Leptospirosis remains a major public health concern in tropical regions, including Malaysia, where it is endemic. To date, evidence from remote and understudied areas remains limited. This study aims to assess the epidemiological evidence of leptospirosis among market traders in Sabah, addressing the limited available data for this potentially high-risk occupational group in this region. A cross-sectional study was conducted among 295 market traders in Kudat Town, Sabah. Sociodemographic, occupational, environmental and behavioural data were collected using a modified validated questionnaire. Venous blood samples were collected to test for Leptospira antibodies using the Microscopic Agglutination Test; a titre of 1:50 or higher was considered seropositive. Seroprevalence was estimated descriptively, while factors associated with leptospiral seropositivity were examined using univariable and multivariable logistic regression analyses. The seroprevalence of leptospirosis among market traders was 5.4% (95% CI: 3.1–8.7). Antibodies were detected against 11 Leptospira serovars/strains, mainly Lep175. In univariable analysis, predictors for seropositivity included trading frequency, rodent presence, and proximity to garbage piles. With only 16 cases, the initial multivariable model had a low EPV of 4, suggesting possible overfitting. After reducing the model (EPV = 8), proximity to garbage piles remained strongly associated (AOR = 52.61; 95% CI: 13.50–205.13; p < 0.001), while rodents at home also became significant (AOR = 5.70; 95% CI: 1.12–29.08; p = 0.037). This study provides the first empirical evidence of leptospirosis exposure among market traders in Sabah. Although the overall seroprevalence was low, the findings indicate a measurable risk and underscore the need for further large-scale studies and integrated One Health interventions to reduce exposure in this vulnerable population.

Methods: This study analyzed US public interest in lumbar decompression and fusion surgery using Google Trends data from January 1, 2015 to December 31, 2024. The relative search volume (RSV) was used to measure public interest. Temporal trends were assessed using linear regression analysis, whereas seasonal and geographic variations were evaluated using analysis of variance and Tukey’s post hoc tests. Statistical significance was set at p<0.05. Results: Public interest in lumbar decompression and fusion surgery showed a statistically significant upward trend from 2015 to 2024 (p<0.05), with R2 ranging from 0.0141 to 0.6748 across search terms. Monthly analysis revealed that the average search volume was highest in October (78.8%) and lowest in December (68.7%). No significant differences were observed among seasons (p=0.102). Geographically, the highest mean RSV was in the Northeast (43.3%), followed by the West (19.5%), Midwest (12.6%), South (11.1%), and Southwest (10.2%), with significant differences among these regions (p<0.001). Conclusions Public interest in lumbar decompression and fusion surgery has shown a consistent and statistically significant upward trend over the past decade. Although seasonal variation was not significant, notable geographic differences in search volume were observed, with the northeast showing the highest level of interest. These findings highlighted regional disparities in public engagement and may serve as a basis for future healthcare resource planning and patient education strategies.

Objective: To assess the safety and performance of the Cereglide 71 aspiration catheter, launched in February 2024, by analyzing adverse events from the Manufacturer and User Facility Device Experience (MAUDE) database. Methods: Reports from the MAUDE database (February 2024 onward) were analyzed using Microsoft Excel and Jamovi, with 36 reports reviewed—27 device-related and 14 patient-related. Results: The most common device-related issues were cracks (8 occurrences), material bending (6), and deformation/stretching (5). Adverse events included thromboembolism (4 cases), intracranial hemorrhage (2), carotid-cavernous fistula (2), and cerebral vasospasm (2). The most common indication was middle cerebral artery stroke (9 cases). Conclusions This study highlights the importance of post-market surveillance to improve patient outcomes, identifying key complications that require careful consideration in clinical practice. This is the first report on complications related to the Cereglide 71 aspiration catheter from the FDA MAUDE database.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models like chat generative pre-trained transformer (ChatGPT) have found success in various sectors, but their application in the medical field remains limited. This study aimed to assess the feasibility of using ChatGPT to provide accurate medical information to patients, specifically evaluating how well ChatGPT versions 3.5 and 4 aligned with the 2012 North American Spine Society (NASS) guidelines for lumbar disk herniation with radiculopathy. Methods: ChatGPT's responses to questions based on the NASS guidelines were analyzed for accuracy. Three new categories—overconclusiveness, supplementary information, and incompleteness—were introduced to deepen the analysis. Overconclusiveness referred to recommendations not mentioned in the NASS guidelines, supplementary information denoted additional relevant details, and incompleteness indicated omitted crucial information from the NASS guidelines. Results: Out of 29 clinical guidelines evaluated, ChatGPT-3.5 demonstrated accuracy in 15 responses (52%), while ChatGPT-4 achieved accuracy in 17 responses (59%). ChatGPT-3.5 was overconclusive in 14 responses (48%), while ChatGPT-4 exhibited overconclusiveness in 13 responses (45%). Additionally, ChatGPT-3.5 provided supplementary information in 24 responses (83%), and ChatGPT-4 provided supplemental information in 27 responses (93%). In terms of incompleteness, ChatGPT-3.5 displayed this in 11 responses (38%), while ChatGPT-4 showed incompleteness in 8 responses (23%). Conclusion ChatGPT shows promise for clinical decision-making, but both patients and healthcare providers should exercise caution to ensure safety and quality of care. While these results are encouraging, further research is necessary to validate the use of large language models in clinical settings.

