Pub Date : 2024-01-08DOI: 10.3390/biomedinformatics4010007
Sarfaraz K. Niazi, Zamara Mariam, R. Z. Paracha
The three-dimensional protein structure is pivotal in comprehending biological phenomena. It directly governs protein function and hence aids in drug discovery. The development of protein prediction algorithms, such as AlphaFold2, ESMFold, and trRosetta, has given much hope in expediting protein-based therapeutic discovery. Though no study has reported a conclusive application of these algorithms, the efforts continue with much optimism. We intended to test the application of these algorithms in rank-ordering therapeutic proteins for their instability during the pre-translational modification stages, as may be predicted according to the confidence of the structure predicted by these algorithms. The selected molecules were based on a harmonized category of licensed therapeutic proteins; out of the 204 licensed products, 188 that were not conjugated were chosen for analysis, resulting in a lack of correlation between the confidence scores and structural or protein properties. It is crucial to note here that the predictive accuracy of these algorithms is contingent upon the presence of the known structure of the protein in the accessible database. Consequently, our conclusion emphasizes that these algorithms primarily replicate information derived from existing structures. While our findings caution against relying on these algorithms for drug discovery purposes, we acknowledge the need for a nuanced interpretation. Considering their limitations and recognizing that their utility may be constrained to scenarios where known structures are available is important. Hence, caution is advised when applying these algorithms to characterize various attributes of therapeutic proteins without the support of adequate structural information. It is worth noting that the two main algorithms, AlfphaFold2 and ESMFold, also showed a 72% correlation in their scores, pointing to similar limitations. While much progress has been made in computational sciences, the Levinthal paradox remains unsolved.
{"title":"Limitations of Protein Structure Prediction Algorithms in Therapeutic Protein Development","authors":"Sarfaraz K. Niazi, Zamara Mariam, R. Z. Paracha","doi":"10.3390/biomedinformatics4010007","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010007","url":null,"abstract":"The three-dimensional protein structure is pivotal in comprehending biological phenomena. It directly governs protein function and hence aids in drug discovery. The development of protein prediction algorithms, such as AlphaFold2, ESMFold, and trRosetta, has given much hope in expediting protein-based therapeutic discovery. Though no study has reported a conclusive application of these algorithms, the efforts continue with much optimism. We intended to test the application of these algorithms in rank-ordering therapeutic proteins for their instability during the pre-translational modification stages, as may be predicted according to the confidence of the structure predicted by these algorithms. The selected molecules were based on a harmonized category of licensed therapeutic proteins; out of the 204 licensed products, 188 that were not conjugated were chosen for analysis, resulting in a lack of correlation between the confidence scores and structural or protein properties. It is crucial to note here that the predictive accuracy of these algorithms is contingent upon the presence of the known structure of the protein in the accessible database. Consequently, our conclusion emphasizes that these algorithms primarily replicate information derived from existing structures. While our findings caution against relying on these algorithms for drug discovery purposes, we acknowledge the need for a nuanced interpretation. Considering their limitations and recognizing that their utility may be constrained to scenarios where known structures are available is important. Hence, caution is advised when applying these algorithms to characterize various attributes of therapeutic proteins without the support of adequate structural information. It is worth noting that the two main algorithms, AlfphaFold2 and ESMFold, also showed a 72% correlation in their scores, pointing to similar limitations. While much progress has been made in computational sciences, the Levinthal paradox remains unsolved.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139447275","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}
Pub Date : 2024-01-08DOI: 10.3390/biomedinformatics4010008
Tin Lai
Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand–supply imbalance in healthcare. Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. However, compared to traditional machine learning approaches, deep learning models are complex and are often treated as a “black box” that can cause uncertainty regarding how they operate. Explainable artificial intelligence (XAI) refers to methods that explain and interpret machine learning models’ inner workings and how they come to decisions, which is especially important in the medical domain to guide healthcare decision-making processes. This review summarizes recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT, enabling transparency in medical diagnosis applications.
