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PubMed2026

Impaired adaptation to smoke-derived phenolic compounds in Listeria monocytogenes CC204 from smoked salmon and trout.

Listeria monocytogenes is a major foodborne pathogen in ready-to-eat smoked fish products. This study evaluated whether clonal complex affiliation contributes to variability in growth responses to stresses representative of smoked salmon and trout processing. Ten strains were studied, including strains from CC121, CC26 and CC204, the three major clonal complexes reported in the French smoked salmon and trout sectors, together with the EGDe reference strain. Strains were exposed to salt, cold, smoke-derived phenolic compounds and combined stress conditions. Growth responses were compared with whole-genome-based phylogeny, and the impaired phenotype observed under phenolic exposure was further investigated using viable counts, live/dead microscopy and comparative genomics. Growth profiles were partly structured by clonal complex, with strains from the same clonal complex showing similar behaviour across stress conditions. Salt and cold reduced growth globally, while smoke-derived phenolic compounds were the most discriminating conditions. CC204 strains showed markedly lower growth rates under phenolic exposure than CC121, CC26 and EGDe. This phenotype was not associated with loss of cultivability or significant loss of membrane integrity. Comparative genomics did not identify a clear gene-content determinant explaining the CC204 phenotype. These results suggest that CC204 has an impaired adaptive response to smoke-derived compounds, likely involving regulatory or physiological mechanisms.

Listeria monocytogenes↗

PubMed2026

Delphi study robot consenso: Strategies for the implementation of robotic surgery in general surgery in the Spanish hospital network.

INTRODUCTION: The implementation of robotic surgery in public hospitals presents multiple logistical, educational, and organizational challenges. In the absence of unified guidelines, a national consensus is required to optimize its safe and efficient adoption. This study aimed to establish a set of consensus-based and measurable recommendations for the implementation of robotic surgery programs in hospitals within the Spanish National Health System, based on the experience of centres with established robotic programs and intended to serve as guidance for hospitals that are initiating or planning their implementation. METHODS: A national Delphi study was conducted with the participation of robotic surgery experts from 26 public hospitals. The expert panel was composed exclusively of digestive surgeons with experience in robotic surgery. Three iterative rounds of expert panel evaluation were conducted between March 2024 and March 2025. The questions were grouped into five thematic blocks. Consensus was defined as an agreement level of ≥66.7%. Kendall's W coefficient was used to assess concordance. RESULTS: High levels of consensus were achieved on key aspects related to infrastructure, structured training, cost evaluation, and quality assurance mechanisms. Areas of disagreement were also identified, such as the need for a dedicated anaesthesiologist, purchase of accessory instruments during the initial phase, and official accreditation pathways. CONCLUSIONS: This study provides a guideline for developing a national robotic surgery strategy focused on patient safety, program sustainability, and standardized training of surgical teams. These recommendations can guide hospitals at different stages of robotic technology adoption. Given that the consensus was reached from an exclusively surgical perspective, the recommendations focus on patient safety, program sustainability, and standardized training of the surgical team, and should be interpreted in an adaptable manner according to each centre's context, case volume, and available resources.

Cirugía Asistida por Robot↗

PubMed2026

Systematic evaluation of one-dimensional-to-two-dimensional near-infrared spectroscopy transformations with deep learning for quantifying coconut sap adulteration.

Near-infrared (NIR) spectroscopy have limitations when combined with deep learning (DL) algorithms because they rely on low-dimensional datasets. Therefore, we investigated the potential of transforming one-dimensional (1D) NIR spectra into two-dimensional (2D) spectrograms using synchronous and asynchronous techniques and the continuous wavelet transform (CWT) and their effectiveness by integrating with DL for detecting adulteration in coconut sap. NIR spectra (12,500-4000 cm-1) were collected from binary mixtures (0%-100%;w/w). The performance of all DL (convolutional neural networks-CNN, AlexNet and ResNet) models was compared with that of partial least squares (PLS). The models were ranked in the mentioned order based on their performances: 2D-CWT > 2D-asynchronous > 2D-synchronous > 1D/2D-PLS. The important features of the best model can be explained and visualized using gradient-weighted-class-activation-mapping. The findings highlight that the 1D-to-2D NIR data transformation combined with DL is a highly robust approach because it addresses the feature representation gap in NIR data and effectively captures the spatial-spectral correlations.

