eBioMedicine

ObjectivePostoperative pain outcomes following spinal fusion surgery remain difficult to predict, as structural and surgical indicators alone offer limited insight into who will experience meaningful relief. A substantial proportion of patients continue to report persistent pain after surgery, underscoring the need for objective markers that can help identify those at risk of poor recovery. Peak alpha frequency (PAF) has emerged as a promising trait-like neural signature of pain sensitivity in experimental models, where individuals with slower PAF tend to exhibit heightened pain sensitivity. Yet despite this link, its ability to forecast longer-term postoperative pain trajectories remains unclear. MethodsSeventeen adults undergoing cervical or lumbar fusion surgery were included. Resting-state, eyes-closed EEG was recorded preoperatively and at multiple visits after surgery. PAF was extracted from central electrodes using the centre-of-mass method. Pain intensity was assessed longitudinally on standardised self-report pain scales. Associations between PAF measures and postoperative pain change were examined using correlation analyses, and receiver operating characteristic (ROC) analyses evaluated discrimination of pain responders ([≥]50% improvement). ResultsPreoperative peak alpha frequency (PAF) was positively associated with longer-term pain reduction at the 3-month follow-up, but showed no consistent relationship with early postoperative pain. Across pain measures, a consistent pattern emerged across the Brief Pain Inventory (BPI), visual analogue scale (VAS), and numerical rating scale (NRS), but not the verbal rating scale (VRS) or Short-Form McGill (SF-MPQ). At the 3-month follow-up, associations reached statistical significance for BPI-Worst ({rho} = 0.67, p = 0.017), and BPI-Average Pain ({rho} = 0.62, p = 0.033). VAS and NRS showed moderate-to-strong effects that approached significance in non-parametric analyses and were significant for VAS when treated as an approximately interval measure (Pearson r = 0.63, p = 0.022). ROC analyses using BPI-Worst pain improvement demonstrated good discriminative ability of preoperative PAF for identifying treatment responders at 3 months (AUC = 0.84; 95% CI: 0.61-1.00), with high specificity and moderate sensitivity at the Youden-optimal threshold of 10.11 Hz. By contrast, changes in PAF over time were not reliably related to changes in pain scores, suggesting that PAF functions more as a stable, trait-like predictor than a dynamic biomarker in this context. ConclusionThis study demonstrates the feasibility and potential clinical value of preoperative EEG for characterising individual differences in postoperative pain recovery following spinal fusion. The results identify faster preoperative PAF as a stable neural signal that captures meaningful variability in longer-term pain reduction, with convergent support across multiple patient-reported measures. While replication in a larger cohort is required, these findings establish a clear foundation for evaluating PAF as a candidate neurophysiological marker to inform preoperative risk profiling and potentially personalised perioperative pain-management strategies in spinal fusion patients.

Cardiac surgery patients experience rapidly evolving hemodynamics in early post-operative period requiring intensive support. Identifying hemodynamic subphenotypes from these data can inform personalized management. Using 24-hour high-resolution physiologic and treatment data from 6,630 MIMIC-IV and 1,963 SICdb patients, we trained a transformer encoder with a reconstruction-contrastive objective to derive patient-level embeddings capturing multivariate temporal dynamics within first 24h of ICU stay and compared them against those generated by dynamic time warping (DTW). Spectral clustering uncovered three reproducible hemodynamic subphenotypes. Compared with subphenotype 1, subphenotype 3 received more IV fluids, vasopressors, inotropes, and exhibited higher in-hospital mortality (OR 5.85, 95 % CI 2.43-14.13), longer ICU stay (7.12 days, 95% CI: 5.52-8.73) and hospitalization (8.86 days, 95% CI: 6.57-11.16). DTW derived subphenotypes had weaker prognostic separation. Thus, contrastive-transformer framework identified more clinically meaningful temporal hemodynamic subphenotypes that may optimize post-operative risk stratification and inform personalized management.

