High comorbidity among mental disorders challenges the utility of categorical classifications. The transdiagnostic perspective focuses on common psychological processes, potentially overcoming these limitations. The aim of this study was to explore transdiagnostic factors related to post-traumatic stress and psychopathology in a clinical sample.

Sixty-one patients (73.8% women; aged 20-66 years, M = 43.79, standard deviation (SD) = 12.75) from the Adult Mental Health Unit of the Hospital Clínico Universitario of Valencia were assessed on symptomatology (post-traumatic stress and psychopathology) and psychological variables (emotional dysregulation, resilience, personality, social support, and stressful life events).

Our results indicated elevated post-traumatic stress scores (M = 70.49, SD = 20.33), suggesting clinically significant distress, together with low exposure to stressful life events (Median (Mdn) = 2.00; interquartile range (IQR) = 2.00). Post-traumatic stress and psychopathology were positively correlated with emotional dysregulation and negatively with resilience, social support, extraversion, and conscientiousness. Higher post-traumatic stress and psychopathology were linked to low social support, low extraversion, and high emotional dysregulation. Emotional dysregulation emerged as a key moderating variable, potentially diminishing the protective effects of resilience on post-traumatic stress.

Overall, findings support the transdiagnostic approach, highlighting that factors like emotion regulation contribute not only to symptom development but also critically influence how risk and protective factors affect mental health outcomes, emphasizing the importance of targeting these processes in clinical intervention and prevention efforts.

Diagnostic stability in child and adolescent psychiatry is a key indicator of validity and essential for clinical decision-making. Few longitudinal studies have examined diagnostic trajectories after a first emergency psychiatric contact.

We conducted a retrospective observational cohort study at Santa Maria University Hospital (Lleida, Spain). A total of 583 patients aged 4-18 years presenting for their first psychiatric emergency visit between 2017 and 2023 were included, with 24-month follow-up. Sociodemographic and clinical data were extracted from Electronic Health Records. Diagnostic transitions were summarized using transition matrices. An exploratory association analysis (Apriori algorithm) identified frequent T1→T2 patterns, reported with confidence and lift. Diagnostic stability was defined as the proportion of patients retaining the same diagnosis at follow-up.

Median age at baseline 14.9 years (interquartile range [13-16]); 54.55% were female. Schizophrenia/psychosis showed the highest stability (71%), followed by intellectual disability with gender identity disorder (67%). Mood disorders showed moderate stability (~44%), while others such as eating disorders (26%) or conduct disorders (17%) had lower stability. The strongest associations were "no prior diagnosis → eating disorder" (confidence = 1.00; lift = 12.76) and "autism spectrum disorder + attention-deficit/hyperactivity disorder (ADHD) → conduct disorders" (confidence = 0.66; lift = 2.55).

Diagnostic stability is heterogeneous, with high persistence in schizophrenia/psychosis and low in eating disorders and ADHD. Association analysis identified specific trajectories that may help anticipate clinical evolution. Findings highlight the importance of longitudinal evaluation in early psychiatric care.

Adolescents with moderate depression and suicidal ideation constitute a high-risk psychiatric population. Major depressive disorder with suicidal ideation in this age group is a disabling psychiatric disorder. Current selective serotonin reuptake inhibitor treatments are limited by their low efficacy rates (approximately 50%-60%) and delayed onset of action. Informed by the gut-brain axis theory, this study aimed to evaluate the synergistic efficacy and anti-inflammatory mechanisms of sertraline combined with a Bacillus subtilis probiotic preparation in this high-risk population.

This retrospective cohort study included 160 adolescents meeting International Classification of Diseases 10th Revision diagnostic criteria were identified and categorised into either monotherapy (sertraline) or combination therapy (sertraline + probiotics) groups. Over a 12-week treatment period, clinical symptoms were assessed using the Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale and Beck Scale for Suicide Ideation-Chinese Version, and serum inflammatory factors, namely, interleukin-6 (IL-6), interleukin-1β (IL-1β), tumour necrosis factor-α (TNF-α) and C-reactive protein (CRP) and peripheral blood inflammatory ratios, namely, platelet-to-lymphocyte, neutrophil-to-lymphocyte and monocyte-to-lymphocyte ratios, were measured.

The proportion of patients with severe depression was significantly reduced in the combination group (1.25% vs. 7.5%, p = 0.004), and anxiety symptoms showed significant improvement (severe anxiety proportion: 1.25% vs. 11.25%, p = 0.008). Biomarker analysis revealed significantly reduced levels of IL-6 (p = 0.007), IL-1β (p = 0.002), TNF-α (p = 0.005) and CRP (p = 0.001) in the combination group, and IL-6 and CRP showed strong positive correlations with depression scores (r = 0.35-0.39).

This study confirms that modulating the intestinal smicroecology can enhance antidepressant efficacy by reducing neuroinflammation.

This study explores the impact of the COVID-19 pandemic on the psychological well-being of adolescents in the province of Pavia.

We analysed data from a non-clinical sample of 1 779 adolescents, aged 13 to 19 years, to assess internalising and externalising psychological symptoms. Participants completed a comprehensive self-report survey.

