IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
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IntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
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Designed to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
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After a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
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Our innovative Book Series format brings you:
\n\n
\n\t
Topic Focused Publications - Each topic showcases high impact subject areas
\n\t
Renowned Editorial Expertise - Series Editors, Topic Editors, and a team of international Board Members that permanently support each Book Series
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Fast Publishing - quick turnaround which is unique for book publishing
\n\t
The benefit of ISSN and ISBN for increased citation and indexing possibilities
\n
\n\n\n\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\n
IntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
We invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
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Note: Edited in October 2021
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1. Introduction
All medical cares, including clinical laboratories, carries an intrinsic risk of errors that can result in harm, disability, and even death so today their activities have seen a significant increase in monitoring [1]. In the past, laboratory processes performed in clinical laboratories focused only on results, while today, they focus on issues related to reliability, safety and effectiveness. It is very important in health world, being aware of the error rate attributable to health system that has great impact on patients [2]. Currently, some strategies are proposed to analyze and to see how you can decline the rate of preventable errors. In order to guarantee reliable results and improved data consistency, while operating with reduced funding, laboratories need to acquire a new culture of management, more tools and specific training [3]. Research management founded on a quality approach is emerging as an essential tool to ensure valuable, vigorous and dependable consequences, within a framework of the best practice. Risk management has been disseminated in clinical laboratories only for the last years, although it has been applied in healthcare since the 80s. That was partly due to constant inspections during the cycle of laboratory examination, rework, removal of any defects and adjustment after the identification of possible causes of flaws or errors. One of the instruments used in risk management is the analysis of failure modes and effects analysis (FMEA). The FMEA model has been applied in various medical fields, including clinical laboratory activities to improve patient safety before serious harm to their health. By reducing/eliminating errors, the FMEA model helps to prevent and control failures and their risks in clinical approaches [4].
2. Failure mode and effect analysis: background and description
Failure mode and effect analysis (FMEA) which was first developed in the 1940s is a systematic technique for identifying all possible errors in a system or process. Adoption of this analysis by National Aeronautics and Space Administration (NASA) in relation with aerospace missions in mid-1960s made its practical application possible. Since then, this analysis has been widely used in diverse industries such as oil and gas, food and automotive and electronics systems. In recent years FMEA has been also successfully applied in the health system as an effective tool for improving patient safety and performance in hospitals. Today, The FMEA is emerging as a tool for assessing the risk of clinical trial processes and clinical analytical methods. However, there are still too few reports about this last use and even fewer data are available on the application of the methodology in clinical laboratories [5, 6, 7, 8]. The risk assessment in this technique involves identification of potential errors, determining the severity (S), occurrence (O) and effects of each error and reviewing the control actions implemented to prevent or detect (D) errors [9] (Figure 1). In the traditional FMEA, to measure these criteria, a numeric scale of 1 to 10 is used (Table 1). Thus, each failure mode is been ranked by a scale called Risk Priority Number (RPN) characterized by multiplying the numbers of three criteria (S, O, D) together. Therefore, the higher the RPN value, the more important the error is and its correction has more priority. So, RPN is so beneficial to identify high risk failures modes requiring priority functions [10, 11].
Figure 1.
FMEA elements.
Severity scale (scale 1 [least severe] to 10 [most severe] for each effect)
Minor (1)
Low (2,3)
Moderate (4-6)
High (7,8)
Very high (9,10)
The minor nature of this failure will not have a significant effect on the patient or the choice of treatment
Because of this failure, the patient experiences only a minor injury or a minor discomfort
Failure can lead to patient dissatisfaction, which may include discomfort or failure
Dissatisfaction with the nature of the failure leads to serious disruption and risk to the patient’s health
This failure affects safety or increases mortality. This may endanger the patient’s life
(I)
Probability scale (scale 1 [least frequent] to 10 [most frequent] for the occurrence)
Remote (1)
Very low (2)
Low (3,4,5)
Moderate (6,7)
High (8,9)
Very high (10)
Failure is unlikely; This failure was never observed
Only a few separates failures have ever been observed or reported
Isolated failures have been encountered
Occasional minor failures have been encountered
Failure is often encountered
Failure is almost inevitable
(II)
Detection scale for occurrence (scale 1 [always detected] to 10 [never detected] for each occurrence)
Very high (1,2)
High (3,4)
Moderate (5,6)
Low (7,8)
Very low (9)
No detection (10)
It is almost certain to detect the failure mode
There is a good chance of detecting the failure mode
One may detect the existence of the failure mode
There is a poor chance of detecting the existence of the failure mode
One probably will not detect the existence of the failure mode
The existence of the failure mode will not or cannot be detected
(III)
Table 1.
Failure Modes and Effects Analysis Scale for Severity, Probability, and Detection. (I): Severity score (S): 1 to 10 scales from least to most severe (II) Probability score (P): 1 to 10 scales from least to most probable (III) Detectability score (D): 1 to 10 scales from most to least detectable.
Prevention, reducing or excluding of errors and their risk is an essential requirement in clinical analytical tests which is been established by the laboratory according to RPN limit. The laboratory decided the assessing scale of frequency, the severity and errors detection which is being different for each test. There are three main categories of errors [12, 13]:
Critical errors – Mainly through request for analysis, if not identified and corrected early, have serious consequences for the patient’s health
Major errors – resulting from the inappropriate application of the sampling method
Minor errors – considered so, because of the low probability of occurrence, the high probability of detection or low/absent severity. These errors are taken into account only with the purpose to review the method and the technical instruction
Classification of potential errors occurred in the clinical laboratory processes which are subjected to the samples shown in Table 2 (The following items only examples of errors and do and does not include all clinical laboratory failure modes) [14, 15].
Pre-analytical
Analytical
Post-analytical
Incorrect identification of the patient
Procedural non-conformity
Incorrect result
Mislabeling of samples
Errors of equipment or reagents
Result sent to a different patient
Incorrect tube for sampling or incorrect storage
Discrepancies in the results of the internal control
Introducing incorrectly the results in the system
Improper or prolonged transport conditions
Delay in analyzing the samples
Lack of information about the limits concerning the results’ interpretation
Table 2.
Potential errors occurred in the clinical laboratory processes.
In clinical laboratories all errors should be controlled by quality indicators. To monitor and assess periodically laboratories’ involvement in patients’ care the implementation of quality indicators is necessary. ISO/TS 22367 supports the non-conformities, errors and incidents identifying in the clinical laboratory, with an emphasis on the pre-analytical and post-analytical processes. These processes are the most critical and the most difficult ones to control due to involving of various specialists, sections and centers [16]. Clinical laboratory process map is shown in Figure 2. The processes map together with the risk map can give us an overview of the failures distribution in each of the processes [3].
Figure 2.
Processes map of clinical laboratory.
Like any analytical method, FMEA should be thoroughly understood prior to being introduced in laboratory practice. There are five stages in its implementation which will be explained in more detail in the methodology [17, 18, 19].
FMEA assessment resulted in actions to address the root causes, determining the following situations:
risk reduction through the development of a preventive action plan to promote process improvement;
immediate removal of the risk source when the pieces of equipment were increased;
change in the probability of certain risks when the selection process for new employees was initiated;
sharing the risk with other staff members when the clinical emergency staff was involved in the potential problem.