Background: The gluteus maximus (GM) is a major hip extensor essential for mobility and metabolic health. Most MRI studies rely on global measures, such as muscle volume or fat fraction, which can overlook spatially localised remodelling. Here, we integrate conventional volumetric and fat fraction metrics with 3D mesh-based shape phenotypes to provide a spatially resolved characterisation of GM morphology in relation to anthropometric, lifestyle, and cardiometabolic factors, with a focus on type 2 diabetes (T2D) and sex-specific effects. Methods: We analysed T1 Dixon MRI from UK Biobank participants to quantify GM muscle volume, fat fraction, and regional surface morphology using 3D meshes. Statistical parametric mapping was used to assess regional associations with anthropometric, lifestyle, and clinical variables Bi-directional causal mediation analyses were performed using GM volumetric and principal components (PCs) of shape variation. PCs were also tested for associations with prevalent and incident disease. Longitudinal changes in GM composition were evaluated in participants with repeated imaging evaluations. Results: GM muscle volume and fat fraction were strongly associated with age, adiposity, and physical activity. Shape analysis revealed spatially localised remodelling patterns not captured by global measures, with region-specific surface shrinkage linked to age, BMI, alcohol intake, grip strength, physical activity, frailty, osteoporosis, and cardiometabolic disease. T2D showed marked sex-differences, with regional shrinkage in men and relative expansion in women. PCA reduced high-dimensional shape variation into interpretable components. Mediation analyses indicated that T2D-related differences in GM morphology partly mediated increases in fat fraction, suggesting that disease effects manifest through spatially patterned shape changes rather than overall muscle size. PCs capturing variations in the central-upper posterior and anterior GM, differentiated between T2D cases from controls, and were associated with incident T2D risk (Men: PC6 HR per SD: 0.81 [0.70-0.95], false discovery rate (FDR)-adjusted p = 0.038, in left GM; 0.76 [0.65-0.88], p = 0.002, in right GM; women; PC5 HR = 1.32, [1.08-1.61], p = 0.032, in right GM). Conclusions: Integrated 3D quantification of GM composition and morphology provides spatially resolved biomarkers that go beyond muscle volume and fat fraction. By capturing region-specific GM remodelling, linked to anthropometric, lifestyle and cardiometabolic factors, this approach offers a more nuanced characterisation of muscle-fat phenotypes and enhances mechanistic insight and risk stratification in population-based imaging studies.

Biomarkers for the early identification of acute coronary syndrome (ACS) risk remain inadequately investigated, particularly in patients with type 2 diabetes mellitus (T2DM), for whom timely clinical intervention may substantially enhance prognostic outcomes. The gut microbiota and serum metabolites may serve as pivotal mediators in the occurrence and progression of ACS among patients with T2DM, and whether these factors can be used for the precise discrimination of patients with T2DM complicated by ACS remains to be explored. Overall, 76 participants were enrolled (38 patients diagnosed with T2DM complicated by ACS and another 38 with T2DM without ACS). 16S rRNA sequencing combined with untargeted LC-MS metabolomics revealed a dysregulated gut-serum axis in patients with T2DM complicated by ACS: enrichment of proinflammatory microorganisms (Enterococcus spp.), reduction of butyrate producers (Butyricimonas spp.) and concomitant dysregulation of circulating lipid metabolites-upregulation of PC(16:0/9:0 (CHO)) and arachidonic acid alongside downregulation of cholesterol sulfate. By integrating multiomics data and applying various feature selection methods, we subsequently identified six key biomarkers. The final constructed combined model robustly distinguished patients with T2DM complicated by ACS from those with T2DM alone (AUC = 0.983), outperforming the other single omics models. Our study revealed that the gut microbiota and related serum metabolites serve as key mediators in the onset and progression of ACS among patients with T2DM, and demonstrated their potential value as noninvasive biomarkers for the early diagnosis of T2DM complicated by ACS. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSWe combined 16S rRNA sequencing with untargeted LC-MS metabolomics to dissect the gut-serum axis in patients with T2DM complicated by newly diagnosed ACS. We identified distinct gut microbial and serum metabolic signatures that distinguish ACS progression within the T2DM population. A multiomics classifier integrating clinical, microbial, and metabolic variables achieved robust diagnostic performance (AUC = 0.983) and outperformed single omics models. What Are the Clinical Implications?Integrated multiomics biomarkers facilitate the early identification of ACS progression in patients with T2DM, offering novel avenues for precision prevention, dynamic clinical surveillance, and individualized therapeutic strategies. Dysregulations of the gut microbiota and serum metabolome may play an important role in the development and progression of ACS in patients with T2DM.

