Overview of major international spontaneous reporting systems that can be searched via online systems.
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A.",surname:"Makela",fullName:"Pirjo Makela",slug:"pirjo-makela"},{id:"104282",title:"Dr.",name:"Jouko",middleName:null,surname:"Kleemola",fullName:"Jouko Kleemola",slug:"jouko-kleemola"},{id:"128038",title:"Mr.",name:"Paavo",middleName:null,surname:"Kuisma",fullName:"Paavo Kuisma",slug:"paavo-kuisma"}]},{id:"34018",type:"chapter",title:"Land Flooding Irridation Treatment System for Water Purification in Taiwan",slug:"land-flooding-irridation-treatment-system-for-water-purification-in-taiwan",totalDownloads:2013,totalCrossrefCites:0,signatures:"Yu-Kang Yuan",reviewType:"peer-reviewed",authors:[{id:"80027",title:"Dr.",name:"Yu-Kang",middleName:null,surname:"Yuan",fullName:"Yu-Kang Yuan",slug:"yu-kang-yuan"}]}]},relatedBooks:[{type:"book",id:"713",title:"Irrigation Systems and Practices in Challenging Environments",subtitle:null,isOpenForSubmission:!1,hash:"f4906d58c1ed14c504112886206ce496",slug:"irrigation-systems-and-practices-in-challenging-environments",bookSignature:"Teang Shui Lee",coverURL:"https://cdn.intechopen.com/books/images_new/713.jpg",editedByType:"Edited by",editors:[{id:"111060",title:"Dr.",name:"Teang Shui",surname:"Lee",slug:"teang-shui-lee",fullName:"Teang Shui Lee"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"34104",title:"Effects of Irrigation on the Flowering and Maturity of Chickpea Genotypes",slug:"effects-of-irrigation-on-the-flowering-and-maturity-of-chickpea-genotypes",signatures:"Kamel Ben Mbarek, Boutheina Douh and Abdelhamid Boujelben",authors:[{id:"80212",title:"Dr.",name:"Kamel",middleName:null,surname:"Ben Mbarek",fullName:"Kamel Ben Mbarek",slug:"kamel-ben-mbarek"}]},{id:"34105",title:"Deficit (Limited) Irrigation - A Method for Higher Water Profitability",slug:"deficit-limited-irrigation-a-method-for-higher-water-profitability-in-agriculture",signatures:"Saeideh Maleki Farahani and Mohammad Reza Chaichi",authors:[{id:"78156",title:"Dr.",name:"Saeideh",middleName:null,surname:"Maleki Farahani",fullName:"Saeideh Maleki Farahani",slug:"saeideh-maleki-farahani"},{id:"129155",title:"Dr.",name:"Mohammad Reza",middleName:null,surname:"Chaichi",fullName:"Mohammad Reza Chaichi",slug:"mohammad-reza-chaichi"}]},{id:"34106",title:"Water Productivity and Fruit Quality in Deficit Drip Irrigated Citrus Orchards",slug:"water-productivity-and-fruit-quality-in-deficit-drip-irrigated-citrus-orchards",signatures:"Ana Quiñones, Carolina Polo-Folgado, Ubaldo Chi-Bacab, Belén Martínez-Alcántara and Francisco Legaz",authors:[{id:"83625",title:"Dr.",name:"Ana",middleName:null,surname:"Quinones",fullName:"Ana Quinones",slug:"ana-quinones"}]},{id:"34107",title:"Crop Evapotranspiration and Water Use Efficiency",slug:"crop-evapotransportation-and-water-use-efficiency",signatures:"Bergson Guedes Bezerra",authors:[{id:"79271",title:"Ph.D.",name:"Bergson",middleName:"Guedes",surname:"Bezerra",fullName:"Bergson Bezerra",slug:"bergson-bezerra"}]},{id:"34108",title:"Strategies for Improving Water Productivity and Quality of Agricultural Crops in an Era of Climate Change",slug:"strategies-for-improving-water-productivity-and-quality-of-agricultural-crops-in-an-era-of-climate-c",signatures:"Zorica Jovanovic and Radmila Stikic",authors:[{id:"77140",title:"Prof.",name:"Zorica",middleName:null,surname:"Jovanovic",fullName:"Zorica Jovanovic",slug:"zorica-jovanovic"},{id:"80319",title:"Prof.",name:"Radmila",middleName:null,surname:"Stikic",fullName:"Radmila Stikic",slug:"radmila-stikic"}]},{id:"34109",title:"A Review on Creating Drought Tolerant Crop Varieties",slug:"a-review-on-creating-drought-tolerant-crop-varieties",signatures:"Ramesh Thatikunta",authors:[{id:"83537",title:"Dr.",name:"Ramesh",middleName:null,surname:"Thatikunta",fullName:"Ramesh Thatikunta",slug:"ramesh-thatikunta"}]},{id:"34110",title:"Drought Stress and the Need for Drought Stress Sensing in a World of Global Climate Change",slug:"drought-stress-and-the-need-for-drought-stress-sensing-in-a-world-of-global-climate-change",signatures:"Rita Linke",authors:[{id:"87392",title:"Dr.",name:"Rita",middleName:null,surname:"Linke",fullName:"Rita Linke",slug:"rita-linke"}]},{id:"34111",title:"Sustainable Rice Yield in Water-Short Drought-Prone Environments: Conventional and Molecular Approaches",slug:"sustainable-rice-yield-in-water-short-drought-prone-environments-conventional-and-molecular-approach",signatures:"B. 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Imamul Huq",authors:[{id:"110352",title:"Dr.",name:"Imamul",middleName:null,surname:"Huq",fullName:"Imamul Huq",slug:"imamul-huq"}]},{id:"34114",title:"Effects of Irrigation-Fertilization and Irrigation-Mycorrhization on the Alimentary and Nutraceutical Properties of Tomatoes",slug:"effects-of-irrigation-fertilization-and-irrigation-mycorrhization-on-the-alimentary-and-nutraceutica",signatures:"Luigi Francesco Di Cesare, Carmela Migliori, Valentino Ferrari, Mario Parisi, Gabriele Campanelli, Vincenzo Candido and Domenico Perrone",authors:[{id:"92003",title:"Dr",name:"Luigi Francesco",middleName:null,surname:"Di Cesare",fullName:"Luigi Francesco Di Cesare",slug:"luigi-francesco-di-cesare"},{id:"92602",title:"Dr.",name:"Carmela",middleName:null,surname:"Migliori",fullName:"Carmela