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PubMed2026

A risk-need-responsivity (RNR)-informed systematic review of needs during the pretrial period.

OBJECTIVE: Pretrial risk assessments are becoming increasingly popular in the United States. Despite the importance of assessing and intervening around "needs" in the risk-need-responsivity model, few pretrial risk assessments include comprehensive assessment of needs. We aim to provide a systematic review of the prevalence of and predictive utility of needs within the pretrial population. HYPOTHESES: There were no hypotheses given the nature of the study. METHOD: We conducted searches for articles in the EBSCO, ProQuest, and Google Scholar databases using key words related to 11 needs domains: antisocial personality, procriminal attitudes, procriminal associates, substance use, family/marital relationships, school/work, prosocial recreational activities, self-esteem, housing, mental health, and physical health. We identified 215 articles that reported on the prevalence of needs or explored their predictive associations with pretrial misconduct outcomes in adult populations. RESULTS: Overall, we find few comprehensive investigations of needs in the pretrial domain, apart from substance use. Variation in methodology and operationalization contributes to wide variability in prevalence estimates. We found only 15 articles that examined predictive associations between pretrial needs and outcomes, which were limited to investigations of behavioral health, employment, and housing needs. Substance use and housing needs emerged as the only consistent predictors of pretrial misconduct. CONCLUSIONS: Researchers should more directly assess the prevalence and predictive utility of needs within the pretrial period to bolster the evidence base for including these factors in pretrial risk assessments. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Humans↗

PubMed2026

Manual, digital, and AI tumour-infiltrating lymphocyte scoring: a secondary analysis of the APHINITY randomised trial.

BACKGROUND: Stromal tumour-infiltrating lymphocytes (sTILs) are prognostic in early-stage HER2-positive breast cancer, but their role in the context of dual HER2 blockade remains undefined. We evaluated manual, digital, and artificial intelligence (AI)-based sTIL quantification, together with AI-derived spatial metrics, for prognostic and treatment-benefit stratification using tumour samples from the phase 3 APHINITY trial. METHODS: In the APHINITY trial, 4805 patients were randomly assigned to receive chemotherapy plus trastuzumab with pertuzumab or chemotherapy plus trastuzumab with placebo. Median follow-up was 74·1 months (IQR 68·3-75·4). We analysed 4262 haematoxylin and eosin-stained images using manual assessment, an automated digital approach, AI-based lymphocyte quantification (AI percentage lymphocytes), and two AI-derived spatial features (AI-TIL and immune hotspot). Interobserver reproducibility was assessed in 262 randomly chosen tumour samples scored independently by five pathologists. Multivariable Cox models were used to assess associations between TIL levels and invasive disease-free survival (primary outcome in APHINITY), distant recurrence-free interval, and overall survival. The heterogeneity of pertuzumab benefit was evaluated using subgroup analyses, subpopulation treatment effect pattern plot analyses, and nested Cox models with treatment-by-biomarker interaction terms. FINDINGS: Manual scoring showed high interobserver reproducibility (intraclass correlation coefficient 0·84 [95% CI 0·79-0·88]). Concordance between manual and automated methods was modest. AI-based scoring (AI percentage lymphocytes) reclassified 120 (11·6%) of 1035 node-positive tumours from immune-low (by manual scoring) to immune-high; this subgroup of patients showed greater separation of 5-year invasive disease-free survival curves between pertuzumab and placebo groups compared with patients whose tumours were concordantly classified as immune-low by both manual and AI-based approaches. Higher levels of TILs were associated with improved invasive disease-free survival for all sTIL measurement approaches and spatial measurements (hazard ratios [HRs] 0·41-0·93). Pertuzumab was associated with improved invasive disease-free survival at higher sTIL levels across all measurement approaches (HRs 0·36-0·48), but was not associated with higher values of spatial measures. The largest 6-year absolute improvements with pertuzumab were observed in patients with node-positive disease whose tumours scored in the highest level of immune infiltration of manual sTIL scoring (≥70·0%; mean absolute improvement 12·1 percentage points [SD 2·8]). In nested prognostic and predictive models, AI-based immune hotspot scores provided the most consistent additional information when combined with any sTIL measurement (all p<0·010). INTERPRETATION: Standardised manual sTIL scoring was reproducible, and digital and AI-based methods showed consistent prognostic stratification and potential for treatment-benefit stratification despite only modest correlation between platforms. AI spatial metrics provided complementary information beyond sTIL density and could support more scalable immune assessment. Future studies are needed to validate these approaches in independent cohorts and to clarify their clinical utility for stratifying contemporary HER2-directed therapies. FUNDING: None.

