Intimate partner violence (IPV) is a serious public health concern linked with adverse health consequences such as posttraumatic stress disorder, depression, and anxiety. Although substance use disorders (SUDs) have been associated with IPV, there is limited research on IPV screening and disclosure among health care patients with SUDs, particularly veterans.

To examine the rates of IPV screening and disclosure among veterans with alcohol use disorder (AUD) and opioid use disorder (OUD) and to identify IPV subtypes and lethality risks, disaggregated by sex and SUD type.

This study analyzed IPV screening and disclosure rates among veterans in the Veterans Health Administration (VHA) diagnosed with AUD and/or OUD from January 2016 to December 2021.

790,384 veterans diagnosed with AUD, OUD, or both in VHA electronic health records.

Data included rates of IPV screening, rates of positive IPV screens (IPV+), IPV subtypes (psychological, physical, sexual), and risk of IPV-related lethality (escalation, strangulation, belief of being killed).

In our sample, 22.36% (n = 176,739) were screened for IPV, with 12.24% (n = 16,086) of those with a valid administration, screening positive. Female veterans had higher rates of being screened for IPV and positive IPV disclosures than males. Veterans with co-occurring AUD and OUD were more likely to screen positive for IPV than those with only AUD or OUD. Female veterans, particularly those with co-occurring AUD and OUD, were more likely to report high-lethality IPV compared to male veterans with AUD only.

Approximately 1 in 5 veterans in the cohort were screened for IPV. Female veterans with co-occurring AUD and OUD had the highest screen positive rate and severity of IPV. These findings highlight the need for routine IPV screening in VHA, tailored interventions, and integrated treatments addressing both SUD and IPV to improve health outcomes.

Growing evidence reveals relative stability in long-term clinical and functional outcomes of schizophrenia spectrum disorders (SSD), and links poor premorbid adjustment (PA) to worse prognoses. This study investigated associations between PA across developmental stages and long-term outcomes. Data on 496 participants (age 26.8 ± 6.5, male 58.5%) from the Danish randomised OPUS I trial were used (January 1998-December 2000; NCT00157313). Participants, with a first diagnosis within SSD, were recruited from outpatient and inpatient mental health services in Denmark and followed up for twenty years (retention = 29.0%). PA was assessed retrospectively at baseline using the Premorbid Adjustment Scale. Academic and social domain scores in childhood, and their subsequent changes in early and late adolescence, were used. Mixed models examined their effects on social functioning (Global Assessment of Functioning) and negative symptoms (Scales for the Assessment of Negative Symptoms). Low social functioning was associated with worse Social (β[95%CI] = 6.46 [0.54, 12.38], P ≤ 0.033) and Academic (9.40 [3.35, 15.44]; P ≤ 0.002) PA in childhood. Severe negative symptoms were associated with impaired Social PA in childhood (1.23 [0.73, 1.73], P ≤ 0.001), deterioration of Social PA in early adolescence (1.64 [0.93, 2.35], P ≤ 0.001), and declined Academic PA in late adolescence (0.77 [0.14, 1.39], P ≤ 0.016). Poor PA in childhood and PA decline during adolescence are related to Severer negative symptoms and lower social functioning 20 years after SSD onset. Our findings suggest that early signs may emerge in childhood, alongside age-dependent vulnerability-accumulation processes, when developing SSD. Support for maladjusting individuals during development is necessary to mitigate long-term impairments.

The case of mental health disorders has been a main topic in the clinical and psychological field. The advancement of computing studies, especially in Natural Language Processing (NLP)-a subset of Machine Learning, created a system of detection that can detect the mental health state of a person in early stage to prevent the eventuality of the worst case. This is crucial since there has been a lot of case of mental health disorder-such as depression and suicide, remains undetected and untreated-especially when the internet usage is more prevalent than ever even among the most vulnerable users, which are the preadolescent users. This study explores the models that can accurately predict mental health disorder with the provided six labels the model can predict. The labels are anxiety, depression, personality disorder, stress, bipolar, and normal. The dataset is gathered from a Kaggle repository which is then processed and refined further for the training process. From multiple evaluations across diverse amount of texts from different users, our Bi-LSTM-XGBoost model outperforms the other models with an accuracy of 0.9035 and 0.4320 loss, while other models fall short within 50-84% accuracy. Further improvement can be made with our model, whether from improving the model's parameters further or by improving the quantity and quality of the dataset gathered.