As of early 2026, over 115 million US adults (more than 1 in 3) have prediabetes, a condition with an annual conversion rate of 5%-10% to type 2 diabetes. Total diabetes (diagnosed and undiagnosed) affects approximately 40.1 million Americans, or 12% of the population, with roughly 1.5 million new cases diagnosed annually. Continuous Glucose Monitoring (CGM) provides real-time, 24/7 insights into glycemic variability, detecting dangerous highs, lows, and trends that HbA1c (a 3-month average) misses. It enables, for instance, identification of nocturnal hypoglycemia or postprandial spikes, enhancing personalized, actionable treatment decisions and improving safety. The Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights (AI-READI) dataset was produced by the National Institutes of Health (NIH) Common Fund Data Ecosystem (CFDE) Bridge2AI program. This dataset offers a rich resource for diabetes research, providing comprehensive biosensor data from over 1,067 participants. However, like many medical datasets, AI-READI contains label inaccuracies due to self-reported health surveys and static HbA1c indicators, which can undermine model effectiveness. We developed a strong classification framework using Convolutional-Bidirectional Long Short-Term Memory (Conv+BiLSTM) to analyze and accurately classify glycemic health states from continuous glucose monitoring time-series data. Our aim was to establish and correct any misclassified labels through hybrid unsupervised-supervised learning methods and validated our results with expert-in-the-loop clinical review. We analyzed 784 participants from the AI-READI dataset, which represented four health states: healthy, prediabetes lifestyle controlled, oral medication, and insulin-dependent. Based on recommendations from the literature and our own expertise, we sought to compare the self-provided "healthy" group labels with a cluster-agnostic, CGM-defined healthy (CGM-H) reference derived from the CGM metrics using K-means clustering (K=6) on standardized CGM summary features to identify CGM-H participants and then applied XGBoost-based iterative label refinement. We identified a misclassification rate of 56.9% (161/283) in the initially labeled "healthy" group. After eight iterations of XGBoost refinement with dual-criterion relabeling ([≥]80% probability + unanimous out-of-fold voting), the cleaned dataset increased CGM-H participants from 122 to 195 for binary classification. Next, we developed a Conv+BiLSTM model combining Convolutional layers (32, 64 filters) for local temporal feature extraction with Bidirectional LSTM layers (64, 32 units) for sequence modeling, using time-series engineered features including rolling statistics, glucose derivatives, and circadian rhythm encoding. Class imbalance was addressed with per-class weighting, and 5-fold stratified cross-validation estimated generalization performance, computing a global decision threshold (0.374) by maximizing Youdens J statistic on concatenated out-of-fold predictions. Additionally, we analyzed heart rate, activity level, and stress and sleep data and validated it against CGM data. The Conv+BiLSTM model achieved ROC-AUC {approx} 0.932 on the held-out test set and 0.907 {+/-} 0.026 in cross-validation, with well-calibrated predictions (Expected Calibration Error = 0.075, temperature scaling T = 1.00). A 3-tier confidence-based decision system achieved 82% detection rate with only 6% OGTT burden, enabling actionable clinical recommendations. This hybrid approach addressed label noise while achieving high discrimination. This framework demonstrates potential for real-time glycemic state monitoring and early intervention in diabetes progression.

Accessible screening for type 2 diabetes (T2D) is critical, with millions of cases remaining undiagnosed globally. Here, we present the largest known real-world validation study for a speech-based T2D prediction model, trained on speech data from over 21,000 individuals, that works on features extracted from 20-second speech recordings. The model was evaluated in two stages: 1) Against self-reported diagnoses in 7,319 English-speaking participants using AUC, and 2) Against HbA1c blood tests in a subset of 801 participants drawn from the full cohort. Performance was also compared against QDiabetes and in the presence of key confounding variables. The model demonstrated clinically useful predictive capacity on self-reported data (AUC = 0.80 {+/-} 0.03), approaching QDiabetes (AUC = 0.86 {+/-} 0.03). It was robust to most demographic confounds (e.g., age and sex) and medication use, with reduced performance in the presence of comorbidities (e.g., cardiovascular disease and hypertension). At diabetes threshold of HbA1c [≥]48 mmol/mol, the model achieved an AUC of 0.75 ({+/-}0.07). This biomarker-validated speech-based tool demonstrates potential to complement existing methods through accessible, scalable screening requiring only a 20-second speech sample.

Lipoprotein composition is altered in sepsis, and supplementation with high-density lipoproteins has been reported to improve outcomes in experimental settings. In this study, we aimed to investigate the nature and inter-individual variability in the lipoprotein proteome to inform risk stratification and opportunities for precision medicine approaches. In a large proteomic dataset including 1134 patients (1781 samples) with sepsis and 149 healthy volunteers, we analysed 18 protein components of lipoproteins. We characterise heterogeneity of the lipoprotein proteome, defining three step-wise sub-phenotypes associated with increasing disease severity, one close to health, then an early phase patient group showing increased abundance of proteins that integrate HDL under inflammatory conditions (SAA1 and SAA2), then a group with decreased abundance of proteins that are components of HDL under healthy conditions that was associated with higher organ failure intensity (SOFA score) and increased mortality. We developed and externally validated a quantitative score reflective of lipoproteins alterations in sepsis, and machine learning predictive models to predict the LP class, advancing future individualised lipoproteins-based therapeutics in sepsis.