Humans↗

PubMed2026

Relationships among mechanisms in psychosocial treatments for chronic pain: mechanism to mechanism lagged effects and relationships with outcomes.

Results suggest that psychosocial treatments for chronic pain work via several mechanisms, and that they often do so to similar degrees and in similar ways. Extant research, however, has focused on individual and/or independent effects of mechanisms on outcomes. Whether successful outcomes are also partly because of sequential and meaningful relationships among and between mechanisms-mechanism-to-mechanism effects-has not been examined. Secondary analyses were conducted of an RCT that compared cognitive therapy, mindfulness-based stress reduction, and behavior therapy to treatment as usual in a sample (N = 521) of people with chronic low back pain. Results of hierarchical linear modeling revealed that (1) Treatment Condition × Mechanism interactions predicting changes in other mechanisms were nonsignificant; (2) lagged prior session mechanism changes predicted next session changes in another mechanism; (3) lagged relationships between pain catastrophizing and pain self-efficacy were reciprocal, whereas links between lagged pain catastrophizing and mindfulness changes and lagged pain catastrophizing changes and behavioral activation changes were unidirectional; and (4) individual differences in the strengths of mechanism-to-mechanism relationships predicted pre- to post-treatment changes in outcomes. Results reveal heretofore hidden therapeutic processes that cognitive therapy, mindfulness-based stress reduction, and behavior therapy may share. Namely, that mechanism-to-mechanism lagged effects do indeed emerge beyond mechanism-to-outcome effects. Findings show not only that mechanisms may change in definable sequences relative to each other but that individual differences in the strengths of mechanism-to-mechanism relationships may themselves be predictive of outcomes.

Humans↗

PubMed2026

Exploratory proteomic and metabolomic profiling of pleural effusions identifies histone H4 and alanine as promising complementary markers for pleural tuberculosis.

The diagnosis of pleural tuberculosis (Pl-TB) remains challenging. Histopathological analysis and pathogen detection in pleural biopsies are informative but limited. We investigated differentially expressed proteins and metabolites in pleural effusions from patients with Pl-TB, malignancies, and other pathologies. A proteomic analysis of pooled pleural effusions identified 45 proteins exclusively detected or upregulated in Pl-TB samples, many linked to infectious processes. Conversely, 18 proteins were uniquely found or upregulated in malignant pleural effusions, mainly associated with detoxification and hemostasis. To validate these findings, we employed targeted proteomics in individual samples. Eight proteins were validated: S100-A9, histone H4, insulin-like growth factor-binding protein 2, fibrinogen beta chain, ficolin-3, immunoglobulin heavy constant alpha 1, sulfhydryl oxidase 1, and histidine-rich glycoprotein. Additionally, NMR-based metabolomics identified 13 metabolites with differential abundance between Pl-TB and non-TB samples. Notably, N-acetyl-glycoprotein and the branched-chain amino acids, alanine and lysine differed between groups. Proteomic and metabolomic analyses revealed distinct molecular profiles between Pl-TB and non-TB patients, despite intra-group variability. To address this, we applied classification models. Histone H4 and alanine consistently emerged as discriminative features. Overall, this study provides novel insights into the molecular landscape of Pl-TB. The combined quantification of proteins and metabolites may improve differential diagnosis, although should be further validated in larger, independent cohorts before clinical application.

