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PubMed2026

Temporal redistribution of control reveals age-related differences in task switching at the level of preparation.

Task-switching studies often report minimal age-related differences in switch costs, leading to the conclusion that switching-related control processes are relatively preserved in aging. However, this conclusion is based on paradigms that confound preparatory and execution processes. This study examined whether age-related differences in semantic task-set reconfiguration may be underestimated due to this confound. In Experiment 1 (36 young and 30 older adults), participants performed an externally paced task-switching paradigm without control over preparation. In Experiment 2 (28 young and 28 older adults), a self-paced paradigm allowed participants to initiate stimulus onset, enabling measurement of preparation time. Across both experiments, reaction time (RT) and error rate (ER) showed reliable age effects but no interactions between age and condition, whereas switching-related condition effects varied across measures and experiments. The expression of switching-related costs differed across measures and task structures. Local switch costs were expressed in ER in Experiment 1 but in RT in Experiment 2. Global switch costs (all-switch vs. all-repeat) were observed in execution measures only in Experiment 1. In Experiment 2, preparation time showed reliable mixing, local, and global switching effects, with age-related amplification emerging specifically for global switching. These findings indicate that switching-related costs are redistributed across processing stages and behavioral measures. The results suggest that age-related modulation of semantic task-set reconfiguration may emerge more clearly during preparation than task execution, particularly under continuous switching demands. Preparation time is interpreted cautiously as reflecting participant-regulated preparatory processes rather than a pure measure of preparation efficiency.

Humans↗

PubMed2026

A Dynamic Nomogram to Predict Metabolic Dysfunction-Associated Fatty Liver Disease in Patients with Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) involves multiple metabolic disorders. This study aimed to identify high-risk populations for metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with MetS and to establish a dynamic predictive nomogram. METHODS: A total of 627 patients with MetS from six regions in Zhejiang Province were enrolled and categorized into MAFLD and non-MAFLD groups, then randomly assigned to training and validation sets at a ratio of 7:3. Independent predictors of MAFLD were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses. These predictors were then used to construct a dynamic nomogram. RESULTS: A total of 627 patients with MetS were included in the final analysis, of whom 77.0% (483/627) were diagnosed with MAFLD. Multivariable logistic regression analysis identified body mass index (BMI), waist circumference (WC), total cholesterol (TC), alanine aminotransferase (ALT), MetS-defined dysglycemia, and education level as independent risk factors for MAFLD. MetS-defined dysglycemia showed the highest odds ratio (OR) for MAFLD development [OR = 1.87, 95% confidence interval (CI): 1.07-3.29]. Although the number of MetS components and the metabolic syndrome score were significantly associated with MAFLD in univariate analysis, they were not independently associated with MAFLD in the multivariate model. A dynamic nomogram for predicting MAFLD risk in patients with MetS was developed and internally validated. The area under the receiver operating characteristic curve was 0.834 (95% CI: 0.787-0.880) in the training set and 0.839 (95% CI: 0.771-0.899) in the validation set, indicating strong predictive performance. Bootstrap internal validation demonstrated good agreement between predicted and observed outcomes in calibration curves. Decision curve analysis further indicated favorable clinical applicability of the nomogram. CONCLUSION: BMI, WC, TC, ALT, MetS-defined dysglycemia, and education level are independent risk factors for MAFLD. A dynamic nomogram for predicting MAFLD risk in patients with MetS was successfully developed and validated.

Humans↗

PubMed2026

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

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

Humans↗

PubMed2026

Integrated proteomic and acetylomic analyses reveal the metabolic reprogramming associated with increased tylosin-equivalent concentration in Streptomyces xinghaiensis sf106-B1.

Deciphering the metabolic basis of high-yield antibiotic production in Streptomyces is crucial for strain optimization. Atmospheric and room-temperature plasma (ARTP) mutagenesis of Streptomyces xinghaiensis sf106 generated a mutant with a 30% increase in tylosin-equivalent concentration (μg/mL). 4D-FastDIA quantitative proteomics identified 279 differentially abundant proteins enriched in the Type I polyketide synthase (PKS) pathway, with increased abundance of key macrolide-biosynthesis-related proteins. Lysine-acetylome profiling identified 1152 differentially abundant acetylation sites and revealed altered acetylation of enzymes involved in fatty acid metabolism and the tricarboxylic acid (TCA) cycle, suggesting adjustments in central metabolism associated with acyl-CoA precursor availability and energy generation. Integration of proteomic and acetylomic data suggests coordinated changes in protein abundance and lysine acetylation associated with the increased tylosin-equivalent concentration. These results highlight candidate nodes for rational metabolic engineering of S. xinghaiensis.