ObjectiveAlthough T-cell-mediated inflammation is a hallmark of Type 2 Diabetes (T2D), the contribution of CD20-expressing T cells--a highly potent subset recently implicated in various autoimmune conditions--to T2D pathogenesis remains unknown. This study aimed to characterize the frequency, functional profile, and glucose-responsiveness of CD20+ cytotoxic T lymphocytes (CTLs) in patients with T2D. Research Design and MethodsPeripheral blood mononuclear cells (PBMCs) from 25 treatment-naive T2D patients and 20 age-matched healthy controls (HC) were analyzed using multicolor flow cytometry. We assessed the frequency of CD3+CD8+CD20+ cells and their production of cytotoxic molecules (Granzyme B, Perforin, Granzyme K) and pro-inflammatory cytokines (IFN-{gamma}, TNF-, GM-CSF). To further establish the link with hyperglycemia, HC-derived CTLs were exposed to increasing glucose concentrations (100-450 mg/dL) in vitro. Single-cell RNA sequencing (scRNA-seq) data from a public T2D dataset was utilized to validate the molecular signature of MS4A1 (CD20)+ CTLs. ResultsThe frequency of circulating CD20+ CTLs was significantly elevated in T2D patients compared to HCs (p<0.0001) and demonstrated a strong positive correlation with HbA1c, fasting glucose, and triglyceride levels. Notably, CD20+ CTLs from T2D patients exhibited a "hyperfunctional" phenotype, characterized by significantly higher degranulation (CD107a), elevated expression of Granzymes B/K and Perforin, and increased production of IFN-{gamma} and TNF- compared to HCs (p<0.01 for all). In contrast, no such differences were observed in the CD20-CTL compartment. In vitro experiments revealed that escalating glucose levels directly enhanced the proliferation and cytotoxic potential of CD20+ CTLs, suggesting a nutrient-sensing mechanism. scRNA-seq analysis further confirmed the distinct pro-inflammatory and effector transcriptional profile of MS4A1+ T cells in T2D. ConclusionsOur findings identify CD20+ CTLs as a novel, glucose-sensitive, and hyperfunctional immune subset in T2D. The strong correlation between these cells and clinical metabolic parameters suggests that CD20+ CTLs may act as a critical link between chronic hyperglycemia and systemic inflammation, representing a potential new therapeutic target for immunomodulation in T2D.

BackgroundAI-based radiomics has demonstrated promising diagnostic performance for pancreatic cystic neoplasms, yet clinical translation remains limited. Whether this reflects insufficient model performance or structural limitations of the evidence base remains unclear. MethodsWe performed a systematic review and diagnostic test accuracy meta-analysis of AI-based radiomics in pancreatic cyst (2015-2025), addressing two clinically relevant tasks (Q1: cyst type differentiation/Q2: malignancy or high-grade dysplasia prediction). Training and validation datasets were synthesized independently using hierarchical models. Study evaluation extended beyond diagnostic performance to a four-dimensional framework integrating RQS 2.0, METRICS, TRIPOD+AI and PROBAST+AI explicitly contrasting pooled diagnostic performance with reporting quality, methodological rigor, and risk of bias. The review was pre-registered (PROSPERO) and conducted according to PRISMA 2020. ResultsTwenty-nine studies were included (Q1: n = 15; Q2: n = 14), predominantly retrospective and single center. Training-based analyses showed high apparent diagnostic performance for Q1 (pooled sensitivity/specificity: 0.89 [95% CI, 0.85-0.92]/ 0.90 [0.85-0.93]), but there was substantial heterogeneity ({tau}{superscript 2} = 0.56/0.78; {rho} = 0.38). Validation-based performance remained high (0.86 [0.82-0.89]/ 0.88 [0.81-0.93]), while heterogeneity persisted and prediction regions exceeded confidence regions. Training-based analyses demonstrated similarly high apparent performance (0.88 [0.79-0.95]/0.89 [0.81-0.94]) for Q2, with pronounced heterogeneity ({tau}{superscript 2} = 1.98/1.61; {rho} = 0.63). Validation-based performance was slightly lower, yet still clinically comparable (0.82 [0.75-0.89]/0.86 [0.80-0.91]), and heterogeneity persisted ({tau}{superscript 2} = 0.71/0.43; {rho} = 0.15). Across both tasks, high diagnostic accuracy occurred alongside incomplete reporting, limited validation and an elevated risk of bias. ConclusionAI-based radiomics for pancreatic cysts has reached a structural performance plateau. Further improvements in diagnostic accuracy alone are insufficient to achieve clinical translation and must be accompanied by a paradigm shift from performance-driven model development toward decision-anchored study designs, robust validation strategies, transparent reporting standard, and clinically integrated evaluation frameworks. SummaryAlthough pancreatic cystic lesions are increasingly being detected, imaging-based decision-making remains limited, particularly regarding differentiating between cyst types and stratifying malignancy risk. In this PRISMA-compliant and PROSPERO-registered systematic review and meta-analysis of diagnostic tests, we evaluated the use of AI-based radiomics for these two tasks, as well as its contextualized performance. In addition, a four-dimensional framework was employed to conduct the evaluation, incorporating diagnostic accuracy, reporting quality, risk of bias, and radiomics maturity. Across studies published between 2015 and 2025, the pooled diagnostic performance was consistently high, with only modest declines observed from the training to the validation stage. Nevertheless, considerable heterogeneity between studies and limited transportability remained evident. Multidimensional evaluation indicated a systematic dissociation between reported performance and methodological robustness, characterized by incomplete reporting, restricted validation, and an elevated risk of bias. These limitations were consistent across both clinical questions and were not resolved by increasing model complexity. The findings of this meta-analysis suggest that the structural performance of AI-based radiomics for pancreatic cysts has plateaued. To progress towards clinical translation, it is necessary to employ study designs anchored in decision-making processes, robust multi-center validation, and transparent, reproducible evaluation frameworks. This is preferred to further optimization of model architecture alone.

