Introduction Suicide is a major global public health problem, responsible for over 1 in 100 deaths worldwide, with South Asian countries bearing a particularly high burden. In Pakistan, the absence of a national suicide surveillance system obscures the true scale of the issue. This review aims to systematically map the existing literature on suicide in Pakistan, with a focus on identifying key contributing factors, commonly used methods, and the role of underlying mental health conditions associated with suicidal ideation, deliberate self-harm, and suicidal attempts. Method This scoping review followed methodological approach outlined by Arksey and O'Malley, integrating the evidence from both peer-reviewed sources and grey literature. We searched National library of medicine MEDLINE (PUBMED), Directory of open access Journals (DOAJ), Cochrane trial registers (CRG), Pakistan Medical Research Council (PMRC) Publications, Pakmedinet.com, Google scholar, PubMed Central (PMC) from year 2010-2025 using a combination of key terms. A total of 61 studies were included, including a thesis from the grey literature. Results Most of the included studies were cross-sectional and urban-based, with limited focus on rural areas, indicating a significant data gap. Females showed higher rates of deliberate self-harm and suicide. At-risk groups included young adults and transgender individuals. The key contributing factors were domestic violence, academic pressure, emotional strain, and low socioeconomic status. Self-poisoning was the most common method, with increasing cases involving paraphenylene diamine. Emerging trends also include suicide related to online gaming (e.g., PUBG). Hanging and firearm use remain prevalent methods. Although formal psychiatric diagnoses were rarely reported, depression and anxiety were frequently associated with suicide. Conclusion Suicide remains a critical yet under-reported issue in Pakistan, driven by easy access to lethal means, weak regulation, and evolving risk factors such as online gaming. Vulnerable groups include women, transgender individuals, and youth, often affected by violence, academic pressure, and untreated mental health conditions. Addressing this crisis requires improved surveillance, restricted access to means, early mental health intervention, and coordinated prevention efforts across sectors.

Stigmatizing attitudes toward mental illness among health professional students may influence future practice and contribute to workforce shortages in mental health services. Evidence from Nepal regarding nursing students' attitudes and career intentions in mental health nursing remains limited. This study assessed attitudes toward people with mental illness and career intentions in mental health nursing, and examined associated factors.

A cross-sectional census study was conducted among all Bachelor of Science in Nursing (BSN) students (n = 287) from two institutions in Pokhara, Nepal. Data were collected using a modified Mental Health Nurse Education Survey. Construct validity was examined using exploratory factor analysis, and internal consistency was assessed using Cronbach's alpha. Associations were analyzed using Mann-Whitney U tests and Spearman's correlations. Multivariable linear regression identified independent predictors of career intentions.

Students demonstrated generally positive perceptions of mental health nursing; however, only 24.0% intended to pursue a career in mental health nursing. Knowledge and preparedness (β = 0.36, p < 0.001), clinical preparedness (β = 0.14, p = 0.01), and lower stigma toward mental illness (β = 0.19, p < 0.001) were independently associated with career intentions. The model explained 25.6% of the variance (R² = 0.256, p < 0.001). Knowledge and preparedness showed the strongest association with career intentions.

Although undergraduate nursing students expressed favorable attitudes toward mental health nursing, career intentions remained modest. Higher knowledge, clinical preparedness, and lower stigma were associated with stronger career intentions in mental health nursing; however, career intentions are likely influenced by additional unmeasured factors.

Ketamine has demonstrated rapid antidepressant efficacy in treatment-resistant depression (TRD), but clinical decision-making is challenging due to variability in individual response. Current trial-and-error prescribing practices may expose patients to ineffective treatment and avoidable adverse effects, underscoring the need for reliable predictive tools to optimize treatment selection and support personalized, evidence-based care. We developed a machine-learning model (support vector classifier) to predict antidepressant response to ketamine using pre-treatment structural MRI data. The model was trained on 99 adults with TRD given a single intravenous ketamine infusion (0.5 mg/kg). Clinical response was defined as a ≥50% reduction in MADRS scores 24 h post-infusion. Internal validation used repeated nested cross-validation, and generalizability was tested in two independent ketamine-treated cohorts (n = 51) and a saline-treated control group (n = 49). Among ketamine-treated participants, 52 (52.5%) responded to treatment. The model achieved a balanced accuracy of 72.2% (sensitivity = 72.3%, specificity = 73.1%, AUC = 0.72) in the discovery sample and 60.0% (p = 0.01, AUC = 0.65) in external validation. Greater gray matter volume in frontal regions predicted response, whereas greater cerebellar volume predicted non-response. Performance dropped to chance in the saline cohort (BAC = 41.1%, AUC = 0.45), supporting pharmacologic specificity. These findings present the first machine-learning model for the prediction of ketamine response in TRD using structural neuroimaging and highlight its potential utility for stratified treatment planning and biomarker-informed interventions while providing mechanistic insight into neuroanatomical predictors of antidepressant response.

