Despite intensive lipid-lowering therapy, substantial residual cardiovascular risk persists, and antigen-specific adaptive immunity may contribute to the inflammatory burden that remains after lipid levels are controlled. Apolipoprotein B (ApoB)-containing lipoproteins accumulate in the arterial wall and undergo local modification. During this process, ApoB-containing material may be taken up by antigen-presenting cells, processed into self-peptides, and presented through MHC-II molecules to CD4⁺ T cells. This review focuses on ApoB antigen processing and presentation, the functional heterogeneity of ApoB-reactive CD4⁺ T cells, and emerging antigen-specific therapeutic approaches. Evidence from selected mouse models and human HLA-restricted systems indicates that ApoB-reactive T cells can acquire FoxP3⁺ regulatory features. However, under hyperlipidemic conditions and during advanced disease, these regulatory features may overlap with, or be replaced by, T-bet⁺, memory-like, and pro-inflammatory phenotypes. Human studies using HLA-DR-p18 tetramers, activation-induced marker assays in peripheral blood mononuclear cells, and single-cell TCR analyses have begun to define disease-associated differences in ApoB-reactive T-cell states. Even so, current data do not yet prove longitudinal transitions within the same T-cell clones. Interpretation is further limited by HLA restriction, the rarity of ApoB-reactive cells, and relatively small study cohorts. In preclinical models, ApoB peptide vaccination and mucosal tolerance approaches suggest that antigen-specific immunity can be shifted toward regulation without broad immunosuppression. Taken together, ApoB-specific immunity appears to reflect a changing balance between tolerance and inflammation, influenced by antigen burden, HLA background, antigen-presenting cell status, and lesion stage.

Perinatal mental health disorders affect approximately 20% of pregnant and postpartum individuals, and are associated with substantial maternal and infant morbidity. Traditional assessment relies on infrequent, subjective self-reports. Mobile devices, including smartphones and wearables, offer opportunities for continuous and objective measurement, but evidence on their assessment utility in perinatal populations remains fragmented.

This review aimed to examine the application of wearable devices and smartphones for detecting and predicting perinatal mental health outcomes, with emphasis on predictive performance, informative features, and methodological rigor.

We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (PROSPERO CRD420251249218). Six databases (PubMed, Web of Science, Scopus, PsycINFO, IEEE Xplore, and ACM Digital Library) were searched initially in January 2026 and supplemented by an amended search in June 2026, with no publication date restrictions. Evidence was synthesized narratively, and the risk of bias was assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool With AI extension).

The initial and supplementary searches yielded 1952 unique records after deduplication, of which 10 studies met the inclusion criteria. The included studies covered postpartum depression, prenatal stress, discrete emotions during pregnancy (eg, happiness, anxiety, and sadness), and maternal loneliness. High discrimination metrics were reported for postpartum depression in individual studies, including a multiclass area under the curve of 0.85, a binary area under the curve of 0.871, and an F₁-score of 0.9872. Heart rate variability, GPS-derived mobility, physical activity, and sleep features were most frequently reported as useful, and their interpretation requires perinatal-specific contextualization. Methodological quality was limited, with 80% (12/15) of PROBAST+AI assessment units rated as having high overall quality concern or risk of bias, mainly due to small samples, limited validation, inadequate handling of missing data, and potential overfitting in the analysis domain.

Mobile sensing shows preliminary potential for perinatal mental health assessment, but current evidence does not yet support clinical screening or decision-making, and independent external validation in perinatal populations is currently lacking. Progress toward clinical utility requires broader mental health outcome coverage, larger longitudinal cohorts, standardized analytical and reporting practices, adoption of modeling approaches better suited to perinatal trajectories, independent external validation, and human-centered monitoring designs.

Veterans generally accept the rationale for suicide risk screening; however, the screening process can be affected by barriers to disclosure. The objective of this analysis was to describe veteran and staff perspectives on fears of involuntary hospitalization related to screening and how staff can help alleviate these concerns to facilitate disclosure.

We used semi-structured interviews to collect qualitative data and applied thematic analysis to transcripts using a conventional/directed hybrid approach. The sample comprised 90 veterans recently screened for suicidal ideation and 53 Veterans Health Administration (VHA) clinical staff members in primary care, mental health, and emergency department settings.

