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PubMed2026

Discrimination, chronic stress, and multimorbidity in cohort of Black and Latina transgender women with HIV: Longitudinal findings from the LITE Plus study.

Black and Latina transgender women with HIV (BLTWH) are exposed to repeated, intersecting discrimination based on race, gender, and serostatus. Minority stress theory conceptualizes discrimination as minority-specific stressors that drive health inequities. Allostatic load theory posits a pathway between discrimination and chronic disease through multisystem physiological dysregulation caused by chronic stress. To test this pathway, a longitudinal cohort of 108 BLTWH, enrolled December 2020 - June 2022 in Boston, New York City, and Washington, DC, were followed for 24 months, with biomarkers measured at baseline, 12, and 24 months. Questionnaires administered every 6 months assessed anticipated discrimination, everyday discrimination, perceived stress, and other psychosocial factors. Multimorbidity was measured via self-reported non-HIV chronic conditions. In mixed-effects mediation models, allostatic load did not mediate relationships between multimorbidity outcomes and anticipated discrimination (β: -0.0004 [95% CI: -0.004, 0.003]) nor everyday discrimination (β: -0.002 [95%CI: -0.009, 0.003]). Perceived stress demonstrated indirect effects on multimorbidity in unadjusted models of anticipated discrimination (β: 0.024, [95% CI: 0.015, 0.081]) and everyday discrimination (β: 0.021 [95%CI: 0.016, 0.077]). Indirect effects remained significant, with attenuated effects (β: 0.020 for anticipated discrimination; β: 0.017 for everyday discrimination) after adjusting for social support, community connection, and resilient coping. Total effects were only significant for the adjusted model of everyday discrimination (β: 0.056 [0.014, 0.099]). Findings suggest discrimination impacted health through specific psychosocial pathways. Alongside efforts to eliminate intersectional discrimination, stress-lowering interventions and increased access to social support and community connection may be effective approaches to reducing multimorbidity in this highly marginalized group.

Humans↗

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

A single session of high-definition transcranial direct current stimulation does not modulate effects of knee two-point discrimination training or sensorimotor function in healthy adults: Double-blind randomised controlled trial.

BACKGROUND: Benefits of sensory-training interventions on sensorimotor outcomes are inconsistent, and it is unclear whether transcranial direct current stimulation (tDCS) can enhance proprioception, which is fundamental to neuromuscular control. Existing evidence is dominated by upper-limb studies, so transferability to the knee is unclear. This study investigated whether a single session of anodal high-definition (HD)-tDCS, alone or combined with brief two-point discrimination (TPD) training, enhances knee sensorimotor function and performance in healthy adults. METHODS: In a double-blind randomised-controlled trial, 57 healthy participants received (1) 20-min, 1 mA anodal HD-tDCS over the knee primary somatosensory (S1) map or sham stimulation, and (2) 15-min knee TPD training or no training. Knee somatosensory, sensorimotor, and functional performance measures were assessed pre- and immediately post-intervention. Three-way mixed-design ANOVAs, equivalence testing (smallest effect size of interest η2 = 0.02), Bayes factors, and linear mixed-effects models quantified effects. RESULTS: HD-tDCS, TPD training, and their sequential combination had no effect on any outcome measure (p ≥ 0.05; η2 ≤ 0.015). Confidence intervals spanned equivalence bounds, and equivalence testing results were non-significant (p ≥ 0.05). Bayes factors (<0.33) showed moderate evidence for the null (no effect). Mixed-effects modelling attributed ≤8% of total variance to intervention fixed effects, with the remainder captured by participant-level random effects. CONCLUSION: A single 20-minute session of 1 mA HD-tDCS, with or without brief TPD training, does not acutely modify knee somatosensory, sensorimotor, or functional performance in healthy adults. Existing evidence at the hand may not translate to the lower limb. Future work should investigate higher-dose, multi-session, task-concurrent, or network-targeted strategies in clinical populations.

Humans↗

PubMed2026

Reported exposure to news portrayals about mental health problems and their impact: Findings from the 2025 National Survey of Stigma and Discrimination.

