Special Issue "Big Data and Mathematical Modeling in Biomedicine"

Special Issue Editors

Prof. Jianhong Wu
E-Mail Website
Guest Editor
Department of Mathematics and Statistics, University of York, Toronto, Canada
Interests: biomathematics and mathematical modelling; mathematics for public health; mathematical epidemiology; mathematical modelling of communicable disorders; Big Data analytics

Special Issue Information

Dear Colleagues,

In recent years, Big Data have garnered significant interest from the scientific community. The concept of Big Data refers to extremely large and massive data sets that, because of their complexity and high degree of heterogeneity, cannot be analyzed and interpreted by means of conventional approaches (such as multivariate regression analyses and similar techniques). Being technically demanding and computationally challenging, they require particular efforts: New algorithms are required to effectively handle, manipulate, and coherently integrate data (the so-called “Big Data analytics”). These methodologies enable scholars to extract significant and relevant patterns in terms of trends, interactions, associations, and correlations. Big Data are classically characterized by three Vs: (i) velocity (in terms of the speed of data acquisition and data processing, Big Data as “fast data”); (ii) volume (in terms of amount of information); and (iii) variety (in terms of the number of sources and streams that can produce Big Data). There are different kinds of Big Data, depending on their generating sources, including: (i) “molecular Big Data”, produced by wet-lab techniques (and recent OMICS-based approaches like genomics, proteomics, postgenomics, metabolomics/metabonomics, epigenomics, etc.); (ii) “digital Big Data” generated by imaging technologies and sensors (in particular, wearable sensors); and (iii) “computational Big Data” produced by sources like the internet, mobiles, smart phones, and other devices. In the field of biomedicine, Big Data can be used for a variety of purposes and tasks, such as: (i) performing disease surveillance and signal detection, (ii) predicting disease risk, (iii) targeting treatment interventions, and, last but not least, (iv) understanding the etiopathogenetic mechanisms of diseases. Mathematical modelling is of paramount importance in Big Data visualization, mining, clustering, analysis, and interpretation.

Dr. Nicola Bragazzi
Prof. Jianhong Wu
Guest Editors

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Keywords

  • Big Data
  • Big Data analytics
  • Big Data mining
  • Big Data clustering
  • Mathematical modelling
  • Computational biomedicine

Published Papers (14 papers)

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Editorial

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Editorial
Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa
Int. J. Environ. Res. Public Health 2021, 18(15), 7890; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18157890 - 26 Jul 2021
Viewed by 839
Abstract
COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants [...] Read more.
COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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Research