Background and ObjectivesWhite matter hyperintensities (WMH) of presumed vascular origin are a neuroimaging hallmark of cerebral small vessel disease (CSVD). Their spatial heterogeneity may reflect different clinical phenotypes. Most prior studies relied on principal component analysis to characterise such heterogeneity, which has limited ability to stratify individuals into discrete and interpretable WMH subtypes. We therefore propose a data-driven framework to identify WMH spatial subtypes, characterise their demographic and clinical profiles, and investigate their predictive value for future WMH progression. MethodsWe analysed MRI scans from 63,338 individuals across 4 major cohorts (internal data): ADNI3, Insight46, SABRE and UK Biobank (UKB), and validated our findings in the OASIS-3 dataset (n=844). WMH were automatically segmented and regionally quantified using a 36-region bullseye framework. Clustering was applied to the relative regional distributions of WMH. A stability-based approach was used to identify robust WMH subtypes. Their associations with 19 risk factors of interest were analysed using multivariable regression. In a subset with follow-up MRI scans (internal: n=5,274, OASIS-3: n=182), we evaluated the predictive value of these subtypes combined with other volumetric or spatial WMH variables for WMH progression. ResultsFive WMH location patterns with different lesion burden and spatial distribution were identified (stability score 0.946) and reproduced in OASIS-3. These patterns showed distinct associations with demographic, vascular, metabolic, inflammatory and genetic risk factors. Higher-burden patterns were independently associated with older age, higher blood pressure, diabetes and smoking, indicating a gradient of vascular risk across spatial subtypes. WMH location patterns were largely preserved over 18-30 months, with most individuals remaining within the same pattern (71.5%). While global baseline WMH volume remained a strong predictor of future WMH progression (balanced accuracy 0.693, 95% CI: 0.664-0.723), models including baseline regional WMH volumes consistently outperformed other candidates (best balanced accuracy 0.737, 95% CI: 0.706-0.764). DiscussionWe presented a robust and scalable framework for spatial WMH phenotyping. We discussed clinical and prognostic implications of the spatial subtypes beyond total lesion burden. Our findings supported the value of WMH spatial characterisation in stratifying risk that may help guide personalised approaches to managing CSVD.

Stressful life events impact individual's functionality and contribute to disease outcomes, yet the biological pathways underlying life stress remain unclear. We characterized the metabolomic profiles of stressful life events using data from 3,264 participants (5,163 observations) of the Dutch NESDA cohort. 98 metabolites were identified, with upregulated metabolites overrepresented in phosphatidylethanolamine and downregulated metabolites overrepresented in fatty acid metabolism. 92 of these metabolites were available in the Dutch NEO cohort (N=599): 11 were significantly replicated including six lipids (e.g., three bile acids (glycochenodeoxycholate 3-sulfate)), one carbohydrate, and one xenobiotic. 21 overlapping metabolites were additionally available in the Chinese GBCS cohort (N=200): 10-undecenoate (11:1n1) (fatty acid) and glycochenodeoxycholate 3-sulfate (bile acid) showed consistent associations across both Dutch and Chinese cohorts. Stressful life events are associated with metabolic dysregulation, particularly involving fatty acid and bile acid pathways, highlighting promising biological targets to reduce the impact of stress on mental and somatic health.

IntroductionPolygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabetes risk prediction beyond single-omics extensions and the clinical Cambridge Diabetes Risk Score (CDRS), which includes HbA1c measurements. MethodsWe analysed data from 23,325 UK Biobank participants without diagnosed diabetes at baseline. Data for a PRS for type 2 diabetes, 11 metabolites, and 15 proteins were added to the CDRS to develop multi-omics prediction models. Model performance was evaluated using Harrells C-index and the net reclassification index (NRI). ResultsDuring 10 years of follow-up, 719 participants developed incident type 2 diabetes. Among individual omics layers, proteomics contributed the greatest improvement in predictive performance, increasing the C-index from 0.857 (clinical CDRS) to 0.880 ({Delta}C-index; +0.023; P < 0.001), with an NRI of 30.0%. The full multi-omics model, further significantly increased the C- index compared to a model combining the clinical CDRS with proteomics data (C-index, 0.886; {Delta}C-index; +0.006; P < 0.033). ConclusionIntegrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins.

