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PubMed2026

A systematic review of the impact of Mental Health First Aid on medical, nursing and allied healthcare professional students.

BACKGROUND: Healthcare professional (HCP) students are at high risk of mental health problems, but stigma and fear of career repercussions often deter them from seeking help. Mental Health First Aid (MHFA) is a globally disseminated course teaching the public to identify and respond to people experiencing mental health problems. MHFA training may address some of the challenges faced by HCP students, by improving mental health knowledge and by enhancing well-being and peer support. AIMS: To systematically review the available literature regarding the impact of MHFA training on HCP students' mental health literacy, confidence and intentions to provide help, stigma, peer support and self-care. METHOD: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (International Prospective Register of Systematic Reviews ID: CRD42024589509), five databases were searched. Primary studies evaluating the above outcome measures in HCP students were included. Two authors independently screened references and extracted data. Quality was assessed using the Modified Medical Education Research Study Quality Instrument and Cochrane Risk of Bias tools. A narrative synthesis was performed. RESULTS: Of 2367 records screened, 26 met inclusion criteria. Confidence in supporting others and mental health literacy showed the most consistent improvements following MHFA training, whereas evidence for changes in stigma was mixed. Peer support, self-care and student well-being were infrequently examined, although qualitative data suggested that MHFA had improved openness to help-seeking. CONCLUSIONS: MHFA shows promise in enhancing mental health literacy, confidence and intentions, and in reducing stigma, particularly when supplemented with experiential learning. HCP students may benefit from tailoring of such courses to their specific needs, fostering a culture of peer support, enhancing well-being and introducing basic concepts in mental health.

MHFA↗

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

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

Emergence of a Novel, Phenotypically Difficult-to-Detect Vancomycin-Resistant Enterococcus faecium Clone (ST117/CT7799).

A significant increase of vancomycin-resistant Enterococcus faecium (VREfm) infections was observed in South-Eastern Austria since 2024. The prolonged outbreak is caused by a novel vanB-VREfm clone (ST117/CT7799, "VREfmstyr"). This study characterizes the atypical difficult-to-detect resistance phenotype and assesses the genomic relatedness of the isolates. Patient and outbreak characteristics were investigated including whole genome sequencing of the isolates. Sensitivity of broth microdilution (BMD), gradient tests (GT), disk diffusion (DD), and automated susceptibility testing (VITEK2) was compared. The performance of commercial screening media was evaluated. From sporadic detections in early 2024 case numbers began to rise during the year. In 30/31 (97%) of all cases, intra-hospital transmission was considered likely and an association with invasive procedures was identified in most cases. Core genome multilocus sequence typing revealed only six allelic differences between VREfmstyr isolates collected in a 12-month period, all belonging to the E. faecium ST117/CT7799 lineage. BMD detected vancomycin resistance (MIC > 4 mg/L) in no more than 16/31 (52%) of isolates after 24 h incubation, while GT and DD misclassified all isolates. Only prolonged incubation improved the performance of these assays. VITEK2 analysis, however, correctly classified all 31 isolates. Of four commercially available VRE-screening agars, only one was capable of detecting VREfmstyr after 24 h incubation. The emergence and clonal dissemination of VREfm ST117/CT7799 reveals a serious diagnostic gap as commonly used diagnostic algorithms fail to reliably detect this resistance phenotype. Our findings should help to further evaluate the true geographical distribution and clinical significance of this novel VREfm clone.

Enterococcus faecium↗

PubMed2026

Stigma, discrimination-related events, and determinants among adult people living with systemic lupus erythematosus (SLE): Systematic review and indicator-level meta-analysis.