Streptomyces↗

PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗

PubMed2026

Metabolic engineering of Candida yeasts for biotechnological applications.

Candida yeasts represent a versatile yet underexploited platform for industrial biotechnology. These yeasts utilize a remarkably broad range of carbon sources, particularly for hydrophobic carbon sources, coupled with robust growth and diverse biosynthetic capacities, making them promising hosts for sustainable production of chemicals, fuels, and proteins. Despite these advantages, industrial deployment of Candida species has been hindered by concerns regarding opportunistic pathogenicity and the historical lack of efficient genetic manipulation tools, leading to a substantial gap between metabolic potential and practical utilization. Recent advances in functional genomics, genome editing, and systems metabolic engineering are rapidly overcoming these barriers, enabling more precise and efficient strain development. In this review, we systematically summarize recent progress in the metabolic engineering of Candida species as microbial cell factories, with particular emphasis on expanding genetic toolkits, utilizting renewable and non-conventional carbon sources, and biosynthesizing high-value compounds. In addition, we propose a biosafety-oriented classification framework to support their safe industrial deployment. Finally, we discuss current challenges and emerging opportunities, emphasizing that the synergy of synthetic biology and artificial intelligence-driven design holds the key to unlocking the biotechnological potential of Candida yeasts.

Candida↗

PubMed2025

Influence of soil types with different soil-forming process on the qualitative and quantitative detection of microplastics by near-infrared spectroscopy.

Microplastics (MPs) have become a pressing global environmental threat, with soils-acting as sinks for MPs from multiple sources-gaining increasing attention. Near-infrared (NIR) spectroscopy offers a promising tool for MPs detection due to its rapid, non-destructive, and field-applicable features. Although previous studies have focused on the effects of individual soil components on the NIR detection performance of MPs, there is still a lack of systematic research on how the complex background-formed by the coupling of multiple physicochemical properties in natural soils-affects detection performance. This study focuses on soil types with different soil-forming processes, selected five representative agricultural soils to systematically evaluate how the combinations of physicochemical properties they represented affect the performance of NIR-based qualitative and quantitative analysis of MPs in soils. The results demonstrated that soil type significantly affected both the spectral response and detection performance of MPs. Brown Pedocals and Brown Earth exhibited clearer characteristic absorption and stronger linear responses, achieving higher identification accuracy under low (<1.5 %) or zero MPs concentrations and the best quantitative performance (R2 ≥ 0.988, prediction set root mean square error (RMSEP) ≤ 0.110 %). In contrast, Phaeozem and Red Soil were more prone to misclassification at low concentrations, while Fluvo-aquic Soil showed the poorest quantitative performance. This study is the first to reveal, at a holistic level, the critical constraints posed by natural soil complexity on the NIR detection of MPs, offering targeted empirical evidence to support the application of NIR technology in real-world soil environments.

Soil↗

PubMed2026

Metagenome-scale modeling to assess microbiome metabolic complementarity for precision microbiota transplantation therapies.

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable and donor-selection strategies remain limited, in part because the role of donor‒recipient metabolic interactions in shaping the post-FMT community remains poorly understood. Here, we leverage metagenome-scale metabolic modeling to quantify metabolic niche complementarity between donor and recipient microbiomes and predict post-FMT community composition. Using MICOM-derived metabolic models, we show that donor genomes whose metabolic flux profiles are more dissimilar from the recipient community colonize at significantly higher rates in a murine FMT model. In a human IBS trial, the same metric predicted post-FMT community composition via leave-one-out cross-validation and captured known disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to evaluate personalized donor screening, identifying super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition. Together, these results support metabolic niche complementarity as a potential determinant of post-FMT community composition and provide a mechanistic basis for evaluating donor-recipient metabolic compatibility. This framework offers a scalable approach for generating testable hypotheses for personalized donor selection.

