Search Results (2,052)

Background: Diabetic nephropathy (DN) is characterized by complex and region-specific metabolic dysregulation that is not captured by conventional biomarkers. However, the spatiotemporal organization of metabolic alterations across renal compartments in type 1 diabetes remains poorly understood. Methods: In this study, spatial metabolomics based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) was applied to investigate metabolic alterations in kidney tissues from alloxan-induced diabetic rats at 4 and 8 weeks post-induction. Complementary LC–MS/MS metabolite profiling and label-free proteomic analysis were performed to support pathway interpretation. Results: Spatial metabolomics revealed pronounced region- and time-dependent metabolic reprogramming in diabetic kidneys. Early-stage (DN-4w) changes were characterized by elevated glucose and activation of glucose-associated pathways, including the polyol pathway, accompanied by accumulation of acylcarnitines and lipid intermediates, indicating metabolic substrate overload. At later stages (DN-8w), glucose and related metabolites declined, reflecting impaired metabolic capacity and mitochondrial dysfunction. Broad remodeling of lipid metabolism, including glycerophospholipids, fatty acids, and hexosylceramide, was observed, along with dysregulation of amino acid metabolism and redox-related pathways. These alterations exhibited clear regional heterogeneity across renal cortex and medulla, highlighting compartment-specific metabolic vulnerability. Conclusions: This study provides a comprehensive spatial characterization of metabolic perturbations during DN progression, revealing coordinated alterations in glucose utilization, lipid metabolism, and mitochondrial function. The findings demonstrate the value of spatial metabolomics in uncovering region-specific metabolic mechanisms and provide new insights into the pathogenesis of diabetic nephropathy. Full article

Food-induced anaphylaxis (FIA) is a life-threatening allergic reaction, while wheat-dependent exercise-induced anaphylaxis (WDEIA) is triggered by wheat ingestion plus cofactors. To elucidate their differences, we profiled serum extracellular vesicle (EV) proteomes from 240 participants, including WDEIA, FIA, oral allergy syndrome (OAS), and healthy controls. All blood samples were obtained at least one month after the most recent acute allergic reaction, using TMT-based LC-MS/MS with ELISA validation. A total of 583 EV proteins were confidently identified, revealing distinct immune features. Compared with controls, EV-derived C1-inhibitor (C1-INH) significantly decreased in both WDEIA and FIA, showing diagnostic potential for systemic anaphylaxis. Seventy-six proteins differed between WDEIA and FIA, with reduced apolipoprotein E (APOE) in FIA and elevated eosinophil cationic protein (ECP) in WDEIA, both exhibiting good discriminatory power. These findings indicate that serum EV proteomics can reveal unique immune signatures and identify C1-INH, APOE, and ECP as potential biomarkers distinguishing food-related anaphylaxis subtypes. Full article

Obesity is a major global health challenge characterized by chronic low-grade inflammation, oxidative stress, and an increased risk of cardiometabolic disorders. Although sex-related differences in inflammatory and redox biomarkers have been reported in obese populations, the molecular mechanisms underlying this heterogeneity remain incompletely understood. In this study, we applied a proteomics-based approach to investigate urinary extracellular vesicles from 45 obese individuals (BMI 30–40 kg/m²; age 50–70 years) in order to identify molecular signatures associated with metabolic dysregulation. Shotgun proteomics analysis performed by nanoLC–MS/MS enabled the identification of 3822 proteins. Hierarchical clustering of proteomic profiles revealed two distinct molecular groups, predominantly enriched in males (Group I) and females (Group II). Label-free quantitative analysis identified 466 differentially abundant proteins between the two clusters. Functional enrichment analysis highlighted pathways associated with immune response, metabolic regulation, and redox homeostasis, including glycolysis/gluconeogenesis, lysosome activity, leukocyte transendothelial migration, and glutathione, cysteine and methionine metabolism. Notably, proteins related to ferroptosis were enriched, suggesting the involvement of iron-dependent oxidative cell death mechanisms in the metabolic imbalance observed in a subset of subjects. Furthermore, the non-enzymatic glycosylation of urinary proteins was significantly higher in Group I compared with Group II (p = 0.0002), indicating increased formation of advanced glycation products in individuals with a more pronounced pro-oxidant state. Preliminary follow-up data suggested a higher incidence of pathological events, including cardiovascular complications, among individuals belonging to Group I. Overall, these findings demonstrate that urinary proteomic profiling can identify distinct molecular phenotypes among obese individuals and highlight oxidative stress, ferroptosis, and protein glycation as potential determinants of metabolic vulnerability, supporting the use of non-invasive proteomic approaches for improved risk stratification in obesity. Full article