Migliori",slug:"carmela-migliori"},{id:"92603",title:"Dr.",name:"Valentino",middleName:null,surname:"Ferrari",fullName:"Valentino Ferrari",slug:"valentino-ferrari"},{id:"92605",title:"Dr.",name:"Mario",middleName:null,surname:"Parisi",fullName:"Mario Parisi",slug:"mario-parisi"},{id:"92608",title:"Dr",name:"Gabriele",middleName:null,surname:"Campanelli",fullName:"Gabriele Campanelli",slug:"gabriele-campanelli"},{id:"95058",title:"Dr.",name:"Vincenzo",middleName:null,surname:"Candido",fullName:"Vincenzo Candido",slug:"vincenzo-candido"},{id:"123090",title:"Dr.",name:"Domenico",middleName:null,surname:"Perrone",fullName:"Domenico Perrone",slug:"domenico-perrone"}]},{id:"34115",title:"Experimentation on Cultivation of Rice Irrigated with a Center Pivot System",slug:"experimentation-on-cultivation-of-rice-irrigated-with-a-center-pivot-system",signatures:"Gene Stevens, Earl Vories, Jim Heiser and Matthew Rhine",authors:[{id:"82842",title:"Prof.",name:"Gene",middleName:null,surname:"Stevens",fullName:"Gene Stevens",slug:"gene-stevens"}]},{id:"34116",title:"Large-Scale Pressurized Irrigation Systems Diagnostic Performance Assessment and Operation Simulation",slug:"large-scale-pressurized-irrigation-systems-diagnostic-performance-assessment-and-operation-simulatio",signatures:"Daniele Zaccaria",authors:[{id:"99711",title:"Dr.",name:"Daniele",middleName:null,surname:"Zaccaria",fullName:"Daniele Zaccaria",slug:"daniele-zaccaria"}]},{id:"34117",title:"Sustainable Irrigation Practices in India",slug:"sustainable-irrigation-practices-in-india",signatures:"Rajapure V. 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E. Toriman and M. Mokhtar",authors:[{id:"79054",title:"Dr.",name:"Mohd Ekhwan",middleName:null,surname:"Toriman",fullName:"Mohd Ekhwan Toriman",slug:"mohd-ekhwan-toriman"},{id:"129566",title:"Prof.",name:"Mazlin",middleName:null,surname:"Mokhtar",fullName:"Mazlin Mokhtar",slug:"mazlin-mokhtar"}]}]}],publishedBooks:[{type:"book",id:"6376",title:"Breeding and Health Benefits of Fruit and Nut Crops",subtitle:null,isOpenForSubmission:!1,hash:"82ffd254d17a5d2f0af4ba338c38afc0",slug:"breeding-and-health-benefits-of-fruit-and-nut-crops",bookSignature:"Jaya R. Soneji and Madhugiri Nageswara-Rao",coverURL:"https://cdn.intechopen.com/books/images_new/6376.jpg",editedByType:"Edited by",editors:[{id:"120848",title:"Dr.",name:"Jaya",surname:"Soneji",slug:"jaya-soneji",fullName:"Jaya Soneji"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6377",title:"Plant, Abiotic Stress and Responses to Climate Change",subtitle:null,isOpenForSubmission:!1,hash:"bfa5cbd0ee1ca1d9060ff51fd9f6468d",slug:"plant-abiotic-stress-and-responses-to-climate-change",bookSignature:"Violeta Andjelkovic",coverURL:"https://cdn.intechopen.com/books/images_new/6377.jpg",editedByType:"Edited by",editors:[{id:"104331",title:"Dr.",name:"Violeta",surname:"Andjelkovic",slug:"violeta-andjelkovic",fullName:"Violeta Andjelkovic"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6422",title:"Recent Advances in Tomato Breeding and Production",subtitle:null,isOpenForSubmission:!1,hash:"450cb677ac2da7c8d5a582417ada3745",slug:"recent-advances-in-tomato-breeding-and-production",bookSignature:"Seloame Tatu Nyaku and Agyemang Danquah",coverURL:"https://cdn.intechopen.com/books/images_new/6422.jpg",editedByType:"Edited by",editors:[{id:"182528",title:"Dr.",name:"Seloame Tatu",surname:"Nyaku",slug:"seloame-tatu-nyaku",fullName:"Seloame Tatu Nyaku"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6544",title:"Next Generation Plant Breeding",subtitle:null,isOpenForSubmission:!1,hash:"bce25a82b2b6a9ff08933753b82fc1e6",slug:"next-generation-plant-breeding",bookSignature:"Yelda Özden Çiftçi",coverURL:"https://cdn.intechopen.com/books/images_new/6544.jpg",editedByType:"Edited by",editors:[{id:"95553",title:"Prof.",name:"Yelda",surname:"Ozden Çiftçi",slug:"yelda-ozden-ciftci",fullName:"Yelda Ozden Çiftçi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6627",title:"Brassica Germplasm",subtitle:"Characterization, Breeding and Utilization",isOpenForSubmission:!1,hash:"f11a68d95e239f899f787ef2ecd31466",slug:"brassica-germplasm-characterization-breeding-and-utilization",bookSignature:"Mohamed Ahmed El-Esawi",coverURL:"https://cdn.intechopen.com/books/images_new/6627.jpg",editedByType:"Edited by",editors:[{id:"191770",title:"Dr.",name:"Mohamed A.",surname:"El-Esawi",slug:"mohamed-a.-el-esawi",fullName:"Mohamed A. El-Esawi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],publishedBooksByAuthor:[]},onlineFirst:{chapter:{type:"chapter",id:"63595",title:"Evolving Roles of Spontaneous Reporting Systems to Assess and Monitor Drug Safety",doi:"10.5772/intechopen.79986",slug:"evolving-roles-of-spontaneous-reporting-systems-to-assess-and-monitor-drug-safety",body:'\nPrescription of a medication is based on a balance between expected benefits, already investigated before marketing authorization, and possible risks (i.e., adverse effects), which become fully apparent only as time goes by after marketing authorization. Premarketing development, in fact, provides evidence on efficacy of drugs in ideal clinical setting of use (i.e., clinical trials); only the most frequent side effects are recognized in this step. The use of drugs in the real-world circumstances will show the actual risk-benefit profile.
