopes",hash:"dad04a658ab9e3d851d23705980a688b",volumeInSeries:3,fullTitle:"Vitamin A",editors:[{id:"261969",title:"Dr.",name:"Leila",middleName:null,surname:"Queiroz Zepka",slug:"leila-queiroz-zepka",fullName:"Leila Queiroz Zepka",profilePictureURL:"https://mts.intechopen.com/storage/users/261969/images/system/261969.png",biography:"Prof. Dr. Leila Queiroz Zepka is currently an associate professor in the Department of Food Technology and Science, Federal University of Santa Maria, Brazil. She has more than fifteen years of teaching and research experience. She has published more than 550 scientific publications/communications, including 15 books, 50 book chapters, 100 original research papers, 380 research communications in national and international conferences, and 12 patents. She is a member of the editorial board of five journals and acts as a reviewer for several national and international journals. 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He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. 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He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"117248",title:"Dr.",name:"Andrew",middleName:null,surname:"Macnab",slug:"andrew-macnab",fullName:"Andrew Macnab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"322007",title:"Dr.",name:"Maria Elizbeth",middleName:null,surname:"Alvarez-Sánchez",slug:"maria-elizbeth-alvarez-sanchez",fullName:"Maria Elizbeth Alvarez-Sánchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",country:{name:"Mexico"}}},{id:"337443",title:"Dr.",name:"Juan",middleName:null,surname:"A. 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Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",hasOnlineFirst:!1,hasPublishedBooks:!0,annualVolume:11404,editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",slug:"adriano-andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",biography:"Dr. Adriano de Oliveira Andrade graduated in Electrical Engineering at the Federal University of Goiás (Brazil) in 1997. He received his MSc and PhD in Biomedical Engineering respectively from the Federal University of Uberlândia (UFU, Brazil) in 2000 and from the University of Reading (UK) in 2005. He completed a one-year Post-Doctoral Fellowship awarded by the DFAIT (Foreign Affairs and International Trade Canada) at the Institute of Biomedical Engineering of the University of New Brunswick (Canada) in 2010. Currently, he is Professor in the Faculty of Electrical Engineering (UFU). He has authored and co-authored more than 200 peer-reviewed publications in Biomedical Engineering. He has been a researcher of The National Council for Scientific and Technological Development (CNPq-Brazil) since 2009. He has served as an ad-hoc consultant for CNPq, CAPES (Coordination for the Improvement of Higher Education Personnel), FINEP (Brazilian Innovation Agency), and other funding bodies on several occasions. He was the Secretary of the Brazilian Society of Biomedical Engineering (SBEB) from 2015 to 2016, President of SBEB (2017-2018) and Vice-President of SBEB (2019-2020). He was the head of the undergraduate program in Biomedical Engineering of the Federal University of Uberlândia (2015 - June/2019) and the head of the Centre for Innovation and Technology Assessment in Health (NIATS/UFU) since 2010. He is the head of the Postgraduate Program in Biomedical Engineering (UFU, July/2019 - to date). He was the secretary of the Parkinson's Disease Association of Uberlândia (2018-2019). Dr. Andrade's primary area of research is focused towards getting information from the neuromuscular system to understand its strategies of organization, adaptation and controlling in the context of motor neuron diseases. 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