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PubMed2026

From host response to genomic targets: electrochemical biosensing of tuberculosis biomarkers.

Tuberculosis (TB) remains one of the leading causes of death from a single infectious agent worldwide, with timely diagnosis continuing to be a major challenge, particularly in resource-limited settings. Conventional TB diagnostic methods are limited by low sensitivity, long turnaround times, and an inability to reliably differentiate latent from active disease. Biomarker-based diagnostic strategies have therefore gained increasing attention as they offer the potential to improve early detection, disease differentiation, and treatment monitoring. Herein, we examine electrochemical biosensing strategies for TB diagnostics using a biomarker-class-driven framework, covering host-response biomarkers (IFN-γ and TNF-α), pathogen-derived antigens (ESAT6, CFP10, CFP10-ESAT6, MPT64, Ag85, HspX and LpqH), cell-wall signatures and whole-cell markers (LAM and whole cell Mtb), and genomic markers (Mtb DNA and IS6110). Through structured comparison of recognition elements, biointerface designs, signal amplification strategies, electrochemical techniques, matrices, and validation levels, this review identifies the most promising technical approaches for different TB biomarker classes. It further highlights key translational bottlenecks, including limited clinical validation, buffer-based testing, complex multistep amplification, redox-probe dependence, matrix fouling, and insufficient evidence of manufacturability. This review therefore provides practical guidance for developing electrochemical TB biosensors that are analytically sensitive, clinically relevant, and suitable for decentralized diagnostic applications.

Biosensing Techniques↗

PubMed2026

MicroRNAs in Oral Bio-Fluids as Predictive Biomarkers of Orthodontic Tooth Movement: A Systematic Review.

This systematic review was designed to assess scientific evidence of the association of microRNA expression during orthodontic tooth movement through various time points. A systematic review was performed in accordance with the PRISMA checklist. A search strategy was developed in electronic databases including Med Line, Scopus, EBSCO Host and ProQuest Dissertations & Theses Global until June 2025. Eligibility criteria included studies that investigated microRNA expression in saliva/GCF during orthodontic treatment. The risk of bias of the included studies was analysed using the QUADAS-2 and RoB-2 tools. The search retrieved 2800 records, of which nine studies were selected. Minor variations in GCF collection were noted, while stimulated saliva was collected in one study. RT-PCR and the Fluro meter accounted for the majority of miRNA estimation. Thirteen miRNAs were identified as target biomarkers for OTM regulation. Despite the high risk of bias, the evidence from the current systematic review indicates that microRNAs can be considered as potential biomarkers of orthodontic tooth movement in oral biofluids. Trial Registration: Prospero ID-CRD420251153064.

Humans↗

PubMed2026

Tripled-Stranded Antisense Oligonucleotide for Biomarker-Activated Suppression of Essential Genes.

Conditional activation of antisense oligonucleotides (ASOs) is a promising strategy for selective suppression of cancer cells without affecting normal cells. In this study, we developed a tripled-stranded ASO (tsASO) that is rendered inactive through complexation with two additional oligonucleotides. The key innovation is the use of partial overlap between the parent ASO and the biomarker sequence, combined with toehold-mediated strand displacement, enabling precise conditional activation. The tsASO effectively triggered RNase H-mediated degradation of DYNC1I2 and DARS1 RNAs exclusively in the presence of the ERBB2 sequence. In cell-free systems, the tsASO demonstrated high cleavage efficiency (up to 81%), comparable to the parent ASO efficiency, with minimal background activity in the absence of the biomarker sequence, validating the concept at the molecular level. However, in cells using lipid-based transfection, the tsASO exhibited nonspecific cytotoxicity that did not correlate with biomarker presence or target gene expression. Detailed analysis showed no clear support for known sequence-driven toxicity mechanisms (CpG/TLR9, G-quadruplexes) in the nonimmune cell lines, suggesting that the primary limitation is intracellular delivery rather than the tsASO design. Future work should focus on optimizing delivery platforms to achieve controlled cellular uptake and biomarker-dependent release, unlocking the therapeutic potential of this conditional gene silencing approach.

