Deciphering the Potential of Pre and Pro-Vitamin D of Mushrooms against Mpro and PLpro Proteases of COVID-19: An In Silico Approach.

Vitamin D's role in combating the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the virus causing COVID-19, has been established in unveiling viable inhibitors of COVID-19. The current study investigated the role of pre and pro-vitamin D bioactives from edible mushrooms against Mpro and PLpro proteases of SARS-CoV-2 by computational experiments. The bioactives of mushrooms, specifically ergosterol (provitamin D2), 7-dehydrocholesterol (provitamin-D3), 22,23-dihydroergocalciferol (provitamin-D4), cholecalciferol (vitamin-D3), and ergocalciferol (vitamin D2) were screened against Mpro and PLpro. Molecular docking analyses of the generated bioactive protease complexes unravelled the differential docking energies, which ranged from -7.5 kcal/mol to -4.5 kcal/mol. Ergosterol exhibited the lowest binding energy (-7.5 kcal/mol) against Mpro and PLpro (-5.9 kcal/mol). The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) and MD simulation analyses indicated that the generated complexes were stable, thus affirming the putative binding of the bioactives to viral proteases. Considering the pivotal role of vitamin D bioactives, their direct interactions against SARS-CoV-2 proteases highlight the promising role of bioactives present in mushrooms as potent nutraceuticals against COVID-19.

The impact of tocilizumab on respiratory support states transition and clinical outcomes in COVID-19 patients. A Markov model multi-state study.

BACKGROUND: The benefit of tocilizumab on mortality and time to recovery in people with severe COVID pneumonia may depend on appropriate timing. The objective was to estimate the impact of tocilizumab administration on switching respiratory support states, mortality and time to recovery. METHODS: In an observational study, a continuous-time Markov multi-state model was used to describe the sequence of respiratory support states including: no respiratory support (NRS), oxygen therapy (OT), non-invasive ventilation (NIV) or invasive mechanical ventilation (IMV), OT in recovery, NRS in recovery. RESULTS: Two hundred seventy-one consecutive adult patients were included in the analyses contributing to 695 transitions across states. The prevalence of patients in each respiratory support state was estimated with stack probability plots, comparing people treated with and without tocilizumab since the beginning of the OT state. A positive effect of tocilizumab on the probability of moving from the invasive and non-invasive mechanical NIV/IMV state to the OT in recovery state (HR = 2.6, 95% CI = 1.2-5.2) was observed. Furthermore, a reduced risk of death was observed in patients in NIV/IMV (HR = 0.3, 95% CI = 0.1-0.7) or in OT (HR = 0.1, 95% CI = 0.0-0.8) treated with tocilizumab. CONCLUSION: To conclude, we were able to show the positive impact of tocilizumab used in different disease stages depicted by respiratory support states. The use of the multi-state Markov model allowed to harmonize the heterogeneous mortality and recovery endpoints and summarize results with stack probability plots. This approach could inform randomized clinical trials regarding tocilizumab, support disease management and hospital decision making.

Darunavir/Cobicistat Is Associated with Negative Outcomes in HIV-Negative Patients with Severe COVID-19 Pneumonia.

The aim of this study was to evaluate both positive outcomes, including reduction of respiratory support aid and duration of hospital stay, and negative ones, including mortality and a composite of invasive mechanical ventilation or death, in patients with coronavirus disease 2019 (COVID-19) pneumonia treated with or without oral darunavir/cobicistat (DRV/c, 800/150 mg/day) used in different treatment durations. The secondary objective was to evaluate the percentage of patients treated with DRV/c who were exposed to potentially severe drug-drug interactions (DDIs) and died during hospitalization. This observational retrospective study was conducted in consecutive patients with COVID-19 pneumonia admitted to a tertiary care hospital in Modena, Italy. Kaplan-Meier survival curves and Cox proportional hazards regression were used to compare patients receiving standard of care with or without DRV/c. Adjustment for key confounders was applied. Two hundred seventy-three patients (115 on DRV/c) were included, 75.8% males, mean age was 64.6 (±13.2) years. Clinical improvement was similar between the groups, depicted by respiratory aid switch (p > .05). The same was observed for duration of hospital stay [13.2 (±8.9) for DRV/c vs. 13.4 (±7.2) days for no-DRV/c, p = .9]. Patients on DRV/c had higher rates of mortality (25.2% vs. 10.1%, p < .0001. The rate of composite outcome of mechanical ventilation and death was higher in the DRV/c group (37.4% vs. 25.3%, p = .03). Multiple serious DDI associated with DRV/c were observed in the 19 patients who died. DRV/c should not be recommended as a treatment option for COVID-19 pneumonia outside clinical trials.

Hypokalemia in Patients with COVID-19.

BACKGROUND: Patients with COVID-19 experience multiple clinical conditions that may cause electrolyte imbalances. Hypokalemia is a concerning electrolyte disorder closely associated with severe complications. This study aimed to estimate prevalence, risk factors and outcome of hypokalemia in a cohort of patients with confirmed COVID-19. METHODS: A retrospective analysis was conducted on 290 non-ICU admitted patients with COVID-19 at the tertiary teaching hospital of Modena, Italy, from February 16 to April 14, 2020. RESULTS: Hypokalemia was detected in 119 out of 290 patients (41%) during hospitalization. Mean serum potassium was 3.1 ± 0.1 meq/L. The majority of patients (90.7%) patients experienced only a mild decrease in serum potassium level (3-3.4 mEq/L). Hypokalemia was associated with hypocalcemia, which was detected in 50% of subjects. Urine potassium-to-creatinine ratio, measured in a small number of patients (n = 45; 36.1%), revealed an increase of urinary potassium excretion in most cases (95.5%). Risk factors for hypokalemia were female sex (odds ratio (OR) 2.44; 95% CI 1.36-4.37; P 0.003) and diuretic therapy (OR 1.94, 95% CI 1.08-3.48; P 0.027). Hypokalemia, adjusted for sex, age and SOFA score, was not associated with ICU transfer (OR 0.52; 95% CI 0.228-1.212; P = 0.131), in-hospital mortality (OR, 0.47; 95% CI 0.170-1.324; P = 0.154) and composite outcome of ICU transfer or in-hospital mortality (OR 0.48; 95% CI 0.222-1.047; P = 0.065) in our cohort of patients. CONCLUSIONS: Hypokalemia was a frequent disorder in subjects with COVID-19. Female sex and diuretic therapy were identified as risk factors for low serum potassium levels. Hypokalemia was unrelated to ICU transfer and death in this cohort of patients.

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency.

AIMS: The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia. METHODS: This was an observational prospective study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients' medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio <150 mmHg in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Shapley Additive exPlanations values were used to quantify the positive or negative impact of each variable included in each model on the predicted outcome. RESULTS: A total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth "boosted mixed model" included 20 variables was selected from the model 3, achieved the best predictive performance (AUC = 0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example. CONCLUSION: This study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels.