Workplace violence (WPV) against healthcare workers (HCWs)is a global issue. Resilience, a constructive psychological resource, can help HCWs better cope with WPV. This study examined resilience levels and factors influencing HCWs exposed to WPV.

Using a convenience sampling method, 180 HCWs from two tertiary hospitals in Shanghai completed a self-administered questionnaire assessing demographic characteristics, hospital WPV, social support, general self-efficacy, emotion regulation, and resilience. Data were collected using the Questionnaire Star platform and analyzed using SPSS 27.0 software.

Over the past 12 months, verbal violence being the most prevalent (90.0%) among HCWs who have experienced WPV. Participants had high resilience scores (70.21 ± SD 12.255). Linear regression analysis demonstrated that self-efficacy (β = 0.363, P < 0.001) and cognitive reappraisal (β = 0.312, P < 0.001) positively affected resilience.

The prevalence of WPV among HCWs was high. Although their resilience levels were strong, further reinforcement is needed by enhancing self-efficacy and fostering an adaptive cognitive perspective. This study suggests that self-efficacy and cognitive reappraisal are key intervention focal points that contribute to the resilience of HCWs exposed to WPV.

Research suggests that sleep disturbances are common among people who use psychostimulants, including cocaine use and methamphetamine use. These sleep disturbances hinder recovery, heighten relapse risk, and worsen physical and mental health outcomes. This study aimed to estimate the prevalence and severity of insomnia among individuals who use cocaine and methamphetamine and to explore potential psychological factors that are associated with more severe insomnia. The sample included participants who completed in-person screening assessments for ongoing studies at an outpatient research clinic. As a part of this screening, participants completed the Insomnia Severity Index (ISI) and validated assessments of psychological domains related to negative affect, such as distress tolerance, anhedonia, emotion regulation, and posttraumatic stress symptoms. Regularized regression identified and retained the most important psychological variables for predicting the presence of clinically-significant insomnia. Results indicated that the prevalence of clinically significant insomnia was 36.6 % in those who use cocaine and 44.7 % in those who use methamphetamine, and the prevalence of subthreshold insomnia was 35.3 % in individuals who use cocaine and 39.3 % in individuals who use methamphetamine. Regression results indicated that depression symptoms were the strongest predictor of insomnia severity, along with difficulties in emotion regulation and posttraumatic stress symptoms. Similarities and differences between cocaine and methamphetamine subgroups were also identified. These results suggest that insomnia in individuals who use cocaine and methamphetamine may be shaped by a collection of modifiable psychological processes, and highlight the need for tailored, integrated interventions that address sleep, affective functioning, and trauma-related processes within stimulant-focused SUD treatment.

Emotion Regulation (ER) is the ability to manage emotional responses. ER is important for maintaining mental health and handling social interactions, especially under stress. This study explores the brain activity involved in ER using electroencephalography (EEG) and machine learning (ML) models to predict successful and unsuccessful ER. Study participants viewed emotional and neutral images under two conditions: regular viewing and being asked to reduce their emotions. At the end of each experimental trial, participants rated the intensity of their emotional response to the image. Ratings of low intensity (1 and 2) were classified as successful ER, whereas ratings of high intensity (3 and 4) were considered indicative of unsuccessful ER. EEG signals were analyzed in both time and frequency domains to identify patterns linked to ER. In the time domain, significant differences in Global Field Power (GFP) were observed, especially in the frontal and central regions of the brain. Frequencydomain analysis using Power Spectral Density (PSD) showed that theta, beta, and gamma bands were important for regulating emotions. Using these analysis results, machine learning models were trained to predict regulation success. Among the models, a neural network with Maximum Mean Discrepancy (MMD) loss performed the best, achieving an F1-score macro of 75.57% with a subject-independent approach. These machine-learning models highlight the importance of frontal and central brain regions and beta brain frequency signals in the prediction of ER levels. It shows that combining EEG data with advanced machine learning methods can create accurate models for understanding and predicting emotional responses. Additionally, this integrated EEG-based approach represents a novel framework for ER assessment, offering a promising direction for future research and enabling personalized mental health treatments.

The present study undertook the adaptation and psychometric validation of the Hungarian version of the General Attitudes toward Artificial Intelligence Scale (GAAIS) to assess both positive and negative attitudes toward artificial intelligence (AI) in relation to psychosocial functioning and personality traits.

