Elderberry Extracts: Characterization of the Polyphenolic Chemical Composition, Quality Consistency, Safety, Adulteration, and Attenuation of Oxidative Stress- and Inflammation-Induced Health Disorders.

Elderberry is highly reputed for its health-improving effects. Multiple pieces of evidence indicate that the consumption of berries is linked to enhancing human health and preventing or delaying the onset of chronic medical conditions. Compared with other fruit, elderberry is a very rich source of anthocyanins (approximately 80% of the polyphenol content). These polyphenols are the principals that essentially contribute to the high antioxidant and anti-inflammatory capacities and the health benefits of elderberry fruit extract. These health effects include attenuation of cardiovascular, neurodegenerative, and inflammatory disorders, as well as anti-diabetic, anticancer, antiviral, and immuno-stimulatory effects. Sales of elderberry supplements skyrocketed to $320 million over the year 2020, according to an American Botanical Council (ABC) report, which is attributable to the purported immune-enhancing effects of elderberry. In the current review, the chemical composition of the polyphenolic content of the European elderberry (Sambucus nigra) and the American elderberry (Sambucus canadensis), as well as the analytical techniques employed to analyze, characterize, and ascertain the chemical consistency will be addressed. Further, the factors that influence the consistency of the polyphenolic chemical composition, and hence, the consistency of the health benefits of elderberry extracts will be presented. Additionally, adulteration and safety as factors contributing to consistency will be covered. The role of elderberry in enhancing human health alone with the pharmacological basis, the cellular pathways, and the molecular mechanisms underlying the observed health benefits of elderberry fruit extracts will be also reviewed.

Deep learning application detecting SARS-CoV-2 key enzymes inhibitors.

The fast spread of the COVID-19 over the world pressured scientists to find its cures. Especially, with the disastrous results, it engendered from human life losses to long-term impacts on infected people's health and the huge financial losses. In addition to the massive efforts made by researchers and medicals on finding safe, smart, fast, and efficient methods to accurately make an early diagnosis of the COVID-19. Some researchers focused on finding drugs to treat the disease and its symptoms, others worked on creating effective vaccines, while several concentrated on finding inhibitors for the key enzymes of the virus, to reduce its spreading and reproduction inside the human body. These enzymes' inhibitors are usually found in aliments, plants, fungi, or even in some drugs. Since these inhibitors slow and halt the replication of the virus in the human body, they can help fight it at an early stage saving the patient from death risk. Moreover, if the human body's immune system gets rid of the virus at the early stage it can be spared from the disastrous sequels it may leave inside the patient's body. Our research aims to find aliments and plants that are rich in these inhibitors. In this paper, we developed a deep learning application that is trained with various aliments, plants, and drugs to detect if a component contains SARS-CoV-2 key inhibitor(s) intending to help them find more sources containing these inhibitors. The application is trained to identify various sources rich in thirteen coronavirus-2 key inhibitors. The sources are currently just aliments, plants, and seeds and the identification is done by their names.

The Impact of COVID-19 on Distress Tolerance in Pakistani Men and Women

The novel coronavirus (COVID-19) is an infectious disease that spread across the world, bringing with it serious mental health problems for men and women. Women in Pakistan are infected with COVID-19 at a much lower rate than men, yet report worse mental health. To explain this paradox, we surveyed 190 participants (46% male) shortly following the country lockdown, focusing on perceptions of the COVID-19 impact and positive adjustment. Measures used in this study included the Warwick-Edinburgh Mental Well-being Scale and Distress Tolerance Scale. Factor analysis revealed five distinct areas related to COVID-19, which did not differ by sex. However, men reported higher levels of both distress tolerance and well-being than women. High endorsement of actions to protect against COVID-19 was related to lower distress tolerance scores, but in different ways for men and women. Men, but not women, who endorsed more protective measures to stop the pandemic reported higher DTS absorption scores, and therefore being more consumed by distress;women who endorsed more protective measures to stop the pandemic reported less acceptance of distress than men, as reflected in DTS appraisal scores. An in-depth analysis of women’s beliefs and behaviors related to COVID-19 is warranted to understand why Pakistani women who are infected with COVID-19 at lower rates than men show more mental health symptoms.

A Generic Deep Learning Based Cough Analysis System From Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels.

