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BackgroundSince the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection.MethodsBiochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman’s rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score.ResultsThe parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count.ConclusionThe current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

Background: Dementia is a global challenge with 10 million individuals being diagnosed every year. Currently, there are no established disease-modifying treatments for dementia. Impaired nutrient sensing has been implicated in the pathogenesis of dementia. Compounds that inhibit the glycogen synthase kinase-3 (GSK3) pathway have been investigated as a possible treatment to attenuate the progression of the disease, particularly the suppression of the hyper-phosphorylation process of the tau protein.Aims: Systematically summarizing compounds which have been tested to inhibit the GSK3 pathway to treat cognitive impairment and dementia.Methods: PubMed, Embase and Web of Science databases were searched from inception until 28 July 2021 for articles published in English. Interventional animal studies inhibiting the GSK3 pathway in Alzheimer’s disease (AD), Parkinson’s dementia, Lewy body dementia, vascular dementia, mild cognitive impairment (MCI) and normal cognitive ageing investigating the change in cognition as the outcome were included. The Systematic Review Centre for Laboratory animal Experimentation’s risk of bias tool for animal studies was applied.Results: Out of 4,154 articles, 29 described compounds inhibiting the GSK3 pathway. All studies were based on animal models of MCI, AD or normal cognitive ageing. Thirteen out of 21 natural compounds and five out of nine synthetic compounds tested in MCI and dementia animal models showed an overall positive effect on cognition. No articles reported human studies. The risk of bias was largely unclear.Conclusion: Novel therapeutics involved in the modulation of the GSK3 nutrient sensing pathway have the potential to improve cognitive function. Overall, there is a clear lack of translation from animal models to humans.

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