Biogerontology

Geroscience clinical trials need biomarker surrogate endpoints for healthspan. Leading candidates are omics-based composites developed from machine learning analysis of aging phenotypes including calendar age, survival, functional capacity, and Pace of Aging. Existing Pace of Aging biomarkers were developed in the Dunedin Longitudinal Study, limiting inference about strengths/weaknesses of the method as distinct from the Study, a unique single-year birth cohort followed through midlife with near-perfect retention and uniform measurement of multi-organ-system function across two decades of follow-up. We adapted our Pace of Aging method for mixed-age cohorts with variable follow-up of organ-function measures and applied it to develop a novel DNA methylation biomarker of Pace of Aging in data from the Framingham Heart Study Offspring Cohort, FraminghamPACE. Validation analyses across four independent cohorts and one clinical trial establish advantages for the Pace of Aging method in developing biomarkers that are both predictive of healthspan and responsive to geroprotective intervention.

Lipid peroxidation (LPO) is widely used as a biomarker of oxidative stress in coral bleaching research, yet its measurement remains poorly standardized across the field. A systematic review of the coral LPO literature reveals substantial variation in methodological approaches, including tissue fraction analysis, lysis protocols, assay choice, and normalization metrics, confounding cross-study comparison and obscuring the biological interpretation of results. We experimentally investigate two key sources of variation: the use of bulk holobiont vs separated host and algal symbiont fractions, and the choice of normalization metric. To do so, we used Montastraea cavernosa (n = 6 colonies) exposed to ambient (28C), heat stress (30.5C), and heat stress + artificial upwelling (AU; heat stress intermitted by daily pulses of cooler water, 30.5/27.5C) conditions in a controlled mesocosm experiment. Using a TBARS-based MDA assay with a lysis buffer optimized for coral tissue, we measured LPO separately in coral host and algal symbiont fractions across four time points throughout the day. Host MDA remained stable across all treatments and time points, consistent with either sufficient antioxidant buffering capacity or thermal acclimation over the experimental period. Algal symbiont MDA, in contrast, exhibited pronounced diel and treatment-specific dynamics, and the two fractions responses were decoupled from one another. Normalizing MDA to coral surface area instead of total protein content produced largely consistent diel and treatment patterns, but the two metrics diverged at specific time points, indicating that normalization choice is not interchangeable and can itself affect interpretation. Together, our literature review and empirical results demonstrate that host and algal symbiont LPO dynamics are not comparable when aggregated and argue for host-symbiont fraction separation and consistent, explicitly reported normalization as minimum standards for interpretable and cross-comparable coral LPO measurement.

Background and objectives: In recent years, the need to develop analytical strategies for healthy aging has assumed great importance. In this study, we introduce DyViA, a generative artificial intelligence (genAI) platform that can construct personalized trajectories capable of predicting the plausible progression of selected phenotypes with advancing age. Research design and methods: DyViA presents a suite of deep learning models covering two major GenAI approaches: DyViA-Diff, a new diffusion model; and DyViA-mGAN, an improved version of a recent Generative Adversarial Network model. It demonstrated the dynamic progression of femoral neck bone mineral density (BMD) using data from a longitudinal cohort study of women in the U.S. of age 65 years or above. Results: Using very few initial measurements, DyViA generated individual-specific continuous trajectories of BMD, with a corresponding region of acceptable predictions, from 66 to 89 years. The results were subjected to rigorous quality-control and comparative analysis across multiple methods. While DyViA-Diff is the superior model with more coherent and accurate predictions, DyViA-mGAN allows for encoding population- and individual-level effects with a better control. Discussion and implications: Given the prevalence of osteoporosis in the aging population, the main impact of DyViAs genAI-driven contribution in the form of personalized, plausible models of BMD progression with age lies in the systematic yet rigorous transition from otherwise static models of inference about a clearly dynamic phenomenon to a continuous one. The foresight offered by DyViAs outputs empowers an individual by conferring a certain degree of strategic preparedness in the course of aging.

