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PubMed2026

A system-level metastable model of cancer evolution: integrating replication stress, cell cycle deregulation and chromosomal instability.

INTRODUCTION: Cancer cell proliferation occurs within the context of persistent genomic instability. In this review, we propose the RS-CCD-CIN axis as a systems-level framework in which replication stress (RS), cell cycle deregulation (CCD) and chromosomal instability (CIN) form an interdependent triad that shapes tumour evolution. This axis represents a constrained metastable state in which genomic instability is tolerated and buffered. The objective of this review is to synthesize the current understanding of how the RS-CCD-CIN axis contributes to tumour heterogeneity, adaptability and therapy response. DISCUSSION: Evidence indicates that RS, CCD and CIN operate as a dynamic, interconnected network rather than as independent processes. Replication stress induces DNA damage and mutagenesis, while partial checkpoint disruption permits cells with unresolved lesions to proliferate. Chromosomal instability generates both structural and numerical alterations, contributing to intratumoural heterogeneity. Together, these processes facilitate adaptation to environmental and therapeutic pressures. Extrachromosomal DNA, micronuclei formation and cytosolic DNA signalling, including the cGAS-STING pathway, connect genomic instability to adaptive responses and immune modulation. Single-cell and spatial profiling reveal temporal and spatial variability in RS, CCD and CIN states, highlighting the limitations of static biomarkers. Therapeutically, targeting individual components often yields limited durability, whereas approaches that simultaneously perturb multiple aspects of the RS-CCD-CIN axis may improve clinical outcomes. CONCLUSIONS: This review highlights the RS-CCD-CIN axis as a fragile and metastable architecture that supports cancer evolution, while also being susceptible to collapse. A deeper understanding of this interconnected framework may inform the development of therapeutic strategies and enhance the management of resistance.

Humans↗

PubMed2026

The effect of metformin on the pharmacokinetics of rifampicin, isoniazid, and pyrazinamide in adults with tuberculosis.

Metformin is under investigation as adjunctive host-directed therapy for tuberculosis (TB), which might interact with first-line TB treatment. We used population pharmacokinetic modeling to assess whether metformin alters first-line TB-drug pharmacokinetics in HIV/TB-coinfected adults without diabetes. Rifampicin, isoniazid, and pyrazinamide pharmacokinetics were investigated in participants from a randomized clinical trial of adjunctive metformin (500 mg twice daily to week 12) in adults starting HIV-associated TB treatment. Antiretroviral therapy (ART)-naïve participants initiated dolutegravir-based ART within 8 weeks. Intensive and semi-intensive sampling was conducted at week 5; concentration-time data were analyzed using non-linear mixed-effects modeling. Data from 78 individuals (43 receiving metformin, median weight 60.8 kg, 62.8% male, 79.5% on ART) were analyzed. Rifampicin and pyrazinamide were described by one-compartment models with linear elimination; typical clearances were 15.8 L/h (95% CI: 13.5-18.7) and 3.88 L/h (95% CI: 3.54-4.08), respectively. Isoniazid followed a two-compartment model with a mixture model for acetylator status; clearance was 10.5 L/h (95% CI: 9.44-11.9) in slow acetylators and 28.8 L/h (95% CI: 25.6-31.0) in fast/intermediate acetylators. Metformin reduced isoniazid bioavailability by 15.6% (95% CI: 4.45-26.4%, P < 0.009) and rifampicin bioavailability by 24.0% (95% CI: 7.83-36.3%, P < 0.007), decreasing the area under the curve from 0 to 24 h from 18.2 to 15.6 mg·h/L and from 35.9 to 27.1 mg·h/L, respectively. No significant effect on pyrazinamide was detected. We found that non-diabetic patients on metformin had lower isoniazid and rifampicin bioavailability. Lowered rifampicin exposure might be clinically relevant; simulations suggest that this could be offset by a single 150 mg rifampicin dose.CLINICAL TRIALSThis study is registered with ClinicalTrials.gov as NCT04930744.