Veterans consistently reported fear of involuntary hospitalization as a barrier to disclosing suicidal thoughts and behaviors, and staff reported experiences with veterans who expressed fear of hospitalization as a concern during suicide risk screening. Training to adequately assess suicide risk, building a trusting relationship between patients and staff, and transparent communication were considered paramount in addressing fears of hospitalization.

Veteran and staff participants reported that fear of hospitalization was present for veterans during screening and evaluation for suicidal thoughts. Veterans and staff recognize that fear of hospitalization impacts how veterans engage with the suicide risk screening and can be affected by the staff's level of training and experience. These findings support a patient-centered approach to screening, which includes facilitating an honest conversation and involving veterans in decision-making.

Antipsychotic treatment response in schizophrenia shows substantial interindividual variability. While pharmacogenomics (PGx) holds potential for personalized prescribing, current models fall short in integrating multimodal factors and meeting real-time clinical decision-making needs.

The precision medicine platform integrating multi-dimensional factors can provide guidance for selecting antipsychotic medications for patients with schizophrenia.

This study employed a retrospective cohort design for model development (2019-2021, N = 735) and external validation (2021-2022, N = 90). We integrated PGx profiles, clinical characteristics, and medication data, selected features via random forest recursive feature elimination (RF-RFE), and built a Super Learner ensemble model (incorporating 5 machine learning algorithms) to predict Positive and Negative Syndrome Scale (PANSS) reduction. For 13 prioritized single-nucleotide polymorphisms (SNPs), we developed a kompetitive allele-specific PCR-based microfluidic chip and validated its accuracy against Sanger sequencing in 24 clinical samples. A clinical decision support (CDS) tool integrating genotyping and predictive analytics was deployed.

The Super Learner model achieved a cross-validated RMSE of 7.08 (R2 = 0.89) and an external validation RMSE of 9.02 (R2 = 0.76). The microfluidic chip showed 100% concordance with Sanger sequencing across all 13 SNPs. The integrated CDS system demonstrated sample-to-report feasibility within 3 h.

Microfluidic genotyping and Super Learner platform enables rapid, real-time prediction of antipsychotic treatment response in schizophrenia, providing a scalable tool for early personalized treatment selection during hospitalization.

Cognitive behavior therapy is an evidence-based, time-limited psychotherapy that uses goal-oriented interventions to treat various mental and physical health conditions across the lifespan. It is used alone or with pharmacologic interventions to help patients modify problematic thoughts, behaviors, and emotions that maintain functional impairment. It is a primary treatment for tinnitus, fatigue, and trauma, somatic symptom, tic, insomnia, and borderline personality disorders. Cognitive behavior therapy or medication is the primary treatment for sexual dysfunction, anxiety, depressive, and obsessive-compulsive and related diagnoses. Cognitive behavior therapy can be a primary treatment or combined with medication-assisted treatment for substance-related and addictive disorders. It is an adjunctive treatment for attention-deficit/hyperactivity disorder, bipolar disorder, chronic pain conditions, irritable bowel syndrome, weight management, and menopause. Telehealth and digital interventions have demonstrated clinical benefits. Efficacy of cognitive behavior therapy ranges from small to large effect sizes compared with control conditions or treatment as usual. Integrated behavioral health teams may administer brief cognitive behavior interventions in the primary care setting.

Academic performance in nursing education is influenced by multiple psychosocial factors, including academic self-efficacy, defined as students' confidence in their ability to complete academic learning tasks successfully, which is consistently relevant to student achievement and adaptation. Gender disparities in academic self-efficacy and academic achievement have been observed across educational settings; however, limited research has examined whether gender moderates the relationship between academic self-efficacy and academic performance among nursing students in Saudi Arabia, particularly during curriculum transition.

This study aimed to explore the relationship between academic self-efficacy and academic performance among nursing students at the University of Hail, Saudi Arabia, and investigate whether gender moderates this relationship.

A quantitative cross-sectional comparative-correlational design was used among 441 fourth-year nursing students from two cohorts under a revised curriculum. Using total enumeration, all 458 eligible students were invited; 441 completed the survey. Academic self-efficacy was measured using the eight-item Self-Efficacy for Learning and Performance subscale of the Motivated Strategies for Learning Questionnaire (MSLQ), whereas academic performance was based on registrar-obtained cumulative Grade Point Average (GPA). Because self-efficacy was measured in the fourth year and GPA incorporated earlier academic performance, analyses were interpreted as associations rather than temporal predictions. Data were analyzed using descriptive statistics, independent-samples t tests with Cohen's d, Pearson correlation, and hierarchical multiple regression with a mean-centered interaction term.