Humans↗

PubMed2026

Peripheral immune markers and choroid plexus volumes as predictors of change in depressive symptoms: Insights from the EMBARC study.

Changes in choroid plexus (ChP) volume and peripheral inflammation have been associated with Major Depressive Disorder (MDD), yet their individual and combined impact on depressive symptoms is unclear. This study investigated whether baseline immune markers and ChP volumes predict changes in depressive symptoms during the 8-week treatment period among Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study participants who received either sertraline or placebo. Adults (n = 222) with MDD with peripheral blood samples were included. Circulating chemokines and cytokines were examined using a 40-plex assay. Depressive symptoms were assessed over 8 weeks using the Hamilton Depression Rating Scale (HAMD-17). Principal component analysis (PCA) was used for dimension reduction. Mixed-effects models were used to examine whether immune profiles and ChP volumes, and their interaction predicted HAMD-17, adjusting for demographic/clinical covariates and baseline depression severity. PCA identified three immune profiles. One profile, characterized by higher levels of cytokines and chemokines including IL-6, TNF-α, and IL-1β, was associated with greater depression severity, higher BMI, age, and CRP at baseline. Higher levels of these immune markers were associated with less improvement in depressive symptoms at 8 weeks (estimate = 1.211, p = 0.018) in models adjusting for right and left ChP volume (right ChP model: estimate = 1.034, p = 0.005; left ChP model: estimate = 0.993, p = 0.007). Interactions between immune markers and ChP volumes were not significant. Future investigations are warranted to examine the relationships between immune markers and ChP volume beyond structural changes in the context of depression symptoms.

Adult↗

PubMed2026

Searching for New Genes That Cause Usher Syndrome.

PURPOSE: The purpose of this project was to identify novel Usher syndrome (USH) candidate genes from phenotyping data of 9139 knockout (KO) mouse lines. METHODS: We evaluated phenotype data for concurrent retinopathy and hearing abnormalities in single-gene KO mice generated by the International Mouse Phenotyping Consortium (IMPC). A search was performed to determine whether each gene had been previously associated with retinopathy and/or deafness in humans. Bioinformatic tools were used to predict protein interactions, molecular functions, signaling pathways, and the expression of human orthologues of candidate genes in the retina and inner ear. RESULTS: We identified 18 single-gene KO lines exhibiting hearing abnormality and retinopathy after ear and eye examinations, respectively, and/or by histopathology. The molecular functions and signaling pathways of the human orthologues of the 18 candidate genes partially overlapped with those of USH genes. Particularly, FER and DYRK1B proteins were predicted to interact with proteins encoded by known ciliopathy genes. ADIPOR1, ATP8B1, and MPDZ were associated with retinal degeneration in humans. CHSY1 and IDUA may be pathogenic causes of hearing impairment in people. Furthermore, CHSY1, CSTB, and SPRED1 were located adjacent to unsolved genetic loci related to USH. CONCLUSIONS: A screen of 9139 KO mouse lines revealed 18 candidate genes exhibiting both retinal and inner ear abnormalities consistent with the principal clinical features associated with USH. As the observed phenotypes are attributed to gene deletion in mice, these genes warrant further study to determine the causation of retinal degeneration and hearing loss in patients.

Animals↗

PubMed2026

Association of the Charlson Comorbidity Index With 1-Year Outcomes in Patients With Macular Edema Secondary to Retinal Vein Occlusion.

