",isbn:"978-1-80356-966-6",printIsbn:"978-1-80356-965-9",pdfIsbn:"978-1-80356-967-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"f86a9f720cc3ac0f1c385d0367ea89b9",bookSignature:"Dr. Fiaz Ahmad and Prof. Muhammad Sultan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11624.jpg",keywords:"Agricultural Waste, Reuse, Reduction, Soil Health, Recycling, Agriculture and Environment, Modelling and Simulation, Agro-Industrial Waste, Bioresource Processing, Processing and Management, Crop Residue, Forest Waste",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 8th 2022",dateEndSecondStepPublish:"June 16th 2022",dateEndThirdStepPublish:"August 15th 2022",dateEndFourthStepPublish:"November 3rd 2022",dateEndFifthStepPublish:"January 2nd 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Fiaz Ahmad is a researcher in the field of Agricultural Engineering with fifteen years of field and academic experience, currently in charge of the Agricultural Machinery Design Laboratory at Bahauddin Zakariya University. He applied for two patents at the national level.",coeditorOneBiosketch:"A renowned researcher in the field of Agricultural Engineering with 14 years of academic experience at Bahauddin Zakariya University. Winner of various prestigious fellowships, awards, and research grants. Published 250+ articles along with several books and chapters. Guest editor of seven ISI-SCI journals for publishers like SAGE, MDPI, and Frontiers.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"338219",title:"Dr.",name:"Fiaz",middleName:null,surname:"Ahmad",slug:"fiaz-ahmad",fullName:"Fiaz Ahmad",profilePictureURL:"https://mts.intechopen.com/storage/users/338219/images/system/338219.png",biography:"Dr. Fiaz Ahmad is an assistant professor and lecturer at the Department of Agricultural Engineering, Bahauddin Zakariya University, Multan, Pakistan. He obtained his Ph.D. in Agricultural Bioenvironmental and Energy Engineering from Nanjing Agriculture University, China, in 2015, and completed his postdoctorate in Agricultural Engineering from Jiangsu University, Zhenjiang, China, in 2020. He was awarded a fellowship from the Higher Education Commission of Pakistan for Ph.D. studies and from the Chinese Government for post-doctoral studies. He earned a BSc and MSc (Hons) in Agricultural Engineering from the University of Agriculture, Faisalabad, Pakistan, in 2004 and 2007, respectively. He is the author of more than fifty journal and conference articles. He has supervised six master’s students to date, and is currently supervising six master and two doctoral students. Dr. Ahmad has completed three research projects with his research interest focusing on the design of agricultural machinery, agricultural waste management, artificial intelligence (AI), and agricultural bioenvironment.",institutionString:"Bahauddin Zakariya University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Bahauddin Zakariya University",institutionURL:null,country:{name:"Pakistan"}}}],coeditorOne:{id:"199381",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sultan",slug:"muhammad-sultan",fullName:"Muhammad Sultan",profilePictureURL:"https://mts.intechopen.com/storage/users/199381/images/system/199381.png",biography:"Muhammad Sultan is an Assistant Professor at the Department of Agricultural\r\nEngineering, Bahauddin Zakariya University, Multan (Pakistan). He completed his Ph.D.\r\nand Postdoc from Kyushu University (Japan) in the field of Energy & Environmental\r\nEngineering. He was an awardee of MEXT and JASSO fellowships (from the Japanese\r\nGovernment) during Ph.D. and Postdoc studies, respectively. He also did a Postdoc as\r\na Canadian Queen Elizabeth Advance Scholar at Simon Fraser University (Canada) in\r\nthe field of Mechatronic Systems Engineering. He worked for Kyushu University\r\nInternational Institute for Carbon-Neutral Energy Research (WPI-I2CNER) for two years.\r\nCurrently, he is working on 4 research projects funded by the Higher Education\r\nCommission (HEC) of Pakistan. He has completed six projects in past in the field of\r\nagricultural engineering. He has supervised 10+ M.Eng. and Ph.D. thesis and 10+\r\nstudents are currently working under his supervision. He has published 120+ journal\r\narticles, 100+ conference articles, 13 book chapters, and 6 books. He is serving as guest\r\neditor for the journals like Sustainability (MDPI), Agriculture (MDPI), Energies (MDPI),\r\nAdvances in Mechanical Engineering (SAGE), Frontiers in Mechanical Engineering, and\r\nEvergreen Journal of Kyushu University. His research is focused on developing energy-\r\nefficient temperature and humidity control systems for agricultural storage, greenhouse,\r\nlivestock, and poultry applications. His research keywords include desiccant air-\r\nconditioning, evaporative cooling, adsorption heat pump, Maisotsenko cycle (M-cycle),\r\nenergy recovery ventilators; adsorption desalination; wastewater treatment.",institutionString:"Bahauddin Zakariya University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Bahauddin Zakariya University",institutionURL:null,country:{name:"Pakistan"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"5",title:"Agricultural and Biological Sciences",slug:"agricultural-and-biological-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"440212",firstName:"Elena",lastName:"Vracaric",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/440212/images/20007_n.jpg",email:"elena@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. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"10454",title:"Technology in Agriculture",subtitle:null,isOpenForSubmission:!1,hash:"dcfc52d92f694b0848977a3c11c13d00",slug:"technology-in-agriculture",bookSignature:"Fiaz Ahmad and Muhammad Sultan",coverURL:"https://cdn.intechopen.com/books/images_new/10454.jpg",editedByType:"Edited by",editors:[{id:"338219",title:"Dr.",name:"Fiaz",surname:"Ahmad",slug:"fiaz-ahmad",fullName:"Fiaz Ahmad"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6418",title:"Hyperspectral Imaging in Agriculture, Food and Environment",subtitle:null,isOpenForSubmission:!1,hash:"9005c36534a5dc065577a011aea13d4d",slug:"hyperspectral-imaging-in-agriculture-food-and-environment",bookSignature:"Alejandro Isabel Luna Maldonado, Humberto Rodríguez Fuentes and Juan Antonio Vidales Contreras",coverURL:"https://cdn.intechopen.com/books/images_new/6418.jpg",editedByType:"Edited by",editors:[{id:"105774",title:"Prof.",name:"Alejandro Isabel",surname:"Luna Maldonado",slug:"alejandro-isabel-luna-maldonado",fullName:"Alejandro Isabel Luna Maldonado"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10359",title:"Landraces",subtitle:"Traditional Variety and Natural Breed",isOpenForSubmission:!1,hash:"0600836fb2c422f7b624363d1e854f68",slug:"landraces-traditional-variety-and-natural-breed",bookSignature:"Amr Elkelish",coverURL:"https://cdn.intechopen.com/books/images_new/10359.jpg",editedByType:"Edited by",editors:[{id:"231337",title:"Dr.",name:"Amr",surname:"Elkelish",slug:"amr-elkelish",fullName:"Amr Elkelish"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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1. Introduction
\n
Iron deficiency anemia (IDA) is the most common nutritional deficiency worldwide. It can cause reduced work capacity in adults [1] and impact motor and mental development in children and adolescents [2]. There is some evidence that iron deficiency without anemia affects cognition in adolescent girls [3] and causes fatigue in adult women [4]. IDA may affect visual and auditory functioning and is weakly associated with poor cognitive development in children.
\n
The term “anemia” is used for a group of conditions in which the number of red blood cells in the blood is lower than normal, or the red blood cells do not have enough hemoglobin. The estimates of the prevalence of anemia vary widely and accurate data are often lacking, and it can be assumed that significant proportions of young children and women of childbearing age are anemic [5, 6].
\n
Iron deficiency results when iron demand by the body is not met by iron absorption from the diet. Thus, patients with IDA presenting in primary care may have inadequate dietary intake, hampered absorption, or physiologic losses in a woman of reproductive age. It also could be a sign of blood loss, known or occult. IDA is never an end diagnosis; the work-up is not complete until the reason for IDA is known.
\n
The risk factors associated with IDA includes the following: low socioeconomic status, race as black women have a lower mean hemoglobin and a wider standard deviation than white women, inadequate dietary intake or parity, suggesting that there may be an unidentified, possibly racial factor predisposing these women to iron deficiency [7].
\n
Anemia cannot be reliably diagnosed by clinical presentation. Fatigue, the most common reason to check hemoglobin, was caused by anemia in only one out of 52 patients in a primary care practice [8]. In a hospital setting, pallor predicted anemia with a likelihood ratio (LR) of 4.5. However, the absence of pallor was less helpful in ruling out anemia, giving an LR of 0.6 even when anemia was defined as less than 9 g per dL (90 g per L), a lower diagnostic level than that of the World Health Organization (WHO) or the Centers for Disease Control and Prevention (CDC) [9]. Other classic symptoms such as koilonychia (spoon nails), glossitis, or dysphagia are not common in the developed world [10].
\n
The diagnosis of IDA requires that a patient be anemic and show laboratory evidence of iron deficiency. Red blood cells in IDA are usually described as being microcytic (i.e., mean corpuscular volume less than 80 μm3 [80 fL]) and hypochromic; however, the manifestation of iron deficiency occurs in several stages [11]. Patients with a serum ferritin concentration less than 25 ng per mL (25 mcg per L) have a very high probability of being iron deficient. The most accurate initial diagnostic test for IDA is the serum ferritin measurement. Serum ferritin values greater than 100 ng per mL (100 mcg per L) indicate adequate iron stores and a low likelihood ratio of IDA [12]. In some populations, such as those with inflammatory disease or cirrhosis, these tests must be interpreted slightly differently because ferritin is an acute-phase reactant. Cutoffs for abnormality in these patients generally are higher [13].
\n
Another laboratory change that occurs in patients with IDA is an increase in the iron-carrying protein transferrin. The amount of iron available to bind to this molecule is reduced, causing a decrease in the transferrin saturation and an increase in the total iron-binding capacity. The serum transferrin receptor assay is a newer approach to measuring iron status at the cellular level. Increased levels are found in patients with IDA, and normal levels are found in patients with anemia of chronic disease [14].
\n
The treatment arms were chosen by block randomization in batches of eight using computer-generated random numbers to assign women to one of the four combinations of trial intervention. Block randomization was chosen to ensure that the experimental groups would not become unbalanced if the rate of recruitment at sites differed greatly. A research fellow with no other role in the project is overseeing the labeling and packing of all the trial medications and holding the randomization schedule until the code is broken.
\n
1.1. Level of significance
\n
This is the set standard to decide the cutoff value between treatment groups when comparing the two groups. If the results are significant at this set level (α = 0.05), the null hypothesis will be rejected.
