Factors Influencing Excessive Gestational Weight Gain Among Pregnant Women in Urban Malaysia Introduction Excessive gestational weight gain (GWG) is a growing public health concern due to its association with higher risk of adverse maternal and perinatal outcomes. Recognising the key factors of underlying GWG incidence is thus crucial for developing targeted interventions. This study aims to determine the prevalence of excessive GWG and its associated factors among pregnant women in an urban district of Klang Valley, Malaysia. Methods A retrospective cross-sectional study was conducted (betweenNovember 2017 and October 2018) among 472 pregnant women who gave birth between January 2015 and December 2016. Data on sociodemographic characteristics, medical illnesses, and pre-pregnancy body mass index (BMI) were retrieved from the birth registry and maternal health records. The classification of GWG is based on the Institute of Medicine's recommendation Results The classification of GWG is based on the Institute of Medicine's recommendation. A total of 113 (23.9%) pregnant women had excessive GWG. Multiple logistic regression showed the odds of getting excessive GWG were higher among those with lower education levels (Adj. OR = 1.85; 95% CI = 1.084, 3.147), being employed (Adj. OR = 2.30; 95% CI = 1.342, 3.928), nulliparous (Adj. OR = 2.46; 95% CI = 1.450, 4.164), and having pre-pregnancy BMI of ≥25kg/m2(Adj. OR = 6.47; 95% CI = 3.909, 10.721). Conclusions Lifestyle interventions, such as weight reduction programs and health education, should focus on women with high pre-pregnancy BMI, nulliparous women, working women and women with low education levels. Pre-pregnancy clinics and workplaces can serve as platforms to promote healthy dietary choices and encourage regular physical exercise.

Introduction: Accurate diagnosis of thyrotoxicosis, a condition resulting from excessive thyroid hormone activity, is essential for appropriate management. However, access to diagnostic tools such as thyrotropin receptor antibody (TRAb) assays and thyroid ultrasonography remains limited in resource-constrained settings, highlighting the need for cost-effective alternatives. Recent studies suggest that the free triiodothyronine to free thyroxine (FT3/FT4) ratio may serve as a potential biomarker for differentiating the causes of thyrotoxicosis. Methodology: This cross-sectional study evaluated the FT3/FT4 ratio in newly diagnosed thyrotoxicosis patients aged ≥18 years recruited from Hospital Tengku Ampuan Rahimah, Hospital Banting, Klinik Kesihatan Pelabuhan Klang, and Klinik Kesihatan Pandamaran between February and December 2025. All participants underwent thyroid function testing (FT3, FT4, and TSH) and autoantibody assessment (TRAb and anti-thyroid peroxidase [anti-TPO]). Diagnostic performance of the FT3/FT4 ratio for Graves’ disease was assessed using receiver operating characteristic (ROC) curve analysis. Results: Fifty-eight patients were included, of whom 58.6% were diagnosed with Graves’ disease. Patients with Graves’ disease had significantly higher FT3 levels (median 16.8 pmol/L; IQR 10.9–25.7) compared to those with non-Graves’ thyrotoxicosis (median 8.3 pmol/L; IQR 5.3–13.5; p <0.001), with similar trends observed for FT4 levels (p <0.001). However, the FT3/ FT4 ratio did not differ significantly between groups (p >0.05), with an overall ROC AUC of 0.572, indicating poor discriminatory ability. Subgroup analysis based on FT4 levels improved performance; at FT4 <30 pmol/L, the FT3/FT4 ratio demonstrated 75.0% sensitivity, 91.7% specificity, and 87.5% diagnostic accuracy at a cutoff of 0.3445 (AUC = 0.813; 95% CI: 0.570–1.000; p = 0.069). No significant association was observed between the FT3/FT4 ratio and TRAb or anti-TPO. Conclusion The FT3/FT4 ratio has limited overall diagnostic utility but may provide adjunctive value in selected biochemical contexts, particularly in settings with limited access to immunological testing.
Diagnosis, Differential ; Thyroxine ; Triiodothyronine ; Thyrotoxicosis ; Cross-Sectional Studies