人工智能（AI）的最新进展促进了其在初级医疗服务中的广泛应用，从而解决了医疗保健供需失衡的问题。视觉转换器（ViT）已成为最先进的计算机视觉模型，得益于自我关注模块。然而，与传统的机器学习方法相比，深度学习模型非常复杂，通常被视为一个 "黑盒子"，可能会导致其运行方式的不确定性。可解释人工智能（XAI）指的是解释和诠释机器学习模型内部工作原理及其如何做出决策的方法，这在医疗领域指导医疗决策过程尤为重要。本综述总结了最近的 ViT 进展和解释性方法，以了解 ViT 的决策过程，实现医疗诊断应用的透明化。
{"title":"Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care","authors":"Tin Lai","doi":"10.3390/biomedinformatics4010008","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010008","url":null,"abstract":"Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand–supply imbalance in healthcare. Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. However, compared to traditional machine learning approaches, deep learning models are complex and are often treated as a “black box” that can cause uncertainty regarding how they operate. Explainable artificial intelligence (XAI) refers to methods that explain and interpret machine learning models’ inner workings and how they come to decisions, which is especially important in the medical domain to guide healthcare decision-making processes. This review summarizes recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT, enabling transparency in medical diagnosis applications.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448250","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}
Pub Date : 2024-01-02DOI: 10.3390/biomedinformatics4010006
Carson K. Leung
Biomedical informatics can be considered as a multidisciplinary research and educational field situated at the intersection of computational sciences (including computer science, data science, mathematics, and statistics), biology, and medicine. In recent years, there have been advances in the field of biomedical informatics. The current article highlights some interesting state-of-the-art research outcomes in these fields. These include research outcomes in areas like (i) computational biology and medicine, (ii) explainable artificial intelligence (XAI) in biomedical research and clinical practice, (iii) machine learning (including deep learning) methods and application for bioinformatics and healthcare, (iv) imaging informatics, as well as (v) medical statistics and data science. Moreover, the current article also discusses some existing challenges and potential future directions for these research areas to advance the fields of biomedical informatics.
{"title":"Biomedical Informatics: State of the Art, Challenges, and Opportunities","authors":"Carson K. Leung","doi":"10.3390/biomedinformatics4010006","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010006","url":null,"abstract":"Biomedical informatics can be considered as a multidisciplinary research and educational field situated at the intersection of computational sciences (including computer science, data science, mathematics, and statistics), biology, and medicine. In recent years, there have been advances in the field of biomedical informatics. The current article highlights some interesting state-of-the-art research outcomes in these fields. These include research outcomes in areas like (i) computational biology and medicine, (ii) explainable artificial intelligence (XAI) in biomedical research and clinical practice, (iii) machine learning (including deep learning) methods and application for bioinformatics and healthcare, (iv) imaging informatics, as well as (v) medical statistics and data science. Moreover, the current article also discusses some existing challenges and potential future directions for these research areas to advance the fields of biomedical informatics.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139389679","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}
Pub Date : 2024-01-01DOI: 10.3390/biomedinformatics4010005
Kazuya Tokumura, Koki Sadamori, Makoto Yoshimoto, Akane Tomizawa, Yuki Tanaka, Kazuya Fukasawa, E. Hinoi
Glioma stem cells (GSCs) contribute to the pathogenesis of glioblastoma (GBM), which is the most malignant form of glioma. The implications and underlying mechanisms of protein glycosylation in GSC phenotypes and GBM malignancy are not fully understood. The implication of protein glycosylation and the corresponding candidate genes on the stem cell properties of GSCs and poor clinical outcomes in GBM were investigated, using datasets from the Gene Expression Omnibus, The Cancer Genome Atlas, and the Chinese Glioma Genome Atlas, accompanied by biological validation in vitro. N-linked glycosylation was significantly associated with GSC properties and the prognosis of GBM in the integrated bioinformatics analyses of clinical specimens. N-linked glycosylation was associated with the glioma grade, molecular biomarkers, and molecular subtypes. The expression levels of the asparagine-linked glycosylation (ALG) enzyme family, which is essential for the early steps in the biosynthesis of N-glycans, were prominently associated with GSC properties and poor survival in patients with GBM with high stem-cell properties. Finally, the oxidative phosphorylation pathway was primarily enriched in GSCs with a high expression of the ALG enzyme family. These findings suggest the role of N-linked glycosylation in the regulation of GSC phenotypes and GBM malignancy.
