Medinformatics最新文献

{"title":"Environmental Epigenetics and Its Impacts on Disease Susceptibility: A Comprehensive Review","authors":"Taiwo Temitope Ogunjobi, T. Gbayisomore, Patrica Okwuchi Nneji, O. Olofin, Emmanuel Niyi Olowe, Chimaobi Divine Gigam-Ozuzu, Jolayemi Ibidunni Afolabi, Ngozi Benedette Okwuokei, Victor Abiodun Boluwaji, Taiwo Paul Ojeniran, I. O. Ogini, Seth Olorunmijuwomlo Adesope, T. D. Yissa, Cyril Arinze Eji","doi":"10.47852/bonviewmedin42022945","DOIUrl":"https://doi.org/10.47852/bonviewmedin42022945","url":null,"abstract":"Environmental epigenetics, an emerging field, investigates the interplay between environmental factors and epigenetic changes, shaping an individual's disease vulnerability. This review highlights the associations among genetics, environment, and health. Environmental epigenetics significantly impacts disease susceptibility through mechanisms like non-coding RNAs, histone modifications, and DNA methylation, responding to external stimuli and dynamically interacting with genes. Lifelong epigenetic imprints, influenced by the prenatal environment, early experiences, diet, chemical exposures, and lifestyle, dictate health trajectories across the lifespan, elevating the risk of various illnesses, including cancer, cardiovascular, metabolic, and neurological conditions. Major findings demonstrate how antenatal environmental factors—like mother's diet, food and stress level—shape fetal epigenetic programming and impact health consequences in the future. Furthermore, it has been demonstrated that environmental pollutants and dietary nutrients can modify epigenetic markers, highlighting the crucial role that lifestyle decisions have in reducing the risk of disease. Significantly, the notion that epigenetic alterations may be passed down through generations raises the possibility that environmental exposures may have an impact on health consequences for future generations. Environmental epigenetics revolutionizes our understanding of gene-environment interactions and their role in disease etiology. The study concludes by highlighting the revolutionary implications of environmental epigenetics for comprehending the etiology of disease and customizing public health approaches, and by promoting the incorporation of both genetic and environmental components within public health frameworks. The knowledge acquired calls for an evolution of paradigms in medicine toward customized care utilizing epigenetic biomarkers and calls for more studies to improve therapies that lessen the harmful epigenetic consequences of exposure to the environment.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"25 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925489","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":"Machine Learning Predicts 1-[(4-Fluorophenyl) Methyl] Indole-2,3- Dione as Drug Lead for Peptide Deformylase in Plasmodium falciparum","authors":"O. O. Charles, Zainab Hassan, Okunbi Favour Onasokhare, Aisha Bello, O. F. Olaiyapo, Blessing Ojajuni, Sulaimon Olajuwon Abdul, Roseline Boboye, Martin Egbulefu, Chiziterem Jaachinma Ezeano, Ahmed Rufai Nurudeen, Rasaq Awosemo, Marian Mark","doi":"10.47852/bonviewmedin42022318","DOIUrl":"https://doi.org/10.47852/bonviewmedin42022318","url":null,"abstract":"Malaria continues to be a serious illness for world health. Deadly parasites that cause malaria infect female Anopheles mosquitoes and bite humans are the disease's vector. Since Plasmodium falciparum is the deadliest of the five Plasmodium species and research indicates that medicines for malaria are increasingly revealing drug-resistance mechanisms, it is imperative that new and effective medications be developed. This project aims to identify and produce a novel therapeutic lead against a P. falciparum target that has been validated. Target2Scan, a programmed tool, was employed in this investigation to find potential therapeutic targets for P. falciparum peptide deformylase. After the tool received the target signature, it ran a BLAST methodology to find targets that resembled peptide deformylase. To ascertain the binding affinities of protein-ligand complexes, molecular docking was performed using PyRx and CBDock. Of the 12,561 compounds produced, 11,304 were used as the training set and 1,256 as the test set. Six ligands were produced by machine learning as potential therapeutic leads, and using molecular docking, 1-[(4-fluorophenyl) methyl] indole-2,3-dione showed the greatest biding effect on peptide deformylase when compared to 5-chloro-1-(2-phenylethyl) indole-2,3-dione. The higher binding affinity of 1-[(4-fluorophenyl) methyl] indole-2,3-dione over 5-chloro-1-(2-phenylethyl) indole-2,3-dione and other ligands on peptide deformylase suggests that it has the ability to suppress P. falciparum pepide deformylase activity.