COVID-19: An Overview of SARS-CoV-2 Variants-The Current Vaccines and Drug Development.

The world is presently in crisis facing an outbreak of a health-threatening microorganism known as COVID-19, responsible for causing uncommon viral pneumonia in humans. The virus was first reported in Wuhan, China, in early December 2019, and it quickly became a global concern due to the pandemic. Challenges in this regard have been compounded by the emergence of several variants such as B.1.1.7, B.1.351, P1, and B.1.617, which show an increase in transmission power and resistance to therapies and vaccines. Ongoing researches are focused on developing and manufacturing standard treatment strategies and effective vaccines to control the pandemic. Despite developing several vaccines such as Pfizer/BioNTech and Moderna approved by the U.S. Food and Drug Administration (FDA) and other vaccines in phase 4 clinical trials, preventive measures are mandatory to control the COVID-19 pandemic. In this review, based on the latest findings, we will discuss different types of drugs as therapeutic options and confirmed or developing vaccine candidates against SARS-CoV-2. We also discuss in detail the challenges posed by the variants and their effect on therapeutic and preventive interventions.

Prediction of acute organophosphate poisoning severity using machine learning techniques.

Poisoning with organophosphate compounds is a significant public health risk, especially in developing countries. Considering the importance of early and accurate prediction of organophosphate poisoning prognosis, the aim of this study was to develop a machine learning-based prediction model to predict the severity of organophosphate poisoning. The data of patients with organophosphate poisoning were retrospectively extracted and split into training and test sets in a ratio of 70:30. The feature selection was done by least absolute shrinkage and selection operator method. Selected features were fed into five machine learning techniques, including Histogram Boosting Gradient, eXtreme Gradient Boosting, K-Nearest Neighborhood, Support Vector Machine (SVM) (kernel = linear), and Random Forest. The Scikit-learn library in Python programming language was used to implement the models. Finally, the performance of developed models was measured using ten-fold cross-validation methods and some evaluation criteria with 95 % confidence intervals. A total of 1237 patients were used to train and test the machine learning models. According to the criteria determining severe organophosphate poisoning, 732 patients were assigned to group 1 (patients with mild to moderate poisoning) and 505 patients were assigned to group 2 (patients with severe poisoning). With an AUC value of 0.907 (95 % CI 0.89-0.92), the model developed using XGBoost outperformed other models. Feature importance evaluation found that venous blood gas-pH, white blood cells, and plasma cholinesterase activity were the top three variables that contribute the most to the prediction performance of the prognosis in patients with organophosphate poisoning. XGBoost model yield an accuracy of 90.1 % (95 % CI 0.891-0.918), specificity of 91.4 % (95 % CI 0.90-0.92), a sensitivity of 89.5 % (95 % CI 0.87-0.91), F-measure of 91.2 % (95 % CI 0.90-0.921), and Kappa statistic of 91.2 % (95 % CI 0.90-0.92). The machine learning-based prediction models can accurately predict the severity of organophosphate poisoning. Based on feature selection techniques, the most important predictors of organophosphate poisoning were VBG-pH, white blood cell count, plasma cholinesterase activity, VBG-BE, and age. The best algorithm with the highest predictive performance was the XGBoost classifier.

The efficacy of mouthwashes in reducing SARS-CoV-2 viral loads in human saliva: A systematic review.

This systematic review aimed to evaluate existing randomized controlled trials (RCT) and cohort studies on the efficacy of mouthwashes in reducing SARS-CoV-2 viral loads in human saliva. Searches with pertinent search terms were conducted in PubMed, MEDLINE, Scopus, and Web of Science databases for relevant records published up to Oct 15, 2022. Google Scholar and ProQuest were searched for grey literature. Manual searches were conducted as well for any pertinent articles. The protocol was prospectively registered at PROSPERO (CRD42022324894). Eligible studies were critically appraised for risk of bias and quality of evidence to assess the efficacy of mouthwash in reducing the SARS-CoV-2 viral load in human saliva. Eleven studies were included. The effect on viral load using various types of mouthwash was observed, including chlorhexidine (CHX), povidone-iodine (PI), cetylpyridinium chloride (CPC), hydrogen peroxide (HP), ß-cyclodextrin-citrox mouthwash (CDCM), and Hypochlorous acid (HCIO). Eight articles discussed CHX use. Five were found to be significant and three did not show any significant decrease in viral loads. Eight studies reviewed the use of PI, with five articles identifying a significant decrease in viral load, and three not showing a significant decrease in viral load. HP was reviewed in four studies, two studies identified significant viral load reductions, and two did not. CPC was reviewed in four studies, two of which identified significant viral load reductions, and two did not. CDCM was reviewed in one article which found a significant decrease in viral load reduction. Also, HCIO which was evaluated in one study indicated no significant difference in CT value. The current systematic review indicates that based on these eleven studies, mouthwashes are effective at reducing the SARS-CoV-2 viral load in human saliva. However, further studies should be performed on larger populations with different mouthwashes. The overall quality of evidence was high.