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PubMed2026

Diagnostic and Predictive Value of Circulating and Exosomal microRNAs in Ferroptosis-Associated Neurological Conditions: A Systematic Review and Meta-analysis.

Circulating microRNAs (miRNAs) have emerged as potential non-invasive markers for intracranial pathology, yet their diagnostic accuracy and relationship with ferroptosis-mediated neuronal damage remain poorly defined. The primary objective of this study was to evaluate the diagnostic and predictive potential of circulating and exosomal miRNAs across ferroptosis-associated neurological conditions and to explore their associations with ferroptosis-related pathways. Following PRISMA-DTA guidelines, a systematic literature search was conducted across PubMed, Scopus, Cochrane, and ScienceDirect, identifying 205 records. After screening for human clinical cohort validation, 7 studies were included in the qualitative synthesis and 5 in the quantitative meta-analysis. Pooled Area-under-the-Curve (AUC) was calculated using a random-effects inverse-variance model, while prognostic correlation coefficients (r) were synthesized using Fisher's Z-transformation. Methodological quality was assessed via QUADAS-2. Analysis of 7 clinical cohorts provided heterogeneous evidence on the diagnostic and prognostic potential of miRNAs. Random-effects pooling of the two eligible diagnostic AUC estimates yielded an exploratory pooled AUC of 0.87 (95% CI, 0.79-0.94; I2 .90%). Prognostic synthesis of Group 2 identified an exploratory association between miRNA levels and clinical severity scales (exploratory pooled correlation coefficient of 0.67 (95% CI: 0.56-0.76; I2 .714.4%). Selected miRNAs were mapped to ferroptosis-associated regulators, including SLC7A11, ABCB8, and SLC40A1. Exosomal miRNAs hold potential to indicate disease-associated molecular information, although comparative clinical evidence remains yet to be explored. Circulating and exosomal miRNAs show promising diagnostic and prognostic potential across selected neurological conditions. These findings highlight a potential mechanistic association between miRNA expression and ferroptosis-mediated neuronal injury.

Humans↗

PubMed2026

Prevalence of Claudin 18.2 Expression in Gastric and Gastroesophageal Junction Adenocarcinoma: A Systematic Review and Meta-Analysis.

BACKGROUND: Claudin 18 isoform 2 (CLDN18.2) has emerged as a clinically validated therapeutic target in gastric and gastroesophageal junction (GEJ) adenocarcinoma following the regulatory approval of zolbetuximab in combination with first-line chemotherapy. Accurate prevalence data at the clinically validated immunohistochemical threshold are essential for patient selection, healthcare resource planning, and treatment strategy. Reported prevalence estimates vary widely across studies due to differences in populations, methodologies, and immunohistochemical protocols. This systematic review and meta-analysis aimed to generate a robust pooled prevalence estimate of CLDN18.2 expression at the threshold used in pivotal phase III trials. METHODS: PubMed, Embase, and the Cochrane Library were searched from database inception through March 12th, 2026. Studies reporting CLDN18.2 expression in gastric or gastroesophageal junction adenocarcinoma using the ≥ 75% moderate-to-strong membranous staining threshold were included. Prevalence proportions were pooled using a random-effects model with logit transformation and restricted maximum-likelihood estimation of between-study variance. Heterogeneity was assessed using the I² statistic and Cochran's Q test, and a 95% prediction interval was calculated. Pre-specified subgroup analyses assessed antibody clone and geographic region, with additional exploratory analyses according to disease setting and specimen type. Sensitivity analyses were performed to assess the robustness of the pooled estimate. RESULTS: Twenty-two predominantly retrospective cohort studies comprising 12,173 patients were included. The pooled prevalence of CLDN18.2 positivity using a random-effects model was 33.99% (95% CI: 30.13%-38.07%; 95% prediction interval: approximately 18%-55%), with high between-study heterogeneity (I² = 92.4%). Subgroup analysis by antibody clone showed no statistically significant difference between studies using the 43-14 A clone (32.79%, 95% CI: 28.86%-36.97%) and those using other reported antibody clones (41.74%, 95% CI: 26.76%-58.42%; p = 0.281). One study with an unreported antibody clone was excluded from this subgroup analysis. Geographic subgroup analysis excluding the multinational Shitara et al. cohort demonstrated a non-significant trend toward higher prevalence in non-Asian populations (37.85%, 95% CI: 31.59%-44.54%) compared with Asian populations (32.10%, 95% CI: 27.28%-37.34%; p = 0.169). All three sensitivity analyses confirmed robustness of the pooled estimate. No significant evidence of publication bias was detected (Egger's test p = 0.56). CONCLUSIONS: Approximately one-third of patients with gastric and GEJ adenocarcinoma express CLDN18.2 at the clinically validated ≥ 75% threshold. However, because the included studies encompassed heterogeneous disease settings and were predominantly HER2-unselected, the pooled estimate should not be interpreted directly as the proportion of patients eligible for zolbetuximab. The estimate was robust across sensitivity analyses and provides an evidence base for understanding CLDN18.2 prevalence and biomarker-testing requirements. Standardisation of immunohistochemical assessment methods is warranted to reduce between-study heterogeneity in future research.

