Advanced Science

BackgroundRetrieval-augmented generation (RAG) frameworks such as RAPID [1] have demonstrated that staged planning and retrieval grounding improve long-form text generation. However, most implementations remain similarity-driven and open-domain, lacking the epistemic safeguards required for biomedical synthesis, where mechanistic completeness, temporal governance, traceability, and explicit gap classification are essential. ObjectiveTo develop and evaluate a topology-aware, graph-augmented retrieval framework for structured biomedical narrative synthesis, and to position it as a domain-constrained evolution of staged RAG aligned with structural principles of digital evidence-based medicine (dEBM). MethodsWe implemented a two-layer architecture operating on a closed, version-controlled corpus of 11,861 peer-reviewed text chunks on iron deficiency. A metadata-constrained vector retriever (RAG01) was extended with a Graph-RAG (RAG02) overlay (RAG02) constructed from chunk-level entity extraction and weighted co-occurrence networks (30 nodes; 118 directed edges). Topic planning was organized through predefined mechanistic axes functioning as structured hypothesis probes. Retrieval was performed under identical deterministic constraints (top-k = 5; cosine threshold = 0.50; publication year [≥] 2023), and graph diagnostics--including local connectivity, induced subgraph density, modular overlap, and multi-hop stability--were used to distinguish retrieval insufficiency from corpus-level evidentiary scarcity. ResultsIn a case study of obesity-associated iron deficiency, the entity network exhibited a centralized regulatory topology with hepcidin as a high-connectivity hub. Axis-based retrieval combined with graph auditing consistently reinforced an inflammation-mediated hepcidin pathway linking obesity to iron deficiency, while alternative mechanisms lacked stable multi-hop embedding. Compared with vector-only retrieval, graph augmentation preserved semantic alignment and increased mean cosine similarity from 0.673 to 0.694 while reducing similarity dispersion (SD 0.056 to 0.035) under identical constraints. Graph activity ratio was 1.00 in the temporally filtered corpus. ConclusionsBy integrating mechanistic axis decomposition, topology-aware auditing, causal scaffolding, and expert-driven iterative refinement, the proposed framework implements selected structural constraints inspired by evidence-based medicine within a controlled digital synthesis environment. The approach advances retrieval-augmented generation beyond similarity-based summarization toward a reproducible model of topology-aware biomedical evidence interrogation with implications for AI-assisted systematic reviews.

Biological age estimators quantify aging-related variation but provide limited insight into organ-specific aging processes. The retina enables non-invasive visualization of microvascular and neural structures and has emerged as a promising modality for biological age prediction. However, existing retinal aging models typically produce unidimensional age estimates with limited interpretability. Here we develop a deep learning framework based on a large-scale vision foundation model to estimate retinal biological age from fundus images and to characterize the physiological heterogeneity underlying retinal aging. Using a reference cohort of 56,019 relatively healthy individuals, the model achieved a Mean Absolute Error of 2.48 years in age prediction. Analysis of age deviations in a real-world clinical cohort (n = 46,369) revealed non-linear associations with cardiometabolic risk and population heterogeneity in aging patterns. Integrating multidimensional physiological profiling, feature attribution and unsupervised analysis, we identified distinct retinal aging signatures associated with systemic inflammation and hemodynamic variation. To further characterize age-related deviations, we introduced a residual learning framework that decomposes retinal aging signals into a normative age-related component and additional components associated with physiological variation, achieving a Mean Absolute Error of 1.80 years on the independent healthy test set. This approach provides an interpretable representation of retinal aging and a framework for studying organ-level aging processes and their relationship to systemic health using large-scale imaging data.

