日内瓦大学医院

Global population aging and increased chronic stress due to numerous mass disasters including those related to pandemics, climate change, war, displacement, and political unrest all challenge our collective resilience, with a growing burden of late-life neuropsychiatric and neurodegenerative disorders placing unprecedented demands on health and social systems worldwide. With these considerations in mind, understanding and promoting brain health is becoming a priority for the prevention of neuropsychiatric disorders across the lifespan. Brain health represents a dynamic balance of neural, cognitive, and emotional processes that determine resilience to neuropsychiatric illness. In later life, this balance becomes particularly critical as neurobiological and psychosocial stressors converge to shape trajectories of neuropsychiatric and neurodegenerative disorders. This review synthesizes current evidence on the determinants of brain health in aging, emphasizing resilience as a modifiable pathway linking neuropsychiatric illness risk and prevention. We integrate insights from neuroscience, lifestyle medicine, geroscience, and social determinants of health to emphasize the value of a whole-person, life-course approach. Particular attention is given to the interplay between stress physiology, interoceptive regulation, emotional resilience, and cognitive and brain reserve across the aging continuum. Emerging frameworks including brain clocks, precision biomarkers, digital phenotyping, and artificial intelligence, are considered as tools for risk stratification, early detection, and personalized intervention. By linking resilience mechanisms to measurable biological indices, we argue for the integration of neurobiological, psychological, behavioral, and sociocultural domains to inform next-generation strategies in neuropsychopharmacology, prevention science, and the promotion of healthy brain aging.

Osteoporosis is a major and growing health concern in the Asia-Pacific region, y et it remains widely underdiagnosed and undertreated due to limited access to dual-energy X-ray absorptiometry (DXA) in many areas. Artificial intelligence (AI) offers new opportunities to improve osteoporosis screening and management, but unvalidated tools pose risks of inconsistent care. This consensus was developed to provide regionally harmonized guidance on the safe, effective, and equitable use of AI in osteoporosis care. Purpose The aim of this work was to establish expert consensus recommendations on the role of AI in osteoporosis screening and management in the Asia-Pacific region. Key objectives were to define appropriate applications of AI (e.g., imaging-based bone assessment and fracture risk prediction) and specify minimum standards for validation and reporting, addressing region-specific implementation challenges and ensuring that AI use aligns with clinical guidelines and ethical principles. Methods This consensus was developed through multidisciplinary collaboration among experts across the Asia-Pacific region. Each participant reviewed draft statements, contributed feedback during virtual meetings, and provided insights based on clinical experience and current evidence. Consensus was reached iteratively until full agreement was achieved for all statements. The process integrated global best practices and regional adaptations, drawing from peer-reviewed studies, international AI guidelines, and local fracture registry data. The final recommendations emphasize the validation, transparency, and ethical implementation of AI within regional healthcare systems, ensuring compatibility with local regulations. Ultimately, twelve consensus statements were established to guide the responsible use of AI for osteoporosis screening and management in the Asia-Pacific region. Results The panel produced 12 consensus statements covering the role of AI as an adjunct for opportunistic osteoporosis screening rather than a diagnostic tool, requirements for imaging quality and AI model transparency, standards for validation and performance reporting, integration of AI with clinical risk stratification, demonstration of clinical utility in real-world settings, adherence to data protection laws and ethical AI principles, training of clinicians in AI use, strategies for implementation and monitoring (including post-market surveillance and feedback loops), and recognition of technical, clinical, and equity limitations of AI. All 12 statements give extensive recommendations for using AI to improve osteoporosis management while ensuring patient safety, accuracy, and equity. Conclusion This first Asia-Pacific consensus on AI in osteoporosis concludes that AI, when appropriately validated and implemented, can help bridge the osteoporosis care gap by identifying high-risk patients who would otherwise remain undiagnosed, thus facilitating earlier intervention. It emphasizes that AI should complement-not replace-standard diagnostic methods and clinical judgment. The guidance emphasizes validation, transparency, and ethical oversight to facilitate early intervention while minimizing risks associated with unvalidated or premature AI adoption.

There is limited data available on short-term outcomes on a cut-block positioning robotic system. The purpose of this study was to compare 12-month clinical outcomes between robotic-assisted (raTKA) and conventional total knee arthroplasty (cTKA) with multiple outcomes and surgical centers. This was a non-randomized controlled trial of patients who received either raTKA (n = 120) or cTKA (n = 101) at 6 different surgical centers. Variables of interest included occurrence of soft tissue release, complications and revisions at minimum one-year follow-up. Satisfaction, pain (numeric rating scale [NRS]), 5-dimensional European Quality of Life (EQ-5D-5 L) questionnaire (index and visual analog scale [VAS]), Oxford Knee Score (OKS), and the Forgotten Joint Score (FJS-12) were collected pre-operatively, and at six weeks, three months, and 12 months post-operative. There were significantly less soft tissue releases with raTKA (28/120, 23.3

Cardiovascular drug therapy in adults is steadily progressing. However, the extent to which children with heart disease in Switzerland benefit from this progress remains unknown. This survey aimed to investigate the current outpatient prescribing practices among paediatric cardiologists in Switzerland. We conducted a cross-sectional survey among Swiss paediatric cardiologists. The physicians were asked to state how often they administer drugs on a pre-defined list for the following indications: heart failure, arrhythmia and thromboembolism. Forty-three (56

The aim of this study was to characterize the anatomical and imaging features of adult-type diffuse gliomas across histomolecular subtypes. Clinical 3-Tesla brain MRI images from 644 patients with pathologically confirmed adult-diffuse glioma before treatment was retrospectively evaluated: 527 IDH-wildtype glioblastoma, 71 astrocytoma, and 46 oligodendroglioma. Pre- and post-contrast T1-weighted, T2-weighted and FLAIR sequences were part of the MRI protocol. Contrast-enhancing tumors and non-enhancing lesions (NEL) were automatically segmented using HD-GLIO. We used a voxel-wise Fisher-exact-test followed by random-permutation (ADIFFI) to identify regions with higher occurrence of tumor associated with IDH-mutation status or 1p/19q-codeletion status. Mann-Whitney-U-test was used to compare signal intensities in CET and NEL across the three different subtypes of adult-diffuse glioma investigated here. We observed a significant correlation of IDH-mutant gliomas with a predominance in the frontal lobe adjacent to the rostral extension of the lateral ventricles. IDH-wildtype tumors had larger NEL volumes than IDH-mutant gliomas (p < 0.0001). Signal intensity analysis demonstrated consistently lower T1w and T1-CE values and higher T2w and FLAIR values in IDH-mutant gliomas (all p < 0.0001). Oligodendrogliomas showed higher signal intensity on T1-CE images compared to astrocytomas (p = 0.035). We analyzed a large radio-genomic patient cohort consisting of glioblastoma, astrocytoma and oligodendroglioma. Our findings are in line with previously analyzed smaller patient cohorts. Our findings underline the importance of the IDH-mutation for determining tumor location and potentially point to a cell of origin along the rostral extension of the lateral ventricles. Adult-type diffuse gliomas with IDH-mutation showed a predominance in the frontal lobe adjacent to the rostral extension of the lateral ventricles. Signal intensity analysis demonstrated consistently lower T1w and T1-CE values and higher T2w and FLAIR values in IDH-mutant gliomas. Oligodendrogliomas showed higher mean signal intensity of NEL on T1-CE images than astrocytomas.