BackgroundSystemic lupus erythematosus (SLE) is a complex autoimmune disease with 0.4 million new cases diagnosed annually. With its wide variety of visible and invisible manifestations, people living with SLE report being exposed to stigmatization, which impacts their personal and professional lives. However, the current literature is unclear on whether healthcare management teams assess this concern during follow-up. This study aims to synthesize existing evidence on the prevalence and determinants of stigma among people living with SLE.MethodsThis systematic review and meta-analysis gathered evidence from observational studies identified from three databases on 16 July 2025. Dual independent screening, data extraction, and risk-of-bias assessment (using the Newcastle-Ottawa Scale) were performed. Results were synthesized using descriptive statistics, narrative synthesis, and indicator-level meta-analyses.ResultsWithin the past two decades, 11 studies comprising 2254 people living with SLE reported and measured stigma- and discrimination-related events using various scales. Stigma was found to be prevalent across its three constructs: interpersonal, perceived, and intrapersonal stigma. This review demonstrated that people living with SLE reported a moderate overall burden of stigma (34.71 [95% CI 26.15, 43.27]), with average stigma scores indicating psychological impact. Additionally, nearly one in two persons (46% [95% CI 28-66%]) experienced at least one form of stigma or discrimination, most commonly social isolation and unfair treatment. Mental health associations were correlated with higher stigma burden.ConclusionThis review demonstrates that stigma and discrimination are not just social challenges but also critical determinants of health. With cautious interpretation, pooled evidence reveals a consistent high prevalence of stigma and discrimination, which act as "toxic" stressors, creating a vicious cycle with psychological stress and psychiatric manifestations and disease activity. There is an urgent clinical need to move beyond a mere biological approach to disease assessment and management and to begin screening for the "invisible" burden of invalidation and discrimination.

Humans↗

PubMed2026

Global prevalence of metabolic syndrome in adults with obstructive sleep apnea: a systematic review and meta-analysis.

STUDY OBJECTIVES: Metabolic syndrome (MetS) is considered to exhibit increased prevalence among adults with obstructive sleep apnea (OSA), but the reported prevalence estimates among such patients vary. Thus, this systematic review and meta-analysis aimed to investigate the global prevalence of MetS in adults with confirmed OSA. MATERIALS AND METHODS: Ovid Medline, Embase, CINAHL, and the Cochrane Library databases were searched for all primary studies published in English that used standard polysomnography for OSA diagnosis and reported MetS estimates. At least two reviewers independently screened for eligible studies, extracted data, and graded the risk of bias using the Risk of Bias Assessment Tool for Non-randomised Studies. Three-level random-effects model was applied for the meta-analysis, reporting pooled prevalence estimate with 95% confidence intervals (CIs). Pre-specified subgroup analyses and meta-regression were also performed. Heterogeneity was quantified using I2 and chi-square statistics. RESULTS: A total of 102 studies were eligible for inclusion (34 013 adults with OSA from 28 countries). The combined MetS prevalence was 55.4% (95% CI: 51.0%, 59.8%). Considerable heterogeneity was noted among the included studies (I2 = 97.8%), whilst the risk of bias ranged from low to high. Subgroup analysis examining the effects of geographic region, study design, MetS definition, and apnea-hypopnea index threshold showed a significant variation in prevalence estimates across most subgroups (p < 0.0001). Meta-regression analysis indicated a positive association between mean body mass index (β = 0.0772, t = 4.56, p < 0.0001) and MetS prevalence. CONCLUSIONS: MetS has a high prevalence among adults with polysomnography-confirmed OSA, underscoring the need for prompt MetS screening in these patients. Future longitudinal and genetic/mechanistic studies should investigate the factors accounting for this association. PROSPERO REGISTRATION NUMBER: CRD420251073055.

Humans↗

PubMed2026

Enhancing Hemoglobin Bart's hydrops fetalis syndrome prevention: a single-tube multiplex real-time PCR assay for the comprehensive detection of four significant α0-thalassemia deletions (--SEA, --THAI, --CR, and --SA) found in Thailand.