Humans↗

PubMed2026

Adaptive Sports Exposure Across Physical Medicine and Rehabilitation Residency and Sports Medicine Fellowship Programs: A National Cross-Sectional Website Analysis.

Adaptive sports improve health and quality of life for people with disabilities, yet the extent of adaptive sports exposure in physical medicine and rehabilitation (PM&R) training is unclear. This cross-sectional study reviewed the public websites of 115 accredited physical medicine and rehabilitation residency programs and 26 sports medicine fellowship programs for any mention of adaptive sports, characterizing exposure type, target populations, and associated program characteristics. Adaptive sports were mentioned by 32 residency programs (27.8%) and 8 fellowship programs (30.8%). Mention varied significantly by geographic region in both cohorts (residency, West 64% vs. South 19%, P=.017; fellowship, Midwest 80% vs. South 0%, P=.035) and, among residencies, was associated with larger program size and a greater number of associated subspecialty fellowships. Residency exposure was predominantly volunteer-based, whereas fellowship exposure was exclusively clinical or undisclosed; target populations served were frequently unspecified. Programs have an opportunity to expand structured adaptive sports training and to clearly convey these opportunities to applicants and patients.

Adaptive sports↗

PubMed2026

Histoplasmosis in children: emerging insights and evolving guidelines.

PURPOSE OF REVIEW: This review provides an update on the epidemiology, risk factors, clinical presentation, diagnosis, and management recommendations incorporating recommendations from recent publications including the Infectious Disease Society of America guidelines for the management of pulmonary and disseminated histoplasmosis. RECENT FINDINGS: Updates to the epidemiology of histoplasmosis indicate a broader geographic range than historically defined. Updated guidelines do not recommend routine treatment for asymptomatic, mild and moderate pulmonary histoplasmosis although itraconazole can be offered for immunocompromised children or for prolonged or worsening symptoms. Liposomal amphotericin B is recommended as initial treatment for severe histoplasmosis syndromes (severe pulmonary and disseminated histoplasmosis). Fibrosing mediastinitis, a late complication of histoplasmosis is treated with stenting of vessels and bronchi. Recent studies demonstrate that rituximab (anti-CD20 monoclonal antibody) may stop progression or lead to regression of progressive fibrosis. SUMMARY: Histoplasmosis has manifold manifestations, many of which are self-limited and do not require treatment. Severe histoplasmosis and its complications should be treated with liposomal amphotericin B followed by itraconazole. Clinical trials are needed to assess the efficacy of rituximab for the treatment of fibrosing mediastinitis.

Humans↗

PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗

PubMed2026

Risk of mortality and complications in people with depressive disorder and co-occurring diabetes mellitus: a systematic review and meta-analysis.