Oligonucleotides, Antisense↗

PubMed2026

Revealing potential biomarkers and metabolic mechanisms of ovarian aging in hens during late laying period based on machine learning and metabolomics.

Ovarian function decline during the late laying period represents a major bottleneck for the economic efficiency of the global poultry industry. However, the underlying metabolic mechanisms and reliable early-warning biomarkers for ovarian aging remain poorly understood. In this study, we performed the first untargeted LC-MS/MS metabolomics analysis of ovarian tissues from Taihe silky fowls at peak laying (30 weeks) and late laying (50 weeks) stages, and employed an ensemble machine learning strategy integrating LASSO, random forest, and support vector machine (SVM) algorithms to identify high-confidence core biomarkers of ovarian aging. Gene expression analysis was further conducted to validate the potential molecular mechanisms. Our results showed that the metabolic profiles of ovarian tissues differed significantly between the two groups. A total of 6 core biomarkers were identified, 4 of which were long-chain acylcarnitines. Mechanistic analysis revealed that downregulation of key genes in the carnitine shuttle system led to impaired mitochondrial fatty acid β-oxidation, which in turn triggered excessive oxidative stress and compromised ovarian endocrine function. In conclusion, this study identifies long-chain acylcarnitines as potential metabolic biomarkers for ovarian aging in Taihe silky fowls. These findings provide novel insights into the metabolic basis of poultry ovarian aging and lay a theoretical foundation for the precise regulation of reproductive performance in indigenous poultry breeds.

Animals↗

PubMed2026

Integrated multi-omics profiling of amniotic fluid identifies predictive biomarkers for fetal growth restriction trajectories.

BACKGROUND: Fetal growth restriction (FGR) is a complex condition with highly heterogeneous clinical outcomes, making prenatal distinction between transient and persistent growth failure challenging. This study aims to identify amniotic fluid (AF) biomarkers capable of differentiating distinct FGR trajectories and characterizing persistent growth failure mechanisms. METHODS: Integrated proteomic and metabolomic profiling was performed on AF samples from transient FGR (n = 11), persistent FGR (n = 9), and healthy controls (n = 13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n = 69). RESULTS: Multi-omics analysis revealed distinct molecular signatures for FGR stratification. A two-protein diagnostic panel (PDGFA and phospho-STAT5A) achieved an AUC of 1.000 in the discovery stage and 0.780 in the external validation cohort. For prognostic assessment, a molecular signature including IREB2, HLA-C, and PLXNB2 accurately predicted persistent growth failure from transient recovery (AUC = 0.966). Cross-platform integration highlighted the mass spectrometry-derived WASHC2C as a central hub protein with a significant progressive increase across the control, transient, and persistent groups (p < 0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular signatures reflecting the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗

PubMed2026

A genome-wide coverage-based pipeline for the identification of host-derived candidate DNA biomarkers from cell-free blood.

We have created a new data-analysis pipeline for the discovery of host-specific candidate DNA biomarkers derived from sequencing data of cell-free blood. Unlike approaches that rely on specific molecular or genetic signatures, our method leverages the coverage distribution of cell-free DNA sequences mapped to a reference genome, applying statistical analyses to identify informative short genomic regions for biomarker discovery. The pipeline is applicable to diverse diseases and can be used to analyze cell-free DNA sequences from plasma or serum to identify candidate biomarkers that are characteristic of disease states in mammals. Core functionalities were developed in Java and integrated with open-source software tools for the preprocessing of raw sequencing data, complemented by Python scripts for the machine-learning analysis and statistical validation. The pipeline is designed for HPC use and users can access the pipeline through a Galaxy workflow, which offers a user-friendly web interface for input selection prior to execution and analysis progress monitoring. Performance tests, carried out using duplicate sets of COVID-19 samples and controls, showed linear scalability of execution time with an increasing dataset size, as well as a substantial reduction in execution time through parallelized computation, whereby each HPC node is used to process the data of one chromosome. Further statistical tests confirmed the quality of the pipeline's results by showing that the set of identified candidate biomarkers remained stable across varying dataset sizes.

