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PubMed2026

Biomonitoring of industrial heavy metal pollution via enzymatic and metabolic responses in desert ants (Cataglyphis savignyi) and beetles (Tentyrum sp) as bioindicators.

The current work seeks to evaluate the effectiveness of Cataglyphis saviginyi and Tentyrum sp as indicators of pollution in the city's main industrial regions by analyzing their enzymatic activity and primary metabolites. Soil samples were collected at each site under investigation to analyze soil characteristics and heavy metal content. C. saviginyi and Tentyrum sp were collected across four consecutive seasons (2023-2024) to investigate enzymatic (GPT, GOT, ALP, ACP, LDH) and metabolic (lipid, protein, carbohydrate) biomarkers. The physicochemical properties of the soil differed substantially between the industrial areas and the control site. Soil heavy metal buildup was highest at industrial sites (1 and 4) compared to the control site, with the order being Zn > Cr > Cd > Cu. Heavy metal pollution indices were determined. Increased industrial activity from metal industries, ceramics, and chemical painting companies defines this area, as seen by the high Cdeg, mCd, PI, and PLI values derived for industrial sites 1 and 4. While C. saviginyi and Tentyrum sp deconcentrated and released Cr, Cd, and Zn into the soil via the biological accumulation factor (BAF), Cu acted as a macro-concentrator. Compared with the control site, industrial environments were shown to increase levels of GPT, GOT, LDH, ACP, protein, and carbohydrates in C. saviginyi. However, lipid and ALP activity was suppressed. at industrial sites, Tentyrum sp carbohydrate content was higher than at control sites, but GPT, GOT, ALP, ACP, LDH, protein, and lipid activities were all suppressed. Consequently, enzymatic and metabolic biomarkers proved to be sensitive indicators for assessing industrial heavy metal pollution in desert ecosystems.

Animals↗

PubMed2026

Beyond multidimensionality: a systematic review of recurrent frailty archetypes in community-dwelling older adults.

BACKGROUND: Frailty is a clinically heterogeneous geriatric syndrome commonly summarised using physical or multidomain severity scores. Whether person-centred analyses identify recurring within-frailty configurations has not been systematically examined in community-dwelling older adults. METHODS: We searched PubMed, Embase, MEDLINE, and CINAHL (January 2000-November 2025) for cross-sectional studies using latent class, latent profile, or analogous clustering methods to derive frailty subgroups. Quality was assessed using the AHRQ checklist and a purpose-built appraisal of person-centred model reporting. Study-derived classes were mapped in duplicate to a structured archetype framework developed through comparison of class-defining features across studies. RESULTS: Fourteen reports representing 12 independent datasets from eight countries were included. Six configurations were identified: minimally impaired reference, mobility-physical, nutritional-metabolic, cognitive-predominant, combined cognitive-physical, and psychosocial/mood-predominant. Convergence was measurement-dependent. The reference and mobility-physical configurations recurred across physical-only and multidomain indicator sets, while the combined cognitive-physical configuration appeared across several multidomain frameworks but required cognition to be measured. The remaining configurations emerged only when their defining domains were included. Evidence of prognostic value beyond aggregate frailty severity came from one deficit-index study. Collapsing shared-provenance reports and excluding the boundary-eligible study did not alter recurrence; excluding the Croatian dataset left five configurations recurrent, with the cognitive-predominant configuration supported by one independent dataset. CONCLUSIONS: Person-centred analyses identify recurring within-frailty configurations, but their apparent stability is partly measurement-dependent. A five-configuration core persisted after exclusion of the Croatian dataset, whereas the cognitive-predominant configuration remained weakly replicated. Harmonised indicators and rigorous external validation are needed before clinical application.

Humans↗

PubMed2026

Effectiveness of Multidomain Cardiac Rehabilitation After Myocardial Infarction by Patient Frailty: Prespecified Subgroup Analysis of the PIpELINe Trial.

