During the first COVID-19 lockdown, researchers of the University of Vienna assessed the influence of stress, mood, and social interactions on "unhealthy" eating habits in nearly 800 participants across Austria, Italy, and Germany. Surprisingly, results showed that negative mood and less social interactions led to a decrease in "unhealthy" food consumption and enjoyment. At the same time eating "unhealthy" meals in fact seemed to help maintain already positive emotions and reduce stress – but not to uplift a negative mood. These findings highlight the role of food in coping with emotional challenges. The study, led by psychologists Ana Stijovic and Giorgia Silani from the University of Vienna, is now published in the scientific journal Biological Psychiatry.

A platform developed nearly 20 years ago previously used to detect protein interactions with DNA and conduct accurate COVID-19 testing has been repurposed to create a highly sensitive water contamination detection tool.

About The Study: In this study, confidence in organizations to use health data responsibly was largely unchanged from 2020 to 2022, but polarization increased between politically liberal and conservative respondents. Compared with 2020, in 2022 liberal respondents reported increased confidence in the federal government to use digital health data responsibly, while conservative respondents reported decreased confidence in the federal government and agencies. Changes in confidence may be related to the prominence of these agencies (CDC, NIH) during the COVID-19 pandemic, a 2020 national election with a change in the governing political party, and political polarization.

A new supplemental issue of The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences presents new measures, methods, and data collected during Round 4 (2021 to 2023) of the National Social Life, Health, and Aging Project (NSHAP) — with a focus on cognition and how researchers adapted to working with participants as a result of the COVID-19 pandemic.

About The Study: The results of this study suggest that mRNA COVID-19 vaccination may be a protective factor against post–COVID-19 condition in children following SARS-CoV-2 infection. These findings suggest benefits of COVID-19 vaccination beyond those associated with protection against acute COVID-19 and may encourage increased pediatric uptake.

About The Study: This cohort study found no increase in sudden cardiac arrest/sudden cardiac death in young competitive athletes in the U.S. during the COVID-19 pandemic, suggesting that reports asserting otherwise were overestimating the cardiovascular risk of COVID-19 infection, vaccination, and myocarditis. Many athlete cases shown in social media video montages occurred before the pandemic yet claimed COVID-19 infection or vaccination raised the risk of sudden cardiac arrest/sudden cardiac death.

Researchers at the University of Kansas conducted a study in which they gauged respondents' attitudes about COVID-19 vaccines. They then showed them media content designed to lead to feelings of anger or embarrassment. They found people preferred discussing vaccines with an AI chatbot when they felt embarrassed, but preferred talking to a human when they were angry. The study, which used eye-tracking technology, helps illustrate that health communicators and marketers should take care to use tech like AI in a way that people like to most effectively reach people, researchers argue.

Advances in technology, the evolution of patient- and-family centered care, and infection control challenges—evidenced during the COVID-19 pandemic—highlight the possibilities and challenges of intensive care unit (ICU) design. For example, prior ICU design guidelines in 1995 and 2012 did not envision remote manipulation of ventilator settings or infusion pumps, or the unique problems presented by pandemic care. As a result, the Society of Critical Care Medicine (SCCM) sought to update the 2012 ICU design guidelines. Published in Critical Care Medicine, the journal of SCCM, these new guidelines provide evidence-based recommendations for clinicians, administrators, and healthcare architects to optimize design strategies in new or renovation projects.

U.S. outpatient prescriptions for hydroxychloroquine and ivermectin increased 2- to 10-fold above pre-pandemic rates, respectively, to treat COVID-19, despite strong evidence disproving their effectiveness.

A novel machine learning framework – Mal-ID – can decipher an individual’s immune system’s record of past infections and diseases, according to a new study, providing a powerful tool with the potential for diagnosing autoimmune disorders, viral infections, and vaccine responses with precision. Traditional clinical diagnostic methods for autoimmune diseases or other immunological pathologies tend to rely on a combination of physical examination, patient history, and various laboratory testing for cellular or molecular abnormalities – a lengthy process often complicated by initial misdiagnoses and ambiguous systems. These approaches make limited use of data from the patient’s individual adaptive immune system’s B cell receptors (BCRs) and T cell receptors (TCRs). In response to pathogens, vaccines, and other antigenic stimuli, BCR and TCR repertoires undergo changes through clonal expansion, somatic mutation, and selective reshaping of immune cell populations. Sequencing BCRs and TCRs could provide a comprehensive diagnostic tool, potentially enabling simultaneous detection of infectious, autoimmune, and immune-mediated diseases in a single test. However, the extent to which immune receptor repertoire sequencing alone can reliably and broadly classify diseases remains uncertain.

 

To address this, Maxim Zaslavsky and colleagues developed Mal-ID (MAchine Learning for Immunological Diagnosis) – a 3-model machine learning framework that analyzes immune receptor datasets to identify signatures of infectious and immunological diseases and vaccine responses in patients. Zaslavsky et al. trained Mal-ID on BCR and TCR data systematically collected from 593 individuals, including patients with COVID-19, HIV, and type-1 diabetes, as well as influenza vaccine recipients and healthy controls. According to the findings, Mal-ID effectively distinguished six distinct disease states in 550 paired BCR and TCR samples with a multiclass AUROC score of 0.986, indicating exceptionally high classification accuracy. This metric reflects the model’s ability to rank positive cases above negative ones across all disease comparisons. Although the model was successful in differentiating COVID-19, HIV, lupus, T1D, and healthy individuals – illustrating its potential as a powerful diagnostic tool – Zaslavsky et al. note that the approach still needs to be refined, using clinical information, before it could be used with confidence in clinical applications.

Paxlovid does not significantly reduce COVID-19 hospitalization and mortality among vaccinated older adults. The study questions the assumption that Paxlovid’s effectiveness in reducing COVID-19 hospitalizations and deaths in unvaccinated adults also applies to vaccinated adults.