In an attempt to reduce the infection rate of the COrona VIrus Disease-19 (Covid-19) countries around the world have echoed the exigency for an economical, accessible, point-of-need diagnostic test to identify Covid-19 carriers so that they (individuals who test positive) can be advised to self isolate rather than the entire community. Availability of a quick turn-around time diagnostic test would essentially mean that life, in general, can return to normality-at-large. In this regards, studies concurrent in time with ours have investigated different respiratory sounds, including cough, to recognise potential Covid-19 carriers. However, these studies lack clinical control and rely on Internet users confirming their test results in a web questionnaire (crowdsourcing) thus rendering their analysis inadequate. We seek to evaluate the detection performance of a primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positive and 6,041 Covid-19 negative) under quantitative RT-PCR (qRT-PCR) from certified laboratories. All collected samples were clinically labelled, i.e., Covid-19 positive or negative, according to the results in addition to the disease severity based on the qRT-PCR threshold cycle (Ct) and lymphocytes count from the patients. Our proposed generic method is an algorithm based on Empirical Mode Decomposition (EMD) for cough sound detection with subsequent classification based on a tensor of audio sonographs and deep artificial neural network classifier with convolutional layers called 'DeepCough'. Two different versions of DeepCough based on the number of tensor dimensions, i.e., DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform prototype web-app 'CoughDetect'. Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of [Formula: see text] 98 . 80 % ± 0 . 83 % , sensitivity of [Formula: see text] 96 . 43 % ± 1 . 85 % , and specificity of [Formula: see text] 96 . 20 % ± 1 . 74 % and average AUC of [Formula: see text] 81 . 08 % ± 5 . 05 % for the recognition of three severity levels. Our proposed web tool as a point-of-need primary diagnostic test for Covid-19 facilitates the rapid detection of the infection. We believe it has the potential to significantly hamper the Covid-19 pandemic across the world.

Psychosocial Barriers of Public Transport Use and Social Exclusion among Older Adults: Empirical Evidence from Lahore, Pakistan.

Transport planning and public health have been intertwined historically. The health impact of public transport services, such as social exclusion, is a widely discussed research topic. Social exclusion is a paramount concern for older adults' health in the wake of emerging global challenges. However, there remains a significant research gap on how psychosocial barriers faced by older adults in using public transport services influence the social exclusion behavior. The present research provides empirical evidence and shows the impact of certain psychosocial barriers of public transportation on older adults' social exclusion. A total of 243 Pakistani older adults (aged 60-89 years old) voluntarily participated in this cross-sectional study. The participants provided self-reports on their psychosocial barriers (including perceived norms, attitude, personal ability, habits, neighborhood social constraints, and intention) and the corresponding social exclusion. Partial Least Square Structural Equation Modeling (PLS-SEM) was utilized for the data analysis. The structural path model supported the significant associations between psychosocial barriers and social exclusion. Except for perceived descriptive norms, all other psychosocial barriers predicted older adults' social exclusion. The research portrays the significance of the psychosocial factors to examine social exclusion and offers practical implications for urban and transport planners. The concerned policymakers can use the research findings to develop age-sensitive, socially sustainable, and healthy cities.

Development of a preoperative Early Warning Scoring System to identify highly suspect COVID-19 patients.

BACKGROUND AND AIMS: The coronavirus disease 2019 (COVID-19) is spreading at an unprecedented speed. Lack of resources to test every patient scheduled for surgery and false negative test results contribute to considerable stress to anesthesiologists, along with health risks to both caregivers and other patients. The study aimed to develop an early warning screening tool to rapidly detect 'highly suspect' among the patients scheduled for surgery. METHODS: Review of literature was conducted using terms 'coronavirus' OR 'nCoV 2019' OR 'SARS-CoV-2' OR 'COVID-19' AND 'clinical characteristics' in PUBMED and MedRxiv. Suitable articles were analysed for symptoms and investigations commonly found in COVID-19 patients. Additionally, COVID-19 patient's symptomatology and investigation profiles were obtained through a survey from 20 COVID-19 facilities in India. Based on literature evidence and the survey information, an Early Warning Scoring System was developed. RESULTS: Literature search yielded 3737 publications, of which 195 were considered relevant. Of these 195 studies, those already included in the meta-analyses were not considered for independent assessment. Based on the combined data from meta-analyses and survey, risk factors of COVID-19 disease identified were as follows: history of exposure, fever, cough, myalgias, lymphocytopaenia, elevated C-reactive protein (CRP)/lactate dehydrogenase (LDH) and radiographic infiltrates. CONCLUSION: Development of this Early Warning Scoring System for preoperative screening of patients may help in identifying 'highly suspect' COVID-19 patients, alerting the physician and other healthcare workers on the need for adequate personal protection and also to implement necessary measures to prevent cross infection and contamination during the perioperative period.

Jatrophane and rearranged jatrophane-type diterpenes: biogenesis, structure, isolation, biological activity and SARs (1984-2019).

Diterpene compounds specially macrocyclic ones comprising jatrophane, lathyrane, terracinolide, ingenane, pepluane, paraliane, and segetane skeletons occurring in plants of the Euphorbiaceae family are of considerable interest in the context of natural product drug discovery programs. They possess diverse complex skeletons and a broad spectrum of therapeutically relevant biological activities including anti-inflammatory, anti-chikungunya virus, anti-HIV, cytotoxic, and multidrug resistance-reversing activities as well as curative effects on thrombotic diseases. Among macrocyclic diterpenes of Euphorbia, the discovery of jatrophane and modified jatrophane diterpenes with a wide range of structurally unique polyoxygenated polycyclic derivatives and as a new class of powerful inhibitors of P-glycoprotein has opened new frontiers for research studies on this genus. In this review, an attempt has been made to give in-depth coverage of the articles on the naturally occurring jatrophanes and rearranged jatrophane-type diterpenes isolated from species belonging to the Euphorbiaceae family published from 1984 to March 2019, with emphasis on the biogenesis, isolation methods, structure, biological activity, and structure-activity relationship.