CoronavirusesPub Date : 2024-03-25DOI: 10.2174/0126667975296097240321060634
K.D.S. Balasooriya, R. Rupasingha, B. Kumara
{"title":"Analysis of Suicide-related Tweets During the COVID-19 Pandemic","authors":"K.D.S. Balasooriya, R. Rupasingha, B. Kumara","doi":"10.2174/0126667975296097240321060634","DOIUrl":"https://doi.org/10.2174/0126667975296097240321060634","url":null,"abstract":"\u0000\u0000The COVID-19 virus started in 2019 and badly affected the different sectors\u0000of many countries around the world. Based on this, financial difficulties, loss of loved ones, sudden\u0000anger, relationships, family disputes, and psychological distress increased, and individuals were\u0000stalled from carrying out their lifestyle in a normal way, and some individuals were even motivated\u0000to commit suicide.\u0000\u0000\u0000\u0000It is important to reduce the number of suicides and identify the reasons for this situation.\u0000Through this research, the focus is on identifying the main topics discussed relevant to suicides during\u0000the COVID-19 pandemic.\u0000\u0000\u0000\u0000Individuals use Twitter, a social media platform, to share their ideas freely and publically.\u0000We collected 9750 primary data through Twitter API (Application Programming Interface). After\u0000preprocessing and feature extraction by TF-IDF (Term Frequency-Inverse Document Frequency), we\u0000applied the LDA (Latent Dirichlet Allocation) and Probabilistic Latent Semantic Analysis (PLSA)\u0000topic modeling algorithms to identify topics.\u0000\u0000\u0000\u0000Based on the LDA results, we extracted ten different topics under the three themes, such as\u0000the impact of COVID-19, human feelings, getting support, and having awareness. Intertopic Distance\u0000Map, Most Salient Terms, and Word Clouds Visualization are used to check the results. The coherence\u0000score and perplexing value are used to measure how interpretable the extracted topics are to\u0000humans. PLSA also extracted 25 topics with their probabilities, and Kullback–Leibler (KL) divergence\u0000was used to check the results.\u0000\u0000\u0000\u0000We were able to gain insight into human emotions and the main motivations behind\u0000suicide attempts using the topics we extracted. Expert feedback proved that LDA results were better\u0000than PLSA. Based on that, we found the main impact of COVID-19 on human lives, how human\u0000feelings were changed positively and negatively during that period, what supporting and awareness\u0000methods people used, and what they preferred. The required measures can then be taken by those\u0000responsible authorities and individuals to prevent, reduce, and get ready for this kind of suicidal incident\u0000in the future.\u0000","PeriodicalId":10815,"journal":{"name":"Coronaviruses","volume":" 67","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140384666","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":"Identification of Autoantigen Markers for SARS CoV-2 Infection with\u0000Machine Learning-based Feature Selection: An Insight into COVID\u0000Symptoms","authors":"Aruna Rajalingam, Chaitra Mallasandra Krishnappa, Shanker G, Anjali Ganjiwale","doi":"10.2174/0126667975296293240320041641","DOIUrl":"https://doi.org/10.2174/0126667975296293240320041641","url":null,"abstract":"\u0000\u0000Severe acute respiratory syndrome coronavirus 2 (SARS\u0000CoV-2) infection has been shown to trigger autoimmunity, and the phenomenon leads to several\u0000chronic human diseases such as Type-1 diabetes, Crohn’s disease, vasculitis, Guillian-Barrė syndrome,\u0000etc. The mechanism underlying SARS CoV-2-induced autoimmune response is unknown and\u0000is an active area of interest for the researchers.\u0000\u0000\u0000\u0000The primary objective of this study is to identify the autoantigen markers for the classification\u0000of SARS CoV-2 (COVID-19 positive and negative samples) that trigger an immune response\u0000leading to autoimmunity using a machine learning approach that provides information to obtain a\u0000more accurate diagnosis for COVID-induced diseases.\u0000\u0000\u0000\u0000Our study reports the transcriptomic profile of the COVID patient's whole\u0000blood samples collected from 0 to 35th day of acute infection as described in the GSE215865 dataset.\u0000The binary classification algorithm from the sci-kit learn python library, namely logistic regression\u0000and random forest with 10-fold cross-validation, was applied to the processed data, followed by a\u0000selection of the 20 best gene features with recursive feature elimination from a set of 10,719 gene\u0000features to obtain the classification accuracy of 87%.\u0000\u0000\u0000\u0000The fidgetin, microtubule severing factor (FIGN), SH3 and cysteine-rich domain (STAC),\u0000Cadherin-6 (CDH6), docking protein 6 (DOK6), nuclear RNA export factor 3 (NXF3) and maternally\u0000expressed 3 (MEG3) are the autoantigens markers identified for classification of COVID-positive\u0000and negative samples.\u0000\u0000\u0000\u0000The identified autoantigen markers from transcriptomic datasets using machine learning\u0000techniques provide a deeper understanding of COVID-induced diseases and may play an important\u0000role as potential diagnostic and drug targets for COVID-19.\u0000","PeriodicalId":10815,"journal":{"name":"Coronaviruses","volume":" March","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140383238","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}