Background: Traumatic brain injury (TBI) remains a major cause of neurological morbidity and mortality. Mitochondria, being embedded as one of the key organelles disrupted after injury, play a central role in regulating neuronal metabolism, oxidative balance, and cell survival, hence the growing interest in their role after TBI. Methods: We present a narrative review of the literature on mitochondrial dysfunction after TBI to highlight the potential role in diagnosis, monitoring, prognostication and treatment strategies. Following SANRA guidelines we conducted a synthesis of 159 selected references published between 1997 and 2026, including 70 references published from 2020 onward. Results: Mitochondrial dysfunction underpins bioenergetic failure through the impairment of critical regulatory pathways, including oxidative phosphorylation, dysregulated reactive oxygen species production, and dysregulated calcium handling. These changes trigger downstream processes of oxidative damage, epigenetic and proteomic remodeling, and activation of regulated cell death pathways such as apoptosis, necroptosis, and ferroptosis in the context of an inflammatory milieu. As such, mitochondrial-derived molecules (such as mitochondrial DNA and microRNA) are emerging candidate biomarkers of TBI severity and prognosis. Additionally, therapeutic approaches under investigation include inhibition of the mitochondrial permeability transition pore, mitigation of mitochondrial oxidative stress using targeted antioxidants, restoration of NAD⁺-dependent metabolic pathways, and metabolic support through ketogenic interventions. Conclusions: Mitochondrial biology is advancing our understanding of TBI and offers a promising framework for improving its management. Full article

Introduction: SGLT2 inhibitors reduce renal composite endpoints and proteinuria, yet RCTs uniformly show an acute eGFR dip within 2 weeks to 2 months after initiation. However, demographic and clinical predictors of an acute eGFR dip demonstrate considerable heterogeneity across studies. This study aims to identify urinary protein biomarkers of this early eGFR dip and integrate them with routine variables to build a clinically actionable prediction model. Methods and analysis: This three-stage proteomics study includes retrospective discovery, prospective internal validation, and external validation cohorts (total n ≈ 600–700). DIA mass spectrometry will screen for urinary proteins associated with ≥10% eGFR decline at 1 month post-SGLT2i initiation in CKD stages 3–4. Top candidates (FDR < 10%, FC > 1.5, ion intensity > 1 × 10⁴, unique gene families) will be validated by ELISA. A LASSO-logistic regression model will integrate the top three proteins with seven routinely available clinical variables: age, BMI, diabetes status, heart failure, systolic blood pressure, baseline eGFR, and diuretic use. Model performance will be assessed using the C-statistic, NRI, IDI, and calibration metrics. Adaptive stopping rules are pre-specified. Ethics and dissemination: Approved by the Ethics Review Committee at Chinese PLA General Hospital (S2025-859-02, 2025KY126-KS002), all participants will provide written informed consent prior to enrollment, and the study will adhere to the Declaration of Helsinki. Data will be pseudonymized and stored securely according to institutional regulations. Findings will be published in peer-reviewed journals and presented at international nephrology conferences. Trial Registration: Registered Report Identifier: ChiCTR2600119772. Date of registration: 3 March 2026. Full article

