Antipsychotic-induced weight gain (AIWG) is a pervasive and concerning side effect of treatment with antipsychotic medications (APs) that has grave implications for morbidity and mortality rates in individuals with serious mental illness. As such, acknowledging, identifying, and addressing the metabolic side effects of these medications are critical to improve the overall health and quality of life of individuals treated with APs. This review summarizes the key risk factors and predictors of AIWG and provides a comprehensive and prescriptive overview of the best-researched and evidenced nonpharmacological and pharmacological therapies available to address this problem. Age, prior exposure to APs, and AP type are strong determinants of AIWG. Metabolic monitoring and lifestyle changes remain the methods of choice for addressing metabolic risk in this population. Clinical guidelines have recommended the off-label use of metformin when such interventions are not effective, and there is emerging evidence for the effectiveness of other novel weight loss agents in this population that may represent an opportunity for greater metabolic improvements. Most of the research and guidelines to date have focused on treating AIWG; however, prevention efforts may confer greater benefits given the challenges of reversing weight gain. To effectively mitigate the metabolic risks associated with these medications, substantial system-level reforms in both education and clinical service delivery are essential, with a focus on proactive monitoring and early intervention based on up-to-date evidence and best-practice recommendations.

There is increasing interest in smartphone digital mental health interventions (DMHIs) for managing schizophrenia. Currently, numerous applications (or, apps) that target different specific domains (e.g., medication adherence, social functioning, relapse prediction, and general mental well-being) have been developed and evaluated in research studies; however, few are commercially available to patients. The emergence of DMHIs has also highlighted the critical role of app engagement and the therapeutic alliance in effective treatments. This has driven the adoption of hybrid care models that integrate traditional psychotherapy with smartphone-based tools for symptom monitoring and intervention delivery, such as psychoeducation, cognitive-behavioral therapy for psychosis modules, and other targeted supports. This clinical review examines the current landscape of DMHIs for managing schizophrenia, including research evaluating outcomes related to effectiveness and implementation.

Post-traumatic stress disorder (PTSD) is increasingly recognized as a significant contributor to cardiovascular morbidity and mortality. The aim of this review was to synthesize current evidence on the biological, behavioral, and clinical mechanisms linking PTSD with cardiovascular disease (CVD) and to highlight clinical implications for screening, prevention, and management. To achieve this, relevant studies were identified through targeted searches of PubMed, Scopus, Web of Science, and Google Scholar using combinations of terms related to PTSD, CVD, autonomic dysfunction, inflammation, endothelial function, and trauma-informed care. Priority was given to original clinical studies, systematic reviews, meta-analyses, and experimental research published between 1990 and 2025. Findings across large epidemiologic studies show that PTSD is associated with significantly higher rates of hypertension, coronary artery disease, myocardial infarction, stroke, metabolic syndrome, and cardiovascular mortality. Veterans represent the most extensively studied group, with multiple cohorts demonstrating a dose-response relationship between PTSD severity and cardiovascular risk. The review identifies several interconnected biological pathways that explain this association, including chronic sympathetic activation, reduced parasympathetic tone, impaired hypothalamic-pituitary-adrenal axis regulation, systemic inflammation, endothelial dysfunction, platelet hyperreactivity, coagulation abnormalities, oxidative stress, and metabolic disturbances. These mechanisms are further amplified by behavioral factors such as elevated smoking rates, physical inactivity, unhealthy diet, poor sleep, and reduced adherence to cardioprotective medications. Clinical challenges including healthcare avoidance, mistrust of medical environments, and frequent psychiatric comorbidities contribute to delayed diagnosis and poorer outcomes. Intervention studies suggest that treating PTSD through trauma-focused psychotherapy, selective serotonin reuptake inhibitors, prazosin for sleep symptoms, and structured exercise may improve physiological parameters relevant to cardiovascular risk. Integrated models of care that combine mental health and cardiovascular services, as well as emerging digital tools such as wearables and telepsychiatry, show promise for enhancing monitoring and preventive care. Overall, current evidence indicates that PTSD is a substantial and modifiable cardiovascular risk factor. Recognition of this relationship supports routine cardiovascular risk assessment in individuals with PTSD and systematic PTSD screening in cardiac care settings. Multidisciplinary, trauma-informed care models may be essential for reducing long-term cardiovascular morbidity in this high-risk population.

