Medinformatics最新文献

{"title":"End-to-end Technologies in Medicine, Pharmacy, Biology","authors":"Olga Denisova, Yaroslava Abramishvili","doi":"10.47852/bonviewmedin42022446","DOIUrl":"https://doi.org/10.47852/bonviewmedin42022446","url":null,"abstract":"The article raises the topical issue of the use of end-to-end technologies for medicine, pharmacy, and biology. The analysis of the use of new technologies in these areas is carried out at the present time. The authors are looking for ways to solve the problem of teaching humanities and natural sciences at a medical university using the capabilities of modern technologies. A random sampling method using the capabilities of Goole forms was used to conduct a survey of 2nd year students of the medical university and teachers working with them. The level of training, the needs of students for new knowledge on modern end-to-end technologies, and the possibilities of implementing training programs are discussed. The survey showed that despite the widespread introduction of end-to-end technologies, the knowledge of students and teachers in this area is extremely limited. Students have a need to acquire new knowledge that can be applied in their future profession. Several solutions to the problem of teaching student’s new technologies and their practical application in medicine, pharmacy, and biology are proposed. The survey also showed that there are two possible options: 1) the introduction of a separate optional course into the curriculum; 2) teachers can talk about the possibilities of new technologies and their use during the study of the discipline. For effective teaching, teachers must improve their skills in a timely manner by taking courses. With an integrated approach, a quick and effective solution to the task is possible.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"220 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140233482","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":"Artificial Intelligence with Great Potential in Medical Informatics: A Brief Review","authors":"Hao Lin","doi":"10.47852/bonviewmedin42022204","DOIUrl":"https://doi.org/10.47852/bonviewmedin42022204","url":null,"abstract":"In the 1950s and 1960s, in molecular biology, information technology was mainly applied to the molecular evolution of proteins and DNA, and later expanded to multiple fields such as sequence alignment, protein structure prediction, and gene splicing. Entering the 21st century, the completion of the Human Genome Project marks the arrival of the era of biomedical big data, providing a large amount of data for the application of artificial intelligence in this field. Especially in recent years, the continuous accumulation of medical data has pushed the application of artificial intelligence in the medical field to a broader and more practical level. This paper briefly introduces the applications of artificial intelligence in genomics, proteomics, transcriptomics, epigenetics, drug development, and other fields. I hope this review can clearly introduce which biomedical fields artificial intelligence can be applied to, and also promote doctors and related scholars to actively use artificial intelligence technology to solve specific biomedical problems.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"208 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140446027","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":"Analyzing Phytocompounds, Antioxidants, and In-Silico Molecular Docking of Plant-Derived Potential Andrographis Paniculated Inhibitory Action to Managed Beta Thalassemia","authors":"Soumya Khare, Tanushree Chatterjee, Shailendra Gupta, Ashish Patel","doi":"10.47852/medin42021979","DOIUrl":"https://doi.org/10.47852/medin42021979","url":null,"abstract":"It has long been known that medicinal plants are vital to human health and have been used for therapeutic purposes since the beginning of civilization. The goal of this study is to evaluate the scientific data supporting the use of the herbal remedy Andrographis paniculata in the management of beta thalassemia. Phytocompounds, one of the bases of traditional use, were assessed both qualitatively and quantitatively in five different extracts with varying solvent properties (methanol, chloroform, hexane, ethyl acetate, and aqueous), providing the fundamental explanation for the therapeutic relevance of this plant. The presence of flavonoids, phenols, tannins, saponins, and alkaloids in plant extracts was assessed. It was shown that ethanol extract had the highest concentration of phytocompounds among the various extracts, while chloroform extract had the lowest concentration. The plant's antioxidant activity was also assessed, and the results showed that the methanolic extract has potential antioxidant activity, as shown by the DPPH's lowest half-inhibitory concentration (IC50) values. To further evaluate the phytocompounds for their in-silico study, molecular docking techniques were employed. These phytocompounds (DL-alphatocopherol, 3,19-O-diacetylanhdroandrographolide, and 14-acetylandrographolide) to the fetal haemoglobin target protein PDBID:4MQJ were also found to have interactions with significantly minimum binding energies of -12.52 kcal/mol, -11.22 kcal/mol, and -11.08 kcal/mol, where DL-alphatocopherol was found to have the highest binding affinity (-12.524 kcal/mol) and interaction with the active site HIS97. The SwissADME was used to assess the drug-likeness and ADMET characteristics of phytocompounds derived from Andrographis panicluata. Based on chemical characteristics, drug-likeness score, and ADMET model, the present study selected and screened the phytocompounds DL-alphatocopherol, 3,19-O-diacetylanhdroandrographolide, and 14-acetylandrographolide, which are expected to have better drug-like features with improved toxicity profiles. Therefore, it makes sense to send the compounds reported in this work to an in vivo analysis of therapeutic applicability due to their significant biological features.