Efficacy of polyherbal formulations for prevention of COVID-19 infection in high-risk subjects: A randomized open-label controlled clinical trial.

COVID-19 is arguably the biggest health crisis the world has faced in the 21st century. Therefore, two of the polyherbal formulations, Infuza and Kulzam were assessed for the prevention of COVID-19 infection as a repurposed medication. Four hundred seven high-risk subjects were recruited in the present open-label randomized controlled clinical trial for eligibility. After assessment for eligibility, remaining 251 subjects were randomized to the test and control groups. Further, 52 high-risk subjects in Infuza, 51 in Kulzam, 51 in Infuza & Kulzam and 53 in control group completed the 14 days of intervention/assessment. The phenotyping of lymphocytes at baseline (0 day) and after 14 days of treatment was carried out by flow cytometry assays. A total of 15.09% high-risk subjects in control group turned positive as compared to only 7.69% in Infuza, 3.92% in Kulzam and 1.96% in Infuza & Kulzam groups. The rate of conversion to COVID-19 infection in Infuza & Kulzam group was minimal and statistically significant as compared to control group (p0.017). No significant changes in phenotype of lymphocytes (T, B, NK cells), absolute lymphocyte count and cytokine levels were found in study groups. However, there was a decreasing trend of hs-CRP level in high-risk subjects after intervention of polyherbal formulations for 14 days. The combination of Infuza and Kulzam may synergistically prevent COVID-19 infection in high-risk subjects of COVID-19.

Changes in psychosocial functioning among urban, school-age children during the COVID-19 pandemic.

BACKGROUND: There is concern about the effect of the COVID-19 pandemic on psychosocial functioning among school-age children, who have faced unusual stressors during this time. Our goal was to assess mental health symptoms and social risks during COVID-19, compared to before the pandemic, for urban, racial and ethnic minority school-age children, and investigate the relationship between mental health and social risks. METHODS: We conducted a cohort study from September 2019 until January 2021 of children age 5-11 years old recruited from an urban safety net hospital-based pediatric primary care practice. We measured emotional and behavioral symptoms (including attention, internalizing, and externalizing symptoms) before and during the pandemic with the Pediatric Symptom Checklist (PSC-17). We measured social risks (including food and housing insecurity) before and during the pandemic with the THRIVE screener. We measured additional mid-pandemic COVID-related stressors with items on school participation, screens/media use, illness exposure, and caregiver mental health. We compared pre- and mid-pandemic PSC-17 symptom scores across 4 domains (total, attention, internalizing, and externalizing) and used path analysis to examine the relationship between mental health and social risks pre- and mid-pandemic. RESULTS: Caregivers of 168 children (54% non-Hispanic Black, 29% Hispanic, and 22% non-English speaking) completed the study. Children had significantly higher levels of emotional and behavioral symptoms midpandemic- vs. pre-pandemic in all domains. Significantly more children had a positive PSC-17 total score (18% vs. 8%, p < 0.01) and internalizing (depression and anxiety) score (18% vs. 5%, p < 0.001) during the pandemic vs. before, indicating clinical concerns in these areas. Caregivers reported significantly more social risks during vs. before the pandemic (p < 0.001). Mental health symptoms significantly correlated with number of social risks before the pandemic, but not during the pandemic. Less school assignment completion, increased screen time, and caregiver depression were all significantly associated with worse mid-pandemic mental health in children. CONCLUSION: The COVID-19 pandemic has led to a dramatic increase in depression/anxiety problems and social risks among urban, racial and ethnic minority school-age children compared to before the pandemic. More research is needed to understand if these changes will persist.

Stochastic covid-19 model with fractional global and classical piecewise derivative.

Several methodologies have been advocated in the last decades with the aim to better understand behaviours displayed by some real-world problems. Among which, stochastics modelling and fractional modelling, fuzzy and others. These methodologies have been suggested to threat specific problems; however, It have been noticed that some problems exhibit different patterns as time passes by. Randomness and nonlocality can be combined to depict complex real-world behaviours. It has been observed that, covid-19 virus spread does not follow a single pattern; sometimes we obtained stochastic behaviours, another nonlocal behaviour and others. In this paper, we shall consider a covid-19 model with fractional stochastic behaviours. More precisely a covid-19 model, where the model considers nonlocalities and randomness is suggested. Then a comprehensive analysis of the model is conducted. Numerical simulations and illustrations are done to show the efficiency of the model.

CRISPR-Cas enzymes: The toolkit revolutionizing diagnostics.

The programmable nature of sequence-specific targeting by CRISPR-Cas nucleases has revolutionized a wide range of genomic applications and is now emerging as a method for nucleic acid detection. We explore how the diversity of CRISPR systems and their fundamental mechanisms have given rise to a wave of new methods for target recognition and readout. These cross-disciplinary advances found at the intersection of CRISPR biology and engineering have led to the ability to rapidly generate solutions for emerging global challenges like the COVID-19 pandemic. We further discuss the advances and potential for CRISPR-based detection to have an impact across a continuum of diagnostic applications.

Numerical analysis of COVID-19 model with constant fractional order and variable fractal dimension.

This work has considered a mathematical model describing the spread of COVID-19 in a given population. The model comprised 5 systems of equations that take into account different classes describing the impact of COVID-19 in a given population. The time differential operator was replaced with three different types of nonlocal operators. These operators are defined as the convolution of variable order fractal differential operators with different kernels including power law, exponential decay law, and Mittag-Leffler functions. We presented the well-poseness of the models for different differential operators that were presented in detail. A novel numerical scheme was used to solve numerically the system and numerical simulations were provided.

Mathematical model of Ebola and Covid-19 with fractional differential operators: Non-Markovian process and class for virus pathogen in the environment.

Differential operators based on convolution definitions have been recognized as powerful mathematics tools to help model real world problems due to the properties associated to their different kernels. In particular the power law kernel helps include into mathematical formulation the effect of long range, while the exponential decay helps with fading memory, also with Poisson distribution properties that lead to a transitive behavior from Gaussian to non-Gaussian phases respectively, however, with steady state in time and finally the generalized Mittag-Leffler helps with many features including the queen properties, transitive behaviors, random walk for earlier time and power law for later time. Very recently both Ebola and Covid-19 have been a great worry around the globe, thus scholars have focused their energies in modeling the behavior of such fatal diseases. In this paper, we used new trend of fractional differential and integral operators to model the spread of Ebola and Covid-19.