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807620","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":"Interactome Based Computational Approach to Identify Association Between Cardiomyopathy and Cardiovascular Disease","authors":"Tammanna R. Sahrawat, Muskan, Rijul Sharma, Suhani Dange, Ritika Patial, S. K. Gahlawat","doi":"10.47852/bonviewmedin42023102","DOIUrl":"https://doi.org/10.47852/bonviewmedin42023102","url":null,"abstract":"Cardiovascular diseases (CVD), including heart failure (HF), represent a major global health concern, with significant indisposition and mortality rates. Cardiomyopathy is a myocardial disease that impedes the heart's ability to efficiently pump blood throughout the body, ultimately leading to heart failure. For the identification of critical genes and proteins linking different diseases, computational biology tools and \"omics\"play a significant role. Therefore, the present study was undertaken to identify underlying molecular factors responsible for cardiomyopathy to decipher its molecular association with CVD and heart failure using an integrative network system biology approach. Microarray and RNA-seq datasets for cardiomyopathy were retrieved from the Gene Expression Omnibus database and 51 common DEGs were identified. Subsequently, a Protein-Protein Interaction (PPI) network was constructed using STRING, followed by its analysis using various Cytoscape plug-ins. Nine hub genes, namely LPA, APOA2, ABCA1, LCAT, APOB, APOA4, CLU, APOC3, and APOA1 were identified that were found to be involved in cholesterol metabolism, fat digestion and absorption, lipid metabolism, and atherosclerosis pathways. Therefore, the proteins identified in the present study belonging to the APO family and their associated proteins may prove as useful biomarkers for cardiomyopathy and therapeutic targets to prevent CVD and heart failure.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"132 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141656635","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":"Bacterial Pangenome: A Review on the Current Strategies, Tools and Applications","authors":"Nikita Chordia Golchha, Anand Nighojkar, Sadhana Nighojkar","doi":"10.47852/bonviewmedin42022496","DOIUrl":"https://doi.org/10.47852/bonviewmedin42022496","url":null,"abstract":"The genomic research has grown by the advancement of sequencing technologies, which aim to capture the \"totality\" of life diversity by progressing from single strain to species or even higher taxa. Out of this scientific progress and inquisitiveness, the term \"pangenome\" was created. The goal of a pangenome is to represent all of a species genetic variation. It divides the genome into core, accessory and unique genes. Since the introduction of pangenomics, many software programs have been actively used for pangenome analysis. This review focuses on providing a snapshot of useful tools with their use, execution and computational details. In addition to tools for pangenome, the components, approaches, mechanism of variation, applications and challenges associated with pangenome analysis are also discussed in this article. Here, we have also discussed the increasing trend of bacterial pangenome studies with few studies and their significant findings. It has been proved that pangenomics is useful for understanding the clade of bacteria that helps in developing strategies for various application based on biological similarities and differences.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350961","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":"Diagnosis of Schizophrenia and Psychosis in Māori People Using Speech Assay: A Natural Language Processing Approach","authors":"Randall Ratana, Hamid Sharifzadeh, Jamuna Krishnan","doi":"10.47852/bonviewmedin42022849","DOIUrl":"https://doi.org/10.47852/bonviewmedin42022849","url":null,"abstract":"Indigenous peoples and ethnic minorities worldwide have a higher incidence of psychosis, primarily schizophrenia. In New Zealand, Māori individuals experience greater rates of anxiety and depression compared to non-Māori, with a significantly higher incidence of mental illness among Māori. We propose natural language processing (NLP) trained on speech samples from Māori patients as a potential solution to the problem of culturally biased psychometric screening tools for psychosis and schizophrenia. This research examines NLP's ability to diagnose psychosis in Māori patients by analysing speech and language abnormalities as indicators of severe mental illnesses such as schizophrenia. Our research emphasizes the need for inclusive language models and investigates cross-cultural applicability. We employed a three-part method: conducting clinical interviews, pre-processing data with the Natural Language Toolkit (NLTK) and applying language classifiers. The study's results demonstrate the promise of NLP, but limited patient data necessitates further research, including standardizing datasets and integrating NLP with indigenous languages. This research represents a step towards improving diagnostic accuracy and support for Māori people suffering from psychosis, aligning with healthcare's goal of fair and culturally responsive mental health screening.