BACKGROUND: Frailty is common among older patients surviving myocardial infarction, is associated with adverse outcomes, and is often perceived as a barrier to cardiac rehabilitation (CR). The aim of this study is to determine whether frailty influences prognosis after myocardial infarction, and whether frailty modifies the clinical benefit of multidomain CR. METHODS: We performed a prespecified subgroup analysis of the PIpELINe (Physical Activity Intervention in Elderly Patients With Myocardial Infarction) randomized clinical trial conducted in Italy, which enrolled 512 patients aged ≥65 years recovering from myocardial infarction and randomized them in a 2:1 ratio to CR or usual care. Frailty was assessed using the Fried Frailty Phenotype, and patients were categorized as nonfrail (robust) or prefrail/frail. Time-to-event outcomes were analyzed using Kaplan-Meier estimates and Cox proportional hazards models, including treatment-by-frailty interaction terms to evaluate effect modification of the multidomain CR. The primary outcome was a composite of cardiovascular death or unplanned hospitalization for cardiovascular causes within 1 year after randomization. RESULTS: Overall, 350 patients (68.4%) were classified as prefrail/frail, of whom 232 were randomized to intervention arm (66%). Frail patients were older (median age, 80 [75-85] years) and more frequently female (41.7% versus 24.7%). Compared with robust patients, prefrail/frail patients had a higher risk of the primary outcome (16 [9.9%] versus 62 [17.7%]; hazard ratio, 1.59 [95% CI, 0.89-2.82]; adjusted P=0.117). Among prefrail/frail patients, assignment to multidomain CR was associated with a lower risk of the primary outcome compared with usual care (hazard ratio, 0.57 [95% CI, 0.34-0.94]; P=0.028), with no statistically significant interaction in the treatment effect on the primary end point (P=0.57). CONCLUSIONS: Among older patients recovering from myocardial infarction, frailty is associated with worse prognosis but does not diminish the benefit of multidomain CR. These findings support the use of frailty assessment to guide rather than limit access to CR. REGISTRATION: ClinicalTrials.gov; Unique identifier: NCT04183465.

Humans↗

PubMed2026

ESC quality indicators for post-myocardial infarction care: transition and chronic coronary syndrome phases.

AIMS: We aimed to develop the European Society of Cardiology (ESC) quality indicators (QIs) for myocardial infarction (MI), from 1 year after hospital discharge, corresponding to transition to the chronic coronary syndrome phases. METHODS AND RESULTS: We collaborated with the European Association of Preventive Cardiology (EAPC) and developed QIs for the long-term management of patients following MI. We applied the ESC methodology for QI development by (i) determining key domains of post-MI care; (ii) developing candidate QIs by performing a systematic review of the literature, and (iii) selecting the final set of QIs using a modified Delphi approach. In total, 18 QIs were identified across seven domains of care including (i) structural framework, (ii) risk assessment and follow-up, (iii) pharmacological management, (iv) rehabilitation, behavioural, and preventive interventions, (v) coronary revascularization, (vi) clinical outcomes, and (vii) patient-reported outcomes. CONCLUSION: We present the ESC QIs from 1 year after hospitalization for MI, to standardize and address gaps in care for this high-risk group. These QIs are supported by evidence from contemporary literature, endorsed by expert consensus, and aligned with the 2024 ESC guidelines on the management of chronic coronary syndromes. LAY SUMMARY: Measures to evaluate and improve the long-term management of patients following a heart attack are needed. In this paper, we identified key aspects of care that can help clinicians, decision-makers and patients improve the quality of care, from one year after a heart attack onwards, and help address inequalities and variations in clinical practice.

Humans↗

PubMed2026

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

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

Humans↗

PubMed2026

Longitudinal Neurocognitive Changes for Patients With Hematologic Malignancies Undergoing Hematopoietic Stem Cell Transplantation: A Systematic Review With Structured Narrative Synthesis.