Humans↗

PubMed2026

Artificial intelligence in treatment prediction for skeletal Class III malocclusion: A systematic review.

In skeletal Class III patients, treatment options range from orthodontics to orthognathic surgery. Choosing the optimal approach requires a comprehensive clinical evaluation, which may be supported by AI tools. The aim of this study was to assess the performance of AI models in predicting the need for orthognathic surgery and in identifying predictors influencing treatment decisions. A PRISMA-guided electronic database search (PubMed, Web of Science; 2009-2024; English/French) was performed to identify studies using machine learning (ML) or deep learning (DL) on cephalometric and clinical data. After screening and assessment for eligibility, 15 studies were critically appraised. Model performance was summarized using accuracy, sensitivity, specificity, and the area under the curve (AUC). ML algorithms (particularly Random Forest and XGBoost) and DL models (ResNet-based convolutional neural networks (CNNs)) achieved high accuracy for predicting surgical need. Frequently selected predictors included Wits appraisal, ANB angle, the maxillomandibular ratio (Mx/Md), overjet, and the divergence of the lower gonial angle. AI methods show promise for assisting treatment decisions in Class III malocclusion, with Random Forest and XGBoost performing well on tabular cephalometric data and CNNs on imaging. Larger, multicentre datasets and external validation are needed to improve reliability, address bias, and support clinical implementation.

Humans↗

PubMed2026

Comparing trajectories of cognitive functioning in treatment-resistant and non-resistant depression: a multicentre linear mixed-effects analysis.

BACKGROUND: Impaired cognitive functioning is a severe symptom in major depressive disorder (MDD). Recent evidence suggests it may be a central characteristic in its treatment resistant form (TRD), potentially constituting a clinical marker for treatment resistance and a target amenable to intervention. To date, cognitive functioning in TRD remains poorly understood and longitudinal investigations are scarce. METHODS: This observational prospective cohort study, including 320 patients diagnosed with MDD from the multicentre PROMPT study, examined differences in cognitive functioning between 118 TRD and 202 non-TRD patients over a period of twelve weeks in a real-world setting, using linear mixed modelling. Patients that failed to respond to at least two prior antidepressants trials at baseline were classified as TRD. RESULTS: TRD patients showed significantly poorer baseline performances than non-TRD patients in attention/processing speed (β = -0.45; 95%CI[-0.70, -0.19]; FDR-p = 0.003) and verbal memory (β = -0.45; 95%CI[-0.72, -0.18]; FDR-p = 0.003). Significant time × group interactions were observed in motor speed and verbal fluency tasks. Post-hoc-analyses revealed stagnation in TRD patients and significant improvement in non-TRD patients. Across all other tasks improvement was observed in both groups, and random effects showed large heterogeneity between patients, indicating notable individual differences in cognitive performances. CONCLUSIONS: The results suggest distinct recovery patters between non-TRD and TRD patients, and diminished functioning in TRD patients at the domain level. However, intact and diminished performances likely occur in both groups, warranting further investigation of cognitive heterogeneity. These short-term findings highlight the need for more comprehensive longitudinal research on cognition in TRD.

Humans↗

PubMed2026

Reliability-aware hierarchical learning for Chagas disease screening from 12-lead ECGs: tackling label uncertainty and class imbalance.

Objective.Chagas disease, a neglected tropical disease (NTD) with significant cardiovascular impact, remains underdiagnosed in resource-limited regions. Electrocardiogram (ECG) screening offers a low-cost tool for detecting cardiac involvement, yet algorithm development is challenged by label noise, data scarcity, and the latent nature of infection. This study proposes a robust ECG-based screening framework that explicitly addresses these constraints.Approach.We introduce aReliability-Aware Hierarchical Learningstrategy that calibrates supervision according to data provenance, prioritizing serology-confirmed labels over noisy self-reports. To mitigate data scarcity, we compare a specialized convolutional neural network (CNN) trained from scratch with a transfer learning approach based on a Spatio-Temporal ECG foundation Model (FM). Performance is evaluated across varying data scales, and the representation structure is analyzed to interpret model behavior.Main results.On the official hidden test set of the George B. Moody PhysioNet/Computing in Cardiology Challenge 2025, our approach achieved a Challenge Score of 0.163. We observe that while the specialized CNN performs competitively in data-rich regimes, the FM exhibits superior robustness in extreme low-resource settings. Furthermore, performance reaches a plateau imposed by underlying disease physiology. Bimodal score distributions suggest that models distinguish established cardiomyopathy from indeterminate infection, which remains electrophysiologically indistinguishable from healthy controls.Significance.These findings clarify both the potential and intrinsic limits of ECG-based AI screening for NTD-associated cardiac involvement. Reliability-aware supervision and data-efficient transfer learning provide a practical framework toward scalable and clinically meaningful ECG screening systems in resource-constrained environments.