Introduction: Sodium-glucose cotransporter-2 (SGLT2) inhibitors are cornerstone therapies for heart failure and diabetes, offering proven cardiorenal benefits. However, expanded use necessitates vigilance regarding adverse effects, particularly genitourinary infections. While mild cystitis is common, serious upper urinary tract infections remain rare and potentially life-threatening. We describe a case of renal abscess presenting as recurrent urinary tract infections (UTI) following SGLT2 inhibitor initiation, emphasizing the need for clinical vigilance. Case: A 69-year-old male with a significant cardiovascular history, including heart failure with reduced ejection fraction (HFrEF), hypertrophic cardiomyopathy with an implantable cardioverter-defibrillator for non-sustained ventricular tachycardia, diabetes mellitus, hypertension, and hyperlipidemia, presented with a 1-week history of right flank pain, dysuria, urinary frequency, and fever. Initial labs confirmed infection: leukocytosis (22.6 × 10³/ µL), markedly elevated CRP (200 mg/L), and bacteriuria. He was diagnosed with a UTI and started on IV Cefuroxime. This marked his third UTI admission in 5 months, following discharge just 3 weeks prior for septic shock secondary to UTI, establishing a relapsing pattern. Subsequent urine and blood cultures were unremarkable. Medication review revealed Dapagliflozin had been initiated for HFrEF 8 months ago. Following clinical improvement from each prior UTI episode, Dapagliflozin was consistently restarted. Despite an initial antibiotic response, symptoms recurred after discharge each time. The recurrent nature of his infections prompted a renal ultrasound revealing a large (5.3 × 7.5 × 7.4 cm), non-drainable, heterogeneously hypoechoic collection at the left kidney’s mid-lower pole, diagnostic of an early renal abscess. Conclusion This report highlights renal abscess as a rare and severe complication of SGLT2 inhibitor therapy. It serves as a critical reminder that recurrent or relapsing UTIs in patients on these agents should prompt immediate investigation with renal imaging to rule out deep-seated pathology rather than simple cystitis. While these drugs offer proven cardiorenal benefits, their role in promoting urological infections necessitates a cautious approach.
Abscess ; Sodium-Glucose Transporter 2 Inhibitors

Background/Aims: Amitriptyline is prescribed off-label for irritable bowel syndrome (IBS). We conducted a meta-analysis to assess its efficacy. Methods: A systematic literature review was conducted until November 10, 2023, using MEDLINE, Embase, Cochrane Library, and Web of Science to study the efficacy of amitriptyline in patients with IBS. We included all randomized controlled trials that compared amitriptyline to placebo. Revised Cochrane risk-of-bias tool was used to assess the quality of studies. Meta-analyses were performed using a bivariate random-effects model. Statistical analyses were performed using R Software 4.2.3 and heterogeneity was assessed with I 2 statistics. Results: Seven trials were included with 796 patients (61% female). Amitriptyline was associated with better treatment response (OR, 5.30; 95% CI, 2.47 to 11.39; P < 0.001), reduced Irritable Bowel Syndrome Symptom Severity Scores (MD, –50.72; 95% CI, –94.23 to –7.20; P = 0.020) and improved diarrhea (OR, 10.55; 95% CI, 2.90 to 38.41; P < 0.001). No significant difference between the 2 groups regarding the adverse effects was observed. Three trials showed an overall low risk of bias, 2 trials showed an overall high risk of bias due to randomization and missing data, and 2 trials had some concerns regarding missing data. Conclusions Amitriptyline was found to be well-tolerated and effective in treating IBS compared to placebo. These findings support the use of amitriptyline for the management of IBS, particularly among patients with the IBS diarrhea subtype. Future research should focus on the dose-dependent effects of amitriptyline in IBS to better guide clinicians in personalized titration regimens.

Objectives: The purpose of this study is to examine the impact of subjective socioeconomic status and social capital on self-rated health and quality of life among a minority group in Iran. Methods: This cross-sectional study involved 800 individuals from a minority group in Iran. The sampling method was clustering, and data collection was conducted using a questionnaire. Data analysis was performed using SPSS version 18 and Stata version 8. Results: The results of logistic regression analysis revealed that subjective socioeconomic status (odds ratio [OR], 1.47; 95% confidence interval [CI], 1.34 to 1.61), belonging and empathy (OR, 1.09; 95% CI, 1.03 to 1.15), and trust (OR, 1.06; 95% CI, 1.00 to 1.13) significantly impacted the quality of life. Additionally, the logistic regression analysis for factors influencing self–rated health demonstrated significant effects for the age group of 31-50 years (OR, 0.59; 95% CI, 0.38 to 0.91), gender (OR, 0.65; 95% CI, 0.46 to 0.92), academic education (OR, 2.00; 95% CI, 1.22 to 3.26), subjective socioeconomic status (OR, 1.27; 95% CI, 1.16 to 1.38), chronic disease (OR, 4.52; 95% CI, 2.49 to 8.19), belonging and empathy (OR, 1.06; 95% CI, 1.01 to 1.11), and participation (OR, 1.12; 95% CI, 1.00 to 1.24). Conclusions The findings indicate that bonding social capital significantly influences health levels and quality of life. Focusing on delegating local responsibilities to community members and striving to promote participation in health programs, along with increasing the socioeconomic status of minority groups, can effectively improve their health and quality of life.