OBJECTIVE: To determine the predictive value of the Charlson Comorbidity Index (CCI) for outcomes in patients with macular edema secondary to retinal vein occlusion (RVO). DESIGN: Retrospective clinical cohort study. SUBJECTS: Patients seen between 2013 and 2023 at the Cole Eye Institute, Cleveland Clinic, were included. All patients were >18, diagnosed with RVO (International Classification of Diseases (ICD)-9 and 10 codes), had a complete CCI score, and had at least 1 year of ophthalmic follow-up data after their first intravitreal injection (baseline). Patients with ocular surgery, trauma, or panretinal photocoagulation were excluded. METHODS: Age-adjusted CCI scores were calculated for each patient from chart review. For patients with bilateral RVO, one eye was selected randomly. Patients were stratified into tertiles by CCI distribution: tertile 1 (CCI 0-5; mean 3.4), tertile 2 (age-CCI 4.1-6; mean 4.9), and tertile 3 (CCI ≥ 8; mean 9.6). Multivariable linear regression was performed to determine the predictive value of CCI and other covariates on visual and anatomical outcomes. MAIN OUTCOME MEASURES: Best-corrected visual acuity (BCVA) and central subfield thickness (CST) at 1-year follow-up. RESULTS: A total of 972 patients met all criteria, with an average age-adjusted CCI score of 6.2. Each one-point increase in CCI predicted 0.38 fewer letters in BCVA at follow-up (P < .001). Baseline BCVA was a significant predictor of follow-up BCVA in all tertiles (P < .001). In the third tertile, each one-point increase in CCI was associated with a 0.72 letter reduction in follow-up BCVA (P < .001). For CST, baseline CST was strongly predictive of final CST (P < .001), while CCI was only significant in the first tertile, where each point increase in CCI predicted a 13.7 µm increase in CST (P = .02). In RVO subtype interaction models, the age-adjusted CCI × CRVO interaction was not statistically significant for either 1-year BCVA (P = .269) or 1-year CST (P = .695). CONCLUSIONS: Higher CCI scores are significantly associated with worse visual outcomes in patients with RVO, particularly in the combined population and most comorbid patients (tertile 3). CCI was significantly associated with higher (thicker) CST only among the least comorbid patients (tertile 1).

Humans↗

PubMed2026

Multiscale Modeling Primer: Focus on Chromatin and Epigenetics.

A central challenge in modern biology is to understand how molecular interactions produce cellular and organismal functions across vast spatiotemporal scales. Nowhere is this challenge more apparent than in the study of chromatin, where meters of DNA compact into a micron-sized nucleus. How this polymer folds is a dynamic process, regulated by epigenetic modifications-chemical changes to DNA and histones that involve only a handful of atoms. These small changes cooperate to produce emergent, higher-order structures that define cellular identity and function. To explain this system, we must integrate static, high-resolution snapshots from techniques like cryo-EM with dynamic, lower-resolution data from microscopy and genomics. Multiscale computational models are essential tools that bridge these experimental gaps and reveal the mechanisms of emergent behavior. However, the communication divide between experimental biologists and quantitative modelers often hampers progress. This primer addresses that gap. It first introduces the fundamental biology of chromatin and epigenetics at an introductory level for non-biologists audiences. We then survey the landscape of computational approaches, from atomistic to systems-level models, and connect them to the experimental data that inform and validate them at an introductory level for non-computationalists. We argue that the next frontier will require us to build integrative models that can predict how molecular perturbations mechanistically alter cellular phenotypes, which will open a new era of chromatin-targeted therapeutics.

Chromatin Dynamics↗

PubMed2026

Comment on: "Machine learning-based prediction of multi-level antimicrobial resistance in Klebsiella pneumoniae using whole-genome sequencing data".

Explore the source record for details and available documents.

Letter↗

PubMed2025

Predictive Validity of Violence Screening Tools in Emergency and Psychiatric Services: A Systematic Review.

Violence against healthcare staff, including a threat or an act of violence toward people during their work, poses a physical and psychological risk to workers internationally. Screening is an important strategy in preventing violence against healthcare professionals. The aim of this systematic review was to synthesize evidence on the predictive validity of risk assessment tools used to screen for violence and aggression risk toward healthcare workers in emergency and psychiatric departments (PD). Primary studies that examined the predictive validity of risk assessment tools for workplace violence were identified via a systematic search of Medline, PsycINFO, Embase, and the Cochrane databases. There were 62 eligible studies, ten of which had a lower risk of bias (RoB). Those studies with high RoB were primarily due to a failure to present calibration measures as part of the analysis. All included studies adopted a longitudinal design and were conducted in PDs. The ten highest-quality studies reported on eight different instruments, four of which showed acceptable to outstanding predictive performance. The Dynamic Appraisal of Situational Aggression and the Brøset Violence Checklist showed the best predictive performance; they were also validated in emergency departments and are best suited for short-term risk prediction. We recommend that the selection of a risk assessment tool should consider the following: (a) the target population, (b) the violence operationalization, and (c) the purpose of the monitoring. We note that the use of a screening tool should be a part of a multicomponent strategy to ensure staff safety.