OBJECTIVES: Neurocognitive changes in hematological malignancies (HM) and as a sequela of hematopoietic stem cell transplantation (HSCT) remain under-recognized. These changes may substantially affect patients' quality of life. Therefore, this review systematically evaluated longitudinal neurocognitive changes in patients with HM undergoing HSCT. METHODS: Following PRISMA guidelines, PubMed, Scopus, and CINAHL were searched on October 30, 2025. Longitudinal studies assessing neurocognitive changes in patients with HM undergoing HSCT, with or without healthy controls, were included. Risk of bias was assessed using an adapted National Institutes of Health quality assessment tool for before-after studies. Effect sizes with 95% confidence intervals were calculated to quantify changes in neurocognitive performance. RESULTS: Nine of 2012 studies met the inclusion criteria, with overall moderate study quality. Improvements may occur post-HSCT; however, allogeneic HSCT and myeloablative conditioning were main risk factors identified for persistent cognitive decline. In line with white matter damage, executive function and attention/processing speed impairments likely represent core deficits, which may affect other cognitive impairments. Post-HSCT changes depend on task characteristics, cognitive load, and conditioning intensity. CONCLUSIONS: Future research should emphasize regular neurocognitive assessment to guide cognitive rehabilitation, implement pre-transplant cognitive rehabilitation strategies to mitigate post-HSCT cognitive decline, and enhance treatment outcomes.

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PubMed2026

Identifying biomarkers of accelerated ageing in cancer patients from routine clinical data.

INTRODUCTION: Cancer and ageing have a bidirectional relationship: age is the strongest risk factor for cancer, and cancer and treatments can accelerate ageing. Therefore, biological age can differ from chronological age; biomarkers are needed to stratify interventions to minimise accelerated ageing. METHODS: PhenoAge was calculated from routine blood test results of patients attending a Geriatric Oncology clinic. PhenoAgeAccel was the residual from a regression of PhenoAge against age. RESULTS: Data were available for 173 patients (62% male). Mean PhenoAge was higher than age (84.3 (12.6) vs 76.2 (7.24), p < 0.001), though the two were correlated (r = 0.579, p < 0.001). Unlike age, PhenoAge and PhenoAgeAccel were associated with one-year mortality (PhenoAge OR=1.083, 95% CI: 1.038-1.136; PhenoAgeAccel OR=1.096, 95% CI: 1.047-1.155). PhenoAge correlated with Clinical Frailty Score and Timed Up and Go (CFS: Rs=0.31, p < 0.001; TUG: Rs=0.25, p < 0.005); there were no correlations with age. PhenoAgeAccel correlated with the number of CGA interventions made (Rs=0.17, p < 0.05), unlike age and PhenoAge. Patients with diabetes mellitus had a higher PhenoAgeAccel compared to those without (3.40 vs -1.71, p = 0.002). In patients receiving systemic anti-cancer treatment, patients with PhenoAgeAccel calculated pre-treatment had less age acceleration than those with PhenoAgeAccel calculated post-treatment, both overall (2.18 vs -2.87; p = 0.048) and in matched samples (n = 21, 7.76 vs -2.87, p < 0.001). CONCLUSIONS: PhenoAgeAccel is a greater predictor of risk than chronological age in older people with cancer. This makes it a promising biomarker to stratify patients for holistic geriatric assessment, dose reductions, or future geroprotective measures which could be integrated within electronic healthcare record systems.

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PubMed2026

Thrombus Metabolism-Based Molecular Subtyping for Prognostic Risk Stratification in Acute Ischemic Stroke: A Preliminary Study.

AIMS: To preliminarily characterize metabolic molecular subtypes of cerebral thromboemboli and evaluate their clinical significance in anterior circulation acute ischemic stroke due to large vessel occlusion (AIS-LVO). METHODS: Untargeted metabolomics was performed on thromboemboli retrieved from 36 patients with anterior circulation AIS-LVO using ultra-performance coupled liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Unsupervised hierarchical clustering was employed to identify distinct metabolic molecular subtypes, and their associations with stroke etiology, radiographic severity, and functional outcomes were analyzed. RESULTS: Two distinct thrombus metabolic molecular subtypes (C1 and C2) were identified based on 12 metabolites significantly associated with both short-term (7-day ∆NIHSS) and long-term (90-day mRS) functional outcomes. The C1 subtype, predominantly cardioembolic, exhibited enhanced lipid metabolism, whereas the C2 subtype, primarily atherothrombotic, demonstrated increased folate metabolism. Patients with C1 thromboemboli presented more severe admission ischemic lesions (as indicated by ASPECTS) and experienced poorer short-term and long-term outcomes. A six-metabolite signature derived from LASSO regression was identified for exploratory discrimination of thrombus metabolic subtypes, etiological subtypes, and 90-day outcomes. CONCLUSION: This preliminary exploratory study identifies two metabolically distinct thrombus molecular subtypes with clinical implications in anterior circulation AIS-LVO, providing a novel basis for risk stratification and personalized secondary prevention and warrants further investigation.