Background & AimsPerihilar cholangiocarcinoma is an aggressive malignancy with clinical heterogeneity and poor long-term outcomes after resection. Current prognostic assessment relies mainly on anatomical staging and pathological features, which incompletely capture the entire postoperative risk. We aimed to determine whether integrative analysis of clinical, surgical, pathological and tumor genomic data could improve time-resolved, individualized recurrence-risk prediction after curative-intent resection. MethodsWe performed a multicenter retrospective study including patients undergoing curative-intent resection for perihilar cholangiocarcinoma in ten Spanish hospitals (2003-2023). Overall and disease-free survival were analyzed using Cox models. Outcome-agnostic clinical phenotypes were derived by unsupervised clustering of clinical and surgical features. Targeted tumor sequencing of cancer-associated hotspot regions and selected genes was performed. Prognostic models integrating clinical and genomic data were trained and evaluated in independent training/test sets using penalized and latent-component Cox frameworks, with time dependent discrimination. ResultsThe final cohort comprised 142 patients, with a median follow-up of 26.4 months. Recurrence occurred in 61.3% of patients, and 53.5% died during follow-up. Classical pathological factors were strongly associated with survival and recurrence. Unsupervised outcome-agnostic clustering identified three reproducible clinical phenotypes with markedly different recurrence patterns and survival, only partially explained by anatomical staging. Integrative clinical-genomic modelling further improved recurrence-risk prediction, achieving high discrimination in independent validation (time-dependent AUC [~]0.8). Moreover, the integrative model assigned higher risk over time to patients who relapsed. Patients combining unfavorable clinical phenotype with high genomic-derived risk exhibited a high probability of early recurrence. ConclusionsIntegrated clinical phenotyping and targeted genomic profiling substantially refine recurrence-risk stratification after resection of perihilar cholangiocarcinoma beyond anatomical staging alone. This provides a pragmatic framework for risk-adapted postoperative surveillance and therapeutic decision-making. Impact and ImplicationsThis study provides a data-driven framework integrating clinical, surgical and targeted genomic information to refine prognostic stratification after resection of perihilar cholangiocarcinoma, addressing the limitations of anatomy-based staging in capturing biological heterogeneity. The results are particularly relevant for clinicians managing postoperative surveillance and adjuvant strategies, as they identify patient subgroups with markedly different risks of early recurrence despite similar conventional staging. In practical terms, the combination of unsupervised clinical phenotyping and a targeted, biologically informed genomic panel could support risk-adapted follow-up intensity, selection for adjuvant or experimental therapies, and enrolment into clinical trials. While prospective validation is required before routine implementation, this approach offers a feasible and interpretable pathway toward precision postoperative management in a highly aggressive malignancy.

The daily cortisol cycle is a critical indicator of hypothalamic-pituitary-adrenal (HPA) axis function. The current analytical approaches produce several outputs difficult to integrate into simple statistical models, clinical workflows, and ML/AI pipelines requiring single-value inputs. We developed the Cortisol Sine Score (CSS), a model-free scalar metric that quantifies daily cortisol exposure by computing a weighted sum of cortisol measurements across the day, using sine-transformed time-of-day weights. The CSS produces positive values for morning-dominant patterns, negative values for evening-shifted profiles, and near-zero values for flattened rhythms characteristic of chronic stress and circadian disruption. We validated the CSS performance in 3,006 samples from 501 pregnant women enrolled in the March of Dimes program, with cortisol values measured at 6 time points per day collected during the second trimester of pregnancy. The CSS showed strong correlations with observed and model-estimated amplitude and acrophase from Cosinor regression and JTK_CYCLE approaches, with excellent classifying performance (AUC=0.89, high versus low). The CSS successfully captured established associations between social disadvantage and cortisol dysregulation, and demonstrated utility in predicting gut microbiome composition in metagenomic analyses. Importantly, the CSS maintains excellent fidelity to the full 6-sample protocol with as few as 3-4 daily measurements. The 4-sample protocol achieves great performance (r = 0.952, MAE = 0.087) while reducing participant burden. The 06:00 time point was identified as essential for accurate CSS quantification. The CSS bridges the gap between circadian analysis and practical implementation by providing a simple, interpretable, and robust assessment of cortisol daily cycle in large-scale epidemiological studies, clinical screening, and biomedical sensors. HighlightsO_LICurrent state-of-the-art approaches estimating the daily cortisol exposures produce multi-output information difficult to implement in simple statistical analyses or ML/AI multi-omics approaches C_LIO_LICortisol Sine Score is a novel model-free scalar metric expressing cortisol daily exposure and rhythmicity (morning vs evening exposure) C_LIO_LICortisol Sine Score was validated using 3006 salivary samples from clinical data and golden standards in circadian analyses such as Cosinor and JTK_CYCLE C_LIO_LICortisol Sine Score was the top performer in our benchmarking approach predicting association with social disadvantage and gut microbiome composition C_LIO_LIReliable with 3-4 daily samples, reducing participant burden C_LIO_LIOpen-source R package CortSineScore democratizes cortisol cycle analysis C_LI