\n
\n
\n
2. Patients, materials, and methods
\n
2.1. Study design
\n
The study was based on an experimental, randomized double-blind clinical trial. The study had been conducted according to principles of good clinical practice (i.e., an informed consent was obtained before enrollment and proper history and clinical examination were recorded on each follow-up), and the study was carried out during May 2003 to June 2004. A randomized double-blind experimental design was employed to test the hypotheses; therefore, by manipulating the independent variables (efficacy, side effects), any effects on dependent variable (herbal and allopathic treatment) could be monitored.
\n
\n
2.2. Patients
\n
The study was conducted on 50 patients aged 12–40 years who were attending gynecological outpatient visits in Shifa-ul-Mulk Memorial Hospital.
\n
\n
2.3. Setting
\n
The study was conducted in the Department of Gynecology and Obstetrics at Shifa-ul-Mulk Memorial Hospital for Eastern Medicine at Hamdard University in Karachi.
\n
\n
2.4. Sample selection
\n
In this study, only the patients selectively enrolled were diagnosed with IDA through clinical history and laboratory investigations were enrolled. Diagnosis of IDA was based on the typical signs and symptoms and laboratory finding. Complete blood picture (CBC), hemoglobin, erythrocyte sedimentation rate (ESR), and urine (routine and microscopic) tests were also performed.
\n
Recruiting GPs identify eligible women in their clinical practice and invite them to consider participation in the trial, after provision of sufficient information to make an informed decision. Women who meet the eligibility criteria and agree to participate are required to give written informed consent. Recruiting GPs also obtain demographic and relevant past and current medical data, particularly data relevant to risk factors for developing iron deficiency anemia. The interventions being tested are as follows:
\n
(i) Control group received allopathic treatment (syrup Ferplex two teaspoons for seven days), (ii) and the test group received the herbal medicine (two teaspoons of syrup Foulad for seven days). All participants were observed for three follow-up visits over the course of treatment until they improved.
\n
Blood samples were collected for CBC when the clinical picture shows the complete improvement to access the efficacy of the trial and confirmation and improvement in hemoglobin status. Same parameters were followed in control group.
\n
\n
2.5. Assessment
\n
The normal hemoglobin count in reference to age set standard of WHO, blood morphology, was used as the primary outcome of the study. Secondary outcomes included the total symptoms score, the global assessment of the treatment by the investigator, and the women safety of the drugs and severity of adverse events at each follow-up visit. The relationship of each event to the study drug was also assessed. The safety outcome measure was the incidence of treatment-emergent adverse events in both groups. A blood specimen for routine CBC was obtained prior to the treatment.
\n
\n
2.6. Inclusion criteria
\n
Persons may be included in the trial, if they meet the following criteria:
\n
Female patients between the aged of 12–40 years suffering from IDA.
All socioeconomical classes were included.
Verbal consent and willingness to participate in all scheduled study visits and tests.
Patients suffering from IDA.
Patients living in Karachi, Pakistan.
\n
\n
2.7. Exclusion criteria
\n
Patients were excluded, if they have any of the following criteria:
\n
Patients having other associated pathologies such as uncontrolled diabetes, hypertension, liver disorders, etc.
Patients having other types of anemia such as protein deficiency anemia, pernicious anemia, sickle cell anemia, and thalassaemia.
Known cases of iron therapy failure.
Patients suffering from iron deficiency in secondary to malignancies.
Patient belonging to any area outside Karachi because of intrinsic difficulty to follow up.
\n
\n
\n
3. Results
\n
The present study is to investigate formulated herbal medicine syrup Foulad for the treatment of IDA. The clinical screening of hematopoietic activity between Foulad and Ferplex was carried out to determine the efficacy and side effects towards off this malaise. These evaluations were based on clinical and laboratory findings so as to ascertain the rate of improvement in hemoglobin. In this study, a total of 50 patients were initially randomized and screened, the intent-to-treat population enrolled. The patients were evenly distributed to test or control group with the ratio of 1:1, that is, 25 in each group. This loss was distributed evenly between the treatment groups. The demographic and baseline characteristics of the intent-to-treat group were comparable for the herbal medicine and allopathic medicine treatment.
\n
3.1. Patient characteristics
\n
There were no significant differences in the mean age (26.12 ± 7.92 vs. 26.48 ± 4.75 test and control group, respectively, (Table 1) values between the treatment groups at the start of the clinical trial. All the patients were distributed in five-class interval ranging from age 12 to 40 years. The mean age of the married women’s was 32.0 versus 32.46 and mean age of the single patients was 19.75 versus 20.0.
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Marital status
\n
Treatment group
\n
Mean
\n
Number (n)
\n
Standard deviation
\n
Sum
\n
\n\n\n
\n
Married
\n
Control
\n
32.46
\n
13
\n
4.75
\n
422
\n
\n
\n
\n
Test
\n
32.00
\n
13
\n
4.98
\n
416
\n
\n
\n
\n
Total
\n
32.23
\n
26
\n
4.78
\n
838
\n
\n
\n
Single
\n
Control
\n
20.00
\n
12
\n
3.86
\n
240
\n
\n
\n
\n
Test
\n
19.75
\n
12
\n
5.17
\n
237
\n
\n
\n
\n
Total
\n
19.88
\n
24
\n
4.47
\n
477
\n
\n
\n
Total
\n
Control
\n
26.48
\n
25
\n
7.65
\n
662
\n
\n
\n
\n
Test
\n
26.12
\n
25
\n
7.98
\n
653
\n
\n
\n
\n
Total
\n
26.30
\n
50
\n
7.74
\n
1315
\n
\n\n
Table 1.
Marital status by treatment group.
\n
\n
3.2. Treatment assignment and follow-up
\n
All subjects were clinically studied and completed assigned therapy during the period May 2001 to June 2004. Results presented below represent an intention-to-treat analysis, as stipulated by this study protocol. Baseline patient characteristics for all study variables were balanced among treatment groups (Table 2).
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Anemia history and examination at baseline
\n
Treatment group
\n
Total n = 50
\n
pvalue
\n
\n
\n
Control
\n
Test
\n
\n\n\n
\n
Severity of anemia
\n
Mild
\n
10
\n
7
\n
17
\n
0.650
\n
\n
\n
Moderate
\n
12
\n
15
\n
27
\n
\n
\n
\n
Severe
\n
3
\n
3
\n
6
\n
\n
\n
\n
Symptoms of anemia
\n
Asymptomatic
\n
5
\n
2
\n
7
\n
0.613
\n
\n
\n
Fatigue
\n
2
\n
5
\n
7
\n
\n
\n
\n
Reduced concentration
\n
0
\n
1
\n
1
\n
\n
\n
Loss of appetite
\n
3
\n
1
\n
4
\n
\n
\n
Pica
\n
1
\n
2
\n
3
\n
\n
\n
Signs of anemia
\n
Koilonychia
\n
1
\n
1
\n
2
\n
\n
\n
Brittle nails
\n
1
\n
0
\n
1
\n
\n
\n
Pallor
\n
9
\n
11
\n
20
\n
\n
\n
Tachycardia
\n
3
\n
2
\n
5
\n
\n
\n
Causes of anemia
\n
Blood loss
\n
14
\n
13
\n
27
\n
0.990
\n
\n
\n
Decreased iron utilization
\n
6
\n
7
\n
13
\n
\n
\n
Dietary inadequacy
\n
4
\n
4
\n
8
\n
\n
\n
Malabsorption
\n
1
\n
1
\n
2
\n
\n\n
Table 2.
Baseline demographic variables.
\n
\n
3.3. Baseline demographic variables
\n
The baseline pretreatment analyses of IDA history and examination were performed. The clinical evaluation proforma of IDA was filled at the time of enrollment in both treatment groups. Patient’s baseline demographic variables for IDA history and general physical examination were summarized for each treatment group. As depicted in Table 2, patient characteristics were equally balanced between the test and control groups. The two treatment groups did not differ significantly (all p < 0.05) from each other at any time point. The most common IDA symptom was pallor 44.0% in allopathic treated and 36.0% in herbal-treated patients. Whereas the common cause of IDA noted in this trial was the blood loss 56% in allopathic treated and 52% in herbal-treated patients.
\n
\n
3.4. Baseline severity of anemia
\n
The assessment of severity of IDA at the time of enrollment exhibited following results test group versus control group, mild anemia 7 patients (28%) versus 10 (40%), moderate anemia 15 patients (60%) versus 12 (48%), severe anemia 3 patients (12%) versus 3 (12%) patients noticed in the both treatment groups. Baseline severity of anemia did not differ between the two groups. Comparative analysis of the baseline data using chi-square test confirms that there were no baseline differences among the treatment group as evident from p values in Table 2.
\n
\n
3.5. Effects of therapy on hemoglobin status
\n
Hemoglobin increased dramatically in both treatment groups after therapy (as dissipated in graph 2). The rates of the improvement in hemoglobin concentration were higher in the herbal treatment group at all times after treatment and it revealed that the efficacy of herbal treatment is as superior to allopathic treatment (p = 0.001). The total duration of treatment was 4 weeks in both treatment groups. The clinical success rates on the basis of self-assessment of patient regression of complaints and physician examination on follow-up were more effective in test group (as in graph 2).
\n
\n
Clinical failures or no significant improvement in hemoglobin after treatment occurred in 1/25 patients (4%) receiving herbal medicine and in 7/25 patients (28%) receiving allopathic medicines (graph 2). Clinical success rates for those with mild-to-moderate infections and those with severe infections were higher in test treatment group. For the overall, clinical success was observed in 15/25 patients (60%) of cases in herbal-treated patients and in 2/25 (8%) of cases in allopathic-treated patients.
\n
The overall evaluation was mainly based on the efficacy of drugs in reducing anemia in terms of both objective and subjective symptoms. The syrup Foulad produced a better result than allopathic medicines, which showed an overall cure rate of 60% versus 8% significantly effective as confirmed by chi-square test and the test treatment has superior efficacy than control treatment (p = 0.001).
\n
\n
3.6. Safety evaluations
\n
All the patients enrolled in the study were evaluated for safety. Adverse effects observed after administration of medicine are summarized in Table 3. The majority of adverse events were assessed as mild in severity. Adverse events categorized by the physician (researcher) as possibly or definitely drug related were reported in 3/25 patients (12%) receiving herbal medicine and in 15/25 patients (60%) given allopathic medicine.