BACKGROUND: Hemoglobin (Hb) Bart's hydrops fetalis is a major public health concern in Southeast Asia, particularly in Thailand. Current screening strategies target the two most common α0 -thalassemia deletions (--SEA and --THAI). METHOD: In this study, we developed a single-tube multiplex real-time PCR assay for the simultaneous detection of four clinically relevant α0-thalassemia deletions (--SEA, --THAI, --CR, and --SA). The assay was validated using 538 clinical samples with diverse thalassemia genotypes and compared against conventional gap-PCR as the reference method. Analytical performance, including sensitivity, specificity, and limit of detection (LOD), was evaluated. In addition, clinical utility was assessed in 22 prenatal diagnosis cases at risk of Hb Bart's hydrops fetalis. RESULTS: The study cohort demonstrated substantial genetic heterogeneity, comprising 43 distinct genotypes. The developed assay achieved 100% sensitivity and specificity for all targeted deletions, with complete concordance with gap-PCR results. No cross-reactivity was observed with α+-thalassemia. The assay demonstrated a high analytical sensitivity with a LOD of 9.76 × 10-3 ng per reaction. Whereas in prenatal diagnosis, all 22 fetal genotypes were accurately identified, including five cases of homozygous --SEA and one rare compound heterozygous --SEA/--CR fetus. CONCLUSIONS: This study presents a rapid, accurate, and cost-effective multiplex real-time PCR assay capable of detecting both common and rare α0-thalassemia deletions in a single reaction. The assay demonstrates strong potential for implementation in routine clinical laboratories and large-scale population screening, contributing to improved prevention and control of severe thalassemia syndromes in high-prevalence regions.

Humans↗

PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗

PubMed2026

Prevalence and pattern of psychological symptoms in form of dental fear and anxiety in children and adolescents with traumatic dental injuries: a systematic review.

BACKGROUND/AIMS: This systematic review aimed to evaluate the level and pattern of dental fear and anxiety (DFA) amongst children and adolescents with traumatic dental injuries (TDI) and, where available, to compare these outcomes with non-traumatised controls. METHODS: An a priori protocol was developed and registered in PROSPERO (CRD420261329946). A comprehensive literature search was conducted in PubMed, EMBASE, Web of Science, and Scopus on 3 March 2026, with additional grey literature, citation, and reference searching. No language or time restrictions were applied. Observational clinical studies assessing DFA in individuals with TDI using validated tools were included. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Joanna Briggs Institute checklist. Certainty of evidence was evaluated using the GRADE approach. RESULTS: A total of 2885 records were identified, of which 4 cross-sectional studies met the inclusion criteria after screening. Studies were conducted in Croatia and Kosovo and included paediatric populations. Sample sizes ranged from 147 to 505 participants. Different validated scales (CFSS-DS, CDAS, S-DAI) were used to assess DFA. Overall, children with TDI demonstrated predominantly low levels of dental fear and anxiety across assessment tools. However, findings were inconsistent, with some studies reporting lower or similar anxiety levels in TDI patients compared to controls. Risk of bias was moderate or high in three of the four studies, and the overall certainty of evidence was rated as low to very low due to methodological limitations, heterogeneity, and imprecision. CONCLUSION: The limited available evidence suggests that children and adolescents with TDI do not consistently present with higher levels of dental fear and anxiety than non-traumatised controls. These findings indicate that TDI does not inherently cause DFA, underscoring the critical role of empathetic behaviour support in mitigating post-traumatic dental fear. However, the evidence is based on a small number of regionally concentrated cross-sectional studies with low to very low certainty. Future prospective studies using standardised TDI classifications, validated DFA instruments, and clearly defined assessment time points are needed.

Dental anxiety↗

PubMed2026

Qualitative evidence of service user experiences and perspectives on long-acting injectable buprenorphine for opioid treatment - a scoping review.

