BioMedInformatics最新文献

{"title":"An Interactive Dashboard for Statistical Analysis of Intensive Care Unit COVID-19 Data","authors":"Rúben Dias, Artur Ferreira, Iola Pinto, Carlos Geraldes, Cristiana P Von Rekowski, Luís Bento","doi":"10.3390/biomedinformatics4010026","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010026","url":null,"abstract":"Background: COVID-19 caused a pandemic, due to its ease of transmission and high number of infections. The evolution of the pandemic and its consequences for the mortality and morbidity of populations, especially the elderly, generated several scientific studies and many research projects. Among them, we have the Predictive Models of COVID-19 Outcomes for Higher Risk Patients Towards a Precision Medicine (PREMO) research project. For such a project with many data records, it is necessary to provide a smooth graphical analysis to extract value from it. Methods: In this paper, we present the development of a full-stack Web application for the PREMO project, consisting of a dashboard providing statistical analysis, data visualization, data import, and data export. The main aspects of the application are described, as well as the diverse types of graphical representations and the possibility to use filters to extract relevant information for clinical practice. Results: The application, accessible through a browser, provides an interactive visualization of data from patients admitted to the intensive care unit (ICU), throughout the six waves of COVID-19 in two hospitals in Lisbon, Portugal. The analysis can be isolated per wave or can be seen in an aggregated view, allowing clinicians to create many views of the data and to study the behavior and consequences of different waves. For instance, the experimental results show clearly the effect of vaccination as well as the changes on the most relevant clinical parameters on each wave. Conclusions: The dashboard allows clinicians to analyze many variables of each of the six waves as well as aggregated data for all the waves. The application allows the user to extract information and scientific knowledge about COVID-19’s evolution, yielding insights for this pandemic and for future pandemics.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139854749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble Methods to Optimize Automated Text Classification in Avatar Therapy","authors":"A. Hudon, K. Phraxayavong, Stéphane Potvin, A. Dumais","doi":"10.3390/biomedinformatics4010024","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010024","url":null,"abstract":"Background: Psychotherapeutic approaches such as Avatar Therapy (AT) are novel therapeutic attempts to help patients diagnosed with treatment-resistant schizophrenia. Qualitative analyses of immersive sessions of AT have been undertaken to enhance and refine the existing interventions taking place in this therapy. To account for the time-consuming and costly nature and potential misclassification biases, prior implementation of a Linear Support Vector Classifier provided helpful insight. Single model implementation for text classification is often limited, especially for datasets containing imbalanced data. The main objective of this study is to evaluate the change in accuracy of automated text classification machine learning algorithms when using an ensemble approach for immersive session verbatims of AT. Methods: An ensemble model, comprising five machine learning algorithms, was implemented to conduct text classification for avatar and patient interactions. The models included in this study are: Multinomial Naïve Bayes, Linear Support Vector Classifier, Multi-layer perceptron classifier, XGBClassifier and the K-Nearest-Neighbor model. Accuracy, precision, recall and f1-score were compared for the individual classifiers and the ensemble model. Results: The ensemble model performed better than its individual counterparts for accuracy. Conclusion: Using an ensemble methodological approach, this methodology might be employed in future research to provide insight into the interactions being categorized and the therapeutical outcome of patients based on their experience with AT with optimal precision.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139796025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble Methods to Optimize Automated Text Classification in Avatar Therapy","authors":"A. Hudon, K. Phraxayavong, Stéphane Potvin, A. Dumais","doi":"10.3390/biomedinformatics4010024","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010024","url":null,"abstract":"Background: Psychotherapeutic approaches such as Avatar Therapy (AT) are novel therapeutic attempts to help patients diagnosed with treatment-resistant schizophrenia. Qualitative analyses of immersive sessions of AT have been undertaken to enhance and refine the existing interventions taking place in this therapy. To account for the time-consuming and costly nature and potential misclassification biases, prior implementation of a Linear Support Vector Classifier provided helpful insight. Single model implementation for text classification is often limited, especially for datasets containing imbalanced data. The main