Humans↗

PubMed2026

Single-cell transcriptome revealed the aberrant keratinocytes activation in antigen presentation in atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD), a common chronic inflammatory skin disease, has been extensively studied using single-cell genomics. However, keratinocytes, as key effector cells in AD, have underlying mechanisms remain incompletely understood and require further investigation. METHODS: We integrated single-cell transcriptomic data from skin tissues of healthy controls, chronic active AD patients, spontaneously healed AD (SHAD) patients, and an ovalbumin-induced AD mouse model. The study particularly emphasized the gene expression and cellular dynamics of keratinocytes across the different groups, as well as their interactions with immune cells. RESULTS: Compared to healthy controls, we observed significant changes in the keratinocyte transcriptome, cellular state, and keratinocyte-immune cell ligand-receptor interactions in AD skin, particularly the marked activation of genes involved in antigen processing and presentation. Interestingly, such gene activation was not observed in keratinocytes from the ovalbumin-induced AD mouse model, despite its phenotype closely resembling human AD. Furthermore, in SHAD, we identified a recovery of both the ligand-receptor interaction patterns and antigen processing and presentation genes, accompanied by a notable shift in the transcriptome. This involved a significant downregulation of genes related to cytoplasmic transcription and oxidative phosphorylation. Notably, this pattern was not observed in the self-healing mouse model following the removal of ovalbumin stimulation. CONCLUSION: Our results suggest that the persistent activation of antigen processing and presentation pathways in keratinocytes may be a key driver of chronic inflammation in AD. Therefore, redirecting anti-allergic therapeutic strategies from solely targeting immune cells to targeting of keratinocyte-mediated antigen presentation may offer a more effective approach. Furthermore, we raise concerns about the use of ovalbumin-induced mouse models to recapitulate human chronic AD, as the underlying mechanisms may differ significantly.

Dermatitis, Atopic↗

PubMed2026

Exploring China's Clean Air Act and associated cardiovascular disease risk: a prospective, quasi-experimental, and causal inference modelling study.