OBJECTIVE: Media portrayals of people with mental illness have the power to mitigate or perpetuate stigma related to mental health. This study aimed to investigate reported real-world exposure to news media portrayals about people with mental health problems in the past 12 months and the impact of these. METHODS: Data were from a nationally representative survey of 6032 Australians exploring attitudes towards people with mental health problems. Participants were asked about their exposure to positive news stories about a person with a mental health problem, as well as negative portrayals, in which someone was harmed by a person with a mental health problem. Further questions covered the sources (traditional or social media) and impact of these exposures. RESULTS: Regression models were used to explore sociodemographic predictors of impact. Most participants reported exposure to negative news portrayals (68.4%, 95% confidence interval = [66.9, 69.8]), while fewer reported exposure to positive news stories (33.7%, 95% confidence interval = [32.3, 35.2]). Most people exposed to the negative news stories reported a negative impact (69.0%, 95% confidence interval = [67.2, 70.7]), and most exposed to positive news stories reported a positive impact (80.2%, 95% confidence interval = [77.6, 82.5]). Age and gender were associated with reported impact but not lived experience of mental illness. CONCLUSIONS: Exposure to negative news stories about mental health problems was prevalent. Given their impact on news audiences broadly, negative news stories need to be accurate and responsible to mitigate negative impacts. A renewed focus on generating and promoting positive and stigma-challenging news stories is needed to increase subsequent positive impacts.

Humans↗

PubMed2026

Experiences of stigma, bias, and communication challenges among pregnant healthcare workers: A systematic review of qualitative evidence.

BACKGROUND: Healthcare work environments are fraught with occupational hazards that can impact pregnant healthcare workers' health as well as patient care. Despite the feminization of healthcare globally, systematic discrimination against pregnant workers persists across diverse healthcare settings and cultural contexts. The intersection of stigma, bias, and communication challenges creates substantial barriers to career advancement and wellbeing. However, no systematic review has synthesized qualitative evidence on how these three constructs interact across healthcare professions and cultural contexts using an integrated theoretical framework. OBJECTIVE: To systematically review and synthesize qualitative evidence on experiences of stigma, bias, and communication challenges among pregnant healthcare workers across different healthcare settings and cultural contexts using an integrated theoretical framework. DESIGN: Systematic review of qualitative studies following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with thematic synthesis. DATA SOURCES: Seven databases were searched from inception to January 2026. REVIEW METHODS: Included qualitative studies were appraised using the Critical Appraisal Skills Programme (CASP) checklist and synthesized through theory-guided thematic synthesis. Confidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual) approach. RESULTS: Fourteen studies encompassing 1223 participants across 17 countries revealed four major themes: (1) professional identity stigma and workplace discrimination through systematic labeling and stereotyping; (2) gender-based institutional bias rooted in masculine organizational logic; (3) multilevel communication failures creating fear-based climates; and (4) individual and collective resistance strategies developed despite constraints. Occupational hazards specific to pregnancy included exposure to infectious diseases, imaging, physical tasks, cleaning products, patient violence, and medication administration. Support from coworkers and supervisors was identified as the most critical facilitator for avoiding hazards and making necessary modifications, while the desire to be 'supernurses' and fear of consequences emerged as significant barriers. These patterns were consistent across healthcare professions, settings, and cultural contexts, with specialty culture and healthcare system type moderating discrimination intensity. Confidence in core findings was rated high using GRADE-CERQual. CONCLUSIONS: Pregnant healthcare workers globally experience interconnected stigma, bias, and communication challenges that are systematically embedded within healthcare organizational structures. These challenges operate synergistically, requiring comprehensive multilevel interventions beyond policy compliance. Healthcare organizations must implement evidence-based strategies addressing stigma reduction, bias interruption, and communication transformation simultaneously to retain skilled workers and ensure quality patient care.

Female↗

PubMed2026

Diagnostic performance of machine learning models versus established risk stratification for intracranial aneurysm rupture: a systematic review and bivariate meta-analysis.

