Probing the Neuro-psychological Changes Observed with the Administration of COVID-19 Drugs.

The COVID-19 virus caused countless significant alterations in the human race, the most challenging of which was respiratory and neurological disorders. Several studies were conducted to find a robust therapy for the virus, which led to a slew of additional health issues. This study aims to understand the changes in the neurological system brought about by COVID-19 drugs and highlights the drug-drug interaction between COVID-19 drugs and psychiatric drugs. Alongside this, the study focuses on the neuropsychological changes in three critical mental disorders, such as schizophrenia, Alzheimer's disease, and Parkinson's disease. The comprehensive and narrative review being performed in this paper, has brought together the relevant work done on the association of COVID-19 drugs and changes in the neurological system. For this study, a systematic search was performed on several databases such as PubMed, Scopus, and Web of Science. This study also consolidates shreds of evidence about the challenges confronted by patients having disorders like Schizophrenia, Alzheimer's disease, and Parkinson's disease. This review is based on the studies done on COVID-19 drugs from mid-2020 to date. We have identified some scopes of crucial future opportunities which could add more depth to the current knowledge on the association of COVID- 19 drugs and the changes in the neurological system. This study may present scope for future work to investigate the pathophysiological changes of these disorders due to COVID-19.

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

In recent years, COVID-19 has been regarded as the most dangerous pandemic for several countries. On various social media platforms, such as Twitter, Facebook, and Instagram, a variety of rumours, hypes, and news are published. This might have a detrimental impact on people’s life. As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. Support vector machine (SVM), random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) were used for classification, while AdaBoost and convolutional neural network (CNN) were utilized for future effects. The tweets dataset from Kaggle was used to train the SVM, RF, KNN, and DT models, which were then assessed on multiple evaluation criteria such as accuracy, precision, recall, and F1-score, using a 70 : 30 ratio. The CNN and AdaBoost, on the other hand, have been taught to detect the mean square error, root mean square error, and mean absolute error. With 0.74 and 0.73 percent score out of 1, respectively, RF and SVM exhibit the best accuracy in impact when classifying the outcomes on the obtained dataset. In terms of a regression problem, CNN beat the ADA Regressor across the board.