AIMS: People with depressive disorder have increased premature mortality and higher rates of diabetes mellitus than general population. Evidence shows that diabetes may further increase their risk of premature death from diabetes-related complications, especially cardiovascular diseases (CVDs). Earlier studies examining depression-associated outcomes in diabetes patients have shown mixed results and were hindered by important limitations, especially the use of self-reported questionnaires to ascertain depression, causing misclassification bias by identifying subclinical symptoms or diabetes distress. Associations of depression with specific diabetes complications have not been systematically evaluated. This meta-analysis aimed to investigate the risk of mortality and complications among patients with depression and co-occurring diabetes (depression-diabetes group) relative to patients with diabetes-only (diabetes-only group), on their all-cause mortality rates, and if applicable cause-specific mortality rates, and occurrence of specific diabetes complications. METHODS: We systematically reviewed and quantitatively synthesized diabetes-related outcomes in patients with depression by searching Embase, MEDLINE, PsycInfo and Web-of-Science from inception to 20 December 2024, and included studies that examined mortality and complication outcomes in depression-diabetes group relative to diabetes-only group. Results were synthesized by random-effects meta-analytic models, with stratified-analyses (subgroup analyses and meta-regression) by study-level characteristics, including age, gender, study period, geographic region, follow-up duration and nature of diabetes sample. The study was registered with PROSPERO (CRD42024595145). RESULTS: Twenty-six studies were identified from nine geographic regions. Regarding mortality risk, depression-diabetes group exhibited increased risks of all-cause mortality (RR = 1.30 [95% CI: 1.21-1.39]) and CVD-specific mortality (1.15 [1.02-1.29]) relative to diabetes-only group. Regarding complication risk, depression-diabetes group showed increased risk of complications (1.28 [1.18-1.40]) relative to diabetes-only group, especially in incident-diabetes sample signifying advanced disease stage upon presentation, with stratified-analyses showing higher risk of metabolic complications (1.63 [1.33-1.99]) and cardiovascular complications (1.20 [1.11-1.29]), and lower likelihood of retinopathy (0.84 [0.76-0.94]), albeit comparable rates of cerebrovascular complications (1.36 [0.99-1.87]), nephropathy (1.09 [0.93-1.27]) and peripheral-vascular complications (0.97 [0.79-1.18]). Both overall mortality and complication risks were present in various regions and persisted over time. Heterogeneities were noted and could not be entirely explained by stratified analyses. CONCLUSIONS: Our study demonstrated that patients with depression and co-occurring diabetes were associated with elevated overall mortality risk and complication risk (particularly metabolic and cardiovascular-complications) than non-depressed counterparts, suggesting an overall poorer glycemic control that might eventually drive their earlier death. Comprehensive and multipronged interventions are needed for individualized risk estimation of diabetes-related outcomes, with consequent early interventions to minimize the avoidable physical morbidity and premature mortality in this vulnerable population.

Humans↗

PubMed2026

Automated CEAP Classification of Venous Duplex Reports Using Multimodal Artificial Intelligence.

OBJECTIVE: To develop and internally validate a prototype multimodal artificial intelligence system for automated CEAP (Clinical, Etiological, Anatomical and Pathophysiological) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams. METHODS: Single centre retrospective observational study using routinely collected clinical data. One thousand consecutive venous duplex ultrasound reports from Cambridge University Hospitals NHS Foundation Trust, UK (July 2024 - May 2025) were labelled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (Great Saphenous Vein, Small Saphenous Vein, Deep system, Perforators), combined using late fusion with probability averaging. RESULTS: The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-AUC of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving SSV performance but reducing Deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70-0.92 and macro-AUC from 0.80-0.92. CONCLUSION: This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Despite class imbalance affecting minority class predictions, the strong discriminatory performance validates this multimodal ML model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision making.

Artificial intelligence↗

PubMed2026

Operationalizing Local Ecological Knowledge for Aquatic Biodiversity Conservation: A Systematic Review and Management Framework.

Effective conservation and management of aquatic biodiversity is severely constrained by the absence of long-term ecological data in small-scale, tropical, and data-poor fisheries, where roughly one-quarter to one-third of freshwater fish species and 37.5% of elasmobranchs are threatened with extinction once Data Deficient species are accounted for. Conventional monitoring and stock-assessment tools are often financially and technically inaccessible in these systems, leaving managers without the evidence needed to prioritize conservation action or implement precautionary governance. Local Ecological Knowledge (LEK) is a largely underutilized resource for natural resource management that can provide temporal depth, spatial resolution, and species-specific ecological insights unavailable from scientific records. We conducted a systematic review and bibliometric synthesis of 60 peer-reviewed studies (1997-2025) applying LEK to assess fish conservation status, examining how, where, and through what methods this knowledge has been used. Our analysis identifies four complementary pathways through which LEK informs conservation management: reconstructing multi-decadal population changes, documenting spatial contraction and habitat loss, detecting extreme rarity and local extirpation, and characterizing intrinsic sensitivity to exploitation based on life-history traits. Despite growing methodological rigor, freshwater systems and African fisheries remain critically underrepresented, and formal integration of LEK into fisheries governance and biodiversity assessment remains the exception rather than the rule. We propose a practical three-stage framework to operationalize LEK within existing management and conservation systems. Recognizing fishing communities as legitimate co-producers of ecological knowledge is both scientifically necessary and an equity imperative for achieving global biodiversity commitments under the Kunming-Montreal Global Biodiversity Framework.

