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PubMed2026

Understanding Suicide through Coroners' Narratives: implications for primary care from a mixed‑methods study of 157 Coroners' reports.

Suicide is a major public health concern, and general practice is often a recent point of contact before death. While mental illness is well recognised, the broader social and contextual factors influencing suicide risk remain under-reported in primary care and epidemiological research Aim To describe the demographic, clinical, and psychosocial characteristics of individuals who died by suicide, integrating coronial quantitative data with qualitative narrative accounts to identify implications for primary/ secondary care and public health. Design and setting Explanatory sequential mixed‑methods study of 157 consecutive deaths by suicide recorded by coroners (2018-19) across five English local authorities. Method Demographic, clinical, and social data were extracted from coroners' records and summarised descriptively. Narrative case summaries were coded and analysed thematically to identify contextual, relational, and service factors preceding death. Results Of 157 individuals: 79% were male; 65% lived in the most deprived IMD quintile; 85% had a diagnosed mental health condition; 62% had a long‑term physical illness; 41% had a previous suicide attempt. About half consulted a GP in the preceding three months; mental health featured in about half of those consultations. Common stressors were relationship breakdown (37.2%), housing instability (22.1%), and work pressures (18.2%). Seven interlinked themes were identified: Mental health; Alcohol/Substance use, Physical health; Social connectedness; Life course trauma, Socioeconomic and Structural Vulnerability; Healthcare access. Service transitions were key vulnerability points Conclusion Coroners' records offer important insights into the complex circumstances preceding suicide and highlight opportunities for GPs to recognise intersectional complexity and support integrated, cross-sector suicide prevention approaches.

General Practice↗

PubMed2026

The Role of Artificial Intelligence for Intimate Partner Violence Prevention: A Systematic Review.

INTRODUCTION: Intimate partner violence (IPV), encompassing physical, sexual, emotional and economic abuse, remains a pervasive global health concern. Traditional prevention efforts face obstacles such as underreporting, delayed detection and limited personalised support. Emerging artificial intelligence (AI) approaches offer new opportunities to enhance IPV prevention. AIM: This systematic review maps and synthesises evidence on AI-driven tools in IPV prevention based on studies published between 2004 and 2024. METHODS: Following PRISMA 2020 guidelines and PROSPERO registration, we searched PubMed, Embase, CINAHL, PsycINFO, IEEE Xplore and Web of Science. Eligible studies explicitly evaluated AI technologies targeting IPV prediction, screening, intervention or support delivery. Study quality was appraised using the Mixed Methods Appraisal Tool (MMAT). RESULTS: Of 1304 records initially identified, 41 studies met eligibility criteria. AI applications ranged from machine learning (ML) for risk prediction and natural language processing (NLP) for IPV detection in clinical and social media data, to image analysis for forensic evaluation and chatbot-based support. Predictive modelling demonstrated strong discriminative performance, while NLP-based screening detected IPV with notable sensitivity. Chatbots showed feasibility and user acceptability, but evidence of their direct impact on reducing IPV incidence was limited, with one randomised controlled trial showing a modest reduction. Key challenges identified included algorithmic bias, data privacy risks and barriers to integration across health and social care systems. DISCUSSION: AI-informed interventions show promise for improving IPV detection, risk assessment, and scalable support, but questions remain about long-term effectiveness, ethical fairness, transparency and equitable implementation. Future interdisciplinary research should address these concerns to responsibly deploy AI in IPV prevention. RELEVANCE TO CLINICAL PRACTICE: The findings highlight the importance of trauma-informed, culturally responsive care and provider training in AI applications. Nurse-led innovation and policy advocacy will be crucial for safe, equitable integration of AI in IPV prevention.

Artificial Intelligence↗

PubMed2026

Transcriptomic and Metabolomic Profiling Identifies a Core Gene-Metabolite Axis Driving African Swine Fever Virus Replication in the Soft Tick Ornithodoros lahorensis.