BACKGROUND: Substantial improvements in air quality have been recorded following the implementation of China's Clean Air Act (CCAA) in 2013. However, the association between CCAA implementation and individual-level cardiovascular disease (CVD) risk remains unclear. We aimed to examine the long-term association between CCAA implementation and individual-level predicted CVD risk. METHODS: In this prospective, quasi-experimental study, we used data from the China Kadoorie Biobank, a prospective cohort study that recruited participants from five urban and five rural areas across China between 2004 and 2008, with three resurveys conducted after the baseline survey (in 2008, 2013-14, and 2020-21). We included 34 862 individuals (mean age 51·3 years) who participated in at least one resurvey and had no history of CVD at baseline. Participants were classified into intervention (n=25 497) and control (n=9365) groups based on the local government's targets for particulate matter reduction. We estimated the 10-year risk of incident CVD morbidity or mortality using a validated risk prediction model. We used a difference-in-difference model to assess the long-term association between CCAA implementation and predicted risk, with adjustments made for regional confounders and individual-level characteristics, including demographics, lifestyle factors, medical history, and indoor air pollution exposure. The relationship between changes in long-term exposure to PM2·5, PM10, and O3 and predicted risk after CCAA implementation was analysed using a linear model. The estimated risk differences associated with air pollutant changes were estimated based on the magnitude of changes and their corresponding effect sizes. FINDINGS: After the CCAA was implemented, PM2·5 and PM10 concentrations declined in both groups, but O3 concentrations increased. The intervention group showed a 3·95% (95% CI 3·18-4·72%) lower increase in predicted risk than the control group, with larger estimated differences under stricter enforcement. Between 2013 and 2021, each 10 μg/m3 change in PM2·5 concentration was positively associated with a 1·80 (1·34-2·27) percentage point change in predicted CVD risk, whereas each 10 μg/m3 change in PM10 concentration was associated with a 1·24 (0·84-1·63) percentage point change and each 10 μg/m3 change in O3 concentration with a 0·58 (0·33-0·83) percentage point change. Overall, the observed changes in air pollutants during the study period were associated with an average 6·6 percentage point reduction in predicted CVD risk. INTERPRETATION: The CCAA and improved air quality were associated with a slower increase in predicted CVD risk, supporting the necessity for stricter, multipollutant air quality policies to maximise public health benefits. FUNDING: National Natural Science Foundation of China, Kadoorie Charitable Foundation, Noncommunicable Chronic Diseases-National Science and Technology Major Project, National Key R&D Program of China, Chinese Ministry of Science and Technology, and UK Wellcome Trust.

Journal Article↗

PubMed2026

Process evaluation of a nurse-led transitional care model (Cardiolotse) within a randomized controlled trial aiming to improve care coordination for patients with cardiovascular diseases in Germany.

BACKGROUND: Patients with higher age suffering from cardiovascular disease discharged from hospital are at greater risk of readmission within 30 days. We evaluated an innovative care program providing post-discharge support and helping patients to navigate through the healthcare system. This paper reports the findings of the process evaluation of the randomized controlled trial Cardiolotse, a nurse-led transitional care model improving care coordination for patients with cardiovascular diseases in Germany. METHODS: A process evaluation, following the guidelines of the Medical Research Council (MRC) Framework, was performed. Semi-structured interviews with all relevant target groups were conducted to gain more insight about implementation processes. Questionnaires and medical records were used to explore mechanisms of impact and understand how change was produced in the intervention. Qualitative data were analysed using content analysis with deductive and inductive categories. Descriptive statistics and subgroup analyses were utilized to explore quantitative data. RESULTS: Overall, the designed training programme was perceived positively by the study nurses, so called Cardiolotsen (CLs). Patients receiving support by the CLs reported positive satisfaction ratings. Interactions between CLs and patients were reported as trustworthy and reliable. A total of approximately 12,500 contacts were made over the course of the intervention. However, changes in satisfaction scores between intervention and control groups in terms of medical treatment or the interaction between medical health providers involved in the treatment could not be determined. Furthermore, data suggested reach issues with respect to office-based physicians, as regular CL contact could not be achieved with 90% of the participating general practitioners and cardiologists. CONCLUSIONS: The CLs served as an important source of support for the participating patients throughout the intervention. At regular intervals, they checked a patient's health status and their adherence to therapies after discharge. However, the process evaluation identified cross-sectoral communication and information exchange between CLs and office-based physicians as an implementation challenge. TRIAL REGISTRATION: The study was retrospectively registered at German Clinical Trial Register, http://www.drks.de/DRKS00020424 (Trial Registration Number DRKS00020424) on 18 June 2020.

Humans↗

PubMed2026

Colchicine attenuates cardiac hypertrophy by targeting the macrophage-driven Interleukin-6 suppression.