Background Acute respiratory distress syndrome (ARDS) is characterised by substantial physiological heterogeneity, which contribute to a very variable clinical outcomes and therefore inconsistent responses to ventilatory strategies. We aimed to externally validate physiological ARDS subphenotypes previously identified using routine ventilatory and gas-exchange variables, assess their prognostic relevance across independent cohorts, and examine heterogeneity of treatment effect according to PEEP strategy. Methods Unsupervised Gaussian Mixture Modelling was used to identify physiological subphenotypes based on ventilatory mechanics and gas-exchange parameters. Labels were subsequently used to train and validate supervised classifiers using XGBoost. Prognostic relevance was assessed across three independent cohorts, including two randomised controlled trials (ALVEOLI and LOVS). Predictive enrichment for PEEP strategy was evaluated using individual patient data from ALVEOLI and LOVS (n = 1,532) using intention-to-treat analyses, applying both one-stage and two-stage fixed-effects IPD meta-analytic approaches to test for interaction between physiological subphenotype and PEEP strategy. Results Two distinct physiological subphenotypes, termed Efficient and Restrictive, were replicated across independent cohorts. Across each cohort, patients classified as Restrictive consistently exhibited higher all-cause 28-day mortality compared to Efficient patients. When pooled across studies, the Restrictive subphenotype was associated with a significantly increased risk of death (pooled odds ratio 1.75, 95% CI 1.36-2.24), with no evidence of between-study heterogeneity. Predictive analyses showed a statistically significant interaction between physiological subphenotype and PEEP strategy in the one-stage IPD model (p for interaction = 0.037), with concordant findings in the two-stage fixed-effects IPD meta-analysis (interaction OR 1.91, 95% CI 1.00-3.66; I2 = 0%). Higher PEEP was associated with increased mortality in Efficient patients and reduced mortality in Restrictive patients, indicating effect modification by physiological subphenotype. Interpretation Physiological ARDS subphenotypes derived from routinely collected bedside data provide robust and externally validated prognostic stratification across observational and randomised trial cohorts. The observed interaction with PEEP strategy suggests that underlying physiological profiles may influence treatment response, supporting the concept that physiology-based be a starting point for personalized medicine and therefore better ventilatory strategies in future clinical trials.

Background: Host genetics alone explains limited susceptibility to tuberculosis (TB), particularly in people with HIV (PWH). Protein quantitative trait loci (pQTLs), genetic variants that regulate plasma protein levels, may bridge genetic and immunological mechanisms underlying TB progression. Methods: We conducted cis-pQTL mapping in 60 PWH who progressed to active TB and 194 matched controls from the Swiss HIV Cohort Study. Plasma proteomes were quantified via high-resolution mass spectrometry (dia-PASEF), and genotype-protein associations were analyzed separately in TB and control groups. Results: TB progressors harbored 26 cis-pQTLs linked to 12 proteins uniquely enriched in immune pathways (antigen presentation, complement activation, phagocytosis, and T-cell regulation). Controls showed 107 cis-pQTLs linked to 14 targets. Gene Ontology enrichment revealed 46 immune biological processes in TB versus only 1 in controls, with HLA-C, C4B, and CHIT1 as key TB-specific proteins. Conclusions: Integrating proteomics with genomics suggests differential regulation of immune proteins associated with TB progression in PWH. hese genetically anchored protein candidates support follow-up studies and future biomarker evaluation for TB risk prediction.

Type 2 diabetes mellitus (T2DM) is a progressive metabolic disorder characterized by persistent hyperglycemia, insulin resistance, and chronic low-grade inflammation. Despite the widespread use of established therapies such as metformin, long-term glycemic control remains suboptimal, and disease progression is often not adequately prevented. This highlights the need for novel therapeutic strategies that address both metabolic dysfunction and the underlying immunometabolic components of the disease. In this study, GLX10 (GLXM100) was evaluated as a novel immune modulator in a high-fat diet (HFD) and low-dose streptozotocin (STZ)-induced rat model of T2DM over a 91-day period. Glycemic outcomes were assessed using terminal random blood glucose and oral glucose tolerance testing (OGTT), with glucose exposure quantified by area under the curve (AUC 0-120). Complementary in vitro investigations were performed in hepatic and macrophage cell models to assess cytocompatibility, nitric oxide production, and modulation of pro-inflammatory cytokines, including IL-6 and TNF-. GLX10 treatment resulted in a significant reduction in random blood glucose levels and a marked improvement in glucose tolerance compared to diabetic control animals. Importantly, GLX10 demonstrated greater improvement in OGTT AUC compared to metformin under the same experimental conditions, indicating enhanced dynamic glucose regulation. In vitro, GLX10 maintained viability in normal hepatic cells while significantly suppressing nitric oxide production and inflammatory cytokine outputs in macrophages, supporting a favorable safety and immune profile. Collectively, these findings demonstrate that GLX10 exerts robust antidiabetic activity through a dual mechanism involving metabolic regulation and suppression of inflammatory signaling. The integration of in vivo efficacy with supportive in vitro safety and mechanistic data provides a strong preclinical foundation and supports the further development of GLX10 as a promising therapeutic candidate for T2DM.

