Analysis on Prediction of COVID-19 with Machine Learning Algorithms

During the pandemic, the most significant reason for the deep concern for COVID-19 is that it spreads from individual to individual through contact or by staying close with the diseased individual. COVID-19 has been understood as an overall pandemic, and a couple of assessments is being performed using various numerical models. Machine Learning (ML) is commonly used in every field. Forecasting systems based on ML have shown their importance in interpreting perioperative effects to accelerate decision-making in the potential course of action. ML models have been used for long to define and prioritize adverse threat variables in several technology domains. To manage forecasting challenges, many prediction approaches have been used extensively. The paper shows the ability of ML models to estimate the amount of forthcoming COVID-19 victims that is now considered a serious threat to civilization. COVID-19 describes the comparative study on ML algorithms for predicting COVID-19, depicts the data to be predicted, and analyses the attributes of COVID-19 cases in different places. It gives an underlying benchmark to exhibit the capability of ML models for future examination.

Evidence mapping and review of long-COVID and its underlying pathophysiological mechanism.

PURPOSE: Apart from the global disease burden of acute COVID-19 disease, the health complications arising after recovery have been recognized as a long-COVID or post-COVID-19 syndrome. Evidences of long-COVID symptoms involving various organ systems are rapidly growing in literature. The objective was to perform a rapid review and evidence mapping of systemic complications and symptoms of long-COVID and underlying pathophysiological mechanisms. METHODS: Publications reporting clinical trials, observational cohort studies, case-control studies, case-series, meta-analysis, and systematic reviews, focusing on the squeal of the disease, consequences of COVID-19 treatment/hospitalization, long-COVID, chronic COVID syndrome, and post acute COVID-19 were reviewed in detail for the narrative synthesis of frequency, duration, risk factors, and pathophysiology. RESULTS: The review highlights that pulmonary, neuro-psychological, and cardiovascular complications are major findings in most epidemiological studies. However, dysfunctional gastrointestinal, endocrine, and metabolic health are recent findings for which underlying pathophysiological mechanisms are poorly understood. Analysis of the clinical trial landscape suggests that more than 50% of the industry-sponsored trials are focused on pulmonary symptoms. In contrast to the epidemiological trends and academic trials, cardiovascular complications are not a focus of industry-sponsored trials, suggestive of the gaps in the research efforts. CONCLUSION: The gap in epidemiological trends and academic trials, particularly concerning cardiovascular complications not being a focus of industry-sponsored trials is suggestive of the gaps in research efforts and longer follow-up durations would help identify other long-COVID-related health issues such as reproductive health and fertility.

Aptamer-based diagnostic and therapeutic approaches in animals: Current potential and challenges.

Fast and precise diagnosis of infectious and non-infectious animal diseases and their targeted treatments are of utmost importance for their clinical management. The existing biochemical, serological and molecular methods of disease diagnosis need improvement in their specificity, sensitivity and cost and, are generally not amenable for being used as points-of-care (POC) device. Further, with dramatic changes in environment and farm management practices, one should also arm ourselves and prepare for emerging and re-emerging animal diseases such as cancer, prion diseases, COVID-19, influenza etc. Aptamer - oligonucleotide or short peptides that can specifically bind to target molecules - have increasingly become popular in developing biosensors for sensitive detection of analytes, pathogens (bacteria, virus, fungus, prions), drug residues, toxins and, cancerous cells. They have also been proven successful in the cellular delivery of drugs and targeted therapy of infectious diseases and physiological disorders. However, the in vivo application of aptamer-mediated biosensing and therapy in animals has been limited. This paper reviews the existing reports on the application of aptamer-based biosensors and targeted therapy in animals. It also dissects the various modifications to aptamers that were found to be successful in in vivo application of the aptamers in diagnostics and therapeutics. Finally, it also highlights major challenges and future directions in the application of aptamers in the field of veterinary medicine.