The pace of aging can be delayed by mutations, dietary manipulations, and drugs, yet the metabolic mechanisms underlying longevity interventions remain poorly understood. Here we present a multi-tissue metabolomic analysis of male UM-HET3 mice treated from 4 to 12 months of age with five validated longevity interventions: rapamycin, acarbose, 17-estradiol, canagliflozin, or caloric restriction. Using a feature-stabilized XGBoost pipeline applied to seven tissues, we show that metabolomic profiles can identify treated mice as likely recipients of a lifespan-extending intervention well before survival differences emerge. A leave-one-intervention-out procedure confirmed that models trained on any four interventions successfully classified mice from a fifth, unseen intervention, implying shared metabolic alterations across mechanistically distinct treatments. The most influential metabolites -- defined as the minimum set explaining 50% of cumulative model gain -- differed substantially across tissues. Only ergothioneine, a dietary antioxidant, ranked highly in more than two tissues: it was elevated by all five interventions in plasma and brain, and by four of five in muscle. Enrichment analyses further identified coordinated remodeling of lipid classes in plasma, perigonadal fat, and kidney. These findings reveal tissue-specific metabolic reprogramming shared across mechanistically distinct longevity interventions and, pending validation against interventions that do not extend lifespan, suggest a path toward metabolomic screening of candidate anti-aging drugs.

Background German cockroach (Blattella germanica) infestation is an important urban environmental health menace associated with food contamination, allergic disease, and reduced quality of life. Long-term control depends not only on professional pest management, but also on residents knowledge and preventive behaviors. This study assessed the knowledge, Health belief model (HBM) constructs, self-efficacy, and preventive practices related to German cockroach infestation among urban residents in Tehran, Iran. Methods In this cross-sectional study, 120 adults with professionally confirmed household German cockroach infestation were recruited from licensed pest-control companies in Tehran. Data were collated using a 39-item HBM-based questionnaire assessing knowledge, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and preventive practices. Descriptive statistics, Pearson correlation, and multiple linear regression were performed. Results Participants demonstrated modest knowledge regarding German cockroach biology (mean score: 0.538) and moderate preventive practices (3.157). Preventive practices were positively correlated with knowledge (r = 0.256, P = 0.005), perceived benefits (r = 0.292, P = 0.001), and self-efficacy (r = 0.244, P = 0.007). Regression analysis showed that the model explained 17.3% of the variance in preventive practices (R2 = 0.173, P = 0.001). Knowledge ({beta} = 0.191, P = 0.036), perceived benefits ({beta} = 0.231, P = 0.010), and self-efficacy ({beta} = 0.229, P = 0.012) were significant predictors. Conclusions Urban residents with confirmed German cockroach infestation showed limited knowledge and moderate preventive behaviors. Knowledge, perceived benefits, and self-efficacy were independently associated with preventive practices and demonstrated modest predictive value. Interventions targeting these behavioral factors, alongside environmental and structural improvements, may enhance sustainable household cockroach control.

Abstract Background: The influence of taurine on biological ageing remains unclear, particularly whether it acts as a causal driver or a functional biomarker. We aimed to disentangle the distinct roles of plasma taurine relative abundance, dietary taurine supply, and genetic metabolic capacity on all-cause mortality and unhealthy ageing. Methods: This prospective study used data from the Korean Genome and Epidemiology Study (2001~2022). A subcohort of 2,321 participants (mean age 56.5 years; 51.4% female) with complete metabolomic, dietary, and genomic data was analyzed. Three independent pathways were evaluated: (1) plasma taurine/total amino acid (AA) ratio, (2) dietary taurine to protein ratio, and (3) a weighted genetic risk score (GRS) from 21 SNPs in taurine biosynthesis and transport genes. Primary outcomes were all-cause mortality and unhealthy ageing (Physiological Healthy Ageing Index [PHAI] score [≤] 25th percentile). Results: A higher plasma taurine/total AA ratio was consistently associated with improved ageing outcomes. Participants in the highest quartile showed 29% lower all-cause mortality (Hazard Ratio [HR], 0.71; 95% Confidence Interval [CI], 0.52-0.98; P for trend = .04) and lower risk of PHAI-based unhealthy ageing (HR, 0.77; 95% CI, 0.59-1.00; P for trend = .04) versus the lowest quartile. Dietary taurine-to-protein ratio was not associated with mortality (P for trend = .70), nor was the GRS (P for trend = .74). Conclusions: The protective association of taurine was linked to its relative abundance within the systemic amino acid pool, rather than dietary intake or genetic predisposition, supporting taurine as a functional biomarker of metabolic efficiency rather than a deterministic causal driver of ageing.