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"71 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141385636","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":"Integrated Bioinformatics Analysis Reveals ceRNA Triplet Related to Apoptosis in Gastric Cancer","authors":"Anju R. Nath, Jeyakumar Natarajan","doi":"10.47852/bonviewmedin42022434","DOIUrl":"https://doi.org/10.47852/bonviewmedin42022434","url":null,"abstract":"Apoptosis is a form of programmed cell death and evading the apoptosis is a milestone in gastric cancer (GC) tumorigenesis. Various RNAs such as miRNA (microRNA), mRNA (messenger RNA), lncRNA (long non-coding RNA), circRNA (circular RNA), play crucial roles in the apoptosis mechanism. In this present study, we aimed to understand the role played by these RNAs in apoptosis in GC. We constructed a ceRNA (competitive endogenous RNA) network using the expression profiles obtained from SRA (Sequence Read Archive) and GEO (Gene Expression Omnibus) datasets. After network construction, the differentially expressed mRNAs were used for gene prioritization. The prioritized genes and the RNAs interacting with them were analyzed to study their role in GC apoptosis. Then, we performed functional and pathway analysis to understand the role played by these genes in gastric cancer. This resulted in 37 miRNA-mRNA interactions, one miRNA-lncRNA interaction, and 17 miRNA-circRNA interactions. The binding site analysis resulted in 10 miRNAs that share common MRE (miRNA Response Elements) among mRNA, lncRNAs, circRNA. Besides, we found 33 miRNA-mRNA-circRNA and two miRNA-mRNA-lncRNA valid interactions. Integration of multiple omics datasets revealed dysregulated genes, including IGF1, MME, CCND2, CDH2, and COL1A1, implicated in GC apoptosis. Functional enrichment analysis highlighted pathways related to apoptosis, with CCND2 emerging as a key player. The integrative methodology has revealed a new potential diagnostic biomarker for the regulation of gastric cancer (GC) apoptosis: the CCND2-miR-141-3p-hsa_circ_0008035 ceRNA triplet. This discovery offers fresh perspectives into the intricate regulatory pathways governing gene expression in GC, promising significant insights into its pathology.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"115 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140967898","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":"Exploring Disrupted Gene Networks in Human 22q11.2 Microdeletion","authors":"Camila Cristina de Oliveira Alves, I. R. Wolf, B. F. Gamba, Lucilene Arilho Ribeiro Bicudo, Guilherme Targino Valente","doi":"10.47852/bonviewmedin42022652","DOIUrl":"https://doi.org/10.47852/bonviewmedin42022652","url":null,"abstract":"Several deletions are observed at the 22q11 locus and are responsible for 22q11.2 deletion syndrome (22q11DS), also known as DiGeorge syndrome, conotruncal anomaly face syndrome, or velocardiofacial syndrome. These microdeletions on human chromosome 22 range from 0.7 to 3 Mb. Many genes are affected by 22q11.2 deletion. However, despite the well-established clinical signs for the diagnosis of 22q11.2 deletion syndrome, the interactome background of 22q11.2 deletion syndrome is unknown. Here, we analyzed protein–protein interaction networks (PPIs) to assess the influences of 3 Mb 22q11.2 deletion on this network. We compared the general human PPI network against a network without 48 genes of the 3 Mb 22q11.2 locus in a homozygous condition: we compared topological metrics, enrichment of gene ontology terms, community assignments, and edge rewiring. The PPI networks revealed that this deletion affected the relevance of hundreds of nondeleted genes. Additionally, this 22q11.2 deletion induces intense rewiring of subnetworks, promoting an accumulation of proteins associated with DiGeorge clinical signs (CTCF, YY1, TFAP2A, PPARG, PAX6, RAX, and E2F3) in a single community (community 1). Therefore, we identified new genes that may be associated with the 22q11.2 deletion syndrome. Altogether, the systemic approaches used here yielded new insights into the 22q11.2 deletion syndrome.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"76 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141011320","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":"Decoding Medical Diagnosis with Machine Learning Classifiers","authors":"S. Chattopadhyay","doi":"10.47852/bonviewmedin42022583","DOIUrl":"https://doi.org/10.47852/bonviewmedin42022583","url":null,"abstract":"Clinical Decision Support Systems (CDSS) are gaining popularity in disease screening and grading in the current era of digital healthcare. This paper attempts to model how a computer learns to grade an Infectious Disease (ID), e.g., Typhoid fever using the Machine Learning (ML)-based approach mimicking how a novice doctor learns to diagnose a case with the help of senior doctors. To achieve the goal, ten Virtual Junior Clinicians (VJCs) are developed using ten Machine Learning Classifiers (MLC)-based CDSS, which are then trained with 'weighted' [0, 1] sign-symptoms and the corresponding 'labeled'grade of synthetic Typhoid fever cases (N = 198). Weights and labels are assigned by ten Senior Clinicians (SC) providing their rich Clinical Knowledge Base (CKB). The performance of each VJC is then measured in terms of their diagnostic Accuracy, Precision, Recall, and F-score. Results show that Random Forest (RF, i.e., VJC9) and Decision Tree (DT, i.e., VJC4)-based CDSS can grade with an average of 87% accuracy, which is even higher than human clinicians' accuracy. The reason behind RF and DT's appreciable performance is that clinicians use tree-search-based methods with probabilistic 'yes' and 'no' logic to learn the disease patterns alike the working principles of DT and RF for diagnosing and grading any ID. Apart from modeling, the paper provides insight into how to select the right Machine Learning Classifier (MLC) algorithm in the field of ID diagnosis. It also throws light on various hardships and challenges with MLC-based CDSS implementations in the real-world scenario.