Spectroscopy, Near-Infrared↗

PubMed2026

Oblique Lateral Interbody Fusion With Lateral Vertebral Screw Fixation Versus Transforaminal Lumbar Interbody Fusion for Severe Lumbar Stenosis: Results of a Multicenter Randomized Controlled Trial.

BACKGROUND AND OBJECTIVES: The benefits of oblique lateral interbody fusion (OLIF) vs transforaminal lumbar interbody fusion (TLIF) in severe lumbar stenosis (Schizas C/D) remain uncertain. This randomized trial compared clinical, radiographic, and safety outcomes of OLIF and TLIF. METHODS: From November 2018 to December 2021, a prospective, multicenter, randomized controlled trial enrolled 260 adults with single-level severe stenosis and instability. In total, 224 patients were randomized to OLIF or TLIF. Prespecified outcomes followed consolidated standards of reporting trials. Primary outcomes were visual analog scale back/leg pain and Oswestry Disability Index (ODI), with minimal clinically important difference thresholds of ODI ≥12-13 points or ≥30% improvement, and visual analog scale ≥1.5-2.0 points. Radiographic measures included disc height, lumbar and segmental lordosis, and canal cross-sectional area (CSA). Complications were recorded. Ethics approval was obtained from the institutional review board, the trial was registered with ISRCTN.com , and all patients provided written informed consent. RESULTS: In total, 224 patients were randomized, 5 were lost to follow-up (TLIF n = 2, OLIF n = 3). Baseline features were comparable. OLIF was associated with shorter operative time, less blood loss, earlier ambulation, and shorter hospital stay (all P < .05). Both groups achieved significant, clinically meaningful improvements. OLIF showed greater back pain reduction at 3-6 months and 2 years ( P < .05) and superior ODI improvement at 3 and 6 months ( P < .001), although long-term ODI scores were similar. Radiographically, OLIF provided greater restoration of disc height and segmental lordosis (all P < .001) and demonstrated progressive CSA increase (dynamic decompression), whereas TLIF achieved immediate, sustained CSA enlargement. Fusion rates were comparable at 1-2 years. Complication rates were low and similar (7.3% TLIF vs 5.5% OLIF), with most OLIF-specific events transient. CONCLUSION: Both OLIF and TLIF yield improvements in severe lumbar stenosis. OLIF offers perioperative advantages, earlier functional recovery, radiographic restoration, and dynamic canal remodeling, supporting its role as an equivalent alternative for lumbar spinal stenosis with some secondary advantages.

Humans↗

PubMed2026

Toward real-time quantification of driving risks: a systematic review and research agenda of risk field theory.

In complex traffic systems, driving risk often evolves in a continuous and progressive manner prior to crash occurrence. How to effectively represent and analyze such latent risk states remains a central challenge in traffic safety research. In recent years, risk field-based approaches have introduced spatial and spatiotemporal continuous modeling paradigms, providing new perspectives for characterizing the distribution of traffic risk and its dynamic evolution. Motivated by the rapid growth of this research area and the lack of a systematic synthesis, this paper presents a comprehensive review of studies applying risk field theory to driving safety and traffic risk analysis. Following the PRISMA guidelines, relevant literature was collected through multi-database searches and analyzed using a combination of bibliometric analysis and qualitative review. The review systematically summarizes the theoretical foundations, modeling elements, data sources, analytical methods, and application domains of risk field-related research. Particular attention is given to studies that conceptualize traffic risk as a continuous field, complemented by a broader review of traffic risk factor literature to identify key elements and analytical dimensions involved in risk field modeling. On this basis, the paper synthesizes research progress in major application areas, including traffic safety state representation, driving behavior analysis, traffic conflict assessment, and autonomous driving and human-machine cooperative systems. Differences and commonalities among existing studies are compared in terms of modeling strategies, data support, and application scenarios. Through this systematic review, the paper clarifies the main research themes and methodological trends of risk field-based studies, providing a structured framework for understanding the evolution and application of this approach and offering methodological insights for risk perception modeling and safety-oriented decision support in intelligent transportation systems (ITS).

