Long-acting injectable cabotegravir (CAB-LA) offers an alternative to oral PrEP, with potential benefits for adherence and stigma reduction. We investigated behavioral and psychosocial factors among early CAB-LA adopters in Italy, focusing on mental health, stigma, and prevention practices.

In a multicentre study, participants initiating CAB-LA completed baseline and follow-up questionnaires (weeks 4-20) assessing mental health, stigma, and prevention behaviors. Descriptive statistics summarized findings.

Of the 388 respondents, most (302, 89.2%) had prior experience with injectable medications, and 256 (75.9%) described these experiences positively. About half (198, 58.7%) reported recreational drug use, while 42 (12.4%) and 72 (21.3%) reported depressive and anxiety symptoms. Nearly one in four (78, 23.2%) were actively engaged in psychotherapy, while an additional 98 (28.9%) reported past engagement. Consistent condom use declined over time, with selective strategies increasing. A strong preference for biannual injections emerged (235, 69.8%), primarily administered in clinical settings (103, 30.5%). PrEP-related stigma was reported by 72 (21.3%) (strangers and friends as the most common sources), though some decline was observed during follow-up.

CAB-LA PrEP shows high acceptability, adherence, and evolving prevention practices. Although its discreet delivery may alleviate some concerns about visibility and disclosure, integration with mental health support and stigma-sensitive care is essential.

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTLs) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we conducted analyses of emulated trials for 11 drugs across 290 comparisons and identified three drugs significantly associated with reduced colorectal cancer risk: caffeine vs. paroxetine (hazard ratio [HR], 0.51; 95% confidence interval [CI], 0.41-0.64), haloperidol vs. prochlorperazine (HR, 0.47; 95% CI, 0.33-0.68), and trazodone hydrochloride vs. paroxetine (HR, 0.49; 95% CI, 0.38-0.63). Conversely, caffeine was associated with increased cancer risk in comparison with finasteride (colorectal cancer) and fluoxetine (breast cancer). Meta-analysis identified six drugs significantly associated with cancer risk, including acetazolamide, which was associated with reduced colorectal cancer risk (HR, 0.79; 95% CI, 0.72-0.87). This study identifies previously unreported protein biomarkers and candidate drug targets across six major cancer types and highlights several approved drugs with potential chemopreventive effects.

South Africa's mental health law is person-centred, has a strong human rights emphasis, and includes the appointment of mental health review boards (MHRB) to provide oversight and consider appeals against involuntary admissions. Owing to the low number of appeals, this study aimed to determine the proportion of involuntary patients who were aware of their right to appeal at two public sector psychiatric hospitals in KwaZulu Natal Province.

A group of conveniently selected involuntary patients was interviewed in a descriptive study that entailed the collection of quantitative data through the administration of a study-specific questionnaire, and the Birchwood Insight Scale from June 2020 to December 2020.

Of the 131 participants, most were unemployed (72.5 %) and had at least a high school level of education (61.06 %). The majority were diagnosed with a psychotic disorder (79.4 %), with a median duration of admission of 17.00 days (IQR 9.00, 69.00), and 63.4 % had good insight into their illness. Not a single patient appealed the current involuntary admission, and only one patient appealed a previous admission. Only 11.5 % of the participants were aware that they had the right to appeal their admission, and 8.4 % were aware of the existence of the MHRB.

Most patients were unaware of their legal right to appeal and of the MHRB, highlighting the challenges in implementing the letter and practice of the law in underresourced settings without the necessary pre-conditions to fully realise its spirit.

Cognitive function may decline with age, increasing the risk of dementia. Cognitive training can help to slow down this process. Digital Cognitive Enhancement System (DCES) is a novel home-based adaptive cognitive training system. We aim at evaluating and analyzing the efficacy of DCES in enhancing cognitive functions among the elderly and its neural mechanisms.

This 10-week study included 64 healthy elderly individuals, conducted as a single-blind, block-randomized controlled trial, registered at the China Clinical Trial Registry (ChiCTR2400091044). Participants were divided into the DCES group and the control group, with sessions occurring 5 days a week, each lasting 0.5 h, for a total of 10 weeks. Cognitive and brain function changes were assessed before and after the intervention, with results analyzed using linear-regression analysis and correlation coefficients calculated.

After 10 weeks of intervention, the DCES group showed significant improvements in overall cognitive function, visuospatial function, memory and attention. DCES training significantly reduced fALFF values in the bilateral supramarginal gyrus (SMG). Its enhancement of immediate memory is closely linked to baseline activation levels in the left SMG.

The DCES training showed positive intervention effects, and changes in bilateral SMG activity may provide neurological support for cognitive ability enhancement.

