Mental Health Perspectives of Youth & Young Adults at a Medical Sciences University in Bhutan

Purpose of Study: Since 1972, Bhutan has followed the philosophy of Gross National Happiness (GNH) - an alternative to GDP which quantifies the holistic wellness of its citizens' as opposed to the production of materials as a metric of the nation's development & prosperity. Nonetheless, the government identified youth and young adult (YYA) mental health to be a major/increasing point of concern from a 2015 national survey, indicating need for a closer look at Bhutan's mental health burden. By exploring YYA perspectives of mental health via an online survey, we hope to inform resource creation, support existing infrastructure, & guide future research. Methods Used: Survey questions were developed via collaboration of Canadian and Bhutanese shareholders to ensure cultural appropriateness and relevance. In all, 11 demographics questions, 17 10-point Likert scale questions, and 7 written answer questions were included in the survey. Students enrolled at university in Thimphu, Bhutan, between the ages of 18-25 inclusive and who are Bhutanese citizens or who have lived the majority of their life in the country were recruited via email. 289 participants consented to & completed the online survey via Qualtrics. Survey responses were quantitatively analyzed in R-Studio, save for written responses still under qualitative analysis in NVivo and not included in this discussion. Summary of Results: On average, YYA indicated feeling sad, anxious, and/or depressed more frequently than they perceived peers/family to feel the same. Strong agreement was seen regarding treatability of mental health conditions (median of 9 on 10-point Likert scale). Environmental factors, adverse childhood experiences, & personal weakness were noted to have detrimental impacts on wellbeing. The majority of respondents indicated positive familial relationships & confidence in parents to understand/support mental health issues, with medians for all family-related 10-point Likert scale questions being above 7.5. Most respondents agreed they were able to get through difficult times and be self-reliant, with all associated 10-point Likert questions having a median over 5 for moderate agreement. A general positive response to using apps or websites as mental health resources was noted. Relating to COVID-19, minimal negative outcomes in terms of diet, anxiety/depression, home life, & substance use were reported. Strong agreement (median of over 7.5 on 10-point Likert scale) was noted with regard to increases in domestic violence as a result of the pandemic. Conclusion(s): Differences between self-ratings and perceptions of others' mental health by Bhutanese YYA indicate a lack of open communication about the topic. Directing resources towards discussions about mental health may be beneficial - especially if technologically accessible. Impacts of the pandemic may continue to develop as Bhutan emerges from lockdown protocols;increasing domestic violence is a primary concern of Bhutanese YYA meriting research.

Tillandsia-Inspired Composite Materials for Atmospheric Water Harvesting

Atmospheric water harvesting (AWH) is a potentially promising small-scale approach to alleviate the water crisis in arid or semiarid regions. Inspired by the asymmetric structure of tillandsia leaves, a plant species native to semiarid regions, we report the development of a bioinspired composite (BiC) to draw moisture for AWH applications. With the advent of the post-COVID era, the nonwoven materials in used masks are discarded, landfilled, or incinerated along with the masks as medical waste, and the negative impact on the environment is inevitable. The nonwoven sheet has porosity, softness, and certain mechanical strength. We innovatively developed BiCs, immobilizing hygroscopic salt with a nonwoven mask for fast vapor liquefaction and using a polymer network to store water. The resulting BiC material manages to achieve a high-water adsorption capacity of 1.24 g g-1 under a low-moderate humidity environment and a high-water release ratio of ca. 90% without the use of photothermal materials, while maintaining high structural integrity in cyclic testing. © 2023 American Chemical Society.

Some considerations in dermatology teaching about national public health emergencies.

Recently, the 2019 novel coronavirus has rapidly spread throughout China. The medical staff at dermatology departments of some hospitals in Hubei province also participated in the fight against coronavirus disease 2019. However, many problems emerged during this period. Some medical staff at dermatology departments lacked abilities to deal with emergencies, self-protect, and to conduct surveys on skin diseases occurring in this period. In this article, the authors highlight common problems in and give advice on dermatology teaching about national public health emergencies to healthcare workers, undergraduate students, graduate students and senior residents, hoping that healthcare workers at dermatology departments will calmly deal with public health emergencies in the future.Copyright © 2020 by the Chinese Medical Association.

A Machine Learning Pipeline for Accurate COVID-19 Health Outcome Prediction using Longitudinal Electronic Health Records

Current COVID-19 predictive models primarily focus on predicting the risk of mortality, and rely on COVID-19 specific medical data such as chest imaging after COVID-19 diagnosis. In this project, we developed an innovative supervised machine learning pipeline using longitudinal Electronic Health Records (EHR) to accurately predict COVID-19 related health outcomes including mortality, ventilation, days in hospital or ICU. In particular, we developed unique and effective data processing algorithms, including data cleaning, initial feature screening, vector representation. Then we trained models using state-of-the-art machine learning strategies combined with different parameter settings. Based on routinely collected EHR, our machine learning pipeline not only consistently outperformed those developed by other research groups using the same set of data, but also achieved similar accuracy as those trained on medical data that were only available after COVID-19 diagnosis. In addition, top risk factors for COVID-19 were identified, and are consistent with epidemiologic findings.