Humans↗

PubMed2026

Quantitative assessment of the fingerprint evidential value using machine learning.

Fingerprints as physical evidence have long supported criminal investigation and adjudication. In practice, however, fingerprint identification relies mainly on examiners' experience. Furthermore, expert opinions tend to be categorical, even though the opinions with the same conclusion could differ substantially in evidential strength. To quantitatively assess fingerprint evidential value, this study proposes a machine learning-based framework as an interpretable decision-support tool. A lightweight residual one-dimensional convolutional neural network was constructed, incorporating channel recalibration and a similarity-driven attention mechanism to learn adaptive contribution weights for different matched minutiae (minutiae for short). Controlled experiments revealed that the predicted evidential value increased with the number of minutiae and was significantly influenced by the quality of minutiae. With 10 minutiae, the mean predicted scores were 4.49, 7.00, and 9.09 for blurred, moderately blurred, and clear minutiae, respectively. Multiple regression analysis indicated that replacing a pair of blurred minutiae with a pair of clear minutiae increased the score by 0.492, whereas replacing it with a pair of moderately blurred minutiae increased the score by only 0.216. By mapping predicted scores to graded levels of evidential strength, the framework contributes to a paradigm shift from categorical expert opinions to graded ones, helping courts evaluate fingerprint evidence more scientifically.

Humans↗

PubMed2026

Implicit and explicit statistical learning in reading: Evidence from a randomized controlled-learning study and computational modeling.

A key challenge in reading acquisition is understanding how learners extract the complex probabilistic mappings between print, meaning, and sound. Statistical learning (SL) theory offers a mechanistic account of how such mappings are acquired, whether implicitly through exposure or explicitly through instruction. We conducted a randomized controlled-learning study in Chinese, a writing system characterized by multiple sub-lexical regularities linking orthography, semantics, and phonology. Ninety-five 2nd-3rd graders with or at risk for dyslexia were randomly assigned to one of three groups: an implicit-SL training group exposed to repeated lexical and sublexical orthography-semantics-phonology associations, an explicit-SL training group receiving the same input plus explicit instruction on the sublexical print-sound mapping, and a no-SL control group. Both SL groups outperformed controls on the characters they were trained on, as well as on untrained characters that required generalization. However, only the explicit group demonstrated abstraction of print-sound mapping to novel items. Neural network simulations further revealed distinct mechanisms supporting implicit and explicit SL, consistent with a dual-system account of reading acquisition. Together, these findings (1) clarify how implicit and explicit learning distinctly support the discovery of statistical structure in written language and (2) underscore the implicit-explicit dual learning mechanism underlying reading acquisition.

Humans↗

PubMed2026

Non-linear predictive modeling and comprehensive meta-analysis of rectal temperature in Santa Inês sheep: a systematic review of thermal challenges and biometerological trends.

A systematic and bibliometric review, combined with a meta-analysis, was used to adjust an equation for estimating the physiological responses of Santa Inês sheep subjected to different thermal challenges. The systematic review compiled data on physiological responses and the thermal environment, which were then used in the meta-analysis to adjust regression models. The bibliometric analysis mapped the relationships among studies, highlighting their usefulness in interpreting research findings and biases. Addressing prior methodological critiques, the core of this study involves replacing the linear approach with a non-linear segmented regression model to accurately define the Thermal Neutral Zone (TNZ). The Segmented Regression Model was crucial, establishing the upper limit of the Thermal Neutral Zone (TNZ) at an air temperature (tair) of 34.64 °C, where trectal begins to increase abruptly. The model, while identifying a biologically significant breakpoint, exhibited a moderate Multiple R-squared of 0.3529, highlighting the high heterogeneity and methodological variability in the current Santa Inês literature. This non-linear approach offers a biologically superior tool for identifying the onset of thermal distress.