Biomarkers↗

PubMed2026

Machine learning-ready genomic biomarkers: ATF3 polymorphisms predict postoperative analgesic demand through AI-compatible phenotyping.

PURPOSE: To determine whether ATF3 polymorphisms can serve as genetic biomarkers for machine learning-based precision analgesia by establishing a genotype-phenotype association suitable for predictive modeling of postoperative opioid requirements. METHODS: In a prospective cohort of 167 adults undergoing abdominal surgery, ATF3 SNPs rs3122721 and rs3125293 were genotyped. A structured dataset architecture was developed to represent genetic profiles as input features for supervised learning models, enabling translational analysis of genotype‑dependent opioid consumption over 72 h. RESULTS: Patients with homozygous genotypes of the ATF3 SNPs had significantly higher opioid requirements than non‑carriers, despite reporting similar subjective pain scores. This consistent genotype‑dependent pattern provided a clinically relevant phenotype suitable for integration into predictive algorithms. CONCLUSION: ATF3 genotyping offers a promising biomarker for computationally informed precision analgesia. By linking genomic variability to clinically meaningful outcomes within a structured clinical and genomic framework, this approach supports the future development of risk-stratified clinical decision-support systems to optimize postoperative pain management.Trial registration ChiCTR1900021991, registered 30 April 2019. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13755-026-00480-9.

ATF3↗

PubMed2026

Effects of Acute Low- and Moderate-Dose Alcohol on Chronic Disease-Related Biomarkers in Healthy Light and Heavy Drinkers.

BACKGROUND: Alcohol consumption is a major contributor to global chronic disease, with growing evidence indicating health risks even at low levels of intake. However, mechanistic understanding of these risks relies heavily on preclinical models and observational data, leaving a critical gap in controlled experimental evidence regarding how alcohol perturbs human biological systems in vivo. METHODS: The present study utilized plasma samples from a randomized, placebo-controlled trial to evaluate the effects of low-dose (0.35 g/kg) and moderate-dose (0.60 g/kg) alcohol on disease-relevant biomarkers in 32 healthy adults (mean age = 25.0 ± 3.8 years; 21 female/11 male), characterized by light (n = 15) or heavy (n = 17) drinking. This design enabled evaluation of effects across dose, timescale, and drinking history, as well as assessment of their interactions. Plasma was collected at prebeverage baseline and hourly for 4 h afterward. Immunoassays quantified 10 disease-related biomarkers: adiponectin, angiogenin, D-dimer, high-sensitivity C-reactive protein (hsCRP), Intercellular Adhesion Molecule-1 (ICAM-1), Lipocalin-2 (LCN2), Matrix Metalloproteinase-7 (MMP-7), Matrix Metalloproteinase-9 (MMP-9), soluble Receptor for Advanced Glycation End-products (sRAGE), and Triggering Receptor Expressed on Myeloid cells 2 (TREM2). RESULTS: Main effects of group indicated that even in this young healthy sample, heavy drinking status was associated with higher levels of adiponectin, angiogenin, ICAM-1, LCN2, and sRAGE, a profile suggesting altered vascular and metabolic activity. Acute alcohol administration induced changes in sRAGE and hsCRP. Specifically, moderate-dose alcohol triggered an increase in the immunoglobulin sRAGE, which may reflect an acute compensatory response to inflammation and/or oxidative stress. Compared to placebo, hsCRP was lower in the low-dose alcohol condition; however, this finding should be interpreted in light of CRP biology. MMP-7, MMP-9, and LCN2 showed time-dependent fluctuations that were independent of experimental condition, highlighting the critical importance of placebo-controlled designs to account for diurnal/postprandial variation in immune biomarkers. CONCLUSION: Findings provide translational evidence that alcohol is associated with multisystem biomarker changes relevant to chronic disease and that alcohol-related biomarker perturbations vary by dose and chronicity.