BackgroundMetformin is the cornerstone therapy for type 2 diabetes, but gastrointestinal intolerance commonly limits dose escalation and long-term adherence. In the ProGasMet trial, multi-strain probiotic supplementation improved metformin tolerability. However, the underlying microbiome-metabolome mechanisms remain unclear. Methods and analysisWe performed an exploratory multi-omics analysis using Period 1 of a randomized, double-blind, placebo-controlled trial. Participants with metformin intolerance received a multi-strain probiotic or placebo for 12 weeks. Paired stool samples collected at baseline (Visit 2) and end of treatment (Visit 5) were available from 34 participants (68 samples). We integrated shotgun metagenomic species profiles, predicted gut metabolic modules, and untargeted faecal LC-MS metabolomics using multi-block sparse PLS (DIABLO), complemented by longitudinal feature-level analyses and associations with gastrointestinal symptom burden (QACSMI and a simplified GI score). ResultsMulti-omics integration showed moderate concordance across taxonomic, functional, and metabolomic blocks and separated probiotic from placebo profiles at 12 weeks. Bile acid-related metabolites were among the strongest contributors to group separation, with hyodeoxycholic acid and related compounds enriched in the probiotic arm. Global biodiversity and community-wide turnover did not differ materially between groups. Feature-level analyses suggested modest, directionally coherent changes in selected taxa, functional modules, and metabolites. Higher hyodeoxycholic acid concentrations at Visit 5 were associated with lower gastrointestinal symptom burden in probiotic-treated participants, a pattern not observed under placebo; statistical support was exploratory. ConclusionProbiotic supplementation may be associated with coordinated microbiome-metabolome shifts in metformin-intolerant type 2 diabetes, highlighting bile acid remodelling, particularly hyodeoxycholic acid, as a plausible mechanistic candidate for improved tolerability.

BackgroundDrug responses in pancreatic ductal adenocarcinoma (PDAC) vary sharply across in vitro culture formats, but most 2D-3D comparisons conflate microenvironmental cues with time-dependent cellular adaptation. As a result, conventional assays frequently overestimate drug efficacy and poorly reflect clinical pharmacology. Main findingsWe profiled MiaPaCa-2, PANC-1, and CFPAC-1 grown in an extracellular-matrix (ECM) hydrogel for 1-12 days, defining extended 3D cultures ([&ge;]10 days) as mature tumoroids, and quantified 72 h drug responses to a multi-class oncology panel using growth-rate (GR) metrics to normalize for proliferation across formats and durations. Prolonged 3D pre-culture induced broad tolerance, with typical 10-100x reductions in sensitivity to standards of care (5-fluorouracil, SN38, oxaliplatin, gemcitabine, paclitaxel), following a reproducible susceptibility hierarchy (MiaPaCa-2 > PANC-1 > CFPAC-1) after GR correction. In mature tumoroids, GR values closely approximated clinically observed plasma exposures (e.g., within <4x for 5-FU and <0.5x for gemcitabine), whereas 2D and short-term organoid assays markedly underestimated resistance, often by >100x, thereby overstating drug activity. Notably, CFPAC-1 exhibited increased sensitivity to SN38 and trametinib under mature-organoid conditions, demonstrating that microenvironmental conditioning can invert responses for selected mechanisms. Transcriptomic profiling revealed coordinated up-regulation of multiple ABC transporters with extended 3D residence, tracking resistance phenotypes across lines and implicating transporter-linked tolerance programs. SignificanceTogether, these data identify time-in-3D and the emergence of mature tumoroids as dominant, previously under-controlled determinants of PDAC pharmacology that both induce tolerance and unmask context-dependent vulnerabilities. We propose incorporating both short-term and mature-tumoroid screening arms into preclinical workflows, reporting pre-culture duration alongside GR-normalized effect sizes, and leveraging transporter-informed biomarkers to guide regimen prioritization and sequencing. This framework enhances physiological relevance, reproducibility, and translational fidelity in PDAC drug discovery.