Gait variability is a critical functional indicator of dynamic balance and neurocognitive decline in health. Its translation into clinical practice is, however, challenged by a lack of age-related normative trajectories and reference values under real-world ecological settings. Furthermore, the conventional metrics used to estimate gait variability (Coefficient of Variation, CV; Standard Deviation, SD) have a fundamental methodological flaw: the inherent sensitivity of conventional metrics to the statistical outliers and environmental noise in real-world walking. In this study, we mitigate this factor by applying a robust statistical framework to quantify gait variability. Analysing a large-scale cohort of community-dwelling older adults (n=2,193), we first demonstrate that free-living gait data follows a heavy-tailed distribution, necessitating the use of robust estimators like the Robust Coefficient of Variation (RCV-MAD) and Median Absolute Deviation (MAD). Leveraging these metrics, we established the normative trajectory and reference values of real-world gait variability across the ageing lifespan, revealing a distinct, age-dependent increase in spatio-temporal fluctuations, indicating a decline in rhythmicity and steadiness with age. We further demonstrated the clinical utility of these robust metrics: RCV-MAD consistently yielded larger effect sizes than conventional CV in discriminating between fallers and non-fallers across all gait parameters. Furthermore, we illustrate the potential of long-term unsupervised monitoring to capture intrinsic variability during real-world walking. Validated for consistency and reliability, this robust framework provides the necessary ecological validity to transform gait variability into a standardised, rapid clinical metric for assessing functional decline at an early timepoint.

BackgroundIn lung transplantation, de novo immunodominant donor-specific anti-HLA antibodies recognizing HLA-DQ antigens (dn-iDSA-DQ) are predominant and can induce chronic lung allograft dysfunction (CLAD). We previously developed a method to measure the active concentration of dn-iDSA-DQ. We aimed to determine whether this new quantitative biomarker is associated with transplantation outcomes. MethodsThis retrospective multicentre cohort study included 90 lung transplant recipients (LTRs) developing dn-iDSA-DQ, evidenced through single antigen flow beads (SAFB) follow-up. We measured the active concentration of dn-iDSA-DQ at the time of their first detection (T0) for all LTRs, and within the 2 years after DSA detection, whenever possible. SAFB dn-iDSA-DQ characteristics and clinical data were retrieved up to 5 years after DSA detection. ResultsWe tested 184 sera with SPR (n=90 at T0, n=94 within the 2 years after DSA detection), among which 63 (34.4%) had a quantifiable concentration of the dn-iDSA-DQ ([&ge;]0.3 nM). The median SAFB mean fluorescence intensity (MFI) of the dn-iDSA-DQ with a concentration [&ge;]0.3 nM was higher (p<0.0001), yet the correlation between SAFB MFI and active concentration was low (r=0.758, p<0.0001). In multivariate analysis, a concentration of the dn-iDSA-DQ [&ge;]0.3 nM at T0 was independently associated with a lower 2-year CLAD-free survival (HR 2.06, p=0.02). A concentration of the dn-iDSA-DQ [&ge;]0.3 nM within the 2 years from DSA detection was associated with a lower graft survival in univariate analysis. ConclusionsActive concentration of dn-iDSA-DQ appears as a valuable biomarker to identify pathogenic DSA at their first detection because of its association with CLAD.

The biomarker MetaboHealth represents a novel indicator of overall health in middle age and may potentially be suitable as actionable health check in prevention strategies. MetaboHealth is a blood-based metabolomic composite score that predicts a wide range of age-related conditions and mortality in large European cohorts. Here, we investigated whether MetaboHealth can be personalised and limited to clinically validated metabolomic markers. Next, we assessed whether the updated MetaboHealth score predicts all-cause mortality and cardiometabolic disease incidence and can be improved by a lifestyle intervention. To personalise MetaboHealth, we scaled the metabolomic markers using a Dutch reference population (i.e. the Biobanking and BioMolecular Research Infrastructure Netherlands) and, in addition, based the score solely on clinically validated metabolic markers. The novel version of the score, Personal-MetaboHealth, retained predictive accuracy for all-cause mortality and showed an even stronger association with incident cardiometabolic disease in the Leiden Longevity Study (LLS) in which 2,404 participants were followed for up to 22 and 16 years for mortality and morbidity, respectively. The association of Personal-MetaboHealth with all-cause mortality remained robust after adjusting for smoking, alcohol use, and medication, while the cardiometabolic disease association was partially driven by smoking. Each standard deviation decrease in Personal-MetaboHealth was associated with a 11.7 year earlier onset of the first cardiometabolic disease in the LLS. Next we showed that Personal-MetaboHealth can be improved by a 3-month combined lifestyle intervention in middle aged individuals (Growing Old Together study), specifically in those at risk with an unhealthy score at baseline. Personal-MetaboHealth thus offers a potential actionable health check in middle age for early prevention and extension of healthy lifespan.