Emerging as a significant global health challenge, Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that causes memory loss and cognitive decline. Despite the ever-increasing waiting time for a specialist diagnosis, the need for a cost-effective and fast diagnostic technique is evident. This study explores the development of an explainable deep learning model to diagnose AD using only routine and low-cost clinical data, including demographic information, patient history, and results of neuropsychological tests (limited to those that can be automatically acquired). The analysis was carried out using a dataset provided by the National Alzheimer's Coordinating Center, comprising 167,364 observations and 1,024 features. The findings demonstrate diagnostic performance comparable, and slightly superior, to that of clinicians when evaluated under similar informative constraints. This study introduces two classification models to discriminate whether the presumptive etiological cause of cognitive impairment is Alzheimer's disease. The deep neural network achieved an accuracy of 90\% with an area under the receiver operating characteristic curve (ROC-AUC) of 0.96, whereas the Light Gradient Boosting Machine reached the same accuracy with a ROC-AUC of 0.97.

Radiation exposure induces systemic and cellular damage, contributing to acute radiation syndrome and long-term effects such as premature aging and carcinogenesis. At the cellular level, radiation triggers apoptosis, mutation, and transformation through oxidative damage and activation of pathways including ER stress-mediated autophagy. Autophagy plays a context-dependent dual role in stressed cells, but its contribution to intestinal recovery after acute radiation remains unclear. Here, we evaluated combinatorial radiomodification using gamma radiation (8 Gy) and autophagy modulators in whole-body irradiated C57BL/6 mice (8-10 weeks old, n = 10). Mice were treated with autophagy inducers or inhibitors and euthanized at 3-, 8-, and 30-day post-irradiation. The jejunal-ileal region was analyzed via antioxidant assays, immunoblotting, H&E staining, and immunohistochemistry. Radiation significantly altered oxidative stress and autophagy markers, including increased LC3-II and decreased SQSTM1/p62. Autophagy induction enhanced intestinal proliferation (as measured by Ki-67), whereas inhibition impaired regeneration. Rapamycin pretreatment improved survival and reduced markers of intestinal injury following 8 Gy total body irradiation (TBI), whereas chloroquine exacerbated several injury-associated parameters. Overall, our findings suggest that targeted modulation of autophagy is a promising strategy for alleviating radiation-induced gastrointestinal injury and provide mechanistic insights relevant to therapeutic development.

