Voltage-gated sodium channels (VGSCs) play pivotal roles in cancer progression and have emerged as promising therapeutic targets and biomarkers. VGSCs comprise multiple subtypes with distinct tissue distributions, influencing tumour characteristics in different ways. Among these, the tetrodotoxin-sensitive α-subunits and the β1 subunit, commonly found in breast cancer, have been implicated in metastasis and tumour aggressiveness. The NaV1.5 channel and its neonatal variant (nNaV1.5) are overexpressed in aggressive cancers such as breast, prostate, colorectal, and lung cancers, thereby enhancing their invasive capacity. nNaV1.5 is particularly significant due to its tumour-specific expression and strong association with poor prognosis, especially in breast cancer, where it regulates cell proliferation, invasion, and tumour microenvironment remodelling. This review highlights nNaV1.5 as a critical ion channel that drives metastasis through ion regulation, extracellular acidification, and cytoskeletal remodelling. We further evaluate current therapeutic strategies, including siRNA, monoclonal antibodies, and small-molecule inhibitors, while addressing translational challenges such as tumour heterogeneity, drug delivery limitations, and off-target cardiotoxicity due to its similarity with the adult isoform. In addition, we explore the potential of nNaV1.5 as a biomarker subject to epigenetic regulations by factors including RE1-silencing transcription factor (REST) and histone deacetylase 2 (HDAC2), which may facilitate patient stratification and treatment optimization. By integrating mechanistic insights, therapeutic opportunities, and translational challenges, this review goes beyond descriptive summaries to provide a framework for advancing nNaV1.5 research from preclinical studies toward clinical application in cancer therapy.

Patient cancer care education aims to inform and empower patients by providing clear, relevant information about cancer, including its mechanisms, treatment options, and management strategies. This study evaluated the effectiveness and accuracy of existing patient cancer care education materials in Malaysia, specifically assessing patients’ understanding of treatment, side effects, and their management. Method: A descriptive, correlational study was conducted involving 20 cancer patients at Gleneagles Penang Medical Center, using the Patient Cancer Education Needs Assessment questionnaire. Data analysis was performed using SPSS version 21.0 and Microsoft Excel 2013. Result: Patient age, education level, occupation, and socioeconomic status were not significant factors influencing understanding. Findings highlight the need to improve existing educational materials to enhance patient comprehension and ensure materials are comprehensive, culturally appropriate, and innovative. Conclusion: Patient cancer care education materials should be comprehensive, interactive, and patient is friendly. Current materials require revision and enhancement to keep pace with technological developments, ensuring they are accessible, understandable, and effective in supporting patients’ knowledge of cancer treatment, side effects, management strategies, and psychosocial aspects.

Nursing education has undergone a significant transformation as a result of artificial intelligence (AI). Chatbots, specifically ChatGPT, have emerged as vital AI technologies within the nursing domain as it is a computer program designed to simulate human conversation through text or voice interactions. This study aims to conduct a bibliometric analysis to gain insights into the publication trends, citation impact, and thematic evolution in nursing education and practice concerning ChatGPT and chatbots. A comprehensive bibliometric analysis was performed using VOSViewer, concentrating on citation networks for data analysis and visualisation. A review of LENS.org identified 344 relevant research publications regarding chatbots and ChatGPT within the nursing discipline, all of which were utilised in the study. The study examined various aspects, including types of publications, prominent authors, leading journals, participating nations, institutions, and the impact of ChatGPT on nursing practice. The primary objectives included categorising the papers, identifying the most influential authors, delineating the prominent areas and institutions in the field, and examining the impact of ChatGPT on nursing education and practice. The findings indicate that ChatGPT positively impacts nursing education by enhancing learning experiences, improving communication, and aiding clinical decision-making. The findings indicate that journal articles accounted for 76% of publications, with the U.S. leading in research output. The findings indicate that ChatGPT positively impacts nursing education by enhancing learning experiences, improving communication, and aiding clinical decision-making. Future research should focus on establishing frameworks for integrating ChatGPT into nursing education, addressing ethical implications, and assessing the long-term impacts on patient care.