{"title":"The Bioinformatics Identification of Potential Protein Glycosylation Genes Associated with a Glioma Stem Cell Signature","authors":"Kazuya Tokumura, Koki Sadamori, Makoto Yoshimoto, Akane Tomizawa, Yuki Tanaka, Kazuya Fukasawa, E. Hinoi","doi":"10.3390/biomedinformatics4010005","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010005","url":null,"abstract":"Glioma stem cells (GSCs) contribute to the pathogenesis of glioblastoma (GBM), which is the most malignant form of glioma. The implications and underlying mechanisms of protein glycosylation in GSC phenotypes and GBM malignancy are not fully understood. The implication of protein glycosylation and the corresponding candidate genes on the stem cell properties of GSCs and poor clinical outcomes in GBM were investigated, using datasets from the Gene Expression Omnibus, The Cancer Genome Atlas, and the Chinese Glioma Genome Atlas, accompanied by biological validation in vitro. N-linked glycosylation was significantly associated with GSC properties and the prognosis of GBM in the integrated bioinformatics analyses of clinical specimens. N-linked glycosylation was associated with the glioma grade, molecular biomarkers, and molecular subtypes. The expression levels of the asparagine-linked glycosylation (ALG) enzyme family, which is essential for the early steps in the biosynthesis of N-glycans, were prominently associated with GSC properties and poor survival in patients with GBM with high stem-cell properties. Finally, the oxidative phosphorylation pathway was primarily enriched in GSCs with a high expression of the ALG enzyme family. These findings suggest the role of N-linked glycosylation in the regulation of GSC phenotypes and GBM malignancy.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139391980","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}
Pub Date : 2023-12-31DOI: 10.3390/biomedinformatics4010004
Hilmi Demirhan, Wlodek Zadrozny
Multimodal medical question answering (MMQA) is a vital area bridging healthcare and Artificial Intelligence (AI). This survey methodically examines the MMQA research published in recent years. We collect academic literature through Google Scholar, applying bibliometric analysis to the publications and datasets used in these studies. Our analysis uncovers the increasing interest in MMQA over time, with diverse domains such as natural language processing, computer vision, and large language models contributing to the research. The AI methods used in multimodal question answering in the medical domain are a prominent focus, accompanied by applicability of MMQA to the medical field. MMQA in the medical field has its unique challenges due to the sensitive nature of medicine as a science dealing with human health. The survey reveals MMQA research to be in an exploratory stage, discussing different methods, datasets, and potential business models. Future research is expected to focus on application development by big tech companies, such as MedPalm. The survey aims to provide insights into the current state of multimodal medical question answering, highlighting the growing interest from academia and industry. The identified research gaps and trends will guide future investigations and encourage collaborative efforts to advance this transformative field.