\nThe World Health Organization (WHO) previously defined
After the adoption in 2012 of the new pharmacovigilance legislation (
Many sources of data and relevant methods of analysis are used in PhV: from disproportionality analyses (DAs) in spontaneous reporting systems (SRSs) to analytical studies (cohort or case-control designs). These traditional approaches are now integrated by innovative strategies (e.g., social media mining and case-population studies) in the
In this chapter, current and emerging roles of DAs in SRSs will be critically discussed, keeping in mind both regulator and researcher viewpoints. A panorama on key data sources (and their proper selection) will be described, followed by a critical appraisal of methodological issues and debated future applications, including exploitation and challenges in evidence integration (i.e., merging and combining heterogeneous sources of data into a unique indicator of risk) and patient’s reporting
Not only notification of suspected adverse drug events is mandatory for health professionals, but also other subjects can report events to the relevant regulatory authorities. According to ICH-E2 guidelines (International Conference on Harmonization, http://www.ich.org/products/guidelines/efficacy/article/efficacy-guidelines.html), each National Drug Agency maintains its specific SRS to collect all notifications and routinely use data-mining algorithms (DMAs) to process data, with the aim of identifying possible signals of unknown drug-effect associations. These DMAs identify drug-reaction pairs occurring with a significant disproportion in comparison with all other pairs, through the method of
Clinical pharmacology knowledge is requested to design and interpret results from DMAs and to decide if further examination is needed (either within the same source of data or by other types of data) or specific bias affects the validity of the findings. Other healthcare data sources are available for PhV to corroborate results of SRS data mining, despite developed for other reasons. As a general classification, they can be pooled into two main groups: electronic medical records (EMRs) and claim databases.
\nElectronic medical records (EMRs) aim to assist physicians in daily clinical practice (including appropriate prescription) by collecting sociodemographic and clinical information (diagnoses, risk factors, treatments, and outcomes). Primary care is the most frequent setting to develop these kinds of databases such as Clinical Practice Research Datalink (CPRD; formerly General Practice Research Database—GPRD, in UK); Health Search (by the Italian College of General Practitioners); The Health Improvement Network (THIN), in UK; and Interdisciplinary Processing of Clinical Information (IPCI), in the Netherlands. The high quantity of data makes them valuable sources to address clinical pharmacology questions, including new effects of drugs (especially on primary endpoints, to confirm premarketing evidence) and assessment of appropriate drug use (closer to the main purpose of the registries).
\nClaim databases were mainly created for administrative purposes, and together with hospital databases provide valuable sources to address PhV questions: data provided (e.g., diagnoses of hospital admissions, reimbursed prescriptions of drugs and diagnostic procedures in ambulatory care) are generally used for reimbursement and other economic issues, and, as a secondary aim, they represent an important source of information for epidemiological questions (taking into account that nonreimbursed intervention is usually not recorded, information on lifestyle and actual exposure to medicines is lacking.
\nEach National Drug Agency collects its own reports in a dedicated spontaneous reporting database, and some international SRSs gather reports originating both by systematic flows from national databases and by direct submission of the reporter. Each source has specific characteristics and limitations to be considered when planning a drug safety analysis (e.g., completeness of data and options for database interrogation); however, collecting information from all these accessible sources is the mainstay in PhV.
\nTable 1 shows an overview of main international PhV databases, which cover a very large population and heterogeneous patterns of drug use and ADR reporting attitudes. Public access to SRSs is becoming a standard, as addressed in Section 8.2.