In the human reproductive system, extracellular vesicles (EVs) have been recognized as playing a vital role in mediating cell–cell communication. They are considered critical for embryo development, implantation, gamete interaction, and fertilization. The various cargoes carried by EVs, depending on the physiological and pathological state of the cell, include proteins, lipids, nucleic acids, and mitochondrial components. EVs are recognized as critical carriers of redox-related signals and mitochondrial components, linking oxidative stress (OS) to reproductive failure and influencing gamete quality and embryo competence. Although considerable progress has been made, research remains poorly integrated, despite individual omics technologies providing valuable molecular insights. The use of multi-omics technologies, including transcriptomics, proteomics, metabolomics, and microbiome analysis, has been proposed as a global approach to understanding the complexities associated with EVs and discovering new biomarkers associated with infertility. ML and AI have been proposed to identify predictive signatures linked to ART effectiveness and reproductive outcomes, with a strong capacity to handle high-dimensional data. The review aims to provide an overview of current knowledge on EV-mediated redox–mitochondrial signaling in human reproduction, while highlighting the importance of emerging multi-omics and AI technologies for EV-mediated biomarker development. The review discusses the promise of EVs in the development of minimally invasive diagnostic approaches and therapeutic interventions, as well as the challenges in the standardization, integration, and clinical translation of EV-mediated research. In addition, the review proposes integrating computational approaches to better understand molecular pathways involved in the development of next-generation precision medicine in human reproduction. Full article

Proteomics has become central to pharmacological research by providing quantitative readouts of protein abundance, post-translational modifications, interactions, and spatial context. However, proteomic datasets are high-dimensional, heterogeneous, and frequently affected by missingness, batch effects, and limited cohort size. Artificial intelligence (AI) and machine learning (ML) can help convert these complex data into decision-relevant outputs for target identification, biomarker discovery, pharmacodynamic monitoring, and drug repurposing. This review critically compares supervised learning, ensemble methods, dimensionality reduction, clustering, deep learning, graph learning, survival modeling, causal inference, and calibration approaches in proteomics-driven drug discovery. We also summarize major software ecosystems for mass-spectrometry processing, targeted assays, spectrum prediction, phosphoproteomics, structure modeling, and reproducible workflows. Emphasis is placed on model selection, benchmarking, missing-data handling, batch correction, interpretability, uncertainty, experimental validation, and translational readiness. Finally, we highlight emerging directions, including contrastive learning, diffusion models, graph-based integration, and federated analytics. Full article

Pancreatic cystic lesions (PCLs) represent a growing clinical challenge due to their diverse biological behaviors and the substantial overlap in imaging features between benign, premalignant, and malignant entities. Traditional diagnostic approaches relying on cross-sectional imaging or isolated morphologic criteria frequently fail to achieve adequate risk discrimination. Advances in endoscopic ultrasound (EUS) now permit detailed morphologic assessment complemented by cyst-fluid biochemical markers, proteomic signatures, and comprehensive genomic profiling using next-generation sequencing. Parallel progress in artificial intelligence (AI) further strengthens diagnostic precision by integrating EUS features with multimodal biomarker data to reduce subjectivity and support individualized clinical decision-making. This review introduces an EUS-based multimodal diagnostic framework of PCLs that integrates morphological evaluation, cyst-fluid biochemical testing, molecular profiling, and AI-assisted analysis. By synthesizing current evidence, we outline how the integrative approach enhances diagnostic accuracy, biological interpretability, and individualized risk stratification for PCLs. Full article