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Article
The Incidence and Predictors of Solid- and Hematological Malignancies in Patients with Giant Cell Arteritis: A Large Real-World Database Study
Int. J. Environ. Res. Public Health 2021, 18(14), 7595; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18147595 - 16 Jul 2021
Viewed by 861
Abstract
Background: The association between giant cell arteritis (GCA) and malignancies had been widely investigated with studies reporting conflicting results. Therefore, in this study, we aimed to investigate this association using a large nationwide electronic database. Methods: This study was designed as a retrospective [...] Read more.
Background: The association between giant cell arteritis (GCA) and malignancies had been widely investigated with studies reporting conflicting results. Therefore, in this study, we aimed to investigate this association using a large nationwide electronic database. Methods: This study was designed as a retrospective cohort study including GCA patients first diagnosed between 2002–2017 and age, sex and enrollment time-matched controls. Follow-up began at the date of first GCA-diagnosis and continued until first diagnosis of malignancy, death or end of study follow-up. Results: The study enrolled 7213 GCA patients and 32,987 age- and sex-matched controls. The mean age of GCA diagnosis was 72.3 (SD 9.9) years and 69.1% were women. During the follow-up period, 659 (9.1%) of GCA patients were diagnosed with solid malignancies and 144 (2.0%) were diagnosed with hematologic malignancies. In cox-multivariate-analysis the risk of solid- malignancies (HR = 1.12 [95%CI: 1.02–1.22]), specifically renal neoplasms (HR = 1.60 [95%CI: 1.15–2.23]) and sarcomas (HR = 2.14 [95%CI: 1.41–3.24]), and the risk of hematologic malignancies (HR = 2.02 [95%CI: 1.66–2.47]), specifically acute leukemias (HR = 1.81 [95%CI: 1.06–3.07]), chronic leukemias (HR = 1.82 [95%CI: 1.19–2.77]), Hodgkin’s lymphomas (HR = 2.42 [95%CI: 1.12–5.20]), non-Hodgkin’s-lymphomas (HR = 1.66: [95%CI 1.21–2.29]) and multiple myeloma(HR = 2.40 [95%CI: 1.63–3.53]) were significantly increased in GCA patients compared to controls. Older age at GCA-diagnosis (HR = 1.36 [95%CI: 1.25–1.47]), male-gender (HR = 1.46 [95%CI: 1.24–1.72]), smoking (HR = 1.25 [95%CI: 1.04–1.51]) and medium-high socioeconomic status (HR = 1.27 [95%CI: 1.07–1.50]) were independently associated with solid malignancy while age (HR = 1.47 [95%CI: 1.22–1.77]) and male-gender (HR = 1.61 [95%CI: 1.14–2.29]) alone were independently associated with hematologic- malignancies. Conclusion: our study demonstrated higher incidence of hematologic and solid malignancies in GCA patients. Specifically, leukemia, lymphoma, multiple myeloma, kidney malignancies, and sarcomas. Age and male gender were independent risk factors for hematological malignancies among GCA patients, while for solid malignancies, smoking and SES were risk factors as well. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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Article
Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory
Int. J. Environ. Res. Public Health 2021, 18(14), 7376; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18147376 - 09 Jul 2021
Viewed by 1044
Abstract
The impact of the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic has been and is still vast, affecting not only global human health and stretching healthcare facilities, but also profoundly disrupting societal and economic systems worldwide. The nature of the way the virus [...] Read more.
The impact of the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic has been and is still vast, affecting not only global human health and stretching healthcare facilities, but also profoundly disrupting societal and economic systems worldwide. The nature of the way the virus spreads causes cases to come in further recurring waves. This is due a complex array of biological, societal and environmental factors, including the novel nature of the emerging pathogen. Other parameters explaining the epidemic trend consisting of recurring waves are logistic–organizational challenges in the implementation of the vaccine roll-out, scarcity of doses and human resources, seasonality, meteorological drivers, and community heterogeneity, as well as cycles of strengthening and easing/lifting of the mitigation interventions. Therefore, it is crucial to be able to have an early alert system to identify when another wave of cases is about to occur. The availability of a variety of newly developed indicators allows for the exploration of multi-feature prediction models for case data. Ten indicators were selected as features for our prediction model. The model chosen is a Recurrent Neural Network with Long Short-Term Memory. This paper documents the development of an early alert/detection system that functions by predicting future daily confirmed cases based on a series of features that include mobility and stringency indices, and epidemiological parameters. The model is trained on the intermittent period in between the first and the second wave, in all of the South African provinces. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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Article
Illicit Drug Use in Canada and Implications for Suicidal Behaviors, and Household Food Insecurity: Findings from a Large, Nationally Representative Survey
Int. J. Environ. Res. Public Health 2021, 18(12), 6425; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18126425 - 14 Jun 2021
Cited by 1 | Viewed by 943
Abstract
Background and Aims: Illicit drug use is an ongoing health and social issue in Canada. This study aimed to investigate the prevalence of illicit drug use and its implications for suicidal behaviors, and household food insecurity in Canada. Design: Cross-sectional population [...] Read more.