Humans↗

PubMed2026

Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits.

Mixed-type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed-type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit. When interest lies in identifying a combination of genomic features associated with a mixed-type outcome, any method used would require a variable selection strategy for high-dimensional data. Unfortunately, few variable selection methods exist for modeling a mixed-type outcome when the covariate space is high dimensional. This study develops a high-dimensional penalized cumulative probability model (CPM), to allow for the identification of genomic features associated with mixed-type outcome of interest. We demonstrated how such model may be estimated using the iterative penalization procedure-the generalized monotone incremental forward stagewise (GMIFS) algorithm. The Model-X knockoffs procedure was combined with the estimation algorithm to control the false discovery rates (FDR) when performing variable selection. Through extensive simulation studies, our penalized CPM was shown to outperform alternative methods in terms of controlled variable selection performance by achieving high statistical power with the FDR being controlled at the target level. We demonstrate the utility of our method by applying it to predict estimated glomeruli filtration rate (eGFR) in kidney transplant recipients at 24 months post-transplant using baseline gene expression data as predictors. Our CPM model identified five genes associated with this mixed-type outcome which have important links to renal disease, which may provide prognostic guidance for kidney transplantation recipients.

Models, Statistical↗

PubMed2026

Ruling out pediatric bacterial epididymo-orchitis with urinalysis - The case for minimizing unnecessary antibiotic prescription.

INTRODUCTION: Epididymo-orchitis in pediatric patients is predominantly non-bacterial, often stemming from viral or reactive etiologies. Despite guidelines recommending conservative management for non-bacterial cases, antibiotic overtreatment remains prevalent in the outpatient setting. We evaluated the diagnostic accuracy of urinalysis in ruling out bacterial infection to support antibiotic stewardship in this population. METHODS: We conducted a cross-sectional diagnostic accuracy study using electronic health records from a large health maintenance organization in Israel. The cohort included patients younger than 18 years with a diagnosis of epididymo-orchitis or clinically overlapping entities (acute scrotum, appendage torsion) who had paired urinalysis and urine culture results within one week of diagnosis. Logistic regression and ROC curve analysis were performed to assess the ability of urinalysis parameters to predict positive urine cultures. RESULTS: Of 682 eligible cases, confirmed bacterial infection was rare, occurring in only 17 patients (2.5%). Nitrite positivity was the strongest independent predictor of infection (OR 43.98; p < 0.001). A prediction model incorporating all urinalysis parameters yielded an area under the curve (AUC) of 0.825 and achieved a 97.7% classification accuracy for correctly predicting negative cultures. Despite the low prevalence of infection, antibiotics were prescribed in 237 cases (34.7%). Urinary anatomic abnormalities were significantly associated with culture positivity. CONCLUSIONS: Bacterial coinfection in pediatric epididymo-orchitis is uncommon. Urinalysis serves as a highly accurate screening tool to rule out bacterial etiology. A negative urinalysis supports withholding antibiotics in this setting, reserving treatment for children with positive markers or known anatomic abnormalities. This evidence-based approach This evidence-based approach has the potential to reduce unnecessary antibiotic exposure, however prospective studies are needed to validate these findings before broad implementation.

Humans↗

PubMed2026

[Analysis of a Chinese pedigree affected with Townes-Brocks syndrome due to a novel variant of SALL1 gene and a literature review].