Humans↗

PubMed2025

Mediators of Change in Cognitive Behavioral and Mindfulness-Based Online-Interventions for Hypoactive Sexual Desire Dysfunction in Women.

Low sexual desire is a common sexual problem among women. When it is accompanied by significant personal distress, it may be diagnosed as hypoactive sexual desire dysfunction (HSDD). Both cognitive behavioral therapy (CBT) and mindfulness-based therapy (MBT) are effective treatments for HSDD when delivered in person or online. In this randomized controlled treatment study, CBT and MBT consisted of eight guided self-help modules delivered online, and participants completed measures at pretreatment and after 3, 6, and 12 months. Nine variables were examined as potential mediators of treatment outcomes (i.e., sexual desire and sexual distress), namely mindfulness, self-compassion, rumination, body connection, self-consciousness, relationship satisfaction, sexual communication, depression, and anxiety. In total, 212 women diagnosed with HSDD were randomized to either CBT or MBT (Mage = 36.3, SD = 10.2). Improvements in self-compassion, rumination, body connection, and self-consciousness partially mediated treatment outcomes in at least one of the treatment groups. Mediation effects were mostly small, explaining up to 15% of the total effects. No systematic differences in mediation pathways between CBT and MBT were found. These findings emphasize the importance of emotion regulation, metacognitive processes, and embodiment for the effective treatment of HSDD. Future research should refine treatment components to enhance efficacy and ensure that psychological interventions adequately address common concerns among women with HSDD.

Humans↗

PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗

PubMed2026

Effect of a PROtein-enriched MEDiterranean diet and EXercise (PROMED-EX) on nutritional status and cognitive performance in older adults at risk of undernutrition and cognitive decline: the PROMED-EX randomized controlled trial.

BACKGROUND: Undernutrition in older adults is associated with adverse health outcomes including cognitive decline, yet evidence for effective preventive strategies is limited. OBJECTIVES: The objective of this study was to investigate effects of a protein-enriched Mediterranean diet, with and without exercise, on nutritional status and cognitive performance in "at risk" community-dwelling older adults. METHODS: A total of 105 participants (69% female; aged 67.7 ± 6.1 y) at risk of undernutrition and cognitive decline were randomized to 1 of 3 groups: 1) PROMED-EX (personalized dietary counseling plus home-based exercise); 2) PROMED (personalized dietary counseling only); or 3) CON (healthy eating leaflet). The primary outcome was change in nutritional status at 6 mo, measured by the Mini Nutritional Assessment (MNA; 0-30 points). Secondary outcomes included neurocognitive test battery (NTB) z-score, PROMED diet quality score (0-14), physical performance, and health-related quality of life. Analyses followed an intention-to-treat approach using linear regression to assess between-group differences in 6-mo outcomes. RESULTS: At baseline, the mean MNA score was 22.5 ± 2.3. After 6 mo, nutritional status improved significantly in both intervention groups compared with CON: mean differences in MNA were 2.7 [95% confidence interval (CI): 1.3, 4.2] for PROMED and 2.9 (95% CI: 1.5, 4.3) for PROMED-EX (both P < 0.001). Cognitive function also improved, with NTB z-score differences of 0.3 (95% CI: 0.1, 0.5; P = 0.01) in PROMED and 0.2 (95% CI: 0.0, 0.4; P = 0.02) in PROMED-EX compared with CON. Diet quality scores significantly increased with mean differences of 4.0 (95% CI: 2.9, 5.0) for PROMED and 3.9 (95% CI: 2.8, 4.9) for PROMED-EX compared with CON (both P < 0.001). Despite low adherence to exercise, additional benefits were observed for physical performance and quality of life. CONCLUSIONS: Dietary intervention improved nutritional status in community-dwelling older adults at risk of undernutrition. Correcting undernutrition could help to slow cognitive decline and promote physical health and quality of life during aging. This study was registered at clinicaltrials.gov as NCT05166564.

