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PubMed2026

Predictive Models for Hypoglycemia Risk in Haemodialysis Patients With Diabetic Kidney Disease: Systematic Review and Meta-Analysis.

AIM: To provide evidence for selecting and developing reliable clinical assessment tools for hypoglycemia in diabetic kidney disease patients during haemodialysis. DESIGN: Review. METHODS: Systematic searches were performed in 9 Chinese and English databases to collect literature regarding the development of hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease. Two reviewers independently performed literature screening, data extraction, risk-of-bias assessment, and applicability evaluation. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability of the included studies. Meta-analysis was conducted using R software. DATA SOURCES: CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, EMbase, Web of Science, and CINAHL. The search period covered from the establishment date of each database to December 2025. RESULTS: Six studies, comprising six prediction models, were included. Two studies performed internal validation, and three conducted external validation. All models reported the area under the curve, ranging from 0.813 to 0.866, and calibration measures. Four studies were rated as having a high risk of bias, while all six demonstrated good overall applicability. The meta-analysis showed that the pooled AUC value of the six studies was 0.846 (95% CI: 0.823-0.867). CONCLUSION: Research on hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease remains in the developmental stage. Although the included prediction models exhibited satisfactory apparent discriminatory ability and clinical applicability, most of the original studies suffered from a high risk of bias and lacked adequate validation. The true predictive performance and clinical application value of these models remain to be further verified. Accordingly, routine and unconditional clinical application is not recommended at this stage. Future studies should include more high-quality, multicenter external validation and develop models with high generalizability, favourable clinical applicability, and robust predictive performance to facilitate early identification of hypoglycemia risk in this population. IMPACT: This study systematically evaluated the hypoglycemia risk prediction models for diabetic kidney disease patients during haemodialysis, and the research on hypoglycemia risk prediction models for maintenance haemodialysis patients during dialysis is still in the development stage. This study provides a reference for clinical medical staff to select or develop hypoglycemia risk prediction and assessment tools for diabetic kidney disease patients during haemodialysis. REPORTING METHOD: This study was conducted in accordance with the relevant guidelines of the EQUATOR Network and followed the TRIPOD-SRMA Checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: PROSPERO: CRD420251243352.

Humans↗

PubMed2026

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

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

Artificial intelligence↗

PubMed2026

Impact of PerioperAtive LidocAine Infusions on Enhanced Recovery After Noncardiac Surgery (IMPALA-ERAS) in an inpatient setting: rationale, design and protocol for a sequential, repeated crossover trial.

INTRODUCTION: Multimodal analgesic strategies designed to minimise perioperative opioid exposure are fundamental components of enhanced recovery after surgery (ERAS) pathways. Despite widespread implementation of ERAS protocols, the optimal analgesic regimen remains undefined, as the individual contributions of specific agents to overall analgesic efficacy and opioid-sparing effects are not fully elucidated. Intravenous lidocaine, a widely utilised local anaesthetic, possesses both analgesic and anti-inflammatory properties and has been associated with improved gastrointestinal recovery. This study seeks to pragmatically evaluate the impact of incorporating perioperative intravenous lidocaine infusion into established ERAS pathways on postoperative functional recovery. METHODS AND ANALYSIS: The Impact of PerioperAtive LidocAine Infusions (IMPALA) on ERAS trial is a single-centre, pragmatic, cluster-randomised, double-blinded, placebo-controlled study. A total of 2290 patients undergoing elective colorectal surgery, emergency general surgery, urology, ventral hernia repair, surgical oncology or spine surgery will be randomly assigned to receive either intraoperative and postoperative intravenous lidocaine infusions (administered for up to 48 hours) or placebo as part of a standardised multimodal analgesic regimen integrated into established ERAS pathways. The primary outcome is case mix index-adjusted resource length of stay, defined as the time interval from surgical initiation to hospital discharge adjusted for case mix index. The primary outcome is total inpatient opioid consumption within the first 72 hours, reported in oral morphine milligram equivalents. Secondary outcomes include various in-hospital clinical endpoints derived from the electronic health record. ETHICS AND DISSEMINATION: This protocol and accompanying statistical analysis plan outline the study design, primary and secondary endpoints and analytic methodology. The IMPALA-ERAS trial has received ethical approval from the Vanderbilt University Institutional Review Board (IRB: 250617). The findings will be disseminated via peer-reviewed publications and presentations at national conferences. Results from this trial are expected to inform evidence-based practices regarding perioperative lidocaine infusion and its potential contributions to enhanced postoperative recovery in surgical patients. TRIAL REGISTRATION NUMBER: NCT07224711.