Heart failure (HF) is a significant global health concern, particularly in Asia, where over half of the world's population resides. Despite advances in treatment, the burden of HF is expected to rise in the region due to the aging population and an increase in non-communicable diseases associated with HF risk. This narrative review examines the current state of HF in Asia, highlighting differences in treatment utilization, underrepresentation of Asian individuals in clinical trials, emerging therapies, and implementation strategies, including the potential use of polypills and the need for expanded HF training opportunities for healthcare providers.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Objective: Large language models like chat generative pre-trained transformer (ChatGPT) have found success in various sectors, but their application in the medical field remains limited. This study aimed to assess the feasibility of using ChatGPT to provide accurate medical information to patients, specifically evaluating how well ChatGPT versions 3.5 and 4 aligned with the 2012 North American Spine Society (NASS) guidelines for lumbar disk herniation with radiculopathy. Methods: ChatGPT's responses to questions based on the NASS guidelines were analyzed for accuracy. Three new categories—overconclusiveness, supplementary information, and incompleteness—were introduced to deepen the analysis. Overconclusiveness referred to recommendations not mentioned in the NASS guidelines, supplementary information denoted additional relevant details, and incompleteness indicated omitted crucial information from the NASS guidelines. Results: Out of 29 clinical guidelines evaluated, ChatGPT-3.5 demonstrated accuracy in 15 responses (52%), while ChatGPT-4 achieved accuracy in 17 responses (59%). ChatGPT-3.5 was overconclusive in 14 responses (48%), while ChatGPT-4 exhibited overconclusiveness in 13 responses (45%). Additionally, ChatGPT-3.5 provided supplementary information in 24 responses (83%), and ChatGPT-4 provided supplemental information in 27 responses (93%). In terms of incompleteness, ChatGPT-3.5 displayed this in 11 responses (38%), while ChatGPT-4 showed incompleteness in 8 responses (23%). Conclusion ChatGPT shows promise for clinical decision-making, but both patients and healthcare providers should exercise caution to ensure safety and quality of care. While these results are encouraging, further research is necessary to validate the use of large language models in clinical settings.

This systematic review and meta-analysis aimed to examine the effect of the antioxidant alpha-lipoic acid (ALA) on various cardiometabolic risk factors and hormonal parameters in patients with polycystic ovary syndrome (PCOS). We searched PubMed, EMBASE, SCOPUS, Cochrane Library, and Web of Science databases without language restrictions until May 2023 to find randomized controlled trials (RCTs) that assessed the impact of ALA supplementation on anthropometric, glycemic, lipid, oxidative stress, and hormonal parameters in women with PCOS. Outcomes were summarized using the standardized mean difference (SMD) and 95% confidence interval (CI) in a random-effects model. An I2 statistic of >60% established significant between-study heterogeneity. The overall certainty of the evidence for each outcome was determined using the grading of recommendations, assessment, development, and evaluations system. Seven RCTs met the inclusion criteria. The ALA group had significant reductions in fasting blood sugar (fasting blood sugar (FBS), n=7 RCTs, SMD, -0.60; 95% CI, -1.10 to -0.10; I2=63.54%, moderate certainty of evidence) and homeostatic model assessment for insulin resistance (homeostatic model assessment of insulin resistance (HOMA-IR), n=4 RCTs, SMD, -2.03; 95% CI, -3.85 to -0.20; I2=96.32%, low certainty of evidence) compared with the control group. However, significant differences were observed between the groups in body mass index, insulin, estrogen, follicle-stimulating hormone, luteinizing hormone, testosterone, low-density lipoprotein, highdensity lipoprotein, triglyceride, total cholesterol, malondialdehyde, or total antioxidant capacity profiles. ALA supplementation improves FBS and HOMA-IR levels in women with PCOS. ALA consumption is an effective complementary therapy for the management of women with PCOS.

Objective: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. Methods: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. Results: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.