{"title":"Survey of Multimodal Medical Question Answering","authors":"Hilmi Demirhan, Wlodek Zadrozny","doi":"10.3390/biomedinformatics4010004","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010004","url":null,"abstract":"Multimodal medical question answering (MMQA) is a vital area bridging healthcare and Artificial Intelligence (AI). This survey methodically examines the MMQA research published in recent years. We collect academic literature through Google Scholar, applying bibliometric analysis to the publications and datasets used in these studies. Our analysis uncovers the increasing interest in MMQA over time, with diverse domains such as natural language processing, computer vision, and large language models contributing to the research. The AI methods used in multimodal question answering in the medical domain are a prominent focus, accompanied by applicability of MMQA to the medical field. MMQA in the medical field has its unique challenges due to the sensitive nature of medicine as a science dealing with human health. The survey reveals MMQA research to be in an exploratory stage, discussing different methods, datasets, and potential business models. Future research is expected to focus on application development by big tech companies, such as MedPalm. The survey aims to provide insights into the current state of multimodal medical question answering, highlighting the growing interest from academia and industry. The identified research gaps and trends will guide future investigations and encourage collaborative efforts to advance this transformative field.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139132249","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}
Pub Date : 2023-12-22DOI: 10.3390/biomedinformatics4010002
Tin Lai, Yukun Shi, Zicong Du, Jiajie Wu, Ken Fu, Yichao Dou, Ziqi Wang
The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which heightened the need for timely and professional mental health support. Online psychological counselling emerged as the predominant mode of providing services in response to this demand. In this study, we propose the Psy-LLM framework, an AI-based assistive tool leveraging large language models (LLMs) for question answering in psychological consultation settings to ease the demand on mental health professions. Our framework combines pre-trained LLMs with real-world professional questions-and-answers (Q&A) from psychologists and extensively crawled psychological articles. The Psy-LLM framework serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress. Additionally, it functions as a screening tool to identify urgent cases requiring further assistance. We evaluated the framework using intrinsic metrics, such as perplexity, and extrinsic evaluation metrics, including human participant assessments of response helpfulness, fluency, relevance, and logic. The results demonstrate the effectiveness of the Psy-LLM framework in generating coherent and relevant answers to psychological questions. This article discusses the potential and limitations of using large language models to enhance mental health support through AI technologies.
{"title":"Supporting the Demand on Mental Health Services with AI-Based Conversational Large Language Models (LLMs)","authors":"Tin Lai, Yukun Shi, Zicong Du, Jiajie Wu, Ken Fu, Yichao Dou, Ziqi Wang","doi":"10.3390/biomedinformatics4010002","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010002","url":null,"abstract":"The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which heightened the need for timely and professional mental health support. Online psychological counselling emerged as the predominant mode of providing services in response to this demand. In this study, we propose the Psy-LLM framework, an AI-based assistive tool leveraging large language models (LLMs) for question answering in psychological consultation settings to ease the demand on mental health professions. Our framework combines pre-trained LLMs with real-world professional questions-and-answers (Q&A) from psychologists and extensively crawled psychological articles. The Psy-LLM framework serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress. Additionally, it functions as a screening tool to identify urgent cases requiring further assistance. We evaluated the framework using intrinsic metrics, such as perplexity, and extrinsic evaluation metrics, including human participant assessments of response helpfulness, fluency, relevance, and logic. The results demonstrate the effectiveness of the Psy-LLM framework in generating coherent and relevant answers to psychological questions. This article discusses the potential and limitations of using large language models to enhance mental health support through AI technologies.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944737","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}
Pub Date : 2023-12-21DOI: 10.3390/biomedinformatics4010001
Alexandre G. de Brevern
Welcome to BioMedInformatics (ISSN: 2673-7426) [...]
欢迎访问 BioMedInformatics（ISSN：2673-7426）[......］
{"title":"BioMedInformatics, the Link between Biomedical Informatics, Biology and Computational Medicine","authors":"Alexandre G. de Brevern","doi":"10.3390/biomedinformatics4010001","DOIUrl":"https://doi.org/10.3390/biomedinformatics4010001","url":null,"abstract":"Welcome to BioMedInformatics (ISSN: 2673-7426) [...]","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951584","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}
Pub Date : 2023-12-08DOI: 10.3390/biomedinformatics3040070
Shadi Askari, Alireza Ghofrani, Hamed Taherdoost
In pharmaceutical research and development, pursuing novel therapeutics and optimizing existing drugs have been revolutionized by the fusion of cutting-edge technologies and computational methodologies. Over the past few decades, the field of drug design has undergone a remarkable transformation, catalyzed by the rapid advancement of computer-aided discovery techniques and the emergence of biosimilar agents. This dynamic interplay between scientific innovation and technological prowess has expedited the drug discovery process and paved the way for more targeted, effective, and personalized treatment approaches. This review investigates the transformative computer-aided discovery techniques for biosimilar agents in reshaping drug design. It examines how computational methods expedite drug candidate identification and explores the rise of cost-effective biosimilars as alternatives to biologics. Through this analysis, this study highlights the potential of these innovations to enhance the efficiency and accessibility of pharmaceutical development. It represents a pioneering effort to examine how computer-aided discovery is revolutionizing biosimilar agent development, exploring its applications, challenges, and prospects.