The self-efficacy subscale demonstrated good internal consistency (Cronbach's alpha = .870). Students reported high academic self-efficacy (M = 5.33, SD = 0.89) and Very Good academic performance (GPA M = 3.20, SD = 0.38). Female students had significantly higher GPA than male students, t(439) = -2.818, p = .005, d = -0.27, although academic self-efficacy did not differ significantly by gender, t(439) = 1.200, p = .231. The first cohort had significantly higher GPA than the second cohort, t(439) = 6.310, p < .001, d = 0.60, whereas the cohort difference in academic self-efficacy was not significant. Academic self-efficacy had a weak positive association with GPA, r(439) =.103, p = .030. Gender did not significantly moderate this association, delta R-squared = .004, F change(1, 437) = 1.641, p = .201.

Nursing students demonstrated high academic self-efficacy and Very Good academic performance. Fourth-year academic self-efficacy was positively but weakly associated with cumulative GPA, and this association did not differ significantly by gender. Because the variables were measured cross-sectionally and cumulative GPA included earlier academic performance, the findings do not establish that current self-efficacy predicted past achievement. Nursing education programs should therefore consider broader academic, curricular, psychosocial, and contextual factors when designing student support strategies.

Collaborative, recovery-oriented psychological interventions are in demand within mental health settings, but layers of barriers can impede their implementation, particularly within acute services. Psychological formulation is a recovery-oriented, collaborative process involving the development of a shared understanding of a service user's mental health difficulties. Psychological formulation has the potential to support a shift away from the embedded medical model; however, few, if any, frameworks have involved service users at the development stage. This study examines the acceptability of a collaboratively developed model of psychological formulation, piloted with staff within a specialist inpatient setting as well as a wider sample of individuals involved in delivering or receiving mental health services. A theory-driven and collaboratively developed psychological formulation model was developed (the 'Personal Narrative Model') and acceptability was evaluated with (1) acute inpatient staff working on an adult ward, and (2) a wider sample of professionals and individuals with lived experience. A focus group (n = 7), surveys (n = 26), and interviews were conducted (n = 8) and analysed using reflexive thematic analysis. The formulation model demonstrated promising acceptability although it was not meaningfully implemented within the acute setting. Three overarching themes are presented from the data: 1) the value of collaborative formulation, 2) implementation barriers, and 3) 'selling' and embedding change. Although there is an appetite for more contextualised ways of viewing and supporting mental health, underlying practical, cultural, and attitudinal barriers exist. Practical recommendations are shared for future researchers or practitioners seeking to embed psychological interventions within their applied settings.

The Strengths and Difficulties Questionnaire (SDQ) is a commonly used screening instrument for assessing emotional and behavioral strengths and difficulties among children and adolescents. However, although the SDQ's psychometric properties have been tested extensively in the literature, its current form is under debate as to whether it is suitable as a tool to assess adolescent mental health, especially among refugees. Therefore, this study aims to validate the SDQ scale among Adolescent Palestinian refugees in the West Bank, Palestine, and Jordan.

The psychometric properties of the original SDQ and other suggested alternatives were tested. First, construct validity for the original SDQ was tested through confirmatory factor analysis (CFA). Then, exploratory factor analysis (EFA) was used to evaluate the latent factorial structure for suggested alternatives, followed by CFA to provide additional evaluation of each alternative. Then, convergent, discriminant, and criterion validity were assessed. Finally, the internal consistency and reliability were assessed using multiple tests.

This study included 2,828 adolescent girls living in Palestine refugee camps in the West Bank and Jordan. Of these, 2,725 participants were included in the final analysis based on completion of the SDQ. CFA indicated that the original five-factor SDQ demonstrated poor fit in this sample (CFI/TLI < 0.40; RMSEA = 0.081; SRMR = 0.110), with several items showing low standardized loadings. EFA and CFA analyses supported alternative factorial structures. A four-factor model showed acceptable fit (CFI = 0.896; RMSEA = 0.040; SRMR = 0.039) after removing poorly performing items, but a three-factor solution provided the best overall fit (CFI = 0.942; TLI = 0.930; RMSEA = 0.034; SRMR = 0.033). The final three-factor model retained 15 items representing Emotional Symptoms, Prosocial behavior, and Conduct Problems. The weak correlation between the difficulties did not support convergent validity score and WHO-5 well-being (r = -0.245). Criterion validity analyses showed acceptable discrimination for overall difficulties (AUC = 0.776). Reliability analyses indicated adequate internal consistency for the composite difficulties scale (α = 0.71).