\n\n | FAERS | \nWHO—VigiBase | \nEudraVigilance | \nAustralian Database of Adverse Event Notifications (DAEN) | \nCanada Vigilance Adverse Reaction Online Database | \nJapanese Adverse Drug Event Report database (JADER) | \n
---|---|---|---|---|---|---|
Website | \n\nhttp://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/default.htm\n | \n\nhttp://www.vigiaccess.org\n | \n\nhttp://www.adrreports.eu\n | \n\nhttps://www.tga.gov.au/database-adverse-event-notifications-daen\n | \n\nhttps://www.canada.ca/en/health-canada/services/drugs-health-products/medeffect-canada/adverse-reaction-database.html\n | \n\nhttp://www.pmda.go.jp/ | \n
Access | \nFull data access (download) since 2004a | \nWeb-based interface (VigiLize™, VigiFlow™, VigiMine, applications for full data accessc) | \nWeb-based interface (different access policies for full data accessd) | \n\n | Full data access (download) | \nFull data access (download) | \n
Timeframe | \n1969–present | \n1968–present | \n2001–present | \n1971–present | \n1965–present | \n2004–present | \n
Products covered | \nAll drugs and biologicsb | \nAll drugs and biologics, including vaccines | \nAll drugs and biologics authorized in the European Union | \nAll medicines, including vaccines, used in Australiae | \nAll drugs, biologics, vaccines, and natural health products licensed in Canadaf | \nAll drugs and biologics, including vaccines used in Japan | \n
Source of reports | \nHealthcare professionals, drug companies, patients/consumers | \nNational and regional pharmacovigilance centers (which may receive reports from patients, healthcare professionals, or drug companies) | \nNational competent authorities and marketing authorization holders (currently, no direct reporting from patients and healthcare professionals) | \nHealthcare professionals, consumers, and market authorization holders | \nHealthcare professionals, consumers, and market authorization holders | \nHealthcare professionals, consumers, and market authorization holders | \n
Current number of reports available | \n>12 million (as of April 2015), more than 1,000,000 per year (2012–2014) | \n>10 million (as of 2016) | \n>1 million received in 2013 | \nUnknown (no public statistics provided) | \nUnknown (no public statistics provided) | \n~500,000 (as of 2017) | \n
Origin of submitted reports | \nUSA and serious/unexpected reports from EU, Japan, and other extra-US countries | \nWorldwide (107 official members and 33 associate members), but majority from EU and the US | \nEU | \nAustralia | \nCanada | \nJapan | \n
Coding system for event | \nMedDRA | \nMedDRA | \nMedDRA | \nMedDRA | \nMedDRA | \nMedDRA | \n
Search strategy through “free text” in the narratives | \nNo (a Freedom of Information Act can be requested to the FDA) | \nNo | \nNo | \nNo | \nNo | \nNo | \n
Overview of major international spontaneous reporting systems that can be searched via online systems.
Different web-based tools are provided; see Böhm et al. [14]. Recently, the FDA has launched the FAERS Public Dashboard, a highly interactive web-based tool that allows to query FAERS data in a user-friendly fashion (https://fis.fda.gov/sense/app/777e9f4d-0cf8-448e-8068-f564c31baa25/sheet/7a47a261-d58b-4203-a8aa-6d3021737452/state/analysis).
Devices, vaccines, and other products are not included, as they are specifically recorded in
Freely available for all members in the WHO Program for International Drug Monitoring.
Specific access policies are described depending on stakeholder groups. For details, see the following link: http://www.ema.europa.eu/ema/index.jsp?curl=pages/news_and_events/news/2011/07/news_detail_001299.jsp&murl=menus/news_and_events/news_and_events.jsp&mid=WC0b01ac058004d5c1. On November 22, 2017, a new and improved version of EudraVigilance was launched. The new system has enhanced features for the reporting and analysis of suspected adverse reactions to support a better safety monitoring of medicines and a more efficient reporting process for stakeholders.
Medical devices are not included, as they are specifically recorded in the
Data on human blood and blood components have only been included since September 1, 2015; data on vaccines used for immunization have only been included since January 1, 2011; the majority of vaccine reports are submitted to the Canadian Adverse Events Following Immunization Surveillance System (CAEFISS).
The identification of the most appropriate source of data is a key step to properly address the research question, considering strength and limitations of the different approaches (Table 2). For instance, SRSs represent the best source of data to investigate the so-called designated medical events (DMEs), usually rare with strong drug-attributable component (e.g., Torsades de Pointes and Stevens-Johnson Syndrome) [5, 6]. Conversely, possible role of drugs in events with high background incidence (e.g., myocardial infarction) can be better investigated by healthcare databases (EMRs and claim databases) [7, 8]. No matter of the type of ADR, a typical time sequence to detect safety profile of drugs considers data mining of SRSs as the first step of the analysis, followed by investigation through healthcare databases to confirm or refuse statistically significant associations.
\n\n | Strengths | \nWeaknesses | \n
---|---|---|
Disproportionality approach | \nIt can be conducted rapidly, and it is easy to implement. It can be conducted on spontaneous reporting systems and healthcare databases. Good performance (accuracy in discriminating false from true positives) when major confounders and biases are accounted for. Highly suitable for rare events with high drug-attributable risk (e.g., TdP and DILI). | \nDoes not provide risk estimates. Loss of information due to aggregated data. Unable to handle numerous confounders. Sensitive to protopathic and indication biases. Less suitable for events with high background incidence (e.g., myocardial infarction). | \n
Traditional pharmacoepidemiological designs | \nIt provides risk estimates (cohort and case-control design). It allows controlling for confounders if matching and nesting are performed (case-control design). Robust to confounders that are stable over time (case crossover, self-controlled cohort, and self-controlled case series). Highly suitable for events with high background incidence (e.g., myocardial infarction). | \nIt needs very large dataset to have enough power to detect signals in case of rare events (cohort and case-control design). Less suitable for rare events with high drug-attributable risk (e.g., TdP and DILI). | \n
Prescription sequence symmetry analysis (PSSA) | \nRapid and easy to be performed (it only requires patient identifier, medication code, and medication dispensed date). Graphical output can be generated to help data visualization and interpretation. Highly specific and moderate sensitivity. It can control for time-constant confounders. | \nIt does not provide risk estimates (it complements disproportionality approach). Prescribing trends are affected by external factors (adjustment is required). Inappropriate identification of new use (exclusion/censoring of switchers is required). Time-variant confounders. Sensitive to inverse causality, protopathic, and indication biases. | \n
Systematic review with meta-analysis | \nIt can provide risk estimates (especially if RCT is the primary source). It does not require additional data collection. It can be conducted rapidly. It can highlight gaps in research. | \nValidity depends on the scientific rigor of the methods, quality, and type of primary source (RCT or observational studies). Meta-analysis of nonrandomized studies (observational) is currently not standardized. | \n
Overview of study designs to assess safety of medicines.
Modified from [9]. DILI: drug-induced liver injury; RCT: randomized controlled trial; TdP: torsade de pointes.