BACKGROUND: There is substantial literature on opioid treatment program (OTP) formulations and how they relate to the pharmacotherapy service user experience. As a newer formulation, less is known about service user experiences of long-acting injectable buprenorphine (LAIB). The aim of this scoping review is to map the qualitative evidence and gaps in the literature on service user experiences and perspectives of LAIB. METHODS: Our search strategy included Medline, Embase, PsycINFO, CINAHL, Scopus and Web Science, and citation chaining, from January 2016 to June 2025. Studies were included if reporting qualitative descriptions of LAIB service user experiences of treatment for opioid dependence, inclusive of qualitative, mixed methods (description of qualitative data only), case reports and English language. Articles were screened by two reviewers. A living experience first author led the analysis using inductive coding and thematic analysis, to produce a descriptive summary of synthesised findings alongside key study characteristics and quality appraisal, adhering to the Systematic reviews and Meta-Analysis for Scoping Reviews (PRISMA-ScR) checklist. RESULTS: After screening 838 titles/abstracts and reviewing 150 full texts, 40 studies met the eligibility criteria. All were conducted in high income countries, principally the US (n=12); Australia (n=10); and England and Wales (n=9). We identified five themes: Navigating LAIB treatment; Embodied and relational effects of LAIB; Impact and role of the service provider; Narratives of harm reduction and recovery; Stigma and criminalisation. LAIB was commonly experienced as increasing convenience, stability and freedom from daily supervised dosing, enabling improved work, travel, privacy and social participation. Reduced clinic/dosing contact often lessened enacted stigma and treatment burden. However, experiences were heterogenous. Some participants described injection-site discomfort, uncertainty about dose adequacy, reduced flexibility once injected, and ambivalence about LAIB effects. There was inconsistency in LAIB service user reports on service connection, isolation and psychosocial support. Treatment experiences were strongly shaped by provider practices. CONCLUSIONS: Findings underscore the need for integrated, flexible, harm-reduction oriented and person-centred LAIB treatment models that prioritise choice, autonomy and therapeutic relationships to maximise benefit for service users. However, evidence of LAIB service user experiences is concentrated in high-income countries, and the absence of perspectives from low- and middle-income country settings represents a substantial gap in the evidence base.

LAIB↗

PubMed2026

Ten-Year Update of Nurse Practitioner Service Impact on Patient and Health Service Outcomes in Emergency Care Settings-A Systematic Review.

AIMS: To provide a 10-year update on the best available evidence evaluating the impact of nurse practitioner services on cost, waiting times, patient satisfaction, representation rates, and length of stay in emergency and urgent care settings. DESIGN: Systematic review. DATA SOURCES: The search was completed on January 28, 2025, in Embase (Elsevier), Medline (EBSCOhost), CINAHL (EBSCOhost), Cochrane Library (Wiley), Emcare (Ovid), Web of Science Core Collection (Clarivate) and Scopus (Elsevier). The data range (2014-2024) was used to limit the search. METHODS: The search was conducted with results imported into Covidence. In Covidence, two reviewers conducted screening, data extraction, and quality appraisal of articles, and findings were analysed using a narrative synthesis approach. Eligible studies examined nurse practitioner services in emergency or urgent care settings, reporting outcomes of cost, waiting times, patient satisfaction, representation rates, and length of stay. RESULTS: Title and abstract screening were performed on 2329 records. Of these, 236 full-text articles were reviewed, and 17 underwent critical appraisal and data extraction. Narrative analysis of outcome measures yielded mixed results, with both favourable and unfavourable findings reported regarding nurse practitioner services. CONCLUSIONS: Global evaluation of nurse practitioner services in emergency care remains inconsistent. Nevertheless, emerging evidence supports their positive impact, particularly in improving patient outcomes. To effectively inform policy, workforce planning and clinical integration, there is a need for professional benchmarks that provide clear frameworks for the evaluation of patient-centred outcomes and operational impacts in emergency departments. IMPLICATIONS: Evidence related to nurse practitioner services in emergency and urgent care clinics highlights the positive impact of nurse practitioner services on patient wait times and satisfaction; however, there is limited and variable evidence of impact on health care costs and outcomes. IMPACT: This paper recommends that evaluating emergency nurse practitioner services requires homogeneous research using consistent professional benchmarks and evaluation frameworks. REPORTING METHOD: This systematic review follows the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. PATIENT OR PUBLIC CONTRIBUTION: This study did not include patient or public involvement in its design, conduct, or reporting. TRAIL REGISTRATION: PROSPERO 2025 CRD420250645148.