African swine fever virus (ASFV) causes an incurable swine disease with nearly 100% mortality, posing a catastrophic threat to global pig production. The soft tick Ornithodoros lahorensis acts as a critical biological vector that sustains persistent ASFV replication and mediates long-distance viral transmission, yet the molecular mechanisms governing ASFV-tick interplay remain poorly understood. Here, we integrated transcriptomics and metabolomics to systematically dissect molecular changes in O. lahorensis across three infection stages: Uninfected control, early infection (7 days post-infection, dpi), and late persistent infection (21 dpi). Multi-omics integration revealed that ASFV extensively remodels tick host metabolism, predominantly activating purine/pyrimidine metabolism, lipid biosynthesis, and energy metabolism. We further characterized a conserved regulatory module consisting of 12 core genes and 8 signature metabolites that collectively support ASFV genome replication and virion assembly. Three hub metabolic genes (TK1, ATP5F1B, and IMPDH) were selected for functional validation via siRNA silencing in ticks; individual gene silencing suppressed ASFV loads by 89.2%, 91.5%, and 87.8%, respectively (p < 0.001***). This work represents the first comprehensive multi-omics investigation of ASFV infection in O. lahorensis. We identified tick-specific molecular targets to block vector-mediated ASFV spread and established a standardized multi-omics analytical pipeline for tick-virus interaction research. Our findings elucidate the mechanistic basis of long-term ASFV persistence in soft ticks and deliver novel actionable clues for developing vector-targeted ASF intervention strategies.

Animals↗

PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗

PubMed2026

Brain network alterations underlying cue reactivity and craving in abstinent methamphetamine users: a systematic review of functional MRI findings.

BACKGROUND: Methamphetamine use disorder (MUD) is marked by intense craving and high relapse risk, often triggered by drug-related cues. Functional magnetic resonance imaging (fMRI) provides key insight into the neural basis of this cue reactivity, implicating large-scale brain networks for reward, motivation, and control. Yet, findings remain inconsistent across studies due to differences in task design, abstinence duration, and participant characteristics. OBJECTIVE: This systematic review synthesises evidence on how abstinence influences brain network alterations underlying cue reactivity and craving in methamphetamine users, integrating task-based and resting-state fMRI findings within leading neurobiological models of addiction. METHODS: A systematic search of PubMed, Scopus, Web of Science, and Ovid was conducted up to August 10, 2025, following PRISMA 2020 guidelines. Eligible fMRI studies examined cue reactivity or craving in abstinent methamphetamine users. Data were extracted on activation, connectivity, and brain-behaviour associations, and synthesised narratively. RESULTS: Task-based studies revealed heightened activation across reward, salience, and control networks during cue exposure, which diminished as parietal and executive control systems re-engaged with longer abstinence. Resting-state findings showed disrupted intrinsic connectivity among default mode, salience, and frontoparietal networks, reflecting persistent imbalances linked to craving and use severity. CONCLUSION: fMRI evidence shows that MUD is marked by network-level disruption linking reward, salience, and control systems. Task-based findings reveal strong cue reactivity in reward circuits, while resting-state data show persistent imbalance among default mode and control networks. With abstinence, partial restoration of network integrity emerges, highlighting both vulnerability and opportunities for targeted, recovery-based interventions.

Humans↗

PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗

PubMed2026

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans↗

PubMed2026

Adjuvant CDK4/6 inhibitors in early-stage breast cancer: Clinical evidence and considerations for risk stratification and treatment selection.

Hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer is the most common biologic subtype and carries a persistent risk of recurrence, particularly in patients with high-risk, early-stage disease. Cyclin-dependent kinase 4 and 6 inhibitors, initially established as a standard component of first-line therapy in the metastatic setting based on improvements in progression-free and overall survival, have since been evaluated in the adjuvant setting. While adjuvant palbociclib did not improve invasive disease-free survival, the monarchE and NATALEE trials demonstrated that abemaciclib and ribociclib, respectively, reduce recurrence risk in patients with high-risk, early-stage disease, with emerging overall survival data further supporting their use. However, the absolute magnitude of benefit varies substantially with baseline risk, and treatment-related toxicity and adherence challenges must be considered, as approximately 20% to 25% of patients discontinue therapy before completion. The integration of these agents into clinical practice also intersects with ongoing efforts to deescalate axillary surgery, as treatment eligibility has been largely defined by anatomic staging, particularly nodal status. Available data suggest that the incremental impact of axillary surgery on identifying candidates for cyclin-dependent kinase 4 and 6 inhibition is modest, especially among the favorable-risk populations now eligible for surgical deescalation. As the field evolves, advances in molecular risk stratification, genomic profiling, and dynamic biomarkers are poised to shift treatment selection from anatomic staging toward biologically driven approaches. Multidisciplinary decision-making that integrates tumor biology, anticipated absolute benefit, toxicity, patient preferences, and surgical considerations will be essential to ensure individualized care.

Humans↗

PubMed2026

Impacts of climate-driven yield changes on the affordability of healthy diets: a modelling study.

BACKGROUND: Food security is central to global nutrition improvement and public health goals, and healthy diets represent a higher-level aspiration beyond merely avoiding hunger. Climate change poses an increasing threat to food systems by affecting crop yields and food prices. Although climate change-driven risks to hunger have been widely studied, the extent to which climate change undermines the affordability of healthy diets while accounting for socioeconomic responses and regional inequalities remains insufficiently understood. This study aimed to quantify the effects of climate change on the future affordability of healthy diets under alternative socioeconomic and climate scenarios. METHODS: We developed an integrated modelling framework that explicitly couples multimodel crop-yield projections with an integrated assessment model (Global Change Analysis Model [GCAM]). Yield responses from six global gridded crop models driven by four climate models were integrated into GCAM, allowing endogenous socioeconomic adjustments such as land-use shifts, production reallocation, and price responses to emerge under shared socioeconomic pathways (SSPs). Diet affordability was then assessed using the Food and Agriculture Organization of the UN's Cost and Affordability of a Healthy Diet framework across three socioeconomic-climate scenarios (SSP1-2.6, SSP2-4.5, and SSP3-6.0). FINDINGS: Under a high-emissions pathway (ie, SSP3-6.0), climate change was projected to render healthy diets unaffordable for a model-mean of 119 million people globally by 2100, even when CO2 fertilisation effects are included, with the upper end of the model ensemble reaching about 1·6 billion people. In contrast, climate-induced affordability losses were found to be negligible under both a low-emissions pathway (ie, SSP1-2.6; -0·3 million) and a medium-emission pathway (SSP2-4.5; +0·2 million). Under a high-emission pathway, model-mean projections indicated that diet costs could increase by up to 12% in the most affected regions by the end of the century. Under medium emissions, cost increases were projected to remain below 4%, whereas under low emissions, affordability changes were projected to be minimum across regions (within approximately 0·5%). Substantial regional disparities emerged, with the largest and most consistent affordability losses concentrated in low-income regions that contributed least to historical greenhouse gas emissions. Under SSP3-6.0, these disparities persisted particularly in regions of Africa and Asia despite projected three-to-five-fold increases in income over the century, with climate-induced disruptions to food systems increasing the number of people unable to afford a healthy diet through mid-century. INTERPRETATION: Climate change is likely to exacerbate global nutritional inequalities by disproportionately increasing the affordability risks of healthy diets in regions that have contributed least to historical greenhouse gas emissions. Under high-warming scenarios, socioeconomic development alone is insufficient to fully offset these risks, highlighting the structural vulnerability of low-income food systems to climate-driven price shocks. These findings suggest that in the absence of targeted interventions, climate change could continue to undermine progress towards equitable and health-oriented nutrition outcomes. FUNDING: Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China; National Aeronautics and Space Administration Goddard Institute for Space Studies Climate Impacts Group; Future of Life Institute; and Global Alliance for Improved Nutrition.

Journal Article↗

PubMed2026

Genome-Wide Characterization of β-Glucosidase (TaBGLU) Genes in Bread Wheat and Their Expression Under Drought, Cold, and Combined Stress.

