BackgroundVitamin D has an immunomodulatory role but the effect of therapeutic vitamin D supplementation in SARS-CoV-2 infection is not known.AimEffect of high dose, oral cholecalciferol supplementation on SARS-CoV-2 viral clearance.DesignRandomised, placebo-controlled.ParticipantsAsymptomatic or mildly symptomatic SARS-CoV-2 RNA positive vitamin D deficient (25(OH)D<20 ng/ml) individuals.InterventionParticipants were randomised to receive daily 60 000 IU of cholecalciferol (oral nano-liquid droplets) for 7 days with therapeutic target 25(OH)D>50 ng/ml (intervention group) or placebo (control group). Patients requiring invasive ventilation or with significant comorbidities were excluded. 25(OH)D levels were assessed at day 7, and cholecalciferol supplementation was continued for those with 25(OH)D <50 ng/ml in the intervention arm. SARS-CoV-2 RNA and inflammatory markers fibrinogen, D-dimer, procalcitonin and (CRP), ferritin were measured periodically.Outcome measureProportion of patients with SARS-CoV-2 RNA negative before day-21 and change in inflammatory markers.ResultsForty SARS-CoV-2 RNA positive individuals were randomised to intervention (n=16) or control (n=24) group. Baseline serum 25(OH)D was 8.6 (7.1 to 13.1) and 9.54 (8.1 to 12.5) ng/ml (p=0.730), in the intervention and control group, respectively. 10 out of 16 patients could achieve 25(OH)D>50 ng/ml by day-7 and another two by day-14 [day-14 25(OH)D levels 51.7 (48.9 to 59.5) ng/ml and 15.2 (12.7 to 19.5) ng/ml (p<0.001) in intervention and control group, respectively]. 10 (62.5%) participants in the intervention group and 5 (20.8%) participants in the control arm (p<0.018) became SARS-CoV-2 RNA negative. Fibrinogen levels significantly decreased with cholecalciferol supplementation (intergroup difference 0.70 ng/ml; P=0.007) unlike other inflammatory biomarkers.ConclusionGreater proportion of vitamin D-deficient individuals with SARS-CoV-2 infection turned SARS-CoV-2 RNA negative with a significant decrease in fibrinogen on high-dose cholecalciferol supplementation.Trial register numberNCT04459247.

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Emotions are the accelerators of human intellect and innovation and creativity, so the ability to recognize emotions is in high demand. Real-time hardware has hurdles of Noise and hardware factors as compared to simulations. An electrocardiogram (ECG) sensor (AD8232), a temperature sensor (LM35), and a signal processing circuit is hardware of the proposed real-time emotion identification. The RR intervals are calculated from the ECG data. Emotions prediction using machine learning makes use of RR intervals and body temperature as features. One of the four emotions (namely 1. Happy 2. Stressed 3. Neutral 4. Sad.) is visualized at the serial port of the processor by using WESAD benchmark dataset and the HRV, serial, and pickle libraries.This article's innovation factors are (1) Use of ECG for emotion detection rather than disease detection with Emotion induction method, RR interval capturing and design of RR interval GUI for real time capture of temperature and ECG (2) Display of current emotion on Arduino serial port. (3) Measurement of Class performance using F1 score, macro average, and weighted average instead of general term accuracy. (4) Use of the probability based Navies Bayes as compared to traditional KNN, SVM, Random Forest nethods (5) Class wise performance for example Navies Bayes' specificity or accuracy is lower than SVM's (0.96), but its recall or sensitivity is higher (0.97) vs. (0.94) for stress.In this article, we presented performance parameters in terms of interactive computations, tabular form and graphical display.

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