FMEA contributed to quality planning, allowing the evaluation of interconnected activities designed to generate products and assisting in the identification of controls.
2.1 FMEA in clinical laboratory activities and patient’ safety
Errors in the laboratory activities can lead to consequences in patients’ safety. That’s why these errors should be identified, controlled and reduced. Effective patient treatment can be improved by prevention and detection of the errors at the time of occurrence which in turn ensures the patient’ safety. Currently, the tendency to move from the traditional technical adopted like internal quality control (IQC) and external quality assessment (EQA) to risk management is seen in all quality systems of clinical laboratories. It is conclusive the need for risk management in clinical laboratories and monitoring them within the quality plan, a fact that would lead to an increase on patient safety. Studies have revealed that FMEA is useful for detecting errors and improving patients’ safety and it can yield benefits, for failures management and general process improvement, within a laboratory system where time and team input is limited, and within a process that was considered to have few obstacles [1, 2, 3, 4]. Former study showed that FMEA can effectively reduce errors in clinical chemistry laboratories [20].
Woodhouse et al. showed applying FMEA for identified processes in a hemotherapy service, can reduced the possibility of error occurrence and increased the probability of detection [21]. Momenizadeh et al. concluded that implementing FMEA can significantly reduce laboratory errors [22]. Molavi-Taleghani et al. argued that FMEA method is very effective in identifying the possible failure of treatment procedures, determining the cause of each failure mode, and proposing improvement strategies [23]. Applying the FMEA risk assessment tool to laboratory processes can increase effectiveness, efficiency and reproducibility of the results [24]. Risk management in the clinical laboratory by FMEA can decrease the possibility of errors occurrence and ensures the accuracy of results and patient’s safety. Risk management guidelines recommended that the clinical laboratories must have a proactive and individualized role in reducing the potential errors by developing an appreciate Quality Control Plan (QCP). The laboratories must create their own analytic process to identify the weakness of each testing stage. As errors and their risks were identified, the laboratories select the appropriate control processes to detect and to prevent the occurrence of errors. All errors and control processes are mentioned in the QCP [25].
2.2 FMEA benefits and barriers
The Benefits of implementing FMEA approach in clinical laboratories include enhancing patients’ safety, improving quality of tests, reducing the chances of repeating the same failure, cost and time and encouragement for teamwork and effective communication between functions – collaboration [26]. In comparison with other quality improvement tools FMEA can be fairly compared, its risk can be assessed, and a score can be assigned.
FMEA also has some barriers such as limits of human error analysis (traditional FMEA uses potential equipment/system failures) and missing interactions between faults and external influences. The reproducibility and generalizability of FMEA in clinical laboratory approaches are factors of concern but since this method is based on hypothetical possibilities uncertainty is still likely to remain [27]. Previous study showed that using FMEA is more time-consuming than other hazard analysis that identifies failure modes but the improvement potentially obtainable by FMEA in a clinical laboratory is high, and this fact should suggest further experiences in this field. Despite the barriers, FMEA represents an appreciate, comprehensive, and organized approach to known potential patient safety failure modes in clinical laboratory [28, 29, 30]. processes. According to the risk-based thinking introduced by new ISO 9001:2015 standard, FMEA is an appropriate approach errors analysis of operational processes under an ISO-certified Quality Management System [31].
3. Methodology
Analytical methodology of FMEA is very effective in maintaining patient safety. Laboratory staffs trained in FMEA methodology can greatly reduce time requirements and guarantee that all activities involved are coordinated increasing the accuracy of laboratory results [17, 18, 19].
The FMEA process including 5 steps as follow:
Selecting a process for study;
Assembling a multidisciplinary team;
Collecting and organizing data about the selected process;
Analysis of hazards;
Developing and implementing appropriate actions and measuring the outcomes;
3.1 Selecting a process for study
The intricacy of laboratory processes increases the probability of undesirable errors. The more steps in the process and the greater their dependency, the greater the chance of error. In this step the laboratory identifies the critical processes based on the severity of possible harmful errors and the potentially dangerous impact on patient safety [17, 18, 19].
3.2 Assembling a multidisciplinary team
Gathering specialists with different levels and types of training, with specific knowledge and experience of the selected process. A team head can lead team members through the process, and can help ensure that team members complete each step and record the results of FMEA [17, 18, 19].
3.3 Collecting and organizing data about the selected process
In this step the assembled team creates an accurate diagram of potential failure modes of each listed activity using focus laboratory staff activities and reaching a common conclusion and recording it on FMEA form (Table 3) [17, 18, 19].
Project: Product: System:
Date: Prepared by:
FMEA number: Reference documents:
SystemComponentFunction
Potential failure mode
Potential consequences/effects of failure
Severity
Critical
Potential causes of failure
Probability
Current design controls
Detection
RPN
Recommended actions
Responsibility And completion date
Table 3.
FMEA form.
3.4 Analysis of hazards
This step including identifying failure modes in each step, determining the potential effect of each failure mode, ranking the severity of failure mode effects, ranking the probability and detectability of each failure mode and identifying the critical failure modes [17, 18, 19].
3.5 Developing and implementing appropriate actions and measuring the outcomes
Identifying the root causes of critical failures is an important step in developing an appropriate action plan. Traditional root cause analysis methods are used to determine the underlying cause of each critical failure so that appropriate actions can be taken. Once the root causes of critical failures have been identified, the team’s aim is to eliminate the risk of failures, reduce the likelihood of failure or mitigate the effects of failure should it affect the patient [17, 18, 19].
4. Discussion
Clinical laboratories processes tend to errors due to human interactions and instrumental mistakes. Therefore, it is essential to design plans to make errors preventable. In the clinical laboratory, most errors are in the pre-analytical phase. The criteria for risk assessment designing plans for preventive errors were defined in the laboratory. There is no standard for developing and implementing of these plans in the laboratory, impediment the comparison between pairs and application of best practices. Some of the staff laboratory features, namely the ability to think analytical and simultaneously to establish standard policies and strict adherence to protocols, helped in the prevention of the potential errors. These plans for risk assessment can help reduce the occurrence of adverse errors. FMEA may become the common standard for measurement and comparison, particularly in clinical laboratories. In fact, the total testing process is intricate, consisting of numerous steps that are not always taken under the control of laboratory experts. Current evidence on the stratification of errors in clinical laboratory strongly supports the introduction of FMEA for further reducing error rates, particularly in the extra-analytical steps. While the first aim of FMEA is to promote an approach to ensuring the safety of laboratory processes, total cost reduction should be simultaneously achieved when considering the entire process of patient safety [13, 14, 15, 16].
Mascia et al. shows that the FMEA risk management approach as applied to a scientific processes is in line with the current needs of management models to raise effectiveness and efficiency, to enhance reproducibility, and to facilitate a rapid industrialization of obtained results [24]. In order to achieve reliable results in long run of clinical laboratory approaches Momenizadeh et al. suggested that the managers of the laboratories of Markazi province (Iran) should focus on the implementation of the FMEA [22]. Sudhakar et al. reported that FMEA is a beneficial technique to decrease quality failures in clinical biochemistry laboratories. As compared to other prospective risk analysis approaches, FMEA prevent and solve high risk failure modes in clinical laboratories [32]. Previous study stated the efficiency of FMEA risk assessment to detect and to adjust the quality control procedures in order to improve the analytical performance of clinical chemistry laboratories [33].
