Background: The early detection of mental health crises is crucial for timely interventions and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises. Methods: We developed a multimodal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over 12 months. Its performance was evaluated using standard metrics and validated against expert psychiatric assessments. Results: The AI model demonstrated a high level of accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827–0.872) and platforms (F1 scores: 0.839–0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying levels of accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%). Conclusions: AI-powered analysis of social media data shows promise for the early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges, including privacy concerns, potential stigmatization, and cultural biases, need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration of the method with existing mental health services, and developing personalized, culturally sensitive models.

Background/Objectives: This study comprehensively examines how artificial intelligence (AI) technologies are transforming clinical practice in plastic and reconstructive surgery across the entire patient care continuum, with the specific objective of identifying evidence-based applications, implementation challenges, and emerging opportunities that will shape the future of the specialty. Methods: A comprehensive narrative review was conducted analyzing the integration of AI technologies in plastic surgery, including preoperative planning, intraoperative applications, postoperative monitoring, and quality improvement. Challenges related to implementation, ethics, and regulatory frameworks were also examined, along with emerging technological trends that will shape future practice. Results: AI applications in plastic surgery demonstrate significant potential across multiple domains. In preoperative planning, AI enhances risk assessment, outcome prediction, and surgical simulation. Intraoperatively, AI-assisted robotics enables increased precision and technical capabilities beyond human limitations, particularly in microsurgery. Postoperatively, AI improves complication detection, pain management, and outcomes assessment. Despite these benefits, implementation faces challenges including data privacy concerns, algorithmic bias, liability questions, and the need for appropriate regulatory frameworks. Future directions include multimodal AI systems, federated learning approaches, and integration with extended reality and regenerative medicine technologies. Conclusions: The integration of AI into plastic surgery represents a significant opportunity to enhance surgical precision, improve outcome prediction, and expand the boundaries of what is surgically possible. However, successful implementation requires addressing ethical considerations and maintaining the human elements of surgical care. Plastic surgeons must actively engage with AI development to ensure these technologies address genuine clinical needs while aligning with the specialty’s core values of restoring form and function, alleviating suffering, and enhancing quality of life.

Background/Objectives: Mental health disorders among children and adolescents represent a significant global health challenge, with approximately 50% of conditions emerging before age 14. Despite substantial investment in services, persistent barriers such as provider shortages, stigma, and accessibility issues continue to limit effective care delivery. This narrative review examines the emerging application of conversational artificial intelligence (AI) in pediatric mental health contexts, mapping the current evidence base, identifying therapeutic mechanisms, and exploring unique developmental considerations required for implementation. Methods: We searched multiple electronic databases (PubMed/MEDLINE, PsycINFO, ACM Digital Library, IEEE Xplore, and Scopus) for literature published between January 2010 and February 2025 that addressed conversational AI applications relevant to pediatric mental health. We employed a narrative synthesis approach with thematic analysis to organize findings across technological approaches, therapeutic applications, developmental considerations, implementation contexts, and ethical frameworks. Results: The review identified promising applications for conversational AI in pediatric mental health, particularly for common conditions like anxiety and depression, psychoeducation, skills practice, and bridging to traditional care. However, most robust empirical research has focused on adult populations, with pediatric applications only beginning to receive dedicated investigation. Key therapeutic mechanisms identified include reduced barriers to self-disclosure, cognitive change, emotional validation, and behavioral activation. Developmental considerations emerged as fundamental challenges, necessitating age-appropriate adaptations across cognitive, emotional, linguistic, and ethical dimensions rather than simple modifications of adult-oriented systems. Conclusions: Conversational AI has potential to address significant unmet needs in pediatric mental health as a complement to, rather than replacement for, human-delivered care. Future research should prioritize developmental validation, longitudinal outcomes, implementation science, safety monitoring, and equity-focused design. Interdisciplinary collaboration involving children and families is essential to ensure these technologies effectively address the unique mental health needs of young people while mitigating potential risks.

Background/Objectives: Neurodegenerative diseases represent a growing global health challenge with limited therapeutic options. Physical exercise has emerged as a promising non-pharmacological intervention with potential neuroprotective effects. This narrative review examines the mechanisms through which exercise induces neuroplasticity and their implications for neurodegenerative disease prevention. Methods: We synthesized evidence from molecular, animal, and human studies on exercise-induced neuroplasticity and neurodegenerative disease prevention through a comprehensive literature review. Results: Exercise enhances neuroplasticity through multiple pathways: (1) neurotrophic signaling (BDNF, IGF-1, VEGF), (2) neuroendocrine regulation, (3) epigenetic modifications, and (4) metabolic pathway optimization. These molecular changes support structural adaptations including hippocampal neurogenesis, enhanced synaptic plasticity, improved cerebrovascular function, and optimized brain network connectivity. Exercise directly impacts pathological features of neurodegenerative diseases by reducing protein aggregation, attenuating excitotoxicity and oxidative stress, and enhancing mitochondrial function. Clinical evidence consistently demonstrates associations between physical activity and reduced neurodegenerative risk, with intervention studies supporting causal benefits on cognitive function and brain structure. Conclusions: Exercise represents a multi-target intervention addressing several pathological mechanisms simultaneously across various neurodegenerative conditions. Its accessibility, minimal side effects, and multiple health benefits position it as a promising preventive strategy. Future research should focus on understanding individual response variability, developing sensitive biomarkers, and creating personalized exercise prescriptions for optimal neuroprotection.

We examined the inoculation and stress sensitization explanations concerning mental health outcomes in 223 predominately middle-aged and older adults after a flood (M age = 49.6 years, SD = 17.7 years, range: 18-88 years). In multiple linear regression models, having flood damage was associated with higher levels of posttraumatic stress disorder (PTSD) and depressive symptoms, while social support was associated with fewer symptoms. Greater lifetime trauma and flood-related stress were associated with more symptoms of depression and PTSD, respectively. Older age was associated with more religious coping and fewer depressive and worry symptoms. Future directions for research on postdisaster vulnerabilities and resilience are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

INTRODUCTION: DeepSeek was evaluated in gastroenterology board examination performance against legacy ChatGPT offline models, which previously showed failing performance. METHODS: The performances of the DeepSeek base R1 model and search-augmented R1 model were assessed using American College of Gastroenterology self-assessments (455 questions). RESULTS: DeepSeek exceeded the passing threshold. Search-augmented DeepSeek scored 81.5% across all questions, and the R1 base model scored 77.1%. Both search-augmented and offline DeepSeek models surpassed offline ChatGPT-3 (65.1%) and ChatGPT-4 (62.4%) ( P < 0.001). DISCUSSION: DeepSeek exhibited passing performance on the gastroenterology board examination but had gaps in niche topics and image exclusion limit utility. It may supplement education if validated by specialists.