Background: Elevated C-Reactive Protein (CRP), interleukin-6 (IL-6) and testosterone deficiency are associated with advanced age and chronic inflammatory diseases; while normal testosterone levels have been shown to decrease inflammation through several mechanisms. Cross-sectional studies have shown an inverse relationship between CRP, IL-6 and total testosterone (TT) levels, yet mixed findings have been reported when individual components of metabolic syndrome are considered. We evaluated the relationship between CRP, IL-6 and TT levels in men from 2004-2018 using the Baltimore Longitudinal Study of Aging to determine if low testosterone status is associated with a high inflammatory profile. Methods: Participants were selected from the Baltimore Longitudinal Study of Aging. Male participants with serum TT level measured during at least three visits were included in our cohort. Common measures of inflammatory disease such as CRP, High-Density Lipoprotein (HDL) and Triglyceride levels were collected via blood specimens. Comorbidity data were documented at each visit. Panel regression was used to analyze the relationship of a series of independent variables collected in pooled cross-sectional observations over time with a dependent variable for modeling. Results: A total of 347 patients were included in this study (median age = 70, IQR = 18, average follow up time = 6.7 +/- 3.2 years). Participants had a median CRP level of 1.0 mg/dL, median IL-6 level of 3.6, a median TT level of 446 ng/dL. On univariable analysis, increasing TT and HDL levels were associated with a decline in CRP, while high Body Mass Index (BMI), congestive heart failure (CHF), Diabetes, and increased serum triglycerides were associated with increased CRP. Age was not associated with CRP. On multivariable analysis, we found that increasing TT level was associated with a decline in CRP levels, independent of comorbidities (p = 0.018; Table 1). As expected, increased BMI was associated with a significant increase in CRP (p = 0.001, Table 1). Age, CHF, Diabetes, HDL, and Triglycerides were not significant predictors of CRP on multivariable analysis. Similarly, on multivariable analysis, increasing TT levels were independently associated with lower IL-6 levels. Higher HDL cholesterol levels were also associated with lower IL-6 levels, whereas increasing age was associated with higher IL-6 levels. BMI, CHF, diabetes, and triglycerides were not significant predictors of IL-6. Conclusions: Lower levels of serum total testosterone are associated with an increase in CRP in older men over time, independent of chronic inflammatory disease. Given the importance of CRP in pathogenesis of chronic disease, we highlight the potential benefits of using total testosterone as a biomarker of chronic inflammatory states.

Many insects enter diapause, a programmed state of developmental arrest that enables survival under adverse environmental conditions. In Drosophila melanogaster Meigen, 1830, diapause is characterized by reproductive arrest and reduced intestinal growth, accompanied by suppressed intestinal stem cell (ISC) activity. Juvenile Hormone (JH) promotes ISC proliferation under favorable conditions, but its capacity to modulate stem cell dynamics during cold-induced diapause remains unclear. Here, we investigated whether JH signaling can reactivate midgut remodeling in adult females maintained at 11. At this temperature, flies exhibited pronounced gut atrophy and elevated Phospho-histone H3 (PH3+) cell abundance, consistent with temperature-dependent G2/M phase arrest JH treatment significantly increased the proportion of Delta-positive progenitor cells in the anterior (R2) and posterior (R5) midgut regions at both 11 and 25, demonstrating that JH acts as a conserved mitogen for the ISC pool irrespective of thermal environment. A trend toward reduced PH3+ accumulation in the posterior midgut following JH treatment (p = 0.061) suggests possible facilitation of mitotic exit, though this effect did not reach statistical significance. Despite cellular-level changes, JH treatment did not restore overall gut size, indicating that the 72-84 hour exposure window was insufficient for subsequent tissue hypertrophy. Additionally, we identified a recurrent cold-induced pathology of gut distension, provisionally termed Lumen Obstruction Syndrome (LOS), which was independent of JH signaling. These findings reveal an uncoupling of JH-driven stem cell expansion from gross organ growth under diapause conditions, highlighting the selective sensitivity of the ISC compartment to endocrine signaling during environmental stress.