Humans↗

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

Impact of Diabetes on Outcomes of Contemporary PCI Guided by OCT vs Angiography: The ILUMIEN IV Trial.

BACKGROUND: Patients with diabetes are at higher risk for adverse events after percutaneous coronary intervention (PCI) compared with patients without diabetes. OBJECTIVES: This study sought to assess the influence of diabetes and complex lesions on the outcomes of patients undergoing PCI with and without optical coherence tomography (OCT) guidance during a follow-up period of 2 years. METHODS: Patients in ILUMIEN IV randomized to OCT-guided vs angiography-guided PCI were grouped into those with (n = 1,044) and without diabetes (n = 1,443). Study endpoints were target vessel failure (TVF) and serious major adverse cardiovascular events (MACE). RESULTS: After adjustment for differences in clinical and angiographic characteristics, the 2-year rates of both TVF (9.9% vs 6.3%; adjusted HR: 1.48; 95% CI: 1.09-2.02; P = 0.01) and serious MACE (5.3% vs 2.7%; adjusted HR: 1.77; 95% CI: 1.14-2.76; P = 0.01) were increased in diabetic compared with nondiabetic patients, consistently in patients with and without complex lesions (Pinteraction = 0.22 and 0.14, respectively), although the highest 2-year rates were in patients with diabetes and complex lesions. In all randomized patients, OCT guidance compared with angiography guidance did not reduce TVF or serious MACE. These effects were consistent in patients with and without diabetes (Pinteraction = 0.41 and 0.20, respectively), and were not modified by treatment of complex lesions. CONCLUSIONS: In the large-scale ILUMIEN IV trial, patients with diabetes remained at increased risk for adverse events after PCI compared with nondiabetic patients despite the use of OCT procedural guidance. Patients with diabetes and complex lesions were at particularly high risk for adverse outcomes after PCI.

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PubMed2026

Microbial diversity, functional activities, and safety risks in fermented tea: a comprehensive review.

Microbial fermented teas are gaining global popularity due to their unique sensory profiles and health benefits. The quality and safety of these products are governed by complex microbial ecosystems that orchestrate the biotransformation of tea leaf components. This review addresses a critical paradox in the field: the same microbial activities that generate desirable bioactive metabolites, such as theabrownins and organic acids, also create ecological niches for mycotoxigenic fungi, posing significant health risks from contaminants like ochratoxin A, citrinin, and aflatoxins. While extensive research has cataloged the microbial diversity in these systems, a comprehensive framework linking processing environments to microbial community assembly, functional outcomes, and quantifiable safety risks remains elusive. This review systematically bridges this gap by synthesizing current knowledge on the microbial consortia-dominated by Aspergillus, Penicillium, Bacillus, and Lactiplantibacillus species-that drive tea fermentation. We critically analyze their functional roles in enhancing flavor, bioactivity, and potential probiotic activity while simultaneously evaluating the mechanisms of mycotoxin production and accumulation. By integrating microbial ecology, biochemistry, and food safety, we propose a forward-looking perspective focused on transitioning the industry from traditional, spontaneous fermentation to modern, controlled biotechnological processes. This approach, centered on the use of defined starter cultures, predictive modeling, and active biocontrol strategies, provides a roadmap for ensuring the consistent quality and safety of fermented tea products, ultimately unlocking their full potential as high-quality functional foods.

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PubMed2026

Urban stormwater infrastructure as a microplastic superhighway: a critical review of transport dynamics, modelling, and mitigation across pavements and drainage networks.