objective of this study is to evaluate the change in accuracy of automated text classification machine learning algorithms when using an ensemble approach for immersive session verbatims of AT. Methods: An ensemble model, comprising five machine learning algorithms, was implemented to conduct text classification for avatar and patient interactions. The models included in this study are: Multinomial Naïve Bayes, Linear Support Vector Classifier, Multi-layer perceptron classifier, XGBClassifier and the K-Nearest-Neighbor model. Accuracy, precision, recall and f1-score were compared for the individual classifiers and the ensemble model. Results: The ensemble model performed better than its individual counterparts for accuracy. Conclusion: Using an ensemble methodological approach, this methodology might be employed in future research to provide insight into the interactions being categorized and the therapeutical outcome of patients based on their experience with AT with optimal precision.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"1 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139855713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-Contact Blood Pressure Estimation Using Forehead and Palm Infrared Video","authors":"Thomas Stogiannopoulos, N. Mitianoudis","doi":"10.3390/biomedinformatics4010025","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010025","url":null,"abstract":"This study investigates the potential of low-cost infrared cameras for non-contact monitoring of blood pressure (BP) in individuals with fragile health, particularly the elderly. Previous research has shown success in developing non-contact BP monitoring using RGB cameras. In this study, the Eulerian Video Magnification (EVM) technique is employed to enhance minor variations in skin pixel intensity in specific facial regions captured by an infrared camera from the forehead and palm. The primary focus of this study is to explore the possibility of using infrared cameras for non-contact BP monitoring under low-light or night-time conditions. We have successfully shown that by employing a series of straightforward signal processing techniques and regression analysis, we were able to achieve commendable outcomes in our experimental setup. Specifically, we were able to surpass the stringent accuracy standards set forth by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI) protocol.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"51 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139798146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-Contact Blood Pressure Estimation Using Forehead and Palm Infrared Video","authors":"Thomas Stogiannopoulos, N. Mitianoudis","doi":"10.3390/biomedinformatics4010025","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010025","url":null,"abstract":"This study investigates the potential of low-cost infrared cameras for non-contact monitoring of blood pressure (BP) in individuals with fragile health, particularly the elderly. Previous research has shown success in developing non-contact BP monitoring using RGB cameras. In this study, the Eulerian Video Magnification (EVM) technique is employed to enhance minor variations in skin pixel intensity in specific facial regions captured by an infrared camera from the forehead and palm. The primary focus of this study is to explore the possibility of using infrared cameras for non-contact BP monitoring under low-light or night-time conditions. We have successfully shown that by employing a series of straightforward signal processing techniques and regression analysis, we were able to achieve commendable outcomes in our experimental setup. Specifically, we were able to surpass the stringent accuracy standards set forth by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI) protocol.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"74 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139858005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unravelling Insights into the Evolution and Management of SARS-CoV-2","authors":"A. G. Mushebenge, S. Ugbaja, Nonkululeko Avril Mbatha, Rene B. Khan, H. Kumalo","doi":"10.3390/biomedinformatics4010022","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010022","url":null,"abstract":"Worldwide, the COVID-19 pandemic, caused by the brand-new coronavirus SARS-CoV-2, has claimed a sizable number of lives. The virus’ rapid spread and impact on every facet of human existence necessitate a continuous and dynamic examination of its biology and management. Despite this urgency, COVID-19 does not currently have any particular antiviral treatments. As a result, scientists are concentrating on repurposing existing antiviral medications or creating brand-new ones. This comprehensive review seeks to provide an in-depth exploration of our current understanding of SARS-CoV-2, starting with an analysis of its prevalence, pathology, and evolutionary trends. In doing so, the review aims to clarify the complex network of factors that have contributed to the varying case fatality rates observed in different geographic areas. In this work, we explore the complex world of SARS-CoV-2 mutations and their implications for