{"title":"Transforming Drug Design: Innovations in Computer-Aided Discovery for Biosimilar Agents","authors":"Shadi Askari, Alireza Ghofrani, Hamed Taherdoost","doi":"10.3390/biomedinformatics3040070","DOIUrl":"https://doi.org/10.3390/biomedinformatics3040070","url":null,"abstract":"In pharmaceutical research and development, pursuing novel therapeutics and optimizing existing drugs have been revolutionized by the fusion of cutting-edge technologies and computational methodologies. Over the past few decades, the field of drug design has undergone a remarkable transformation, catalyzed by the rapid advancement of computer-aided discovery techniques and the emergence of biosimilar agents. This dynamic interplay between scientific innovation and technological prowess has expedited the drug discovery process and paved the way for more targeted, effective, and personalized treatment approaches. This review investigates the transformative computer-aided discovery techniques for biosimilar agents in reshaping drug design. It examines how computational methods expedite drug candidate identification and explores the rise of cost-effective biosimilars as alternatives to biologics. Through this analysis, this study highlights the potential of these innovations to enhance the efficiency and accessibility of pharmaceutical development. It represents a pioneering effort to examine how computer-aided discovery is revolutionizing biosimilar agent development, exploring its applications, challenges, and prospects.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138587907","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}
Pub Date : 2023-12-07DOI: 10.3390/biomedinformatics3040069
Vishakha Vashisht, A. Vashisht, A. Mondal, Jaspreet Farmaha, Ahmet Alptekin, Harmanpreet Singh, P. Ahluwalia, Anaka Srinivas, R. Kolhe
Emerging infectious diseases (EIDs) pose an increasingly significant global burden, driven by urbanization, population explosion, global travel, changes in human behavior, and inadequate public health systems. The recent SARS-CoV-2 pandemic highlights the urgent need for innovative and robust technologies to effectively monitor newly emerging pathogens. Rapid identification, epidemiological surveillance, and transmission mitigation are crucial challenges for ensuring public health safety. Genomics has emerged as a pivotal tool in public health during pandemics, enabling the diagnosis, management, and prediction of infections, as well as the analysis and identification of cross-species interactions and the categorization of infectious agents. Recent advancements in high-throughput DNA sequencing tools have facilitated rapid and precise identification and characterization of emerging pathogens. This review article provides insights into the latest advances in various genomic techniques for pathogen detection and tracking and their applications in global outbreak surveillance. We assess methods that leverage pathogen sequences and explore the role of genomic analysis in understanding the epidemiology of newly emerged infectious diseases. Additionally, we address technical challenges and limitations, ethical and legal considerations, and highlight opportunities for integrating genomics with other surveillance approaches. By delving into the prospects and obstacles of genomics, we can gain valuable insights into its role in mitigating the threats posed by emerging pathogens and improving global preparedness in the face of future outbreaks.