The adaptation followed international test-adaptation standards, involving translation, back-translation, and expert review. A total of 704 participants (557 women, 144 men) aged 18-60 years (M = 27.8, SD = 10.6) completed the GAAIS together with several validated self-report measures: the Mental Health Continuum-Short Form (MHC-SF), Self-Concept Clarity Scale (SCCS), frequency of AI usage, Problematic Internet Use Questionnaire (PIUQ), and Schizotypal Personality Questionnaire-Brief Revisited (SPQ-BR).

The Hungarian version showed solid internal consistency (Cronbach's α = 0.85 for the positive and 0.81 for the negative subscale) and a clear two-factor structure, supported by confirmatory factor analysis (CFI = 0.951, RMSEA = 0.058). The frequency of AI use in daily life emerged as the strongest predictor of both positive and negative attitude scores lending further support to the construct validity of the scale. The association analysis revealed that the behavioral components of AI-related attitudes are shaped by the competing motivational forces-approach (positive) and avoidance (negative). Specifically, the frequent use of AI is linked to the positive attitudes of GAAIS. In contrast, the unfavorable use of AI is associated with the negative attitudes of GAAIS. In the affective domain, anxiety sensitivity is associated with a negative attitude, and in the cognitive domain, schizotypal cognitive characteristics and difficulties in self-integration are linked to elevated negative attitudes in GAAIS. However, on the other pole of this cognitive dimension, adequate self-integration does not play a significant role in the formation of an AI-related positive attitude.

These findings confirm the reliability and validity of the Hungarian GAAIS and highlight the importance of personality traits in shaping adaptive and maladaptive attitudes toward AI. The results underscore the value of a multidimensional framework for understanding AI attitudes. Adaptive traits were associated with psychological resilience, effective self-regulation, and constructive digital engagement, whereas maladaptive traits were correlated with social anxiety and problematic interactions with the internet and artificial intelligence (AI) technologies. A critical question remains: What outcomes may arise from when individuals hold positive attitudes toward AI but simultaneously experience difficulties with self-integration? This paradox highlights the need for further research into the complex interplay between personality structure and digital adaptation.

Growing evidence highlights the role of dietary habits in shaping mental health outcomes, particularly among young adults. This study aimed to examine the associations between dietary intake of fat and sugar and mental health outcomes-specifically depression and anxiety-among university students in Lebanon.

A cross-sectional survey was conducted between January and May 2025 across five major universities in Lebanon. A total of 646 students aged 18-30 years completed a self-administered online questionnaire. Dietary intake of fats and free sugars was assessed using the validated Dietary Fat and Free Sugar Questionnaire (DFS). Mental health outcomes were measured using the Patient Health Questionnaire-9 for depression (PHQ-9) and the General Anxiety Disorder (GAD-7). Descriptive statistics, bivariate analyses, and logistic regressions were performed to evaluate associations between dietary fat or sugar intake and mental health outcomes, adjusting for relevant confounders.

Among 646 university students (64.9% females, mean age = 20.8 ± 3.7 years), 74.8% reported depressive symptoms, 72.1% reported anxiety. The mean consumption of fat and sugar was 29 ± 6.7 and 15 ± 4.4, respectively using the DFS Fat and Sugar subscales. In multivariable analysis, depression was significantly associated with overall health, moderate alcohol consumption, physical activity, fat intake and sugar intake, while anxiety was significantly linked to overall health, caffeine consumption, sugar intake, but not fat intake.

Our findings call for integrated, holistic health promotion strategies within university settings that combine nutrition education, physical activity encouragement and mental health support.

Non-communicable diseases (NCDs) account for 74% of global deaths, with low- and middle-income countries (LMICs) bearing a disproportionate burden. In Bangladesh, NCDs are responsible for 66% of all deaths, projected to rise to 75% by 2030. A major contributor to this trend is the high consumption of free sugars, particularly through sugar-sweetened beverages (SSBs). Although SSB taxation has been shown to reduce sugar consumption and improve health outcomes, Bangladesh has yet to implement such a tax. This study explores the perspectives of key stakeholders involved in the development of a proposed sugar tax on foods and non-alcoholic beverages in Bangladesh.

A qualitative study was conducted, involving thirty key informant interviews with interest holders from government, private industry, civil society, and public health sectors. Data was analyzed thematically. The study examined the feasibility, challenges, and opportunities associated with implementing a sugar tax in Bangladesh.