From data cleaning (a mere data managing step, see later) to statistical analyses, all steps of data management are considered tasks to address questions on ADRs. Usually, each source of data requires specific data-mining approaches (e.g., disproportion calculation for SRSs and multiple regression analysis for EMRs), but emergent strategies to better exploit the more accessible sources are now appearing in the literature (e.g., self-controlled time series and prescription sequence symmetry analysis—PSSA) [9]. In fact, data mining could virtually provide as many associations as possible between drug and effect, but without consensus among experts on the methodological steps and confirmation of pathophysiological pathways, the association can easily conduct to interpret errors.
\nTraditionally, regulatory decision-making has relied on detection of safety signals through spontaneous reports. Today, things are changing for several reasons, including increased awareness of prescribers on the importance of PhV and the emerging role of different health professionals and patients.
\nA modern model involves signal detection, signal validation (i.e., signal should represent a novel causal relationship between a drug and an event), signal prioritization (evaluation of clinical impact of the safety issue), and some other steps to drive the decision-making, also on the basis of data on how drugs are used in a population and how their utilization can be influenced. Drug consumption is also now frequently analyzed by regulators to evaluate the actual impact of risk minimization strategies in a specific settings, such as the risk of progressive multifocal leukoencephalopathy with multiple sclerosis therapies [10].
\nRegulatory agencies routinely perform analyses of SRSs to detect disproportionality signals, especially for new drugs. Although the Food and Drug Administration (FDA) and the European Medicine Agency (EMA) have different frameworks, they are promoting rigorous scientific information exchange for optimal post-approval drug safety monitoring [11]. Both agencies publicly posted the list of signals emerging from internal analyses, with the aim to promote transparency and stimulate research while avoiding alarm. Usually, many of these signals remain (fortunately) unnoticed by clinicians, and only a minority of them result in measures affecting clinical practice, such as ketoacidosis with sodium-glucose cotransporter-2 inhibitors, which in turn prompted the FDA to revise relevant labels.
\nAlso for old drugs, the importance of spontaneous reports should not be overlooked, especially because the amount of time of a drug on the market (drug age) is correlated with the number of signals detected [12]. The recent case of tiocolchicoside, restricted in recommended dose and treatment duration by the EMA, is noteworthy: after withdrawal of tetrazepam, the use of alternatives (including tiocolchicoside) and relevant spontaneous reporting increased, which made evident specific safety concerns [13].
\nIn the past, regulatory actions on a given safety issue did not support clinical practice. The case of haloperidol and the risk of torsade de pointes (TdP) is a typical example: an ECG before administration was indeed recommended in some circumstances before administering the medicine. However, it was not duly taken into account that a psychotic crisis does not usually allow appropriate ECG measurement, and this results in the inability to use injectable haloperidol in the emergency setting. The clinical consequence was a loss of this therapeutic option and its substitution with alternatives, which are not necessarily better.
\nDisproportionality analyses (DAs) are attracting considerable interest in the medical literature for several reasons:
there is increasing availability of publicly accessible SRSs and open-access tools to independently analyze international databases [14]; the various web-based resources mainly differ in terms of data transparency, possibility to customize searches and analyses (e.g., correction for confounders);
DAs are inexpensive and relatively quick and easy to perform, at least by frequentist methods such as reporting odds ratio (ROR) and proportional reporting ratio (PRR); these methods can be applied systematically to analyze a given pharmacological class or specific DMEs such as TdP [15];
they are likely to be published in a high ranking journal, especially when sophisticated analyses are presented, claiming to correct for multiple confounders [16], and a strong signal emerges. This aspect raises ethical issues: on one hand, the researcher may be more prone toward an alarming interpretation of the findings to increase the impact of the publication. On the other hand, when broadly looking at the published literature in the past 5 years, only a minority of industry-sponsored studies provided “negative findings,” that is, the lack of statistically significant DAs [17, 18].
This “uncontrolled” scenario has generated what someone coined “apophenia,” that is, the perception of meaningful patterns and causal connections among random data [19], or the so-called pharmacovigilance syndrome, that is, the incorrect use of spontaneous adverse event reports to infer that a drug causes an adverse reaction, what the incidence or prevalence of such events may be, and whether one drug has lower or higher risk than another [20]. This in turn increases the complexity in the risk-benefit assessment [21] and may generate false alarm among clinicians [22].
\nIt must be emphasized that statistical techniques, usually referred to as quantitative analyses [23], cannot be used as a standalone approach to assess a drug-related risk because no risk quantification can be offered: they should be viewed in conjunction with a qualitative analysis of individual reports, whenever feasible, and other pieces of evidence (e.g., observational studies). In other words, they cannot replace a proper clinical judgment in the individual patient.
\nIn the recent past, a debate arose on the proper use of DAs and the benefit of their publication [24, 25]. However, no actions have been taken so far. The key applications of DAs are summarized as follows:
A recent systematic review highlighted that only a minority of studies aimed at confirming or supporting previous regulatory decisions on a given safety aspect [52], thus strengthening the aforementioned concept that DAs do not usually support, on their own, regulatory actions but must be integrated with other data sources.
\nApart from DAs, the value of case-by-case assessment should not be disregarded. In fact, the individual evaluation of reports performed by pharmacovigilance experts with medical background has multiple aims: (a) it may
As a conclusive remark, it should be recognized that most researchers are from academia, and in fact, their additional role is university teaching. In the last few years, experts of medical teaching have strengthened the importance of PhV in the core curriculum of undergraduate students of healthcare courses (i.e., medicine, pharmacy, dentistry, nursing, etc.). WHO and the most active national PhV centers are committed to better define knowledge, skills, and attitudes that students should acquire in order to have an active role in pharmacovigilance [58].