{"title":"Genomics for Emerging Pathogen Identification and Monitoring: Prospects and Obstacles","authors":"Vishakha Vashisht, A. Vashisht, A. Mondal, Jaspreet Farmaha, Ahmet Alptekin, Harmanpreet Singh, P. Ahluwalia, Anaka Srinivas, R. Kolhe","doi":"10.3390/biomedinformatics3040069","DOIUrl":"https://doi.org/10.3390/biomedinformatics3040069","url":null,"abstract":"Emerging infectious diseases (EIDs) pose an increasingly significant global burden, driven by urbanization, population explosion, global travel, changes in human behavior, and inadequate public health systems. The recent SARS-CoV-2 pandemic highlights the urgent need for innovative and robust technologies to effectively monitor newly emerging pathogens. Rapid identification, epidemiological surveillance, and transmission mitigation are crucial challenges for ensuring public health safety. Genomics has emerged as a pivotal tool in public health during pandemics, enabling the diagnosis, management, and prediction of infections, as well as the analysis and identification of cross-species interactions and the categorization of infectious agents. Recent advancements in high-throughput DNA sequencing tools have facilitated rapid and precise identification and characterization of emerging pathogens. This review article provides insights into the latest advances in various genomic techniques for pathogen detection and tracking and their applications in global outbreak surveillance. We assess methods that leverage pathogen sequences and explore the role of genomic analysis in understanding the epidemiology of newly emerged infectious diseases. Additionally, we address technical challenges and limitations, ethical and legal considerations, and highlight opportunities for integrating genomics with other surveillance approaches. By delving into the prospects and obstacles of genomics, we can gain valuable insights into its role in mitigating the threats posed by emerging pathogens and improving global preparedness in the face of future outbreaks.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138591918","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}
Pub Date : 2023-12-06DOI: 10.3390/biomedinformatics3040067
Sabrina Giguère, Laura Dellazizzo, M. Beaudoin, Marie-Andrée Lapierre, Marie Villeneuve, K. Phraxayavong, Stéphane Potvin, A. Dumais
Considering the harmful effects of cannabis on individuals with a severe mental disorder and the limited effectiveness of current interventions, this case report showcases the beneficial results of a 10-session Avatar intervention for cannabis use disorder (CUD) on a polysubstance user with a comorbid schizoaffective disorder. Virtual reality allowed the creation of an Avatar representing a person significantly related to the patient’s drug use. Avatar intervention for CUD aims to combine exposure, relational, and cognitive behavioral therapies while practicing real-life situations and learning how to manage negative emotions and cravings. Throughout therapy and later on, Mr. C managed to maintain abstinence from all substances. Also, an improvement in the severity of CUD, as well as a greater motivation to change consumption, was observed after therapy. As observed by his mother, his psychiatrist, and himself, the benefits of Avatar intervention for CUD extended to other spheres of his life. The drastic results observed in this patient could be promising as an alternative to the current treatment available for people with a dual diagnosis of cannabis use disorder and psychotic disorder, which generally lack effectiveness. A single-blind randomized control trial comparing the treatment with a classical intervention in a larger sample is currently underway to evaluate whether the results are reproducible on a larger sample.
{"title":"Avatar Intervention for Cannabis Use Disorder in a Patient with Schizoaffective Disorder: A Case Report","authors":"Sabrina Giguère, Laura Dellazizzo, M. Beaudoin, Marie-Andrée Lapierre, Marie Villeneuve, K. Phraxayavong, Stéphane Potvin, A. Dumais","doi":"10.3390/biomedinformatics3040067","DOIUrl":"https://doi.org/10.3390/biomedinformatics3040067","url":null,"abstract":"Considering the harmful effects of cannabis on individuals with a severe mental disorder and the limited effectiveness of current interventions, this case report showcases the beneficial results of a 10-session Avatar intervention for cannabis use disorder (CUD) on a polysubstance user with a comorbid schizoaffective disorder. Virtual reality allowed the creation of an Avatar representing a person significantly related to the patient’s drug use. Avatar intervention for CUD aims to combine exposure, relational, and cognitive behavioral therapies while practicing real-life situations and learning how to manage negative emotions and cravings. Throughout therapy and later on, Mr. C managed to maintain abstinence from all substances. Also, an improvement in the severity of CUD, as well as a greater motivation to change consumption, was observed after therapy. As observed by his mother, his psychiatrist, and himself, the benefits of Avatar intervention for CUD extended to other spheres of his life. The drastic results observed in this patient could be promising as an alternative to the current treatment available for people with a dual diagnosis of cannabis use disorder and psychotic disorder, which generally lack effectiveness. A single-blind randomized control trial comparing the treatment with a classical intervention in a larger sample is currently underway to evaluate whether the results are reproducible on a larger sample.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138594754","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}
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