vaccine efficacy and therapeutic interventions. The dynamic viral landscape of the pandemic poses a significant challenge, leading scientists to investigate the genetic foundations of the virus and the mechanisms underlying these genetic alterations. Numerous hypotheses have been proposed as the pandemic has developed, covering various subjects like the selection pressures driving mutation, the possibility of vaccine escape, and the consequences for clinical therapy. Furthermore, this review will shed light on current clinical trials investigating novel medicines and vaccine development, including the promising field of drug repurposing, providing a window into the changing field of treatment approaches. This study provides a comprehensive understanding of the virus by compiling the huge and evolving body of knowledge on SARS-CoV-2, highlighting its complexities and implications for public health, and igniting additional investigation into the control of this unprecedented global health disaster.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"16 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139867470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unravelling Insights into the Evolution and Management of SARS-CoV-2","authors":"A. G. Mushebenge, S. Ugbaja, Nonkululeko Avril Mbatha, Rene B. Khan, H. Kumalo","doi":"10.3390/biomedinformatics4010022","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010022","url":null,"abstract":"Worldwide, the COVID-19 pandemic, caused by the brand-new coronavirus SARS-CoV-2, has claimed a sizable number of lives. The virus’ rapid spread and impact on every facet of human existence necessitate a continuous and dynamic examination of its biology and management. Despite this urgency, COVID-19 does not currently have any particular antiviral treatments. As a result, scientists are concentrating on repurposing existing antiviral medications or creating brand-new ones. This comprehensive review seeks to provide an in-depth exploration of our current understanding of SARS-CoV-2, starting with an analysis of its prevalence, pathology, and evolutionary trends. In doing so, the review aims to clarify the complex network of factors that have contributed to the varying case fatality rates observed in different geographic areas. In this work, we explore the complex world of SARS-CoV-2 mutations and their implications for vaccine efficacy and therapeutic interventions. The dynamic viral landscape of the pandemic poses a significant challenge, leading scientists to investigate the genetic foundations of the virus and the mechanisms underlying these genetic alterations. Numerous hypotheses have been proposed as the pandemic has developed, covering various subjects like the selection pressures driving mutation, the possibility of vaccine escape, and the consequences for clinical therapy. Furthermore, this review will shed light on current clinical trials investigating novel medicines and vaccine development, including the promising field of drug repurposing, providing a window into the changing field of treatment approaches. This study provides a comprehensive understanding of the virus by compiling the huge and evolving body of knowledge on SARS-CoV-2, highlighting its complexities and implications for public health, and igniting additional investigation into the control of this unprecedented global health disaster.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"506 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139807807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention","authors":"Dimitrios G. Boucharas, Chryssa Anastasiadou, Spyridon Karkabounas, E. Antonopoulou, George Manis","doi":"10.3390/biomedinformatics4010021","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010021","url":null,"abstract":"Cancer, which is currently rated as the second-leading cause of mortality across the globe, is one of the most hazardous disease groups that has plagued humanity for centuries. The experiments presented here span over two decades and were conducted on a specific species of mice, aiming to neutralize a highly carcinogenic agent by altering its chemical structure when combined with certain compounds. A plethora of growth models, each of which makes use of distinctive qualities, are utilized in the investigation and explanation of the phenomena of chemically induced oncogenesis and prevention. The analysis ultimately results in the formalization of the process of locating the growth model that provides the best descriptive power based on predefined criteria. This is accomplished through a methodological workflow that adopts a computational pipeline based on the Levenberg–Marquardt algorithm with pioneering and conventional metrics as well as a ruleset. The developed process simplifies the investigated phenomena as the parameter space of growth models is reduced. The predictability is proven strong in the near future (i.e., a 0.61% difference between the predicted and actual values). The parameters differentiate between active compounds (i.e., classification results reach up to 96% in sensitivity and other performance metrics). The distribution of parameter contribution complements the findings that the logistic growth model is the most appropriate (i.e., 44.47%). In addition, the dosage of chemicals is increased by a factor of two for the next round of trials, which exposes parallel behavior between the two dosages. As a consequence, the study reveals important information on chemoprevention and the cycles of cancer proliferation. If developed further, it might lead to the development of nutritional supplements that completely inhibit the expansion of cancerous tumors. The methodology provided can be used to describe other phenomena that progress over time and it has the power to estimate future results.