Glycoside hydrolase 1 (GH1) β-glucosidases were known to activate hormone conjugates and defense metabolites, yet their genomic organization and stress-response dynamics in wheat remained incompletely defined. We therefore performed an integrated characterization of TaBGLUs spanning phylogeny, gene structure and conserved motifs, subcellular localization, promoter cis-elements, Gene Ontology enrichment, protein-protein interaction networks, and targeted expression profiling. Wheat TaBGLUs partitioned into well-supported clades that shared canonical GH1 catalytic residues and a largely conserved motif scaffold. Subcellular localization predictions indicated predominant nuclear and chloroplast targeting, with a smaller cohort directed to secretory or endomembrane compartments. Promoters were enriched for light-responsive, hormone-related (ABA, JA/SA, auxin, GA) and stress-associated (MYB/WRKY, heat, low temperature) cis-elements, and functional annotations were consistent with roles in carbohydrate and cell-wall metabolism, hormone homeostasis, and defense. Network analysis revealed a densely connected TaBGLU submodule embedded within broader carbohydrate and defense interaction networks, suggesting coordinated or cooperative functions. Expression profiling under cold, drought, and combined drought and cold demonstrated broad stress inducibility, with early activation detected by 6 h, cold-responsive maxima typically at 12 h, drought-responsive peaks predominating at 24 h, and combined stress eliciting both earlier and more sustained expression maxima between 12-24 h. Representative strongly responsive genes included TaBGLU20, TaBGLU44, TaBGLU6, and TaBGLU23, which showed pronounced late induction under combined stress, TaBGLU30, which exhibited an earlier combined-stress peak, and TaBGLU12, which displayed a marked late drought-specific response. Taken together, this integrated genomic, regulatory, and expression atlas refined the wheat BGLU repertoire relative to previous gene model inventories, highlighted candidate TaBGLUs with central network positions and strong stress inducibility, and provided concrete entry points for functional validation and breeding for improved stress resilience.

Triticum↗

PubMed2026

From commensal to pathobiont: The emergence of virulence-enhanced Escherichia coli in China's food-animal systems - insights with future implications.

A fundamental shift in Escherichia coli epidemiology is being driven by convergence of virulence determinants and antimicrobial resistance within linked human-animal-environment systems. In China, the rapid growth of food-animal production, extensive antimicrobial use, and complex food networks are accelerating the emergence and dissemination of virulence-enhanced E. coli pathobionts. This review synthesizes recent epidemiological, genomics, and outbreak data to characterize China's evolving landscape of food-animal-associated E. coli. We highlight a significant shift from classical pathotypes to hybrid lineages that simultaneously carry virulence factors and last-resort antibiotic resistance determinants, including mcr-1, tet(X4), and blaNDM. These traits disseminate rapidly via plasmid-mediated horizontal gene transfer, facilitating rapid adaptation and enabling cross-sectoral One Health transmission. National surveillance, foodborne outbreak investigations, and whole-genome sequencing data show that food-animal reservoirs are active evolutionary niches that drive pathogen diversity and fitness, rather than serving merely as contamination sources. Whole-genome sequencing also pinpoints high-risk clones (e.g., ST394) and plasmid-mediated co-selection of virulence and AMR. The emergence of hybrid pathotypes (e.g., STEC/ETEC) and AMR-virulence co-selection challenges traditional classification and limits the effectiveness of conventional surveillance approaches. The 2017 colistin ban reduced mcr-1, yet ongoing resistance and emerging tet(X4) demand integrated surveillance. Collectively, these findings call for reconceptualizing E. coli as a dynamic genomic entity embedded within a unified ecological network. Addressing this threat requires an integrated One Health strategy including genomic surveillance, agricultural antimicrobial stewardship, and coordinated food-environment-clinical monitoring to prevent high-risk clone emergence and global spread.

Animals↗

PubMed2026

Form and function of actin impacts actin health and aging.