\n
\n
\n
\n
\n
\n
\n\n
\n
Observed Side effects
\n
Treatment group
\n
Total
\n
p value
\n
\n
\n
Control
\n
Test
\n
\n\n\n
\n
Constipation
\n
2
\n
1
\n
3
\n
0.010
\n
\n
\n
Diarrhea
\n
3
\n
1
\n
4
\n
\n
\n
Nausea
\n
9
\n
1
\n
10
\n
\n
\n
Vomiting
\n
1
\n
0
\n
1
\n
\n
\n
No complaints
\n
10
\n
22
\n
32
\n
\n
\n
Total patients
\n
25
\n
25
\n
5
\n
\n\n
Table 3.
Side effects on patient’s self-assessment.
\n
Nausea was the most common drug-related events among allopathic medicine (36%) and herbal group (4%) recipients. Overall side effects (p = 0.010) were greater in control-treated participants than in test participants. No severe or serious adverse side effects were observed that interfere with activities of daily living. Comparison of data recorded by participants relating to these variables showed highly significant differences between test and control groups for measurements side effects as shown in Table 3.
\n
Consequently, the generated data rejected the null hypothesis (when p < 0.05); hence, the null hypothesis was rejected on the basis of statistical findings in regard to efficacy and safety.
\n
\n
\n
4. Discussion
\n
Iron deficiency anemia (IDA) is most often a polysymptomatic disease. The use of allopathic drug combination has been considered as one of the effective therapy. But this is not feasible, as besides being cost prohibitive, they are not without side effects. The herbal formulation syrup Foulad contains herbs, which are known for its wide range of clinical use in indigenous medicine. It has been proved that these herbs exert profound activity for the improvement of hemoglobin percentage.
\n
This unicenter trial demonstrated that herbal medicine was more effective in the management of patients with IDA. Herbal treatment resulted in a 60% clinical cure or improvement rate, which is superior to that achieved with allopathic therapy 8% clinical success rate. The response rate of hemoglobin improvement status before and after treatment suggests that syrup Foulad has the higher efficacy as allopathic medicine (p > 0.001).
\n
In light of study that the authors have presented, it is concluded that phytomedicine administered under a randomized double-blind trial is exhibiting desirable effects with a profound margin of safety. The plus point is that the formulations are absolutely cost-effective and have shown promising results even in surveillance studies. The spectrum of herbal medicine has been widening following the modalities of integrated medicine like our eastern system of medicine.
\n
The medicinal word is switching over to alternative medicine, including herbal medicine especially South Asia, due to its tremendous potential that is being confirmed by current researches. In this unicenter study, syrup Foulad was well tolerated and had a rate of drug-related adverse events less than to that of patients treated with syrup Ferplex (p = 0.010). Mild nausea was the most commonly reported adverse events in test treatment groups and control group.
\n
\n
5. Conclusion
\n
Based on the statistical result of present clinical trial, it can be concluded that
\n
a comparative evaluation of the IDA treatment by Sharbat Foulad vis-a-vis the syrup Ferplex differs in treatment response, the herbal medicine is superior to allopathic medication.
there was less untoward manifestation associated with the use of syrup Foulad and this is found a good acceptability by most of the treated patients. Syrup Foulad has added the benefit of safety.
\n
\n\n',keywords:"iron deficiency, herbal medicine, anemia",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/51044.pdf",chapterXML:"https://mts.intechopen.com/source/xml/51044.xml",downloadPdfUrl:"/chapter/pdf-download/51044",previewPdfUrl:"/chapter/pdf-preview/51044",totalDownloads:1586,totalViews:412,totalCrossrefCites:1,totalDimensionsCites:1,totalAltmetricsMentions:0,introChapter:null,impactScore:1,impactScorePercentile:67,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"October 14th 2015",dateReviewed:"April 25th 2016",datePrePublished:null,datePublished:"July 20th 2016",dateFinished:"June 9th 2016",readingETA:"0",abstract:"Iron deficiency is a common nutritional disorder in developing countries and contributes significantly to reduced work productivity and economic output as well as to increased morbidity and mortality. There are well-established biochemical tests for assessing iron status in developed countries. However, cost and interference from infectious conditions make it difficult to assess iron status in many developing country settings. Examination of the hemoglobin distribution in the population and assessment of the hemoglobin response to supplementation are alternative approaches to define iron status and the nature of anemia. Prevention and control of iron deficiency requires the combined approach of dietary improvement, fortification of a common staple food when feasible, and appropriate iron supplementation for infants and pregnant women. In all these intervention activities, operational research is needed to improve effectiveness. In addition, controlling iron deficiency requires coordination with other nutrition and primary health care programs as part of an integrated approach to improved health and nutrition of the population. A randomized, controlled double-blind clinical trial was conducted to compare the efficacy and safety of herbal medicinal treatment syrup Sharbat-a-Folad versus syrup Ferplex for the treatment of iron deficiency anemia (IDA).",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/51044",risUrl:"/chapter/ris/51044",book:{id:"5176",slug:"nutritional-deficiency"},signatures:"Halima Nazar and Khan Usmanghani",authors:[{id:"179274",title:"Dr.",name:"Halima",middleName:null,surname:"Nazar",fullName:"Halima Nazar",slug:"halima-nazar",email:"halimanazar76@gmail.com",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/179274/images/4365_n.jpg",institution:{name:"Hamdard University",institutionURL:null,country:{name:"Pakistan"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Level of significance",level:"2"},{id:"sec_3",title:"2. Patients, materials, and methods",level:"1"},{id:"sec_3_2",title:"2.1. Study design",level:"2"},{id:"sec_4_2",title:"2.2. Patients",level:"2"},{id:"sec_5_2",title:"2.3. Setting",level:"2"},{id:"sec_6_2",title:"2.4. Sample selection",level:"2"},{id:"sec_7_2",title:"2.5. Assessment",level:"2"},{id:"sec_8_2",title:"2.6. Inclusion criteria",level:"2"},{id:"sec_9_2",title:"2.7. Exclusion criteria",level:"2"},{id:"sec_11",title:"3. Results",level:"1"},{id:"sec_11_2",title:"3.1. Patient characteristics",level:"2"},{id:"sec_12_2",title:"3.2. Treatment assignment and follow-up",level:"2"},{id:"sec_13_2",title:"3.3. Baseline demographic variables",level:"2"},{id:"sec_14_2",title:"3.4. Baseline severity of anemia",level:"2"},{id:"sec_15_2",title:"3.5. Effects of therapy on hemoglobin status",level:"2"},{id:"sec_16_2",title:"3.6. Safety evaluations",level:"2"},{id:"sec_18",title:"4. Discussion",level:"1"},{id:"sec_19",title:"5. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Haas JD, Brownlie T, Iron deficiency and reduced work capacity: a critical review of the research to determine a causal relationship, J Nutr 2001;131(2 suppl):676S–88S; discussion 688S-90S.'},{id:"B2",body:'Halterman JS, Kaczorowski JM, Aligne CA, Auinger P, Szilagyi PG, Iron deficiency and cognitive achievement among school-aged children and adolescents in the United States, Pediatrics 2001;107:1381–6.'},{id:"B3",body:'Algarin C, Peirano P, Garrido M, Pizarro F, Lozoff B, Iron deficiency anemia in infancy: long-lasting effects on auditory and visual system functioning, Pediatr Res 2003;53:217–23.'},{id:"B4",body:'Verdon F, Burnand B, Stubi CL, Bonard C, Graff M, Michaud A, Iron supplementation for unexplained fatigue in non-anaemic women: double blind randomized placebo controlled trial, BMJ 2003;326:1124.'},{id:"B5",body:'Aftab Saeed, Director Hamdard Research institute of unani Medicine hrium@live.com. Iron deficiency anaemia: assessment, prevention and control. 2001, Geneva, World Health Organization, 2001 (document WHO/NHD/01.3).'},{id:"B6",body:'Focusing on anaemia: towards an integrated approach for effective anaemia control: joint statement by the World Health Organization and the United Nations, Children’s Fund, 2004, Geneva, World Health Organization.'},{id:"B7",body:'Ramakrishnan U, Frith-Terhune A, Cogswell M, Kettel Khan L, Dietary intake does not account for differences in low iron stores among Mexican American and non-Hispanic white women: Third National Health and Nutrition Examination Survey, 1988–1994, J Nutr 2002;132:996–1001.'},{id:"B8",body:'Elnicki DM, Shockcor WT, Brick JE, Beynon D, Evaluating the complaint of fatigue in primary care: diagnoses and outcomes, Am J Med 1992;93:303–6.'},{id:"B9",body:'Sheth TN, Choudhry NK, Bowes M, Detsky AS, The relation of conjunctival pallor to the presence of anemia, J Gen Intern Med 1997;12:102–6.'},{id:"B10",body:'Cook JD, Diagnosis and management of iron-deficiency anaemia, Best Pract Res Clin Haematol 2005;18:319–32.'},{id:"B11",body:'Zanella A, Gridelli L, Berzuini A, Colottie MT, Mozzi F, Milani S, Sensitivity and predictive value of serum ferritin and free erythrocyte protoporphyrin for iron deficiency, J Lab Clin Med 1989;113:73–8.'},{id:"B12",body:'Guyatt GH, Oxman AD, Ali M, Willan A, McIlroy W, Patterson C, Laboratory diagnosis of iron-deficiency anemia: an overview [published correction appears in J Gen Intern Med 1992;7:423], J Gen Intern Med 1992;7:145–53.'},{id:"B13",body:'Intragumtornchai T, Rojnukkarin P, Swasdikul D, Israsena S, The role of serum ferritin in the diagnosis of iron deficiency anaemia in patients with liver cirrhosis, J Intern Med 1998;243:233–41.'},{id:"B14",body:'Cook JD, Flowers CH, Skikne BS. The quantitative assessment of body iron. Blood.2003;101:3359–3364.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Halima Nazar",address:"halimanazar76@gmail.com",affiliation:'
Business Development Department, Rafay Laboratories Private Limited, Karachi, Pakistan
Research and Development Department, Herbion Pakistan Private Limited, Karachi, Pakistan
'}],corrections:null},book:{id:"5176",type:"book",title:"Nutritional Deficiency",subtitle:null,fullTitle:"Nutritional Deficiency",slug:"nutritional-deficiency",publishedDate:"July 20th 2016",bookSignature:"Pınar Erkekoglu and Belma Kocer-Gumusel",coverURL:"https://cdn.intechopen.com/books/images_new/5176.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-953-51-2438-2",printIsbn:"978-953-51-2437-5",pdfIsbn:"978-953-51-4193-8",reviewType:"peer-reviewed",numberOfWosCitations:40,isAvailableForWebshopOrdering:!0,editors:[{id:"109978",title:"Prof.",name:"Pınar",middleName:null,surname:"Erkekoglu",slug:"pinar-erkekoglu",fullName:"Pınar Erkekoglu"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"185037",title:"Dr.",name:"Belma",middleName:null,surname:"Kocer-Gumusel",slug:"belma-kocer-gumusel",fullName:"Belma Kocer-Gumusel"},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1130"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"50622",type:"chapter",title:"Zinc Deficiency and Depression",slug:"zinc-deficiency-and-depression",totalDownloads:2234,totalCrossrefCites:0,signatures:"Anna Rafalo, Magdalena Sowa‐Kucma, Bartlomiej Pochwat, Gabriel\nNowak and Bernadeta Szewczyk",reviewType:"peer-reviewed",authors:[{id:"129632",title:"Prof.",name:"Gabriel",middleName:null,surname:"Nowak",fullName:"Gabriel Nowak",slug:"gabriel-nowak"},{id:"180643",title:"Ph.D.",name:"Bernadeta",middleName:null,surname:"Szewczyk",fullName:"Bernadeta Szewczyk",slug:"bernadeta-szewczyk"},{id:"180644",title:"MSc.",name:"Anna",middleName:null,surname:"Rafało",fullName:"Anna Rafało",slug:"anna-rafalo"},{id:"185041",title:"Dr.",name:"Magdalena",middleName:null,surname:"Sowa-Kucma",fullName:"Magdalena Sowa-Kucma",slug:"magdalena-sowa-kucma"},{id:"185042",title:"Dr.",name:"Bartlomiej",middleName:null,surname:"Pochwat",fullName:"Bartlomiej Pochwat",slug:"bartlomiej-pochwat"}]},{id:"51098",type:"chapter",title:"Zinc Deficiency",slug:"zinc-deficiency",totalDownloads:2164,totalCrossrefCites:9,signatures:"Ann Katrin Sauer, Simone Hagmeyer and Andreas M. 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1. Introduction
In the recent years, machine learning techniques have been utilized to solve problems at hand across multitudes of industries and topics. In the healthcare industry, these techniques are often applied to a variety of healthcare claims and electronic health records data to garner valuable insights into diagnostic and treatment pathways in order to help optimize patient healthcare access and treatment process [1]. Unfortunately, many of these applications resulted in inaccurate or irrelevant research results, as proper research protocols were not fully followed [2]. On the other hand, statistics has been the basis of analysis in healthcare research for decades, especially, in the areas of clinical trials and health economics and outcomes research (HEOR), where the precision and accuracy of analyses have been the primary objectives [3]. Furthermore, the classical statistics methodologies are often preferred in those research areas to ensure the ability to replicate and defend the results and ultimately, the ability to publish the research content in peer-reviewed medical journals [3]. The increased availability of data, including data from wearables, provided the opportunity to apply a variety of analytical techniques and methodologies to identify patterns, often hidden, that could help with optimization of healthcare access as well as diagnostic and treatment process [4].