In measurement-based care or routine outcome monitoring self-report questionnaires are used to monitor how a patient fares in therapy. Interpretation and utilization of test results are improved and communication facilitated when raw scores are converted to common metrics, such as standard scores (T-scores) and percentile rank scores. Both types of common metrics are described and reviewed and a warning is issued that percentiles may be misinterpreted at the tails of the scale.

Data from the Manchester Short Assessment (MANSA) was used to investigate various approaches to obtain T-scores. The study analyzed cross-sectional data from two normative samples: a representative sample of the Dutch general population (N = 11,789) and a clinical sample (N = 9987) of patients with substance use disorder. Linear, normalized, and IRT-based T-scores were compared.Results revealed that T-scores derived from a linear conversion were biased at the lower end of the scale. Normalizing raw test scores through either Rankit normalization or by an IRT approach yielded improved and quite similar T-scores. For all possible raw scores on the MANSA, corresponding normalized T-scores are presented, as well as Percentile Rank scores for the two reference groups. Finally, cut-off values for reliable change and clinically significant change are presented for raw scores and T-scores.

Practical guidance is offered for converting raw test scores into two common metrics: normalized T-scores and Percentile Rank scores. For T-scores, simple linear conversions yielded biased results. These findings have implications for test developers, practitioners, and researchers who want to express test results in valid and unbiased common metrics.

The online version contains supplementary material available at 10.1007/s10862-025-10266-0.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, and the existing clinical diagnosis mainly relies on subjective behavioral assessment and lacks objective biomarkers. This paper proposes a hierarchical deep learning architecture, VaeTF, incorporating community-aware mechanisms based on resting-state functional magnetic resonance imaging (rs-fMRI) data. VaeTF introduces a priori knowledge of the functional community, extracts localized features through a variational auto-encoder (VAE), captures global dependencies across brain regions using the Transformer module, and incorporates an improved pooling mechanism to enhance the expressive power and model generalization performance. Experimental results on the ABIDE database show that VaeTF achieves 71.4% accuracy in ASD and typically performs well in group classification tasks. Further feature weighting analysis reveals that VaeTF is capable of identifying local functional abnormalities and cross-network functional synergistic dysfunctions closely related to ASD, thereby uncovering the underlying neurobiological mechanisms. VaeTF not only improves the classification performance of ASD but also provides a new method and theoretical support for objective assessment and early diagnosis based on fMRI.

: Cardiovascular health (CVH), as defined by the American Heart Association's Life's Essential 8 (LE8) metric, is associated with reduced cardiovascular disease (CVD) risk. However, its quantitative impact on acute myocardial infarction (AMI)-including risk reduction magnitude, onset delays, and population-level preventable burden-remains unclear.

: In this prospective cohort study, we analysed 122,914 UK Biobank participants aged 40-69 years who were free from CVD at baseline. CVH was evaluated using LE8 metrics, and was categorised as low (<50), moderate (50-79), or high (≥80). Associations between CVH and AMI risk/onset were assessed through multivariable Cox regression, accelerated failure time models, and restricted cubic splines. Mediation analysis evaluated the contributions of inflammatory (hs-CRP, leukocytes, platelets), metabolic (triglycerides, urate), renal function (eGFR), and mental health status (anxiety and depression).

