Mental and physical health among students at a private university that held in-person classes during the COVID-19 pandemic.

OBJECTIVE: The COVID-19 pandemic is expected to have serious negative consequences on mental and physical health, which may disproportionally affect young adults. The aim of this study was to understand short-term impacts on a population of students at a college that held in-person classes during the pandemic. PARTICIPANTS: This study was conducted at a moderately-sized private university in the southeastern United States where approximately 75% of students were enrolled in undergraduate degree programs and 25% in graduate degree programs. METHODS: A survey was created to assess anxiety and depression symptoms, psychotherapeutic medication use, healthy living, and coping skills. Links to the electronic form were distributed to students via email in Spring 2020 and Fall 2020. Participation was completely voluntary and responses were collected anonymously. RESULTS: The rate of anxiety symptoms in the study cohort was higher than the national average (31%) and increased between Spring 2020 (39%) and Fall 2020 (50%). Rates of psychotherapeutic medication use also rose, with benzodiazepine use increasing from 6% to 11% and antidepressant use increasing from 16% to 20%. Compared to the national average, fewer students in the study cohort rated their overall health as "good" or better (72-76% vs. 82%). Physical exercise, nutrition, and alcohol use worsened between Spring and Fall 2020. Problem-focused engagement was associated with significantly fewer anxiety and depression symptoms. Demographic factors such as gender, race, and sexual orientation interacted with several outcomes studied. CONCLUSIONS: Students at a private university that held in-person classes during the COVID-19 pandemic reported high rates of anxiety that increased between Spring and Fall 2020. Self-reported physical health was below average in Spring 2020 but improved in Fall 2020. Appropriate identification and management of the effects of pandemic-related stressors is critical during this uncertain time.Supplemental data for this article can be accessed online at https://doi.org/10.1080/07448481.2022.2052074 .

Non-Cannabinoid Metabolites of Cannabis sativa L. with Therapeutic Potential.

The cannabis plant (Cannabis sativa L.) produces an estimated 545 chemical compounds of different biogenetic classes. In addition to economic value, many of these phytochemicals have medicinal and physiological activity. The plant is most popularly known for its two most-prominent and most-studied secondary metabolites-Δ9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD). Both Δ9-THC and CBD have a wide therapeutic window across many ailments and form part of a class of secondary metabolites called cannabinoids-of which approximately over 104 exist. This review will focus on non-cannabinoid metabolites of Cannabis sativa that also have therapeutic potential, some of which share medicinal properties similar to those of cannabinoids. The most notable of these non-cannabinoid phytochemicals are flavonoids and terpenes. We will also discuss future directions in cannabis research and development of cannabis-based pharmaceuticals. Caflanone, a flavonoid molecule with selective activity against the human viruses including the coronavirus OC43 (HCov-OC43) that is responsible for COVID-19, and certain cancers, is one of the most promising non-cannabinoid molecules that is being advanced into clinical trials. As validated by thousands of years of the use of cannabis for medicinal purposes, vast anecdotal evidence abounds on the medicinal benefits of the plant. These benefits are attributed to the many phytochemicals in this plant, including non-cannabinoids. The most promising non-cannabinoids with potential to alleviate global disease burdens are discussed.

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. METHODS: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. RESULTS: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict "survival". Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients' age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. CONCLUSIONS: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.

Machine Learning Identifies ICU Outcome Predictors in a Multicenter COVID-19 Cohort. (preprint)

Background. Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.Methods. A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results. 1,039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions. Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models.Trial registration. “ClinicalTrials” (clinicaltrials.gov) under NCT04455451

Antiviral Activity of Jamaican Medicinal Plants and Isolated Bioactive Compounds.

Plants have had historical significance in medicine since the beginning of civilization. The oldest medical pharmacopeias of the African, Arabian, and Asian countries solely utilize plants and herbs to treat pain, oral diseases, skin diseases, microbial infections, multiple types of cancers, reproductive disorders among a myriad of other ailments. The World Health Organization (WHO) estimates that over 65% of the world population solely utilize botanical preparations as medicine. Due to the abundance of plants, plant-derived medicines are more readily accessible, affordable, convenient, and have safer side-effect profiles than synthetic drugs. Plant-based decoctions have been a significant part of Jamaican traditional folklore medicine. Jamaica is of particular interest because it has approximately 52% of the established medicinal plants that exist on earth. This makes the island particularly welcoming for rigorous scientific research on the medicinal value of plants and the development of phytomedicine thereof. Viral infections caused by the human immunodeficiency virus types 1 and 2 (HIV-1 and HIV-2), hepatitis virus B and C, influenza A virus, and the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) present a significant global burden. This is a review of some important Jamaican medicinal plants, with particular reference to their antiviral activity.