This review examines the transport, fate, modelling, and mitigation of Microplastics (MPs) in urban stormwater infrastructure, with emphasis on pavements, runoff pathways, micro-drainage, and macro-drainage systems. Following a systematic review approach, more than 1000 records were screened and approximately 50 core studies were retained when they addressed urban stormwater or drainage-related MP transport with adequate methodological reporting; marine-only studies and biological-effect studies without direct relevance to transport processes were excluded. The evidence shows that stormwater systems function not merely as passive conduits but as dynamic reactive transport systems with temporary storage, where particle mobilisation, sedimentation, resuspension, and temporary retention regulate MP export. Road surfaces, especially high-traffic areas, are major reservoirs of tyre wear, road-marking, atmospheric, and litter-derived particles that are rapidly mobilised during rainfall. Conventional grab sampling may underestimate MP loads, which in some cases exceed treated wastewater effluent loads by up to six-fold. Drainage structures such as manholes can immobilise up to 17.3% of near-neutrally buoyant particles, while biofouling and aggregation may shift buoyant polymers from wash-load to bedload. Mitigation systems, including permeable pavements, bioretention, wetlands, and technical inserts, can achieve high removal of coarse MPs, but performance declines for fine particles below 100 µm. The review highlights the need for standardised flow-proportional sampling, physically informed modelling, and treatment-train strategies targeting both surface sources and in-network storage.

Microplastics↗

PubMed2026

Exploring precision risk in pediatric vesicoureteral reflux: Innate immune gene variations and reflux outcomes in the RIVUR cohort.

INTRODUCTION: Children with vesicoureteral reflux (VUR) are at increased risk for morbidity from recurrent urinary tract infections (UTIs), yet the factors influencing spontaneous VUR resolution remain poorly defined. This study evaluates whether genetic variations in key urinary innate immune effectors (DEFA1A3, DMBT1, and RNASE7) influences VUR resolution and interacts with prophylaxis to alter clinical response. METHODS: We conducted a secondary analysis of 303 RIVUR participants with available DEFA1A3 and DMBT1 copy number variation (CNV) data and RNASE7 rs1263872 genotype. Primary outcomes were (1) VUR improvement (decrease in grade) and (2) VUR resolution at study exit. Multivariable logistic regression models included genotype, treatment, and their interactions, adjusting for age, sex, baseline grade (high vs low), laterality, bowel/bladder dysfunction, and any UTI. Internal validation used 2000-sample bootstrap with bias-corrected and accelerated confidence intervals and influence diagnostics. RESULTS: Clinical covariates did not significantly predict VUR improvement. Children with DEFA1A3 CNV >5 had higher odds of improvement (OR 2.36, 95% CI 1.12-4.96, p = 0.023), an effect that remained significant in bootstrap analyses. High-grade VUR was associated with lower odds of resolution (OR 0.34, 95% CI 0.12-0.94, p = 0.038). A significant interaction was observed between prophylaxis and high DMBT1 copy number for VUR resolution (interaction OR 2.99, 95% CI 1.11-8.04, p = 0.031); no interaction was seen for improvement. RNASE7 rs1263872 was not associated with either outcome. CONCLUSION: Innate immune gene variation may contribute to heterogeneity in VUR outcomes. High DEFA1A3 copy number was associated with reflux improvement and a DMBT1-prophylaxis interaction was associated with reflux resolution. The results of this study is hypothesis-generating and prompt further evaluation to assess whether a subset of children may experience structural benefit from prophylaxis or have a more favorable natural history based on their innate immune genotype.

Humans↗

PubMed2026

Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.

PURPOSE: To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five databases. Meta-analyses used random-effects models with logit-transformed Area Under the Curve (AUCs) and cluster-robust standard errors. AI models were classified as Machine Learning Models (MLM) or Hybrid Nomograms (HN) based on their construction methodology. RESULTS: Of 3,742 articles identified, 53 were included. MLMs demonstrated higher point estimates than radiologists in differential diagnosis (AUC: 0.87 vs. 0.83), though this difference was not statistically significant and carried substantial uncertainty. HNs achieved stronger performance in risk stratification (AUC: 0.87). AI-derived nomograms (AUC: 0.9) and gene signatures (AUC: 0.8) outperformed conventional prognostic markers descriptively. Chemotherapy response prediction remained below clinical utility thresholds across all model types. Only 33.9% of models reported calibration and 24.5% underwent external validation. CONCLUSIONS: AI demonstrates proof-of-concept across multiple NB clinical domains. However, clinical adoption remains premature given persistent gaps in external validation, calibration, dataset size, and pediatric-specific model development. Future studies should test these models prospectively in multicenter pediatric cohorts, ideally through COG or SIOPEN, using shared definitions for diagnosis, risk group, treatment response, and survival outcomes.

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↗