BACKGROUND: Machine learning (ML) models have been proposed to improve the discrimination of intracranial aneurysm rupture status beyond established clinical risk stratification tools. However, reported performance is heterogeneous and the relative contribution of model architecture and feature dominance remains unclear. METHODS: We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-diagnostic test accuracy systematic review and diagnostic meta-analysis of studies evaluating ML models for intracranial aneurysm rupture discrimination. PubMed, Embase and CENTRAL were searched to February 2026. Sensitivity and specificity were pooled using a bivariate random-effects model, with summary receiver operating characteristic curves generated across training, internal testing and external validation datasets. Models were compared with regression-based approaches and Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid haemorrhage, Site of aneurysm (PHASES) scores. Subgroup and meta-regression analyses explored associations between algorithm family and feature domain. RESULTS: Sixty-two retrospective cohorts (29 709 patients 209 models) met the inclusion criteria. In training datasets, pooled sensitivity and specificity for ML were 0.81 (95% CI 0.75 to 0.85) and 0.83 (0.80-0.86), with an area under the curve (AUC) of 0.878, exceeding PHASES (AUC 0.667). In testing datasets, ML retained higher discrimination (AUC 0.837) than regression models (0.806) and PHASES (0.646). In external validation, sensitivity was preserved (0.82), but specificity declined (0.66). Deep learning demonstrated the highest AUCs (training and testing). Incorporation of haemodynamic or radiomic features improved pooled discrimination relative to morphology alone. Evidence of small-study effects and mostly unclear Prediction Model Risk Of Bias Assessment Tool ratings were observed. CONCLUSIONS: ML approaches demonstrate higher pooled discrimination for aneurysm rupture status than conventional risk scores in retrospective datasets, but reduced external validation specificity and heterogeneity limit confidence for clinical translation. Prospective, externally validated, calibrated models are required before integration into routine cerebrovascular risk stratification.

Humans↗

PubMed2026

Plant species identification by genome skimming across the vascular plant tree of life.

Accurate species identification is essential for biodiversity conservation and sustainable use, yet standard plant DNA barcoding often fails to achieve species-level resolution. We present a large-scale empirical evaluation of genome skimming as a tool to improve plant species discrimination. Using standardised data from 1969 individuals representing 475 species from 32 genera across major lineages of the vascular plant tree of life, we compare conventional plastid + internal transcribed spacer (ITS) barcodes with genome skimming approaches. Standard barcoding using rbcL, matK, trnH-psbA and ITS resolved about half of species (49.3%), with six genera showing < 25% species discrimination. By contrast, genome skimming enabled the recovery of complete plastid genomes, yielding 57.6% species discrimination. It also generated sufficient nuclear genomic data for additional resolution from k-mer analysis, achieving 66.8% species discrimination - an average gain of 17.5% over standard barcodes - while eliminating cases of extreme failure (< 25% resolution). The recovery of complete plastomes and ribosomal DNAs from genome skims also ensures backward compatibility with existing barcode datasets. Our results demonstrate that genome skimming provides data that substantially improves species-level resolution across diverse plant lineages and offers a scalable, high-throughput approach for building comprehensive reference resources to support global biodiversity initiatives.

DNA Barcoding, Taxonomic↗

PubMed2026

Externally validated risk prediction models for gestational diabetes mellitus: A systematic review and meta-analysis.

INTRODUCTION: Risk prediction models for gestational diabetes mellitus (GDM) offer potential for early identification and targeted prevention. External validation is crucial to assess model performance across diverse populations. Despite the availability of numerous GDM prediction models, limited evidence exists on their external validation frequency, methodological quality, and clinical applicability. This systematic review evaluated externally validated GDM prediction models, focusing on methodological rigor, reporting standards, and clinical relevance to inform future research and implementation. MATERIAL AND METHODS: Databases including Ovid MEDLINE, Embase, Scopus, Emcare, and CINAHL were searched up to May 1, 2025. Studies reporting external validation of GDM risk prediction models were included. Two reviewers independently screened studies. Data were extracted using the CHARMS framework, and risk of bias and applicability were assessed using PROBAST+AI. The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251125758). RESULTS: Twenty-six studies validated 33 models, with validation sample sizes ranging from 50 to 75 161. Over half used the IADPSG criteria to define GDM. Discrimination metrics were commonly reported, but calibration, overall performance, and clinical utility were often lacking. Meta-analysis was feasible for only four models: Teede et al., Nanda et al., Naylor et al., and Van Leeuwen et al., each showing fair discrimination. The Teede et al. model was the most widely validated, with 11 external validations across six continents and a pooled AUC of 0.72 (95% CI: 0.67-0.76). Despite fewer validations, the Nanda et al. model achieved the highest pooled discrimination (5 validations; pooled AUC 0.77, 95% CI: 0.74-0.80). The Naylor et al. and van Leeuwen et al. models also underwent meta-analysis, as sufficient external validation studies were available to support comparative performance assessment. Notably, 69.23% of studies had a high risk of bias. CONCLUSIONS: While many models showed acceptable predictive performance, most validations were methodologically weak. Future studies should follow best-practice guidelines and promote scalable validation strategies, such as algorithm sharing, to enhance clinical utility.