BackgroundElectronic Health Records(EHR) are very crucial for Clinical Decision Support Systems and for proper care to be delivered to ICU heart failure patients, there is often missing data due to monitoring device errors thus the need for robust imputation methodologies. ObjectiveTo compare and evaluate three different methodologies for imputing missing data for heart failure patients from the MIMIC-III database: Denoising Autoencoder (DAE), Self-Attention Imputation for Time Series (SAITS), and Multiple Imputation by Chained Equations (MICE) with LightGBM. MethodsAnalysis of 14,090 ICU admissions for patients with heart failure was performed using data from the MIMIC-III database. Features were selected based off of clinical relevance, and 19 clinical features were selected through a combination of Random Forest analysis, correlation analysis, and Mutual Information. The introduction of artificial missing values of 20%, 30%, and 50% was applied to the data set, and then 3 imputation methodologies were evaluated with the DAE, SAITS, and MICE+LightGBM. The performance of each imputation methodology was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). ResultsBoth DAE and SAITS had superior performance on the imputation of missing values across all percentages of missing values. At 20% missingness, DAE had mean MAE = 0.004967, RMSE = 0.005217, and NRMSE = 3.260893 while SAITS had mean MAE = 0.005461, RMSE = 0.005797, and NRMSE = 3.244695; thus MICE+LightGBM resulted in a higher number of errors. At 50% missingness, the SAITS methodology demonstrated the best performance followed by DAE and MICE+LightGBM methods demonstrated decreased performance. The deep learning methodologies maintained a consistent level of accuracy between the clinical variables measured. ConclusionsOur analysis indicates that deep learning-based imputation methodologies significantly outperform traditional methodologies for imputing missing values in ICU heart failure data thus supporting the implementation of these methodologies into Clinical Decision Support Systems for heart failure patients.

BackgroundIntrinsic capacity (IC) is a key marker of healthy ageing, which captures an individuals physical and mental capacities, measured across five domains: cognitive, locomotor, psychological, vitality, and sensory. Although genetic factors are known to influence both general IC and its individual domains, existing IC indices have been developed primarily using phenotypic data, without accounting for the underlying biological architecture across domains. In this study, we developed a multi-trait polygenic score (Mt-PGS) model for IC by integrating polygenic scores derived from a broad set of phenotypes spanning the five IC domains and examined its validity. MethodsUsing data from 13,085 participants of the Canadian Longitudinal Study on Aging (CLSA), we computed PGSs for 63 phenotypes related to IC domains. A supervised machine-learning model was applied to develop a mt-PGS model for IC and identify the optimal set of polygenic predictors. The validity of the mt-PGS IC score was evaluated by comparing it with a phenotype-based IC score and by examining its association with mortality. ResultsOur analysis identified PGSs for 33 phenotypes with non-zero coefficients, jointly explaining 2.23% of the variance in IC. Several of the strongest contributors were most closely aligned with vitality-related phenotypes in the literature (including body mass index, grip strength, fat-free mass, diastolic blood pressure, and chronic obstructive pulmonary disease), acknowledging cross-domain relevance, and that predictors from all five IC domains were represented. The mt-PGS IC score was consistent with the phenotype-based IC score, positively correlated with the phenotype-based IC score and was inversely associated with mortality (OR = 0.04; 95% CI: 0.005 - 0.379). ConclusionOur findings support the multisystem biological basis of IC, demonstrating that an mt-PGS model integrating diverse phenotypes is associated with the phenotype-based IC score. PGSs for the phenotypes frequently related to vitality in the literature were the strongest predictors, recognizing that several of these phenotypes may span multiple domains, and that all domains contributed to the model. If replicated across different ancestries and settings, these findings may serve as a foundation for future research for the potential integration of genetic information into IC frameworks.