Alzheimer’s disease (AD) is increasingly recognized as a biological continuum characterized by early neuropathological and molecular changes that precede the onset of clinical symptoms. Fluid biomarkers have transformed the diagnostic landscape by enabling the in vivo detection of core AD pathologies, particularly amyloid-β deposition and tau-related neurodegeneration. Despite the rapid expansion of candidate biomarkers, however, only a limited number have successfully translated into clinical practice. Discovery-phase approaches, primarily driven by mass spectrometry-based proteomics, enable the unbiased identification of novel biomarker candidates across multiple biological pathways. Research-phase methods, including immunoassays such as enzyme-linked immunosorbent assay (ELISA), electrochemiluminescence immunoassays (ECLIA), microfluidic platforms, and ultrasensitive technologies such as single-molecule array (SIMOA), support analytical and clinical validation in well-characterized cohorts. Clinical implementation has been advanced by fully automated platforms, including Lumipulse and Elecsys, which have obtained regulatory approval for cerebrospinal fluid biomarkers and, more recently, blood-based biomarkers. These developments represent a paradigm shift toward minimally invasive and scalable diagnostic strategies that may reduce dependence on neuroimaging techniques. Nevertheless, major challenges remain, including assay standardization, inter-platform variability, demonstration of clinical utility, and barriers to widespread clinical adoption. This review provides a comprehensive overview of analytical methods used to measure AD fluid biomarkers in cerebrospinal fluid and plasma, structured according to the biomarker development pipeline from discovery to clinical implementation. Overall, the review highlights a fit-for-purpose approach to biomarker development and emphasizes the complementary roles of diverse analytical technologies across the different phases of biomarker translation. Full article

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by complex and interconnected pathophysiological mechanisms, including mitochondrial dysfunction, oxidative stress, neuroinflammation, lysosomal impairment, and altered neurotransmitter metabolism. Unlike cerebrospinal fluid or blood, urine offers a truly non-invasive source of biomarkers, reflecting systemic metabolic changes and renal protein excretion linked to neurodegeneration. This review aims to critically synthesize current evidence on urinary biomarkers in PD and to organize this heterogeneous literature into pathophysiologically meaningful domains. Methods: A comprehensive literature search of human studies investigating urinary biomarkers in PD was performed. Eligible studies were comprehensively analyzed and classified according to dominant biological pathways. To facilitate interpretation, findings were organized into six thematic domains: genetic and protein-based biomarkers; metabolic pathways and mitochondrial dysfunction; oxidative stress and neuroinflammation; gut–brain-axis-related metabolites; hormonal and systemic biomarkers; and emerging exploratory markers. Results were summarized in domain-specific tables and integrated using a conceptual framework. Results: A total of 32 human studies met the inclusion criteria, revealing diverse urinary molecular signatures associated with PD across multiple biological domains. Genetic and protein-based markers, including LRRK2-related proteins, α-synuclein species, and lysosomal lipids, showed potential for disease stratification. Metabolomic studies consistently identified alterations in acylcarnitines, organic acids, and amino acid metabolism, reflecting mitochondrial dysfunction. Biomarkers related to oxidative stress, immune activation, gut microbiota metabolism, and hormonal regulation further highlighted the systemic nature of PD. However, most individual biomarkers lacked disease specificity and exhibited methodological heterogeneity. Conclusions: Current evidence supports urine as a valuable source of systemic biomarkers reflecting multiple pathophysiological processes in PD. While single urinary markers remain insufficient for clinical application, integrated omics-based approaches—particularly metabolomics and peptidomics/proteomics—hold promise for identifying combinatorial biomarker signatures. Future longitudinal and standardized studies are required to enhance specificity and translational potential for non-invasive diagnosis and disease monitoring in PD. Full article