OBJECTIVE: To analyze a novel exonic variant of the SALL1 gene and its impact on the binding site of SALL protein. METHODS: Clinical data of three children diagnosed with Townes-Brocks syndrome and their family members who had presented at the First Affiliated Hospital of Shandong First Medical University in April 2022 were retrospectively collected. The pathogenic variant was identified through whole-genome sequencing (WGS) and validated by Sanger sequencing. Protein structural prediction was performed using AlphaFold and PyMOL software to construct three-dimensional models of the wild-type and mutant proteins. Additionally, previously reported cases were systematically reviewed. This study was approved by the Medical Ethics Committee of the hospital (Ethics No.: 2023-386). RESULTS: The proband was one of triplet sisters born at 34+4 gestational weeks. All three cases had presented with anal atresia and rectovaginal fistula, and case 3 also had toe malformation of left foot. WGS revealed a novel heterozygous c.757C>T (p.Gln253*) variant in the SALL1 gene, which was predicted to be pathogenic. Sanger sequencing confirmed co-segregation of the variant with the disease within the family. Protein structural modeling demonstrated that the variant has introduced a premature stop codon at position 253, resulting in a truncated protein. CONCLUSION: Above finding has enriched the mutation spectrum of the SALL1 gene in association with Townes-Brocks syndrome, which also represented a rare case of anal atresia in triplets, and provided a basis for molecular diagnosis, genetic counseling, and further research.

Humans↗

PubMed2026

Predictive value of anal sphincter electromyography for sacral neuromodulation test-phase outcomes.

BACKGROUND: Sacral neuromodulation (SNM) is an established therapy for refractory pelvic organ dysfunction. Anal sphincter electromyography (EMG) is commonly used preoperatively to assess sacral and peripheral nerve integrity. However, the prognostic significance of chronic neurogenic EMG changes for SNM outcomes remains unclear. OBJECTIVE: To evaluate whether chronic neurogenic changes on preoperative anal sphincter EMG predict the outcome of the SNM test phase. METHODS: We retrospectively analysed 62 consecutive patients with bladder and/or bowel dysfunction or pelvic pain who were candidates for SNM treatment and who underwent preoperative anal sphincter EMG. EMG findings were classified as normal or showing chronic neurogenic changes. SNM test-phase success was defined as a ≥50% improvement of symptoms at 24 days. Outcomes were compared between EMG groups. RESULTS: Of the 62 patients (49 women, 13 men), 30 (48%) had normal EMG findings and 32 (52%) showed chronic neurogenic changes. Overall, the SNM test phase was successful in 47 patients (76%). Success rates were similar in patients with normal EMG (72%) and neurogenic EMG changes (79%), with no statistically significant difference (p = 0.878). Sex-stratified analyses revealed no significant association between EMG findings and test-phase success in women or men. CONCLUSIONS: Chronic neurogenic changes on anal sphincter EMG do not predict SNM test-phase outcomes. These findings suggest that abnormal sphincter EMG results should not be used as a standalone criterion to exclude patients from SNM therapy. SIGNIFICANCE: Signs of neurogenic damage on anal sphincter EMG are not an indicator of reduced neuromodulatory capacity or diminished clinical response to SNM.

Humans↗

PubMed2026

From population to individual: advocating personalised digital tools for heat-health early warning in a changing climate.

Escalating heat extremes under climate change are imposing substantial health burdens, with 2023 and 2024 consecutively breaking global temperature records. Mounting evidence suggests that heatwaves elevate the risks of hospitalisation and mortality across multiple disease categories, including ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and acute kidney injury. Nonetheless, most existing heat-health warning systems remain primarily reliant on population-level predictions, and considering individual differences and disease-specific considerations when defining warning levels would benefit the effectiveness of early prevention for high-risk groups. In this Viewpoint, which is based on the framework of precision public health-delivering the right intervention to the right population at the right time-we propose a framework for personalised digital heat-health early warning tools comprising three dimensions: individualised, risk-stratified prediction models that generate tiered early warnings; personalised health prompts coupled with theory-informed behavioural interventions; and adaptive, equity-oriented alert delivery mechanisms tailored to diverse populations. Such tools have the potential to bridge precision disease prevention and climate adaptation, thereby helping to mitigate heat exposure risks and disease burdens, particularly among high-risk populations. Future implementation research will be essential to address substantial challenges related to feasibility, validation, and equity.