In all clinical laboratories a risk assessment approach is required according to ISO 17025:2017 standard dedicated to laboratories measurement, in order to improve uncertainty and thus the reliability and reproducibility of results. Performing FMEA to processes in the laboratory facilitates evaluation high-risk processes tend to failure before an error happen. By assuming and compensating for less-than-perfect human performance, FMEA promotes error prevention through identification of valuable and consensually accepted quality indicators in all steps of the testing process [34].
5. Conclusion
Clinical laboratories are inseparable part of health care system as they help in appropriate diagnosis of patient’s health. Their working process is a complex procedure which may associate with certain errors. Improvement of the patients’ safety by reducing the errors and their risks in clinical laboratories is a great challenge. High-quality clinical laboratories ensure that they perform standard tasks, monitor, and improve their performance, creating a culture of transparency, defining responsibilities, and optimizing patients’ safety. FMEA is very effective and successful technique in preventing errors, improving quality and safety of tests, identifying potential errors, and prioritizing clinical laboratory improvement strategies. FMEA had a multidisciplinary approach and its complex configuration processes involvement facilitated the management of errors. As compared to other prospective risk analysis methods, FMEA analysis provides a good solution for high risk failure modes in clinical laboratories. Therefore, FMEA is a suitable and efficient tool to identify most clinical laboratory errors to improving the quality of laboratory processes and ensuring the accuracy of obtained results and maintaining patient health and safety. The overall purpose of this paper is to encourage clinical laboratories to assess and monitor their own. In addition, it should be possible to identify and monitor error rates to improve upon the process on the basis of objective and desirable quality specifications.
Conflict of interests
The authors declare that they have no conflicts of interest.
Author contributions
All authors contributed equally to this manuscript, and approved the final version of manuscripts.
Ethical declarations
Not applicable.
Financial support
None to be declared.
\n',keywords:"Patient Safety, Medical Laboratory, Risks, Failure Modes, Processes",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/76640.pdf",chapterXML:"https://mts.intechopen.com/source/xml/76640.xml",downloadPdfUrl:"/chapter/pdf-download/76640",previewPdfUrl:"/chapter/pdf-preview/76640",totalDownloads:1137,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,impactScore:0,impactScorePercentile:41,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"February 16th 2021",dateReviewed:"April 6th 2021",datePrePublished:"December 7th 2021",datePublished:"April 20th 2022",dateFinished:"May 7th 2021",readingETA:"0",abstract:"Patient safety is an aim for clinical applications and is a fundamental principle of healthcare and quality management. The main global health organizations have incorporated patient safety in their review of work practices. The data provided by the medical laboratories have a direct impact on patient safety and a fault in any of processes such as strategic, operational and support, could affect it. To provide appreciate and reliable data to the physicians, it is important to emphasize the need to design risk management plan in the laboratory. Failure Mode and Effect Analysis (FMEA) is an efficient technique for error detection and reduction. Technical Committee of the International Organization for Standardization (ISO) licensed a technical specification for medical laboratories suggesting FMEA as a method for prospective risk analysis of high-risk processes. FMEA model helps to identify quality failures, their effects and risks with their reduction/elimination, which depends on severity, probability and detection. Applying FMEA in clinical approaches can lead to a significant reduction of the risk priority number (RPN).",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/76640",risUrl:"/chapter/ris/76640",book:{id:"9808",slug:"contemporary-topics-in-patient-safety-volume-1"},signatures:"Hoda Sabati, Amin Mohsenzadeh and Nooshin Khelghati",authors:[{id:"340486",title:"M.Sc.",name:"Hoda",middleName:null,surname:"Sabati",fullName:"Hoda Sabati",slug:"hoda-sabati",email:"h.sabati@yahoo.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"348872",title:"M.Sc.",name:"Amin",middleName:null,surname:"Mohsenzadeh",fullName:"Amin Mohsenzadeh",slug:"amin-mohsenzadeh",email:"mohsenzadehamin@gmail.com",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/348872/images/15934_n.jpg",institution:null},{id:"348874",title:"MSc.",name:"Nooshin",middleName:null,surname:"Khelghati",fullName:"Nooshin Khelghati",slug:"nooshin-khelghati",email:"noshinkhelghati@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Failure mode and effect analysis: background and description",level:"1"},{id:"sec_2_2",title:"2.1 FMEA in clinical laboratory activities and patient’ safety",level:"2"},{id:"sec_3_2",title:"2.2 FMEA benefits and barriers",level:"2"},{id:"sec_5",title:"3. Methodology",level:"1"},{id:"sec_5_2",title:"3.1 Selecting a process for study",level:"2"},{id:"sec_6_2",title:"3.2 Assembling a multidisciplinary team",level:"2"},{id:"sec_7_2",title:"3.3 Collecting and organizing data about the selected process",level:"2"},{id:"sec_8_2",title:"3.4 Analysis of hazards",level:"2"},{id:"sec_9_2",title:"3.5 Developing and implementing appropriate actions and measuring the outcomes",level:"2"},{id:"sec_11",title:"4. Discussion",level:"1"},{id:"sec_12",title:"5. Conclusion",level:"1"},{id:"sec_16",title:"Conflict of interests",level:"1"},{id:"sec_13",title:"Author contributions",level:"1"},{id:"sec_14",title:"Ethical declarations",level:"1"},{id:"sec_15",title:"Financial support",level:"1"}],chapterReferences:[{id:"B1",body:'Kalra, J. (2011). Medical errors and patient safety: strategies to reduce and disclose medical errors and improve patient safety (Vol. 1). Walter de Gruyter.'},{id:"B2",body:'Plebani, M. (2010). The detection and prevention of errors in laboratory medicine. Annals of clinical biochemistry, 47(2), 101-110.'},{id:"B3",body:'Lao, E. G., García, Á. S., Figuerola, M. B., Moreno, E., & Paraire, A. H. (2017). Errors of clinical laboratory and its impact on patient safety. Open Journal of Social Sciences, 5(3), 243-253.'},{id:"B4",body:'Mascia, A., Cirafici, A. M., Bongiovanni, A., Colotti, G., Lacerra, G., Di Carlo, M., ... & Kisslinger, A. (2020). A failure mode and effect analysis (FMEA)-based approach for risk assessment of scientific processes in non-regulated research laboratories. Accreditation and Quality Assurance, 25(5), 311-321.'},{id:"B5",body:'Sharma, K. D., & Srivastava, S. (2018). Failure mode and effect analysis (FMEA) implementation: a literature review. J Adv Res Aeronaut Space Sci, 5, 2454-8669.'},{id:"B6",body:'Banduka, N., Veža, I., & Bilić, B. (2016). An integrated lean approach to Process Failure Mode and Effect Analysis (PFMEA): A case study from automotive industry. Advances in Production Engineering & Management, 11(4).'