Humans↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Redox Rewiring in Nicotine-Driven Gastric Carcinogenesis: Uncovering ROS-Dependent Oncogenic Circuits.

SIGNIFICANCE: Nicotine from tobacco products, secondhand smoke, and emerging delivery systems remains a major but underappreciated driver of gastric carcinogenesis (GC). Although reactive oxygen species (ROS) have long been implicated in tumor biology, current models incompletely explain how chronic nicotine selectively reprograms gastric epithelial signaling. This review advances the concept of redox rewiring, whereby nicotine establishes a persistent oxidative state that orchestrates multiple oncogenic programs via spatially compartmentalized NOX signaling. RECENT ADVANCES: We synthesize evidence for a unified model wherein nicotine activates nAChR/β-AR signaling, Ca2+ influx, PKC, and compartmentalized NOX-derived ROS to generate distinct oncogenic outputs. Beyond the established NOX/ROS/NF-κB/MAPK-driven IL-8 and MMP-9 axes, we integrate emerging evidence into three interconnected modules governing EMT/metastasis (ABL1/STAT3/COX-2/periostin), survival/chemoresistance (ERK/GLI1/Bcl-2), and invasion/immune evasion (miR-21/PDCD4). Collectively, these circuits suggest that ROS function not merely as damaging byproducts but as spatially organized signaling mediators dictating tumor behavior. CRITICAL ISSUES: A major challenge is distinguishing established mechanisms from incompletely validated models. The three proposed axes are testable hypotheses requiring experimental validation. Most data derive from in vitro studies with nonphysiologic nicotine concentrations, and artifacts from nonspecific ROS probes are common. Compensatory pathway activation and multi-target effects of natural products remain underexplored. FUTURE DIRECTIONS: We outline a precision-redox oncology roadmap linking pathway-specific biomarkers, mechanistically matched natural products, and biomarker-enriched trials. Priorities include genetic validation of the three axes, time-resolved ROS imaging, and pulsed natural product regimens. By reframing nicotine-driven GC as adaptive redox network remodeling, this review provides a framework for prevention, stratification, and next-generation therapy. Antioxid. Redox Signal. 00, 000-000.

gastric cancer↗

PubMed2026

Perceptions of Pharmacogenomic Testing Among People With Treatment Resistant Depression: Legitimization as a Facilitator of Acceptance.

Pharmacogenomic testing for psychiatric medications has been proposed as both an early intervention to optimize treatment response, and for use among patients who have tried multiple medications without symptom remission. Therefore, this testing may be particularly salient to the subset of individuals with major depressive disorder for whom depression has been labeled as "treatment resistant". Understanding the impact of this diagnostic label on illness identity and attitudes towards new therapies is important as genomic technology expands and rates of depression increase. We sought to explore perceptions and attitudes towards pharmacogenomic testing among individuals who had received a diagnosis of treatment resistant depression. We conducted a qualitative study with a constructivist orientation. Participants were recruited from a larger genomic research study and interviewed by phone or video call. We took an inductive approach to coding guided by reflexive thematic analysis. Themes were then organized into a relational framework following principles of interpretive description. Twelve individuals were interviewed. Key themes included internalized acceptance/hopelessness, and external validation/frustration, which were cyclically interconnected. These themes were situated within a larger framework illustrating the ways that illness identity and modifying factors such as relief of guilt, social support, pharmacogenomic testing and depressive symptoms can either facilitate acceptance and validation or contribute to feelings of hopelessness and frustration. Though participants expressed some skepticism around its effectiveness, pharmacogenomic testing may contribute to the shift towards acceptance and validation by legitimizing individuals' experiences with lack of treatment response. Genetic counselors and other healthcare providers should be aware of the complex balance between hope and frustration underlying conversations around pharmacogenomic testing, and factors that are more likely to foster self-acceptance.