Our findings reveal significant sex differences, with females showing higher levels of internalising symptoms such as anxiety and depression compared to males. No significant sex differences were found for externalising symptoms. We examined correlations between these psychological symptoms and factors like age, suicidal ideation, and eating problems.

This study underscores the prevalence of mental health challenges among adolescents in the post-pandemic context and highlights the need for targeted interventions. Our findings may inform efforts to enhance adolescent well-being by addressing both risk and protective factors in collaboration with families and educational communities.

Artificial intelligence (AI) is a tool that could provide useful prevention strategies for people at risk of suicide. However, there are many ethical challenges regarding sensitive or confidential data in the use of AI. This article identifies ethical issues in the use of AI for suicide prevention, analyzed from a mental health perspective and the current Durkheimian approach. A non-systematic review of the literature and a critical analysis of the information were carried out. Data employed for suicide prevention using AI are obtained for other purposes, including untargeted surveys without explicit informed consent, chatbot clinical care records, and non-standardized medical records, which may lead to inappropriate use of information. The use of AI in suicide prevention requires consideration of ethical data management, and issues such as informed consent, privacy, and respect for dignity and autonomy, and must be analyzed in light of social and behavioural transformations.

Rapid eye movement (REM) sleep is increasingly understood as a heterogeneous state composed of two neurophysiologically distinct microstates: tonic REM and phasic REM. Phasic REM, defined by brief clusters of saccadic eye movements and transient cortical activation, has been linked to emotional memory consolidation, sensorimotor integration, and autonomic modulation. Despite its importance, automated quantification of phasic versus tonic REM remains uncommon, mainly because existing electrooculography (EOG) methods rely on fixed thresholds or generic wavelet families that do not accurately capture real saccade morphology in clinical polysomnography (PSG). This study introduces a fully automated framework for detecting phasic REM based on hybrid adaptive segmentation of a single EOG channel. The segmentation algorithm fuses median absolute deviation (MAD) amplitude-change detection with a morphology score derived from a custom saccade kernel built from manually verified EyeCon recordings. Segment boundaries are refined using local derivative extrema to improve temporal alignment. A supervised support vector machine (SVM) classifier further refines segment labels using features based on saccade morphology, including correlations with custom log-sigmoid templates and a morphology similarity measure. All segmentation and classification hyperparameters were optimized exclusively on controlled EyeCon datasets with precise ground-truth event markers. The final model was then applied without modification to 21 full-night clinical PSG recordings. Event-level analysis on EyeCon yielded 92.9% correct detections, with 5.3% fragmentation and 1.8% missed events. When aggregated into saccadic bursts, the resulting REM microstructure was physiologically consistent: phasic REM accounted for 31.8 ± 3.5% of REM duration, and tonic REM for 68.2 ± 3.5%. Additional EEG analysis confirmed increased beta and gamma power during phasic REM, supporting physiological validity. The proposed framework provides an interpretable, morphology-aware, and computationally efficient tool for large-scale REM microstructure research. Its single-channel design and external validation on clinical PSG recordings make it suitable for both retrospective analyses and future clinical applications.

Background/Objectives: Venlafaxine has been reported to exhibit significant interindividual pharmacokinetic heterogeneity across populations, which has been linked to cytochrome P450 polymorphisms and clinical factors. This study aimed to assess the impact of pharmacogenetic (PGx) and clinical determinants on the dose-normalized venlafaxine/O-desmethylvenlafaxine (ODV) metabolic ratios (MRs) in routine clinical settings in Spain. Methods: 29 adult patients receiving venlafaxine were prospectively recruited through the MedeA PGx Implementation Strategy into clinical practice (Extremadura, Spain). CYP2D6, CYP2C19, and CYP2C9 genotypes were determined using TaqMan^® assays, and CYP2D6 activity scores were assigned based on allele functionality. Steady-state trough plasma concentration of venlafaxine and ODV were measured using a validated high-performance liquid chromatography method. Dose-normalized venlafaxine/ODV MRs were compared across CYP2D6-, CYP2C19-, and CYP2C9-genotype-predicted metabolizer groups. The influence of demographic and clinical variables on dose-normalized venlafaxine/ODV MR was also assessed. Results: Significant variability in dose-normalized venlafaxine/ODV MRs was observed across CYP2D6 (p = 0.019) and CYP2C19 (p = 0.008) metabolizer groups. Among clinical variables, sex was significantly associated with differences in dose-normalized venlafaxine/ODV MR (p = 0.0006). Conclusions: CYP2D6 and CYP2C19 genotypes and sex significantly contribute to variability in venlafaxine metabolism in patients treated in routine clinical settings. These results highlight the value of combining PGx and clinical data with drug plasma concentration measurement to optimize venlafaxine therapy within PGx implementation programs.

Artificial intelligence (AI) is increasingly integrated into healthcare decision-making. Public trust in AI remains a critical determinant of its acceptance and effective use. Evidence on the factors shaping trust in AI within Middle Eastern contexts, particularly Saudi Arabia, remains limited. Therefore, we aimed to identify the determinants of trust in AI for health-related decision-making and to examine a theory-informed mediation pathway in which patient satisfaction mediates the association between patient-doctor relationships and trust in AI.