The actin cytoskeleton is a fundamental and highly conserved structure that functions in diverse cellular processes, yet its direct contribution to organismal aging remains unclear. Here, we systematically interrogated how genetic and pharmacologic perturbations of actin structure and function influence lifespan and various hallmarks of aging in Caenorhabditis elegans. Whole-animal and tissue-specific knockdown of actin and key actin-binding proteins (ABPs)-arx-2 (Arp2/3), unc-60 (cofilin), and lev-11 (tropomyosin)-led to premature disruption of filament organization, reduced lifespan, and tissue-specific physiological defects. Actin dysfunction also displayed a more "aged" transcriptome using previously validated transcriptomics clocks, and broadly exacerbated many age-associated phenotypes, including mitochondrial dysfunction, lipid dysregulation, loss of proteostasis, impaired autophagy, and intestinal barrier failure. Pharmacological destabilization with Latrunculin A mirrored genetic knockdowns, while mild stabilization with Jasplakinolide modestly extended lifespan, emphasizing that optimal and finely tuned actin function is critical for healthy aging. Finally, analysis of human genome-wide association data revealed that common ACTB polymorphisms correlate with differences in age-related decline in gait speed, suggesting some links between aging and actin across organisms. Taken together, our results provide a comprehensive and publicly accessible resource that maps, for the first time, how changes in actin integrity correlate with diverse aging phenotypes across tissues. This descriptive framework is intended to enable future mechanistic discovery by offering a deep, unbiased dataset that can be integrated with emerging studies to define how actin dynamics can potentially influence aging.

actin↗

PubMed2026

Implementing Prolonged Exposure Therapy in a Community Substance Use Treatment Program: A Qualitative Study.

INTRODUCTION: Post-traumatic stress disorder (PTSD) commonly co-occurs with substance use disorders (SUD), yet few community-based SUD programs incorporate evidence-based trauma-focused. Prolonged exposure (PE), including its massed format (M-PE) with session frequency of 3-4 times per week, is a gold standard intervention for PTSD; however, concerns about client readiness, logistical demands and relapse risk have limited its adoption within SUD settings. This study examined staff perspectives on the feasibility and acceptability of integrating M-PE into a community-based SUD program. METHODS: Prior to launching a Hybrid Type 1 effectiveness-implementation trial (Project COMET), we conducted semi-structured virtual interviews with 15 community clinic staff: providers (n = 8), administrators (n = 2) and peer specialists (n = 5). Interviews were recorded, transcribed and analysed using a rapid qualitative analysis framework with matrix techniques to compare themes across roles. RESULTS: Four overarching themes captured staff perspectives on integrating M-PE: (Theme 1) Prior Knowledge and Experiences: Most staff were familiar with EMDR, while direct knowledge of PE/M-PE was limited. (Theme 2) Perceptions of M-PE: M-PE was widely viewed as a promising, structured intervention that fits the pacing and duration of SUD care. (Theme 3) Symptom Reduction and Client Impact: Staff anticipated improvements in PTSD and SUD symptoms through trauma-focused treatment. (Theme 4) Barriers and Constraints: Participants identified several potential implementation challenges, including logistical barriers and client readiness. DISCUSSION AND CONCLUSIONS: Findings suggest that staff generally viewed M-PE favourably but emphasised the importance of ensuring client readiness and organisational support. Enhancing feasibility and long-term sustainability may require expanded psychoeducation, targeted provider training and flexible delivery models. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT06968832.

Adult↗

PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗

PubMed2026

Effectiveness and usability of artificial intelligence-powered assistive technologies in Supporting daily activities of children with cerebral palsy: a systematic review.