The integration of artificial intelligence (AI) into the field of mental health diagnosis has garnered increasing scholarly and clinical attention, particularly in relation to the early detection and classification of depression. This study offers a comprehensive review of the current landscape of AI-driven approaches for depression diagnosis, examining the methodologies, data modalities, and performance metrics employed across recent empirical investigations. Emphasizing machine learning and deep learning techniques, the study critically evaluates the utility of linguistic, behavioral, and physiological data sourced from social media, clinical interviews, speech recordings, and wearable devices. The findings suggest that AI systems, particularly those incorporating multimodal data fusion and advanced neural network architectures, demonstrate promising diagnostic accuracy and the potential to augment traditional psychiatric assessments. However, the study also identifies significant methodological, ethical, and practical challenges, including issues of dataset bias, algorithmic transparency, and clinical applicability. In response, the paper outlines key future directions aimed at improving model generalizability, enhancing interpretability, and fostering ethically responsible deployment in real-world settings. This review not only elucidates the transformative capacity of AI in mental health diagnostics but also provides a roadmap for advancing the development of robust, transparent, and clinically integrated AI systems for the detection of depression.

Pathological evaluation of hepatocellular carcinoma (HCC) traditionally relies on surgical resection, posing risks of infection and complications while failing to provide comprehensive pathological insights preoperatively. This study aims to develop HepaPathGPT, which utilises preoperative imaging to deliver detailed pathological interpretations, enabling non-invasive, real-time pathological assessments for patients with HCC.

A retrospective study of 1091 patients with HCC from 10 independent cohorts was used. SegFormer-b5 segmented tumour regions, and vision-language alignment mapped imaging features to pathology descriptions. We fine-tuned four pretrained frameworks using Low-Rank Adaptation (LoRA) to efficiently translate imaging features into structured histological reports, enabling real-time evaluation via an interactive interface.

HepaPathGPT showed robust tumour segmentation (mean Intersection over Union: 0.883 ± 0.007, Dice: 0.934 ± 0.006) and an average accuracy of 0.697 ± 0.024 for six pathological markers in external validation (n = 109). For text generation, BLEU-4 and ROUGE-1 scores were 62.7 ± 1.7 and 84.2 ± 1.1. Five pathologists rated 92.5% and 87.4% of reports as acceptable for accuracy and completeness.

HepaPathGPT offers a approach for non-invasive pathological analysis in patients with HCC. This technology holds significant clinical value for decision-making in patients with HCC and promises scalability to other diseases in the future.

National Natural Science Foundation of China (82090053, 82090052, 12326618, 82272703, 82473201); Tsinghua University Initiative Scientific Research Program of Precision Medicine (2022ZLA007); CAMS Innovation Fund for Medical Sciences (2019-I2M-5-056); Elite Youth Project of Natural Science Foundation of Fujian Province (2023J06056); Science-Health Joint Medical Scientific Research Project of Chongqing (2023MSXM092).

Artificial intelligence-based conversational agents (AI-based CAs) have emerged as essential tools for communication and service delivery in the healthcare industry. However, global synthesis regarding the current applications and effectiveness of this technology remains scarce.

This study aims to provide a comprehensive overview of the current applications of AI-based CAs in healthcare and associated health-related outcomes.

A systematic search of six databases and additional sources was conducted for studies published from January 1, 2000 to August 20, 2025. Inclusion criteria were as follows: a) articles must be peer-reviewed literature reviews conducted systematically with or without quantitative analysis, b) at least 70% of primary studies included in the review article reported applications of AI-based CAs, c) included primary studies were conducted in healthcare context, d) review articles must report health-related outcomes, and e) full-text must be available in English.

In total, 44 review articles were included. AI-based CAs were implemented in a wide range of medical functions, including supporting clinical decision making, supporting mental health, providing educational content, etc. Most articles studied text-based CAs that utilize Deep Learning methods. The most common mode of deployment is stand-alone or embedded in currently available applications. Regarding effectiveness, only articles discussing the effectiveness of AI-based CAs to assist addiction such as smoking and substance use reported all positive health-related outcomes. Some domains, such as clinical decision support and mental health support, receive more attention than others, while understudied areas such as promoting healthy lifestyle offer potential results.

It remains challenging to draw conclusions regarding the overall effectiveness of current AI-based CAs applications in healthcare domain, calling for transparency and standardization in CAs development practice. Moreover, some healthcare domains are more heavily studied than the others, causing an imbalance. Scholarly works on the understudied areas are critical to ensure successful implementation of AI-based CAs.