We conducted a cross-sectional, facility-based survey of adults in Saudi Arabia, using an electronic questionnaire distributed in four primary healthcare centers. We performed multiple linear regression to assess the association of trust in AI for health-related decision-making with patient satisfaction, patient-doctor relationships, sociodemographic characteristics, and healthcare-related factors. A mediation analysis was also employed to evaluate the indirect and direct association linking patient-doctor relationships, patient satisfaction, and trust in AI.

Our findings showed that patient satisfaction was positively associated with trust in AI (β = 0.54, 95% CI: 0.18-0.90), while patient-doctor relationships showed an inverse association (β = -0.34, 95% CI: -0.48 to -0.20), possibly reflecting a greater reliance on physicians' clinical judgment and a reduced perceived need for AI-supported decision-making. Trust in AI varied across age groups, with a lower trust observed in older age categories compared with younger adults. No strong associations were observed for sex, education, body mass index, or healthcare-related factors. Patient-doctor relationship quality was indirectly associated with trust in AI via patient satisfaction (ACME = 0.138, 95% CI: 0.043-0.246), alongside a direct association with trust in AI (ADE = -0.313, 95% CI: -0.456 to -0.160). This means that patient-doctor relationships influenced trust in AI both directly and indirectly through patient satisfaction, suggesting that, while interpersonal care may reduce the reliance on AI (direct effect), enhancing patient satisfaction can partially offset this effect and promote trust in AI (indirect effect).

These findings highlight that fostering patient-centered care and satisfaction may be crucial for promoting public trust in AI, which has important implications for AI governance, ethical deployment, and the design of AI-supported healthcare systems.

Background: Mental health among healthcare professionals is a critical aspect of clinical practice, as they are exposed to demanding working conditions that frequently lead to symptoms of depression, anxiety, and stress. Objective: We aimed to examine the influence of depression-, anxiety-, and stress-related symptoms on mental health in healthcare professionals. Methods: A quantitative, non-experimental, cross-sectional study was conducted with a sample of 123 healthcare professionals from a Peruvian hospital. Two instruments were administered: the Depression, Anxiety, and Stress Scale (DASS-21) and the Mental Health Inventory-5 (MHI-5). Results: More than 75% of healthcare professionals presented mild levels of depression (82.1%) and stress (89.4%), whereas over half reported mild levels of anxiety (63.4%). Significant differences were observed according to age and years of service (p < 0.05). Mental health was significantly and inversely correlated with depression (r = -0.706), anxiety (r = -0.742), and stress (r = -0.698). Moreover, the predictive model explained 59.2% of the variance, with a moderate effect size. Conclusions: Among healthcare professionals, higher levels of depression, anxiety, and stress were associated with significantly lower mental health. The strength of these correlations highlights the need to better understand and address the negative emotional impact experienced by healthcare personnel.

Background/Objectives: Digital environments have intensified exposure to interpersonal stressors and appearance-related evaluation, raising concerns about cyberbullying and problematic internet use among women with eating disorders (EDs). This study examined whether cyberbullying exposure and problematic online use are associated with health-related quality of life in women receiving specialized outpatient care for eating disorders in Spain. Methods: We conducted a cross-sectional analysis of baseline data collected between 2018 and 2019 from a clinical cohort of 124 women in specialized ED treatment. ED symptoms were assessed using the SCOFF and the Bulimic Investigatory Test, Edinburgh (BITE). Problematic online use was measured with the Internet Addiction Test (IAT) and the Bergen Facebook Addiction Scale (BFAS), and cyberbullying exposure was summarized using a composite index. HRQoL was assessed with the EQ-5D index and visual analogue scale (EQ-VAS). Associations were examined using correlation analyses, group comparisons, and exploratory multiple linear regression models adjusting for age, body mass index (BMI), diagnosis, and comorbidity. Results: Cyberbullying exposure was strongly positively correlated with problematic internet and social media use (IAT and BFAS). Its bivariate associations with ED symptom measures were small and not statistically significant. Participants with physical or mental health comorbidities reported lower HRQoL on both the EQ-5D index and EQ-VAS scores (p < 0.01). In the exploratory adjusted regression model predicting EQ-5D, coefficients for cyberbullying exposure, IAT, and BITE severity were small and imprecisely estimated, whereas diagnosis category showed between-group differences (with the "other ED" category reporting lower EQ-5D scores relative to the reference group). The overall model explained approximately 26.7% of the variance in EQ-5D (adjusted R² = 0.22). Conclusions: In this clinical sample, digital-use measures co-occurred strongly with one another, and comorbidity was associated with poorer HRQoL at the bivariate level. In exploratory adjusted models, estimated associations of cyberbullying and problematic online use with HRQoL were imprecise, supporting cautious interpretation. Prospective and intervention studies are needed to determine whether digital interpersonal stressors contribute to HRQoL trajectories in women receiving specialized ED care and whether targeting these stressors improves patient-reported outcomes.