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

Integrative genomic and transcriptomic analyses identify key regulators of skin pigmentation in Larimichthys crocea.

The yellow body coloration of large yellow croaker (Larimichthys crocea) constitutes a crucial economic trait, yet its underlying genetic regulatory mechanisms remain poorly understood. This study systematically elucidated the molecular basis of body color variation by integrating genome resequencing and skin transcriptome analyses, combined with the contextual analysis of key pigmentation-related genes and phenotypic histological validation. 200 phenotyped individuals (including yellow-selected lines, F1 progeny, and normal control groups, all derived from a well-characterized aquaculture stock) identified 39 significantly associated SNPs (-log₁₀(P) ≥ 6), mapping to multiple candidate genes. These genes were significantly enriched in pathways related to pigment deposition (GO:0033059), melanosome organization (GO:0032438), melanogenesis, and tyrosine metabolism. Cross-developmental stage transcriptome analysis revealed 2395 differentially expressed genes (DEGs). Multi-omics integration identified eight overlapping candidate genes, including tyrp1, slc45a2, oca2, and dgat2, among which tyrp1 was prioritized for in-depth validation based on its core regulatory role in eumelanin synthesis, significant SNP association signal, and consistent downregulation in transcriptomic data. Experimental validation demonstrated that the g.895C > T mutation in exon 2 of tyrp1b was strongly significantly associated with the yellow phenotype: the frequency of mutant genotypes (TT/CT) reached 92.86%in the yellow-selected group, whereas the control group exclusively exhibited the wild-type genotype (CC). qPCR confirmed significantly downregulated tyrp1b expression in the skin of yellow individuals, consistent with the transcriptome trend. Histological and stereomicroscopic observations of skin tissues further validated the physiological basis of the yellow phenotype, revealing a significant reduction in melanophore number and abnormal melanosome morphology in yellow-phenotype individuals, accompanied by increased xanthophore density. These results suggest that tyrp1b mutation is strongly associated with the yellow phenotype. However, the presence of a wild-type CC individual in the yellow group indicates that this mutation is not strictly required for yellow coloration, suggesting that other genetic or environmental factors may also contribute to the phenotype, Additionally, downregulation of the carotenoid metabolism gene bco2 coupled with upregulation of xdh, together with the functional changes of slc45a2 and oca2, may synergistically promote xanthophore pigment deposition, contributing to the yellow phenotype. As melanin synthesis in large yellow croaker relies on the conserved tyrosinase pathway and transporter proteins, mutations in associated genes (tyrp1b, slc45a2, oca2) represent a primary underlying cause for the loss of melanin-based coloration and transition to a yellow phenotype in L. crocea. These findings provide key molecular targets and a theoretical foundation for molecular breeding of body color in this species, and also enrich the understanding of xanthism regulatory mechanisms in teleosts.

Animals↗

PubMed2026

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans↗

PubMed2026

Type, severity, frequency and management of adverse reactions associated with ultrasound contrast agents: a systematic review and meta-analysis.