BACKGROUND: Cerebral Palsy (CP) is the main cause of motor disabilities in childhood, necessitating innovative approaches to rehabilitation and assistive technology (AT). Simultaneously, artificial intelligence (AI) is increasingly being integrated into devices to create more adaptive, personalized, and effective AT. This systematic review aimed to evaluate the effectiveness and usability of AI-powered assistive technologies designed to support daily activities and rehabilitation in children with CP. MATERIALS AND METHODS: Five databases, including Scopus, Web of Science, PubMed, Embase, and IEEE Xplore, were systematically searched, and 23 articles were included in the final analysis. Articles were identified, selected, and categorized into emerging thematic areas based on the primary function and application of the technology. RESULTS: Five key thematic topics were identified: 1) AI-driven motor rehabilitation and gait training for functional mobility; 2) intelligent assessment and monitoring systems for clinical decision support; 3) AI-supported communication, social interaction, and intention recognition tools; 4) gamified and virtual reality-based interventions to enhance engagement and usability; and 5) smart assistive systems supporting daily living and independent mobility. The findings demonstrate a strong trend toward the application of AI technologies in personalized, engaging, and data-driven interventions for children with CP. However, the field is predominantly in the proof-of-concept stage, with limitations including small sample sizes, lack of long-term clinical validation, challenges in user-centered design, and usability for children with CP. CONCLUSION: AI-powered assistive technologies hold significant potential for transforming the care of children with CP by enabling highly personalized and engaging interventions. To actualize this potential, future work must realize that practical application remains challenging owing to limited clinical validation, technological integration, and usability barriers for children with CP. Future research must prioritize user-centered design and multidisciplinary collaboration to ensure that AI and robotic advancements improve the usability and quality of life for children with CP.

Humans↗

PubMed2026

Empirical Meropenem Versus Piperacillin/Tazobactam for Critically Ill Adults With Sepsis: Feasibility of a Randomised Trial.

BACKGROUND: Meropenem and piperacillin/tazobactam are commonly used empirical antibiotics in critically ill adults with sepsis, but whether one is superior to the other is uncertain. METHODS: The Empirical Meropenem versus Piperacillin/Tazobactam for Adult Patients with Sepsis (EMPRESS) trial is an ongoing investigator-initiated, randomised, open-label, adaptive clinical trial with an integrated feasibility phase comparing empirical treatment with meropenem versus piperacillin/tazobactam in critically ill adults with sepsis. The integrated feasibility phase enrolled 200 participants across 10 intensive care units (ICUs) in Denmark between 28 June and 12 December 2025. Five pre-specified feasibility criteria were evaluated; if all feasibility criteria were met, the trial would proceed unaltered, whereas failure to meet one or more criteria would require intervention and re-evaluation. RESULTS: We randomised 200 of 284 screened patients (70.4%). The median age was 70 years (interquartile range (IQR): 60-77), 65.5% were males. At randomisation, 80.0% received vasopressors or inotropes, and 43.5% were on invasive mechanical ventilation. Four of five pre-specified feasibility criteria were met: time to completion of the feasibility phase (5.5 months vs. threshold < 12.0 months), recruitment proportion (70.4% vs. threshold ≥ 50.0%), proportion of participants without consent to the continued collection of data (2.5% vs. threshold < 5.0%) and protocol adherence (81.0% vs. threshold ≥ 75.0%). The proportion of participants with timely primary outcome data availability (30-day mortality) within 45 days was 85.5% and below the pre-specified threshold of ≥ 95.0%. The proportions were low in the first 3 months (33.3%, 22.2% and 30.8%, respectively), increasing to 95.8% in the last month of the feasibility phase. All-cause mortality at 30 days was 30.5%, and specific serious adverse reactions occurred in 4.0% of participants. CONCLUSIONS: In this integrated feasibility evaluation of the EMPRESS trial comparing empirical meropenem versus piperacillin/tazobactam in critically ill adults with sepsis, four of five pre-specified feasibility criteria were met. The unmet criterion, timely primary outcome data availability, improved substantially during the feasibility phase. We consider the trial feasible and will proceed without modifications. EDITORIAL COMMENT: This feasibility study assessed recruitment, randomised allocation and data collection for the multicentre EMPRESS trial. For adaptive trials on trial platforms, careful interim checking of trial design functions is an important and necessary process. TRIAL REGISTRATION: Clinical Trials Information System EUCT number: 2023-509703-33-00; ClinicalTrials.gov identifier: NCT06184659; Universal Trial Number: U1111-1301-6379.