With the rapid increase in data from the healthcare and many other industries, it is important to consider how to select well - suited statistical and machine learning methodologies that would be best for the problem at hand, the available data type, and the overall research objectives [5]. Machine learning alone or complemented by statistical modeling is becoming, not just a more common, but a desired convergence to take advantage of the best of both approaches for advancing healthcare outcomes [1]. Please note that this book chapter was originally posted on the Cornell University’s research working article website: https://arxiv.org. The book chapter content is mostly the same between the two versions [6].
2. Machine learning foundation is in statistical learning theory
Machine learning (ML) is considered a branch of artificial intelligence and computer science that focuses on mimicking human behaviors through a set of algorithms and methods that use historical values to predict new values [7], without specifically being coded to do so and thereby learning over time [8, 9]. ML is grounded in statistical learning theory (SLT), which provides the constructs used to create prediction functions from data. One of the first examples of SLT was the creation of the support vector machine (SVM), the supervised learning method that can be used as for both classification and regression and has become a standard in modeling how to recognize visual objects [7]. SLT formalizes the model that makes a prediction based on observations (i.e., data) and ML automates the modeling [7].
SLT sets the mathematical and theoretical framework for ML as well as the properties of learning algorithms [7] with the goals of providing mechanisms for studying inference and creating algorithms that become more precise and improved over time [8]. SLT is based multivariate statistics and functional analysis [8]. Functional analysis is the branch of statistics that measures shapes, curves, and surfaces, extending multivariate vector statistics to continuous functions and finding functions that describe data patterns [8]. Inductive inference is the process of generalizing and modeling past observations to make predictions for the future; SLT formalizes the modeling concepts of inductive inference, while ML automates them [8].
For example, pattern recognition is considered a problem of inductive inference and SLT, as it is a curving-fitting problem, and one of the most common applications of ML [7, 8, 9]. Pattern recognition is not suited for traditional computer programming as the inferences needed are not free of assumptions and the patterns are not easily described or labeled programmatically with deterministic functions. The standard mathematics behind SLT makes no assumptions on distributions, uses stochastic functions that can include humans labeling the “right” classification, i.e., training data, and can assume that the probability of the occurrence of one observation is independent of another thereby including the concept of randomness [7, 8, 9]. These tenets are therefore those of ML as well.
SLT also provides the definition of terms often using in ML such as overfitting, underfitting and generalization. Overfitting is when the presence of noise in the data negatively affects training and the ultimate model performance because the noise is being incorporated into the learning process, thereby giving error when the model sees new data [8, 9]. Underfitting is when the noise impacts both performance on training data as well as new and unseen data [9]. In ML, discussion about underfitting and overfitting are often used to describe models that do not generalize the data effectively and might not present the right set of data elements to explain the data patterns and posited hypotheses [9]. Underfitting is often defined when model which is missing features that would be present in the most optimized model, akin to a regression model not fully explaining all of the variance of the dependent variable [9]. In a similar vein, overfitting is when the model contains more features or different features than is optimal, like a regression model with autocorrelation or multicollinearity [9].
The general goal of learning algorithms and therefore ML model optimization is to reduce the dimensions, features, or data variables to the fewest number needed as that reduces noise or the impact of trivial variables that can overfit or unfit [8, 9]. A regularization model can then become generalized to perform not just on the past or the training data, but also on future and yet unseen data [8, 9]. Although true generalization needs both the right modeling criteria as well as strong subject matter knowledge [8].
Often dimension reduction approaches like Principal Component Analysis (PCA) or boot strapping techniques used along with subject matter expertise can help resolve how to refine models, combat fit challenges, as well as improve generalization potential [9, 10]. Furthermore, understanding the studied population and data characteristics can further help define the data to be used, variable selection, and proper model set up [10].
3. Similarities between machine learning and statistical modeling
Statistical modeling is based on SLT and use of mathematical models and statistical assumptions to generate sample data and make predictions about the real world occurrences. A statistical model is often represented as a collection of probability distributions on a set of all possible outcomes. Furthermore, statistical modeling has evolved in the last few decades and shaped the future of business analytics and data science, including the current use and applications of ML algorithms. On the other hand, machine learning does not require many assumptions and interventions when running algorithms in order to accurately predict studied outcomes [7].
There are similarities between ML and statistical modeling that are prevalent across most analytical efforts. Both techniques use historical data as input to predict new output values, but they vary as noted above on the underlying assumptions and the level of analyst intervention and data preparation.
Overall, machine learning foundations are based from statistical learning theory, and it is recommended for the data scientists to apply SLT’s guiding rules during analysis. While it may seem as a statistical background and understanding is not required when analyzing the underlying data, this misconception often leads to data scientist’s inability to set up proper research hypothesis and analysis due to a lack of understanding of the problem and the underlying data assumptions as well as caveats. This issue can in turn result in biased and irrelevant results as well as unfounded conclusions and insights. With that in mind, it is important to evaluate the problem at hand, and consider both statistical modeling and ML as possible methods to be applied. Understanding the underlying assumptions of the data and statistical inference can help support proper technique selection and guide the pathway to solution [11]. In the later sections of the chapter, application of both techniques will be provided and the reasoning for selecting the methods presented to guide future research.
As mentioned above, the similarities between ML and statistical modeling start with the underlying assumption that data or observations from the past can be used to predict the future [7]. The variables included in the analysis generally represent two types: dependent variables, that in ML are called targets, and independent variables, that in ML are called features. The definition of the variables is the same across both techniques [8]. Furthermore, both ML and statistical modeling leverage the available data in a way that allow for generalization of results to larger population [7]. The loss and risk associated with the models accuracy and representation of the real world occurrence is described frequently in terms of mean squared error (MSE). In statistical modeling, MSE is the difference between the predicted value and the actual value and is used to measure loss of the performance of predictions. In the ML, the same MSE concept is presented via a confusion matrix that evaluates a classification problem\'s accuracy [9].
4. Differences between machine learning and statistical modeling
Differences between machine learning and statistical modeling are distinct and based on purposes and needs for the analysis as well as the outcomes. Assumptions and purposes for the analysis and approach can vastly differ. For example, statistics typically assumes that predictors or features are known and additive, models are parametric, and testing of hypotheses and uncertainty are forefront. On the other hand, ML does not make these assumptions [12]. In ML, many models are based on non-parametric approaches where the structure of model is not specified or unknown, additivity is not expected, and assumptions about normal distributions, linearity or residuals, for example, are not needed for modeling [10].
The purpose of ML is predictive performance using general purpose learning algorithms to find patterns that are less known, unrelated, and in complex data without a priori view of underlying structures [10]. Whereas in statistical modeling, consideration for inferences, correlations, and the effects of a small number of variables are drivers [12].
Due to the differences in the methods’ characteristics, it is important to understand the variations in application of the techniques when solving healthcare problems. For example, one typical application of statistics is to analyze whether a population has a particular medical condition. For some diseases such as diabetes, the condition is easily screened for and diagnosed using distinct lab values, such as elevated and increasing HbA1C over time, high glucose levels and low insulin levels, often due to insulin depletion occurring from unmanaged diabetes. Also conditions such as hypertension can easily be detected at home or in the healthcare provider’s office using simple blood pressure measurement and monitoring, and wearables can identify when patients are experiencing atrial fibrillation, abnormal heart rhythms and even increased patient falls (possible syncope). Therefore, analyses of patients with these easily measurable conditions can be done simply by qualifying patients based on lab values or biomarkers falling within or outside of certain ranges. One of the simplest examples is identifying patients with diabetes [13]. This can be accomplished by using A1C levels to group patients as having no diabetes (A1C < 5.7), pre-diabetes (AIC of 5.7–6.4), or diabetes (A1C > 6.4). These ranges are based on American Diabetes Association Diagnosis Guidelines and a very high, medically accepted correlation between AIC levels and the diagnosis of diabetes [14].