BackgroundNeurodegenerative diseases, including Alzheimers disease (AD), exhibit substantial clinical and molecular heterogeneity, complicating accurate diagnosis and development of effective therapies. Although multi-omics profiling provides unprecedented molecular resolution, systematic integration of high-dimensional, imbalanced data modalities with disease-relevant biological networks remains a methodological challenge. MethodsWe developed a network-informed multi-omics integration framework that combines data-driven molecular networks with brain transcriptomic, proteomic, and metabolomic data from 356 participants in the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP). Utilizing 25 functional, data-driven multi-omics groups (DAD-MUGs) derived by graph embedding from the AD Atlas, co-expression-guided feature extraction and systematic two-phase feature balancing were applied to derive representative molecular features, which were subsequently learned using DAD-MUG-specific autoencoders to generate compact multi-omics expression scores. These were then used to identify molecular subgroups via hierarchical clustering. Subgroup robustness was assessed in an independent ROS/MAP cohort (n=327) using a two-round nested classification strategy. ResultsSubgroup identification based on DAD-MUG-derived expression scores resulted in five molecular subgroups exhibiting significant differences in cognitive performance and core neuropathological measures. Cross-validated nested classification using transcriptomic and proteomic data demonstrated reliable discrimination of subgroups. Applying these classifiers to the replication cohort, subgroup-trait association patterns showed strong agreement with discovery findings (Spearman {rho} = 0.65). Differential expression analysis further revealed stage-dependent biological patterns of brain pathologies, ranging from early synaptic and immune activation to mitochondrial bioenergetic dysfunction at disease transition and proteostatic impairment in advanced stages. ConclusionUsing a balanced, network-informed multi-omics integration framework, we identified five molecular subgroups of brain aging, including a reference control subgroup and a distinct mixed subgroup characterized by amyloid, vascular pathology, and early-life adversity. Three additional subgroups formed a structured spectrum comprising molecularly Alzheimers-like but cognitively and neuropathologically unimpaired At-risk controls, an intermediate stage, and typical Alzheimers disease, with tau pathology differentiating advanced disease, underscoring the value of molecular subgroup identification beyond clinical diagnosis.

Respiratory monitoring in daily-life settings is important for health assessment, yet extracting physiologically interpretable information from breathing signals under natural conditions remains challenging, as breathing is inherently dynamic and strongly modulated by behavior. Here, a portable breathing monitoring device based on a flexible lead zirconate titanate sensor is developed to address this challenge. By exploiting polarity-opposed piezoelectric and pyroelectric responses through sensor orientation, the recorded breathing waveform exhibits a characteristic dual-component structure, consisting of a narrow transient spike followed by a broad quasi-steady peak within each breathing phase. This intrinsic waveform structure enables phase-resolved quantification of how breathing effort is distributed between transient and quasi-steady components during inhalation and exhalation. Pilot measurements in healthy subjects and patients with chronic obstructive pulmonary disease or asthma reveal systematic shifts toward transient-enhanced breathing in patients, providing clearer differentiation than conventional descriptors based on breathing duration or amplitude. By transforming complex breathing dynamics into stable and physiologically meaningful signal components under daily-life conditions, this dual-response sensing approach enables more robust access to function-related changes in natural breathing.