Background and Aims: Illicit drug use is an ongoing health and social issue in Canada. This study aimed to investigate the prevalence of illicit drug use and its implications for suicidal behaviors, and household food insecurity in Canada. Design: Cross-sectional population survey. Setting: Canada, using the 2015–2016 Canadian Community Health Survey, a nationally representative sample selected by stratified multi-stage probability sampling. Participants: A total of 106,850 respondents aged ≥ 12 years who had completed information on illicit drug use. Measurements: Illicit drug use was assessed through a series of questions about illicit drug use methods. Respondents who reported lifetime illicit drug use but no past-year use were considered to have prior illicit drug use. In this survey, illicit drug use included cannabis use. Findings: Overall, the prevalence of lifetime, past-year, and prior illicit drug use was 33.2% (9.8 million), 10.4% (3.1 million), and 22.7% (6.7 million), respectively. In models adjusting for sociodemographic covariates, prior illicit drug use was significantly associated with increased odds of past-year suicidal ideation (adjusted odds ratio [AOR] 1.21, 95% CI 1.04–1.40), and plans (1.48, 1.15–1.91), and past-year household food insecurity (1.27, 1.14–1.41), and the odds were much higher among prior injecting drug users than prior non-injecting drug users. No significant correlation was found between prior illicit drug use and past-year suicidal attempts, but there was a strong association between past-year illicit drug use and past-year suicidal attempts. Conclusions: Our findings suggest that even after people have stopped taking illicit drugs, prior illicit drug use, especially for prior injecting drug use, continues to be associated with increased risks of subsequent suicidal ideation, and plans, and household food insecurity. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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Article
The Utility of 18FDG-PET/CT in Diagnosing Fever of Unknown Origin: The Experience of a Large Tertiary Medical Center
Int. J. Environ. Res. Public Health 2021, 18(10), 5360; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18105360 - 18 May 2021
Viewed by 564
Abstract
Fever of unknown origin (FUO) poses a diagnostic challenge, and 18-fluorodexoyglucose positron emission tomography with computed tomography (18FDG-PET/CT) may identify the source. We aimed to evaluate the diagnostic yield of 18FDG-PET/CT in the work-up of FUO. The records of patients admitted to Sheba [...] Read more.
Fever of unknown origin (FUO) poses a diagnostic challenge, and 18-fluorodexoyglucose positron emission tomography with computed tomography (18FDG-PET/CT) may identify the source. We aimed to evaluate the diagnostic yield of 18FDG-PET/CT in the work-up of FUO. The records of patients admitted to Sheba Medical Center between January 2013 and January 2018 who underwent 18FDG-PET/CT for the evaluation of FUO were reviewed. Following examination of available medical test results, 18FDG-PET/CT findings were assessed to determine whether lesions identified proved diagnostic. Of 225 patients who underwent 18FDG-PET/CT for FUO work-up, 128 (57%) met inclusion criteria. Eighty (62.5%) were males; mean age was 59 ± 20.3 (range: 18–93). A final diagnosis was made in 95 (74%) patients. Of the 128 18FDG-PET/CT tests conducted for the workup of FUO, 61 (48%) were true positive, 26 (20%) false positive, 26 (20%) true negative, and 15 (12%) false negative. In a multivariate analysis, weight loss and anemia were independently associated with having a contributary results of 18FDG-PET/CT. The test yielded a sensitivity of 70%, specificity of 37%, positive predictive value of 70%, and negative predictive value of 37%. 18FDG-PET/CT is a valuable tool in the diagnostic workup of FUO. It proved effective in diagnosing almost half the patients, especially in those with anemia and weight loss. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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Article
An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors
Int. J. Environ. Res. Public Health 2021, 18(8), 4022; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18084022 - 12 Apr 2021
Cited by 2 | Viewed by 1014
Abstract
Experts have predicted that COVID-19 may prevail for many months or even years before it can be completely eliminated. A major problem in its cure is its early screening and detection, which will decide on its treatment. Due to the fast contactless spreading [...] Read more.
Experts have predicted that COVID-19 may prevail for many months or even years before it can be completely eliminated. A major problem in its cure is its early screening and detection, which will decide on its treatment. Due to the fast contactless spreading of the virus, its screening is unusually difficult. Moreover, the results of COVID-19 tests may take up to 48 h. That is enough time for the virus to worsen the health of the affected person. The health community needs effective means for identification of the virus in the shortest possible time. In this study, we invent a medical device utilized consisting of composable sensors to monitor remotely and in real-time the health status of those who have symptoms of the coronavirus or those infected with it. The device comprises wearable medical sensors integrated using the Arduino hardware interfacing and a smartphone application. An IoT framework is deployed at the backend through which various devices can communicate in real-time. The medical device is applied to determine the patient’s critical status of the effects of the coronavirus or its symptoms using heartbeat, cough, temperature and Oxygen concentration (SpO2) that are evaluated using our custom algorithm. Until now, it has been found that many coronavirus patients remain asymptomatic, but in case of known symptoms, a person can be quickly identified with our device. It also allows doctors to examine their patients without the need for physical direct contact with them to reduce the possibility of infection. Our solution uses rule-based decision-making based on the physiological data of a person obtained through sensors. These rules allow to classify a person as healthy or having a possibility of infection by the coronavirus. The advantage of using rules for patient’s classification is that the rules can be updated as new findings emerge from time to time. In this article, we explain the details of the sensors, the smartphone application, and the associated IoT framework for real-time, remote screening of COVID-19. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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Article
Epilepsy as a Comorbidity in Polymyositis and Dermatomyositis—A Cross-Sectional Study