Glycated hemoglobin (HbA1c) is a central biomarker for long-term glycemic control and diabetes management, traditionally quantified using laboratory-intensive chromatographic or immunochemical assays. As the global burden of diabetes continues to rise, there is growing interest in alternative, scalable approaches capable of rapid biochemical assessment. Fourier-transform infrared (FTIR) spectroscopy offers a reagent-free method that captures molecular signatures of protein glycation, but translating complex spectra into clinically interpretable HbA1c values requires robust analytical frameworks. Here, we present a complementary multi-model strategy for predicting HbA1c from FTIR spectra of whole blood. Using 685 blood samples with matched reference HbA1c measurements, we evaluated three analytically distinct yet synergistic approaches: partial least squares regression (PLSR), peak-resolved curve fitting based on pseudo-Voigt functions combined with H2O AutoML, and a convolutional neural network (CNN). PLSR and CNN models were trained on biologically informative spectral regions (800-1800 cm-{superscript 1} and 2800-3400 cm-{superscript 1}), while curve fitting focused on the fingerprint region (1000-1720 cm-{superscript 1}) to extract interpretable biochemical parameters. PLSR achieved the highest predictive accuracy (R{superscript 2} = 0.76), closely followed by the CNN (R{superscript 2} = 0.73), reflecting their ability to capture global linear and nonlinear spectral relationships. Although curve fitting yielded lower predictive performance (R{superscript 2} = 0.59), its peak-level decomposition enabled mechanistic interpretation of glycation-related changes. Explainable AI analysis using SHAP identified lipid- and protein-associated vibrations, carbohydrate-linked glycation bands, and amide-region structural features as key contributors to HbA1c prediction. Rather than treating these approaches as competing alternatives, our results demonstrate that their integration provides a more informative framework than any single model alone. By combining predictive performance with biochemical interpretability, this multi-model FTIR strategy highlights a scalable and mechanistically grounded pathway toward non-invasive HbA1c assessment and broader metabolic screening in diabetes monitoring. The code for this study is freely available at https://github.com/MelnychenkoM/ftir-hba1c-prediction.

Intensive care unit (ICU) monitoring faces a critical challenge: translating continuous physiological data into actionable insights for real-time clinical decisions. We developed STREAM (State Trajectory Representation & Evolution-Aware Monitoring), which applies optimal transport theory to identify distinct physiological states from routine ICU data and maps individual patients onto state progressions. Using the multicenter eICU Collaborative Research Database (N=158,294), STREAM identified five reproducible states. Patients whose measurements differ substantially from their assigned state (state outliers) showed 9-fold higher mortality (45.7%) versus patients who remained inside state boundaries (5.1%). State-based features predicted mortality with AUROC 0.863 at 8 hours and 0.903 at 72 hours, with excellent calibration error score (0.002). External validation on MIMIC-IV (N=84,517) demonstrated robust performance (AUROC 0.798 and 0.857, respectively), with state outliers exhibited a 7.1-fold higher mortality risk (41.1% vs. 5.8%). Importantly, STREAM connects this risk stratification to the underlying clinical measures defining each physiological state, providing accurate mortality predictions and interpretable insight.

PurposePortal hypertension, a major complication of chronic liver disease, leads to significant morbidity and mortality. While portal vein diameter measured on imaging has long been proposed as a non-invasive marker of portal hypertension, normative CT-based reference values and population-level associations remain incompletely characterized. Here, we aim to define contemporary reference values for portal vein diameter on clinically obtained CT and evaluate its associations with demographic, clinical, and imaging factors, as well as its diagnostic performance for portal hypertension. MethodsWe conducted a retrospective analysis of 20,225 clinically obtained CT scans at a single academic medical center. The main portal vein was automatically segmented using Total Segmentator, and maximum diameter extracted using the Vascular Modeling Toolkit. Associations with demographic and imaging factors were evaluated using linear mixed-effects models; prevalent liver disease and portal hypertension using logistic regression; risk of incident ascites and esophageal varices among participants with liver disease using Cox regression; and invasive hepatic venous pressures using correlation analysis and linear regression. ResultsThe mean portal vein diameter was 12.4 mm (95% CI, 12.37-12.45). Larger diameter was independently associated with male sex (+1.4 mm), higher BMI (+0.11 mm/kg/m2), greater height (+0.04 mm/cm), and older age (+0.05 mm/10 years) (all p <0.001), and was substantially larger on contrast-enhanced abdomen/pelvis CT (+2.4 mm, p <0.001). Each 1-mm increase in portal vein diameter was associated with higher odds of prevalent liver disease (OR 1.06; 95% CI, 1.04-1.08) and portal hypertension (OR 1.18; 95% CI, 1.12-1.28). Among individuals with liver disease, greater diameter predicted higher risk of incident esophageal varices (baseline diameter HR 1.50; 95% CI, 1.14-2.08) and ascites (HR per mm increase in diameter 1.06; 95% CI, 1.003-1.12). However, portal vein diameter demonstrated weak to no association with invasively measured hepatic venous pressures. ConclusionIn this large, EHR-linked imaging cohort, the mean portal vein diameter on CT was 12.4 mm and varied with demographic and imaging factors. Larger diameter was associated with liver disease, portal hypertension, and subsequent development of varices and ascites, supporting use of portal vein diameter as a pragmatic screening or enrichment tool within multimodal clinical frameworks. Key ResultsO_LIMean portal vein diameter on routine clinical CT was 12.4 mm (95% CI, 12.37-12.45) and varied with sex, height, BMI, exam type, contrast use, and clinical setting. C_LIO_LIEach 1-mm increase in portal vein diameter was associated with higher odds of prevalent liver disease (OR 1.06) and portal hypertension (OR 1.18). C_LIO_LIAmong individuals with liver disease, larger portal vein diameter predicted higher risk of incident esophageal varices and ascites, independent of demographic and imaging factors. C_LI