},{id:"B7",body:'Yusof, M. B., & Abdullah, N. H. B. (2016). Failure mode and effect analysis (FMEA) of butterfly valve in oil and gas industry. J. Eng. Sci. Technol., 11, 9-19.'},{id:"B8",body:'Goel, A., & Graves, R. J. (2007, May). Using failure mode effect analysis to increase electronic systems reliability. 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Risk Assessment of Clinical Care in Emergency Departments by Health Failure Modes and Effects Analysis. Journal of Holistic Nursing and Midwifery, 30(1), 35-44.'},{id:"B19",body:'Thornton, E., Brook, O. R., Mendiratta-Lala, M., Hallett, D. T., & Kruskal, J. B. (2011). Application of failure mode and effect analysis in a radiology department. Radiographics, 31(1), 281-293.'},{id:"B20",body:'Jiang, Y., Jiang, H., Ding, S., & Liu, Q. (2015). Application of failure mode and effects analysis in a clinical chemistry laboratory. Clinica Chimica Acta, 448, 80-85.'},{id:"B21",body:'Woodhouse, S., Burney, B., & Coste, K. (2004). To err is human: improving patient safety through failure mode and effect analysis. Clinical leadership & management review: the journal of CLMA, 18(1), 32-36.'},{id:"B22",body:'Momenizadeh, E., Riahi, L., & Nazarimanesh, L. (2019). The effect of controlling clinical laboratory errors on patients\' safety in Markazi province laboratories. Razi Journal of Medical Sciences, 26(9), 102-111.'},{id:"B23",body:'Molavi-Taleghani, Y., Ebrahimpour, H., & Sheikhbardsiri, H. (2020). A Proactive Risk Assessment Through Healthcare Failure Mode and Effect Analysis in Pediatric Surgery Department. Journal of Comprehensive Pediatrics, 11(3).'},{id:"B24",body:'Mascia, A., Cirafici, A. M., Bongiovanni, A., Colotti, G., Lacerra, G., Di Carlo, M., ... & Kisslinger, A. (2020). A failure mode and effect analysis (FMEA)-based approach for risk assessment of scientific processes in non-regulated research laboratories. Accreditation and Quality Assurance, 25(5), 311-321.'},{id:"B25",body:'Eliza, D. R., & Minodora, D. (2015). Risk Management in Clinical Laboratory: from Theory to Practice. Acta Medica Marisiensis, 61(4), 372-377.'},{id:"B26",body:'Tosheska-Trajkovska, K., Bosilkova, G., Kostovska, I., Labudovikj, D., Brezovska Kavrakova, J., Cekovska, S., ... & Marija, K. (2019). Risk management in the clinical laboratories-use of the Failure Modes and Effects Analysis (FMEA). Journal of Morphological Sciences.'},{id:"B27",body:'Stravitz, P. E., Cibas, E. S., & Heher, Y. K. (2019). Targeting specimen misprocessing safety events with failure modes and effects analysis. Cancer cytopathology, 127(4), 213-217.'},{id:"B28",body:'Mendes, M. E., Ebner, P. D. A. R., Romano, P., Pacheco Neto, M., Sant’anna, A., & Sumita, N. M. (2013). Practical aspects of the use of FMEA tool in clinical laboratory risk management. Jornal Brasileiro de Patologia e Medicina Laboratorial, 49(3), 174-181.'},{id:"B29",body:'David, R. E., & Dobreanu, M. I. N. O. D. O. R. A. (2015). Failure modes and effects analysis (FMEA)-An assessment tool for risk management in clinical laboratories. Acta Medica Transilvanica, 20(4), 130-34.'},{id:"B30",body:'Potts, H. W., Anderson, J. E., Colligan, L., Leach, P., Davis, S., & Berman, J. (2014). Assessing the validity of prospective hazard analysis methods: a comparison of two techniques. BMC health services research, 14(1), 1-10.'},{id:"B31",body:'Corpuz, R. S. A. (2020). ISO 9001: 2015 Risk-based Thinking: A Framework using Fuzzy-Support Vector Machine. Makara Journal of Technology, 24(3), 149-159.'},{id:"B32",body:'Sudhakar, B., & Sadariya, B. R. (2020). Application of failure mode and effects analysis to minimize quality failures in clinical biochemistry laboratory. International Journal of Clinical Biochemistry and Research, 5(4), 613-616.'},{id:"B33",body:'Xia, Y., Xue, H., Yan, C., Li, B., Zhang, S., Li, M., & Ji, L. (2018). Risk analysis and assessment based on Sigma metrics and intended use. Biochemia medica, 28(2), 195-203.'},{id:"B34",body:'Vasilnakova, A. (2018). RISK MANAGEMENT IN ACCREDITED TESTING LABORATORIES. Annals of DAAAM & Proceedings, 29.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Hoda Sabati",address:"h.sabati@yahoo.com",affiliation:'
Faculty of Science, Biotechnology and Biological Science Research Center, Shahid Chamran University of Ahvaz, Iran
Faculty of Science, Department of Chemistry, Shahid Chamran University of Ahvaz, Iran
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1. Introduction
Macrophages and dendritic cells are key players in activation of the innate and adaptive immune systems. In viral infections, such as HIV-1 and COVID-19, macrophages assume either a more protective or a pathogenic role depending on their classification.
Macrophages are polarized by cytokine signaling from CD4 T cells into the M1 or the M2 type. M1 macrophages are classically activated by interferon-γ (INF-γ), whereas M2 macrophages are activated by interleukin-4 (IL-4) and IL-13 [1]. M1 macrophages have a role in combating infection, whereas M2 macrophages in supporting homeostasis. This inflammatory response, while beneficial against microbes and aggregated molecules, when hyper-activated can lead to tissue damage in the lungs, heart, and brain.
Under conditions of high microbial activity, M1 promote inflammation, synthesize nitric oxide, and induce cytokine production IL-6, IL-12 and tumor necrosis factor (TNF). Type M2 macrophages secrete arginase-I, IL-10 and TGF-β and other anti-inflammatory cytokines promoting wound healing but also contributing to tumor growth. M1 macrophages phagocytize microbes and initiate an adaptive immune response by T cells while M2 macrophages induce collagen repair to maintain tissue repair [1].
The roles of M1 macrophages in opposing the disease and the M2 macrophages in promoting homeostasis, while evident in infectious diseases, is less evident in neurodegenerative diseases. Macrophages of AD patients were classified according to the ratio of cluster of differentiation (CD) surface markers: CD54+ CD80/CD163+ CD206 [2] as follows: the inflammatory M1 macrophage type has the ratio > 4, the anti-inflammatory M2 type has a ratio < 1. Both M1 and M2 are associated with progression of dementia. In AD patients, however, the intermediate M1M2 phenotype with a ratio between 1 and 4 is a pro-resolution type promoting homeostasis and protection against dementia. Infection or other stress can cause an imbalance in the expression of these two macrophage types, as seen by others [3].
Macrophages were first appreciated in tuberculosis, where the dual role of macrophages in containing vs. replicating Mycobacterium tuberculosis (Mtb) has been known for a century. A successful response to mycobacterial infection is a granuloma consisting of lymphocytes, macrophages, Langhans giant cells (fused macrophages around mycobacteria) and fibroblasts [4].
In recent years, the role of macrophages in neurodegenerative diseases, Alzheimer’s disease (AD) and amyotrophic lateral sclerosis (ALS), has been appreciated as a result of immunochemical demonstration of central nervous system (CNS) invasion by peripheral blood monocyte/macrophages (MM) in AD patients [5]. Macrophage targets in AD are amyloid-beta (Aβ) and P-tau, while in ALS macrophage targets are aggregated superoxide dismutase (SOD-1) and microbial nucleic acids. Autologous DNA released into the cytoplasm could be a target in the cGAS-STING pathway [6].
