Mental health and academic experiences among U.S. college students during the COVID-19 pandemic.

When the COVID-19 pandemic began, U.S. college students reported increased anxiety and depression. This study examines mental health among U.S college students during the subsequent 2020-2021 academic year by surveying students at the end of the fall 2020 and the spring 2021 semesters. Our data provide cross-sectional snapshots and longitudinal changes. Both surveys included the PSS, GAD-7, PHQ-8, questions about students' academic experiences and sense of belonging in online, in-person, and hybrid classes, and additional questions regarding behaviors, living circumstances, and demographics. The spring 2021 study included a larger, stratified sample of eight demographic groups, and we added scales to examine relationships between mental health and students' perceptions of their universities' COVID-19 policies. Our results show higher-than-normal frequencies of mental health struggles throughout the 2020-2021 academic year, and these were substantially higher for female college students, but by spring 2021, the levels did not vary substantially by race/ethnicity, living circumstances, vaccination status, or perceptions of university COVID-19 policies. Mental health struggles inversely correlated with scales of academic and non-academic experiences, but the struggles positively correlated with time on social media. In both semesters, students reported more positive experiences with in-person classes, though all class types were rated higher in the spring semester, indicating improvements in college students' course experiences as the pandemic continued. Furthermore, our longitudinal data indicate the persistence of mental health struggles across semesters. Overall, these studies show factors that contributed to mental health challenges among college students as the pandemic continued.

Why Do We Need Food Systems Informatics? Introduction to This Special Collection on Smart and Connected Regional Food Systems

Public interest in where food comes from and how it is produced, processed, and distributed has increased over the last few decades, with even greater focus emerging during the COVID-19 pandemic. Mounting evidence and experience point to disturbing weaknesses in our food systems' abilities to support human livelihoods and wellbeing, and alarming long-term trends regarding both the environmental footprint of food systems and mounting vulnerabilities to shocks and stressors. How can we tackle the "wicked problems” embedded in a food system? More specifically, how can convergent research programs be designed and resulting knowledge implemented to increase inclusion, sustainability, and resilience within these complex systems, support widespread contributions to and acceptance of solutions to these challenges, and provide concrete benchmarks to measure progress and understand tradeoffs among strategies along multiple dimensions? This article introduces and defines food systems informatics (FSI) as a tool to enhance equity, sustainability, and resilience of food systems through collaborative, user-driven interaction, negotiation, experimentation, and innovation within food systems. Specific benefits we foresee in further development of FSI platforms include the creation of capacity-enabling verifiable claims of sustainability, food safety, and human health benefits relevant to particular locations and products;the creation of better incentives for the adoption of more sustainable land use practices and for the creation of more diverse agro-ecosystems;the wide-spread use of improved and verifiable metrics of sustainability, resilience, and health benefits;and improved human health through better diets.

What is the Impact of Vitamin D Levels on COVID-19 Severity?: A Systematic Review

Objective: We assessed studies probing at vitamin D deficiencies in both positive and negative COVID-19 cases. Methods: We measured mean, standard deviations, and 95% Confidence Interval (CI) of many studies to determine if there is a consistent relationship between vitamin D levels and COVID-19. Independent sample t-test compared non-survivors vs. survivors of COVID-19 and vitamin D levels, and moderate vs. severe COVID-19 symptoms and vitamin D levels. Results: We evaluated the difference in vitamin D levels (serum 25(OH)D, nmol/L) among those who tested positive for COVID-19 to those who tested negative. The average median serum 25(OH)D, nmol/L for patients who tested positive was 27.08 nmol/L (± 0.58 SD, 95% CI: 1.88) and the average median of serum 25(OH)D, nmol/L for patients who tested negative was 48.67 nmol/L (± 13.66 SD, 95% CI: 2.17) this difference was near significant (p=0.059). When looking at the relationship between vitamin D levels and severity of COVID-19 progression the result was not statistically significant, t(df)=0.84, p=0.216. When comparing the average values of vitamin D level among those who survived COVID-19 vs. those who did not the results were not statistically significant, t(269)=0.17, p=0.438. Conclusion: There seems to be a correlation between vitamin D deficiency and likelihood of developing severe illness of COVID-19 when observing studies individually. However, when comparing studies on a larger scale it seems that the significant difference seems to fade. [ FROM AUTHOR] Copyright of eLife is the property of eLife Sciences Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Commentary on the use of the reproduction number R during the COVID-19 pandemic.

Since the beginning of the COVID-19 pandemic, the reproduction number [Formula: see text] has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, [Formula: see text] is defined as the average number of secondary infections caused by one primary infected individual. [Formula: see text] seems convenient, because the epidemic is expanding if [Formula: see text] and contracting if [Formula: see text]. The magnitude of [Formula: see text] indicates by how much transmission needs to be reduced to control the epidemic. Using [Formula: see text] in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of [Formula: see text] but many, and the precise definition of [Formula: see text] affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined [Formula: see text], there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate [Formula: see text] vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when [Formula: see text] is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of [Formula: see text], and the data and methods used to estimate it, can make [Formula: see text] a more useful metric for future management of the epidemic.

Targeted Delivery of Zinc Pyrithione to Skin Epithelia.