OBJECTIVES: This systematic review and meta-analysis are aimed at evaluating the incidence of adverse drug reactions (ADRs) following administration of clinically approved ultrasound contrast agents (UCAs) in adults and children, to assess risks in patients with cardiovascular disease and in pregnancy, and to evaluate the effectiveness of emergency management of severe ADRs. MATERIALS AND METHODS: A PRISMA 2020 systematic review was conducted searching PubMed, Scopus, and Embase. Two reviewers independently screened, extracted data, and assessed quality. Incidence estimates were pooled when feasible, stratified by age group, contrast agent, and administration route. RESULTS: Seventy-four studies encompassing > 1 million adults and > 36,000 children were included, contributing multiple analytic cohorts to the quantitative synthesis. Severe acute ADRs were extremely rare (6 and 16 cases per 100,000 in adults and children, respectively) and absent following endocavitary administration in children. Non-severe acute ADRs occurred in 11 and 8 cases per 10,000 adults and children, respectively. Delayed reactions were very rare (< 1 case per million in adults). No significant safety differences emerged between UCA products. The incidence of ADRs in patients with cardiovascular disease was analogous to the general population. No ADRs were reported in pregnant women. Standard emergency management was effective in almost all serious cases, though rare fatalities occurred. CONCLUSION: UCAs show an excellent safety profile in adults and children, with very rare severe ADRs and few non-severe, typically self-limiting reactions. Strict adherence to recommended emergency management protocols mitigates the remaining risks, supporting safe use across a broad range of clinical indications. PROSPERO REGISTRATION: CRD42023432668. KEY POINTS: Question What is the incidence, type, and severity of acute and delayed ADRs associated with clinically approved UCAs across different patient populations? Findings Severe acute adverse reactions are very rare, and non-severe reactions are rare and self-limiting, with no significant safety differences between adults, children, or patients with cardiovascular disease. Clinical relevant UCAs show an excellent safety profile across populations. These findings support their safe clinical use as reliable alternatives to iodine-based and gadolinium-based contrast agents in routine diagnostic imaging.

Contrast Media↗

PubMed2026

The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.

Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST + AI for prediction model studies. Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST + AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection. AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.

Humans↗

PubMed2026

The musculoskeletal pain literacy questionnaire (MSK-PLq) - Part 1: Development of a preliminary version through a systematic review and Delphi consensus.

OBJECTIVE: Chronic musculoskeletal (MSK) pain is a leading cause of disability worldwide, and self-management is a first-line approach recommended by international clinical guidelines. Access to evidence-based information that enhances health literacy may support patients' engagement in their self-management and treatment decision-making, potentially reducing disease burden and pain. However, no tool currently exists to assess health literacy specifically in MSK pain. This study aimed to develop and describe the preliminary version of a knowledge-based questionnaire to evaluate MSK pain literacy, the Musculoskeletal Pain-Literacy questionnaire (MSK-PLq). METHODS: A systematic literature review identified existing health literacy instruments and generated a preliminary list of domains. A two-round Delphi study with 22 panellists (19 experts and three people living with chronic MSK pain), followed by consensus meetings, was used to refine domains and items (≥70% agreement). Readability was assessed using the Flesch Reading Ease (FRE) score and three stakeholders were consulted to review the questionnaire for comprehensibility, clarity, and face validity. RESULTS: Six domains were retained (Understand, Access, Appraise, Apply, Digital, Beliefs), comprising 20 items in the preliminary version of MSK-PLq. Readability was acceptable (mean FRE 74, indicating fairly easy reading), and subject feedback supported the questionnaire's clarity and face validity. CONCLUSIONS: The preliminary version of the MSK-PLq is proposed as the first knowledge-based tool to assess functional, interactive, and critical aspects of MSK pain literacy. It may have applications in clinical practice, research, education, and digital health, by informing tailored patient education and supporting self-management strategies, although further psychometric validation is required.

Humans↗

PubMed2026

Micro- and nanoplastics-induced neurotoxicity: a CNS-centered, evidence-graded adverse outcome pathway framework based on systematic weight-of-evidence assessment.