This chapter will discuss the integral role and modulation of macrophage role in the therapy of neurodegenerative and infectious diseases. The natural substances polyunsaturated fatty acids (PUFA), circuminoids, vitamin D, and polyphenols improve macrophage transcriptome and functions for brain clearance in neurological diseases, but their mechanisms depend on many variables that are under study. The effects of natural substances on macrophage transcriptome and clearance of Aβ and P-tau could potentiate therapeutic outcome of monocl0nal antibodies, which increase phagocytosis but not degradation or export of Aβ and P-tau.
2. Macrophages of Alzheimer’s disease patients fail in clearance of the AD brain
In AD patients, microglia are the first responders to accumulation of Aβ in the brain, which attract monocyte/macrophages (MM) from the blood into the CNS by chemokines, in particular CCL5 (RANTES) [7]. In non-demented individuals, post-mortem immunochemistry and results from a blood-brain barrier (BBB) model suggest that MM migrate across BBB in both directions, clear Aβ in plaques, degrade or export Aβ from the brain without disruption of BBB or accumulation of macrophages around vessels [5, 8], and deposit Aβ into cervical nodes or further downstream [9]. MM of healthy subjects with the pro-resolution M1M2 phenotype effectively upload Aβ into endosomes, fuse endosomes with lysosomes, and degrade Aβ by lysosomal enzymes. MM of AD patients are defective in internalization and degradation of Aβ in endosomes [8].
In the AD brain, MM immigrate into CNS through disrupted tight junctions between endothelial cells. In addition, opening of tight junctions in the blood-brain barrier (BBB) permits brain entry of fibrin and components of the complement. In the brain parenchyma, AD macrophages invade the plaques but upload Aβ only on its cell surface, do not transport Aβ into endosomes and fail to degrade Aβ. Clearance of individual Aβ plaques by macrophages is functionally stochastic, thus some plaques are not fully cleared [5]. After uploading Aβ, MM migrate to vessels but fail to emigrate across BBB, suffer apoptosis and release Aβ into vessels, which develop cerebro-vascular angiopathy (CAA) (Figure 1) [10].
Figure 1.
Macrophage pathway in the AD brain. Created with BioRender.com.
2.1 Natural substances have mixed results in prevention of Alzheimer’s disease
Natural substances, prominently PUFA, have a long history in prevention of AD in the patients with mild cognitive impairment (MCI). PUFA supplementation had positive cognitive effects in MCI patients, but only in those with very mild MCI [11]. A large controlled clinical trial of PUFA supplementation, however, did not slow the rate of cognitive and functional decline in patients with mild to moderate Alzheimer disease [12].
Nevertheless, proven effects of PUFA on macrophage transcriptome and functions designate PUFA for immunotherapeutic studies controlled for baseline immune phenotype, APO E genotype, stage of disease, diet and lifestyle using a high-quality omega-fatty acid preparation protected against oxidation [2]. In an uncontrolled study of mild cognitive impairment (MCI) patients supplemented by the omega-3 fatty acid drink Smartfish, macrophage phenotype changed from either a highly inflammatory M1 or a very low non-inflammatory M2 to the desired pro-resolution M1/M2 (Table 1) [2]. Importantly, in this study, PUFA supplementation improved the cognitive status of MCI/AD patients for 4–17 months and protected against dementia for 4.5 years in certain patients [13]. In vivo supplementation and in vitro stimulation by the Smartfish drink stimulated the pro-resolution M1/M2 macrophage type with increased phagocytosis and energy [14].
AD patients
PUFA-supplemented AD patients
M1-type macrophages
↑
↓
M2-type macrophages
Variable
↑
M1/M2 pro-resolution-type macrophages
↓
↑↑
MGAT3 transcription
↓
↑↑
Inflammation
↑↑
↓
Aβ phagocytosis by macrophages
↓
↑
Table 1.
The effects of PUFA-supplementation on Alzheimer’s disease (AD) patients.
A clinical study of supplementation with a high dose of vitamin B complex, including vitamins B6 (pyridoxine), B9 (folic acid), and B12 (cobalamin), showed a reduction in the rate of brain atrophy in MCI patients with elevated total homocysteine [15].
2.2 Alzheimer’s disease patients’ macrophages have transcriptomic defects that underlie their defective function
AD brain cells and macrophages lack energy due to oxidative damage and nitrosylation by reactive oxygen species (ROS) of the glycolytic enzyme transcripts GAPDH, PKM1, and PDH, and the citric acid cycle enzymes ACO2 and OGDH [16]. The defects in macrophage transcriptome in protein-coding and regulatory sequences for energy, glycosylation, unfolded protein response, glymphatic system, Toll-like receptors, and other genes lead to macrophage malfunction and apoptosis [14]. Macrophages exhibit a faulty unfolded protein response (UPR) to endoplasmic reticulum (ER) stress from Aβ phagocytosis. These defects lead to macrophage apoptosis and inflammation [14].
2.3 Natural substances have positive effects on macrophage biochemistry
The anti-inflammatory molecule bisdemethoxycurcumin up regulated MGAT3 transcripts and increased macrophage phagocytosis of Aβ. 1,25 dihydroxy vitamin D3 potentiated the 4,4-diisothiocyanostilbene-2,2-disulfonic acid-sensitive chloride channel ClC-3 currents essential for Aβ phagocytosis [17]. A clinical study of supplementation with a high dose of vitamin B complex, including vitamins B6 (pyridoxine), B9 (folic acid), and B12 (cobalamin), showed a reduction in the rate of brain atrophy in MCI patients with elevated total homocysteine [15].
RNA-seq analysis of single blood cell types in PUFA-supplemented MCI patients showed up-regulation of the transcripts for glycolysis, tricarboxylic acid cycle, OX-PHOS, nicotinamide dinucleotide (NAD+) synthesis in monocytes, neutrophils, regulatory T cells, memory CD4 and CD8 T cells, and NK cells, but the most consistent effects across the whole spectrum were in monocytes/macrophages (MM). Importantly, the supplementation showed that PUFA provide energy for immune clearance of the brain throughout the diurnal cycle and even in hypo- or hyper-glycemia [13]. Both hypo- and hyper-glycemic glucose concentrations in the medium of macrophages in vitro inhibit Aβ phagocytosis, but phagocytosis is restored in presence of PUFA in the medium. Diabetic patients have an increased risk of AD and may benefit from PUFA supplementation to provide missing energy in hypoglycemia and to enhance correct glycation and protect against glucose adducts in hyperglycemia of mild cognitive impairment (MCI) patients.
2.4 Monoclonal antibodies and PUFA in therapy of AD
The approval of aducanumab in 2021 increased enthusiasm for immune therapy by monoclonal antibodies. Aducanumab cleared both soluble oligomers and aggregated Aβ in a dose- and time-dependent fashion according to amyloid positron emission tomography (PET) imaging and slowed disease progression [18]. Both aducanumab and another monoclonal antibody ponezumab, however, have been associated with cerebro-vascular amyloid angiopathy (CAA) in models and with amyloid-related imaging abnormalities (ARIA) in patients [19]. Although the exact mechanisms of CAA related to aducanumab are not known due to the lack of pathological and biochemical studies, this complication appears to be due to macrophage shuttling Aβ from plaques to vessels, failure of macrophages in Aβ degradation and macrophage apoptosis with a release of Aβ into CAA vessels [10]. As PUFA up regulate the transcription of Aβ degradation enzymes, PUFA supplementation could be synergistic with the monoclonal antibody therapy.