The COVID-19 pandemic necessitated a global paradigm shift in the teaching of human anatomy. Most institutions successfully transitioned from traditional in-person teaching methods, to various distance-learning strategies.Since virtual reality (VR) offers immersive three-dimensional (3D) experiences, this study investigated students’ experiences regarding the capacity of VR to support distance-learning of anatomy. Using the VR application, 3D Organon Virtual Reality Anatomy, anatomy instructors pre-recorded learning content as 360-degree videos with live voice-over and integrated it into the teaching material of the MBBS first-year abdomen, pelvis, and perineum-structure and function course. A 19-item 5-point Likert scale questionnaire, comprising of two major categories, “VR experience in anatomy lessons” and “VR in anatomy lessons vs. traditional cadaveric dissection” was disseminated. Post-evaluation analysis revealed a response rate of 63.5%. Almost 70% of students agreed that VR was instrumental in solidifying their theoretical understanding and improved spatial awareness with better retention of anatomical relationships. Approximately 50% wanted to continue using VR even if instruction becomes onsite. Though 72% of participants agree that VR addressed the session learning objectives only 24% agree that it is similar or better than cadaveric dissection, thus preferring cadaveric dissection to VR. Only 12.1% agree that VR is more beneficial to cadaveric dissection. Our exploration into the integration of VR technology in anatomy teaching has revealed promising opportunities. While VR can augment traditional teaching methods in unprecedented times such as war, floods or global pandemic, it should not replace hands-on cadaveric learning entirely, but rather complement existing approaches.

The COVID-19 pandemic necessitated a global paradigm shift in the teaching of human anatomy. Most institutions successfully transitioned from traditional in-person teaching methods, to various distance-learning strategies.Since virtual reality (VR) offers immersive three-dimensional (3D) experiences, this study investigated students’ experiences regarding the capacity of VR to support distance-learning of anatomy. Using the VR application, 3D Organon Virtual Reality Anatomy, anatomy instructors pre-recorded learning content as 360-degree videos with live voice-over and integrated it into the teaching material of the MBBS first-year abdomen, pelvis, and perineum-structure and function course. A 19-item 5-point Likert scale questionnaire, comprising of two major categories, “VR experience in anatomy lessons” and “VR in anatomy lessons vs. traditional cadaveric dissection” was disseminated. Post-evaluation analysis revealed a response rate of 63.5%. Almost 70% of students agreed that VR was instrumental in solidifying their theoretical understanding and improved spatial awareness with better retention of anatomical relationships. Approximately 50% wanted to continue using VR even if instruction becomes onsite. Though 72% of participants agree that VR addressed the session learning objectives only 24% agree that it is similar or better than cadaveric dissection, thus preferring cadaveric dissection to VR. Only 12.1% agree that VR is more beneficial to cadaveric dissection. Our exploration into the integration of VR technology in anatomy teaching has revealed promising opportunities. While VR can augment traditional teaching methods in unprecedented times such as war, floods or global pandemic, it should not replace hands-on cadaveric learning entirely, but rather complement existing approaches.

Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these "omics" studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data-driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state-of-the-art applications of ML algorithms for chronic disease detection and prediction across datasets, including medical imaging, genomics, wearables, and electronic health records. Specifically, we review and synthesize key studies leveraging major ML approaches ranging from traditional techniques such as logistic regression and random forests to modern deep learning neural network architectures. We consolidate existing literature to date around ML for chronic disease prediction to synthesize major trends and trajectories that may inform both future research and clinical translation efforts in this growing field. While highlighting the critical innovations and successes emerging in this space, we identify the key challenges and limitations that remain to be addressed. Finally, we discuss pathways forward toward scalable, equitable, and clinically implementable ML solutions for transforming chronic disease screening and prevention.

Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these "omics" studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data-driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state-of-the-art applications of ML algorithms for chronic disease detection and prediction across datasets, including medical imaging, genomics, wearables, and electronic health records. Specifically, we review and synthesize key studies leveraging major ML approaches ranging from traditional techniques such as logistic regression and random forests to modern deep learning neural network architectures. We consolidate existing literature to date around ML for chronic disease prediction to synthesize major trends and trajectories that may inform both future research and clinical translation efforts in this growing field. While highlighting the critical innovations and successes emerging in this space, we identify the key challenges and limitations that remain to be addressed. Finally, we discuss pathways forward toward scalable, equitable, and clinically implementable ML solutions for transforming chronic disease screening and prevention.