Introduction: The Triglycerides-Glucose Index (TyG), as a cost-effective and novel biomarker for insulin resistance, plays a pivotal role in the pathogenesis of heart disease. This study aims to assess the TyG’s capacity to predict cardiovascular risk. To investigate the correlation between the TyG and the 10-year risk of heart disease determined by the Framingham Risk Score (FRS). Materials and methods: A comprehensive study of 3,832 Indonesian participants (aged 19-65, Male 3,415). TyG Index threshold determined by ROC curve analyses. Its relationship with cardiovascular risk was assessed using the chi-square test and bivariate correlation analysis. Results: 3,832 participants (1,647 with high TyG≥8.7795, mean age 38.86). There was a significant association between TyG Index and FRS (P=0.02, sensitivity 0.53 specificity 0.57 PR 1.537). TyG-BMI and FRS (P<0.001, sensitivity 0.63, specificity 0.55, PR 2.18). METS-IR (P<0.001, sensitivity 0.59, specificity 0.55, PR 1.862), treadmill exercise test and FRS (P<0.025, sensitivity 0.07, specificity 0.96, PR 2). Bivariate correlation analysis between FRS and TyG, TyG BMI, METS-IR, SBP, heart rate, weight, waist circumference, and fasting blood glucose (P<0.001). In subgroup analyses, there was no significant correlation between TyG Index and FRS in the diabetes and hypertension groups (P=0.360, P=0.344). Conclusion: This study shows a strong connection between the Triglycerides-Glucose Index and an elevated 10-year cardiovascular disease risk as determined by Framingham Risk Score. The effectiveness of The TyG Index in predicting cardiovascular risk is affected by hypertension and diabetes.

Objectives: This study aimed to estimate the prevalence of loss to follow-up (LTFU) among tuberculosis (TB) patients in Indonesia and to identify associated factors. Methods: This study analyzed data from the 2022 Tuberculosis Information System in Indonesia. A total of 71,665 drug-sensitive TB patients were included in the analysis. Age, sex,employment status, diagnosis type, human immunodeficiency virus (HIV) status, diabetesmellitus, TB type, mode of treatment, treatment standard, referral status, and type of residence.Categorical variables were analyzed using chi-square tests, followed by multivariable logistic regression to identify confounder-adjusted independent predictors of LTFU. Results: The prevalence of LTFU was 18.4%. A higher likelihood of LTFU was observed amongolder adults aged ≥65 years (adjusted-prevalence-odds-ratio [aPOR], 1.862), men (aPOR, 1.187), unemployed individuals (aPOR, 1.136), non-referred patients (aPOR, 1.547), patients with HIV (aPOR, 3.712), and those obtaining TB drugs out of pocket (aPOR, 4.998). The strongest predictorwas receipt of non-standard treatment, which was associated with a markedly increasedlikelihood of LTFU (aPOR, 26.912). In contrast, rural residence demonstrated a protective association (aPOR, 0.610). All associations were statistically significant (p < 0.001). Conclusion This study highlights a substantial burden of LTFU among TB patients in Indonesia,with nearly one in five patients discontinuing treatment. Multiple sociodemographic and clinical factors—particularly non-standard treatment, non-referral status, and HIV co-infection—were strongly associated with LTFU, underscoring gaps in the TB care continuum.These findings emphasize the need for targeted, patient-centered interventions, strengthenedreferral pathways, improved communication, and enhanced care coordination to reduce LTFU in high-risk groups.