Anoxia is a major stress for most vertebrates and frequently accompanies harsh winter conditions, particularly in species that spend much of the season frozen solid. North American freeze-tolerant wood frogs (Rana sylvatica) can survive several months without oxygen and endure whole-body freezing for up to eight months of the year, with [~]70% of total body water frozen as extracellular ice, yet revive when temperatures rise in spring. Survival depends on multiple adaptations, including tolerance of prolonged oxygen deprivation while frozen, when breathing and circulation are halted. A key strategy involves hepatic glycogen mobilization, producing large amounts of glucose that are distributed to tissues where it functions both as a cryoprotectant and as a substrate for anaerobic ATP production. The present study examines the role of histone lysine methylation and demethylation in regulating liver proteins under anoxic conditions. Relative protein expression of seven histone methyltransferases (ASH2L-S, ASH2L-L, RBBP5, SETD8, SMYD2, ESET, SETD1), six lysine demethylases (KDM1A, KDM3B, KDM4A, KDM4B, KDM5A, KDM5C), and eight histone marks (H3K4me1, H3K4me2, H3K9me3, H3K27me3, H3K36me3, H3K79me3, H4K20me1, H4K20me3) were evaluated in wood frog liver under control, 4-hour, and 24-hour anoxia exposures. The data indicate that histone lysine methylation and demethylation contribute significantly to transcriptional regulation under anoxia. Specifically, H3K4, H3K36, and H3K79 methylation were associated with transcriptional activation, whereas H3K9, H3K27, and H4K20 methylation correlated with transcriptional repression. These findings highlight the dynamic role of epigenetic regulation in supporting hypometabolism and stress adaptation in freeze-tolerant wood frogs.

Cells release membrane-bound extracellular vesicles into the bloodstream laden with proteins that may reflect their physiological state. How this circulating EV proteome changes across life remains poorly understood. Identifying molecular signatures of aging in accessible biofluids could facilitate earlier intervention and monitoring of age-related disease. Many circulating aging proteome studies rely on affinity-based platforms which suffer from poor cross-species translation, ambiguous signal attribution, and inconsistent agreement between platforms. Here, we present a characterization of the aging plasma EV proteome from a cross-sectional cohort of 86 male and female C57BL/6J mice (5-31 months). We leveraged a species-agnostic EV enrichment (Mag-Net) and mass spectrometry to detect 2,575 protein groups from 15,969 peptides. Protein abundance heterogeneity increased with age and the abundance of 272 proteins were significantly correlated with chronological age including established senescence and frailty markers. Proteins increasing with age were enriched in genome maintenance pathways, while those decreasing were associated with the extracellular matrix organization and lipid metabolism. Notably, several of the strongest age-increased proteins converged on Alzheimer's and Parkinson's disease pathology. We observed sexual divergence in the aging EV proteome not previously characterized at this resolution. A proteomic clock built from this data accurately predicts chronological age, and peptide-level analysis reveals aging signals invisible at protein-level. These findings demonstrate that EV-enriched plasma proteomics can identify known aging markers, reveal novel sex-specific age-related changes, and generate predictive models of chronological age. This study provides a species-agnostic foundation for proteomic clocks that complement epigenetic approaches to monitor aging and evaluate healthspan.

Background: Chronic kidney disease (CKD) is associated with alterations in the gut microbiome that promote the accumulation of gut-derived uremic solutes and contribute to systemic inflammation, vascular dysfunction, and disease progression. Dietary fiber has emerged as a promising modulator of gut microbial metabolism, yet the influence of fiber physicochemical properties, particularly fermentability and viscosity, on uremic metabolite production in CKD remains poorly understood. Objective: To systematically evaluate the effects of isolated dietary fiber interventions, classified by fermentability and viscosity, on gut microbial metabolites in CKD across experimental rodent models and randomized clinical trials, and to determine whether these fiber properties modify microbial metabolites. Methods: A systematic search of PubMed, Embase, CINAHL, and Cochrane Library (through June 2026) identified randomized controlled trials and controlled rodent studies assessing isolated dietary fiber in CKD. Eligible studies reported at least one gut-derived metabolite (i.e., indoxyl sulfate (IS), p-cresyl sulfate (PCS), trimethylamine-N-oxide (TMAO), tryptophan-derived indoles, or short-chain fatty acids (SCFAs)). Random-effects models were used for pooled estimates using weighted mean differences (WMD) for human studies and standardized mean differences (SMD) for animal studies. Subgroup analyses evaluated fiber fermentability, viscosity, intervention dose, duration, and CKD stage. Risk of bias was assessed with ROB-2 and SYRCLE, and evidence certainty with GRADE. Results: Twenty-eight studies (13 human, 15 animal) met eligibility criteria, comprising 511 participants and 312 animals with CKD. Isolated fiber supplementation, primarily fermentable and non-viscous fibers, reduced IS (human: -0.13 mg/dL; 95% CI: -0.25, -0.01; p = 0.03; animal: -1.99; 95% CI: -3.06, -0.92; p < 0.0001) and pCS (human: -0.23 mg/dL; 95% CI: -0.46, 0.001; p = 0.051; animal: -1.56; 95% CI: -2.08, -1.03; p < 0.0001). SCFAs increased in animal studies, including cecal acetate (2.00, 95% CI: 0.78 to 3.22; p = 0.001) and circulating propionate (1.51, 95% CI: 0.054 to 2.96; p=0.04). There were no dose-dependent effects, but longer interventions (>8 weeks) tended to lower pCS (-0.26 mg/dL, 95% CI: -0.55 to 0.02; p=0.06). Some heterogeneity and low-to-moderate certainty were observed. Conclusion: Isolated dietary fiber reduces major gut-derived uremic solutes in CKD, with fermentability influencing metabolic responsiveness, but with minimal studies on viscous fibers. Larger, longer-duration trials with standardized reporting of total fiber intake and clinical endpoints are needed to guide evidence-based dietary recommendations in CKD.