Hypertrophic cardiomyopathy (HCM), the most prevalent inherited cardiovascular disease, is strongly linked to progressive heart failure and sudden cardiac death (SCD). However, its underlying pathogenic mechanisms remain incompletely understood, and effective therapeutic strategies are still lacking. Here, we established two murine HCM models harboring high SCD risk-associated mutations. Single-cell RNA sequencing revealed immune activation and enhanced fibrotic remodeling in the myocardium of these models. Therefore, we hypothesized that colchicine, a widely used anti-inflammatory drug known to reduce cardiovascular events in multiple cardiac disorders, may also represent a promising therapeutic candidate for HCM. As we expected, colchicine treatment attenuated pathological remodeling in our study, as evidenced by reduced cardiomyocyte hypertrophy, decreased fibrosis, and downregulation of cardiac stress markers (Anp, Bnp) and fibrotic mediators (Ctgf, Col1a1, Col3a1). In addition, colchicine attenuated pro-inflammatory macrophage populations and suppressed IL-6 expression, thereby contributing to the preservation of cardiac function. These findings provide the first preclinical evidence that colchicine alleviates myocardial inflammation and fibrosis in HCM, underscoring its potential as a novel therapeutic strategy to reduce fibrosis, lower SCD risk, and improve patient outcomes.

Animals↗

PubMed2026

Assessing comorbidities and predicting risk: A primer for APRNs.

Today's clinical environments are rife with tools designed to comprehensively account for medical complexity and comorbidities while predicting risk for a host of adverse health-related outcomes. Therefore, it is imperative that advanced practice registered nurses (APRNs) understand the structure and function of these tools, their similarities and differences, their limitations, and strategies for appropriate incorporation into practice. This article offers a practical overview for APRNs, emphasizing clinical implications and guidance for aligning assessment tools with the clinical population of interest to improve care delivery, quality, and patient outcomes.

Humans↗

PubMed2026

Recovering membrane interaction kinetics of single molecules from 3D tracking data.

Interactions between cytosolic biomolecules and the bacterial inner membrane are fundamental to many cellular processes, yet directly measuring their binding kinetics in living cells remains challenging. Conventional 2D single-molecule tracking analyses can be insufficient, particularly when membrane association does not markedly alter the diffusion rate. Here, we present a method to recover membrane interaction kinetics from 3D single-molecule trajectories in rod-shaped bacteria. Using simulated 3D tracking data, we identify membrane-associated motion by quantifying how well short trajectory segments follow the circular curvature of the cell membrane. The resulting measure is further analyzed using a hidden Markov modeling framework, enabling robust discrimination between cytosolic and membrane-bound states and capturing the dynamics of state transitions without requiring diffusion-rate changes or direct colocalization with membrane markers. This work establishes a general framework for extracting membrane interaction kinetics from 3D single-molecule tracking data in live bacteria and highlights the value of realistic microscopy simulations for quantitative interpretation and systematic bias assessment.

Kinetics↗

PubMed2026

Comfort and Usability of Digital Versus Conventional Custom-Fit Mouthguards: A Randomized Clinical Trial.

BACKGROUND/OBJECTIVES: The use of mouthguards protects teeth and the supporting tissues against impacts. Digital impressions and 3D-printed models can reduce manufacturing steps and minimize discomfort. This study evaluated the fit, comfort, and usability of custom-made mouthguards, obtained by conventional and digital workflow, in amateur athletes. MATERIALS AND METHODS: Amateur athletes were recruited for this randomized, double-blind, crossover clinical study. Each participant received two 4-mm-thick custom-fit mouthguards made of ethylene vinyl acetate (EVA) sheets using two protocols: conventional impression using alginate to produce dental stone cast (CMP) and intraoral digital scanning to produce 3D-printed resin models (DMP). Each mouthguard was worn during all training and matches over 3 months. The order of mouthguard use was randomly assigned. Patient satisfaction with the impression protocol and mouthguard use, model accuracy, and mouthguard fit were assessed. RESULTS: Overall, 46 participants completed the study. The DMP protocol required less execution time (p = 0.023), caused less discomfort (p = 0.018), anxiety (p = 0.008), nausea (p = 0.027), difficulty breathing (p < 0.001), and unpleasant taste (p = 0.006) compared to the CMP protocol. The pain (p = 0.971) perceived by the participants was similar in both impression protocols. The CMP-mouthguards were considered to be better fitting by the majority of participants (p = 0.027). Between-group comparisons revealed that DMP-mouthguard caused less discomfort immediately postadaptation (p = 0.034), with no significant differences observed at 30 or 90 days. CONCLUSIONS: The digital workflow demonstrated to be efficient in the fabrication of mouthguards. Digital impression was more comfortable, requiring less execution time, with more accurate printed models and produced mouthguards with reported comfort and fit levels similar to those manufactured conventionally.