To synthesise existing qualitative and conceptual literature on the implementation, ethical considerations and policy implications of Medical Assistance in Dying for Mental Disorder as a Sole Underlying Medical Condition (MAiD MD-SUMC) in Canada and internationally.

A qualitative evidence synthesis using a thematic analysis approach. Empirical, conceptual and policy papers addressing MAiD for mental disorders were identified through major databases and grey literature. Studies were thematically analysed to identify recurring ethical, clinical and policy themes related to eligibility, assessment and implementation.

Data was extracted from a systematic search of Medline and Embase for peer-reviewed studies published from 1974 onwards, supplemented by relevant policy documents and legal cases.

Studies were included if they examined MAiD MD-SUMC and explored ethical, legal or clinical considerations or provided stakeholder perspectives. Exclusion criteria included studies focusing solely on non-psychiatric conditions or not published in English.

Two independent reviewers screened, extracted and analysed data using an iterative thematic synthesis approach. Key themes were identified through consensus discussions.

The synthesis identified four major themes: (1) Irremediability and treatment resistance-persistent uncertainty regarding when mental disorders can be considered irremediable. (2) Capacity and vulnerability-ongoing debate about assessing capacity amid fluctuating symptoms and social influences. (3) Ethical and policy considerations-divergent interpretations of autonomy, justice and safeguards highlighting the need for standardised criteria. (4) Public and professional perspectives-public and family support for inclusion, although clinician hesitancy exists.

The evidence supports a thoughtful, structured approach to potential implementation of MAiD MD-SUMC in Canada. Future priorities include refining criteria for irremediability, standardising capacity assessments, addressing disorder-specific complexities and strengthening mental health infrastructure. Continued research, engagement and transparent policy dialogue will be essential to ensure that any expansion of MAiD upholds ethical integrity, protects vulnerable persons and maintains public trust.

To evaluate changes in health-related quality of life (HRQoL) three years after laparoscopic hysterectomy (LH) for benign, prophylactic, and malignant indications, and to identify baseline factors associated with these changes.

Registry-based observational cohort study with longitudinal patient-reported outcomes.

Women undergoing LH at five Norwegian hospitals between 2019 and 2020.

LH for benign, prophylactic, or malignant indications.

HRQoL was assessed preoperatively and at three years using the RAND-36 across eight domains (0-100; higher scores indicating better health).

Of 939 eligible women, 575 (61%) responded at three years. Indications were benign disease (n=445, 77.4%), prophylactic surgery (n=71, 12.3%), and malignancy (n=59, 10.3%). Women with benign conditions showed significant improvements in bodily pain (mean difference (MD): 12.1, 95% confidence interval (CI): 9.3, 14.9) and physical role limitations (MD: 10.9, CI: 6.3, 15.5) (both p ≤ 0.001), with consistent effects across subgroups. Prophylactic surgery showed no significant changes. Malignant indications had mixed outcomes: physical functioning declined (MD: 9.5, CI -14.9, -4.1, p ≤ 0.001), while mental health improved (MD: 7.2, CI: 1.8, 12.5, p = 0.009). Among benign cases, higher baseline HRQoL predicted less improvement (regression coefficient β = -8.0 to -4.6, p < 0.001).

Three years after LH, changes in HRQoL outcomes varied by indication. Women with benign conditions showed significant improvements, while prophylactic surgery showed no significant change and malignant indications yielded mixed effects. Baseline HRQoL was the strongest predictor of long-term change, highlighting its importance in preoperative counseling.

The need to improve predictive analytics in healthcare demands strong frameworks that would be able to interpret the complicated physiological signals well. In this study, the researcher presents a complex multi-modal emotion recognition architecture of mental health care based on EEG, ECG, and GS Rrecordings with aGPT-based NLP interface to understand brief clinical text input and self-reported emotional responses. The framework com bines sophisticated preprocessing and synchronization can be performed using cross-correlation, noise reduction using discrete wavelet transform, and event segmentation then feature extraction can be done using wavelet scattering transform and statistics. The dimensionality reduction is through two-dimensional bidirectional principal component collaborative projection as well as the use of canonical correlation analysis to make sure that fusion of features is effective. According to the experimental assessment, provision of contextual embeddings produced by GPT results in a better interpretability score and contributes to clinical reasoning, which in turn improves the healthcare decision-making. Optimized WOA-KELM model performs significantly better than the traditional classifiers like SVM, k-NN, and XGBoost as well as the standard KELM with high valence and arousal classification rates of 96.93 and 99.05 respectively. Valence and arousal are treated as binary classification tasks for emotion recognition. The GPT module is used only for post-classification interpretation and does not influence the classification performance. Also, the GPT component demonstrates the possibilities of optimized, multi-model solutions to facilitate predictive healthcare analytics meaningfully and provide credible applications in emotion-aware diagnostics, mental health monitoring, adaptive human-computer interaction, and future ways of providing real-time and personalized healthcare solutions.