Biodiversity↗

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

Factors Associated With Menopause Symptoms: A Systematic Review and Meta-Analysis.

BACKGROUND: Menopause, marked by hormonal decline and menstrual cessation, is associated with various symptoms. Socio-demographic and behavioural factors may influence symptom type and severity. Understanding these associations can inform better symptom management. OBJECTIVES: To identify factors associated with the presence and severity of menopausal symptoms through systematic review and meta-analysis. SEARCH STRATEGY: We searched Medline, Embase, CINAHL and Cochrane for studies on demographic, behavioural, or health factors linked to vasomotor, vaginal dryness and joint symptoms in women aged 40-60. SELECTION CRITERIA: Studies reporting odds ratios or raw numbers for symptom presence or severity were included. DATA COLLECTION AND ANALYSIS: Studies were combined for meta-analysis, reporting odds ratios and 95% confidence intervals. Quality assessment was performed to quantify the risk of bias. RESULTS: Of 9228 screened articles, 61 were meta-analysed. Compared with White women, Black women had higher odds of vasomotor symptom presence (OR 1.65, 1.41-1.94) and severity (OR 1.91, 1.10-3.29), and vaginal dryness presence (OR 1.27, 1.10-1.47), while Asian had lower vasomotor symptom presence and severity (OR 0.40, 0.22-0.72; OR 0.55, 0.53-0.56). Higher education (OR 1.31, 1.09-1.56), high income (OR 1.41, 1.01-1.97) and depression (OR 2.36, 1.51-3.70) were associated with increased presence of vasomotor symptoms. Smoking and obesity were associated with both presence (OR 1.63, 1.30-2.04 and 1.35, 1.02-1.78) and severity (OR 1.56, 1.07-2.27 and 1.42, 1.11-1.83) of vasomotor symptoms. CONCLUSION: Socio-demographic and behavioural factors, including ethnicity, education, income, smoking, obesity and depression, influence menopausal symptoms, highlighting the need for personalised care. TRIAL REGISTRATION: PROSPERO number: CRD42023459154.

Humans↗

PubMed2026

Azithromycin-resistant Salmonella enterica Typhi with AcrB R717L/Q mutations in the United States.

BACKGROUND AND OBJECTIVES: Azithromycin is a critical oral treatment for typhoid fever caused by Salmonella enterica serovar Typhi (Salmonella Typhi), since XDR has rendered other first-line treatment options ineffective. Azithromycin resistance conferred by amino acid changes in AcrB, an AcrAB-TolC efflux pump component, represents an emerging public health concern. Leveraging phenotypic and genotypic data from U.S. Salmonella Typhi surveillance systems, this study describes the prevalence, phenotype and genomic epidemiology of Salmonella Typhi with AcrB mutations in U.S. patients since the first detection in 2015. METHODS: AST and WGS data of >3000 Salmonella Typhi isolates were used to identify all cases with an AcrB mutation in the United States (2015-2025). We calculated annual prevalence and MIC ranges. Phylogenetic analysis was used to contextualize U.S. cases of Salmonella Typhi with an AcrB mutation within all globally reported cases. RESULTS: While the prevalence of AcrB mutations in the United States is low (1.5%), it has risen significantly in recent years, from 0.2% in 2016-2022 to 2.2% in 2023-2025. This increase is predominantly driven by clonal expansion of existing strains circulating in South Asia. AcrB mutations do not reliably confer resistance to azithromycin (MIC ≥ 32 mg/L), complicating clinical interpretation. CONCLUSIONS: The prevalence of AcrB mutations in Salmonella Typhi is increasing in the United States, and likely globally, given that U.S. data function as an informal proxy for regions without routine surveillance infrastructure. Clinical outcomes data are needed to inform Salmonella Typhi treatment guidelines and potentially amend clinical breakpoints for azithromycin.

Journal Article↗

PubMed2026

Determinants of private health insurance uptake and its association with healthcare utilization in Gulf Cooperation Council countries: a systematic review.