Int. J. Environ. Res. Public Health 2021, 18(8), 3983; https://doi.org/10.3390/ijerph18083983 - 10 Apr 2021
Viewed by 792
Abstract
Polymyositis (PM) and dermatomyositis (DM) are autoimmune-mediated multisystemic myopathies, characterized mainly by proximal muscle weakness. A connection between epilepsy and PM/DM has not been reported previously. Our study aim is to evaluate this association. A case–control study was conducted, enrolling a total of [...] Read more.
Polymyositis (PM) and dermatomyositis (DM) are autoimmune-mediated multisystemic myopathies, characterized mainly by proximal muscle weakness. A connection between epilepsy and PM/DM has not been reported previously. Our study aim is to evaluate this association. A case–control study was conducted, enrolling a total of 12,278 patients with 2085 cases (17.0%) and 10,193 subjects in the control group (83.0%). Student’s t-test was used to evaluate continuous variables, while the chi-square test was applied for the distribution of categorical variables. Log-rank test, Kaplan–Meier curves and multivariate Cox proportional hazards method were performed for the analysis regarding survival. Of the studied 2085 cases, 1475 subjects (70.7%) were diagnosed with DM, and 610 patients (29.3%) with PM. Participants enrolled as cases had a significantly higher rate of epilepsy (n = 48 [2.3%]) as compared to controls (n = 141 [1.4%], p < 0.0005). Using multivariable logistic regression analysis, PM was found only to be significantly associated with epilepsy (OR 2.2 [95%CI 1.36 to 3.55], p = 0.0014), whereas a non-significant positive trend was noted in DM (OR 1.51 [95%CI 0.99 to 2.30], p = 0.0547). Our data suggest that PM is associated with a higher rate of epilepsy compared to controls. Physicians should be aware of this comorbidity in patients with immune-mediated myopathies. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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Article
Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development
Int. J. Environ. Res. Public Health 2021, 18(6), 3099; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18063099 - 17 Mar 2021
Cited by 2 | Viewed by 1630
Abstract
In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering [...] Read more.
In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes (‘NEURAL-NETWORKS’, ‘CANCER’, ‘ELETRONIC-HEALTH-RECORDS’, ‘DIABETES-MELLITUS’, ‘ALZHEIMER’S-DISEASE’, ‘BREAST-CANCER’, ‘DEPRESSION’, and ‘RANDOM-FOREST’) are depicted in a thematic network. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. The thematic network structure is arranged thusly that its subjects are organized into two different areas, (i) practices and techniques related to data mining in healthcare, and (ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field’s evolution over time. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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Article
A Bibliometric Network Analysis of Coronavirus during the First Eight Months of COVID-19 in 2020
Int. J. Environ. Res. Public Health 2021, 18(3), 952; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18030952 - 22 Jan 2021
Cited by 10 | Viewed by 2295
Abstract
The COVID-19 pandemic has affected all aspects of society. Researchers worldwide have been working to provide new solutions to and better understanding of this coronavirus. In this research, our goal was to perform a Bibliometric Network Analysis (BNA) to investigate the strategic themes, [...] Read more.
The COVID-19 pandemic has affected all aspects of society. Researchers worldwide have been working to provide new solutions to and better understanding of this coronavirus. In this research, our goal was to perform a Bibliometric Network Analysis (BNA) to investigate the strategic themes, thematic evolution structure and trends of coronavirus during the first eight months of COVID-19 in the Web of Science (WoS) database in 2020. To do this, 14,802 articles were analyzed, with the support of the SciMAT software. This analysis highlights 24 themes, of which 11 of the more important ones were discussed in-depth. The thematic evolution structure shows how the themes are evolving over time, and the most developed and future trends of coronavirus with focus on COVID-19 were visually depicted. The results of the strategic diagram highlight ‘CHLOROQUINE’, ‘ANXIETY’, ‘PREGNANCY’ and ‘ACUTE-RESPIRATORY-SYNDROME’, among others, as the clusters with the highest number of associated citations. The thematic evolution. structure presented two thematic areas: “Damage prevention and containment of COVID-19” and “Comorbidities and diseases caused by COVID-19”, which provides new perspectives and futures trends of the field. These results will form the basis for future research and guide decision-making in coronavirus focused on COVID-19 research and treatments. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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Article
Artificial Intelligence Model of Drive-Through Vaccination Simulation
Int. J. Environ. Res. Public Health 2021, 18(1), 268; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18010268 - 31 Dec 2020
Cited by 8 | Viewed by 1804
Abstract
Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. [...] Read more.
Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. Use of drive-through has been suggested as one of the possible effective temporary mass vaccinations among other methods. In this study, we present a machine learning model that has been developed based on a big dataset derived from 125K runs of a drive-through mass vaccination simulation tool. The results show that the model is able to reasonably well predict the key outputs of the simulation tool. Therefore, the model has been turned to an online application that can help mass vaccination planners to assess the outcomes of different types of drive-through mass vaccination facilities much faster. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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Communication
How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic
Int. J. Environ. Res. Public Health 2020, 17(9), 3176; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17093176 - 02 May 2020
Cited by 83 | Viewed by 10248
Abstract
SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help [...] Read more.
SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)