On the other hand, if the objective of the research is to predict which pre-diabetic patients are most likely to progress to diabetes, a myriad of factors influence diabetes progression including extent of chronic kidney disease, high blood pressure, insulin levels over time, body mass index/obesity, age, years with diabetes, success of prior therapy, number and types of prior therapies, family history, coronary artery disease, prior cardiovascular events, infections, etc. A complicated combination of comorbidities, risk factors, and patient behavior can lead to differing diabetes complications and varying outcomes makes prediction more challenging and thus it represents a good candidate for the use of machine learning techniques. Classification models such as gradient boosting tree algorithms have been used to successfully predict diabetes progression, especially earlier in the disease. While there any many diabetes risk factors and co-morbidities, these disease characteristics are well studied over many years, thus enabling stable predictive models which perform well over time [14].
Overall, machine learning is highly effective when the model uses more than a handful of independent variables/features [10]. ML is required when the number of features (p) is larger than the number of records or observations (n) – this is called the curse of dimensionality [15, 16], which increases the risk of overfitting, but can be overcome with dimensionality reductive techniques (i.e., PCA), as part of modeling [15] and clinical/expert input on the importance or lack thereof of certain features, is it relates to the disease or its treatment. Additionally, statistical learning theory teaches that learning algorithms increase their ability to translate complex structures from data at a greater and faster rate than the increase of sample size capture can alone provide [8]. Therefore, statistical learning theory and ML offer methods for addressing high-dimensional data or big data (high velocity, volume and variety) and smaller sample sizes [17], such as recursive feature elimination and support vector machines, boosting, or cross validation which can also minimize prediction error [18].
In the healthcare industry, machine learning models are frequently used in cancer prediction, generally in three areas: (1) predicting a patient with a cancer prognosis/diagnosis, (2) predicting cancer progression, and (3) predicting cancer mortality. Of these, predicting whether a patient may have a cancer prognosis/diagnosis can be more or less difficult depending on the tumor type. Certain cancers such as lung cancer, breast cancer, prostate cancer, and skin cancer are evaluated based on specific signs and symptoms, and non-invasive imaging or blood tests. These cancers are easier to predict. Conversely, cancers with non-descript symptoms such fatigue, dizziness, GI pain and distress, and lack of appetite are much more difficult to predict even with machine learning models as these symptoms are associated with multiple tumor types (for example esophageal, stomach, bladder, liver, and pancreatic cancer) and also mimic numerous other conditions [14].
For cancers with vague symptoms, understanding the patient journey is very important to cancer prediction. If a prediction period is too long and does not reflect the time period before diagnosis when symptoms develop, the model may overfit due to spurious variables not related to the condition. If the prediction period is too short, key risk factors from the patient record could be missing. Variable pruning is required in these situations. A multi-disciplinary team including business and clinical experts can help trim unrelated variables and improve model performance [14].
Model validation is an inherent part of the ML process where the data is split into training data and test data, with the larger portion of data used to train the model to learn outputs based on known inputs. This process allows for rapid structure knowledge for primary focus on building the ability to predict future outcomes [15]. Beyond initial validation of the model within the test data set, the model should be further tested in the real world using a large, representative, and more recent sample of data [19]. This can be accomplished by using the model to score the eligible population and using a look forward period to assess incidence or prevalence of the desired outcome. If the model is performing well, probability scores should be directly correlated to incidence/prevalence (the higher the probability score, the higher the incidence/prevalence). Model accuracy, precision, and recall can also be assessed using this approach [20].
Epidemiology studies and prior published machine learning research in related areas of healthcare can help benchmark the performance of the model relative to the baseline prevalent or incident population for the condition to be predicted. Machine learning models created using a few hundred or thousand patients often do not perform as well in the real world. Careful variable pruning, cohort refinement and adjustment of modeling periods can often resolve model performance problems. Newer software can be used to more quickly build, test, and iterate models, allowing users to easily transform and combine features as well as run many models simultaneously and visualize model performance, diagnosis and solve model issues [21].
5. How to choose between machine learning and statistical modeling
Machine learning algorithms are a preferred choice of technique vs. a statistical modeling approach under specific circumstances, data configurations, and outcomes needed.
5.1 Importance of prediction over causal relationships
As noted above, machine learning algorithms are leveraged for prediction of the outcome rather than present the inferential and causal relationship between the outcome and independent variables/data elements [17, 22]. Once a model has been created, statistical analysis can sometime elucidate and validate the importance and relationship between independent and dependent variables.
5.2 Application of wide and big dataset(s)
Machine Learning algorithms are learner algorithms and learn on large amount of data often presented by a large number of data elements, but not necessarily with many observations [23]. Ability of multiple replications of samples, cross validation or application of boot strapping techniques for machine learning allows for wide datasets with many data elements and few observations, which is extremely helpful in predicting rare disease onset [24] as long as the process is accompanied with real world testing to ensure the models are not suffering from overfitting [18, 19]. With the advent of less expensive and more powerful computing power and storage, multialgorithm, ensembled models using larger cohorts can be more efficiently built. Larger modeling samples that are more representative of the overall population can help reduce the likelihood of overfitting or underfitting [25]. A large cohort imposes various issues and of priority is the ability to identify the set of independent variables that are most meaningful and impactful. These significant independent variables provide a predictive and/or inferential model that can be readily acceptable in providing a real-world application. The variables in such instances may also result into more realistic magnitude and direction of the causal relationship between the independent and outcomes variables of interest.
A recent example for a real-world example in healthcare for machine learning algorithm application is to identify the likelihood of hospitalization for high-risk patients diagnosed with Covid 19. The dataset leveraged included over 20,000 independent variables across healthcare claims data for diagnostics and treatment variables. The best optimal ML model consisted of approximately 200 important predictors variables such as age, diagnosis like Type 2 diabetes/CKD/Hypertension, frequency of office visits, Obesity amongst others. None of the variables in this example were ‘new’, however, the magnitude and direction as a result of the ML exercise may illustrate the ‘true’ impact of each independent variable, a feature that is a serious limitation in traditional statistical modeling [26].
Furthermore, as explained above, statistical models tend to not operate well on very large datasets and often require manageable datasets with a fewer number of pre-defined attributes/data elements for analysis [23]. The recommended number of attributes is up to 12 in a statistical model, because these techniques are highly prone to overfitting [25]. This limitation creates a challenge when analyzing large healthcare datasets and require application of dimension reduction techniques or expert guidance in allowing to eliminate the number of independent variables in the study [23].
5.3 Limited data and model assumptions are required
In machine learning algorithms, there are fewer assumptions that need to be made on the dataset and the data elements [5]. However, a good model is usually preceded by profiling of the target and control groups and some knowledge of the domain. Understanding relationships within the data improve outcomes and interpretability [27].
Machine learning algorithms are comparatively more flexible than statistical models, as they do not require making assumptions regarding collinearity, normal distribution of residuals, etc. [5]. Thus, they have a high tolerance for uncertainty in variable performance (e.g., confidence intervals, hypothesis tests [28]. In statistical modeling emphasis is put in uncertainty estimates, furthermore, a variety of assumptions have to be satisfied before the outcome from a statistical model can be trusted and applied [28]. As a result, the statistical models have a low uncertainty tolerance [25].
Machine learning algorithms tend to be preferred over statistical modeling when the outcome to be predicted does not have a strong component of randomness, e.g., in visual pattern recognition an object must be an E or not an E [5], and when the learning algorithm can be trained on an unlimited number of exact replications [29].
ML is also appropriate when the overall prediction is the goal, with less visibility to describe the impact of any one independent variable or the relationships between variables [30], and when estimating uncertainty in forecasts or in effects of selected predictors is not a requirement [28]. However, often data scientists and data analysts leverage regression analytics to understand the estimated impact, including directionality of the relationships between the outcome and data elements, to help with model interpretation, relevance, and validity for the studied [27]. ML is also preferred when the dataset is wide and very large [23] with underlying variables are not fully known and previously described [5].
6. Machine learning extends statistics
Machine learning requires no prior assumptions about the underlying relationships between the data elements. It is generally applied to high dimensional data sets and does not require many observations to create a working model [5]. However, understanding the underlying data will support building representative modeling cohorts, deriving features relevant for the disease state and population of interest, as well as understanding how to interpret modeling results [19, 27].
In contrast, statistical model requires a deeper understanding how the data was collected, statistical properties of the estimator (p-value, unbiased estimators), the underlying distribution of the population, etc. [17]. Statistical modeling techniques are usually applied to low dimensional data sets [25].
7. Machine learning can extend the utility of statistical modeling
Robert Tibshirani, a statistician and machine learning expert at Stanford University, calls machine learning “glorified statistics,” which presents the dependence of machine learning techniques on statistics in a successful execution that not only allows for a high level of prediction, but interpretation of the results to ensure validity and applicability of the results in the healthcare [17]. Understanding the association and knowing their differences enables data scientists and statisticians to expand their knowledge and apply variety of methods outside their domain of expertise. This is the notion of “data science,” which aims to bridge the gap between the areas as well as bring other important to consider aspects of research [5]. Data science is evolving beyond statistics or more simple ML approaches to incorporate self-learning and autonomy with the ability to interpret context, assess and fill in data gaps, and make modeling adjustment over time [31]. While these modeling approaches are not perfect and more difficult to interpret, they provide exciting new options for difficult to solve problems, especially where the underlying data or environment is rapidly changing [27].
Collaboration and communication between not only data scientists and statisticians but also medical and clinical experts, public policy creators, epidemiologists, etc. allows for designing successful research studies that not only provide predictions and insights on relationships between the vast amount of data elements and health outcomes [30], but also allow for valid, interpretable and relevant results that can be applied with confidence to the project objectives and future deployment in the real [30, 32].
Finally, it is important to remember that machine learning foundations are based in statistical theory and learning. It may seem machine learning can be done without a sound statistical background, but this leads to not really understanding the different nuances in the data and presented results [17]. Well written machine learning code does not negate the need for an in-depth understanding of the problem, assumptions, and the importance of interpretation and validation [29].
8. Specific examples in healthcare
As mentioned earlier in the chapter, machine learning algorithms can be leveraged in the healthcare industry to help evaluate a continuum of access, diagnostic and treatment outcomes, including prediction of patient diagnoses, treatment, adverse events, side effects, and improved quality of life as well as lower mortality rates [24].
As shown in Figure 1, often these algorithms can be helpful in predicting a variety of disease conditions and shortening the time from awareness to diagnosis and treatment, especially in rare and underdiagnosed conditions, estimate the ‘true’ market size, predicting disease progression such as identifying fast vs. slow progressing patients as well as determinants of suitable next line change [32]. Finally, the models can be leveraged for patient and physician segmentation and clustering to identify appropriate targets for in-person and non-personal promotion [30].