Humans↗

PubMed2026

Quality assessment, prognostic factors, and biomarkers for brain tumor analysis: a comprehensive systematic review.

The brain tumors possess different causative factors and properties, making their diagnosis and treatment difficult. Growth of these cancers usually leads to compression of the adjacent nerves and obstruction of the flow of cerebrospinal fluid, thus leading to increase in intracranial pressure. This affects the working of brain in many ways; thus, the difficulty involved in its treatment. With the improvements in technology in neuroimaging, including Diffusion Tensor Imaging (DTI), Positron Emission Tomography (PET), and multiparametric Magnetic Resonance Imaging (mpMRI), the diagnosis process has become easy. The effectiveness of any form of therapy in such patients depends primarily on their prognosis. While it is a common practice that physicians determine the prognosis of the disease by considering the age of the patient, histological grade of the tumor, and resection status, now this method has become more comprehensive by adding molecular signature and genetic analyses to the list of criteria. Next-generation sequencing (NGS) allows a reliable molecular classification. It increases the level of risk stratification, facilitating the application of therapies tailored to individual patients. Thus, molecular oncology has greatly changed our views on brain tumors' pathology and prognosis while neoadjuvant treatments aim at increasing the survival rate. On the other hand, radiogenomics is a field of study that combines non-invasive imaging phenotypes and genomic information in order to find unique molecular signatures of tumors without collecting samples from tumors. Molecular biomarkers are absolutely essential in the diagnosis of cancer, treatment monitoring, and recurrence of cancer. Advances in liquid biopsy technology, particularly the methods for circulating tumor DNA (ctDNA) and Extracellular Vesicle (EV) based analysis, have enabled the possibility of non-invasive monitoring of the progression of the tumors over time. This review highlights key studies and important scientific works about imaging technologies, biomarkers, and prognostic factors of malignant brain tumors.

Humans↗

PubMed2026

Urinary Small Extracellular Vesicle DNA as a Biomarker for the Non-Invasive Diagnosis of Bladder Cancer.

Existing diagnostic technologies for bladder cancer (BC) suffer from low sensitivity, low specificity, or a lack of validation. Therefore, validated, non-invasive diagnostic biomarkers with high sensitivity and specificity for early detection of BC are needed to complement and improve upon the limitations of existing diagnostic methods. We used low-pass whole genome sequencing (LP-WGS) technology to detect copy number variations (CNVs) in small extracellular vesicle (sEV) DNA isolated from urine samples of patients. Based on these results, we constructed and validated a diagnostic model to differentiate between benign and malignant bladder lesions. We conducted a receiver operating characteristic analysis and calculated the area under the curve (AUC) to evaluate the performance of the diagnostic model. The urine sEV-DNA LP-WGS data revealed CNV differences between benign and malignant samples. The diagnostic model achieved an AUC of 0.953, a sensitivity of 86.7%, and a specificity of 100% in the training cohort and an AUC of 0.985, a sensitivity of 90%, and a specificity of 100% in the validation cohort. Even at the lowest coverage depth of 0.01X, the performance of the diagnostic model remained relatively robust. Notably, the performance of this diagnostic model surpassed that of the biomarker neuron-specific enolase (sensitivity: 85.7% vs. 64.3%; specificity: 100% vs. 87.5%) and urinary cytology (sensitivity: 100% vs. 66.7%; specificity: 100% vs. 94.1%). Our study demonstrates that urine sEV-DNA exhibits high discriminatory power in distinguishing between benign and malignant bladder lesions, making it a promising tool for auxiliary diagnosis of BC.