3. Macrophages in amyotrophic lateral sclerosis
In amyotrophic lateral sclerosis (ALS), macrophages target aggregated superoxide dismutase (SOD-1) [20] and may be targeting free autologous DNA in the cytoplasm.
Immunopathological studies of sporadic amyotrophic lateral sclerosis (sALS) patients demonstrated an inflammatory attack by macrophages, cytotoxic T cells, NK and mast cells on motor neurons in the spinal cord [21, 22]. The attack includes gray matter and white matter of the spinal cord as well as peripheral nerves. The invading macrophages are inflammatory expressing cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (iNOS), IL-6 and TNF-α, and they attack both caspase-positive and caspase-negative neurons. In addition, cytotoxic CD8 T cells and NK cells participate in the autoimmune attack expressing the cytotoxic enzymes granzymes.
Aggregated superoxide dismutase-1 (SOD-1) stimulates peripheral blood mononuclear cells (PBMC) of sALS patients inducing IL-1, IL6, and IL23. These cytokines enhance production of the auto-immune pro-inflammatory cytokine IL-17A, which is increased in the serum and spinal cord of ALS patients [23]. The induction of inflammatory pathology in sALS by SOD-1 in aggregated form is an example that in neurodegenerative diseases the aggregated state of the putative culprits SOD-1 in ALS and amyloid-β and P-tau in Alzheimer’s disease is sufficient for induction of inflammatory neuropathology.
4. Macrophages in HIV-1 infection
Trojan transport is the main path for virus penetration the blood-brain barrier in HIV-1 encephalitis. In addition, HIV-1 penetrates across coronary endothelia by transcytosis or by a paracellular route through disrupted tight junction protein ZO-1 [24].
Infiltration of target organs by inflammatory macrophages has a critical role in the progression of HIV-1 encephalitis and HIV-1 myocarditis. Other target organs in AIDS, the liver and the kidneys, are also infiltrated by macr0phages. In patients with HIV-1 encephalitis, cognitive impairment is proportionate to the number of macrophages infiltrating the brain [25]. In a study of postmortem heart tissues from 15 AIDS patients, the failing hearts showed significantly higher infiltration in the left ventricular myocardium by COX-2-positive and iNOS-positive macrophages compared to the functioning hearts. In the hearts with HIV-1 myocarditis, productive infection was exclusively in infiltrating macrophages and T cells, not in cardiomyocytes [26]. Significant infiltration of the failing heart by COX-2-positive macrophages (demonstrated using an antigen-retrieval technique) and production of inflammatory cytokines and mediators, such as NO, quinolinic acid, free radicals, is considered as the main mechanism of heart failure in HIV-1 myocarditis [26].
5. Macrophages in patients with COVID-19 infections
The recent epidemic of SARS-CoV-2 viral infection is associated with severe complications in subjects with risk factors, including older age, obesity, diabetes, renal failure; cancer; chronic obstructive pulmonary disease from smoking; and immunosuppressive conditions. In contrast, SARS-CoV-2 infected subjects without risk factors may suffer only mild pulmonary damage [27].
SARS-CoV-2 and previous coronaviruses responsible for severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS) induce inflammatory activation of monocytes/macrophages (MM). Patients with a low expression of interferon–α/β/γ in macrophages may suffer severe pulmonary damage by lung infiltration with inflammatory MM in a cytokine release syndrome (CRS), a.k.a. cytokine storm. MM are attracted into the lungs by chemokines produced by lung epithelia and fibroblasts. MM are activated by GM-CSF and IL-6, therefore, a blockade with IL-6 receptor antibody (tocilizumab, ActemraR) is used in presence of a high inflammation [27]. CRS is related to the release of IL-1, IL-6 and nitric oxide (NO) from monocyte/macrophages, as shown in vitro and in an animal model by depletion of macrophages. CRS is similar to macrophage activation syndrome (MAS), a complication of several autoimmune diseases, in which well-differentiated macrophages show uncontrolled proliferation. CRS is also a complication of chimeric antigen receptor (CAR) therapy of B cell malignancies, in which macrophages produce IL-6, IL-1 and NO and where IL-1 blockade is therapeutic. COVID 19 pneumonia may be complicated by disseminated intravascular coagulation (DIC), therefore anticoagulation with enoxaparin (LovenoxR) is routinely used in severe CoV2 infections.
In COVID-19 pneumonia, macrophages have a dual role with a beneficial anti-viral role and a detrimental role through excessive release of inflammatory cytokines [28]. The lung has two types of macrophages, interstitial and alveolar macrophages. The alveolar macrophages with M1 type are pivotal in defense against pathogens by phagocytosis of microbes, enhancement of development of cytotoxic T cells and type I interferon production. The M2 type alveolar macrophages dampen the inflammation and repair the damage in association with other immune tissue cells. Macrophages infected by CoV2, however, accumulate lipid droplets in association with virus replication [29].
Angiotensin-converting enzyme 2 (ACE2) is a cell receptor for SARS and CoV-2 on lung epithelial cells. SARS-CoV-2 can infect monocytes and macrophages through ACE2-dependent and ACE2-independent pathways [27]. A second study with similar findings detected ACE2-expressing CD68+ and CD169+ macrophages containing SARS-CoV-2 nucleoprotein antigens in the sinuses of the lymph nodes of COVID-19 patients. These infected macrophages up regulated the pro-inflammatory cytokines IL-6 [30] and IL-1B [31]. The increased production of these cytokines by infected monocytes and macrophages induces hyper-inflammation and the onset of cytokine storm, leading to excess tissue damage and the potential harm of other vital organs in addition to the lungs (Figure 2) [27]. In addition, a subset of macrophages isolated from Covid-19 patients have been found to express genes that promote fibrosis generation and tissue repair. Therefore, the infection of infiltrating macrophages might not only be detrimental because of the promotion of acute inflammation, but also because of fibrotic complications that may arise and that have been observed in patients under mechanical ventilation. Long-term increases in the pro-inflammatory M1 type macrophages are associated with elevated reactive oxygen species (ROS) [32], which can produce harmful long-term side effects.
Figure 2.
Monocyte infiltration of the lungs with differentiation and activation of macrophages with production of cytokines and chemokines. Created with BioRender.com.
6. Macrophages in tuberculosis
Similar to in COVID-19 infections, macrophages play a dual role in tuberculosis. Macrophages permissive for M. tuberculosis (Mtb) replication, they may control and eliminate the infection in the presence of appropriate innate and acquired responses [4].
M1 and M2 macrophages are distinguished by their activation type. M1 cells are activated by the T helper type 1 (Th1) cells. These cells pertain to cell mediated immune responses and emit interleukin-2 (IL-2), IFN-γ, and lymphotoxins [33]. Th1 cells lead to an increased M1 activation due to the production of IFN-y. Nitric oxide production is stimulated by the Th1 activation, rather than T helper type 2 (Th2) cells [34]. Th2 cells cause the activation of M2 macrophages. These cells are responsible for the stimulation of humoral immunity and secrete interleukin-4 (IL-4), interleukin-5 (IL-5), and interleukin-10 (IL-10). Th2 responses are a result of the production of ornithine which activates the M2 response allowing for tissue repair [1]. In tuberculosis, the intersection of Th1 and Th2 activation of M1 and M2 respectively leads to the immunity against pathogens. Along with the production of IFN-y, immunoglobulin A (Ig-A) is necessary for defense against tuberculosis (Table 2) [35].
