Major neurological syndromes with COVID-19: lessons to learn

Objective: Covid-19 is a highly infectious viral disease, and our understanding of the impact of this virus on the nervous system is limited. Therefore, we aimed to do a systematic analysis of the neurological manifestations. Methods: We retrospectively studied the clinical, laboratory, and radiological findings of patients with major neurological syndromes (MNS) in Covid-19 over 6 months. Results: We had 39 patients with major neurological syndromes (MNS). The most common MNS was cerebrovascular disease (CVD) (61.53%), in which ischemic stroke (83.33%), cortical sinus thrombosis (12.50%), and haemorrhagic stroke (4.16%) were seen. Among ischemic stroke patients, 50% had a large vessel occlusion, and 66.66% of patients with CVD had a significant residual disability. Cranial neuropathy (15.38%), GBS (10.26%), encephalitis (7.26%), and myelitis (5.12%) were the other MNS. Among the three encephalitis cases, two had CSF-Covid-19 PCR positivity and had severe manifestations and a poor outcome. Associated comorbidities included hypertension (30.76%), diabetes mellitus (12.82%), chronic kidney diseases (7.69%), and polycythaemia vera (2.56%). Lung involvement was seen in 64.1% of patients. Mortality was 17.94% in MNS with Covid-19. Conclusions: The most common major neurological syndrome associated with Covid-19 is CVD with increased frequency of large vessel occlusion causing significant morbidity and mortality. Simultaneous lung and other systemic involvement in MNS results in a deleterious outcome.

Genetic risk factors associated with SARS-CoV-2 susceptibility in multiethnic populations

Susceptibility to infection from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease COVID-19, may be understood more clearly by looking at genetic variants and their associations to susceptibility phenotype. I conducted a genome-wide association study of SARS-CoV-2 susceptibility in a multiethnic set of three populations (European, African, and South Asian) from a UK BioBank clinical and genomic dataset. I estimated associations between susceptibility phenotype and genotyped or imputed SNPs, adjusting for age at enrollment, sex, and the ten top principal components of ancestry. Three genome-wide significant loci and their top associated SNPs were discovered in the European ancestry population: SLC6A20 in the chr3p21.31 locus (rs73062389-A;P=2.315 x 10-12), ABO on chromosome 9 (rs9411378-A;P=2.436 x 10-11) and LZTFL1 on chromosome 3 (rs73062394;P=4.4 x 10-11);these SNPs were not found to be significant in the African and South Asian populations. A multiethnic GWAS may help elucidate further insights into SARS-CoV-2 susceptibility.

Vehicular Relaxation And Amphan Cyclone: Is It A Double Threat To India Towards Combat Of COVID-19

The COVID-19 spread as a pandemic and more than 185 countries have suffered from it. Various strategies have been devised to combat the virus and prevent it from spreading. In India, lockdown had been initiated since 24th March 2020 and social gatherings had been restricted. But due to the slump in the economy and to boost up trade, certain manufacturing sectors have been opened and vehicular movement was relaxed. PM2.5 and NO2 levels started to rise drastically, which were initially reduced during the lockdown. Moreover, the recent Amphan super cyclonic storm may have increased the risk of COVID spread. A statistical Spearman correlation analysis of the air quality index (AQI) parameters with meteorological variables was carried out to ascertain the significance. Detailed epidemiological studies are warranted to confirm the spike in COVID positive cases may have been related to unforeseen torrential rains as a result of Amphan the cyclone and increased vehicular pollution. © 2021 - Kalpana Corporation

COVID-19 pandemic prediction and forecasting using machine learning classifiers

COVID-19 is a novel virus that spreads in multiple chains from one person to the next. When a person is infected with this virus, they experience respiratory problems as well as rise in body temperature. Heavy breathlessness is the most severe sign of this COVID-19, which can lead to serious illness in some people. However, not everyone who has been infected with this virus will experience the same symptoms. Some people develop cold and cough, while others suffer from severe headaches and fatigue. This virus freezes the entire world as each country is fighting against COVID-19 and endures vaccination doses. Worldwide epidemic has been caused by this unusual virus. Several researchers use a variety of statistical methodologies to create models that examine the present stage of the pandemic and the losses incurred, as well as considered other factors that vary by location. The obtained statistical models depend on diverse aspects, and the studies are purely based on possible preferences, the pattern in which the virus spreads and infects people. Machine Learning classifiers such as Linear regression, Multi-Layer Perception and Vector Auto Regression are applied in this study to predict the various COVID-19 blowouts. The data comes from the COVID-19 data repository at Johns Hopkins University, and it focuses on the dissemination of different effect patterns of Covid-19 cases throughout Asian countries. © 2022, Tech Science Press. All rights reserved.

Prediction of deaths caused by covid-19 using machine learning

Covid-19 prediction models are the most welcome and need of the hour in this current pandemic situation for paving way for decision making by Authorities. India due to its protective characteristics against COVID-19 is supposed to have the least death rate compared to nations having similar number of Covid cases. But the recent increase in the death toll has been a matter of deep concern. The SEIR models which were practiced earlier could not predict death rates accurately due to certain limitations in the procedure. Hence this paper is presented with a model which can efficiently predict the deaths due to covid-19 in every state in India. The aim of this paper is to present a model which predicts the number of fatalities caused by corona with maximum efficacy.