Sodium (23Na) magnetic resonance imaging (MRI) provides valuable metabolic information, but it is limited by a low signal-to-noise ratio (SNR) and long acquisition times. To overcome these challenges, we present a Deep Image Prior (DIP)-based framework that combines anatomically guided proton (1H) MRI and metabolically guided 23Na MRI denoising via a fused proton-sodium prior within a directional total variation (dTV) regularization scheme. The DIP-Fusion approach minimizes a variational loss function combining data fidelity, fused dTV regularization, gradient consistency, and bias-field correction to reconstruct sodium images. MRI data were acquired from healthy volunteers and breast cancer patients. Healthy datasets were retrospectively undersampled at multiple factors, and fully sampled scans served as the ground truth. Patient datasets acquired for clinical purposes were reconstructed using the baseline DIP and the proposed DIP-Fusion methods. Sodium images were reconstructed using sum-of-squares (SoS) and adaptive combined (ADC) coil combination methods. We evaluated reconstruction performance using quantitative image quality metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), mean squared error (MSE), learned perceptual image patch similarity (LPIPS), feature similarity index (FSIM), and Laplacian focus. In healthy volunteers, DIP-Fusion outperformed state-of-the-art reconstruction techniques across all undersampling factors. In patient datasets, DIP-Fusion demonstrated superior performance compared with baseline DIP, achieving improved structural fidelity and sodium-specific signal preservation. These results demonstrate the potential for robust, highquality sodium MRI reconstruction under accelerated acquisition, which could lead to reduced scan times and enhanced clinical feasibility.

Diet is an essential factor influencing biological aging, yet few exsiting dietary indices were specifically developed to target biological aging. We developed a data-driven food-based Empirical Dietary Index for Slower Epigenetic Aging (EDISEA) in the US Health and Retirement Study (HRS, n=7,398), which predicted deceleration of GrimAge, an established DNA methylation-based epigenetic clock. Participants in the highest versus lowest EDISEA quintile had 4.65-year deceleration in GrimAge (P value <0.001). We externally validated EDISEA in an independent US cohort (n=23,830), where it showed consistent associations with several epigenetic clocks and lower all-cause mortality risk. In HRS and a UK aging cohort (n=4,895), EDISEA was associated with lower risks of several aging-related diseases and functional limitations. Outcome-wide analyses in the UK Biobank (n=187,035), together with integrative proteomic, metabolic, and neuroimaging assessments, revealed biological signatures of EDISEA implicating broad vascular, inflammatory, metabolic, and brain-structural pathways through which EDISEA was associated with biological aging. EDISEA provides a scalable, biologically anchored tool to inform the development of precision nutrition strategies aimed at slowing epigenetic aging and mitigating aging-related disease burden.

Executive dysfunction affects nearly 50% of individuals with traumatic brain injuries (TBI), yet interventions targeting the underlying neural mechanisms remain limited. This study examined whether aerobic exercise modulates functional connectivity to improve executive function in individuals with mild TBI and identified the neural pathways mediating these improvements. In this secondary analysis of a 12-week pilot randomized controlled trial, participants with mild TBI (n=24) were randomized to aerobic exercise (n=12) or active balance control (n=12). Resting-state fMRI with multivariate pattern analysis revealed that aerobic exercise selectively altered functional connectivity patterns of the anterior cingulate cortex (ACC) compared to balance control. Post-hoc seed-to-voxel analyses identified widespread ACC connectivity differences between groups post-intervention while controlling for baseline, across 19 cortical regions spanning default mode, frontoparietal control, and salience networks. Critically, greater anticorrelation between the ACC and insula following aerobic exercise was associated with improved Trail Making Test B-A performance in the aerobic group ({beta}=46.92, p=0.04) but not the balance group, indicating that participants who developed stronger ACC-insula functional segregation showed greater reductions in executive function completion times. These findings establish the ACC-insula circuit as a critical neural substrate mediating exercise-induced executive function recovery after TBI and identify this pathway as a promising therapeutic target for exercise-based rehabilitation interventions.

Adaptation to physiological stress is fundamental to health but varies widely among individuals. In humans, this heterogeneity is evident in markedly different gains in fitness in response to identical exercise training. The molecular determinants of this variable "trainability" remain poorly understood. Here we identify insulin-like growth factor binding protein-7 (IGFBP7), a senescence-associated secreted protein, as a circulating constraint on exercise adaptation. Plasma proteomics in older adults enrolled in a randomized exercise trial revealed that IGFBP7 levels inversely predicted fitness gains after one year of high-intensity interval training despite similar baseline fitness. In mice, genetic deletion of IGFBP7 markedly amplified training-induced gains in exercise capacity across distinct training protocols, whereas somatic overexpression abolished this advantage. In the UK Biobank, lower IGFBP7 levels were associated with reduced mortality and multiple incident age-related diseases, mirroring the breadth of ties between fitness and healthspan. Together, these findings identify circulating IGFBP7 as a molecular brake on physiological plasticity in response to exercise, linking training responsiveness, aging biology, and health outcomes.