A unified consensus statement on medication-related osteonecrosis of the jaw (MRONJ) has not yet been established among the Asian member countries or regions of the Asian Federation of Osteoporosis Societies (AFOS). This study aimed to develop a consensus on MRONJ in patients with osteoporosis across these countries and regions. In this study, the term “Asia-Pacific” refers specifically to the Asian member countries and regions of AFOS. A structured survey consisting of nine MRONJ-related questions was distributed across 10 countries and regions to assess the level of agreement and summarize regional perspectives. In addition, a manual literature review and voting were conducted to evaluate the current evidence on MRONJ. The key aspects of MRONJ, including definition, staging, diagnosis, pathogenesis, risk factors, management, and prevention, were generally consistent among the AFOS countries and regions. The annual incidence and incidence rate of MRONJ associated with low-dose antiresorptive therapy in patients with osteoporosis ranged from 0.025% to 0.136% and 21 to 283 cases per 100,000 person-years, respectively. However, evidence regarding the benefits of drug discontinuation before dental surgery, such as tooth extraction, remains insufficient. Large-scale, multinational studies across AFOS countries and regions are warranted to determine the incidence of MRONJ better and evaluate the impact of antiresorptive drug discontinuation before dental procedures. These findings may contribute to the devel opment of effective evidence-based strategies for preventing MRONJ in patients with osteoporosis.

Background: Work-related musculoskeletal disorders (WRMSDs) remain a major cause of occupational disability and productivity loss worldwide. Traditional statistical methods have identified numerous associated factors; however, they often struggle to capture complex non-linear relationships and interactions across multiple domains of risk. Machine learning (ML) offers an alternative analytical approach for modelling such multidimensional relationships. Methods: Following the PRISMA 2020 guidelines (PROSPERO: CRD420250605234), literature searches were conducted in Web of Science, Scopus, and PubMed for studies published between 2020 and 2025. Eligible studies applied ML methods to identify factors associated with WRMSDs using cross-sectional study designs. Included studies were appraised using the Joanna Briggs Institute Critical Appraisal Checklist for analytical cross-sectional studies. Results: Ten studies met the inclusion criteria, representing workers from healthcare, transport, manufacturing, and service sectors across Asia, Africa, and Europe. Frequently applied ML algorithms included random forest, support vector machine, and artificial neural networks, demonstrating strong internal discriminative performance (area under the receiver operating characteristic curve: 0.80–0.99), although the absence of external validation in several studies suggests a potential risk of overfitting. Commonly identified factors included age, sex, awkward posture, vibration exposure, prolonged working hours, stress, and burnout. Psychosocial factors, including post-traumatic stress disorder, job stress, and depression, were ranked among the most influential predictors within ML models. Conclusions ML models demonstrate strong capability in discriminating WRMSDs risk and identifying multidimensional risk factors compared with traditional statistical approaches. These models highlight complex interrelationships between ergonomic and psychosocial exposures. Future research should incorporate external validation, objective exposure measurements, and standardized ML reporting frameworks to enhance methodological transparency and generalizability.

Purpose: Down syndrome (DS) is associated with metabolic dysregulation, obesity, and increased risk of chronic inflammation. This study aimed to assess subclinical inflammation in children with DS by evaluating inflammatory biomarkers, such as high-sensitivity C-reactive protein (hs-CRP), and their association with metabolic parameters including ghrelin, lipid profiles, and vitamin D levels. Methods: A total of 49 children with DS (aged 1–18 years) and 22 age-matched healthy controls were enrolled. Anthropometric data, body fat percentage, and metabolic parameters were assessed. Inflammatory markers (hs-CRP, apolipoprotein-B [Apo B], adiponectin), metabolic hormones (ghrelin, insulin), and lipid profiles were determined from venous blood samples. Statistical analyses included bivariate correlation, analysis of variance, and multiple linear regression to identify predictors of inflammation. Results: Children with DS exhibited significantly higher hs-CRP levels than controls (p=0.03), indicative of increased systemic inflammation. Higher hs-CRP levels were associated with older age (r=0.33, p=0.006), greater obesity (body mass index: r=0.32, p=0.011), and elevated serum insulin and low-density lipoprotein levels. Ghrelin levels correlated negatively with Apo B (r=-0.41, p<0.001) and positively with hs-CRP (r=0.30, p=0.012). Predictors of inflammation (based on hs-CRP) included older age, male sex, higher gamma-glutamyl transferase level, and a diagnosis of DS (adjusted R²=0.276). Conclusion Children with DS are prone to metabolic inflammation, with increasing age and obesity exacerbating inflammatory responses. Clinicians should monitor and manage weight, dyslipidemia, and inflammation in this population to prevent long-term complications such as cardiovascular diseases and insulin resistance.