Type 1 macrophages
Type 2 macrophages
Marker expression
CD80, CD54
CD163, CD206
Activation
Th1, INF-γ, GM-CSF
Th2, IL-4
Pro-inflammatory cytokines
High
Low
Antigen presentation
Yes
No
Production of ROS/NO
High
Low
Purpose
Destruction of microbes
Construct extracellular matrix
Table 2.
Type 1 and type 2 macrophage distinction.
Alveolar macrophages (AM) and dendritic cells phagocytize Mtb in the alveolar space in the lungs. Mtb is transmitted by macrophages to other organs via lymph nodes and blood vessels [36]. Alveolar macrophages present Mtb antigens to the adaptive immune cells to initiate an immune response. Subsequently, granulomas are formed because of the early inflammatory response caused by the AM [37]. Granulomas contain a variety of immune cells in order to contain the antigen. Some of the immune cells present in granulomas include foamy macrophages, epithelioid cells, neutrophils, and dendritic cells.
7. Conclusion
The phenotype of macrophages infiltrating the lesions in AD, ALS, HIV-1 myocarditis and encephalitis should be determined first, as it is the initial information necessary for analyzing macrophage role in each disease. M1 phenotype should be confirmed by staining of NOS, COX-2, TNF alpha, IL1, and IL-6 on macrophages.
Preliminary data suggest the following: (a) the pro-inflammatory M1 macrophage phenotype = CD54 + CD80/CD163 + CD206 > 4.0 is associated with an increased macrophage effector function in viral infections, but also with organ damage best known in HIV-1 myocarditis; b) the pro-resolution phenotype M1/M2 phenotype CD54 + CD80/CD163 + CD206 = in AD is associated with a beneficial role of macrophages in AD. The natural substances polyunsaturated fatty acids, vitamins B and D, energy molecules (carnitine), and flavonoids (resveratrol) may have individual health effects by improving macrophage transcriptome and macrophage pro-resolution type for brain clearance in AD, but the effects are complex and depend on many variables. In ALS patients, the M1/M2 phenotype promoted by fatty acids could be beneficial, but the data in human patients are not yet available.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"Alzheimer’s disease, amyotrophic lateral sclerosis, inflammatory response, cytokines, macrophage, transcriptome, glycome",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80219.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80219.xml",downloadPdfUrl:"/chapter/pdf-download/80219",previewPdfUrl:"/chapter/pdf-preview/80219",totalDownloads:43,totalViews:0,totalCrossrefCites:1,dateSubmitted:"December 20th 2021",dateReviewed:"December 25th 2021",datePrePublished:"March 24th 2022",datePublished:null,dateFinished:"January 25th 2022",readingETA:"0",abstract:"Macrophages have a critical role in the outcome of neurological diseases, including neurodegenerative, autoimmune, vascular and microbial diseases. Macrophage role ranges from beneficial to pathogenic depending upon genetics, other components of innate and adaptive immunity, lifestyle and macrophage targets: aggregated molecules or bacterial and viral pathogens. Macrophages are attracted by chemokines to migrate into the brain and remove or inactivate pathogenic molecules. In the patients with neurodegenerative diseases, macrophages target aggregated molecules, amyloid-β1–42 (Aβ) and P-tau in Alzheimer’s disease (AD), and superoxide dismutase-1 (SOD-1) in amyotrophic lateral sclerosis (ALS), but also have autoimmune targets. In AD and ALS patients, macrophages in the pro-resolution M1M2 state are adapted to brain clearance and homeostasis, whereas in the proinflammatory M1 state are modulate to an anti-viral and antibacterial role, which may be associated with collateral damage to tissues. In HIV-1 and CoV2 viral infections, macrophages in M1 state are anti-viral but also pathogenic through inflammatory damage to the heart and the brain. 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Conclusion",level:"1"},{id:"sec_15",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Orecchioni M, Ghosheh Y, Pramod AB, Ley K. Macrophage polarization: Different gene signatures in M1(LPS+) vs. classically and M2(LPS–) vs. alternatively activated macrophages. Frontiers in Immunology. 2019;10(1084)'},{id:"B2",body:'Famenini S, Rigali EA, Olivera-Perez HM, Dang J, Chang MT, Halder R, et al. Increased intermediate M1-M2 macrophage polarization and improved cognition in mild cognitive impairment patients on omega-3 supplementation. The FASEB Journal. 2017;31(1):148-160'},{id:"B3",body:'Wang N, Liang H, Zen K. Molecular mechanisms that influence the macrophage M1-M2 polarization balance. Frontiers in Immunology. 2014;5:614'},{id:"B4",body:'Guirado E, Schlesinger LS, Kaplan G. Macrophages in tuberculosis: Friend or foe. Seminars in Immunopathology. 2013;35(5):563-583'},{id:"B5",body:'Fiala M, Liu QN, Sayre J, Pop V, Brahmandam V, Graves MC, et al. 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Omega-3 fatty acids increase OXPHOS energy for immune therapy of Alzheimer disease patients. The FASEB Journal. 2020;34(8):9982-9994'},{id:"B14",body:'Olivera-Perez HM, Lam L, Dang J, Jiang W, Rodriguez F, Rigali E, et al. Omega-3 fatty acids increase the unfolded protein response and improve amyloid-beta phagocytosis by macrophages of patients with mild cognitive impairment. The FASEB Journal. 2017;31(10):4359-4369'},{id:"B15",body:'Smith AD, Refsum H. Homocysteine, B vitamins, and cognitive impairment. Annual Review of Nutrition. 2016;36:211-239'},{id:"B16",body:'Butterfield DA, Halliwell B. Oxidative stress, dysfunctional glucose metabolism and Alzheimer disease. Nature Reviews. Neuroscience. 2019;20(3):148-160'},{id:"B17",body:'Fiala M, Liu PT, Espinosa-Jeffrey A, Rosenthal MJ, Bernard G, Ringman JM, et al. Innate immunity and transcription of MGAT-III and toll-like receptors in Alzheimer’s disease patients are improved by bisdemethoxycurcumin. 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Amyotrophic Lateral Sclerosis. 2004;5:1-7'},{id:"B22",body:'Chiot A, Zaïdi S, Iltis C, Ribon M, Berriat F, Schiaffino L, et al. Modifying macrophages at the periphery has the capacity to change microglial reactivity and to extend ALS survival. Nature Neuroscience. 2020;23(11):1339-1351'},{id:"B23",body:'Fiala M, Chattopadhay M, La Cava A, Tse E, Liu G, Lourenco E, et al. IL-17A is increased in the serum and in spinal cord CD8 and mast cells of ALS patients. Journal of Neuroinflammation. 2010;7(1):76-90'},{id:"B24",body:'Fiala M, Looney DJ, Stins M, Way DD, Zhang L, Gan X, et al. TNF-alpha opens a paracellular route for HIV-1 invasion across the blood-brain barrier. Molecular Medicine. 1997;3(8):553-564'},{id:"B25",body:'Lipton JD, Schafermeyer RW. Central nervous system infections. The usual and the unusual. Emergency Medicine Clinics of North America. 1995;13(2):417-443'},{id:"B26",body:'Liu QN, Reddy S, Sayre JW, Pop V, Graves MC, Fiala M. 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EMBO Molecular Medicine. 2021;13(8):e14150'},{id:"B32",body:'Deng Z, Shi F, Zhou Z, Sun F, Sun MH, Sun Q, et al. M1 macrophage mediated increased reactive oxygen species (ROS) influence wound healing via the MAPK signaling in vitro and in vivo. Toxicology and Applied Pharmacology. 2019;366:83-95'},{id:"B33",body:'Dlugovitzky D, Torres-Morales A, Rateni L, Farroni MA, Largacha C, Molteni O, et al. Circulating profile of Th1 and Th2 cytokines in tuberculosis patients with different degrees of pulmonary involvement. FEMS Immunology & Medical Microbiology. 1997;18(3):203-207'},{id:"B34",body:'Mills CD, Kincaid K, Alt JM, Heilman MJ, Hill AM. M-1/M-2 macrophages and the Th1/Th2 paradigm. Journal of Immunology. 2000;164(12):6166-6173'},{id:"B35",body:'Abebe F. Synergy between Th1 and Th2 responses during mycobacterium tuberculosis infection: A review of current understanding. International Reviews of Immunology. 2019;38(4):172-179'},{id:"B36",body:'Garcia-Romo GS, Pedroza-Gonzalez A, Lambrecht BN, Aguilar-Leon D, Estrada-Garcia I, Hernandez-Pando R, et al. Mycobacterium tuberculosis manipulates pulmonary APCs subverting early immune responses. Immunobiology. 2013;218(3):393-401'},{id:"B37",body:'Khan A, Singh VK, Hunter RL, Jagannath C. Macrophage heterogeneity and plasticity in tuberculosis. Journal of Leukocyte Biology. 2019;106(2):275-282'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Mary Dover",address:null,affiliation:'