All Gulf Cooperation Council (GCC) countries have a multi-payer healthcare system that comprises governmental health coverage (GHC), funded by the government, and private health insurance (PHI), mainly sponsored by employers and purchased by individuals. Both are expected to influence healthcare utilization and contribute to system efficiency and patient well-being. This systematic review explored the determinants of PHI uptake and its association with healthcare service utilization in the presence of GHC in GCC countries. We systematically searched CINAHL, PubMed, Scopus, Web of Science, and Cochrane Library for peer-reviewed studies published between January 2012 and October 2022. Study quality was assessed using the Critical Appraisal Skills Programme (CASP) checklists for both quantitative and qualitative studies, following PRISMA guidelines. Twenty-six studies met the inclusion criteria. Determinants of PHI uptake were mapped to Andersen's Behavioral Model of Health Services Use (BMHSU) and categorized into (1) predisposing factors (sex, age, marital status, and education), (2) enabling factors (employment/income and health system-related factors such as access and perceived service quality), and (3) need factors (health status, including chronic noncommunicable diseases). PHI uptake was positively associated with being male, married, highly educated, employed with a high income, and having chronic diseases. PHI was positively associated with healthcare utilization, particularly routine check-ups, preventive services, and the use of prescribed medicines. In GCC countries, PHI uptake is influenced by sociodemographic and socioeconomic characteristics, health status, and perceived service quality. PHI is also associated with higher healthcare utilization, underlining the need for evidence-informed policies that enhance equity and expand coverage.

Humans↗

PubMed2026

Meta-analysis of growth and inactivation kinetics of Legionella.

Quantitative risk assessments intended to inform evidence-based water management plans and public health targets for Legionella in engineered water systems are constrained by fragmented and heterogeneous growth and inactivation kinetics. We conducted a meta-analysis of 25 growth and 39 thermal- and chemical-inactivation studies, fitting microbial persistence models to harmonize parameters. Nonlinear models outperformed first-order formulations, indicating that lag phases and resistant or protected subpopulations are central to Legionella persistence. Random forest analysis identified environmental and methodological drivers of variability based on 226 growth rates and reduction times for thermal (209) and chemical (135) inactivation. Growth was primarily governed by temperature, nutrient availability, and compatible Legionella-host pairings; thermal inactivation by quantification method, temperature, and turbidity; and chemical inactivation by inoculum size, disinfectant type, concentration, and host-associations. Accordingly, temperature-dependent growth parameters and exposure metrics for heat, free-chlorine, and monochloramine, expressed as TT (Temperature×time) and CT (Concentration×time), were derived as condition-specific inputs for predictive models. Growth optima around 37-40 °C, together with lag-time estimates, indicate that hot-water temperature setbacks and energy-saving practices may favor Legionella proliferation under repeated or prolonged lukewarm exposure. Culture- and viability-based TT differences highlight the need to consider viable‑but-non-culturable persistence in monitoring programs. CT comparisons suggest monochloramine may be advantageous because of its lower apparent sensitivity to host-associated protection. Although limited by restricted experimental conditions, the findings show that predictive models should account for microbial ecology, water matrix effects, and quantification endpoints. Future kinetic studies should prioritize realistic multi-host systems, strain pre-adaptation, complementary viability measurements, and standardized protocols and reporting to ensure reproducibility and enable robust system-level predictive modeling.

Legionella↗

PubMed2026

Assessing the public health impact of routinely collected electronic healthcare record data in NICE guidelines: A systematic review of CPRD research.

OBJECTIVES: Evidence used in NICE guidance has traditionally prioritised randomised controlled trials, but increasing availability of electronic health record (EHR) data has expanded opportunities for real-world evidence. The Clinical Practice Research Datalink (CPRD) is a commonly used UK primary care EHR resource, yet the extent to which CPRD studies have informed NICE guidelines in the past decade is unclear. STUDY DESIGN: The systematic review was conducted in accordance with PRISMA guidelines. METHODS: We conducted a systematic review of CPRD studies in PubMed, MEDLINE, and Embase published between 04/16-09/25. For each eligible CPRD study, targeted searches of NICE guidelines were performed to identify explicit citations in NICE guidelines. Two reviewers screened and extracted data independently, resolving disagreements by consensus or third reviewer. Guideline information, number of guidelines over time, type of guidelines, and disease area guidelines (using British National Formulary (BNF) chapters) were described. RESULTS: 7181 records were identified. After de-duplication, 2704 unique CPRD studies were screened against NICE guidelines. Of these, 92 CPRD-based studies met inclusion criteria and were cited across 67 NICE documents. The annual number of NICE guidelines citing CPRD studies increased between 2016 and 2025; 1.5% of identified guidelines published in 2016 and 27.7% in 2025. The guideline citing the most CPRD studies was cancer related. The most common types of guidelines included clinical guidelines (49.3%) and technology appraisals (32.8%). Guidelines made up 12 different BNF categories, most frequently central nervous system related (23.9%; n = 16). CONCLUSION: Observational CPRD studies are increasingly referenced in NICE guidelines across multiple disease areas, supporting the growing role of EHR data in national guideline development.