IntroductionThis clinical trial investigates the efficacy and safety of a personalized 15-day accelerated intermittent theta-burst stimulation (aiTBS) protocol, targeted at either the default mode network (DMN) or the fronto-parietal network (FPN), in individuals with mild Alzheimers disease (AD). Methods45 patients with mild AD were randomized 1:1:1 to receive 15 consecutive days of high-dose aiTBS (7200 pulses/day) targeting the DMN or FPN, or sham. The primary outcome was the change in ADAS-Cog after 15 days of treatment. ResultsBoth active aiTBS groups demonstrated significantly greater ADAS-Cog improvement than sham at the primary endpoint. Response rates for a clinically meaningful improvement ([&ge;]3-points on ADAS-Cog) were significantly higher in the active groups (DMN: 38%; FPN: 47%) than in the sham group (0%). The improvement in active groups was sustained at 3-month follow-up. DiscussionPersonalized aiTBS targeting the DMN or FPN produced clinically meaningful cognitive benefits in mild AD and was safe.

BackgroundIron metabolism disorder is highly prevalent before and after heart transplantation (HTx). The impact of pretransplant and posttransplant iron disorder on posttransplant outcomes is unclear. ObjectivePretransplant serum levels of key regulator proteins of iron metabolism (hepcidin, interleukin-6, erythroferrone) were tested for prediction of the composite outcome 1-year posttransplant all-cause mortality (ACM) or [&ge;]moderate acute cellular rejection (ACR). Furthermore, serum levels of these proteins were measured at 1-year posttransplant to explore their posttransplant course and association with ACR. ResultsIn a multicenter cohort including 276 consecutive HTx recipients, patients with or without outcome (n=118/158, respectively) did not differ for pretransplant demographics, mismatch of donor/recipient sex, mismatch of HLA epitopes, and hepcidin or interleukin-6 levels. However, pretransplant erythroferrone levels were higher (1.40 vs. 1.19 ng/mL; p=0.013) and hemoglobin levels were lower (124.5 vs. 127 g/L; p=0.004) among patients with the composite outcome. Pretransplant erythroferrone levels >2.25 ng/ml (4th-quartile) were significantly associated with the composite outcome in multivariable analysis (OR 2.17; 95% CI 1.19-3.94, p=0.011; reference: 1st-3rd quartiles). In adjusted predicted proportions analysis, the incidence of the composite outcome was higher in 4th-quartile patients when compared to 1-3rd -quartiles patients (58.0 vs. 37.7%; p=0.003). At 1-year posttransplant, 80.4% of patients with pretransplant erythroferrone levels >2.25 ng/ml remained high; 88.4% of patients with pretransplant erythroferrone levels [&le;]2.25 ng/ml had high levels posttransplant. In 1-year survivors with high erythroferrone levels and [&ge;]moderate ACR during the first postoperative year, the ratio of the opponent regulators of hepcidin gene expression, erythroferrone to interleukin-6, was higher when compared to those without ACR (1.18 vs. 0.41; p=0.016). Hepcidin levels were not different between these two subgroups indicating disproportionate ERFE increase. ConclusionHigh pretransplant erythroferrone levels predict the composite posttransplant outcome 1-year ACM and ACR. Disproportionately high posttransplant erythroferrone levels are related with [&ge;]moderate acute cellular rejection.