Review

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Review
Systematic Review and Meta-Analysis of Tocilizumab Therapy versus Standard of Care in over 15,000 COVID-19 Pneumonia Patients during the First Eight Months of the Pandemic
Int. J. Environ. Res. Public Health 2021, 18(17), 9149; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18179149 - 30 Aug 2021
Cited by 1 | Viewed by 792
Abstract
Background. Tocilizumab is an anti-IL-6 therapy widely adopted in the management of the so-called “cytokine storm” related to SARS-CoV-2 virus infection, but its effectiveness, use in relation to concomitant corticosteroid therapy and safety were unproven despite widespread use in numerous studies, mostly open [...] Read more.
Background. Tocilizumab is an anti-IL-6 therapy widely adopted in the management of the so-called “cytokine storm” related to SARS-CoV-2 virus infection, but its effectiveness, use in relation to concomitant corticosteroid therapy and safety were unproven despite widespread use in numerous studies, mostly open label at the start of the pandemic. Methods: We performed a systematic review and meta-analysis of case-control studies utilising tocilizumab in COVID-19 on different databases (PubMed/MEDLINE/Scopus) and preprint servers (medRxiv and SSRN) from inception until 20 July 2020 (PROSPERO CRD42020195690). Subgroup analyses and meta-regressions were performed. The impact of tocilizumab and concomitant corticosteroid therapy or tocilizumab alone versus standard of care (SOC) on the death rate, need for mechanical ventilation, ICU admission and bacterial infections were assessed. Results. Thirty-nine studies with 15,531 patients (3657 cases versus 11,874 controls) were identified. Unadjusted estimates (n = 28) failed to demonstrate a protective effect of tocilizumab on survival (OR 0.74 ([95%CI 0.55–1.01], p = 0.057), mechanical ventilation prevention (OR 2.21 [95%CI 0.53–9.23], p = 0.277) or prevention of ICU admission (OR 3.79 [95%CI 0.38–37.34], p = 0.254). Considering studies with adjusted, estimated, tocilizumab use was associated with mortality rate reduction (HR 0.50 ([95%CI 0.38–0.64], p < 0.001) and prevention of ICU admission (OR 0.16 ([95%CI 0.06–0.43], p < 0.001). Tocilizumab with concomitant steroid use versus SOC was protective with an OR of 0.49 ([95%CI 0.36–0.65], p < 0.05) as was tocilizumab alone versus SOC with an OR of 0.59 ([95%CI 0.34–1.00], p < 0.001). Risk of infection increased (2.36 [95%CI 1.001–5.54], p = 0.050; based on unadjusted estimates). Conclusion: Despite the heterogeneity of included studies and large number of preprint articles, our findings from the first eight of the pandemic in over 15,000 COVID-19 cases suggested an incremental efficacy of tocilizumab in severe COVID-19 that were confirmed by subsequent meta-analyses of large randomized trials of tocilizumab. This suggests that analysis of case-control studies and pre-print server data in the early stages of a pandemic appeared robust for supporting incremental benefits and lack of major therapeutic toxicity of tocilizumab for severe COVID-19. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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Review
Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature
Int. J. Environ. Res. Public Health 2021, 18(17), 8989; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18178989 - 26 Aug 2021
Viewed by 781
Abstract
Medical education refers to education and training delivered to medical students in order to become a practitioner. In recent decades, medicine has been radically transformed by scientific and computational/digital advances—including the introduction of new information and communication technologies, the discovery of DNA, and [...] Read more.
Medical education refers to education and training delivered to medical students in order to become a practitioner. In recent decades, medicine has been radically transformed by scientific and computational/digital advances—including the introduction of new information and communication technologies, the discovery of DNA, and the birth of genomics and post-genomics super-specialties (transcriptomics, proteomics, interactomics, and metabolomics/metabonomics, among others)—which contribute to the generation of an unprecedented amount of data, so-called ‘big data’. While these are well-studied in fields such as medical research and methodology, translational medicine, and clinical practice, they remain overlooked and understudied in the field of medical education. For this purpose, we carried out an integrative review of the literature. Twenty-nine studies were retrieved and synthesized in the present review. Included studies were published between 2012 and 2021. Eleven studies were performed in North America: specifically, nine were conducted in the USA and two studies in Canada. Six studies were carried out in Europe: two in France, two in Germany, one in Italy, and one in several European countries. One additional study was conducted in China. Eight papers were commentaries/theoretical or perspective articles, while five