Humans↗

PubMed2026

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. RESULTS: Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. CONCLUSION: Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.

Humans↗

PubMed2026

Emerging Principles in Spatial Functional Genomics.

Spatial transcriptomic and proteomic atlases have enabled mapping of gene programs within intact tissues, but these measurements remain largely descriptive and do not define the mechanisms controlling tissue biology. Pooled CRISPR screening provides scalable causal interrogation of gene function but remains largely confined to dissociated systems that lack spatial context. In vivo spatial functional genomics (SFG) bridges these approaches by integrating genetic perturbations with in situ transcriptomic and proteomic readouts to measure gene function within intact tissue ecosystems. By preserving spatial organization, SFG enables interpretation of perturbations through effects on cell-cell interactions, diffusible signals, multicellular niches, and tissue architecture. Here, we outline key design axes of SFG: perturbation strategy, barcoding strategy, and phenotypic readout. We discuss computational challenges, including spatial autocorrelation, neighborhood dependence, and context-aware null modeling, and highlight how SFG reveals non-cell-autonomous, architecture-dependent mechanisms of gene function, advancing toward predictive models of tissue organization and gene function.

Genomics↗

PubMed2026

Risk Factors for Long-Term Health-Related Quality-of-Life and Mental Health Outcomes in Traumatic Brain Injury: A Systematic Review and Meta-Analysis.

Traumatic brain injury (TBI) often leads to long-term disability, including persistent mental health issues and lower health-related quality of life (HRQoL). Early interventions can improve recovery, but because resources limit routine monitoring of all patients, trauma care remains largely symptom-driven. The combination of long-term disability and limited capacity for routine follow-up highlights the need for risk-stratified follow-up care and reliable evidence on early prognostic factors. However, the existing literature is sparse and methodologically heterogeneous, limiting the clinical applicability of findings. We therefore conducted a systematic review and meta-analysis to identify early risk factors for poorer long-term mental health and HRQoL outcomes. A systematic search of seven electronic databases identified studies of adult patients with TBI, with outcomes assessed at least 6 months postdischarge. Two authors independently screened the studies, assessed the risk of bias, and extracted the data. We pooled effect estimates using a random-effects meta-analysis and calculated 95% prediction intervals. A narrative synthesis was applied when meta-analysis was not feasible. The review was registered with PROSPERO (CRD42024576912) and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Of the 8,104 articles screened, 64 studies met the inclusion criteria (n = 334,672). Most studies (58%) had a low risk of bias. Female sex, socioeconomic disadvantage, psychiatric history, assaultive-related injuries, and previous TBI were consistently associated with worse long-term outcomes. Across meta-analyses, assault-related injuries more than doubled the odds of post-traumatic stress disorder (odds ratio [OR] = 2.72; 95% confidence interval [CI]: 2.01-3.66, I2 = 0%). Higher odds were also observed among females (OR = 1.33; 95% CI: 1.11-1.59, I2 = 0%), individuals with prior TBI (OR = 1.56; 95% CI: 1.07-2.27, I2 = 0%), and those with psychiatric history (OR = 2.38; 95% CI: 1.83-3.10, I2 = 48%). We found that female sex (OR = 1.72; 95% CI: 1.38-2.16, I2 = 58%), prior TBI (OR = 1.52; 95% CI: 1.25-1.85, I2 = 0%), and psychiatric history (OR = 3.25; 95%CI: 1.86-5.69, I2 = 98%) were associated with higher odds of depression. Furthermore, higher pooled anxiety scores were observed in females and in individuals with a psychiatric history. The study identified several readily available factors present before or at discharge that are associated with poor long-term HRQoL and mental health outcomes. Leveraging these factors in follow-up protocols, prediction modeling, and clinical decision support systems may facilitate risk-stratified postdischarge care for TBI patients.