Figure 1.
Examples of Machine Learning Applications in Healthcare Analytics [22].
There are, however, instances in which machine learning might not be the right tool to leverage, including when the condition or the underlying condition have a few known variables, when the market is mature and has known predetermined diagnostic and treatment algorithm, and when understanding correlations and inference is more important than making prediction [5].
One aspect of the machine learning process is to involve a cross functional team of experts in the healthcare area to ensure that the questions and problem statement along with hypothesis are properly set up [33, 34]. Many therapeutic areas require in-depth understanding of the clinical and medical concepts (i.e., diagnostic process, treatment regimens, potential adverse effects, etc.), which can help with the research design and selection of the proper analytical techniques. If the expert knowledge is not considered or properly captured in the research design, it might lead to irrelevant, invalid, and biased results, and ultimately invalidate the entire research study [33, 34].
9. A practical guide to the predominant approach
Using a real example of a project with the goal of predicting the risk of hypertension due to underlying comorbid conditions or induced by medication, the decision to lead with machine learning vs. statistical modeling can be based on explicit criteria that can be weighed and ranked based on the desired outcome of the work [17, 32]. Please see Figure 2 presenting an example of the approach.
Figure 2.
Criteria for Choosing the Predominant Approach for a Project.
As shown in Figure 2, pending the research objectives, machine learning or statistical modeling or both techniques could be the right method(s) to apply. For example, shifts in market trends, including shifts in patient volume of diagnosis and treatment present a suitable example when a statistical modeling type of analysis should be utilized. On the other hand, trying to predict patients with a high risk for hypertension requires the utilization of ML approaches. Leveraging both methods is best suited when predictive power and explanatory reasoning is needed to understand the important factors driving the outcome and their relative magnitudes and inferences.
10. Conclusions
Machine learning requires fewer assumptions about the underlying relationships between the data elements. It is generally applied to high dimensional data sets and require fewer observations to create a working model [5]. In contrast, statistical model requires an understanding of how the data was collected, statistical properties of the estimator (p-value, unbiased estimators), the underlying distribution of the population, etc. [17]. Statistical modeling techniques are usually applied to low dimensional data sets [25]. Statistical modeling and ML are not at odds but rather complementary approaches that offer choice of techniques based on need and desired outcomes. Data scientists and analysts should not necessarily have to choose between either machine learning or statistical modeling as a mutually exclusive decision tree. Instead, selected approaches from both areas should be considered as both types of methodologies are based on the same mathematical principles but expressed somewhat differently [5, 10].
Note: This book chapter was originally posted on the Cornell University’s research working paper website: https://arxiv.org. The content of the book chapter is mostly the same compared to the version posted on https://arxiv.org [6].
Conflict of interest
The authors declare no conflict of interest.
Funding
Authors work for Symphony Health, ICON plc Organization.
\n',keywords:"machine learning, statistical modeling, data science, healthcare analytics, research design",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/82063.pdf",chapterXML:"https://mts.intechopen.com/source/xml/82063.xml",downloadPdfUrl:"/chapter/pdf-download/82063",previewPdfUrl:"/chapter/pdf-preview/82063",totalDownloads:7,totalViews:0,totalCrossrefCites:0,dateSubmitted:"February 14th 2022",dateReviewed:"May 2nd 2022",datePrePublished:"May 31st 2022",datePublished:null,dateFinished:"May 31st 2022",readingETA:"0",abstract:"Data scientists and statisticians are often at odds when determining the best approaches and choosing between machine learning and statistical modeling to solve their analytical challenges and problem statements across industries. However, machine learning and statistical modeling are actually more closely related to each other rather than being on different sides of an analysis battleground. The decision on which approach to choose is often based on the problem at hand, expected outcome(s), real world application of the results and insights, as well as the availability and granularity of data for the analysis. Overall machine learning and statistical modeling are complementary techniques that are guided on similar mathematical principles, but leverage different tools to arrive at insights. Determining the best approach should consider the problem to be solved, empirical evidence and resulting hypothesis, data sources and their completeness, number of variables/data elements, assumptions, and expected outcomes such as the need for predictions or causality and reasoning. Experienced analysts and data scientists are often well versed in both types of approaches and their applications, hence use best suited tools for their analytical challenges. Due to the importance and relevance of the subject in the current analytics environment, this chapter will present an overview of each approach as well as outline their similarities and differences to provide the needed understanding when selecting the proper technique for problems at hand. Furthermore, the chapter will also provide examples of applications in the healthcare industry and outline how to decide which approach is best when analyzing healthcare data. Understanding of the best suited methodologies can help the healthcare industry to develop and apply advanced analytical tools to speed up the diagnostic and treatment processes as well as improve the quality of life for their patients.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/82063",risUrl:"/chapter/ris/82063",signatures:"Michele Bennett, Ewa J. Kleczyk, Karin Hayes and Rajesh Mehta",book:{id:"11422",type:"book",title:"Machine Learning and Data Mining - Annual Volume 2022",subtitle:null,fullTitle:"Machine Learning and Data Mining - Annual Volume 2022",slug:null,publishedDate:null,bookSignature:"Dr. Marco Antonio Aceves Fernandez",coverURL:"https://cdn.intechopen.com/books/images_new/11422.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-289-7",printIsbn:"978-1-80355-288-0",pdfIsbn:"978-1-80355-290-3",isAvailableForWebshopOrdering:!0,editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez"}],productType:{id:"5",title:"Annual Volume",chapterContentType:"chapter",authoredCaption:"Authored by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Machine learning foundation is in statistical learning theory",level:"1"},{id:"sec_3",title:"3. 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Conclusions",level:"1"},{id:"sec_18",title:"Conflict of interest",level:"1"},{id:"sec_14",title:"Funding",level:"1"}],chapterReferences:[{id:"B1",body:'Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;19(13):1317-1318. DOI: 10.1001/jama.2017.18391'},{id:"B2",body:'Shelmerdine et al. Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare. BMJ Health & Care Informatics. 2021;28(1):e100385. DOI: 10.1136/bmjhci-2021-100385'},{id:"B3",body:'Romano R, Gambale E. Statistics and medicine: The indispensable know-how of the researcher. Translational Medicine @UniSa. 2013;5:28-31'},{id:"B4",body:'Razzak et al. Big data analytics for preventive medicine. Neural Computing and Application. 2020;32:4417-4451. DOI: 10.1007/s00521-019-04095-y'},{id:"B5",body:'Bzdok D, Altman N, Krzywiniski M. Statistics versus machine learning. Nature Methods. 2018;15(4):233-234. DOI: 0.1038/nmeth.4642'},{id:"B6",body:'Bennett M, Hayes K, Kleczyk EJ, Mehta R. Analytics in healthcare: Similarities and differences between machine learning and traditional advanced statistical modeling. Cornell University. 2022:1-16. Available from: https://arxiv.org/abs/2201.02469'},{id:"B7",body:'Von Luxburg U, Scholkopf B. Inductive logic. In: Handbook and History of Logic. Vol. 10. New York: Elsevier; 2011'},{id:"B8",body:'Bousquet et al. Introduction to Statistical Learning. 2003. Available from: http://www.econ.upf.edu/~lugosi/mlss_slt.pdf'},{id:"B9",body:'Field A. Discovering Statistics Using R. London: Sage; 2012'},{id:"B10",body:'Carmichael I, Marron JS. Data science vs. statistics: Two cultures? Japanese Journal of Statistics and Data Science. 2018;1(1):117-138'},{id:"B11",body:'Cahn A, Shoshan A, Sagiv T, Yesharim R, Goshen R, Shalev V, et al. Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model. Diabetes/Metabolism Research and Reviews. 2020;36(2):e3252. DOI: 10.1002/dmrr.3252 Epub 2020 Jan 14'},{id:"B12",body:'Breiman L. Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science. 2001;16(3):199-231'},{id:"B13",body:'Mehta R, Uppunuthula S. Use of machine learning techniques to identify the likelihood of hospitalization for high-risk patients diagnosed with COVID-19. In: ISPOR Conference; Washington DC. 2022'},{id:"B14",body:'American Diabetes Association. Understanding A1C Diagnosis. 2022. Available from: https://www.diabetes.org/diabetes/a1c/diagnosis#:~:text=Diabetes%20is%20diagnosed%20at%20fasting,equal%20to%20126%20mg%2Fdl'},{id:"B15",body:'Bzdok et al. Machine learning: A primer. Nature Methods. 2017;14(12):1119-1120. DOI: 10.1038/nmeth.4526'},{id:"B16",body:'Bellman RE. Adaptive Control Processes. Princeton, NJ: Princeton University Press; 1961'},{id:"B17",body:'Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2ed). Stanford, CA: Springer; 2016'},{id:"B18",body:'Chapman et al. Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development. Psychology Methods. 2016;21(4):603-620. DOI: 10.1037/met0000088'},{id:"B19",body:'Argent et al. The importance of real-world validation of machine learning systems in wearable exercise biofeedback platforms: A case study. Sensors (Basel). 2021;21(7):2346. DOI: 10.3390/s21072346'},{id:"B20",body:'Parikh et al. Understanding and using sensitivity, specificity and predictive values. Indian Journal of Ophthalmology. 2008;56(1):45-50. DOI: 10.4103/0301-4738.37595'},{id:"B21",body:'Mendis A. Statistical Modeling vs. Machine Learning. 2019. Available from: https://www.kdnuggets.com/2019/08/statistical-modelling-vs-machine-learning.html'},{id:"B22",body:'Hayes K, Rajabathar R, Balasubramaniam V. Uncovering the machine learning “Black Box”: Discoveringlatent patient insights using text mining & machine learning. In: Conference Paper Presented at Innovation in Analytics via Machine Learning & AI; Las Vegas, NV. 2019 Available from: https://www.pmsa.org/other-events/past-symposia'},{id:"B23",body:'Belabbas M, Wolfe PJ. Spectral methods in machine learning and new strategies for very large datasets. Proceedings of the National Academy of Sciences. 2009;106(2):369-374. DOI: 10.1073/pnas.0810600105'},{id:"B24",body:'Kempa-Liehr et al. Healthcare pathway discovery and probabilistic machine learning. International Journal of Medical Informatics. 2020;137:104087. DOI: 10.1016/j.ijmedinf.2020.104087'},{id:"B25",body:'Wasserman L. Rise of the machines. In: Past, Present, and Future of Statistical Science. Chapman and Hall; 2013. pp. 1-12. DOI: 10.1201/b16720-49'},{id:"B26",body:'Ranjan R. Calibration in machine learning. 2019. Available from: https://medium.com/analytics-vidhya/calibration-in-machine-learning-e7972ac93555'},{id:"B27",body:'Child CM, Washburn NR. Embedding domain knowledge for machine learning of complex material systems. MRS Communications. 2019;9(3):806-820. DOI: 10.1557/mrc.2019.90'},{id:"B28",body:'Hilliermeir E, Waegerman W. Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning. 2021;110:457-506. DOI: 10.1007/s10994-021-05946-3'},{id:"B29",body:'Goh et al. Evaluating human versus machine learning performance in classifying research abstracts. Scientometrics. 2020;125:1197-1212. DOI: 10.1007/s11192-020-03614-2'},{id:"B30",body:'Chicco D, Jutman G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics. 2020;21(6). DOI: /10.1186/s12864-019-6413-7'},{id:"B31",body:'Ansari et al. Rethinking human-machine learning in Industry 4.0: How does the paradigm shift treat the role of human learning? Procedia Manufacturing. 2018;23:117-122. DOI: 10.1016/j.promfg.2018.04.003'},{id:"B32",body:'Morganstein et al. Predicting population health with machine learning: A scoping review. BMJ Open. 2020;10(10). DOI: 10.1136/bmjopen-2020-037860'},{id:"B33",body:'Terranova et al. Application of machine learning in translational medicine: Current status and future opportunities. The AAPS Journal. 2021;23(74). DOI: 10.1208/s12248-021-00593-x'},{id:"B34",body:'Kleczyk E, Hayes K, Bennett M. Building organization AI and ML acumen during the COVID Era. 2022. In: PMSA Annual Conference. Louisville, KY. 2022'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Michele Bennett",address:null,affiliation:'