Humans↗

PubMed2026

Advancing nursing education through social and emotional learning: A systematic review guided by the Collaborative for Academic, Social, and Emotional Learning framework.

BACKGROUND: With Generation Z entering the nursing workforce in growing numbers, strengthening social and emotional learning is critical for academic success, professional adaptation, and safe practice. However, the existing evidence remains fragmented because of varied interventions and inconsistent approaches. OBJECTIVES: This systematic review examined (1) the social and emotional learning essential for nursing students and nurses within the Collaborative for Academic, Social, and Emotional Learning framework, (2) their impact on educational and clinical outcomes, and (3) implications for advancing nursing education and practice. METHODS: Following Joanna Briggs Institute methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, five international (PubMed, EMBASE, CINAHL, PsycINFO, Cochrane) and three Korean (RISS, KoreaMed, KMBASE) databases were searched up to June 2025. Eighteen studies involving 2,952 participants met the inclusion criteria, including quasi-experimental quantitative studies, descriptive quantitative studies, qualitative studies, and mixed-methods studies. The methodological quality of the included studies was appraised using the Mixed Methods Appraisal Tool. RESULTS: Within the Collaborative for Academic, Social, and Emotional Learning framework, relationship skills and self-management were the most frequently studied competencies, emphasizing teamwork, communication, and stress regulation. Self-awareness and social awareness were underexplored, despite their importance in empathy, resilience, and reflective practice. Responsible decision-making was the least studied competency, despite its importance in ethical reasoning. Social and emotional learning was consistently associated with enhanced adaptation, communication, leadership, relationships, and clinical performance. Effective strategies included blended learning, simulation, reflective activities, and mentorship, which are aligned with Generation Z's learning preferences. CONCLUSION: Although social and emotional learning integration is associated with improvements in educational and clinical outcomes in nursing, current research has largely centered on relational and stress-related competencies while underrepresenting responsible decision-making. To cultivate reflective, empathetic, and ethically grounded nurses, curricula should integrate social and emotional learning through a balanced and structured approach. REGISTRATION: This study was registered on PROSPERO (ID: CRD420251005683).

Humans↗

PubMed2026

Clinical performance evaluation of single-shade versus multi-shade composite resins in non-carious cervical lesions: a 36-month randomized clinical trial.

BACKGROUND: Composite resins are widely used as the material of choice for definitive restorations due to their ability to integrate functional and aesthetic aspects in dental rehabilitation. Recently, one-shade composite resins have been introduced to simplify the clinical workflow. OBJECTIVES: This study aimed to compare the clinical performance of a "chameleon effect" composite resin with that of a multi-shade composite resin in non-carious cervical lesions after 36 months of follow-up. METHODS: This study was a randomized, controlled, double-blind clinical trial with an equivalence design using a split-mouth approach. The sample consisted of 60 patients presenting at least two non-carious cervical lesions, totaling 120 restorations. Restorations were performed using two materials from the same commercial brand: Vittra Unique (one-shade group) and Vittra APS (multi-shade group). The clinical performance of the restorations was longitudinally evaluated according to the FDI criteria. Survival analysis was performed using Kaplan-Meier curves and the log-rank test, while other clinical parameters were compared using the chi-square test (α = 0.05). RESULTS: At baseline, no statistically significant differences were observed between the groups regarding the evaluated biological, functional, and aesthetic parameters (p > 0.05). Both materials demonstrated clinically acceptable performance according to the FDI criteria, with no significant differences between them. CONCLUSIONS: After 36 months, no significant differences were observed between the one-shade and the multi-shade composite resins in the restoration of non-carious cervical lesions. CLINICAL SIGNIFICANCE: This 36-month randomized clinical trial demonstrates that single-shade composite resins achieve structural durability and aesthetic integration equivalent to traditional multi-shade layering when restoring non-carious cervical lesions. This evidence validates a simplified, single-shade restorative workflow, significantly reducing chairside time and operator-dependent variables without compromising the clinical longevity.

Humans↗