Major Depressive Disorder (MDD) is a globally widespread mental health disorder that frequently remains underdiagnosed and inadequately treated. Recent advancements in circular RNAs (circRNAs) have illuminated their potential as biomarkers for a variety of diseases, including MDD. This review emphasizes the advantages of circRNA enrichment methodologies over traditional techniques, particularly isotachophoresis. Furthermore, the intricate role of circRNAs in the pathophysiological processes underlying MDD, as well as their integration with biosensor technology to improve diagnostic accuracy and efficiency, are synthesised. However, the clinical translation of circRNA-based diagnostics faces significant challenges, including the low abundance of circRNAs in bodily fluids, the need for highly sensitive and rapid detection platforms, and the lack of standardized, point-of-care compatible methods. A comprehensive overview of current circRNA detection methods, delineating their similarities and differences, is discussed. Insights for the anticipated advancements in quantitative and rapid circRNA detection is proposed. This review not only presents a thorough assessment of emerging trends in circRNA detection but also elaborates on primary techniques, traditional approaches, and recent innovations within the field of biosensor-based MDD diagnostics.

Wound management remains a significant clinical challenge due to bacterial infection, oxidative stress, and inflammation. To address these issues, we developed a multifunctional hydrogel, NO-GM/Fle@FD, by combining gelatin methacryloyl (GelMA), Pluronic F127 diacrylate (F127DA), the nitric oxide (NO) donor S-nitrosoglutathione (GSNO), and the antibiotic fleroxacin (Fle). The hydrogel allows rapid photopolymerization under 405 nm light, forming a robust network with controlled NO release and localized antibiotic delivery. In infected murine wound model, NO-GM/Fle@FD accelerated wound closure through three mechanisms: (1) infection suppression via fleroxacin-mediated bactericidal activity, (2) ROS scavenging to reduce oxidative damage, and (3) inflammatory modulation through sustained NO release. Histological analysis revealed complete re-epithelialization by day 10, reduced inflammation, and enhanced collagen deposition in the NO-GM/Fle@FD group. Immunofluorescence showed decreased IL-1β (pro-inflammatory) and increased IL-10 (anti-inflammatory), confirming the hydrogel's ability to resolve inflammation and counteract oxidative stress. This study demonstrates that NO-GM/Fle@FD effectively targets the infection-oxidative stress-inflammation triad, providing a promising therapeutic solution for treating infectious wounds, diabetic ulcers, burns, and other chronic complex wounds.

Traditional approaches to the diagnosis of personality disorders, including a clinical interview and a self-report, are usually limited by subjectivity and time constraints. Recent developments in artificial intelligence have opened the possibility of more objective and data-driven psychological testing. This paper introduces an AI-powered system that forecasts personality disorders using natural language processing (NLP), speech recognition, and face recognition. The suggested method should help with the initial diagnosis and more tailored mental health solutions. Two benchmark datasets were used: myPersonality for text analysis and DAIC-WOZ for multimodal analysis of speech and facial expressions. The feature extraction methods were TF-IDF, Vader sentiment scores, Mel-Frequency Cepstral Coefficients, prosodic features, facial action units, and gaze tracking. BiLSTM, CNN, BERT, and GPT-3 models were analyzed through accuracy, precision, recall, F1 score, and AUC-ROC. GPT-3 was the most accurate at 89.1%, followed by BERT at 87.4% and CNN-based facial analysis at 85.6%. The findings indicate that multimodal fusion improves classification by leveraging holistic and complementary behavioral information. These results support the promise of multimodal systems with AI capabilities to make more precise predictions of personality disorders and underscore the need to consider interpretability, fairness, and data privacy in future applications. It should be mentioned that the current study deals with the problem of both normal personality traits prediction (through the Big Five framework) and features that can reflect psychological distress and be related to personality disorders. The myPersonality data set measures normative personality dimensions, whereas the DAIC-WOZ data set measures multimodal behavioral data that is relevant in clinical terms. The paper explains the connections between extreme trait profiles and clinical personality disorders to reconcile the paradigms of trait and disorder assessment.

Obsessive-compulsive disorder (OCD) is characterized by disrupted brain network organization, yet the molecular basis underlying this dysconnectivity remains elusive. Here, we applied voxel-wise functional connectivity density (FCD) mapping to characterize brain network alterations across 145 patients with OCD and 168 healthy controls (HCs), while further evaluating its ability to support diagnostic classification and predict treatment response in OCD. Then, we examined the spatial correlations between FCD alterations, neurotransmitter distributions, and gene expression profiles. Relative to HCs, OCD patients showed increased FCD in the visual network and decreased FCD in the limbic and default mode networks. Support vector machine (SVM) and support vector regression (SVR) analyses demonstrated that FCD could efficiently discriminate OCD patients from HCs and predict treatment response. Additionally, the FCD alterations showed significant spatial correlations with four neurotransmitter distributions as well as gene expression patterns enriched in excitatory and inhibitory neurons, synaptic signaling, neuronal function, and cellular metabolism. By integrating neuroimaging, neurotransmitter profiles, and transcriptomics, this study reveals that aberrant FCD in OCD reflects both its clinical relevance and molecular basis, providing insights into the neurobiological mechanisms and potential targets for personalized intervention.