Stunting remains a major public health concern in Southeast Asia, and is shaped by a complex interplay of genetic, inflammatory, and nutritional factors. This scoping review sought to map genetic polymorphisms associated with stunting in Southeast Asian children and to identify candidate biomarkers for early diagnosis and biologically targeted interventions. Following the Arksey and O'Malley framework and the PCC (Population, Concept, Context) model, a systematic search was conducted across 7 databases. Eligible studies were peer-reviewed, published in English from 2015–2024, involved children under 18 years of age, and investigated gene variants in relation to stunting. A total of 902 records were screened independently by 3 reviewers using predefined criteria, with consensus procedures to resolve any discrepancies. Eleven studies met the final inclusion criteria. Thematic analysis and protein-protein interaction mapping revealed that 5 key polymorphisms—IGF1R, GHSR, MTRR, CASP1, and CARD17—were significant contributors to growth impairment. IGF1R polymorphisms were associated with a 2.46-fold increase in stunting risk (odds ratio [OR], 2.46; 95% confidence interval [CI], 1.60–3.78), while MTRR< variants yielded an OR of 1.93 (95% CI, 1.22–3.05). Similarly, GHSR and CASP1 polymorphisms were linked to increased odds of stunting (OR, 2.15; 95% CI, 1.38–3.34 and OR, 1.67; 95% CI, 1.10–2.54, respectively). These polymorphisms were consistently associated with disrupted growth hormone signaling, chronic inflammation, and nutrient-sensitive pathways. The biological network underlying stunting in this population points to a converging mechanism of impaired endocrine function and inflammatory dysregulation. However, this review’s scope is limited by underrepresentation of some Southeast Asian nations and exclusion of non-English literature. Early genetic screening for high-risk biomarkers and precision-driven nutritional interventions may offer more effective strategies to reduce the burden of stunting in Southeast Asian children.

Objective: To translate the Örebro Musculoskeletal Pain Screening Questionnaire (ÖMPSQ) into Urdu and to determine the psychometric properties of its Urdu version. Methods: This cross-sectional study recruited 300 participants (18–60 years) with non-specific low back pain (NSLBP), who were able to read Urdu. Test-retest reliability was assessed using intra-class correlation coefficient (ICC), and reproducibility through Cronbach’s α. Face and content validity were examined via individual interviews, and construct validity by correlating with relevant reference standards. Exploratory factor analysis (EFA) & confirmatory factor analysis (CFA) was also carried out Results: ÖMPSQ was successfully translated into Urdu version with acceptable face and content validity. ÖMPSQ Urdu version showed acceptable internal consistency (α=0.789) & good test-retest reliability (ICC=0.784, 95% confidence interval, p<0.001) while good correlation was demonstrated between ÖMPSQ and Chronic Pain Grade Scale (pain and disability subscales i.e., r=0.809 and 0.807, respectively). However, Roland-Morris Disability Questionnaire showed moderate correlation (r=0.513). Additionally, no significant floor or ceiling effects were observed in the ÖMPSQ Urdu version. EFA revealed a five-factor solution using twenty items, 89.21% was the total item variance in the database, while CFA demonstrated good model fit with strong factor loadings and acceptable fit indices Conclusion The ÖMPSQ Urdu version is valid and reliable for assessing the risk of long-term disability & workplace absence in NSLBP patients.