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":" 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140683490","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":"Edge Computing Based Cardiac Monitoring and Detection System in the Internet of Medical Things","authors":"Saranya Murugesan, Archana Naganathan","doi":"10.47852/bonviewmedin42022430","DOIUrl":"https://doi.org/10.47852/bonviewmedin42022430","url":null,"abstract":"Currently, cardiac disease becomes one of the major health issues in the world. The death due to cardiac arrests has been increasing day by day. The survival rate for the sudden cardiac arrest is low. It is possible to save 60% human life by continuously monitoring the fitness of the person. In recent times, the Internet of Things is emerging promising technologies. If the doctor cannot monitor the patient face to face, there Internet of Things plays important role in remotely monitoring the patient’s condition. With the rise in the use of smart wearable gadgets, the contribution of IoT will be more. In this paper, about five predictor model are analyzed for a particular dataset and chosen the best as Random forest algorithm. The proposed system integrates oxygen saturation level and heart rate sensor data from MAX30100 sensor for monitoring the patient’s vital signs. The remaining parametric data of the patient (AGE, GENDER, BLOOD SUGAR LEVEL, BLOOD PRESSURE LEVEL, GENETIC ISSUE and CHLOESTEROL LEVEL) are fed through the matrix keypad. The designed system monitors the online patient’s vitals continuously. The patient’s vitals will be stored in the edge server and also analyzed. The chosen predictor model checks the vital data for any abnormality and based on abnormality data detection in the heart rate, the warning message is conveyed to the nearby health care center via Thingspeak cloud. By tracking the patient’s GPS location, the server cloud sends alert notification along with the GPS location to the nearby health center and to the registered mobile numbers.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"99 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140695179","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":"Insilico Annotation and Immunoinformatics Guided Epitope Mapping of Potential Antigenic Proteins of Trichomonas foetus","authors":"Shravya Gorla, Shravani Akula, Deevya Awalaloo, Swathi Pathlavath, Neha Shireen, Alavala Renuka Devi, Geethanjali Karli","doi":"10.47852/bonviewmedin42022148","DOIUrl":"https://doi.org/10.47852/bonviewmedin42022148","url":null,"abstract":"Bovine Trichomonosis is one of the neglected tropical diseases of cattle that is resulting in severe reproductive failure. With present knowledge, disease diagnosis and maintaining the infected animals in the quarantine are the only available strategies. Several spillover incidences of Trichomonas foetus had also resulted in zoonotic transmission to humans. In spite of above circumstances, till date there are no point of care diagnostics developed for screening Bovine Trichomoniasis in cattle. In the light of above circumstances, there exists a demand for cost-effective diagnostic kits to be provided to farming community. This current study highlights evaluation of few surface proteins of Trichomonas foetus for the suitability as sero-diagnostic markers. Proteins such as Adhesin, Immuno-dominant variable surface antigen-like protein, and polymorphic membrane GP-63-like (Clan MA, family M8) protein and Hypothetical protein (OHS95735.1) were analyzed for structural features, Glycosylation patterns using freely available Bioinformatics tools and antigenic potential. Mapping of potential epitopes of all the target proteins was done using Immunoinformatics tools. Among the above proteins, GP63 like protein, Immuno-dominant variable antigenic domain like protein and Polymorphic membrane protein like proteins are most suitable as diagnostic targets, owing to their higher levels of glycosylation, large epitope domains and showing structural similarities with the domains of known toxic proteins. On the other hand, Adhesin protein has the potential to be exploited as a vaccine candidate. The above proteins are suitable to be expressed in suitable host system and validated the immunogenic potential by animal inoculation and by testing with the real samples.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"8 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229711","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}