\nIntegration of heterogeneous data (literature including mass media, clinical trials, observational studies, spontaneous reporting data analysis, case reports, and preclinical data) is currently in the research domain at the preliminary level, with the degree of confidence and reliance on a given source as key unresolved issues. An attempt to achieve a risk score on the pro-arrhythmic potential of drugs was undertaken within the ARITMO project [59], where a Dempster-Shafer model was used to combine evidence from heterogeneous and independent sources using expert judgment [60]. The only published experience on data integration in pharmacovigilance comes from the (useful) interplay between SRSs and healthcare databases to increase the accuracy of signal detection [61, 62].
\nIn the following section, the issue of evidence integration for research purposes will be addressed in the context of systematic reviews, which are increasingly being used as they can make researchers and readers aware about what is known, how it is known, how evidence varies across studies, and thus about what is not already known [63].
\nIssues of data quality and inherent limitations cause remarkable impact in spontaneous reporting studies in which more sources of variability (e.g., missing data) and biases affecting the results could be identified (competition or notoriety bias). Nevertheless, so far, no specific tools or techniques have been developed to select, compare, or pool together data from DAs. This could be due to a relative paucity of this kind of analysis in the medical literature.
\nDisproportionality is used to detect “signals of disproportionate reporting” (SDRs) that, once detected, are usually investigated through other, and more precisely, study designs. It is thus rare to have additional DAs regarding the same outcome related to the same drug or drug class and that used a comparable tool for signal detection (frequentist vs. Bayesian approaches). Nevertheless, at least theoretically, techniques and statistical basis used to perform meta-analysis could also be used to analyze results from disproportionality, at least to evaluate consistency of signal across different databases. A consistent signal found in two databases could be probably prioritized in comparison with inconsistent ones. Notably, raw data cannot be pooled because of the existence of an unquantified degree of redundancy (i.e., duplicates across databases), but results can be combined to reach a single “pharmacovigilance score” [59].
\nIt is well known that results of DA cannot be considered as measures of risk: the number of cases in a spontaneous reporting database does correspond to neither the number of cases that happened under the drug nor to that of cases induced by the drug, and the number of exposed people is not measured. From this point of view, including results of disproportionality in a meta-analysis could be considered inappropriate, although identification of heterogeneity in reporting may be of interest [64]. In the absence of any clear guideline, disproportionality studies could be searched and included in (qualitative) systematic reviews, but their results must be kept separated from pooled risk estimates of (quantitative) meta-analyses [65]. A recent experience by a French team on safety of drugs acting on the nitric oxide pathway in pulmonary hypertension considers together results from a DA of VigiBase and from a meta-analysis of clinical trials and concludes that the safety profiles of riociguat and phosphodiesterase inhibitors were different, thus providing a rationale for safe prescribing [66]. This approach, as the integration of spontaneous reporting analysis in meta or teleoanalysis [67], is still a research question.
\nPreliminary findings raise the hypothesis that, provided that all technical and clinical aspects are addressed, the performance of DAs is remarkable [7] and may approach the relative risks of analytical studies, thus providing an initial indication of the likely clinical importance of an adverse event [68].
\nOnce the research question has been identified, the researcher must keep in mind the various limitations and biases affecting SRSs to reduce the likelihood of detecting spurious signals. Moreover, clinical, pharmacological, and statistical considerations are needed to select the most appropriate dataset, definition of cases, exposure, and covariables for stratification/adjustment.
\nAlthough the discussion on performance, accuracy, and reliability of different approaches to perform DAs was fascinating a decade ago, at present there is still no recognized
As previously described, the various SRSs differ in terms of accessibility, catchment area, drug codification, and other technical issues. For instance, two key steps must be managed when analyzing the publicly available version of FAERS: drug mapping and removal of duplicates. These aspects have been extensively covered in the previous book chapter, and the reader should refer to this publication for details [73]. The FDA is continuously working to develop a probabilistic record-linkage algorithm combining structured and unstructured data (narratives) to improve the detection rate and accordingly reduce the occurrence of false positive signals [74].
\nBefore considering a potential causal relationship for a given identified SDR, main biases that affect signal detection from spontaneous reporting must be eliminated or at least mitigated. Notably, even after accounting for major bias, clinical association cannot be inferred from SRSs, and
Bias | \nExample | \nUnderlying reason | \nMinimization strategy | \n
---|---|---|---|
Angiotensin Converting Enzyme (ACE) inhibitors showing signal of hypoglycemia. | \nThese agents are largely used in diabetic patients. | \nSensitivity analysis including only nondiabetic patients (i.e., using antidiabetic agents). | \n|
Anticoagulants when analyzing drug-induced bleeding. | \nAnticoagulants are expected to cause bleeding as toxic effect of their drug class. | \nAnalysis by excluding reports with anticoagulants. | \n|
Extrapyramidal syndrome (ES) when analyzing first-generation antipsychotics (FGA). | \nES is a typical ADR in FGA-treated patients. | \nAnalysis by excluding ES to detect new safety signal for FGA. | \n|
Rhabdomyolysis occurrence with statins after regulatory warnings. | \nAfter that alert, the number of events arose. | \nStudying signal before the alert. | \n|
Suicide ideation related to new antidepressant. | \nA warning issued for a whole pharmacological class has stronger impact for newer drugs because the new ADR is diluted by other ADRs for older drugs. | \nTaking into account the time of drug approval and investigate different sources of dilution (e.g., warnings, publications, etc.). | \n
Major biases in disproportionality analyses and strategies for their minimization.
Modified from [114].
Overall, we can identify: (A)
The
The 2012 PhV legislation forced national competent authorities and marketing authorization holders to record and report cases of suspected adverse reactions reported by patients [3]. This, in turn, caused legislation remarkable increase of the total number of patient reports (+113%) after 3 years, with the Netherlands, the UK, Germany, France, and Italy accounting for 75% of all patient reports [85]. The relevance of patient reports is heterogeneous, and a recent survey on 141 countries worldwide showed that in one-fourth of them, patients were not allowed to report. Conversely, countries receiving the highest percentage of patient reports in 2014 were the USA (64%) and Canada (30%).