Micro- and nanoplastics (MPs/NPs) are ubiquitous anthropogenic particulate pollutants posing emerging threats to human neurological health. Severe heterogeneity in particle physicochemical properties, environmental aging status, exposure paradigms and experimental platforms has created persistent mechanistic uncertainties in MP/NP neurotoxicology, hindering reliable hazard characterization and risk translation. Here, we systematically consolidate empirical toxicological evidence and construct a dedicated central nervous system (CNS)-targeted adverse outcome pathway (AOP) network integrated with rigorous weight-of-evidence (WoE) grading to elucidate the hierarchical, particle-specific toxic cascades underlying MP/NP-induced neural injury. Our synthesis overturns the conventional linear toxicity paradigm, demonstrating that MPs/NPs trigger neurotoxicity via a complex multi-input mechanistic network. We definitively establish oxidative stress as a robust early convergent key event-rather than a universal molecular initiating event-orchestrating ROS overproduction, lipid peroxidation, mitochondrial dysfunction, and neuroinflammation to propagate neuronal damage. This core module is driven by five distinct particulate upstream triggers: particle-biomolecule interfacial perturbation, corona-facilitated cellular internalization, plastic-associated chemical leaching, aging-derived free radical reactivity, and gut-borne systemic neurotoxic signaling. Downstream pathogenic outcomes encompass glial overactivation, neurotransmitter dyshomeostasis, autophagy-lysosome dysfunction, metabolic reprogramming, regulated neuronal cell death, and behavioral impairments. Tiered WoE analysis confirms strong validation for early oxidative/inflammatory cascades, moderate support for gut-brain axis crosstalk and intracellular trafficking disruption, and nascent evidence for synaptic dysfunction and neurodegeneration-linked proteostatic defects. Extrapolation to human health risk remains constrained by the frequent use of high-dose exposure paradigms, limited validated data on internal dosimetry in the human brain, discrepancies between effective concentrations in experimental models and environmentally relevant human tissue burdens, and insufficient causal validation of distal adverse outcomes. We highlight key research priorities including aged mixed-particle exposure systems, leachate-controlled assays, quantitative internal dose evaluation, and mechanistic intervention verification. This evidence-stratified AOP framework resolves longstanding mechanistic ambiguities in particulate neurotoxicity, providing a standardized, causality-based foundation for future mechanistic exploration and health risk assessment of global plastic pollution.

Adverse outcome pathway↗

PubMed2026

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89-0.94) in cross-validation and 0.88 (95% CI: 0.85-0.91) in independent validation, outperforming clinical models (ΔAUC = +0.18, p < 0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression-free survival (HR = 4.7, p < 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC.

Humans↗

PubMed2026

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

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

Dental anxiety↗

PubMed2026

PGR expression as a pharmacogenomic companion biomarker to GENE70-derived genomic risk in ER-positive/HER2-negative breast cancer.