By utilizing a targeted genetic assay within a Fox Insight cohort (N = 1,987), this research establishes a hybrid, transparent, and interpretable predictive framework. Initial modeling via Firth penalized logistic regression discovered enrichment regarding the GBA N370S locus (OR = 0.01, FDR < .001), highlighting the critical role of epidemiological evaluation in enriched, human study populations. Advanced ensemble learning methods, refined through a meta-learner gradient boosting machine, attained an out-of-sample AUC of 0.929 on 15% of the analysis dataset partitioned via random sampling and strictly held-out from model training. Both global, visual machine learning explanations and local-Shapley interpretations provide transparency into the models and individual predictions representative of practical, collaborative human-artificial intelligence efforts, offering a solution that supports classification while remaining accessible and economical.

Systems-level immune profiling data arising from longitudinal studies of vaccination or infection has an inherent multi-index array structure. While tensor decomposition of such datasets has gained popularity, choosing a rank and trial for a decomposition is not straightforward. We show that taking into account the experimental data model can inspire the development of new metrics to assess the quality of a Non-negative CANDECOMP/PARAFAC (NCPD) decomposition, and can thus be used to choose a rank and trial for the decomposition. Moreover, we show how framing the results via a dictionary learning framework can better enable interpretation of the components of the decomposition.

Crown-of-thorns sea star (CoTS), Acanthaster cf. solaris, outbreaks are a major cause of hard coral cover decline across the west Pacific, threatening coral reefs. Coral taxa vary in susceptibility to CoTS predation from preferred (Acropora spp.) to non-preferred (Porites spp.), yet the mechanisms underlying these differences are poorly understood. We investigated coral defenses during an ongoing CoTS outbreak in Mo'orea, French Polynesia by examining gene expression (including putative toxin genes) in healthy and actively predated colonies of a preferred (Acropora hyacinthus) and a non-preferred (Porites sp.) coral prey species. During predation, A. hyacinthus exhibited molecular signatures of cellular stress responses involving oxidative stress signalling, inflammation, and tissue proteolysis. In contrast, Porites sp. showed enrichment of genes involved in mitochondrial metabolic adjustment and aerobic metabolism, suggesting metabolic compensation to maintain cellular function. Furthermore, A. hyacinthus demonstrated a reactive defense behaviour by differentially expressing toxins (e.g., kunitz-type neurotoxins) while Porites sp. employed constitutive expression of all putative toxins regardless of active predation, suggesting a proactive defense strategy. Together, these findings suggest that preferred and non-preferred coral prey exhibit fundamentally different molecular and defensive strategies during CoTS predation, shedding light on the evolutionary arms race between corals and their predators.