were designed as a case study. Five investigations exploited large databases and datasets, while five additional studies were surveys. Two papers employed visual data analytical/data mining techniques. Finally, other two papers were technical papers, describing the development of software, computational tools and/or learning environments/platforms, while two additional studies were literature reviews (one of which being systematic and bibliometric).The following nine sub-topics could be identified: (I) knowledge and awareness of big data among medical students; (II) difficulties and challenges in integrating and implementing big data teaching into the medical syllabus; (III) exploiting big data to review, improve and enhance medical school curriculum; (IV) exploiting big data to monitor the effectiveness of web-based learning environments among medical students; (V) exploiting big data to capture the determinants and signatures of successful academic performance and counteract/prevent drop-out; (VI) exploiting big data to promote equity, inclusion, and diversity; (VII) exploiting big data to enhance integrity and ethics, avoiding plagiarism and duplication rate; (VIII) empowering medical students, improving and enhancing medical practice; and, (IX) exploiting big data in continuous medical education and learning. These sub-themes were subsequently grouped in the following four major themes/topics: namely, (I) big data and medical curricula; (II) big data and medical academic performance; (III) big data and societal/bioethical issues in biomedical education; and (IV) big data and medical career. Despite the increasing importance of big data in biomedicine, current medical curricula and syllabuses appear inadequate to prepare future medical professionals and practitioners that can leverage on big data in their daily clinical practice. Challenges in integrating, incorporating, and implementing big data teaching into medical school need to be overcome to facilitate the training of the next generation of medical professionals. Finally, in the present integrative review, state-of-art and future potential uses of big data in the field of biomedical discussion are envisaged, with a focus on the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic, which has been acting as a catalyst for innovation and digitalization. Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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Review
Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects
Int. J. Environ. Res. Public Health 2021, 18(10), 5058; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18105058 - 11 May 2021
Cited by 2 | Viewed by 1047
Abstract
Tremendous scientific and technological achievements have been revolutionizing the current medical era, changing the way in which physicians practice their profession and deliver healthcare provisions. This is due to the convergence of various advancements related to digitalization and the use of information and [...] Read more.
Tremendous scientific and technological achievements have been revolutionizing the current medical era, changing the way in which physicians practice their profession and deliver healthcare provisions. This is due to the convergence of various advancements related to digitalization and the use of information and communication technologies (ICTs)—ranging from the internet of things (IoT) and the internet of medical things (IoMT) to the fields of robotics, virtual and augmented reality, and massively parallel and cloud computing. Further progress has been made in the fields of addictive manufacturing and three-dimensional (3D) printing, sophisticated statistical tools such as big data visualization and analytics (BDVA) and artificial intelligence (AI), the use of mobile and smartphone applications (apps), remote monitoring and wearable sensors, and e-learning, among others. Within this new conceptual framework, big data represents a massive set of data characterized by different properties and features. These can be categorized both from a quantitative and qualitative standpoint, and include data generated from wet-lab and microarrays (molecular big data), databases and registries (clinical/computational big data), imaging techniques (such as radiomics, imaging big data) and web searches (the so-called infodemiology, digital big data). The present review aims to show how big and smart data can revolutionize gynecology by shedding light on female reproductive health, both in terms of physiology and pathophysiology. More specifically, they appear to have potential uses in the field of gynecology to increase its accuracy and precision, stratify patients, provide opportunities for personalized treatment options rather than delivering a package of “one-size-fits-it-all” healthcare management provisions, and enhance its effectiveness at each stage (health promotion, prevention, diagnosis, prognosis, and therapeutics). Full article
(This article belongs to the Special Issue Big Data and Mathematical Modeling in Biomedicine)
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