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Local systems include local heating, local air-conditioning, local ventilation, and split systems.",book:{id:"6807",slug:"hvac-system",title:"HVAC System",fullTitle:"HVAC System"},signatures:"Shaimaa Seyam",authors:[{id:"247650",title:"M.Sc.",name:"Shaimaa",middleName:null,surname:"Seyam",slug:"shaimaa-seyam",fullName:"Shaimaa Seyam"},{id:"257733",title:"MSc.",name:"Shaimaa",middleName:null,surname:"Seyam",slug:"shaimaa-seyam",fullName:"Shaimaa Seyam"},{id:"395618",title:"Dr.",name:"Shaimaa",middleName:null,surname:"Seyam",slug:"shaimaa-seyam",fullName:"Shaimaa Seyam"}]},{id:"70315",title:"Some Basic and Key Issues of Switched-Reluctance Machine Systems",slug:"some-basic-and-key-issues-of-switched-reluctance-machine-systems",totalDownloads:1268,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"Although switched-reluctance machine (SRM) possesses many structural advantages and application potential, it is rather difficult to successfully control with high performance being comparable to other machines. Many critical affairs must be properly treated to obtain the improved operating characteristics. This chapter presents the basic and key technologies of switched-reluctance machine in motor and generator operations. The contents in this chapter include: (1) structures and governing equations of SRM; (2) some commonly used SRM converters; (3) estimation of key parameters and performance evaluation of SRM drive; (4) commutation scheme, current control scheme, and speed control scheme of SRM drive; (5) some commonly used front-end converters and their operation controls for SRM drive; (6) reversible and regenerative braking operation controls for SRM drive; (7) some tuning issues for SRM drive; (8) operation control and some tuning issues of switched-reluctance generators; and (9) experimental application exploration for SRM systems—(a) wind generator and microgrid and (b) EV SRM drive.",book:{id:"8899",slug:"modelling-and-control-of-switched-reluctance-machines",title:"Modelling and Control of Switched Reluctance Machines",fullTitle:"Modelling and Control of Switched Reluctance Machines"},signatures:"Chang-Ming Liaw, Min-Ze Lu, Ping-Hong Jhou and Kuan-Yu Chou",authors:[{id:"37616",title:"Prof.",name:"Chang-Ming",middleName:null,surname:"Liaw",slug:"chang-ming-liaw",fullName:"Chang-Ming Liaw"},{id:"306461",title:"Mr.",name:"Min-Ze",middleName:null,surname:"Lu",slug:"min-ze-lu",fullName:"Min-Ze Lu"},{id:"306463",title:"Mr.",name:"Ping-Hong",middleName:null,surname:"Jhou",slug:"ping-hong-jhou",fullName:"Ping-Hong Jhou"},{id:"306464",title:"Mr.",name:"Kuan-Yu",middleName:null,surname:"Chou",slug:"kuan-yu-chou",fullName:"Kuan-Yu Chou"}]}],onlineFirstChaptersFilter:{topicId:"1",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"83153",title:"Perspective Chapter: Cryptocurrencies Effectiveness for Nature",slug:"perspective-chapter-cryptocurrencies-effectiveness-for-nature",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106493",abstract:"The rise of cryptocurrencies based on Blockchain platforms have provided multiple solutions for social and nature projects supported by concerned investors with sustainable development initiatives. Speculation and unclear uses of a cryptocurrency plays a negative role for the projects they claim to support. A positive relationship between coin investors and supported projects must position the coin value on the scale of the community involvement among the coin and project issues, thus placing the project results above speculative moves. Coin nature and social based projects may include a decentralized autonomous organization (DAO), combined with a digital currency to contribute to social and nature improvements. This organization provides a framework for the engagement of investors, beneficiaries, and implementation partners, with results measured by reliable third parties. The potential funding from non fiduciary sources for sustainable development targets may be framed under the fundraising and financial solutions models, addressing the cryptocurrency volatility risks with responsible tokenomics in attention to transaction and regulatory issues. Overall, the more clear are the object and transaction issues of a nature conservation project supported by a currency, the more successful it will be in terms of nature and social improvements and the currency valuation for all parties involved.",book:{id:"11551",title:"Blockchain",coverURL:"https://cdn.intechopen.com/books/images_new/11551.jpg"},signatures:"Luiz Cruz Villares"},{id:"83163",title:"Robust Control Algorithm for Drones",slug:"robust-control-algorithm-for-drones",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.105966",abstract:"Drones, also known as Crewless Aircrafts (CAs), are by far the most multi - level and multi developing technologies of the modern period. This technology has recently found various uses in the transportation area, spanning from traffic monitoring applicability to traffic engineering for overall traffic flow and efficiency improvements. Because of its non-linear characteristics and under-actuated design, the CA seems to be an excellent platform to control systems study. Following a brief overview of the system, the various evolutionary and robust control algorithms were examined, along with their benefits and drawbacks. In this chapter, a mathematical and theoretical model of a CA’s dynamics is derived, using Euler’s and Newton’s laws. The result is a linearized version of the model, from which a linear controller, the Linear Quadratic Regulator (LQR), is generated. Furthermore, the performance of these nonlinear control techniques is compared to that of the LQR. Feedback-linearization controller when implemented in the simulation for the chapter, the results for the same was better than any other algorithm when compared with. The suggested regulatory paradigm of the CA-based monitoring system and analysis study will be the subject of future research, with a particular emphasis on practical applications.",book:{id:"11522",title:"Aeronautics - New Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11522.jpg"},signatures:"Parul Priya and Sushma S. Kamlu"},{id:"83171",title:"Some Results on the Non-Homogeneous Hofmann Process",slug:"some-results-on-the-non-homogeneous-hofmann-process",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106422",abstract:"The classical counting processes (Poisson and negative binomial) are the most traditional discrete counting processes (DCPs); however, these are based on a set of rigid assumptions. We consider a non-homogeneous counting process (which we name non-homogeneous Hofmann process – NHP) that can generate the classical counting processes (CCPs) as special cases, and also allows modeling counting processes for event history data, which usually exhibit under- or over-dispersion. We present some results of this process that will allow us to use it in other areas and establish both the probability mass function (pmf) and the cumulative distribution function (cdf) using transition intensities. This counting process (CP) will allow other researchers to work on modelling the CP, where data dispersion exists in an efficient and more flexible way.",book:{id:"12021",title:"Applied Probability Theory - New Perspectives, Recent Advances and Trends",coverURL:"https://cdn.intechopen.com/books/images_new/12021.jpg"},signatures:"Gerson Yahir Palomino Velandia and José Alfredo Jiménez Moscoso"},{id:"83152",title:"Recycled Synthetic Polymer-Based Electrospun Membranes for Filtering Applications",slug:"recycled-synthetic-polymer-based-electrospun-membranes-for-filtering-applications",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106683",abstract:"Synthetic polymers have been widely applied in various commercial and household applications owing to their fascinating properties of low-cost, lightweight, and processability. However, increasing population and living standards and rising demand for non-biodegradable polymers have led to the accumulation of plastic pollution resulting in the current environmental crisis. Current waste management methods such as landfilling or incineration do not solve these environmental issues. On the other hand, recycling plastic waste is the most valuable strategy for dealing with waste as raw material for high-value products. One of such products is filter membranes. Polymer fiber membranes as masks in pandemics have been one of the most sought-after products in recent years. Some types of plastic waste became a material source for the development of filter materials, which could contribute to the protection of human health. Utilizing the simple, cheap, and industrially available technological solution is also needed. Given the number of advantages, electrospinning is such a beneficial solution. The electrospun polymer waste-based membranes show excellent filtration performance and can carry many other functionalities. Therefore, this review article presents a brief overview of electrospun nanofibrous membranes based on synthetic plastic waste and summarizes the filtration performance of such membranes. This review will discuss the future perspectives of electrospun membranes as well.",book:{id:"11462",title:"Recent Developments in Nanofibers Research",coverURL:"https://cdn.intechopen.com/books/images_new/11462.jpg"},signatures:"Alena Opálková Šišková, Heba M. Abdallah, Smaher Mosad Elbayomi and Anita Eckstein Andicsová"},{id:"83166",title:"General Drag Correlations for Particle-Fluid System",slug:"general-drag-correlations-for-particle-fluid-system",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106427",abstract:"Particle-fluid flows are commonly encountered in industrial applications. It is of great importance to understand the fundamentals governing the behavior of such a flow system for better process design, control, and optimization. Generally, the particle-fluid flow behavior is strongly influenced by the interaction forces between fluid and particles. Among the various kinds of particle-fluid interaction forces, the drag force is the most essential. This chapter reviews the modeling of drag force for particle-fluid systems: from single particle to multiple particles, monosize to multisize, spherical to nonspherical, and Newtonian fluid to non-Newtonian fluid. Typical drag correlations in the literature are compared and assessed in terms of physical meaning, consistency, and generality.",book:{id:"11498",title:"Boundary Layer Flows - Modelling, Computation, and Applications of Laminar, Turbulent Incompressible and Compressible Flows",coverURL:"https://cdn.intechopen.com/books/images_new/11498.jpg"},signatures:"Zheng Qi, Shibo Kuang, Liangwan Rong, Kejun Dong and Aibing Yu"},{id:"82272",title:"Landslide Movement Monitoring with InSAR Technologies",slug:"landslide-movement-monitoring-with-insar-technologies",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.105058",abstract:"Synthetic aperture radar interferometry (InSAR) is a technology that has been widely used in many areas, such as topographic mapping, land and resource survey, geological exploration, disaster prevention and mitigation, volcanic and seismic monitor and so on. Landslide, as a representative geohazard, include a wide range of phenomena involving downhill ground movement. InSAR, a technology which can measure surface deformation at the millimeter