Variants in AFG3-Like Matrix AAA Peptidase, Subunit 2 (AFG3L2) gene are associated with diverse clinical phenotypes. Here, we describe phenotypic findings of two unrelated children with de novo heterozygous variant and one family with inherited heterozygous variant in AFG3L2 gene.

A 3-year-old girl presented with global developmental delay, vision disturbances and frequent falls. She developed choreoathetosis, feeding difficulty and sleep disturbances from 3.5 years of age. Developmental regression, and optic atrophy were identified. An 11-year-old girl presented with left foot dystonia and tremulousness at 2 years of age which later progressed to developmental regression and generalized dystonia. Bilateral optic disc pallor was observed. In contrast, spinocerebellar ataxia 28 phenotype with variable expressivity was observed in adult patients with inherited heterozygous variant in AFG3L2 gene. Treatment with levodopa offered variable clinical benefits.

Our report emphasizes phenotypic heterogeneity in children and adults with heterozygous variant in AFG3L2 gene.

Obesity remains a major global health challenge with profound clinical, psychosocial, and economic consequences. Although body mass index (BMI) is widely used, alternative adiposity indices such as waist-to-height ratio (WtHR), Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), and the Metabolic Score for Visceral Fat (METS-VF) may offer superior insights into central adiposity and cardiometabolic risk. Furthermore, the relationship between obesity and health-related quality of life (HRQoL) remains insufficiently explored in occupational settings, where lifestyle, socioeconomic, and psychosocial factors converge.

We conducted a cross-sectional study of 100,014 Spanish workers (18-69 years) who underwent standardized occupational health assessments between 2021 and 2023. Anthropometric and biochemical data were collected to calculate BMI, WtHR, CUN-BAE, and METS-VF. Sociodemographic characteristics, lifestyle habits (smoking, physical activity, and Mediterranean diet adherence), and HRQoL (12-Item Short Form Health Survey, SF-12) were also recorded. Logistic regression models were used to evaluate associations between obesity indices and sociodemographic, behavioral, and HRQoL variables, adjusting for potential confounders.

Obesity prevalence varied markedly depending on the index employed, ranging from 17.2% with BMI to over 30% with CUN-BAE and METS-VF. Across all measures, older age, male sex, lower social class, smoking, physical inactivity, and poor adherence to the Mediterranean diet were significantly associated with higher odds of obesity. Importantly, workers with lower SF-12 physical and mental component scores consistently showed greater obesity risk, even after adjustment. These findings confirm the bidirectional interplay between excess adiposity and diminished quality of life.

In this large occupational cohort, obesity was strongly associated with adverse sociodemographic profiles, unhealthy lifestyles, and impaired HRQoL. Our results highlight the limitations of BMI alone and underscore the value of alternative adiposity indices in public health surveillance. Integrating HRQoL assessment into obesity prevention and workplace health programs may enhance early detection, risk stratification, and the design of holistic interventions targeting both physical and psychosocial well-being.

Positive childhood experiences (PCEs) and adverse childhood experiences (ACEs) influence adult health and well-being. While ACEs are linked to chronic disease and poor mental health, PCEs have been shown to build resilience and promote positive outcomes. This study investigates associations between PCEs and adult health and socioeconomic outcomes and whether ACEs moderate these relationships.

We analyzed data from the Behavioral Risk Factor Surveillance Survey from 4 states (2015-2020), including 18 773 adults. PCEs and ACEs were categorized and evaluated against 20 adult health and life opportunity outcomes. Propensity score matching and survey-weighted generalized linear models were used to estimate adjusted prevalence ratios, population attributable fractions, and prevented fractions for the population PFPs.

Reports of PCEs and ACEs were inversely related. Higher PCEs were associated with significantly improved outcomes, including reduced depression, better mental health, and increased income and education, regardless of ACEs. Among those reporting ACEs, adults reporting high PCEs reported lower tobacco use, lower chronic diseases (eg, asthma, diabetes, heart disease), and better general health. PFPs indicated that maximizing PCEs at the population level could theoretically prevent up to 36.5% of depression and 30.7% of poor mental health in adulthood.

PCEs were strongly associated with better adult health and well-being, even in the presence of adversity. ACEs moderated some but not all associations, highlighting the independent protective associations of PCEs. These findings support the potential value of PCEs as a public health strategy to improve long-term outcomes and reduce societal costs.