Humans↗

PubMed2026

Association of time-averaged systemic immune-inflammation indices with in-hospital mortality after intracerebral hemorrhage: a retrospective study.

BACKGROUND: Systemic inflammation plays a central role in secondary brain injury following intracerebral hemorrhage (ICH). Although inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) are linked to poor outcomes, their associations with mortality are commonly assumed to be linear, potentially overlooking nonlinear patterns where mortality risk rises steeply at higher levels. METHODS: We conducted a retrospective study using the MIMIC-IV database, including 440 patients with non-traumatic ICH who were alive and remained in the ICU for at least 72 h after admission. Mean NLR, SII, and SIRI were calculated from measurements obtained during this period. Multivariable logistic regression and restricted cubic spline (RCS) analyses were applied to assess their independent and nonlinear associations with in-hospital mortality. Model discrimination and calibration were internally validated using 1,000 bootstrap resamples. RESULTS: The in-hospital mortality rate was 26.1%. After multivariable adjustment, NLR and SIRI remained independently associated with mortality. Patients in the highest SIRI quartile had the highest risk of death (aOR = 5.12; 95% CI: 2.57-12.24; p < 0.001). RCS analysis revealed a significant nonlinear association between SIRI and mortality (p-nonlinearity < 0.05), showing a steep risk increase at higher SIRI levels. Adding SIRI to the base model provided a modest improvement in discrimination (AUC 0.762 to 0.785, p = 0.045) and significantly improved risk reclassification (cNRI = 0.4778, p < 0.001; IDI = 0.0240, p = 0.0151). CONCLUSIONS: Among patients with ICH who met the 72-hour eligibility criterion, higher 72-hour average SIRI was independently associated with in-hospital mortality. As a time-averaged measure, SIRI should be interpreted as a dynamic marker integrating the initial inflammatory state and the early clinical course rather than as a purely baseline prognostic factor. Although adding SIRI to the base model modestly improved discrimination and risk reclassification, it should be considered a candidate prognostic marker requiring external validation before clinical application.

Humans↗

PubMed2026

Plasma proteomic profiling characterizes candidate biomarkers of perimesencephalic non-aneurysmal subarachnoid hemorrhage.

OBJECT: This study aims to explore the plasma proteomic profiles of angiographically confirmed pmSAH and aSAH, and to identify candidate protein biomarkers for discriminating these subtypes on a biological level. METHODS: The differentially abundant proteins of plasma samples from patients with pmSAH (n = 30) and aSAH (n = 30) were analyzed by data-independent acquisition proteomics, and candidate biomarkers were screened. RESULTS: 291 candidate biomarkers were obtained that could be used to distinguish pmSAH patients from aSAH patients, among which 76 were upregulated and 215 were downregulated in pmSAH. Subsequently, the 10 candidate biomarkers were validated by enzyme-linked immunosorbent assay in a validation cohort of 72 subjects. ORM1, ORM2, HP and NMNAT1 were specifically down-regulated in the pmSAH group, while ANP32A was specifically up-regulated in the pmSAH group. FGL2 was specifically up-regulated in the aSAH group. The combined model of ORM2, HP and ANP32A had the best discriminative power (AUC = 0.880). CONCLUSIONS: This study identified ORM2, HP, and ANP32A as candidate biomarkers reflecting biological differences between pmSAH and aSAH. SIGNIFICANCE: Although some proteomic studies have analyzed aneurysmal subarachnoid hemorrhage, to date, there have been no reports on the circulating proteomic analysis of pmSAH. Comparative analysis of the circulating proteomic differences between pmSAH and aSAH may not only help understand the causes of pmSAH, but also contribute to a deeper understanding of mechanisms showing how pmSAH differs from the formation and rupture mechanisms of intracranial aneurysms.

