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Author

Tahmineh Aldaghi

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Review Open access Aug 2026

Post-traumatic stress disorder (PTSD) in war: a comprehensive review of subtypes, risk and protective factors, and therapeutic approaches.

BACKGROUND War-related trauma is associated with substantial mental health burden, particularly post-traumatic stress disorder (PTSD) and complex PTSD (cPTSD). These presentations often co-occur with depression, anxiety, traumatic brain injury, sleep disturbance, functional impairment, and moral injury, complicating assessment and treatment. Despite growing research on trauma in conflict-affected populations, recent evidence remains uneven across active-duty personnel, veterans, civilians, refugees, internally displaced persons, and children. METHODS Studies published in English between 2020 and November 2025 were identified by following the PRISMA 2020 guidelines and conducting systematic searches in the PubMed, Scopus, and Web of Science databases. Eligible studies examined PTSD or cPTSD outcomes, moral injury or potentially morally injurious events, functioning, comorbidities, treatment engagement, or implementation outcomes among war-affected populations. Eligible designs included randomized controlled trials, quasi-experimental studies, cohort and longitudinal studies, case-control studies, cross-sectional surveys, psychometric studies, and implementation-oriented evaluations. Risk of bias was assessed using RoB 2 for randomized trials and the Newcastle-Ottawa Scale for observational studies. Findings were synthesized narratively. RESULTS Twenty-six empirical studies met the inclusion criteria. The evidence base was dominated by military and veteran samples, particularly from high-income health-system settings, limiting generalizability to civilians, refugees, asylum seekers, and internally displaced persons. Across observational studies, combat and interpersonal trauma exposure, unemployment, female sex, low social connectedness, and insomnia were associated with greater PTSD symptom burden or more symptomatic traumatic-stress profiles. Social and vocational well-being, unit cohesion, social connectedness, and sleep health were associated with more favourable outcomes, although these findings should be interpreted as associations rather than causal effects. Randomized controlled trials supported trauma-focused psychotherapies, particularly cognitive processing therapy and prolonged exposure, in veteran, active-duty, and primary-care samples. Sleep-focused augmentation and alternative approaches such as Sudarshan Kriya Yoga appeared promising but were supported by limited evidence. Implementation studies suggested that telehealth, massed, and primary-care delivery formats were feasible and acceptable, but uncontrolled designs limited conclusions about effectiveness. Psychometric evidence provided mixed support for the empirical distinction between ICD-11 PTSD and cPTSD in veteran samples. Moral injury was closely associated with PTSD and related psychological outcomes, but causal mechanisms remain unconfirmed. CONCLUSIONS Trauma-focused psychotherapies remain the best-supported interventions for war-related PTSD, particularly in veteran and military samples. Sleep assessment, social and vocational support, and scalable delivery models such as telehealth may improve service access and treatment planning, but stronger controlled studies are needed, especially among civilians, refugees, internally displaced persons, and children in humanitarian settings.

Ata Amini, M. Ahani, Fatemeh Amani Beni et al. · 0 citations
Review Open access Jul 2026

Probiotics in disease prevention and treatment: integration of bioinformatics and machine learning tools for their characterization

Probiotics play an important role in human health, food safety, and industrial applications due to their ability to modulate the gut microbiota and support host physiological functions. Their therapeutic potential has expanded beyond gastrointestinal health to include benefits in metabolic, inflammatory, and infectious diseases. This review synthesizes current evidence on probiotics, their health effects, and the scientific principles underlying their viability and functionality under different processing and storage conditions. Despite their wide-ranging applications, conventional probiotic research and development face important limitations related to experimental speed, predictive accuracy, and the analysis of large and complex biological datasets. These challenges necessitate the integration of advanced artificial intelligence (AI) and machine learning (ML) approaches to accelerate discovery and optimize probiotic performance. Particular focus is placed on applying ML and bioinformatics tools to improve probiotic strain selection, predict survival under environmental stresses, and analyze genomic features associated with probiotic efficacy. Genera such as Lactobacillus and Bifidobacterium have demonstrated clinical benefits in conditions including diarrhea, gastroenteritis, inflammatory bowel diseases, allergic disorders, and metabolic dysfunction, through mechanisms including pathogen inhibition, immune regulation, and enhancement of gut barrier integrity. AI- and ML-based models enable rapid, accurate, and scalable analysis of multidimensional probiotic datasets, overcoming traditional trial-and-error limitations. ML enables accurate prediction of probiotic stability and performance across variable pH, temperature, and formulation environments, while bioinformatics provides deeper insight into strain-level genomic traits and functional pathways. Together, these computational advances contribute to the development of more effective, targeted, and scientifically optimized probiotic interventions.

Goli Asgari, Mohammadamin Rahmani, Alireza Madandar et al. · 0 citations

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