Accurate COVID-19 Health Outcome Prediction and Risk Factors Identification through an Innovative Machine Learning Framework Using Longitudinal Electronic Health Records

COVID-19 is a respiratory disease that has caused a worldwide pandemic and put a strain on the global healthcare systems. To better protect and treat higher risk individuals and alleviate the burden on healthcare systems, it is crucial to identify risk factors and build accurate predictive models for COVID-19 related health outcomes. However, current predictive models focus on predicting the risk of mortality, and rely on COVID-19 specific medical data such as CT scans and COVID-19 lab tests results after a positive COVID-19 diagnosis. To address these issues, we developed an innovative supervised machine learning framework using synthetic veteran Electronic Health Records (EHR) which were routinely collected during past clinic visits. Our models accurately predicted COVID-19 related health outcomes including mortality, ventilation, days in hospital or ICU. In particular, we developed a series of unique and effective data processing algorithms, including data cleaning, vector representation, initial feature screening. Then we trained models using state-of-the-art machine learning strategies combined with different parameter settings. Our machine learning pipeline not only consistently outperformed those developed by other research groups using the same set of EHR, but also achieved similar accuracy as those trained on medical data that were only available after COVID-19 diagnosis. In addition, top risk factors for COVID-19 were identified, which include age, diabetes, metabolic syndromes, heart disease, kidney disease etc., and are consistent with epidemiology findings. Built on veteran's EHR, our results were especially relevant to veterans and filled in the gap of missing COVID-19 research for veterans who are at much higher risk of severe COVID-19 illness than the general population. This project also demonstrated that longitudinal EHR data can be successfully leveraged to provide a holistic prediction of an individual's health risk based on past health records, which is critical for controlling emerging infectious diseases such as COVID-19. © 2021 IEEE.

Using electronic health records to accurately predict COVID-19 health outcomes through a novel machine learning pipeline

Current COVID-19 predictive models primarily focus on predicting the risk of mortality, and rely on COVID-19 specific medical data such as chest imaging after COVID-19 diagnosis. In this project, we developed an innovative supervised machine learning pipeline using longitudinal Electronic Health Records (EHR) to accurately predict COVID-19 related health outcomes including mortality, ventilation, days in hospital or ICU. In particular, we developed unique and effective data processing algorithms, including data cleaning, initial feature screening, vector representation, and feature normalization. Then we trained models using state-of-the-art machine learning strategies combined with different parameter settings and feature selection. Based on routinely collected EHR, our machine learning pipeline not only consistently outperformed those developed by other research groups using the same data set, but also achieved similar mortality prediction accuracy as those trained on medical data available only after COVID-19 diagnosis. In addition, we identified top COVID-19 risk factors, which are consistent with epidemiologic findings. © 2021 Owner/Author.

Some considerations in dermatology teaching about national public health emergencies

Recently, the 2019 novel coronavirus has rapidly spread throughout China. The medical staff at dermatology departments of some hospitals in Hubei province also participated in the fight against coronavirus disease 2019. However, many problems emerged during this period. Some medical staff at dermatology departments lacked abilities to deal with emergencies, self-protect, and to conduct surveys on skin diseases occurring in this period. In this article, the authors highlight common problems in and give advice on dermatology teaching about national public health emergencies to healthcare workers, undergraduate students, graduate students and senior residents, hoping that healthcare workers at dermatology departments will calmly deal with public health emergencies in the future. Copyright © 2020 by the Chinese Medical Association.

Emergency management of 2019 novel coronavirus: implications for the dermatology department.

Since cases first emerged in December 2019, COVID-19 (a type of coronavirus) has rapidly become pandemic. This fast-tracked paper (published quickly) from China on COVID-19 is written by dermatologists at the epicentre of the outbreak in Wuhan. Dermatology clinic staff may be at risk because protective equipment is not routinely available, and skin lesions might possibly transmit the virus indirectly. These authors suggest preventive measures based on experience in this and previous coronavirus outbreaks. Online consultation for non-urgent patients reduces the numbers of patients attending clinics. Nurse-led triage, to identify patients with possible COVID-19, at the entrances of hospital and skin clinics directs patients with a cough or fever to a specific COVID-19 area and a dermatologist is consulted if the fever might be related to skin disease. Clinic staff wear N95 masks and observe hand hygiene during consultations. Patients are admitted to a ward only if routine blood tests and chest CT scans exclude COVID-19. Triage will not detect patients who are showing no symptoms but who are developing the disease, so the hospital should provide an on-call expert team to discuss inpatients suspected or diagnosed with COVID-19 and refer them to radiology, respiratory or intensive care colleagues as required. Confirmed cases are managed following local policies. Skin disorders in COVID-19 inpatients can usually be managed remotely using photographs, email and teleconferencing. If necessary a multidisciplinary team (a team of medical staff from different specialties) can meet in the clean area of the isolation ward. If the dermatologist must see the patient, all records should be provided in advance to minimise exposure time. With these precautions, as of 20th February 2020 no infected patients were detected in the Wuhan Dermatology Department. This is a summary of the study: Emergency management for preventing and controlling nosocomial infection of 2019 novel coronavirus: implications for the dermatology department.