Humans↗

PubMed2026

Psychometric Evaluation of the Breast Inflammatory Symptom Severity Index Versions 2 and 3 Among Lactating Women.

OBJECTIVE: To evaluate the psychometric properties of Versions 2 and 3 of the Breast Inflammatory Symptom Severity Index (BISSI). DESIGN: Secondary data analysis of clinical trial data. SETTING: Private physiotherapy practices, a public tertiary hospital, and a community in Melbourne, Australia. PARTICIPANTS: Women more than 7 days after birth with inflammatory conditions of the lactating breast (N = 43). METHODS: We performed confirmatory factor analysis of the BISSI Version 2 to examine item loading, which informed development of the BISSI Version 3 (V3). We assessed convergent validity by comparing total BISSI V3 scores with human milk sodium to potassium ratio (Na+:K+) at Trial Days 1, 3, and 10 using Bland-Altman plots. We compared item-level scores for size of affected area with objective receiver operating characteristic curve analysis to assess discriminant validity for symptom severity and Cronbach's alpha for internal reliability. RESULTS: After confirmatory factor analysis, we removed two items, resulting in a six-item BISSI V3. All retained items demonstrated comparable loading on the overall scale. Limits of agreement for total BISSI V3 scores and item-level scores for size of affected area were acceptable at all time points, with more than 90% of observations falling within 2 standard deviations of the mean difference, supporting convergent validity. Discriminant validity of the BISSI V3 was supported. We found high internal reliability at both time points CONCLUSION: Our findings provide evidence for the validity and reliability of the BISSI V3 and support its continued development and for clinical use of the BISSI V3 and human milk Na+:K+ analysis to enhance management of inflammatory conditions of the lactating breast.

breastfeeding↗

PubMed2026

Development and Validation of a Predictive Model for Identification of Cognitive Impairment Risk in Older Adults with Subjective Cognitive Decline：A Longitudinal Study.

BACKGROUND: Subjective cognitive decline (SCD) is a transitional state between objective cognitive impairment and cognitively intact mental status, providing a critical window for implementing preventive interventions to delay objective cognitive decline. AIMS: We aimed to develop a predictive model for SCD progression in older adults with mild cognitive impairment (MCI). This model will facilitate the identification of risk factors and establishment of targeted interventions for community-based SCD management. METHODS: Data from the China Health and Retirement Longitudinal Study (CHARLS) was utilized in this study, extracting 18 indicators. Potential predictors selected through univariate Cox regression and LASSO regression analyses were sequentially incorporated into a multivariable Cox regression model. A nomogram was constructed to establish a predictive model. Model validation encompassed Area Under Curve (AUC) metrics for discriminative capacity, complemented by quantitative assessments using calibration curve analysis for precision verification and decision curve analysis (DCA) for clinical utility evaluation. RESULTS: A total of 1099 older adults with SCD were included in the final analysis, of whom 114 (10.3%) developed MCI. Multivariable Cox regression identified residence, marital status, educational level, social participation, gait speed, and baseline cognitive function. The model demonstrated time-dependent AUC values of 0.885, 0.830, 0.839, and 0.836 in the training set when evaluating discriminative capacity at 2-, 4-, 7-, and 9-year, respectively. The predictive model showed excellent predictive ability according to AUC, calibration curve, and DCA. CONCLUSIONS: A predictive model was created to estimate the risk of developing MCI in older individuals with SCD, offering clinician-actionable intervention benchmarks for preventive care.

Humans↗

PubMed2026

Proprioception Training and Surrogate Outcomes: A Systematic Review of Definitions, Measures, and Effectiveness Claims.