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"2 3-4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139871087","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":"Analyzing Phytocompounds, Antioxidants, and In-Silico Molecular Docking of Plant-Derived Potential Andrographis Paniculated Inhibitory Action to Managed Beta Thalassemia","authors":"Soumya Khare, Tanushree Chatterjee, Shailendra Gupta, Ashish Patel","doi":"10.47852/medin42021979","DOIUrl":"https://doi.org/10.47852/medin42021979","url":null,"abstract":"It has long been known that medicinal plants are vital to human health and have been used for therapeutic purposes since the beginning of civilization. The goal of this study is to evaluate the scientific data supporting the use of the herbal remedy Andrographis paniculata in the management of beta thalassemia. Phytocompounds, one of the bases of traditional use, were assessed both qualitatively and quantitatively in five different extracts with varying solvent properties (methanol, chloroform, hexane, ethyl acetate, and aqueous), providing the fundamental explanation for the therapeutic relevance of this plant. The presence of flavonoids, phenols, tannins, saponins, and alkaloids in plant extracts was assessed. It was shown that ethanol extract had the highest concentration of phytocompounds among the various extracts, while chloroform extract had the lowest concentration. The plant's antioxidant activity was also assessed, and the results showed that the methanolic extract has potential antioxidant activity, as shown by the DPPH's lowest half-inhibitory concentration (IC50) values. To further evaluate the phytocompounds for their in-silico study, molecular docking techniques were employed. These phytocompounds (DL-alphatocopherol, 3,19-O-diacetylanhdroandrographolide, and 14-acetylandrographolide) to the fetal haemoglobin target protein PDBID:4MQJ were also found to have interactions with significantly minimum binding energies of -12.52 kcal/mol, -11.22 kcal/mol, and -11.08 kcal/mol, where DL-alphatocopherol was found to have the highest binding affinity (-12.524 kcal/mol) and interaction with the active site HIS97. The SwissADME was used to assess the drug-likeness and ADMET characteristics of phytocompounds derived from Andrographis panicluata. Based on chemical characteristics, drug-likeness score, and ADMET model, the present study selected and screened the phytocompounds DL-alphatocopherol, 3,19-O-diacetylanhdroandrographolide, and 14-acetylandrographolide, which are expected to have better drug-like features with improved toxicity profiles. Therefore, it makes sense to send the compounds reported in this work to an in vivo analysis of therapeutic applicability due to their significant biological features.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139811324","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":"CLOP-hERG: The Contrastive Learning Optimized Pre-trained Model for Representation Learning in Predicting Drug-Induced hERG Channel Blockers","authors":"Shida He, Xiucai Ye, Tetsuya Sakurai","doi":"10.47852/medin42022049","DOIUrl":"https://doi.org/10.47852/medin42022049","url":null,"abstract":"During drug development, ensuring that drug molecules do not block the hERG (human Ether-à-go-go-Related Gene) channel is critical. If this channel is blocked, it can cause many cardiovascular-related diseases. However, traditional experimental detection methods are expensive and time-consuming. In this work, we proposed a novel deep learning framework CLOP-hERG, that combines contrastive learning with the RoBERTa pre-trained model to predict whether drug molecules will block the hERG channel. We employed data augmentation techniques on molecular structures to ensure that our model can capture the multifaceted information of the molecules. Besides, we used a contrastive learning strategy to enable the model to learn meaningful molecular features from large unlabeled datasets. The RoBERTa pre-trained model played a pivotal role in this process, giving our model with a robust representational learning capability. The model, obtained through contrastive learning, was further fine-tuned to achieve high-precision prediction of hERG blockers. Through a series of experiments, we demonstrated the effectiveness of CLOP-hERG. This work provides a novel and effective strategy for predicting hERG blockers and provides some insights for other similar pharmaceutical tasks.","PeriodicalId":511651,"journal":{"name":"Medinformatics","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140493158","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}