Background: Age is the most important risk factor for coronary artery disease (CAD) independent of traditional risk factors. Aging induces classic pro-inflammatory and prothrombotic vascular phenotypic changes whose molecular mediators remain poorly understood. Medin is a common cleavage product protein that accumulates in vasculature with aging and shown to cause endothelial dysfunction. Its role in CAD is unknown. The study aimed to evaluate the effects of medin on human coronary artery endothelial cell (HCAEC) pro-inflammatory and prothrombotic activation and establish the relationship between medin and coronary atherosclerosis in human decedents. Methods: HCAECs were exposed to physiologic dose of medin (5 M) for 20 hours and ribonucleic acid sequencing (RNAseq) with signaling pathway analyses and reverse transcription polymerase chain reaction of select pro-inflammatory and prothrombotic genes performed. Corresponding protein expression was measured by Western blot or enzyme linked immunosorbent assay in HCAECs exposed to medin (5 M) without or with nuclear factor-{kappa}B (NF{kappa}B) inhibitor RO106-9920 (10 M). Coronary arteries from 40 deceased individuals underwent immunohistochemistry and medin and plaque burden were quantified and their relationship evaluated. Results: RNAseq showed predominant pro-inflammatory gene expression changes induced by medin. HCAECs treated with medin showed increased phosphorylated NF{kappa}B, elevated protein expression of interleukin (IL)-6, IL-8, monocyte chemotactic protein (MCP)-1, intercellular adhesion molecule (ICAM)-1, vascular cell adhesion molecule (VCAM)-1 and plasminogen activator inhibitor (PAI)-1 and reduced protein expression of thrombomodulin; these changes were reversed by RO106-9920 co-treatment. In human tissues, coronary artery medin strongly correlated with plaque burden (R=0.76, p<0.0001) and coronary macrophage content (R=0.72, p<0.0001). Coronary arteries from decedents with myocardial infarction had higher medin than those without (5.53{+/-}2.67% versus 0.02{+/-}0.02%, p=0.0005). Conclusions: Medin induced NF{kappa}B-mediated endothelial cell pro-inflammatory and prothrombotic activation and was strongly associated with coronary plaque burden and inflammation. Medin is a novel candidate mediator linking aging and coronary atherosclerosis.

Despite decades of research, the factors determining the sinking velocity of marine biogenic particles remain poorly constrained, and growing evidence suggests that particle composition and morphology are as important as size in determining particle fate. We compared characteristics of suspended and sinking particles at three depths below the mixed layer and within the layer of maximal flux attenuation during the decline of a Phaeocystis pouchetii bloom in the Labrador Sea using marine snow catchers. Biochemical and morphological characteristics of suspended and sinking particles always differed, with differences depending primarily on bloom stage, and depth accounting for comparatively less variation. Exopolymer particles played a key role, with the relative concentrations of transparent exopolymer particles consistently higher in the suspended than in the sinking particle fraction. In contrast, the partitioning of coomassie-stainable particles changed with the bloom stage, as a function of the Phaeocystis life cycle. Ballast minerals played a negligible role during the late-bloom and bloom-decline stages, and their relative importance increased during the non-bloom stage. The C:N ratio was lower in suspended than sinking particles, with differences in morphological measures depending on bloom stage. Our findings emphasize that export potential is driven not only by particle size, but also by bloom stage, which is closely linked to plankton community composition and plays a key role in the timing and magnitude of carbon flux in the upper mesopelagic. Further, this work highlights the important and diverse roles of exopolymers in regulating carbon flux.

