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While serving as an immediate address for psychological safety and stability, psychological first aid (PFA) currently lacks the incorporation of triage. Without triage, patients cannot be prioritized in correspondence to condition severity that is often called for within emergency conditions. To disentangle the relevance of a potential triage system to PFA, the authors of this paper have developed a method to quantify resilience - a prominent predictor of the capability to recover from a disaster. With this resilience index, they have quantified resilience of differing age, race, and sex demographics to better inform the practice of PFA and potential demographic prioritization via a triage system.

In this study, the authors surveyed a number of students in Singapore to determine how their experiences changed after the implementation of home-based learning during the COVID-19 pandemic.

This study used an improved CMS-seq method to profile 5hmC in ormalin-fixed and paraffin-embedded (FFPE) samples from HNC tumors and adjacent normal tissues, identifying three genes (PRKD2, HADHA, and AIPL1) with promising potential as biomarkers for Head and neck cancer (HNC) diagnosis.

AI analysis of brain scans offers promise for helping doctors diagnose brain tumors. Haider and Drosis explore this field by developing machine learning models that classify brain scans as "cancer" or "non-cancer" diagnoses.

Pediatric cancers pose unique challenges due to their rarity and distinct biological factors, emphasizing the need for accurate survival prediction to guide treatment. This study integrated generative AI and machine learning, including synthetic data, to analyze 9,184 pediatric cancer patients, identifying age at diagnosis, cancer types, and anatomical sites as significant survival predictors. The findings highlight the potential of AI-driven approaches to improve survival prediction and inform personalized treatment strategies, with broader implications for innovative healthcare applications.

This article describes the classification of medical text data using vector databases and text embedding. Various large language models were used to generate this medical data for the classification task.

Some cancer treatments lose efficacy when combined with treatments for excessive stomach acid, due to the changes in the stomach environment caused by the stomach acid treatments. Lin and Lin investigate information on oral cancer drugs to see what information is available on interactions of these drugs.

The authors looked at abundance of bacteria in stool samples from patients with colorectal cancer compared to controls. They found different bacteria that was more prevalent in patients with colorectal cancer as well as bacteria in control patients that may indicate a beneficial gut microbiome.

The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.

Consumption of non-alcoholic beverages, like non-alcoholic beer, is growing in popularity in the United States. These beverages raise important societal questions, such as whether minors should be allowed to purchase or consume non-alcoholic beer. An and An investigate this issue by surveying adults to see if they support minors purchasing and consuming non-alcoholic beer.