Animals↗

PubMed2026

Regional genomic analysis of lineage distribution and transferable multidrug resistance among chicken-associated Salmonella Kentucky isolates in China.

Salmonella enterica serovar Kentucky is an important multidrug-resistant foodborne pathogen in the poultry meat supply chain. Although recent broader genomic studies have elucidated the population structure and epidemiological significance of major lineages in China (e.g., ST198 and ST314), the regional dynamics within local poultry supply chains remain insufficiently characterized. In this study, 31 chicken meat-derived isolates from Shanghai and 39 publicly available genomes from China were analyzed using antimicrobial susceptibility testing, whole-genome sequencing, phylogenetic analysis, conjugation experiments, and complete sequencing of representative plasmids. This enabled a systematic characterization of the molecular epidemiological features of the population and the mechanisms underlying resistance dissemination. Population genomic analysis revealed a lineage composition markedly different from the global epidemiological pattern: ST314 was the predominant sequence type among the Shanghai chicken-derived isolates (74.2%), whereas the internationally recognized high-risk clone ST198 accounted for only 25.8% of the local isolates. However, risk stratification analysis indicated that although ST198 was detected less frequently, it carried a significantly greater burden of acquired resistance genes and therefore represented a higher-risk resistant lineage. Functional and structural validation further elucidated the molecular basis of resistance dissemination within this high-risk lineage. Conjugation experiments confirmed the co-transfer of a multidrug resistance module carrying blaTEM-1 and blaCTX-M-267 to the recipient strain Escherichia coli J53. Complete plasmid analysis revealed that these two β-lactam resistance genes were co-localized on a 242-kb transferable plasmid flanked by Tn1331, Tn3, and multiple transposase-associated elements, thereby providing a structural basis for their horizontal transfer. This study provides important molecular epidemiological evidence for lineage-specific surveillance and risk-stratified control of resistant Salmonella in the poultry meat supply chain and further underscores the need for continuous monitoring of mobile genetic elements within a One Health framework.

Animals↗

PubMed2026

Inducible flocculation in Komagataella phaffii enables enhanced biomass separation for biopharmaceutical production.

Biomass separation represents a critical bottleneck in Komagataella phaffii-based biopharmaceutical processes, as typically high cell densities of 40 - 50 % create significant operational, technical and economic challenges for harvest operations. Yeast cell aggregation (flocculation) provides a solution to accelerate cell sedimentation by increasing particle size, thus allowing to improve biomass-supernatant separation efficiency during both natural gravity settling and (continuous) centrifugation operations. This study demonstrates successful engineering of K. phaffii strains with an inducible flocculation phenotype using CRISPR/Cas9-based genome editing to integrate the Saccharomyces cerevisiae FLO1 (ScFLO1) gene under control of various regulatory elements, including methanol-inducible and derepressible promoters. Flocculation strength could be enhanced by implementing transcriptional positive feedback circuits based on the methanol-inducible AOX1 promoter. To address methanol-free production requirements, we developed alternative systems to retrofit PAOX1-based ScFLO1 expression and exploited the derepressible PDF promoter, offering broader compatibility with biopharmaceutical manufacturing facilities. Flocculating cells cultivated in a bioreactor demonstrated significantly improved sedimentation behavior, with considerably lower supernatant turbidity after short low-speed centrifugation or gravity sedimentation compared to non-flocculating controls. Crucially, cell flocculation had no negative impact on product amount and quality when expressing a multivalent NANOBODY® VHH molecule with pharmaceutical relevance. Thus, this work establishes the first genetically engineered flocculation system in K. phaffii compatible with recombinant protein production, providing the basis for an innovative approach to streamline harvest operations in biopharmaceutical processes.

Flocculation↗

PubMed2026

Playing with Fire, Losing the Drive: Bidirectional Links Between Problematic Smartphone Use and Grit Dimensions.