Purpose The application of machine learning (ML) to osteoporosis prediction has expanded rapidly, yet no comprehensive meta-analysis has synthesized the discriminative performance of these models across all ML categories, data types, and validation strategies. This systematic review and meta-analysis aimed to evaluate the diagnostic and predictive accuracy of ML and deep learning models for osteoporosis prediction in adult populations. Methods Systematic searches of PubMed, Embase, Web of Science, and IEEE Xplore were conducted for studies published between January 2020 and February 2026. Studies developing, validating, or applying ML models for predicting osteoporosis, low bone mineral density, or osteoporotic fractures in adults were included. Methodological quality was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Area under the receiver operating characteristic curve (AUC) values were pooled using random-effects meta-analysis with logit transformation. Subgroup analyses were performed by data type, ML category, external validation status, and population type. The review followed PRISMA 2020 guidelines. Results Thirty-three studies were included in the qualitative synthesis and 27 in the meta-analysis. The pooled AUC was 0.879 (95% CI: 0.853 0.901), with substantial heterogeneity (I = 99.5%). Imaging-based models outperformed clinical data models (AUC = 0.905 vs. 0.872). Deep learning achieved the highest pooled AUC (0.909), followed by ensemble methods (0.874) and traditional ML (0.840). Externally validated models showed lower performance than internally validated ones (AUC = 0.868 vs. 0.897). PROBAST assessment rated 32 of 33 studies (97.0%) as low risk of bias, though this proportion should be interpreted cautiously given that PROBAST was designed for traditional prediction models and may not fully capture ML-specific sources of bias. Egger's test indicated significant publication bias (p < 0.001). Explainable AI methods were employed in 60.6% of studies, identifying age, body weight, and alkaline phosphatase as the most frequent top predictive features. Conclusions Machine learning models demonstrate overall good discriminative performance for osteoporosis prediction, albeit with substantial heterogeneity across studies (I = 99.5%), and show potential as complementary screening tools, particularly in settings with limited DXA access. Deep learning models applied to imaging data and ensemble methods using clinical variables achieved the strongest subgroup estimates. However, extreme heterogeneity, evidence of publication bias, and limited prospective validation warrant cautious interpretation of the pooled estimate. Future research should prioritise multi-centre external validation, standardised reporting following TRIPOD+AI guidelines, and prospective clinical trials to establish real-world clinical impact.

Metabolites from Plantago lanceolata (plantain) biomass have been linked with biological nitrification inhibition (BNI) in soil. After grazing, leaf metabolite chemistry is altered via digestion, and a suite of secondary metabolites are then delivered onto soil via dung and urine. The purpose of this study was to establish if urine from sheep grazed on plantain had BNI activity when added to pasture soil, and to identify the metabolite profile(s) that most likely contribute to the BNI effects observed. Groups of sheep (n=5) were grazed on one of nine different plantain cultivars in autumn and spring with analysis of leaf material, urine, soil incubation and BNI bioassay data used to identify potential metabolite candidates implicated with BNI. The urinary nitrogen and metabolite composition of sheep fed plantain varied significantly between cultivars and season. After 28 days of incubation, all soil microcosms treated with plantain-derived urine had up to 35% less nitrate than comparative ryegrass urine controls in both seasons, except one in autumn. The key phytochemistry associated with lower soil nitrate concentrations was phenylethanoid and iridoid glycosides resulting in a higher output of glucuronidated, methylated and sulfated secondary metabolites in the urine. Among 19 secondary metabolites identified in the urine, hydroxytyrosol-related metabolites as well as catechol glucuronide, 2-methoxyphenyl sulfate and guaiacol-{beta}-D-glucuronide appear to be the most likely target compounds with respect to the BNI effects observed. Variation in metabolites from different plantain cultivars affected the ratio of metabolite derivatives in urine, which ultimately affected soil nitrification rates. Cultivar phytochemistry is therefore an important consideration with respect to BNI under urine patches. HighlightsO_LISheep grazing different plantain cultivars had different urine compositions C_LIO_LIUrines elicited biological nitrification inhibition (BNI) in soil and in vitro C_LIO_LIDifferent BNI response was related to differential expression of urine metabolites C_LIO_LIKey urine metabolites associated with BNI are derived from glycosidic compounds C_LI