\nMore than 70 countries had fewer than 50 reports from patients [86]. The quality and the value of patient reports in the context of signal detection were evaluated in many published studies [87, 88, 89, 90, 91]. The value of the reports as a signal is directly dependent on the amount of clinically relevant information, in addition to the fact that an ADR report requires a thorough examination of the potential drug-event association. Most of the published studies comparing information reported by patients and healthcare professionals focused on the completeness of information [86, 92].
\nPatient reports give detailed descriptions of suspected ADRs, attribute reactions to specific medicines, and provide information useful for assessing causality. Patient reports often have richer narratives than those of healthcare professionals, including detailed information about the impact of the suspected ADR on the patient’s life [91].
\nMany studies, mainly from the UK and the Netherlands, showed that patient reports allow for the identification of new ADRs and lead to the strengthening of signal detection activities [90, 93, 94].
\nIn summary, patient’s reporting offers a different perspective in drug safety assessment and may potentially contribute in signal detection. However, it is important to further investigate its actual role in drug safety assessment; in fact, the large number of reports without clear causal relationship (recently called “precautionary report”) may alter adverse event profile by masking safety signals or, conversely, creating spurious associations [95].
\nThe relevance of patient reporting highlights the need of public access to spontaneous reporting data, and many countries now provide public access to SRSs, with the possibility to have summary presentations for reactions associated to each single drug in the database or a case listing of limited information for each single case report. Both EMA and WHO Uppsala Monitoring Centre (UMC) developed web tools to access a limited set of spontaneous reporting data in their database, EudraVigilance (adrreports.eu) and VigiBase (vigiaccess.org).
\nThe EMA policy includes the possibility for academia or nonprofit organization to ask for a greater access to data as aggregated data outputs or line listings based on core data elements (http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/general/general_content_000674.jsp). However, it has been commented that the EMA’s approach to transparency over PhV data is too timid. The public access of PhV data is even more restricted for vaccines, mainly due to the potential negative impact of this public access to the vaccination campaigns. The reporting of serious adverse events not causally related to the vaccination could lead to a misrepresentation of vaccine risks that could be used by antivaccine movement. To our knowledge, very few European countries (e.g., Italy and the Netherlands) give public access to spontaneous data related to vaccines.
\nA different approach to transparency is followed by UMC and FDA. In VigiBase, custom search service provided by UMC is performed upon request. Any stakeholders can use the custom search services to request a limited set of data for specific studies or projects for a fee.
\nThe best level of transparency is observed for FDA data. Data for both drugs (FAERS) and vaccine (VAERS) can be obtained using web-based search tools that return structured and/or unstructured data. Moreover, the entire database is quarterly downloadable in comma-separated value (CSV) or other formats. This access needs technical skills to properly process the relational database files and any unstructured fields. However, it gives the possibility to any users to analyze FDA spontaneous reporting data even applying DAs [5]. Since June 2014, the FDA developed an innovative platform called openFDA (openfda.gov) to facilitate access and use of big important FDA public datasets by developers, researchers, and the public through harmonization of data across disparate FDA datasets provided via application programming interfaces (APIs) [96]. Recently, the FDA has also launched the FAERS Public Dashboard, a highly interactive web-based tool that will allow to query FAERS data in a user-friendly fashion (https://fis.fda.gov/sense/app/777e9f4d-0cf8-448e-8068-f564c31baa25/sheet/7a47a261-d58b-4203-a8aa-6d3021737452/state/analysis). These different approaches to public access spontaneous reporting data lead to a bizarre situation because the reports included in EudraVigilance, VigiBase, and FAERS are largely overlapped, and it could be possible to have different information for the same report.
\nAn area of emerging interest for research is represented by the use of information provided by patients in social media on personal experiences when using a given drug. At present, it is under investigation whether or not (and how) social media data mining can contribute to signal detection [94, 95].
\nA recent review summarizes prevalence, frequency, and comparative value of information on adverse events of healthcare interventions from user comments and videos in social media. The study assessed over 174 social media sites, with discussion forums (71%) being the most popular. The overall prevalence of adverse event reports in social media varied from 0.2 to 8% of posts. Moreover, there was general agreement on overall concordance between adverse events mentioned in social media and those already documented in other sources (such as drug labels and published trials) [97].
\nThe web-recognizing adverse drug reaction (Web-RADR) project, leaded by EMA and funded within the innovative medicines innovation (IMI), aims to recommend policies, frameworks, tools, and methodologies in the use of social media and mobile technology to improve drug safety [98]. Specific objectives are as follows: (a) to develop the specific mobile application prototypes to support adverse drug reaction reporting and the provision of drug safety information to application users and (b) to assess the usefulness of social media data for PhV and more specifically in signal detection activities.
\nThe theoretical advantages of social media in the context of signal detection rely on potential earlier identification of rare and serious drug-related problems, in comparison with conventional SRSs, considering the opportunity to share information as fast as possible and the large number of active users in the social media. It has been reported that patient reports of suspected adverse reactions, particularly for specific reactions, can precede those of healthcare professionals [99]. One study of social media posts containing discussions of adverse drug events (“Proto-AEs”) found that there were nearly three times as many Proto-AEs found in Twitter data than reported to the FDA by consumers, with rank correlation between them at the distribution of reactions at MedDRA SOC level [100].
\nAnother important value from social media analyses comes from extracting qualitative insights into the actual discussions made by patients around a drug and an adverse event. This can be of great value for addressing issues related to the patient experience around an ADR and its impact on the quality of life [101]. Moreover, mining data from social media gives us a greater chance of capturing ADRs that a patient would not necessarily complain about to their doctor or nurse and can also help assessment of the risk perceptions of patients.