Zinc pyrithione (ZnPT) is an anti-fungal drug delivered as a microparticle to skin epithelia. It is one of the most widely used ingredients worldwide in medicated shampoo for treating dandruff and seborrheic dermatitis (SD), a disorder with symptoms that include skin flaking, erythema and pruritus. SD is a multi-factorial disease driven by microbiol dysbiosis, primarily involving Malassezia yeast. Anti-fungal activity of ZnPT depends on the cutaneous availability of bioactive monomeric molecular species, occurring upon particle dissolution. The success of ZnPT as a topical therapeutic is underscored by the way it balances treatment efficacy with formulation safety. This review demonstrates how ZnPT achieves this balance, by integrating the current understanding of SD pathogenesis with an up-to-date analysis of ZnPT pharmacology, therapeutics and toxicology. ZnPT has anti-fungal activity with an average in vitro minimum inhibitory concentration of 10-15 ppm against the most abundant scalp skin Malassezia species (Malassezia globosa and Malassezia restrica). Efficacy is dependent on the targeted delivery of ZnPT to the skin sites where these yeasts reside, including the scalp surface and hair follicle infundibulum. Imaging and quantitative analysis tools have been fundamental for critically evaluating the therapeutic performance and safety of topical ZnPT formulations. Toxicologic investigations have focused on understanding the risk of local and systemic adverse effects following exposure from percutaneous penetration. Future research is expected to yield further advances in ZnPT formulations for SD and also include re-purposing towards a range of other dermatologic applications, which is likely to have significant clinical impact.

Does Vitamin D Deficiency Contribute to COVID-19 Severity?

Objectives In this systematic review, we assessed studies that probed at vitamin D deficiencies in both positive and negative COVID-19 cases. We compared vitamin D levels to see if there was a noticeable difference. Finally, through the review of several studies, we investigated whether more severe cases of COVID-19 were correlated with low vitamin D levels. Methods The mean and standard deviations of the vitamin D levels in patients who tested positive and negative for COVID-19 were analyzed. We used Practical Meta-Analysis Effect Size Calculator developed by David B. Wilson, Ph.D., George Mason University when looking at COVID-19 status and vitamin D (N = 50–80 nmol/L) deficient levels. In this systematic review, we measured mean, standard deviations, and 95% CI of many studies to determine if there is a consistent relationship between vitamin D levels and COVID-19. We also performed an independent sample t-test comparing non-survivors vs. survivors of COVID-19 and vitamin D levels, and when comparing moderate vs. severe COVID-19 symptoms and vitamin D levels. Results A few studies were compared to evaluate the difference in vitamin D levels (serum 25(OH)D, nmol/L) among those who tested positive for COVID-19 to those who tested negative. It was found that the average median serum 25(OH)D, nmol/L for patients who tested positive was 27.08 nmol/L (±0.58 SD, 95% CI: 1.88) and the average median of serum 25(OH)D, nmol/L for patients who tested negative was 48.67 nmol/L (±13.66 SD, 95% CI: 2.17) this difference was near significant (p = .059). When looking at the relationship between vitamin D levels and severity of COVID-19 progression the result was not statistically significant, t(df) = 0.84, p = .216. When comparing the average values of vitamin D level among those who survived COVID-19 vs. those who did not, the results were not statistically significant, t(269) = 0.17, p = .438. Conclusions It is apparent that there is a trend found in relationships among those who test positive for COVID-19 and their vitamin D levels. There seems to be a correlation between vitamin D deficiency and likelihood of developing severe illness of COVID-19 when observing studies individually. However, when comparing studies on a larger scale it seems that the significant difference seems to fade. Funding Sources None.

An international survey examining the impact of the COVID-19 pandemic on telehealth use among mental health professionals.

BACKGROUND: COVID-19 has profoundly affected the work of mental health professionals with many transitioning to telehealth to comply with public health measures. This large international study examined the impact of the pandemic on mental health clinicians' telehealth use. METHODS: This survey study was conducted with mental health professionals, primarily psychiatrists and psychologists, registered with WHO's Global Clinical Practice Network (GCPN). 1206 clinicians from 100 countries completed the telehealth section of the online survey in one of six languages between June 4 and July 7, 2020. Participants were asked about their use, training (i.e., aspects of telehealth addressed), perceptions, and concerns. OUTCOMES: Since the pandemic onset, 1092 (90.5%) clinicians reported to have started or increased their telehealth services. Telephone and videoconferencing were the most common modalities. 592 (49.1%) participants indicated that they had not received any training. Clinicians with no training or training that only addressed a single aspect of telehealth practice were more likely to perceive their services as somewhat ineffective than those with training that addressed two or more aspects. Most clinicians indicated positive perceptions of effectiveness and patient satisfaction. Quality of care compared to in-person services and technical issues were the most common concerns. Findings varied by WHO region, country income level, and profession. INTERPRETATION: Findings suggest a global practice change with providers perceiving telehealth as a viable option for mental health care. Increasing local training opportunities and efforts to address clinical and technological concerns is important for meeting ongoing demands.

Machine churning

Attempts to use artificial intelligence to diagnose covid-19 have so far been unsuccessful, says Michael Roberts

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.

Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.