Fecal Microbiota Transplantation↗

PubMed2026

ReMeDy: A Flexible Statistical Framework for Region-Based Detection of DNA Methylation Dysregulation.

Region-based epigenome-wide association studies have demonstrated improved statistical power and biological interpretability compared with probe-wise analyses of DNA methylation data. However, most existing region-based methods characterize methylation dysregulation primarily through changes in mean methylation levels associated with a phenotype of interest. Substantial evidence indicates that phenotype-associated methylation alterations may also manifest through changes in methylation variability or through joint shifts in mean and variability. Despite this, no existing statistical framework jointly models mean-variance methylation changes in a region-based manner. We propose ReMeDy, a flexible statistical framework that uses a hierarchical likelihood approach within a generalized linear model setting to identify differentially methylated regions, variably methylated regions, and regions exhibiting joint differential and variable methylation at a genome-wide scale. Unlike existing models, ReMeDy operates directly on biologically defined co-methylated regions, allowing it to naturally capture spatial correlation inherent in DNA methylation array data, while avoiding reliance on heuristic, user-defined tuning parameters such as smoothing spans and kernel bandwidths that can substantially influence results and introduce subjectivity. Through extensive simulation studies and comprehensive benchmarking against popular models, we demonstrate that ReMeDy maintains false discovery and Type-I error rates at nominal levels while achieving consistently higher statistical power across a wide range of realistic scenarios. Application to population-level DNA methylation data further shows that ReMeDy identifies biologically meaningful regions and pathways implicated in complex human diseases that are not captured by conventional mean-based analyses alone. ReMeDy is implemented as an open-source R package and is freely available at https://github.com/SChatLab/ReMeDy.

DNA Methylation↗

PubMed2026

Metabolic depot for nucleated erythrocyte degradation: molecular and structural elucidation of the teleost melanomacrophage center.

The function of melanomacrophage centers (MMCs) has long been controversial. While their foundational function is widely accepted as "metabolic dumps" for waste processing, a widely circulated hypothesis posits that they are primitive germinal centers (GCs) executing adaptive immunity. To elucidate this controversy, this study systematically evaluated the splenic MMCs in a higher teleost ( Micropterus salmoides) by combining transmission electron microscopy (TEM) and high-resolution spatial transcriptomics. Structurally, TEM revealed that the MMC comprises a core with characteristic sparse cellular density, filled with cellular debris and encapsulated by a fibrous layer. Molecularly, under physiological conditions, MMC regions exhibited low transcriptional activity. We did not detect clear enrichment of B cell and T cell lineage genes, and the key GC marker aicda was not observed. Conversely, its predominant molecular signature was characterized by macrophage-driven iron metabolism (e.g., ferritin) and erythrocyte degradation (e.g., hba1). Furthermore, the physicochemical properties of MMCs pigments (e.g., argyrophilia) suggest that traditional histological staining methods warrant cautious interpretation regarding potential non-specific signals. In conclusion, our findings characterize the MMC as a highly specialized metabolic processing and sequestration niche. This study provides new perspectives on the evolution of immune-metabolic homeostasis in poikilothermic vertebrates, advances comparative immunology, and offers a critical scientific reference for the accurate interpretation of MMCs as a biological indicator in pathology and ecotoxicology.

Animals↗

PubMed2026

Are Adverse Childhood Experiences Associated with Metabolic Syndrome in Patients with Severe Mental Illness?