Department of Integrative Physiology and Biology, UCLA School of Life Sciences, Los Angeles, California, USA
Department of Integrative Physiology and Biology, UCLA School of Life Sciences, Los Angeles, California, USA
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5. LANGUAGE COPYEDITING, TECHNICAL EDITING AND TYPESET PROOF
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Your manuscript will be sent to Straive, a leader in content solution services, for language copyediting. You will then receive a typeset proof formatted in XML and available online in HTML and PDF to proofread and check for completeness. The first typeset proof of your manuscript is usually available 10 days after its original submission.
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If you feel that IntechOpen Compacts, Monographs or Edited Books are the right publishing format for your work, please fill out the publishing proposal form. For any specific queries related to the publishing process, or IntechOpen Compacts, Monographs & Edited Books in general, please contact us at book.department@intechopen.com
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2. SUBMIT YOUR MANUSCRIPT
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4. ACCEPTANCE AND PRICE QUOTE
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We will send you your price quote and after it has been accepted (by both the author and the publisher), both parties will sign a Statement of Work binding them to adhere to the agreed upon terms.
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5. LANGUAGE COPYEDITING, TECHNICAL EDITING AND TYPESET PROOF
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After we receive your proof corrections and a final typeset of the manuscript is approved, your manuscript is sent to our in house DTP department for technical formatting and online publication preparation.
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Additionally, you will be asked to provide a profile picture (face or chest-up portrait photograph) and a short summary of the book which is required for the book cover design.
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6. INVOICE PAYMENT
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IntechOpen will help you complete your payment safely and securely, keeping your personal, professional and financial information safe.
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7. ONLINE PUBLICATION, PRINT AND DELIVERY OF THE BOOK
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IntechOpen authors can choose whether to publish their book online only or opt for online and print editions. IntechOpen Compacts, Monographs and Edited Books will be published on www.intechopen.com. If ordered, print copies are delivered by DHL within 12 to 15 working days.
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If you feel that IntechOpen Compacts, Monographs or Edited Books are the right publishing format for your work, please fill out the publishing proposal form. For any specific queries related to the publishing process, or IntechOpen Compacts, Monographs & Edited Books in general, please contact us at book.department@intechopen.com
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He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. 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Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. 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She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:38,paginationItems:[{id:"82531",title:"Abnormal Iron Metabolism and Its Effect on Dentistry",doi:"10.5772/intechopen.104502",signatures:"Chinmayee Dahihandekar and Sweta Kale Pisulkar",slug:"abnormal-iron-metabolism-and-its-effect-on-dentistry",totalDownloads:0,totalCrossrefCites:null,totalDimensionsCites:null,authors:null,book:{title:"Iron Metabolism - Iron a Double‐Edged Sword",coverURL:"https://cdn.intechopen.com/books/images_new/10842.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82291",title:"The Role of Oxidative Stress in the Onset and Development of Age-Related Macular Degeneration",doi:"10.5772/intechopen.105599",signatures:"Emina Čolak, Lepša Žorić, Miloš Mirković, Jana Mirković, Ilija Dragojević, Dijana Mirić, Bojana Kisić and Ljubinka Nikolić",slug:"the-role-of-oxidative-stress-in-the-onset-and-development-of-age-related-macular-degeneration",totalDownloads:0,totalCrossrefCites:null,totalDimensionsCites:null,authors:null,book:{title:"Importance of Oxidative Stress and Antioxidant System in Health and Disease",coverURL:"https://cdn.intechopen.com/books/images_new/11671.jpg",subseries:{id:"15",title:"Chemical Biology"}}},{id:"82195",title:"Endoplasmic Reticulum: A Hub in Lipid Homeostasis",doi:"10.5772/intechopen.105450",signatures:"Raúl Ventura and María Isabel Hernández-Alvarez",slug:"endoplasmic-reticulum-a-hub-in-lipid-homeostasis",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Updates on Endoplasmic Reticulum",coverURL:"https://cdn.intechopen.com/books/images_new/11674.jpg",subseries:{id:"14",title:"Cell and Molecular Biology"}}},{id:"82409",title:"Purinergic Signaling in Covid-19 Disease",doi:"10.5772/intechopen.105008",signatures:"Hailian Shen",slug:"purinergic-signaling-in-covid-19-disease",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}}]},overviewPagePublishedBooks:{paginationCount:32,paginationItems:[{type:"book",id:"7006",title:"Biochemistry and Health Benefits of Fatty Acids",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7006.jpg",slug:"biochemistry-and-health-benefits-of-fatty-acids",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Viduranga Waisundara",hash:"c93a00abd68b5eba67e5e719f67fd20b",volumeInSeries:1,fullTitle:"Biochemistry and Health Benefits of Fatty Acids",editors:[{id:"194281",title:"Dr.",name:"Viduranga Y.",middleName:null,surname:"Waisundara",slug:"viduranga-y.-waisundara",fullName:"Viduranga Y. 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Saxena",hash:"d92a4085627bab25ddc7942fbf44cf05",volumeInSeries:2,fullTitle:"Current Perspectives in Human Papillomavirus",editors:[{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",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Bacterial Infectious Diseases",value:3,count:2},{group:"subseries",caption:"Parasitic Infectious Diseases",value:5,count:4},{group:"subseries",caption:"Viral Infectious Diseases",value:6,count:7}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:2},{group:"publicationYear",caption:"2021",value:2021,count:4},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:3},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:148,paginationItems:[{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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