At present, the gold standard for gastric cancer (GC) confirmation relies mostly on histopathology, an invasive procedure. Noninvasive detection methods using serum for large-scale screening may be useful for the early diagnosis of GC. Helicobacter pylori (HP) infection and chronic atrophic gastritis are major GC risk factors. We recently developed a noninvasive test called the DSC test-based on the patient’s age, sex, their serum PGI and PGII, anti-HP immunoglobulin (IgG), and gastrin G17 levels-predicting GC risk as low (score 0, S0) or high (score 2, S2). The comparative investigation at the serum protein level of the two different patient groups detected by our DCS test (S0 and S2) may undoubtedly help to identify gastric disease-dependent proteins, resulting from bacterial infection or gastric mucosa inflammation, as well as get better insight into the molecular scenario associated with pre-cancerous conditions. We used an untargeted liquid chromatography–tandem mass spectrometry (LC-MS/MS)-based proteomic profiling approach, followed by univariate statistical analysis to compare the different DSC groups across two patient cohorts (exploratory and validation). Significantly differentially abundant proteins differing more than 1.5-fold between S0 and S2 groups were selected and validated, and their putative role(s) in gastritis and GC were discussed. In both the exploratory and the validation cohorts, four proteins (beta-2-microglobulin, EGF-containing fibulin-like extracellular matrix protein 1, complement factor D, and cystatin-C) were more abundant, while two (sex hormone-binding globulin and pregnancy zone protein) were less abundant in the sera of S2 individuals (|fold change| ≥ 0.6, p < 0.05, t-test). The higher presence of beta-2-microglobulin (B2M) and the lower content of pregnancy zone protein (PZP) in S2 sera were validated by immunoblotting. Replacing age and sex in our DSC model with two specific candidate biomarkers can lead to a refined, albeit modest, improvement in classification accuracy. This study identified a proteomic signature that was differentially associated with the sera of patients with a different risk to develop advanced atrophy/GC according to the DSC test. Moving from a demographic model to a proteomic-driven model can better reflect the personalized biology of pathological processes associated with DSC. Full article

The autoantibody reactome refers to the multidimensional repertoire of antibody reactivities against self-antigens across the human proteome or selected antigenic compartments. This offers a scalable systemic layer for precision immunology across spontaneous autoimmunity and treatment-induced immune toxicity. Autoimmune diseases and immune-related adverse events (irAEs) share major features of dysregulated immunity, yet clinically useful tools for risk stratification, early detection, endotyping, and treatment guidance remain limited and slow. A central challenge is that tissue pathology is highly informative but not uniformly accessible across diseases and organ systems, whereas routine serology captures only a narrow fraction of immune heterogeneity. In this perspective, I argue that a global autoantibody reactome can serve as a central unifying framework linking systemic immune history, tissue pathology, and clinical trajectories across autoimmune disorders and irAEs. Rheumatoid arthritis (RA) provides a strong prototype because its serological diversity, major role of post-translationally modified autoantigens, and marked synovial heterogeneity allow reactome features to be interpreted against tissue biology. Immune checkpoint inhibitor-associated inflammatory arthritis serves as an illustrative rheumatic irAE and a model of treatment-induced immune dysregulation with clear opportunities for longitudinal blood-based profiling. Spatial transcriptomics and proteomics are therefore positioned not as stand-alone solutions, but as mechanistic tools that can decode reactome-defined immune states within tissue microenvironments where tissue is accessible. Clinical translation will require integration of autoantibody reactomes with tissue, circulating proteomic, imaging, genetic, and clinical data through transparent multimodal models, as well as a shift from exploratory resources such as AAgAtlas toward analytically validated and clinically interpretable biomarker panels for risk prediction, endotyping, monitoring, and biomarker-guided intervention. This perspective outlines technical and strategic steps toward clinically actionable decision support, including risk stratification before ICI initiation and treatment guidance for patients who develop ICI-induced inflammatory arthritis, through integration of autoantibody reactome profiling, spatial omics and transparent multimodal AI. Full article