Several factors were identified as barriers to the development of a sugar tax. These include a policy paradigm influenced by socio-cultural beliefs that resist regulatory food policies, lack of leadership from the Ministry of Health, strong opposition from the private industry, and limited awareness among key policymakers about the link between diet and non-communicable diseases (NCDs). The power imbalance between the private sector and public health authorities further hinders progress. Despite these challenges, interviewees suggested measures to support the tax's adoption, including strengthening the Ministry of Health's leadership, promoting inter-ministerial collaboration, and increasing public awareness through media advocacy.

The lack of government leadership and strong industry opposition present significant obstacles to implementing a sugar tax in Bangladesh. To overcome these challenges, sustained advocacy for decisive leadership and consistent government and public health messaging is crucial. A coordinated approach will be necessary to advance this policy and effectively address diet-related non-communicable diseases (NCDs).

Cadherin-11 (CDH11), a specialized cell-cell adhesion protein, plays an essential role in tissue injury, inflammation and repair. This study aimed to investigate the role of CDH11 in severe asthma. Bronchial biopsy specimens were obtained from healthy subjects and patients with severe asthma. Two murine models of severe asthma were established using either TDI (toluene diisocyanate) or OVA (ovalbumin)/CFA (complete Freund's adjuvants). A selective CDH11 antagonist SD133 (100 mg/kg) was given to allergen-exposed mice after airway challenge. The effects of recombinant CDH11 were also tested in vivo, and FGFR1 inhibition was used to explore a possible mechanism for CDH11-induced inflammatory responses in the lung. We detected upregulated expression of CDH11 in the airway mucosa of severe asthma patients when compared with the healthy control. In the OVA/CFA-induced model, though CDH11 expression in the lung remained unchanged, pharmacological antagonism of CDH11 with SD133 dramatically decreased airway neutrophil accumulation, as well as IL-6 production, but had no effect on eosinophilic infiltration, type 2 inflammation (IL-4 and IL-5) nor airway hyperresponsiveness. In the TDI model, pulmonary CDH11 expression was upregulated. Treatment with SD133 inhibited TDI-induced airway hyperresponsiveness and neutrophilic inflammation, decreased IL-6 and TNF-α production, with no effect on airway eosinophil counts and type 2 inflammatory cytokines. In addition, intratracheal instillation of recombinant CDH11 led to neutrophil recruitment in the lungs of mice, which could be attenuated by inhibition of FGFR1 signaling. CDH11 contributes to airway neutrophilic inflammation in severe asthma through the FGFR1 pathway.

Although the cardiovascular benefits of GLP-1RAs in type 2 diabetes are established, their effects in type 1 diabetes remain unclear. This study aimed to evaluate the impact of GLP-1RAs as adjunctive therapy on cardiometabolic risk factors in T1DM.

A comprehensive search of randomised controlled trials (RCTs) was conducted in PubMed, Embase, Cochrane Library, Web of Science. The search covered all studies published from database inception to August 30, 2025. The methodology followed the PRISMA 2020 guidelines and the Cochrane Handbook for Systematic Reviews of Interventions, with eligibility criteria based on the PICOS framework. Randomised controlled trials comparing GLP-1 receptor agonists with placebo or standard insulin therapy in patients with type 1 diabetes mellitus were included.

A total of 21 RCTs involving 3417 patients and evaluating five different GLP-1RAs were included. Compared with the control group, GLP-1RAs significantly reduced systolic blood pressure (WMD: -2.65 mmHg, 95% confidence interval [CI]: -3.81 to -1.49, I² = 0.0% and p < 0.001_effect), decreased diastolic blood pressure (WMD: -0.99 mmHg, 95% CI: -1.70 to -0.28, I² = 0.0% and p = 0.006_effect) and increased heart rate (WMD: 3.90 bpm, 95% CI: 2.54 to 5.26, I² = 0.0% and p < 0.001_effect). Similarly, total cholesterol (WMD: -0.15 mmol/L, 95% CI: -0.29 to -0.01, I² = 0.0% and p = 0.031_effect), LDL cholesterol (WMD: -0.12 mmol/L, 95% CI: -0.22 to -0.01, I² = 0.0% and p = 0.028_effect) and CRP (SMD: -0.32, 95% CI: -0.54 to -0.10, I² = 0.0% and p = 0.005_effect) were significantly reduced, whereas no significant changes were observed for triglycerides, VLDL cholesterol, HDL cholesterol, TNF-α or IL-6 (all p > 0.05_effect). Previous meta-analyses have shown the efficacy and safety of GLP-1RAs in T1DM, and this study once again verified that GLP-1RAs can significantly reduce glycosylated haemoglobin (HbA1c) (WMD: -0.21% and 95% CI: -0.26 to -0.17), body weight (WMD: -3.91 kg and 95% CI: -4.53 to -3.30) and body mass index (BMI) (WMD: -1.52 kg/m², 95% CI: -1.88 to -1.16) (all p < 0.05_effect).