: Over 163.2-month median follow-up, 2892 AMI cases (844 STEMI, 1490 NSTEMI) occurred. Each 1-unit LE8 increase reduced AMI risk by 3 % (HR 0.970, 95 % CI: 0.967-0.973). Moderate and high CVH groups exhibited 41.2 % (HR 0.588, 95 % CI: 0.534-0.648) and 75 % (HR 0.25, 95 % CI: 0.205-0.306) risk reductions versus low CVH, with consistent trends for STEMI/NSTEMI. AMI onset was delayed by 14.5 months in the moderate CVH group and 33.6 months in the high group compared with the low group. The population attributable fraction for AMI was 58.01 % (95 % CI, 57.15 %-58.86 %) when comparing the combined moderate or high CVH group with the low CVH group. Inflammatory/metabolic biomarkers mediated 1.57-8.62 % of the CVH-AMI relationship.

: Higher CVH levels were associated with reduced AMI risk and delayed onset, with inflammatory and metabolic biomarkers partially mediating this relationship. In the low-CVH group, a hypothetical shift to higher CVH levels was associated with a scenario-based population attributable fraction of approximately 60 %, highlighting the potential population impact of improving cardiovascular health.

This study aimed to investigate the association between uric acid (UA) levels and cognitive function in a low-income, rural population in Chinese adults aged ≥60 years without hyperuricemia.

Elderly individuals (≥60 years old) without hyperuricemia from rural areas of Tianjin were included in this cross-sectional study. Basic demographic and clinical information were collected, and cognitive impairment was assessed using the Mini-Mental State Examination (MMSE). Binary logistic regression analysis was performed to evaluate the association between UA levels and cognitive impairment, and multivariate linear regression analysis was used to explore the relationship between UA levels and MMSE scores. Subgroup analyses were conducted based on gender and age.

A total of 1,418 participants were included, with 43.1% showing cognitive impairment. Multivariate analysis revealed that the risk of cognitive impairment decreased by 0.2% for each unit increase in UA level and was reduced by 33% in the third quartile of UA levels compared with the lowest quartile (OR = 0.67, 95% CI: 0.49, 0.92, p = 0.014). MMSE scores increased by 0.01 (β = 0.01, 95% CI: 0.002, 0.01, p = 0.006) for each unit increase in UA level. Subgroup analysis showed significant protective associations in men and participants aged 60-69 years. However, no such relationship was found in women or individuals aged ≥70 years.

This study highlights the cognitive protective effect of UA in low-income rural Chinese populations aged ≥60 years without hyperuricemia, particularly in men and those aged 60-69 years. These findings underscore the importance of targeted interventions and health education programs to prevent cognitive decline in this vulnerable population.

We report a rare and uncommon case of ideational apraxia (IA) and conceptual apraxia (CA) in a 70-year-old woman after concurrent cerebral infarctions of the left basal ganglia (BG) extending to the corona radiata and the right pons. The patient abruptly developed difficulty operating devices such as the car's gear shift and smartphone. The brain MRI on admission disclosed infarction in the area involving the left BG and right pons. During rehabilitation therapy, she exhibited features of IA and CA, including sequencing failures when using technological devices and a profound loss of functional knowledge, exemplified by an inability to recall the purpose and functions of an automated teller machine (ATM) despite prior proficiency. A testing error involved using the keyboard rather than the mouse to open a file. Behaviorally, she impulsively initiated actions and sometimes scheduled conflicting appointments. Neuropsychological testing showed preserved scores on the Mini-Mental State Examination (MMSE) and the Hasegawa Dementia Scale-Revised (HDS-R), but memory impairment was evident on the Rivermead Behavioural Memory Test (RBMT) and the Standard Verbal Paired Associates Learning Test (S-PA). The Behavioural Assessment of the Dysexecutive Syndrome (BADS) revealed marked dysexecutive symptoms, and the Clinical Assessment for Attention (CAT) indicated reduced auditory selective attention and diminished inhibitory control. Notably, the Standard Performance Test for Apraxia (SPTA) yielded normal results. Treated with antiplatelets then antihypertensives, she was discharged home four months later. Attention deficits and driving cessation persisted, but daily life was minimally affected. Although IA and CA are typically linked to left-hemisphere damage, particularly to the BG, the right pontine lesion may have contributed to the deficits, possibly through network disruption. This infarct pattern suggests a complex interplay in apraxia development and warrants further mechanistic study.

