Mental health disorders represent a growing burden across the Middle East and North Africa (MENA) region, where depression and anxiety are highly prevalent amid conflict, displacement, and socioeconomic strain, affecting up to 40 percent of adults, yet treatment gaps remain at 80-95% due to provider shortages, financial strain, and cultural barriers. In this context, artificial intelligence (AI), in the form of large language models (LLMs) and specialized psychotherapy chatbots, may offer a scalable adjunct to help address these gaps through anonymous screening, predictive risk modeling, psychoeducation, and brief interventions. This narrative review examines current evidence of AI-driven conversational tools in mental health with a specific focus on their application, acceptance, and limitations within the MENA region. To do so, A structured search of MEDLINE and Embase (2000–2026) identified studies on conversational AI in mental health, prioritizing evidence from the MENA region and supplemented by relevant global literature. Overall, findings suggest that while these tools offer high accessibility and user engagement, particularly for low-intensity support, their effectiveness is limited by linguistic and cultural mismatches, including Arabic diglossia and poor alignment with locally grounded expressions of distress. At the same time, user acceptance reflects a paradox in which stigma and privacy concerns drive reliance on anonymous AI tools while simultaneously limiting trust in their clinical reliability, reinforcing a preference for hybrid models with human oversight. Taken together, these findings indicate that current systems remain insufficiently adapted to the MENA context, underscoring the need for culturally grounded, dialect-sensitive, and clinically supervised approaches to ensure safe and effective integration.
High perceived energy: exploring distinct patterns of energetic and cognitive functioning in older adults
BackgroundA hyperthymic temperament and high perceived energy are often framed as positive dispositional traits, particularly in later life. Indeed, these characteristics are generally associated with a greater tendency to preserve autonomy and with more favorable outcomes in terms of health, engagement, and participation in community life-key dimensions of successful aging. However, the relationship between elevated perceived energy and cognitive performance in older adults remains insufficiently explored. Therefore, this exploratory study aimed to compare cognitive performance and well-being in older adults with different self-reported levels of subjective energy, and to explore potential phenotypic heterogeneity within this population.MethodsWe conducted a cross-sectional study on older adults from the general population. Participants were classified as having higher versus lower levels of perceived energy based on item 10 of the Short Form Health Survey (SF-12) questionnaire. Cognitive performance was assessed using the Addenbrooke’s Cognitive Examination-Revised (ACE-R). Depressive symptoms were evaluated with the Patient Health Questionnaire-9 (PHQ-9), biological and behavioral rhythm regulation with the Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN), and quality of life with the SF-12 were also evaluated to characterize overall well-being.ResultsStatistically significant difference emerged in global cognitive performance between individuals with higher versus lower perceived energy (ES ACE-R p=0.012). Although subscale scores were within the normative range in both groups, individuals with higher perceived energy consistently showed significantly lower scores in language (p = 0.011) and attention/orientation (p = 0.049) compared to the lower perceived energy group. Interestingly, the higher perceived energy group also reported fewer depressive symptoms (p = 0.013), better social and behavioral rhythm regulation (p = 0.005), and higher quality of life (p < 0.001).Discussion/ConclusionsHyperenergy in older adults with preserved well-being is not associated with global cognitive impairment, but rather with subtle domain-specific performance variations, suggesting a heterogeneous underlying profile. These findings support a high-energy profile that does not appear to be associated with functional impairment, but is instead characterized by preserved or enhanced functioning and overall well-being, despite selective areas of lower performance. Longitudinal studies are needed to clarify its clinical and cognitive implications.
Beyond Theory of Mind: mentalization as a relational and developmental framework for autism
Autistic individuals and those around them often navigate social and emotional situations in which behaviors, intentions, and affects are difficult to interpret. Supporting mentalizing processes within child–caregiver interactions may help address these challenges; however, a broader conceptual shift is needed, moving beyond a narrow deficit-based perspective toward understanding mentalization as a multidimensional, relational, and developmental process. By shifting the focus from individual deficits to child–caregiver meaning-making processes, this framework may help clarify assessment and intervention targets and inform future research on psychopathological vulnerability in autism. This targeted narrative mini-review therefore aimed to summarize preliminary evidence suggests that other-related mentalizing may show greater difficulties than self-related mentalizing, although this hypothesis requires further replication. Findings also highlight caregiver mentalization, particularly parental reflective functioning, as a key relational process shaping how children’s behavior is interpreted, regulated, and responded to over time. In this light, preliminary intervention studies suggest that mentalization-based and mentalization-informed approaches may improve parental reflective functioning, cognitive reappraisal, self-efficacy, and inferential style, with potential indirect benefits for children’s emotional outcomes. We therefore propose a relational-developmental framework in which mentalization is conceptualized as a shared and dynamic process of meaning-making under conditions of social and emotional ambiguity. Adopting an individual, relational and developmentally informed perspective may contribute to the development of more precise assessment models, more targeted interventions, and a deeper understanding of mental health vulnerability in autism.
Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas
ObjectiveTo analyze the multidimensional factors associated with postpartum post-traumatic stress disorder (PP-PTSD) in primiparas based on the Integrated Framework for Population Health Risk Management (IFPHRM), multiple machine learning-based predictive models were constructed and externally validated to identify high-risk individuals and to provide a robust evidence base for targeted preventive interventions.MethodsThis cross-sectional study consecutively enrolled 1, 135 primiparous women from the Department of Obstetrics at Hefei Maternal and Child Health Hospital between June 2024 and May 2025. Participants were divided chronologically into a training cohort and an independent temporal validation cohort. Women recruited from June 2024 to January 2025 were included in the training cohort (n = 794), whereas those recruited from February 2025 to May 2025 were included in the temporal validation cohort (n = 341). At six weeks postpartum, PP-PTSD symptoms were assessed using the Post-traumatic Stress Disorder Checklist-Civilian Version (PCL-C), with a score ≥38 indicating probable PP-PTSD. Multidimensional variables, including physiological and psychological factors, environmental and family-related factors, and social-behavioral factors, were collected. Candidate predictors were first screened using univariate analysis and then selected using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression was used to identify independent associated factors. Seven machine learning models, including Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree, Gradient Boosting, AdaBoost, and Linear Discriminant Analysis, were constructed. Model performance was evaluated in the independent temporal validation cohort using receiver operating characteristic curves, calibration curves, decision curve analysis, and the DeLong test. SHAP analysis was used to interpret the optimal model.ResultsAmong the 794 participants in the training cohort, the incidence of PP-PTSD was 25.18%. Five key predictors were selected by LASSO regression: social support, depression, neonatal caregiving style, husband’s participation, and sleep quality. Multivariable logistic regression showed that depression and poor sleep quality were associated with an increased risk of PP-PTSD, whereas higher social support, greater husband’s participation, and parental assistance in neonatal care were associated with a reduced risk. Among the seven models, the Gradient Boosting model achieved the best overall performance in the temporal validation cohort, with an AUC of 0.939, F1 score of 0.700, specificity of 0.943, sensitivity of 0.651, and Youden index of 0.595. The DeLong test showed that Gradient Boosting performed significantly better than Logistic Regression. SHAP analysis further indicated that social support, husband’s participation, sleep quality, and depression were the major contributors to model prediction.ConclusionPostpartum PTSD (PP-PTSD) exhibits a higher incidence among primiparous women and exerts substantial adverse effects on maternal mental health, the mother–infant relationship, and overall family functioning. Guided by the Integrated Framework of Perinatal Health Risk Management (IFPHRM), this study elucidated the multidimensional mechanisms underlying PP-PTSD, encompassing physiological and psychological factors (e.g., sleep quality and depression), environmental and occupational factors (e.g., social support, paternal involvement, and infant caregiving practices), and social behavioral factors. The Gradient Boosting prediction model demonstrated robust performance and high predictive accuracy upon independent external validation, highlighting its potential utility for risk stratification and future clinical translation. Nevertheless, multicentre validation and the development of clinically implementable tools are warranted. Collectively, this study offers a theoretical foundation and methodological framework for the early identification, targeted intervention, and long-term health management of PP-PTSD in primiparous women.
Multiscale in vivo imaging of tumor evolution using a germline conditional triple-reporter mouse
Nature Biotechnology, Published online: 15 July 2026; doi:10.1038/s41587-026-03184-3
Tumors are imaged across scales—from cells to whole body—with a triple-reporter mouse.
ADAR-based DNA adenine base editing with single-nucleotide precision
Nature Biotechnology, Published online: 15 July 2026; doi:10.1038/s41587-026-03228-8
Adenosine deaminases acting on RNA (ADARs) have not been previously exploited for DNA adenine base editing in mammals. We developed a single-nucleotide-resolution adenine base editor (snuABE) that uses ADAR and customized guide RNAs to precisely convert adenines to guanines on DNA targets with minimal bystander editing.
High numerical aperture confocal volumetric mesoscope reveals mesoscale subcellular dynamics in vivo
Nature Biotechnology, Published online: 15 July 2026; doi:10.1038/s41587-026-03231-z
Volumetric mesoscale imaging reveals subcellular immune dynamics in different mammalian organs.
High-fidelity fast fluorescence lifetime imaging by event-based denoising
Nature Biotechnology, Published online: 15 July 2026; doi:10.1038/s41587-026-03222-0
Event-based first-photon fluorescence lifetime imaging enables fluorescence lifetime imaging below one photon per pixel.
When caution becomes harm
Nature Medicine, Published online: 15 July 2026; doi:10.1038/s41591-026-04526-z
Our pregnant patients deserve accurate conversations about risk.
Multi-omics maps human–pig interactions in extracorporeal liver cross-circulation
Nature Medicine, Published online: 15 July 2026; doi:10.1038/s41591-026-04515-2
Spatial and circulating multi-omics of pig-to-human extracorporeal liver cross-circulation reveals an early innate immune response, species-specific complement dynamics and xenograft–platelet interactions, while showing preserved metabolic support even after removal of the native liver. Von Willebrand factor, endothelium, hepatocytes, and resident and infiltrating immune cells were implicated in xenograft-associated thrombocytopenia.