\nKey challenge is represented by the identification of drugs and ADRs in the text strings through a particular type of machine learning called natural language processing (NLP). From the perspective of PhV and NLP specifically, user posts on social media contain colloquial language and also misspellings. Especially when using lexicon-based approaches, these present problems as the accuracy of direct matches decreases. Colloquial and informal language is more difficult to parse, and thus, recent research tasks have focused on developing NLP tools specifically for data from social media [102, 103]. The balance between sensitivity and specificity of these tools in identifying ADRs is a key issue because a high number of false positives could heavily impact the efficacy of signal detection activities.
\nAnother key element is the quality of the information on adverse events reported in the social media, which was analyzed only by a few works. A study where Internet narratives posted by patients were evaluated showed that the informativeness level was very incomplete and makes their assessment and use for PhV purpose difficult [104].
\nConcerning the potential of social media analyzes for earlier signal detection, contrasting data are published [105, 106].
\nSocial media data mining uses information for PhV purposes, which were not primarily shared by the patient for this purpose. This raises a number of ethical questions, especially about identification of individuals by utilizing additional information, such as the geocode location on posting, username, and other potentially personally identifiable information [107], which are still unresolved. How would patient using social media react when approached for additional information by organizations that collect PhV data? Since this is a new area, ethically sound policy guidance needs to be developed.
\nA different approach in the use of Internet data for signal detection is the use of anonymized logs of web searchers [108]. In a recent study, a web-based search query method called “query log reaction score” was developed to detect whether adverse events associated with certain drugs could be found from search engine query data. The web query methods have moderate sensitivity (80%) in detecting signals in web query data compared with reference signal detection algorithms, but many false positives were generated, and this method had low specificity [109].
\nThe continuous increasing number of spontaneous reports and the increasing quality in their systematic archiving and accessing comply scientific community to improve methods of analysis and ways to interpret them for regulatory, clinical, and research purposes.
\nA specific debated issue on the current role of data-mining procedures of SRSs regards the possibility to directly compare drugs within the same therapeutic class [110]. We are in favor of this approach and strongly encourage further research regarding the use of SRSs, under stringently defined conditions, to compare adverse event rates for drugs [111]. To this aim, all the following criteria must be fulfilled:
An emerging application of SRSs, in the era of Big Data, is represented by their integration with other heterogeneous sources of healthcare data (e.g., the availability of prescription-data, hospital admission and discharge, population-based, disease-based, death registries, social media, and literature) to support proactive PhV in the risk-benefit assessment, as performed in the ARITMO projects through the Dempster-Shafer approach [59].
\nFinally, the question arises as to whether all disproportionality studies should be published in scientific journals. Supporters of scientific transparency and full release of datasets via Open Science would undoubtedly call for public availability of study results, including negative findings. A proposal was recently formulated [114].
\nThis controversy on the quality of DAs raises the concern on how best assess it and reach consensus on a “set of minimum requirements to assess the quality of DAs in terms of study conception, performing and reporting.” Provisional criteria have been recently proposed (from the experience of antidiabetic drugs) [114], but further discussion is warranted:
From a technical standpoint, good signal detection practices have been published by the Innovative Medicines Initiative Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium (PROTECT) project, which have formulated 39 recommendations for those working in the PhV community [115].
\nA final issue regards the
All these unsettled issues witness the need and the importance of implementing research to finally clarify the role of DAs in clinical practice.
\nRegulators and especially clinicians are appreciating the importance and the role of DAs to monitor and assess the safety profile of marketed drugs. All “actors” dealing with SRSs must always be aware of the so-called seduction bias and self-deception bias (i.e., over-reliance on mathematical models and the subconscious confidence in expecting a given output from results), thus be reminded of inherent limitations that, at present, do not allow to assess actual risk in clinical practice, mainly because of the lack of certainty in the occurrence of adverse events and the lack of exposure data [121].
\nFrom a research perspective, there is an urgent need to raise the bar, aiming to increase the accuracy and reproducibility (in one word the quality) of this kind of study. From one side, there is a room for improvement in several aspects of the analysis of SRSs, including relevant implications and their appropriate use such as the aspect of “no findings” (i.e., findings of nondisproportional results), which has not received sufficient attention so far. Moreover, different research teams are implementing sophisticated methods to account for confounders in signal detection, so that DAs may approach relative risk. In the meantime, we propose to include disproportionality studies in (qualitative) systematic reviews keeping results separated from pooled risk estimates of (quantitative) meta-analyses [63].
\nIn conclusion, SRSs represent an invaluable source to monitor and assess the safety of medications, including drugs, vaccines, and healthcare products.
\nWe call for a responsible use and publication of DAs, which should be regulated through a consensus approach among experts; this would finally establish the use and transferability of DAs in clinical practice.
\nThis is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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\n\nPlease complete the publishing proposal form. The completed form should serve as an overview of your future Compacts, Monograph or Edited Book. Once submitted, your publishing proposal will be sent for evaluation, and a notice of acceptance or rejection will be sent within 10 to 30 working days from the date of submission.
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Currently, he is a professor of Orthodontics. He holds a Certificate of Advanced Study type A in Technology of Biomaterials used in Dentistry (1995); Certificate of Advanced Study type B in Dento-Facial Orthopaedics (1997) from the Faculty of Dental Surgery, University Denis Diderot-Paris VII, France; Diploma of Advanced Study (DESA) in Biocompatibility of Biomaterials from the Faculty of Medicine and Pharmacy of Casablanca (2002); Certificate of Clinical Occlusodontics from the Faculty of Dentistry of Casablanca (2004); University Diploma of Biostatistics and Perceptual Health Measurement from the Faculty of Medicine and Pharmacy of Casablanca (2011); and a University Diploma of Pedagogy of Odontological Sciences from the Faculty of Dentistry of Casablanca (2013). 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He 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:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{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:'"Politechnica" University Timişoara',institution:null},{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. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. 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. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"4",type:"subseries",title:"Fungal Infectious Diseases",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment",scope:"Fungi are ubiquitous and there are almost no non-pathogenic fungi. Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. 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