Given the critical significance of cognitive functions in daily life and social interactions, the Screen for Cognitive Impairment in Psychiatry (SCIP) scale is particularly important in clinical practice. It can be administered quickly and offers alternative forms, avoiding issues with lengthy administration or a lack of parallel forms. This study examined the psychometric properties of the Turkish version of the SCIP scale (SCIP-TR), which enables rapid and practical assessment of cognitive impairment in psychiatric settings.

This study involved 137 healthy students and hospital staff aged 18-45. Three alternative forms of the SCIP scale were adapted into Turkish using direct and reverse translation methods, and experts approved them to ensure content validity. Participants were administered one of the three alternative forms of the SCIP-TR and the Brief Cognitive Assessment Tool for Schizophrenia (B-CATS), the Standardized Mini-Mental State Examination (SMMSE), and the Cognitive Failures Questionnaire (CFQ) scales. Test-retest reliability was evaluated using different forms of the SCIP-TR at 2-7 day intervals. The feasibility, reliability, and validity of the SCIP-TR forms were examined.

The analysis showed no significant differences between the three forms in total scores. Cronbach's alpha (0.715-0.750) and omega coefficients (0.709-0.784) demonstrated that the scale had acceptable internal consistency. Correlation coefficients confirmed the scale's test-retest reliability. The exploratory factor analysis unveiled a single-factor structure that accounted for 50.2% of the total variance. The moderate correlation of SCIP-TR with B-CATS, which measures similar cognitive domains, supports convergent validity.

Findings demonstrate the validity and reliability of the Turkish version of the SCIP as a simple and practical tool for screening cognitive impairment in the general population. Its simplicity, brevity, and lack of need for a technological platform make it suitable for integration into clinical practice. Further research on SCIP-TR is needed in a more diverse demographic, including those with mental health disorders.

Non-adherence to antipsychotics, which plays a critical role in the treatment of individuals with severe mental disorders, is the most significant predictor of relapse. The effectiveness of long-acting injectable antipsychotics (LAIs) in ensuring treatment adherence and preventing relapse is well established. However, studies examining the factors influencing the clinical choice between first-generation and second-generation LAIs and the adherence differences between them are limited. This study aimed to compare patients with schizophrenia and bipolar disorder, who were hospitalized and discharged on either a first- or second-generation LAI, in terms of LAI choice, factors influencing adherence to LAIs, and adherence differences between the two groups of LAIs.

Our study is a naturalistic, follow-up study. The sample consists of 187 patients (99 females, 88 males) who were hospitalized, initiated on LAI treatment, and discharged. Sociodemographic and clinical information was obtained from patient records. Patients and/or their relatives were contacted by phone at 3, 6, and 12 months after discharge to assess treatment continuation, and this information was verified through health system records.

First-generation LAIs were more frequently chosen for patients who were unemployed, diagnosed with schizophrenia, had a longer illness duration, had a higher number of prior hospitalizations, exhibited homicidal behavior, or had a history of electroconvulsive therapy (p<0.05). Patients with alcohol or substance-use developed non-adherence to treatment earlier (p<0.05). Treatment adherence was higher in patients with bipolar disorder (p=0.025). Adherence to second-generation LAIs at 12 months was higher compared to first-generation LAIs. Adherence was particularly higher with long-acting (LA) aripiprazole and LA paliperidone compared to the other three LAIs (haloperidol, risperidone, and zuclopenthixol) (p<0.05). Adherence to treatment prior to hospitalization significantly predicted LAI adherence at 12 months (p<0.001). Adherence to LAIs administered every four weeks was higher than those administered every two weeks (p=0.014).

For patients expected to face challenges with adherence to LAIs, second-generation agents with longer administration intervals may be preferred.