Humans↗

PubMed2026

Early Worsening of Diabetic Retinopathy Following Initiation of Hybrid Closed-Loop/Automated Insulin Delivery Systems in Type 1 Diabetes: A Systematic Review and Structured Study-Level Synthesis.

BACKGROUND: Hybrid closed-loop (HCL) systems achieve rapid, algorithm-driven improvements in glycaemia in type 1 diabetes (T1D). Paradoxically, rapid improvement in glycaemic control is associated with early worsening of diabetic retinopathy (EWDR), a phenomenon established in the intensive insulin therapy era. Whether HCL initiation carries a clinically meaningful EWDR risk is unknown. No systematic review has previously addressed this question. METHODS: A systematic review and structured quantitative synthesis was performed using study-level estimates only (PROSPERO CRD:420261391951). MEDLINE, SCOPUS and Web of Science were searched to 14th May 2026. Studies reporting retinal outcomes in people with T1D initiating any HCL system were eligible. Two reviewers independently screened studies and extracted data. Risk of bias was assessed using ROBINS-I and certainty of evidence using the GRADE framework. EWDR incidence was summarised using study-level proportions, and comparative studies were summarised using study-specific risk ratios for HCL versus control therapy. Given substantial heterogeneity in EWDR definitions, retinal assessment timing, follow-up duration, and comparator groups, no pooled or meta-analytic estimates were derived. RESULTS: Eight studies (n = 1487 participants; 860 HCL users) were included; all were observational and six were retrospective. EWDR varied markedly with the timing of retinal assessment. In studies assessing the retina within ≤ 12 months of HCL initiation, EWDR rates ranged from 8.9% to 26.5%. Studies with longer follow-up reported lower rates of retinal worsening or incident DR, 6.7% at 24 months and 6.1% over a mean follow-up of 4.9 years, suggesting that these studies may capture background DR progression rather than true early worsening. Three comparative studies included 177 HCL users and 315 controls; EWDR study-specific risk ratios were directionally inconsistent, ranging from 0.32 to 1.51, and were therefore not pooled. The most consistently identified risk factors were higher baseline HbA1c and older age. The magnitude of HbA1c reduction was not a consistent predictor of EWDR in the HCL context, in contrast to pre-HCL era evidence. Risk of bias ranged from moderate to critical and certainty of evidence was very low for all outcomes. CONCLUSIONS: Study-defined retinal worsening was reported in a minority of participants. The current evidence base is dominated by retrospective studies, variable retinal assessment timing, and inconsistent EWDR definitions. Well-designed prospective studies with protocol-specified retinal surveillance anchored to HCL initiation are required to generate reliable incidence estimates, identify risk factors, determine visual consequences, and inform standardised screening guidance.

Humans↗

PubMed2026

Integrated transcriptomic and metabolomic analysis of fluoride tolerance-related pathways and differentially expressed genes in silkworm strain XSKD.

XueSong KD (XSKD) silkworm strain exhibits prominent fluoride tolerance, yet the underlying molecular mechanisms of fluoride tolerance remains unclear. In the present study, fourth-instar pre-molting XSKD silkworms were used as experimental materials for integrated transcriptomic and untargeted metabolomic analyses. In total, 572 differentially expressed genes and 90 differential metabolites were screened. GO enrichment and KEGG enrichment based on the hypergeometric distribution model revealed that 13-Hydroxy-9Z,11E-octadecadienoic acid (13-(S)-HODE) acts as the core differential metabolite, which is significantly enriched in the linoleic acid metabolism pathway. Within this pathway, LOC101737302 and CYP338A1 display opposite expression trends and show correlations with pathway metabolites. Based on multi-omics data, this study preliminarily characterizes the lipid metabolic response under fluoride stress, providing omics dataset support for further in-depth exploration of the molecular mechanism of fluoride tolerance in silkworms.

Animals↗

PubMed2026

Comparative Efficacy of Different AI Systems for Polyp Detection by Size During Colonoscopy: Systematic Review and Network Meta-Analysis.