BACKGROUND: Patients with severe mental disorders (SMD) are at substantially elevated risk for metabolic syndrome (MetS), contributing to excess cardiovascular morbidity and premature mortality. Adverse childhood experiences (ACEs) have been associated with dysregulation of metabolic pathways, yet their contribution to MetS risk in SMD remains poorly understood. OBJECTIVE: This study aimed to investigate the association between ACEs and MetS in outpatients with bipolar disorder (BD) and schizophrenia (SZ) in clinical remission and to identify independent and incremental predictors of MetS using a hierarchical analytical framework. METHODS: This cross-sectional study included 140 outpatients with SMD (96 with BD and 44 with SZ) in clinical remission, recruited from a university hospital in Eastern Turkey. MetS was defined according to NCEP-ATP III criteria, and ACEs were assessed using the Turkish version of the Adverse Childhood Experiences Scale (ACE-TR). Hierarchical and multivariable logistic regression analyses were performed to examine factors associated with MetS. RESULTS: MetS was highly prevalent in this sample (46.4%). ACE-TR total score was independently and consistently associated with MetS across all hierarchical models (odds ratio [OR] range: 1.68-1.77), with each one-unit increase conferring approximately 71% higher odds in the fully adjusted model (OR = 1.71; 95% confidence interval [CI] 1.26-2.32; P = 0.001). The number of hospitalizations was the only other independently associated variable (OR = 1.19; 95% CI 1.02-1.39). Sexual abuse (16.9% vs. 2.7%; P = 0.004), emotional neglect (63.1% vs. 30.7%; P < 0.001), and physical neglect (30.8% vs. 14.7%; P = 0.022) were significantly more prevalent in the MetS group. ACE-TR total score was positively correlated with waist circumference and triglyceride levels. CONCLUSION: The strong and consistent association between ACEs and MetS underscores the importance of trauma-informed care models in psychiatric practice, where metabolic comorbidity remains a leading cause of premature mortality.

Humans↗

PubMed2026

Flux rewiring enables native D-glucosamine production in Escherichia coli.

D-Glucosamine is an industrially important amino sugar used in pharmaceuticals, nutraceuticals, and functional materials, yet its production remains dominated by chemical extraction from chitinous biomass, raising sustainability and allergen concerns. Escherichia coli natively synthesizes D-glucosamine directly from D-glucose through endogenous metabolism, revealing an underutilized amino sugar biosynthetic capability. Building on this native pathway, D-glucosamine production was enhanced through targeted genetic modifications and systematic optimization of nitrogen metabolism and cultivation conditions, reaching 9.2 g L-1 under shake-flask conditions. This work extends a phosphorylation-dephosphorylation strategy previously developed for neutral rare sugars to amino sugar biosynthesis, demonstrating the broader applicability of this metabolic design principle. Phosphatase identity emerged as a key control point for product formation: YbiV was the most effective phosphatase for selective D-glucosamine production, whereas alternative phosphatases redirected flux toward D-sedoheptulose. This enzyme-dependent flux partitioning further enabled tunable co-production of D-glucosamine and D-sedoheptulose. Native amino sugar biosynthesis in E. coli provides a controllable framework for producing chemically distinct sugars through endogenous metabolism and establishes a generalizable strategy for engineering amino sugar and other nitrogen-containing metabolite biosynthesis.

Escherichia coli↗

PubMed2026

Deciphering S-nitrosylation-regulated metabolic networks in postmortem beef based on label-free modificomics: Identification of ferroptosis as a novel quality-related pathway.

This study elucidated the molecular mechanisms of S-nitrosylation on postmortem beef metabolism and quality based on the label-free modificomics. Varying degrees of S-nitrosylation were exogenously induced in beef semimembranosus (SM) muscle. Results indicated that a high S-nitrosylation level significantly increased beef pH and Warner-Bratzler shear force (WBSF) while reducing centrifugal loss (P < 0.05). A total of 828 S-nitrosylated proteins and 1458 modification sites were identified, of which 114 sites on 81 proteins (DSNPs) exhibited differential modification abundance, representing an increase of 125% compared with previous proteomics studies. DSNPs were mainly involved in glycolysis, the tricarboxylic acid cycle, oxidative phosphorylation, calcium signaling, cell structure, and ferroptosis. Notably, this study provides the first evidence in postmortem muscle that S-nitrosylation regulates key ferroptosis-related proteins, including ACSL, CP, and TF, offering new insights into the link between S-nitrosylation and the ferroptosis pathway in meat. Correlation analysis demonstrated that TF was significantly negatively correlated with pH and WBSF, but positively correlated with centrifugal loss (P < 0.05). Collectively, protein S-nitrosylation critically modulates postmortem beef quality through the coordinated regulation of multiple metabolic processes. More importantly, the identification of ferroptosis as a S-nitrosylation-sensitive pathway provides a new perspective for regulating meat quality through protein post-translational modifications.

Animals↗

PubMed2026

Revealing the Shared Genetic Architecture of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Traits Through Genomic Structural Equation Modeling.