The use of GLP-1RAs as adjuvant therapy for T1DM is not only beneficial for glycaemic control and weight loss, but also can lead to important improvements in other cardiometabolic risk factors and provide valuable guidance for clinicians in making treatment decisions for this population.

Opioid use disorder (OUD), cannabis use disorder (CUD) and cocaine use disorder have been associated with a range of adverse health outcomes, including certain ocular manifestations; however, their impact on diabetic retinopathy (DR) remains insufficiently explored. This study aimed to measure the association between OUD, CUD and cocaine use disorder and the risk of DR among patients with type 2 diabetes mellitus (T2DM).

Propensity-score-matched retrospective cohort study.

This study used the TriNetX US Collaborative Network to access electronic health records (EHRs), including data on demographics, diagnoses, medication use and laboratory results.

A total of 131 088 adult patients with T2DM and comorbid OUD, CUD or cocaine use disorder, and 131 088 adult patients with T2DM without these conditions, were identified following propensity score matching.

The primary outcome was the risk of DR evaluated over a 5-year follow-up period. The risks of various DR subtypes and diabetic macular edema (DME) were also assessed. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated.

Over a 5-year follow-up period, patients with T2DM comorbid with OUD, CUD or cocaine use disorder had a statistically significantly higher risk of developing DR [HR (95% CI) = 2.90 (2.55-3.30), P < 0.00] compared with those without any drug use disorder. Drug use disorders were also associated with elevated risks of vision-threatening diabetic retinopathy (VTDR) [HR (95% CI) = 2.78 (2.24-3.46), P < 0.00], non-proliferative diabetic retinopathy (NPDR) [HR (95% CI) = 3.10 (2.61-3.68), P < 0.00], proliferative diabetic retinopathy (PDR) [HR (95% CI) = 3.17 (2.26-4.45), P < 0.00] and DME [HR (95% CI) = 2.64 (2.04-3.42), P < 0.00] among patients with T2DM.

Opioid use disorder, cannabis use disorder and cocaine use disorder appear to be associated with an elevated risk of diabetic retinopathy among patients with type 2 diabetes mellitus.

Indigenous peoples have long demonstrated resilience and holistic approaches to health, grounded in cultural practices and community knowledge. Despite this, Indigenous populations globally experience a disproportionately high burden of Type 2 Diabetes Mellitus (T2DM). Existing healthcare models often fail to address the cultural, social, and structural determinants that influence health outcomes in these communities. Digital health technologies are increasingly recognised as valuable solutions to improve diabetes care, particularly in remote and underserved Indigenous settings.

This scoping review aimed to identify digital health technologies used in the treatment and management of T2DM among Indigenous populations, assess how ethical considerations are integrated into these interventions, and evaluate their effectiveness using the Quintuple Aim framework.

A systematic search of five electronic databases was conducted for studies published between 2010 and 2024. Eligible studies included primary research involving Indigenous populations with T2DM and digital health interventions directly engaging patients. Data were synthesised using qualitative content analysis, with interventions categorised according to the WHO digital health classification, NHMRC ethical guidelines, and the Quintuple Aim framework.

Mobile applications, remote monitoring devices, telemedicine, SMS/online messaging, and websites were the most used technologies. Interventions primarily supported targeted communication and health tracking. Ethical practices were evident across all research phases, with strong alignment to NHMRC values, particularly Respect and Reciprocity. While all studies reported positive impacts on at least one outcome, mixed results were observed across the Quintuple Aim domains. Patient experience was the most frequently and positively impacted domain, whereas cost and care team satisfaction were least addressed.

Digital health interventions demonstrate potential to improve diabetes care among Indigenous populations, particularly when culturally tailored and community led. However, further research is needed to assess long-term effectiveness, economic impact, and equitable access. Future initiatives should prioritise ethical engagement, Indigenous data sovereignty, and inclusive design to ensure sustainable and culturally appropriate digital health solutions.