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, multi-system disease characterized by a multitude of symptoms across various organ systems. Diagnosis has relied heavily on heterogeneous clinical symptom presentation and evolving case definitions, with treatment focused on addressing presenting symptoms due to the paucity of validated biomarkers. Meanwhile, advances have been made in understanding the underlying pathophysiology through strong epidemiologic, clinical, and basic science studies. This narrative review synthesizes recent advances that are likely to drive a shift in understanding from symptom-based classification toward a molecularly defined understanding of the disease. This shift in understanding will likely provide the foundation for future research efforts focused on targeting diagnosis and treatment more effectively. Specifically, we reference the identification of rare genetic risk variants through the HEAL2 deep learning framework, the large-scale DecodeME genome-wide association study, and dynamic epigenetic markers of disease state. In addition, the findings revealed the downstream consequences of this genetic and epigenetic priming: chronic innate immune activation, CD8+ T cell exhaustion characterized by upregulation of the exhaustion-driving transcription factors Thymocyte Selection-Associated HMG Box (TOX) and Eomesodermin (EOMES), and a cellular energy crisis centered on mitochondrial dysfunction. Furthermore, results of recent studies have revealed sex-specific transcriptomic and proteomic signatures of maladaptive recovery. We also highlight the role of machine learning and artificial intelligence integrations in translating high-dimensional multi-omics data into actionable biological insights, including the identification of monocyte subsets via Positive Unlabeled Learning, circulating cell-free RNA diagnostic signatures, and integrated multi-modal disease models such as BioMapAI. The combination of these findings, which highlight multiple identifiable mechanisms of molecular activity, support the feasibility of molecular subtyping, precision diagnostics, and targeted therapeutic strategies for ME/CFS. Full article

Cellular senescence drives aging and age-related disease through the accumulation of senescent cells and their senescence-associated secretory phenotype (SASP). Emerging evidence suggests intermittent (“hit-and-run”) senolytic interventions may improve healthspan by reducing senescent cell accumulation and the SASP. Healthy adults aged 45–79 were recruited for a decentralized, single-arm pilot study (NCT06953518) evaluating 2 days of nutraceutical supplementation (Qualia Senolytic). Fingerstick blood samples and validated quality of life (QoL) questionnaire data were collected on days 0 and 7. Primary outcomes were SASP biomarkers measured by the Olink^® Target 48 Cytokine panel, including tumor necrosis factor (TNF), interleukin-1 beta (IL-1β), interleukin-8 (CXCL8), and vascular endothelial growth factor A (VEGFA). Protein data were analyzed using linear mixed models and Wilcoxon signed-rank tests. Seventy-one adults enrolled and 53 (74.6%) provided paired protein samples. No significant changes occurred in primary outcomes. Exploratory unadjusted analyses revealed significant reductions in the established senescence chemokines CXCL9 and CXCL10, as well as CCL8 and CXCL11, and increases in interleukin-17F and oncostatin M. QoL significantly improved without safety concerns, though results are expectation-sensitive. Preliminary findings support the feasibility of this decentralized approach and identify candidate SASP biomarker signals in healthy adults warranting validation in randomized, placebo-controlled trials. Full article

Objectives: Myelodysplastic syndromes (MDSs) are a heterogeneous group of clonal hematopoietic disorders characterized by ineffective hematopoiesis, genomic instability, and a high risk of progression to acute myeloid leukemia. Oxidative stress (OS) has emerged as a central factor in MDS pathophysiology, contributing to DNA damage, altered cellular signaling, and disease progression. Recent advances in artificial intelligence (AI) and machine learning (ML) offer a transformative approach for integrating multidimensional datasets including oxidative stress markers, hematologic parameters, and molecular profiles to enhance diagnosis, prognostication, and therapeutic monitoring in MDS. Methods: A comprehensive literature search was conducted in PubMed and Scopus, using the keywords “OS biomarkers,” “AI,” and “MDS’’. Results: Modified redox biomarkers can be correlated with oxidative imbalance and disease progression. ML models such as neural networks, decision trees, and support vector machines effectively capture complex relationships among redox biomarkers, enhancing risk stratification and prediction of treatment response. AI-driven proteomic analyses further revealed OS-related protein signatures linked to MDS pathophysiology. Overall, AI and ML enable the transformation of multidimensional OS data into clinically actionable tools for personalized management in MDS. Conclusions: Integrating biomarker research with AI-based analytics holds promise for advancing personalized diagnostics, prognostication, and therapeutic strategies in MDS, paving the way toward precision medicine. Full article