Clinical studies↗

PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗

PubMed2026

Robust error-minimization in the genetic code across physicochemical metrics and variant codes: A graph-theoretic analysis in GF(2)6.

The standard genetic code reduces the impact of point mutations, but the robustness of this property across physicochemical metrics, naturally occurring variant codes, and codon-reassignment mechanisms remains incompletely quantified. Embedding the 64 codons in GF(2)6 represents the hypercube Q6 as a coordinate-dependent subgraph of the encoding-independent single-nucleotide mutation graph H(3,4), and enables continuous ρ-interpolation between the two. Under a quartet-pattern shuffle null (n=10,000), the standard code is significantly low-cost across four established, code-independent physicochemical distance metrics with partially overlapping content (Grant ham p=0.0062; Miyata p<0.001; Woese polar requirement p=0.003; Kyte-Doolittle hydropathy p=0.001), and the signal strengthens monotonically as ρ moves Q6→H(3,4). A structure-aware sensitivity analysis under the alignment-derived ProtSub matrix (Jia & Jernigan 2021) yields the most extreme percentile of any measure tested (p=0.0004; all five p-values pass Bonferroni at α=0.05). Across the 27 NCBI translation tables, near-optimality is preserved: 11 of 12 informative-distance variants retain top-5% placement after BH-FDR correction. Natural codon reassignments avoid disrupting codon-family connectivity: under the encoding-independent H(3,4) adjacency, observed events are topology-breaking at relative risk 0.32 versus the candidate landscape (permutation p≤10-4). The H(3,4) result is stable by construction; the Q6 decomposition is representation-specific and fails to show depletion under 8 of 24 base-to-bit encodings, so we report H(3,4) as the primary test and Q6 as a sensitivity. Event-level conditional-logit modelling shows that topology avoidance and local physicochemical cost provide complementary, only weakly correlated signal (rs=0.15), and that topology adds explanatory value beyond physicochemistry under both Q6 and encoding-independent H(3,4) adjacency. Retrospective reanalysis of nine genome-recoding datasets is consistent with codon-family topology operating as an evolutionary-trajectory constraint distinct from acute engineering fitness. The contribution is the second axis: code evolution is jointly constrained by physicochemical smoothness and codon-family topological integrity, and these two constraints are partly independent.

Codon reassignment↗

PubMed2026

Ancient DNA and Human Physiology.

Ancient DNA (aDNA) enables the reconstruction of chronologically sampled genomes from ancient humans, animals, plants, pathogens, and microorganisms, as well as environmental DNA, providing a record of biological changes through time. Improvements in short and degraded DNA extraction methods and low-cost sequencing now enable the generation of broad, cross-regional datasets that expand evolutionary analyses from past population demography to biological mechanisms. By tracking temporal shifts of allele frequencies, integrating functional genomics resources (e.g., gene expression, chromatin structure variation), modeling population demography to separate selection from genetic drift, and aligning genetic changes with archaeological, cultural, and climatic data, aDNA has the potential to link sequence variation to physiological function within their temporal and environmental contexts. In this review, we summarize illustrative case studies from aDNA research spanning complex traits, dietary adaptations, and responses to pathogens and other environmental changes, showing how human biology has evolved under multiple selective pressures through time. These dated signals help triage experimental work and expose mechanisms that are rare or absent in living cohorts. Although some challenges remain, such as geographic and temporal sampling disparities, limitations in data resolution and variant detection, and genotype-phenotype uncertainties, rapid methodological progress and stronger ethical frameworks are expanding what can be inferred, making aDNA a promising tool for refining physiological pathways, their timing, and their drivers.

Humans↗

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↗