Problematic internet use (PIU) and problematic social-media use have been associated with depressive symptoms and suicidal behaviors among university students, with limited Mediterranean evidence. This study examined their associations with stressful life events, depressive symptoms, and suicidal behaviors.

A cross-sectional anonymous online survey conducted among undergraduates at the Cyprus University of Technology. Participants completed Internet Addiction Test-20 (IAT-20) to assess PIU risk, Bergen Social Media Addiction Scale (BSMAS) to assess problematic social-media use, Center for Epidemiologic Studies Depression Scale (CES-D) to assess depressive symptoms, Life Events Scale for Students (LESS-36) to assess stressful life events, and Suicidal Behaviors Questionnaire-Revised (SBQ-R) to assess suicidal behaviors. Correlation and multivariable linear regression analyses examined associations with depressive symptoms and suicidal behaviors.

1002 students completed the survey (45% response rate); 67.7% were female. PIU risk was minimal (51.1%), mild (38.6%), and moderate (10.3%). BSMAS and LESS-36 scores correlated with depressive symptoms (ρ = 0.47; ρ = 0.30) and suicidal behaviors (ρ = 0.24; ρ = 0.31; all p < 0.001). Adjusted analyses showed depressive symptoms were associated with female gender, mild-moderate PIU, problematic social-media use, and stressful life events. Suicidal behaviors were associated with male gender, non-Cypriot nationality, family history of mental illness, screen time, mild-moderate PIU, stressful life events, and depressive symptoms.

Problematic internet and social-media use and stressful life events were associated with depressive symptoms and suicidal behaviors; longitudinal research is needed to clarify temporal relationships.

Traumatic events produce enduring memories that may be attenuated through extinction learning. Previous work has identified neuronal mechanisms underlying extinction learning that involve the remodeling or inhibition of neuronal ensembles (or engrams) that support the original fear memory. Here we identify a role for microglia in extinction learning in mice. We show that, during extinction, microglia are recruited to the soma and dendritic processes of fear engram neurons in the dentate gyrus. Interactions between microglia and somata mediate transient silencing of engram neurons. Inhibition of microglial recruitment to somata attenuated extinction-induced reductions in engram reactivity and slowed extinction. By contrast, interactions between microglia and dendritic processes promote engulfment of engram synapses and remodeling of engram neurons. Blocking complement signaling in engram neurons prevented extinction-induced engram neuron remodeling and slowed extinction. Together, these findings identify microglia as key regulators of fear engram expression and remodeling during extinction learning.

This study provides prevalence estimates of gaming disorder (GD) risk among Polish adolescents and validates a Polish translation of the Gaming Motivation Inventory (GMI). Moreover, it explores the role of introjected regulation in GD using a comprehensive motivational model. A representative sample of 1060 adolescents (930 gamers) completed measures of GD symptoms, gaming motivation, and involvement. GD risk ranged from 7.7% to 10.8%, higher among boys (8.9%-12.2%) than girls (6.5%-7.5%). The Polish GMI demonstrated strong psychometric properties, supporting its use in assessing gaming motivations among Polish adolescents. Introjected regulation, escape and identity were among the motivations most strongly associated with GD symptoms, with introjected regulation emerging as the strongest predictor.

Local policy makers increasingly have implemented nutrition standards for municipal programs to advance population health and climate change goals. Yet little is known about the impact of these policies. In 2008, New York City established nutrition standards for food purchased and served by city agencies, and in 2022, it revised the standards to limit meat and increase plant-based options. Using menu data from four agencies serving 77 percent of all city meals, we examined changes in their entrée offerings, as well as greenhouse gas and nutrition content associated with their total menu offerings, from fiscal year 2019 through fiscal year 2024. All agencies reduced the frequency of beef entrées offered on menus and increased the frequency of vegetarian entrées. Changes in total menu offerings were associated with an estimated reduction of 0.64 kilograms of carbon dioxide equivalent in greenhouse gas emissions per portion across all agencies and programs, while the nutrition content generally remained consistent. These findings suggest that municipal food standards can support greenhouse gas reductions without compromising nutrition, and they offer a model for other jurisdictions seeking to advance both population and environmental health goals.