This study examined bidirectional longitudinal associations between grit dimensions (consistency of interest [CI] and perseverance of effort [PE]) and problematic smartphone use (PSU) and tested cognitive flexibility as a mediating mechanism. A sample of 1,641 Chinese university students (55.2 percent female; Mage = 20.1 years) completed measures at two time points 6 months apart. A four-variable cross-lagged panel model revealed that CI and PSU negatively predicted each other over time, whereas PSU unidirectionally predicted decreased PE. Cognitive flexibility partially mediated the PSU-to-PE pathway (indirect effect = -0.004, 95 percent bootstrap CI [-0.010, -0.0001]). Competing models analysis confirmed this directionality: the forward mediation (PSU → cognitive flexibility → PE) was significant, whereas the reverse was not. These findings demonstrate that grit dimensions exhibit distinct longitudinal patterns with PSU and identify cognitive flexibility as a cognitive mechanism through which PSU specifically undermines effort persistence. Implications for dimensional approaches to grit and targeted interventions are discussed.

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

Ecological momentary assessment studies on food craving among healthy adults: A systematic review.

Ecological Momentary Assessment (EMA) can capture the dynamic nature of food craving in free-living conditions, addressing limitations of laboratory and retrospective reporting methods. This systematic review synthesized findings from EMA studies to characterize 1) methodological features, 2) the temporal dynamics of food craving, and 3) the relationship between craving and eating behaviors. A systematic search of PubMed and PsycINFO databases was conducted following PRISMA guidelines. The review included 23 studies that utilized EMA designs to assess food craving repeatedly in daily life among healthy adults. Most studies employed EMA protocols that prompted participants to respond at pre-specified time points and utilized single-item craving measures. Most craving measures (71%) lacked specificity regarding the type of food craved. Results revealed a consistent positive within-person association between hunger and food craving. Conversely, associations between stress/negative affect and craving were inconsistent, varying by individual traits and context. Momentary food craving appeared to predict subsequent eating outcomes. Evidence suggested food craving may be a dynamic, transient state that co-fluctuates with hunger, functioning as a proximal antecedent to food intake. However, reliance on non-specific food craving measures and EMA protocols prompting at fixed schedules limits the granular understanding of craving mechanisms. Future research requires refining food craving measurement and incorporating randomized prompting within predefined windows, or participant-initiated sampling triggered by specific events (e.g., eating occasion), to better characterize food craving dynamics in relation to eating behaviors.

Humans↗

PubMed2026

Tigecycline-resistant Staphylococcus in waiting pens of a pig slaughterhouse: genomic insights into a food safety alert.

BACKGROUND: The waiting pens of slaughterhouses represent a critical control point in the 'farm-to-fork' continuum, yet their role in the emergence and dissemination of antimicrobial resistance remains understudied. This study investigated tigecycline-resistant Staphylococcus (TRS) in these high-risk zones to assess their prevalence, resistance mechanisms, and transmission dynamics. METHODS: 400 samples were collected from the waiting pens of a pig slaughterhouse in Guangzhou, China. Antimicrobial susceptibility testing, whole-genome sequencing, phylogenetic analysis, and molecular cloning were employed to characterize resistance mechanisms and transmission patterns. RESULTS: 78 TRS strains were isolated and classified into three species, including S. borealis, S. ureilyticus, and S. pasteuri. These isolates exhibited multidrug-resistant phenotypes and carried new mutations in rpsJ and tet(M), which were functionally confirmed to reduce tigecycline susceptibility. Phylogenetic evidence demonstrated clonal transmission between pig farms and the slaughterhouse. The tet(M) gene was located within Staphylococcal cassette chromosome mec elements mediated by IS257, while tet(L) was carried by plasmids formed through IS256/IS257-mediated recombination. CONCLUSIONS: Waiting pens serve as crucial reservoirs for the amplification and dissemination of antimicrobial resistance. Our findings underscore the urgent need for enhanced biosecurity measures, improved waste management, and routine molecular surveillance in these high-risk zones to mitigate the spread of resistance along the food production chain.

Animals↗

PubMed2026

Genetic regulation of CPEB3-mediated alternative polyadenylation associated with survival of patients with hepatocellular carcinoma.

