Aims: Understanding of the perceived stress and coping strategies adopted by people is important for contemplating the consequences of a pandemic on mental health of people globally. In this study, we intended to assess the perceived stress status under quarantine/isolation globally during the COVID-19 pandemic.Methods: This is a multicentre, multinational cross-sectional study that recruited isolated/quarantined individuals suspected or confirmed to have COVID-19 to assess the psychological impact of the quarantine/isolation experience by answering a survey distributed online.Results: The study was conducted across 63 participating countries, gaining 1,871 valid responses. There was a higher proportion of female participants in the Moderate to High Perceived Stress Scores (MH-PSS) group compared to the Low Perceived Stress Score group (66.0 vs. 52.0%) and a higher proportion of individuals whose marital status was single had MH-PSS (57.1%). Also, individual's religion (Christian, Hindu, and Muslim), no formal education level, being exposed to a confirmed or suspected COVID-19 patient, being forced to be quarantined/isolated, uncomfortable feeling during quarantine period may significantly increase the risk of MH-PSS (p < 0.05).Conclusions: Many factors can predict stress in COVID-19 pandemic including female sex, being single, religion, no formal education, involuntary quarantine, location and reason of quarantine/isolation, and place of exposure.

Background: In this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs). Objective: The aim of this study is to evaluate the applicability of various LLMs based on textual content of scientific ophthalmology papers. Methods: We developed a model based on natural language processing techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we used zero-shot learning LLMs and compared Bidirectional and Auto-Regressive Transformers (BART) and its variants with Bidirectional Encoder Representations from Transformers (BERT) and its variants, such as distilBERT, SciBERT, PubmedBERT, and BioBERT. To evaluate the LLMs, we compiled a data set (retinal diseases [RenD] ) of 1000 ocular disease–related articles, which were expertly annotated by a panel of 6 specialists into 19 distinct categories. In addition to the classification of articles, we also performed analysis on different classified groups to find the patterns and trends in the field. Results: The classification results demonstrate the effectiveness of LLMs in categorizing a large number of ophthalmology papers without human intervention. The model achieved a mean accuracy of 0.86 and a mean F1-score of 0.85 based on the RenD data set. Conclusions: The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval. We performed a trend analysis that enables researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines.

We evaluated the performance of 3 large language models (LLMs) in generating patient education materials (PEMs) and enhancing the readability of prewritten PEMs on idiopathic intracranial hypertension (IIH). This cross-sectional comparative study compared 3 LLMs, ChatGPT-3.5, ChatGPT-4, and Google Bard, for their ability to generate PEMs on IIH using 3 prompts. Prompt A (control prompt): "Can you write a patient-targeted health information handout on idiopathic intracranial hypertension that is easily understandable by the average American?", Prompt B (modifier statement + control prompt): "Given patient education materials are recommended to be written at a 6th-grade reading level, using the SMOG readability formula, can you write a patient-targeted health information handout on idiopathic intracranial hypertension that is easily understandable by the average American?", and Prompt C: "Given patient education materials are recommended to be written at a 6th-grade reading level, using the SMOG readability formula, can you rewrite the following text to a 6th-grade reading level: [insert text]." We compared generated and rewritten PEMs, along with the first 20 googled eligible PEMs on IIH, on readability (Simple Measure of Gobbledygook [SMOG] and Flesch-Kincaid Grade Level [FKGL]), quality (DISCERN and Patient Education Materials Assessment tool [PEMAT]), and accuracy (Likert misinformation scale). Generated PEMs were of high quality, understandability, and accuracy (median DISCERN score ≥4, PEMAT understandability ≥70%, Likert misinformation scale = 1). Only ChatGPT-4 was able to generate PEMs at the specified 6th-grade reading level (SMOG: 5.5 ± 0.6, FKGL: 5.6 ± 0.7). Original published PEMs were rewritten to below a 6th-grade reading level with Prompt C, without a decrease in quality, understandability, or accuracy only by ChatGPT-4 (SMOG: 5.6 ± 0.6, FKGL: 5.7 ± 0.8, p < 0.001, DISCERN ≥4, Likert misinformation = 1). In conclusion, LLMs, particularly ChatGPT-4, can produce high-quality, readable PEMs on IIH. They can also serve as supplementary tools to improve the readability of prewritten PEMs while maintaining quality and accuracy.

Background: Statins or 3‑hydroxy‑3‑methyl‑glutarylcoenzyme A (HMG‑CoA) reductase inhibitors are medications that act by reducing the cholesterol content of liver cells Moreover, statins have been found to improve endothelial function and reduce vascular wall inflammation. A growing body of research suggests that statins are associated with less risk of migraine, and they can be used to treat symptoms. However, the evidence has been inconclusive, so we aim to investigate the nature and strength of the effect of statins on the prevention and prophylaxis of migraines. Methods: We conducted a comprehensive systematic search across multiple electronic databases, including PubMed, Scopus, Web of Science, and the Cochrane Library, from inception until October 2024, to include studies on the association between statins use and migraine. The outcomes of interest involved the association of the HMG-CoA reductase gene with the risk of migraine, as well as the association and efficacy of statins in migraine patients. Results: Thirteen studies were included in our systematic review. Mendelian Randomization (MR) studies revealed that expression of HMGCR was associated with an increased risk of migraine with odds ratio (OR) ranging from 1.38 to 1.55 (P < 0.001). Three observational studies investigating the relationship between statins and migraine risk demonstrated a protective effect, with odds ratios ranging from 0.73 to 0.94 (P < 0.001). The findings suggest a significant reduction in overall migraine risk, particularly for migraines with aura and in patients with higher vitamin D levels. Meta-analysis of randomized controlled trials (RCTs) showed that statins significantly reduced monthly migraine frequency (MD= -3.16, 95%CI= [-5.79, -0.53]; p = 0.02, I2 = 79%; P = 0.03). RCTs supported the efficacy of statins in reducing migraine frequency, days, and intensity compared to placebo. Conclusions: Statins, already well-established for cardiovascular benefits, emerge as a promising dual-purpose therapy for many neurological disorders. The association between the HMGCR gene and increased migraine risk, coupled with the possible efficacy of statins in reducing migraine frequency, may open new avenues for migraine prophylaxis. However, the variability in study design hinders definitive conclusions, so larger studies with longer follow-ups are required to ascertain both findings.