Journal Article↗

PubMed2026

Association of lipoprotein-associated phospholipase A2 with recurrence risk and its predictive value in large artery atherosclerotic stroke.

OBJECTIVE: To investigate the association of lipoprotein-associated phospholipase A2 (Lp-PLA2) with large artery atherosclerotic (LAA) stroke and its predictive value for recurrence. METHODS: We consecutively enrolled 412 acute LAA stroke patients. Using a cutoff of 200 ng/mL, patients were divided into high and low Lp-PLA2 groups, and into recurrence and non‑recurrence groups based on 1‑year follow‑up. Baseline characteristics, lipid profiles, National Institutes of Health Stroke Scale (NIHSS) scores, and vascular stenosis degree were compared. Binary logistic regression and Receiver Operating Characteristic (ROC) analysis were used to identify independent risk factors and evaluate predictive value. RESULTS: The high Lp-PLA2 group had significantly higher low-density lipoprotein cholesterol (LDL-C), small dense low-density lipoprotein cholesterol (sdLDL-C), prevalence of severe stenosis (≥70%), and proportion of NIHSS > 15 (all P < 0.05). The recurrence group showed elevated Lp-PLA2, higher LDL‑C and sdLDL-C, more severe neurological deficits, and more severe stenosis (all P < 0.001). Multivariable regression identified elevated Lp-PLA2 (per 10 ng/mL: OR = 1.139, 95% CI: 1.089-1.191), moderate (OR = 3.145) and severe (OR = 11.663) neurological deficits, and severe stenosis (OR = 9.390) as independent risk factors for recurrence (all P < 0.05). The Area Under the Curve (AUC) of Lp-PLA2 was 0.75 (95% CI: 0.69-0.82), with an optimal cutoff of 208.95 ng/mL. CONCLUSION: Elevated Lp-PLA2 is associated with adverse lipid profiles, more severe neurological deficits, and greater vascular stenosis in LAA stroke patients, and independently predicts 1‑year recurrence. Lp-PLA2 shows moderate predictive value, supporting its potential for risk stratification.

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↗

PubMed2026

Modelling peak microbial pollution events caused by combined sewer overflows in a source-to-sea system.

Predicting peak microbial pollution events in downstream coastal bathing waters caused by combined sewer overflows (CSOs) is essential for protecting public health. In urban areas, wastewater effluents, CSOs, and surface runoff can contribute to elevated microorganism loads to downstream waters. These pressures are likely to be intensified by growing population density and more frequent heavy rainfalls due to climate change. This study developed a process-based model to simulate Escherichia coli (E. coli) emissions, transport, and fate from the initial sources to coastal beaches. A three-year retrospective simulation (2017-2019) shows that E. coli concentrations in CSO discharges varied widely across the catchment (4.6 - 7.3 (log10 CFU 100 ml-1)). 99th percentile E. coli concentrations (4.0 (log10 CFU 100 ml-1)) at the inland water outlet were dominated by local CSO emissions, whereas 90th percentile E. coli concentrations (3.6 (log10 CFU 100 ml-1)) reflected cumulative upstream contributions from both CSO and effluent emissions. With the simulation accuracy of 89%, the model reliably reproduced the E. coli dynamics on the downstream beach and showed strong performance in representing peak concentrations based on Complementary Cumulative Distribution Function (CCDF) analysis. The process-based model enables quantitative tracking of source contributions and identification of pollution hotspots, providing support for mitigation measures. The study lays down a source-to-sea modelling framework for representing pollution transport across the aquatic continuum and provides a transferable tool for microbial pollution forecasting and climate adaptation planning.

Climate projection↗