BACKGROUND: "Proprioception training" is widely advocated in rehabilitation and sports practice, yet the term encompasses heterogeneous constructs, interventions, and outcomes. Many trials infer proprioceptive benefits from surrogate outcomes (balance, strength, or pain) rather than direct psychophysical indices. OBJECTIVE: We aimed to examine how proprioception is defined and measured, and how improvement is claimed, in randomized controlled trials. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, PubMed, Scopus, and Web of Science were searched to October 2025. Eligible randomized controlled trials explicitly described interventions as "proprioceptive" or "sensorimotor training" and reported at least one proprioceptive outcome, either direct (e.g., joint position reproduction, threshold to detection of passive motion, active movement extent discrimination) or indirect (e.g., sway, balance). Methodological quality was appraised with the Physiotherapy Evidence Database (PEDro) scale and risk of bias using the Cochrane Risk of Bias 2 (RoB 2) tool. RESULTS: Fifty-one randomized controlled trials (n = 2319) were included. Comparative synthesis showed that improvements inferred from surrogate outcomes were more frequent and often larger than improvements observed in direct psychophysical measures. Directly targeted practice, angle specific, attentionally demanding, and aligned with the measured proprioceptive submodality and task construct, produced the most consistent benefits in position-reproduction accuracy/error, movement-detection sensitivity, or discrimination performance, depending on the outcome assessed. In contrast, multimodal regimens (balance, strengthening, taping, manual therapy) commonly improved balance, pain, strength, or function without comparably consistent evidence of enhanced direct psychophysical proprioceptive function. CONCLUSIONS: Specific psychophysical components of proprioceptive function appear modifiable, but only when training explicitly targets the sensory construct measured. The field remains conceptually diffuse, with frequent conflation of sensorimotor performance and proprioception. Progress depends on defining proprioceptive submodalities a priori, privileging validated psychophysical outcomes over surrogate outcomes, and aligning intervention content with measurement to substantiate true perceptual learning rather than generic motor adaptation.

Journal Article↗

PubMed2026

A polygenic risk score for peripheral artery disease and major adverse limb events.

BACKGROUND AND AIMS: Large-scale genome-wide association studies have identified common genetic variants that predict the risk of peripheral artery disease (PAD). This study assessed whether a polygenic risk score (PRS) is associated with PAD and the incidence of major adverse limb events (MALE) independent of clinical risk factors in patients with established cardiometabolic disease. METHODS: A genetic analysis was performed, pooling individual patient-level data from six TIMI trials. The association of a recently validated PAD PRS with prevalent PAD and the incidence of MALE (acute limb ischaemia, chronic limb-threatening ischaemia, major amputation, or peripheral revascularization) was assessed. RESULTS: A total of 68 816 patients were included in this analysis, with a median follow-up of 2.6 years. Of these, 5986 (8.7%) had known PAD at baseline. After adjusting for clinical risk factors, a higher PAD PRS was independently associated with a 15% greater odds of prevalent PAD (adjusted odds ratio per 1-SD: 1.15 [95% confidence interval 1.12-1.18], P < .0001), a magnitude of risk as strong as established clinical risk factors. A total of 577 patients experienced MALE during follow-up. A higher PAD PRS was associated with a 30% increased risk of MALE (adjusted hazard ratio per 1-SD: 1.30 [1.19-1.42], P < .0001). Adding the PAD PRS to clinical risk factors resulted in a statistically significant but modest improvement in discrimination (area under the curve went from 0.651 to 0.662 P < .0001). CONCLUSIONS: In a broad spectrum of patients with cardiometabolic disease, the PAD PRS is associated with an increased risk of PAD and the incidence of MALE beyond clinical risk factors; however, the improvement in discrimination was statistically significant but clinically modest.

Humans↗

PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗

PubMed2024

Promoters and Barriers of Vaccine Hesitancy.

This systematic review explores the psychological antecedents of Vaccine Hesitancy, a significant determinant of vaccination behavior. Following PRISMA guidelines, an extensive search was conducted starting from 1673 papers and resulting in 48 publications from various databases. The review identifies psychological factors, specifically cognitive, personality, experiential, and social factors contributing to hesitancy. Cognitive factors include health literacy, conspiracy beliefs, trust, and perceived risk. Personality traits such as extraversion, openness, and psychological capital impact hesitancy, while psychopathy increases it. Personal experiences, like perceived stress and racial discrimination, indirectly affect hesitancy. Social factors, including social relationships and norms, play a significant role in reducing hesitancy. Tailored interventions addressing these factors can enhance vaccine acceptance.

Humans↗

PubMed2026

Diffusion MRI radiomics in meningiomas: imaging correlates of tumor grade and intraoperative consistency.