BackgroundSedentary behavior is highly prevalent following traumatic brain injury (TBI) and compounds existing risks for cardiovascular, neurodegenerative, and affective disorders. The cognitive and behavioral sequelae of TBI, including impaired decision-making, blunted reward processing, and cognitive fatigue, create particular barriers to adopting and maintaining an active lifestyle. Despite this, effective behavior change interventions targeting physical activity in community-dwelling TBI survivors remain scarce. Here, we evaluated the feasibility, compliance, and preliminary efficacy of a 12-week remotely delivered walking intervention combining planning, behavioral reminders, and monetary micro-incentives. MethodsFifty-six adults aged 40-80 years with a mild-to-moderate TBI diagnosed between 3 months and 15 years prior were randomized to either a planning, reminders, and micro-incentives intervention (n=23) or a health advice control condition (n=25). Participants wore a Fitbit Inspire 3 continuously throughout the study. Intervention participants completed weekly phone calls to plan five 30-minute walks for the following week, received daily text message or email reminders on planned walk days, and earned small monetary incentives upon walk completion. Control participants received weekly health education calls. Feasibility was assessed through recruitment, retention, and adverse event rates. Compliance was assessed via phone call completion rates and Fitbit wear time. Efficacy outcomes included weekly walk counts, walking duration, and step counts, modeled using Poisson generalized linear mixed models and linear mixed-effects models over 12 weeks. ResultsForty-eight participants completed the study (retention rate: 84.2%), with high phone call compliance in both groups (intervention: 98.4%; control: 98.1%). Intervention participants completed significantly more walks than controls from week 1 onward (aIRR = 5.33, 95% CI: 2.27-12.5, p < 0.001), with the group difference growing over time (interaction aIRR = 1.09 per week, 95% CI: 1.01-1.17, p = 0.029). Estimated marginal means indicated that intervention participants completed 5.5 times more walks than controls at week 1, increasing to 15.5 times more by week 12. The intervention group also walked significantly longer at week 1 (b = 62.14 min, 95% CI: 1.05-123.23, p = .046), with the advantage growing over time; by week 12, intervention participants walked 5.3 times longer than controls. Similarly, the intervention group accumulated significantly more steps during walks at week 1 (b = 4,779 steps, 95% CI: 45.50-9,513.00, p = .048), accumulating 3.1 times more steps than controls by week 12. ConclusionsA remotely delivered, multicomponent walking intervention targeting planning, behavioral reminders, and micro-incentives was feasible, well-tolerated, and produced meaningful increases in walking activity in community-dwelling adults with TBI. With high retention and compliance, and consistent effects on walk counts, duration, and steps across the intervention period, these findings provide compelling support for a larger, fully powered trial.

ObjectivesArtificial intelligence (AI)-enabled digital stethoscopes combine phonocardiography and electrocardiography to support detection of cardiac rhythm and structural abnormalities. This study evaluated the feasibility and exploratory diagnostic performance of AI-guided cardiac auscultation during routine general practice consultations and home visits. MethodsIn this prospective feasibility study, 50 consecutive patients aged [&ge;]65 years underwent AI-assisted auscultation using the Eko CORE 500 during routine care. Recordings were attempted at four standard cardiac positions. Feasibility outcomes included technical failure, workflow disruption, and proportion of analyzable recordings (defined as successful AI output based on combined ECG and phonocardiography signals). Exploratory diagnostic performance was assessed against previously established diagnoses of atrial fibrillation (AF), heart failure (HF), or valvular heart disease (VHD) documented in the electronic medical record. ResultsAI-guided cardiac auscultation was completed in all patients without device malfunction or meaningful workflow disruption (median acquisition time 1-2 minutes). At least one analyzable recording was obtained in 47/50 patients (94%), and complete four-position analyses in 42/50 (84%). Signal limitations were mainly attributable to obesity, chest hair, or excess breast tissue. Among 47 analyzable patients, 11 had known AF, HF, or VHD. Sensitivity for detecting these conditions was 81.8% and specificity 91.7%. One new case of clinically relevant mitral regurgitation was identified. ConclusionsAI-enabled digital auscultation was feasible in routine general practice, with high rates of analyzable recordings and minimal workflow impact. Larger studies with contemporaneous reference standards are warranted to determine clinical utility.

Algorithmic decision systems mediate access to healthcare, credit, employment and housing, yet individuals who experience adverse decisions face multi-stage barriers when seeking recourse. We formalize these barriers as a series-structured system with 11 empirically parameterized stages across three layers (data integration, data accuracy and institutional access) and prove that single-barrier interventions are bounded by baseline system success. Under baseline parameterization derived from federal datasets and peer-reviewed algorithmic audit studies, end-to-end recourse probability is 0.0018%. Removing any single barrier yields negligible improvement (<0.02%). Factorial decomposition reveals that the three-way cross-layer interaction accounts for 87.6% of achievable improvement, confirmed by Shapley attribution, Sobol sensitivity analysis and bootstrap resampling (n = 1,000). These results provide a structural explanation for the limited impact of incremental reforms and support coordinated multi-layer intervention approaches for clinical AI governance and algorithmic fairness.