Humans↗

PubMed2026

Molecular evaluation of residual disease following neoadjuvant chemotherapy in triple-negative breast cancer CALGB 40603 (Alliance).

BACKGROUNDDespite therapeutic advances in early-stage triple-negative breast cancer (TNBC), residual disease (RD) following neoadjuvant therapy remains a key predictor of a worse prognosis and obstacle to improving patient outcomes.METHODSTo better characterize RD and identify survival-associated features, we performed comprehensive transcriptomic profiling of 340 pretreatment stage II/III TNBCs and 70 matched posttreatment RD samples from the randomized CALGB 40603 (Alliance) phase II clinical trial. To explore preclinical treatment strategies for RD, patient-derived xenograft (PDX) mouse models mimicking RD were treated with antibody-drug conjugates (ADCs).RESULTSOur study shows prognostic genomic features measured pretreatment may differ from prognostic features measured posttreatment from RD specimens. Patients with a genomic PAM50 subtype of basal-like in RD specimens had a poor survival outcome, and their matching pretreatment tumors were characterized by elevated chromosomal amplifications of oncogenic drivers and significantly reduced B and T cell expression features. Paired analyses of basal-like RD and matched pretreatment tumors revealed further lymphocyte depletion in RD, along with lower expression of MHC class I and interferon signaling, indicating an immune-cold RD microenvironment. Treatment of a basal-like and conventional chemotherapy-resistant PDX model, resembling basal-like RD, with sacituzumab govitecan or trastuzumab deruxtecan produced a marked antitumor response.CONCLUSIONRD biology differs from pretreatment tumors, with basal-like subtype RD following neoadjuvant chemotherapy being immune cold and associated with poor survival. Preclinical modeling suggests this high-risk group may benefit from adjuvant ADC therapy.TRIAL REGISTRATIONClinicalTrials.gov NCT00861705.FUNDINGNIH NCI U10CA180821 (Alliance for Clinical Trials in Oncology), NCI U24CA176171 (Alliance for Clinical Trials in Oncology), NCI UG1CA233373 (Alliance for Clinical Trials in Oncology), NCI Breast SPORE program P50-CA058223; Susan G. Komen SAC-160074; Breast Cancer Research Foundation BCRF-23-127; NIH NCI R01-CA229409; UNC LCCC Triple Negative Breast Cancer Center.

Humans↗

PubMed2026

A conserved distal-tail helical extension defines a tailspike attachment architecture in Gram-negative siphophages.

Rapid growth of bacteriophage genome collections has outpaced functional annotation of tail-tip proteins, limiting comparative analysis of host-recognition structures. Starting from a shared distal-tail gene organization in the Salmonella phages 9NA and Jersey, I developed a morphogenetic bioinformatic framework integrating gene synteny, sequence comparison, profile hidden Markov model (HMM) screening, structural evidence, structure-aware searching, and AlphaFold modeling. Comparison with the experimentally characterized lambda and Sf11 tail assemblies identified a predominantly alpha-helical C-terminal extension of the distal-tail (DT) protein associated with tailspike attachment, termed the distal-tail helical extension (DT-helix). Screening 541,986 proteins from 5167 complete NCBI RefSeq tailed-phage genomes, followed by evidence-based evaluation of sequence, genomic context, and structural architecture, identified 165 curated DT-helical-extension-associated phages. Their DT proteins segregated into six sequence groups. In the four principal multi-member groups, cognate tailspikes showed group-specific conservation in proximal N-terminal regions but substantially greater downstream diversity, consistent with sequence constraint at the DT-tailspike attachment boundary. A complementary ProstT5/Foldseek search supported the established groups but revealed no convincing additional highly divergent family. Together with the experimentally characterized Sf11 attachment interface, these findings define a recurrent morphogenetic architecture linking conserved distal-tail scaffolds to more variable receptor-binding proteins across siphophages infecting Gram-negative bacteria. Although universal exchangeability is not established, the identified scaffold-receptor-binding boundaries provide a framework for molecular characterization and rational phage engineering. Accession-level information for the 165 curated phages is available through PhageTailDB.