level over serveral days or years, is suitable to detect landslides with chronical and widespread movements. In this chapter, we introduce main process methods of InSAR data, including Persistent Scatter Interferometry (PSInSAR) and Distributed Scatter Interferometry (DSInSAR). A study area, Daguan County Town, one of the most landslide-prone areas in China is induced to demonstrate the practicability of InSAR in detecting landslides. Combined InSAR results with geological, geotechnical and meterological data, the distribution of landslide in Daguan County in spatial and temporal dimensions would be displayed. We also coupling numerical modeling and InSAR for characterizing landslide movements under multiple loads. The numerical results revealed that body loads dominated the cumulative downhill movements by squeezing water and air from voids, and precipitation caused seasonal movements with the direction perpendicular to the slope surface.",book:{id:"10950",title:"Landslides",coverURL:"https://cdn.intechopen.com/books/images_new/10950.jpg"},signatures:"Peifeng Ma, Yifei Cui, Weixi Wang, Hui Lin, Yuanzhi Zhang and Yi Zheng"}],onlineFirstChaptersTotal:764},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:11,numberOfPublishedChapters:91,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:333,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:11,numberOfPublishedChapters:144,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:126,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:113,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:23,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:13,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:"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"}}}},{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"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"August 18th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"3",title:"Bacterial Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",isOpenForSubmission:!0,annualVolume:11399,editor:{id:"205604",title:"Dr.",name:"Tomas",middleName:null,surname:"Jarzembowski",slug:"tomas-jarzembowski",fullName:"Tomas Jarzembowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKriQAG/Profile_Picture_2022-06-16T11:01:31.jpg",biography:"Tomasz Jarzembowski was born in 1968 in Gdansk, Poland. He obtained his Ph.D. degree in 2000 from the Medical University of Gdańsk (UG). After specialization in clinical microbiology in 2003, he started studying biofilm formation and antibiotic resistance at the single-cell level. In 2015, he obtained his D.Sc. degree. His later study in cooperation with experts in nephrology and immunology resulted in the designation of the new diagnostic method of UTI, patented in 2017. He is currently working at the Department of Microbiology, Medical University of Gdańsk (GUMed), Poland. Since many years, he is a member of steering committee of Gdańsk branch of Polish Society of Microbiologists, a member of ESCMID. He is also a reviewer and a member of editorial boards of a number of international journals.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorTwo:{id:"484980",title:"Dr.",name:"Katarzyna",middleName:null,surname:"Garbacz",slug:"katarzyna-garbacz",fullName:"Katarzyna Garbacz",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003St8TAQAZ/Profile_Picture_2022-07-07T09:45:16.jpg",biography:"Katarzyna Maria Garbacz, MD, is an Associate Professor at the Medical University of Gdańsk, Poland and she is head of the Department of Oral Microbiology of the Medical University of Gdańsk. She has published more than 50 scientific publications in peer-reviewed journals. She has been a project leader funded by the National Science Centre of Poland. Prof. Garbacz is a microbiologist working on applied and fundamental questions in microbial epidemiology and pathogenesis. Her research interest is in antibiotic resistance, host-pathogen interaction, and therapeutics development for staphylococcal pathogens, mainly Staphylococcus aureus, which causes hospital-acquired infections. Currently, her research is mostly focused on the study of oral pathogens, particularly Staphylococcus spp.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorThree:null},{id:"4",title:"Fungal Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",isOpenForSubmission:!0,annualVolume:11400,editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. Board Member and Chair of Mycology Group of Chinese Society of Dermatology.",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null},{id:"5",title:"Parasitic Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",isOpenForSubmission:!0,annualVolume:11401,editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",slug:"amidou-samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",biography:"Dr. Amidou Samie is an Associate Professor of Microbiology at the University of Venda, in South Africa, where he graduated for his PhD in May 2008. He joined the Department of Microbiology the same year and has been giving lectures on topics covering parasitology, immunology, molecular biology and industrial microbiology. He is currently a rated researcher by the National Research Foundation of South Africa at category C2. He has published widely in the field of infectious diseases and has overseen several MSc’s and PhDs. His research activities mostly cover topics on infectious diseases from epidemiology to control. His particular interest lies in the study of intestinal protozoan parasites and opportunistic infections among HIV patients as well as the potential impact of childhood diarrhoea on growth and child development. He also conducts research on water-borne diseases and water quality and is involved in the evaluation of point-of-use water treatment technologies using silver and copper nanoparticles in collaboration with the University of Virginia, USA. He also studies the use of medicinal plants for the control of infectious diseases as well as antimicrobial drug resistance.",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},{id:"6",title:"Viral Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",isOpenForSubmission:!0,annualVolume:11402,editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",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",institutionURL:null,country:{name:"India"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:14,paginationItems:[{id:"83117",title:"Endothelial Secretome",doi:"10.5772/intechopen.106550",signatures:"Luiza Rusu",slug:"endothelial-secretome",totalDownloads:0,totalCrossrefCites:0,totalDimensionsCites:0,authors:[{name:"Luiza",surname:"Rusu"}],book:{title:"Periodontology - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11566.jpg",subseries:{id:"1",title:"Oral Health"}}},{id:"83087",title:"Role of Cellular Responses in Periodontal Tissue Destruction",doi:"10.5772/intechopen.106645",signatures:"Nam Cong-Nhat Huynh",slug:"role-of-cellular-responses-in-periodontal-tissue-destruction",totalDownloads:8,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Periodontology - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11566.jpg",subseries:{id:"1",title:"Oral Health"}}},{id:"83073",title:"Dental and Orofacial Trauma Impacts on Oral-Health-Related—Quality of Life in Children: Low- and Middle-Income Countries",doi:"10.5772/intechopen.105845",signatures:"Yolanda Malele-Kolisa, Nazia Khan, Mpho P. 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Currently, he is a professor of Orthodontics. He holds a Certificate of Advanced Study type A in Technology of Biomaterials used in Dentistry (1995); Certificate of Advanced Study type B in Dento-Facial Orthopaedics (1997) from the Faculty of Dental Surgery, University Denis Diderot-Paris VII, France; Diploma of Advanced Study (DESA) in Biocompatibility of Biomaterials from the Faculty of Medicine and Pharmacy of Casablanca (2002); Certificate of Clinical Occlusodontics from the Faculty of Dentistry of Casablanca (2004); University Diploma of Biostatistics and Perceptual Health Measurement from the Faculty of Medicine and Pharmacy of Casablanca (2011); and a University Diploma of Pedagogy of Odontological Sciences from the Faculty of Dentistry of Casablanca (2013). 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He previously worked as a post-doctoral fellow at the Ben-Gurion University of Negev, Israel; University of the Free State, South Africa; and Central University of Technology Bloemfontein, South Africa. He obtained his Ph.D. in Organic Chemistry from Nagaoka University of Technology, Japan. He has published more than seventy-four journal articles and attended several national and international conferences as speaker and chair. Dr. Kendrekar has received many international awards. He has several funded projects, namely, anti-malaria drug development, MRSA, and SARS-CoV-2 activity of curcumin and its formulations. He has filed four patents in collaboration with the University of Central Lancashire and Mayo Clinic Infectious Diseases. His present research includes organic synthesis, drug discovery and development, biochemistry, nanoscience, and nanotechnology.",institutionString:"Visiting Scientist at Lipid Nanostructures Laboratory, Centre for Smart Materials, School of Natural Sciences, University of Central Lancashire",institution:null},{id:"428125",title:"Dr.",name:"Vinayak",middleName:null,surname:"Adimule",slug:"vinayak-adimule",fullName:"Vinayak Adimule",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/428125/images/system/428125.jpg",biography:"Dr. Vinayak Adimule, MSc, Ph.D., is a professor and dean of R&D, Angadi Institute of Technology and Management, India. He has 15 years of research experience as a senior research scientist and associate research scientist in R&D organizations. He has published more than fifty research articles as well as several book chapters. He has two Indian patents and two international patents to his credit. Dr. Adimule has attended, chaired, and presented papers at national and international conferences. He is a guest editor for Topics in Catalysis and other journals. He is also an editorial board member, life member, and associate member for many international societies and research institutions. His research interests include nanoelectronics, material chemistry, artificial intelligence, sensors and actuators, bio-nanomaterials, and medicinal chemistry.",institutionString:"Angadi Institute of Technology and Management",institution:null},{id:"284317",title:"Prof.",name:"Kantharaju",middleName:null,surname:"Kamanna",slug:"kantharaju-kamanna",fullName:"Kantharaju Kamanna",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284317/images/21050_n.jpg",biography:"Prof. K. Kantharaju has received Bachelor of science (PCM), master of science (Organic Chemistry) and Doctor of Philosophy in Chemistry from Bangalore University. He worked as a Executive Research & Development @ Cadila Pharmaceuticals Ltd, Ahmedabad. He received DBT-postdoc fellow @ Molecular Biophysics Unit, Indian Institute of Science, Bangalore under the supervision of Prof. P. Balaram, later he moved to NIH-postdoc researcher at Drexel University College of Medicine, Philadelphia, USA, after his return from postdoc joined NITK-Surthakal as a Adhoc faculty at department of chemistry. Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"436430",title:"Associate Prof.",name:"Mesut",middleName:null,surname:"Işık",slug:"mesut-isik",fullName:"Mesut Işık",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/436430/images/19686_n.jpg",biography:null,institutionString:null,institution:{name:"Bilecik University",country:{name:"Turkey"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a scientist and Principal Investigator at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering the lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via artificial intelligence-based analyses of exosomal Raman signatures. Dr. Paul also works on spatial multiplex immunofluorescence-based tissue mapping to understand the immune repertoire in lung cancer. Dr. Paul has published in more than sixty-five peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award and the 2022 AAISCR-R Vijayalaxmi Award for Innovative Cancer Research. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. 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