BACKGROUND: The biology of the estrogen receptor-positive (ER+) and human epidermal growth factor receptor 2-negative (HER2-) breast cancers is heterogeneous even when they are categorized by their risk via genomics. Transcriptomic PGR expression reflects endocrine pathway activity and may provide complementary biological information within established GENE70-derived genomic-risk categories. Whether this molecular marker improves the biological interpretation of genomic-risk stratification beyond conventional clinicopathological assessment remains uncertain. OBJECTIVES: The aim of this study was to determine whether transcriptomic PGR expression provides complementary biological and prognostic information within reconstructed GENE70-derived genomic-risk categories and refines the characterization of endocrine-related tumour biology in ER-positive/HER2-negative breast cancer. METHODS: This study analysed publicly available transcriptomic and clinical data from three cohorts: METABRIC (discovery cohort), GSE96058/SCAN-B cohort (validation cohort) and TCGA-BRCA cohort (molecular validation cohort). The GENE70-derived genomic-risk score was reconstructed for each cohort using matched genes. Cox regression, Kaplan-Meier analysis and subgroup comparisons were used to assess relationships between PGR expression, clinicopathologic variables, molecular features and survival outcomes. RESULTS: Across the three independent cohorts, low transcriptomic PGR expression was consistently associated with higher GENE70-derived genomic risk, increased MKI67 expression, reduced ESR1 expression and enrichment of the Luminal B subtype. Survival findings differed between cohorts. In the discovery METABRIC cohort, transcriptomic PGR expression showed heterogeneous associations with survival, particularly within GENE70-derived high-risk subgroups, whereas the external GSE96058/SCAN-B validation cohort demonstrated consistent associations between low PGR expression and poorer overall survival in both the overall ER-positive/HER2-negative population and GENE70-derived high-risk subgroups. CONCLUSION: These findings suggest that transcriptomic PGR provides complementary biological and prognostic information within GENE70-derived genomic-risk categories. However, because treatment response was not evaluated in the present study, the findings should not be interpreted as evidence of predictive or pharmacogenomic utility and prospective studies incorporating treatment-response analyses are required before such applications can be established.

Humans↗

PubMed2026

Effects of transcutaneous electrical acupoint stimulation versus acupressure on the trajectories of multidimensional adverse reactions to chemotherapy in breast cancer patients: a secondary analysis of a randomized controlled trial.

BACKGROUND: Chemotherapy for breast cancer often induces multidimensional adverse reactions such as nausea and vomiting, anxiety, depression, and sleep disturbances. These symptoms are interrelated and may evolve dynamically, impacting patients' treatment outcomes and quality of life. As non-pharmacological interventions, transcutaneous electrical acupoint stimulation (TEAS) and self-acupressure (SA) have shown potential in alleviating symptoms. However, their long-term effects on the joint developmental trajectories of these multidimensional symptoms (nausea and vomiting, anxiety, depression, and sleep disturbances) remain unclear. OBJECTIVE: This study aimed to identify potential trajectory class of multidimensional adverse reactions in breast cancer patients undergoing chemotherapy and to explore the differential effects of TEAS and SA on different trajectory subgroups. METHODS: This was a secondary analysis of a randomized controlled trial. A total of 189 breast cancer patients receiving chemotherapy were included. The Group-Based Multi-Trajectory Model (GBMTM) was employed to identify joint developmental trajectories of acute/delayed chemotherapy-induced nausea and vomiting (CINV), anxiety, depression, and sleep quality during chemotherapy. Subsequently, causal forest was used to analyze the average treatment effects (ATE) of TEAS (vs. control group) and SA (vs. control group) on patients' symptom trajectory. RESULTS: Multidimensional adverse reactions were classified into two heterogeneous trajectories: a "High Symptom Burden-Persistent (HSBP)" type (n = 101) and a "Low Symptom Burden-Relieving (LSBR)" type (n = 88). The persistent high incidence of acute CINV contrasted sharply with the comprehensive relief of other symptoms in the latter group. Causal forest suggested that both TEAS and SA significantly increased the probability of patients being classified into the "LSBR" trajectory. The ATE was 0.147 (95% CI: 0.143, 0.151) for TEAS, slightly lower (P < 0.05) than 0.176 (95% CI: 0.162, 0.190) for SA.  CONCLUSION: Multidimensional adverse reactions in breast cancer patients undergoing chemotherapy exhibit heterogeneity in their trajectories. Both TEAS and SA were associated with a higher probability of patients being classified into a more favorable symptom trajectory-LSBR. The multidimensional trajectory identification with treatment effect estimation may serve as a useful analytical strategy for future longitudinal research in cancer chemotherapy-induced adverse reactions symptom management. CLINICAL TRIAL REGISTRATION: ChiCTR2300077667 (Chinese Clinical Trial Registry, https://www.chictr.org.cn/ ), Registered 15 November 2023.

Humans↗

PubMed2026

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

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

Humans↗