Viral Tail Proteins↗

PubMed2026

Comprehensive source-risk assessment of organophosphate esters in surface water of the Dianchi Lake Basin, Yunnan, China.

Organophosphate esters (OPEs), widely used as flame retardants and plasticizers, have been increasingly detected in aquatic environments. However, investigations of their distribution in high-altitude plateau lakes remain scarce. Identifying and quantifying the sources and associated risks of OPEs are crucial for subsequent water environment management. In this study, an integrated source-risk analysis approach was employed by combining the Positive Matrix Factorization (PMF) model, the Geodetector (GD) model, and risk quotient (RQ). Analysis of 14 OPEs in surface waters of the Dianchi Lake Basin (DLB) revealed 12 detectable compounds, with total OPEs concentrations (ΣOPEs) ranging from not detected (ND)-64.6 ng/L during the wet season and ND-35.8 ng/L during the dry season. Elevated ΣOPEs were primarily observed at inflow sites in the northern part of the lake and in urban rivers. Source apportionment indicated four major contributing sources: agricultural films containing flame-retardant and plasticizer additives, traffic-related particulate emissions, releases from household and personal care products, and industrial production and applications of flame retardants in plastics, electronics, and related products (the predominant source). The ecological impact caused by OPEs ranges from no risk to low risk, with tris(2-chloroethyl) phosphate emitted from industrial source being the primary driver of potential environmental risk. These findings highlight the necessity of prioritizing industrial sources in future management strategies. Overall, this study provides a methodological framework for source apportionment and risk assessment of OPEs and offers scientific evidence to support environmental management of OPEs in the DLB.

Environmental Monitoring↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

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

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma.

BACKGROUND: Immunotherapy resistance in lung adenocarcinoma (LUAD) remains a critical clinical challenge, and the mechanisms underlying resistance-associated intratumoral heterogeneity are poorly characterized. METHODS: We performed single-cell RNA sequencing of LUAD patients receiving neoadjuvant immunotherapy (responders vs. non-responders), integrating inferCNV, GSVA, and differential expression analyses. Cluster-specific genes were validated across seven independent cohorts (TCGA-LUAD, GSE13213, GSE26939, GSE29016, GSE30219, GSE31210, GSE42127). A multi-algorithm machine learning framework was used to construct a prognostic model, and the immune microenvironment was characterized using TCIA scoring, seven infiltration algorithms, and ESTIMATE. ARNTL2 function was assessed by CCK-8 and Transwell assays in A549 and H1299 cells. RESULTS: Non-responders showed significant enrichment of epithelial cells, depletion of cytotoxic T/NK cells, and elevated copy number variation burden versus responders (p < 0.0001). A resistance-enriched malignant subcluster (Cluster 2) exhibited hyperproliferative and metabolic reprogramming signatures with upregulated KRT17, S100A2, and CST6, which showed tumor-specific overexpression, adverse prognostic value, and genomic amplification across cohorts. CoxBoost combined with survivalSVM achieved optimal predictive performance (C-index = 0.686), yielding robust risk stratification (HR: 2.54-10.51, all p < 0.05). Low-risk patients showed greater immune infiltration and higher TCIA immunophenoscores. ARNTL2 was an independent prognostic factor (HR: 2.07-4.64) strongly correlated with risk score (r = 0.69), and its knockdown suppressed proliferation and invasion in both LUAD cell lines (all p < 0.05). CONCLUSION: This study identifies a resistance-associated malignant subcluster in LUAD, constructs a validated CoxBoost + survivalSVM prognostic model with robust immune stratification, and establishes ARNTL2 as a core oncogenic driver and therapeutic target.

ARNTL2↗