Depression is highly prevalent among patients with degenerative cervical spine disease and has been linked to worse postoperative outcomes across various surgical procedures. Prior studies in anterior cervical discectomy and fusion (ACDF) have primarily focused on limited endpoints such as pain or discharge disposition and were conducted before the COVID-19 pandemic. Given the rise in depression prevalence and its multidimensional impact on functional recovery, resilience, and quality of life in the post-pandemic population, a more comprehensive evaluation of its influence on ACDF outcomes is warranted.

A retrospective review of a prospectively maintained database was performed for patients undergoing ACDF between 2020 and 2022 at a single academic institution. Patients were grouped by presence or absence of a pre-existing complete depression diagnosis that were actively being treated. Demographic, perioperative, radiographic, and multiple PROs, were collected preoperatively and at 3 months, 6 months, 1 year, and 2 years postoperatively. Multivariate logistic and linear regression controlled for age, sex, BMI, Charlson Comorbidity Index, smoking status, surgical levels, and relevant preoperative symptoms.

Of 302 patients, 134 (44.4 %) had depression. Compared with non-depressed patients, the depression cohort had higher reoperation rates (18.7 % vs. 4.8 %, p = 0.007) Depression was associated with greater persistent arm pain at 2 years (VAS 5.92 vs. 2.67, p < 0.001) and lower PROMIS Physical (38.26 vs. 44.37, p = 0.008) and Mental (41.15 vs. 50.14, p < 0.001) scores at 1 year. No significant differences were found in cervical sagittal alignment or rates of structural complications.

Unlike prior ACDF studies, which largely evaluated limited endpoints or smaller cohorts, this study employed multiple PROs alongside clinical and radiographic measures to comprehensively characterize depression's postoperative impact, in the context of rising depression rates following the COVID-19 pandemic. Preoperative depression independently predicted an approximately fourfold increased risk of reoperation and consistently worse PROs through 2 years after ACDF, despite similar radiographic correction. Routine preoperative mental-health screening and targeted perioperative interventions, such as expectation setting, adherence support, and activity counseling, may improve outcomes for this high-risk population.

The article examines the concept and methods of positive schema therapy. It is based on the theory of schema therapy and adaptive schemas, which enrich the perspectives of positive psychology, positive CBT and strengths-based CBT. Based on case vignettes, show practical methods associated with this approach.

A literature review was conducted on adaptive schemas, their measurement using the Young Positive Schema Questionnaire (YPSQ) and therapeutic interventions supporting the development of the Healthy Adult and Kind Parent modes. In addition, special attention was paid to integrating schema therapy methods within therapeutic interventions of positive psychology and CBT based on resources. Selected case studies of various psychological problems and disorders are presented as examples of therapeutic work using positive schema therapy methods.

Positive schema therapy has been shown in additional studies to be an effective tool for increasing emotional resilience, coping with stressful situations, and improving emotional regulation and the overall quality of life. Adaptive schemas such as self-compassion, social belonging, and emotional openness significantly support the development of the Healthy Adult mode.

Positive schema therapy is an innovative approach in psychotherapeutic practice, expanding clinical methods of schema therapy work. Despite limited empirical support, it has the potential to develop further, introducing new procedures to strengthen the positive aspects of client´s experiences and behaviour when dealing with psychological problems.

Metabolic dysfunction-associated steatotic liver disease (MASLD), affecting 25-30% of adults globally, is strongly associated with depression, compounding morbidity and mortality. Despite their bidirectional relationship, tools to identify MASLD patients at high depression risk remain limited. This study aimed to develop and validate a predictive model for depression in MASLD using nationally representative data. Using 2017-2023 NHANES data, 6,107 MASLD participants were analyzed. Depression was defined as Patient Health Questionnaire-9 (PHQ-9) scores ≥10. LASSO regression with 10-fold cross-validation identified predictors, followed by multivariable logistic regression to construct a nomogram. Model performance was evaluated via area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Ten predictors were retained: younger age, female gender, never-married status, low family poverty-to-income ratio (PIR), smoking, diabetes, arthritis, cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), and platelet count. The model demonstrated robust discrimination (training set AUC = 0.733; testing set AUC = 0.758), excellent calibration (Hosmer-Lemeshow p > 0.05), and clinical utility across threshold probabilities < 40-50%. Socioeconomic factors (low PIR) and comorbidities (arthritis, CVD, COPD) showed strong associations with depression risk. This nomogram-based tool effectively stratifies depression risk in MASLD patients, integrating demographic, socioeconomic, and clinical variables. It offers clinicians a practical screening instrument for early psychological intervention, addressing the intertwined burden of metabolic and mental health disorders. Implementation could enhance holistic care and reduce adverse outcomes in this high-risk population.