BACKGROUND: Alternative polyadenylation (APA) is a key post-transcriptional mechanism that regulates gene expression by modulating 3'UTR length, its dysregulation has been implicated in carcinogenesis. How genetic variants influence APA to affect hepatocellular carcinoma (HCC) prognosis remains unclear. METHODS: Prognosis-APA quantitative trait loci (apaQTL) were performed using genotype and APA profiling from TCGA data. A two-stage survival analysis in 848 Chinese and 369 TCGA LIHC patients and functional validation were used to identify prognostic apaQTL in HCC progression. RESULTS: A total of 2,025 and 817 significant APA events were identified in Chinese and TCGA cohort, respectively. Besides, 859 events were associated with poor prognosis in HCC and enriched in RNA splicing / metabolism pathways. We detected 32,034 significant apaQTLs, predominantly enriched in 3'UTRs and RBP-binding regions. CPEB3 was prioritized as a key APA regulator RBP; its low expression correlated with poor patient survival and promoted proliferation, migration, and invasion in HCC cells. Notably, a functional apaQTL variant rs2037547, located in GSK3B and mediated by CPEB3, demonstrated a poor survival of HCC patients in both cohort (pooled HR=1.29, p=0.016). Mechanistically, rs2037547 promoted aberrant APA at proximal poly(A) sites of GSK3B through CPEB3, leading to increased expression of short 3'UTR isoform. This regulatory alteration enhanced HCC cell proliferation, invasion, and migration, and contributed to HCC progression. CONCLUSION: These findings elucidated the distinct role of apaQTL-mediated APA dysregulation in HCC prognosis, providing insights for prognostic stratification and potential targets for personalized therapy in HCC.

RNA-binding proteins↗

PubMed2026

Effects of apple phenolics on the human metabolome: modulation of key metabolic pathways.

Apples are widely recognized for their potential health benefits, partly attributed to their phenolic compounds. However, their impact on human metabolism remains incompletely understood. This study investigated metabolic effects of apple-derived phenolic compounds using untargeted metabolomics approach across multiple biofluids. In a crossover intervention study, 30 healthy men consumed a phenolic-rich apple juice or a placebo for two weeks. Blood, urine and saliva samples were collected before and after each intervention and analyzed by direct infusion ultra-high resolution mass spectrometry. Consumption of apple phenolic compounds resulted in significant alterations of the human metabolome, including increased levels of phenolic-derived degradation products and microbial-associated metabolites across all biofluids. Pathway enrichment analysis revealed pronounced effects on phenylalanine and tyrosine metabolism, as well as linoleic and arachidonic acid metabolism, Overall, these findings demonstrate that apple phenolic compounds induce measurable, microbiota-associated and systemic metabolic changes, providing new insights into their metabolic fate and biological relevance.

Humans↗

PubMed2026

Microglial modulation in general anesthesia: molecular.

General anesthetics profoundly alter brain function and consciousness, yet the mechanisms underlying these effects remain incompletely understood. Although traditional studies have primarily focused on neuronal targets, accumulating evidence suggests that microglia dynamically respond to anesthetic exposure and may participate in anesthesia-associated neurophysiological changes. Beyond their established immune functions, microglia are increasingly implicated in synaptic remodeling, metabolic regulation, neuronal activity surveillance, and neuron-glia communication. Recent studies indicate that different classes of anesthetic agents modulate microglial activity through diverse and context-dependent mechanisms involving inflammatory signaling, purinergic pathways, calcium dynamics, mitochondrial metabolism, and neural circuit interactions. These responses are associated with postoperative neurocognitive disorders, altered synaptic plasticity, and anesthesia-related changes in brain states. In this review, we summarize current evidence regarding the effects of volatile anesthetics, intravenous anesthetics, and analgesics on microglial function and discuss the molecular, functional, and circuit-level mechanisms underlying anesthesia-associated neuron-microglia interactions. We further highlight the dynamic and heterogeneous nature of microglial responses during anesthesia and discuss current limitations in the field, including the lack of temporally resolved and cell-specific approaches. Understanding these processes may provide insights into anesthesia-associated neurocognitive dysfunction and support the development of neuroimmune-targeted strategies in anesthesiology.

General anesthesia↗

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↗