BACKGROUND: Colorectal cancer remains a leading cause of death despite being largely preventable through polypectomy. AI systems designed to enhance polyp detection during colonoscopy have shown promise, but the extent to which they improve detection of different-sized polyps remains unclear. OBJECTIVE: This study compared the size-stratified efficacy of AI-assisted colonoscopy vs standard colonoscopy using the Hartung-Knapp-Sidik-Jonkman (HKSJ) method, and generated exploratory rankings while acknowledging all cross-platform comparisons are indirect. METHODS: This systematic review and network meta-analysis (NMA) searched PubMed, Embase, Cochrane CENTRAL, and Web of Science from inception to July 25, 2026, supplemented by citation searching. We included randomized controlled trials (RCTs) comparing AI-assisted vs standard colonoscopy in adults (≥18 years of age), reporting mean polyp detection counts stratified by size (≤5 mm, 6-9 mm, and ≥10 mm). Two reviewers screened studies, extracted data, and assessed risk of bias using the Cochrane Risk of Bias 2.0. We conducted frequentist NMA using the HKSJ method with restricted maximum likelihood estimation, calculated 95% prediction intervals (PIs), and assessed heterogeneity using I2 and τ2. Certainty of evidence was rated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) framework. RESULTS: A total of 13 RCTs (4156 participants) compared 8 AI systems to standard colonoscopy, forming a network without direct AI comparisons. For diminutive polyps (≤5 mm), AI showed a modest advantage (standardized mean difference [SMD] 0.21, 95% CI 0.07 to 0.35, 95% PI -1.12 to 1.54), but substantial heterogeneity (I2=86.6%) and wide PI crossing the null indicated high uncertainty. EndoScreener showed the most consistent evidence (SMD 0.36, 95% CI 0.18-0.54). For small and large polyps, effects were minimal (SMD 0.02, 95% CI -0.02 to 0.06, 95% PI -0.03 to 0.07; SMD 0.01, 95% CI 0.00-0.02, 95% PI -0.01 to 0.03). GRADE certainty was very low for diminutive polyps and low for small and large polyps. Sensitivity analysis excluding Tianjin YuJin did not materially change findings. CONCLUSIONS: AI may modestly enhance diminutive polyp detection, but effects on small and large polyps are minimal, with no platform superiority. Given very low to low certainty, findings are hypothesis-generating. This exploratory NMA provides size-stratified comparisons that can inform future head-to-head trial design. Unlike prior reviews aggregating all polyp sizes, we show the overall AI benefit is driven by diminutive polyp detection, providing a framework for targeted deployment-prioritizing AI for diminutive polyp screening, with limited value for larger lesions. Head-to-head trials are urgently needed. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD420251266932; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251266932.

Colonoscopy↗

PubMed2026

Colorimetric gold nanosensors for monitoring protein aggregation: implications for Alzheimer's disease.

Alzheimer's disease (AD) is the leading cause of dementia worldwide. It remains a major public health challenge due to the lack of early diagnostic tools and effective disease-modifying therapies. Molecularly, AD is characterized by extracellular amyloid-β (Aβ) plaques and intracellular Tau tangles, as well as soluble oligomers that are likely the neurotoxic species. However, the transient and heterogeneous nature of these oligomers makes them difficult to detect using conventional biosensing approaches. Nanomaterial-based colorimetric biosensors have emerged as promising platforms for detecting protein aggregates and discovering aggregation inhibitors. Specifically, the localized surface plasmon resonance properties of metallic nanomaterials can enable rapid, label-free, and visually detectable colorimetric sensing of molecular interactions. These features can be leveraged to monitor protein aggregation processes in real time and achieve high-throughput screening of aggregation inhibitors, which may collectively enable early detection and timely intervention of AD progression. This Review Article presents the design and engineering of gold-nanomaterial-based colorimetric biosensors for monitoring protein aggregation and highlights the current challenges and emerging opportunities for applying these nanosensors to combat AD.

Journal Article↗