Although individual traits related to metabolic dysfunction-associated steatotic liver disease (MASLD) have been investigated through large-scale genome-wide association studies (GWASs), the shared genetic susceptibility across these traits remains unclear. We therefore conducted a multivariate GWAS of key MASLD-related traits to elucidate their common genetic architecture. We applied genomic structural equation modeling to model a latent genetic factor (MASLD-F) underlying genetically correlated MASLD-related traits, leveraging their GWAS-derived genetic correlations. We then performed functional annotations, including fine-mapping, transcriptome-wide association study, and cell- and tissue-type-specific enrichment analyses, and conducted Mendelian randomization analyses to identify modifiable risk factors. Our multivariate MASLD-F GWAS identified 50 independent variants across 48 genomic loci. Transcriptomic imputation identified several MASLD-F-associated genes, including ARNTL, NPC1, BTBD10, VDAC2, TSKU, SFMBT1, and ABHD17C. We observed significant enrichment of MASLD-F-related genetic signals predominantly in brain tissues, pancreatic islets, and the adrenal gland. Additionally, six modifiable risk factors and four modifiable protective factors for MASLD-F were identified. These findings reveal a complex shared genetic architecture underlying MASLD components, thereby expanding our understanding of disease pathogenesis and providing novel insights for precision medicine and public health interventions.

Humans↗

PubMed2026

Changes in hemoglobin levels and cardiometabolic health in adults with metabolic syndrome - a secondary outcome analysis of a six-month randomized controlled trial.

BACKGROUND: Lower hemoglobin (Hb) levels within the normal range have been associated with favorable metabolic traits in cross-sectional studies. This study investigated whether changes in Hb levels correlated with changes in physiological and cardiometabolic parameters during a six-month behavioral intervention in individuals with metabolic syndrome. METHODS: The six-month randomized controlled trial aimed to reduce sedentary behavior in adults with metabolic syndrome (n = 64). Key measurements included fasting blood samples, insulin sensitivity during a hyperinsulinemic-euglycemic clamp, insulin-stimulated liver glucose uptake, liver fat content (LFC), indirect calorimetry, cardiorespiratory fitness, and cardiac function. Correlations between changes in these variables and changes in Hb levels at baseline, three, and six months were examined. RESULTS: Cross-sectionally, higher Hb levels correlated with lower insulin sensitivity (r=-0.35, p = 0.005), higher resting O2 consumption (r = 0.41, p < 0.001), higher resting energy expenditure (r = 0.49, p < 0.001), higher LFC (r = 0.40, p = 0.011), and greater left ventricular wall thickness (r = 0.42, p = 0.001). The intervention did not significantly impact Hb levels, and changes in Hb levels did not correlate with most cardiometabolic changes. However, reduced Hb levels correlated with reduced fasting blood glucose (r = 0.29, p = 0.032), improved insulin sensitivity (r = -0.26, p = 0.045), and increased cardiorespiratory fitness (r = -0.29, p = 0.033). CONCLUSIONS: Changes in Hb levels did not consistently correlate with changes in cardiometabolic markers during the intervention. However, reductions in Hb levels may relate to improved insulin sensitivity and fitness. Along cross-sectional correlations, this may be clinically relevant for individuals with metabolic syndrome. Further studies are merited to clarify the role of Hb levels in this high-risk group.

Humans↗

PubMed2026

Acute and chronic effects of mixed nuts on energy metabolism in women at cardiometabolic risk: a randomized clinical trial.

BACKGROUND AND AIMS: The effect of consuming a mix of Brazilian nuts on energy metabolism has not been explored. Thus, the present study aimed to evaluate the effects of acute and chronic consumption of mixed nuts on markers of energy metabolism in women with overweight/obesity. METHODS AND RESULTS: This is a randomized, controlled, and parallel clinical trial with adult women. In an acute study, participants received a beverage containing mixed nuts (30 g of cashew nuts + 15 g of Brazil nuts) or a control beverage, and energy metabolism markers were assessed for up to 3 h postprandially. For the chronic study, participants received 45 g of a mix of nuts/day and a -500kcal energy-restricted diet (MNG) or only a -500kcal energy-restricted diet free of nuts (CTG) for 8 weeks, and energy metabolism was assessed before and after the intervention period. In the postprandial period, fat oxidation was higher in the MNG than in the CTG (piAUC: 47.53 ± 5.78 mg/min vs. 27.93 ± 6.98 mg/min; p = 0.048). After 8 weeks of the intervention, fasting fat oxidation increased in the MNG (+16.0 ± 7.0 mg/min) and decreased in the CTG (-5.0 ± 6.0 mg/min), with no significant difference between groups. Other acute and chronic markers also showed no significant changes between groups. CONCLUSION: The acute consumption of mixed nuts increased postprandial fat oxidation, whereas chronic intake within an energy-restricted diet did not affect energy metabolism markers in women at cardiometabolic risk. REGISTRATION NUMBER FOR BRAZILIAN REGISTRY OF CLINICAL TRIALS: RBR-3ntxrm.