OBJECTIVE: Despite advancements in imaging studies, the preoperative prediction of the biological behavior and intraoperative consistency of intracranial meningiomas remains limited. This study evaluated the association of volumetric diffusion-based and texture-derived radiomic features extracted from routine MRI with histopathological aggressiveness and intraoperative tumor consistency. METHODS: Ninety-seven intracranial meningiomas resected at two tertiary centers were retrospectively analyzed. Volumetric segmentation was performed on contrast-enhanced T1-weighted MRI and coregistered to apparent diffusion coefficient (ADC) maps. Data on first-order diffusion metrics and selected texture features were collected. The associations between World Health Organization (WHO) grade and Ki-67 index were assessed using nonparametric tests and Spearman correlation analysis. Independent factors associated with intraoperative tumor consistency (Zada grades 1-5) were evaluated via multivariate ordinal logistic regression analysis that adjusted for tumor volume, skull base location, calcification status, and WHO grade. Secondary receiver operating characteristic (ROC) curve analyses were performed to differentiate solid (Zada grades 4-5) from soft (Zada grades 1-2) tumors. ROC analyses were performed within the study cohort and were intended as exploratory assessments of discriminative performance. RESULTS: The mean ADC (ADCmean) and the 10th percentile of the ADC decreased significantly with increasing WHO grade (p < 0.001). ADCmean had a moderate inverse correlation with the Ki-67 index (r = -0.42, p < 0.001) and intraoperative tumor consistency (r = -0.45, p < 0.001). In the multivariate analysis, the ADCmean remained independently associated with increasing tumor firmness. Each 0.1 × 10-3 mm2/sec increase corresponded to a 38% reduction in the odds of belonging to a higher consistency category (OR 0.62, 95% CI 0.51-0.74, p < 0.001). The ROC analysis showed good discrimination for solid tumors (area under the curve 0.847, 95% CI 0.742-0.953) and soft tumors (area under the curve 0.824, 95% CI 0.714-0.935). Texture features had weaker associations with intraoperative tumor consistency. CONCLUSIONS: Volumetric diffusion-derived metrics, particularly ADCmean, are associated with both histopathological aggressiveness and intraoperative tumor firmness in meningiomas. Diffusion imaging may reflect a graded microstructural continuum rather than a purely dichotomous property, providing complementary preoperative insights into surgical complexity.

Humans↗

PubMed2026

Risk Factors and Predictive Model for Postoperative High Myopia in Children Undergoing Congenital Cataract Surgery With Intraocular Lens Implantation.

PURPOSE: To identify risk factors associated with the development of high myopia following congenital cataract surgery and to establish a robust predictive model. DESIGN: Retrospective clinical cohort study. SUBJECTS: This retrospective study included 106 pediatric patients who underwent congenital cataract surgery with primary IOL implantation (mean follow-up 8.19 years). The model was externally validated in an independent cohort of 72 patients with a mean follow-up of 7.83 years. METHODS: Preoperative and postoperative ocular biometric parameters were collected. Risk factors for postoperative high myopia were analyzed using Cox proportional hazards regression, which served as the basis for model construction. The predictive performance of the model was rigorously evaluated for discrimination and calibration. Discriminative ability was quantified using Harrell's C-index and the area under the receiver operating characteristic curve (AUC). Model calibration was assessed via calibration plots by comparing predicted probabilities with actual observed outcomes. Internal validation was performed using a bootstrapping method (500 iterations) to ensure model stability and adjust for potential overfitting. RESULTS: An initial postoperative refraction of <+0.75D, and a higher IOL Power to Axial length Ratio (IOL/AL ratio) were identified as significant risk factors for the development of postoperative high myopia. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. The predictive model demonstrated robust performance, achieving a C-index of 0.711 (internal validation C-index: 0.713). The area under the receiver operating characteristic curve (AUC) values for predicting high myopia at 5 and 10 years were 0.858 and 0.745, respectively. Furthermore, calibration curves demonstrated excellent agreement between the predicted and observed outcomes throughout the follow-up period. In external validation, the model achieved a C-index of 0.825, 5-year AUC of 0.833, and 10-year AUC of 0.713. CONCLUSIONS: Our analysis established that initial postoperative refraction <+0.75D, and an elevated IOL/AL ratio are key determinants of high myopia risk following surgery. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. This predictive framework provides clinicians with a practical tool to optimize preoperative IOL selection and identify high-risk infants who require vigilant myopia prevention and balanced amblyopia management.

Humans↗