The original SDQ structure validity and reliability were not supported for use among Palestinian refugees in the West Bank and Jordan. Instead, the 3-factor and 15-item alternative might be a better alternative, given its superior model fit indices and construct validity. However, it still had limitations in reliability and convergent validity, making it far from being an adequate substitute for the original SDQ. Contexts, culture, and the surrounding environment might influence how adolescents in refugee settings understand, interpret, and respond to SDQ items.

People living with severe mental illness experience substantial health inequalities and high rates of premature mortality, largely driven by modifiable lifestyle-related risk factors, including poor diet quality. Digital technologies may provide scalable and flexible opportunities to support healthier behaviours; however, few interventions have specifically targeted nutrition-related outcomes as a primary focus in this population. MindBia is a co-designed, web-based digital nutrition intervention consisting of six structured modules developed for individuals residing in high-support mental health hostels. The intervention was informed by prior studies with service users and mental health staff. This pilot study aims to evaluate the feasibility, acceptability, and preliminary effectiveness of the MindBia intervention over eight weeks. A mixed-method pre-post design will be employed. Participants will complete baseline and post-intervention assessments examining dietary behaviours, digital literacy, and general wellbeing. Engagement with the web-based application will be monitored using embedded usage analytics. Semi-structured interviews conducted post-intervention will explore participants' experiences, perceived impact, and barriers to engagement. Findings from this pilot study will inform refinement of the MindBia web-based application and guide the design of a larger scale trial evaluating its overall effectiveness.

Borderline personality disorder (BPD) is associated with substantial distress and a high risk for suicide. Individuals with BPD may be unable to access evidence-based treatments like dialectical behavior therapy (DBT). Artificial intelligence conversational agents (AI-CA) are increasingly discussed as scalable tools for mental health support, but little is known about how DBT clinicians understand the possible role of AI-CA in treatment.

This study aimed to explore DBT psychologists' perspectives on integrating AI-CA into DBT for BPD in the future.

Seventeen psychologists in Sweden, each with at least 1 year of clinical DBT experience (mean 6.4 years, SD 5.6), participated in semistructured interviews as part of this qualitative study. Interviews were conducted in Swedish, transcribed verbatim, and analyzed using reflexive thematic analysis within a constructivist framework. Participants did not test a specific AI-CA.

Three main themes were developed from the data. The first main theme, "Who Are We in Therapy?" explored how participants defined AI-CA relationally, positioning it variously as a tool, team member, or supervisor, and a sometimes harmful competitor. How these positionings were configured shaped what AI-CA was seen as allowed to do. The second main theme, "The Stoic Helper," captured how AI-CA was constructed as an extension of the ideal helper: available, competent, adaptable, and tireless, able to provide support in moments when human therapists could not or preferred not to be present. Participants' hopes for what AI-CA could become often mirrored qualities they found difficult to sustain in their own clinical work. The third main theme, "The Well-Intended Accommodator," captured concerns that AI-CA may reinforce dependency and function as a safety behavior by supporting reassurance-seeking rather than autonomy. A central concern was not whether AI-CA could generate validating responses, but whether it could know when validation supports change and when it becomes maladaptive accommodation (AI functional ambiguity).

Perceived benefits mainly centered on accessibility and support for DBT skills generalization, whereas key concerns involved alliance disruption, reinforcement of behaviors that would ideally be targeted for change, dependency, and questions regarding responsibility in high-risk situations. Integrating AI-CA into DBT is not only a technical question but a relational and ethical one. How AI-CA is positioned in relation to the therapist, person in treatment, and team shapes which tasks are considered acceptable and what form integration can take. The findings highlight the need for implementation frameworks that account for relational dynamics, treatment-specific considerations, and AI functional ambiguity that may arise when AI-CA operates in complex therapeutic contexts.