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"10 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139811273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention","authors":"Dimitrios G. Boucharas, Chryssa Anastasiadou, Spyridon Karkabounas, E. Antonopoulou, George Manis","doi":"10.3390/biomedinformatics4010021","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010021","url":null,"abstract":"Cancer, which is currently rated as the second-leading cause of mortality across the globe, is one of the most hazardous disease groups that has plagued humanity for centuries. The experiments presented here span over two decades and were conducted on a specific species of mice, aiming to neutralize a highly carcinogenic agent by altering its chemical structure when combined with certain compounds. A plethora of growth models, each of which makes use of distinctive qualities, are utilized in the investigation and explanation of the phenomena of chemically induced oncogenesis and prevention. The analysis ultimately results in the formalization of the process of locating the growth model that provides the best descriptive power based on predefined criteria. This is accomplished through a methodological workflow that adopts a computational pipeline based on the Levenberg–Marquardt algorithm with pioneering and conventional metrics as well as a ruleset. The developed process simplifies the investigated phenomena as the parameter space of growth models is reduced. The predictability is proven strong in the near future (i.e., a 0.61% difference between the predicted and actual values). The parameters differentiate between active compounds (i.e., classification results reach up to 96% in sensitivity and other performance metrics). The distribution of parameter contribution complements the findings that the logistic growth model is the most appropriate (i.e., 44.47%). In addition, the dosage of chemicals is increased by a factor of two for the next round of trials, which exposes parallel behavior between the two dosages. As a consequence, the study reveals important information on chemoprevention and the cycles of cancer proliferation. If developed further, it might lead to the development of nutritional supplements that completely inhibit the expansion of cancerous tumors. The methodology provided can be used to describe other phenomena that progress over time and it has the power to estimate future results.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"23 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139871146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Depleted-MLH1 Expression Predicts Prognosis and Immunotherapeutic Efficacy in Uterine Corpus Endometrial Cancer: An In Silico Approach","authors":"Tesfaye Wolde, Jing Huang, Peng Huang, Vijay Pandey, Peiwu Qin","doi":"10.3390/biomedinformatics4010019","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010019","url":null,"abstract":"Uterine corpus endometrial carcinoma (UCEC) poses significant clinical challenges due to its high incidence and poor prognosis, exacerbated by the lack of effective screening methods. The standard treatment for UCEC typically involves surgical intervention, with radiation and chemotherapy as potential adjuvant therapies. In recent years, immunotherapy has emerged as a promising avenue for the advanced treatment of UCEC. This study employs a multi-omics approach, analyzing RNA-sequencing data and clinical information from The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis (GEPIA), and GeneMANIA databases to investigate the prognostic value of MutL Homolog 1 (MLH1) gene expression in UCEC. The dysregulation of MLH1 in UCEC is linked to adverse prognostic outcomes and suppressed immune cell infiltration. Gene Set Enrichment Analysis (GSEA) data reveal MLH1’s involvement in immune-related processes, while its expression correlates with tumor mutational burden (TMB) and microsatellite instability (MSI). Lower MLH1 expression is associated with poorer prognosis, reduced responsiveness to Programmed cell death protein 1 (PD-1)/Programmed death-ligand 1 (PD-L1) inhibitors, and heightened sensitivity to anti-cancer agents. This comprehensive analysis establishes MLH1 as a potential biomarker for predicting prognosis, immunotherapy response, and drug sensitivity in UCEC, offering crucial insights for the clinical management of patients.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":"32 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139875635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}