Humans↗

PubMed2026

Insights from changes in NDEV biomarkers of metabolism: effects of PPARγ and GLP1 receptor agonists on brain metabolism.

BACKGROUND: Insulin resistance (IR) is implicated in central nervous system disorders, including depression and Alzheimer's disease (AD). METHODS: We analyzed biological samples from two cohorts of clinical trial participants: (1) participants with unremitted depression after six months of treatment as usual who received pioglitazone (PPARγ agonist, N = 12) or placebo and (2) middle-aged participants at genetic risk for AD who received liraglutide (glucagon-like peptide 1 [GLP1] receptor agonist, N = 15) or placebo. These cohorts, which previously showed treatment-related improvements in peripheral IR, were used to assess the effects of pioglitazone and liraglutide on CNS insulin signaling using neuron-derived extracellular vesicles (NDEVs) as biomarkers. We utilized biological samples to measure biomarkers of IR in NDEVs. Eleven Akt-mTOR pathway proteins were measured before and after 12 weeks of treatment in both groups. RESULTS: Participants who received pioglitazone experienced broader changes, with significant increases in GSK3β (Ser9), mTOR (Ser2448), and RPS6 (Ser235/Ser236; all P ≤ .02) compared with placebo, and 77% of participants showed mTOR (Ser2448) response. Participants who received liraglutide demonstrated significantly increased NDEV-associated phosphorylated Akt (Ser473) and mTOR (Ser2448; P = .04 and P = .025, respectively) compared with placebo, with 40% and 30% of participants in the liraglutide group showing biomarker response in both Akt (Ser473) and mTOR (Ser2448), respectively. These effects appeared relatively independent from changes in fasting plasma insulin and glucose concentration at 120-minutes during the oral glucose tolerance test. DISCUSSION: Our findings demonstrate CNS-specific biomarker responses to both PPARγ agonists and GLP1 receptor agonists.

Humans↗

PubMed2026

Perioperative safety and survival outcomes of robot-assisted partial nephrectomy in elderly patients with localized renal cell carcinoma: an overlap-weighted Asian cohort study.

The value of robot-assisted partial nephrectomy (RAPN) in elderly Asian patients with localized renal cell carcinoma (RCC) remains insufficiently defined. We retrospectively analyzed 339 patients (≥ 70 years) with localized RCC treated at a single Asian center between 2015 and 2025, including 119 undergoing partial nephrectomy (PN) and 220 undergoing radical nephrectomy (RN). Propensity score overlap weighting (OW) was applied to compare PN versus RN and, within the PN cohort, RAPN versus laparoscopic partial nephrectomy (LPN). Three open partial nephrectomy cases were summarized descriptively and retained only in exploratory sensitivity analyses. Weighted logistic regression and Cox models with robust standard errors evaluated Clavien-Dindo grade ≥ II complications and overall survival (OS). After OW, PN was associated with better early postoperative renal functional preservation than RN but a greater incidence of grade ≥ II complications (36.4% vs. 17.6%; weighted p < 0.001); OS was similar. Within the PN cohort, RAPN had longer operative time than LPN (weighted p = 0.030), whereas warm ischemia time, early postoperative eGFR, and grade ≥ II complications (31.8% vs. 40.4%; weighted p = 0.414) were not significantly different. Exploratory analyses favored RAPN, but only one death occurred in this group, and residual confounding remains possible. PN may preserve early renal function in selected older patients, while RAPN appears feasible in experienced centers; its survival association remains hypothesis-generating.

Humans↗