Humans↗

PubMed2026

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans↗

PubMed2026

Candidate biomarkers for early Giardia duodenalis infection revealed by time-resolved secretome proteomics.

Giardia duodenalis is a zoonotic protozoan parasite that causes giardiasis in humans and other mammals. Early diagnosis remains challenging because current diagnostic methods, including microscopy and enzyme-linked immunosorbent assays (ELISAs), primarily detect established infections. Consequently, a critical diagnostic gap exists during the early stage of infection within the first 2-48 h following exposure. To address this limitation, we characterized the proteins released by in vitro-cultured G. duodenalis trophozoites under serum-free conditions and evaluated their potential as early diagnostic biomarkers. Proteomic analysis of culture supernatants collected during early trophozoite incubation identified 31,773 peptides corresponding to 2504 quantifiable proteins. Temporal profiling showed distinct secretion patterns, including proteins that peaked during the early stage, progressively accumulated over time, or remained persistently abundant throughout the incubation period. Based on their secretion characteristics and predicted immunogenic properties, five candidate biomarkers were selected for further evaluation. Polyclonal antibodies raised against selected candidates successfully detected the corresponding proteins in serum-free culture supernatants, providing preliminary evidence for their potential utility as early-stage diagnostic targets. These findings identify stage-associated candidate proteins that may serve as a resource for future early giardiasis diagnostic development, provide a valuable resource for investigating host-parasite interactions, and establish a foundation for future diagnostic assay development. However, further validation in clinical and biological samples is required to confirm their diagnostic applicability. SIGNIFICANCE: Giardiasis, caused by Giardia duodenalis, is a major diarrheal disease worldwide. Although enzyme-linked immunosorbent assays (ELISAs) provide rapid detection, their diagnostic utility is limited by the lack of biomarkers capable of identifying infection during its earliest stages, creating a critical gap in the detection of active infection within 2-48 h following exposure. Using data-independent acquisition proteomics, this study provides a time-resolved characterization of proteins released by G. duodenalis trophozoites into serum-free culture supernatants. Our findings reveal temporal secretion dynamics of protein secretion and identify candidate biomarkers with potential utility for the development of early-stage diagnostic assays pending rigorous biological and clinical validation. In addition, this proteomic resource provides a foundation for investigating host-parasite interactions and may facilitate the development of future point-of-care diagnostic strategies.

Giardiasis↗

PubMed2026

Plasma proteome profiling identifies XPNPEP3 as a novel biomarker associated with metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes mellitus.

OBJECTIVE: To identify plasma protein differences between type 2 diabetes mellitus (T2DM) patients with and without metabolic dysfunction-associated steatotic liver disease (MASLD), and to evaluate the diagnostic potential of X-prolyl aminopeptidase 3 (XPNPEP3) for identifying MASLD in T2DM patients. METHODS: Twenty T2DM inpatients were categorized into groups with and without MASLD and their plasma samples were analyzed using data-independent acquisition mass spectrometry, followed by bioinformatics analysis to identify differentially expressed proteins. The cohort was then expanded to 84 patients, and plasma XPNPEP3 levels were validated by enzyme-linked immunosorbent assay. Correlation between XPNPEP3 and clinical indicators were evaluated, and diagnostic performance was determined via receiver operating characteristic (ROC) analysis. Immunohistochemistry was employed to compare hepatic XPNPEP3 expression between the two groups. RESULTS: Proteomic analysis identified 176 differentially expressed proteins, with XPNPEP3 exhibiting the most significant down-regulation by fold change. In the validation cohort, plasma XPNPEP3 was significantly lower in T2DM+MASLD versus T2DM alone. XPNPEP3 levels were negatively correlated with diabetes duration, liver function markers, and triglyceride levels, and was identified as an independent factor inversely associated with MASLD in T2DM.ROC analysis demonstrated strong diagnostic performance for XPNPEP3, further enhanced when combined with BMI and diabetes duration.  Immunohistochemistry confirmed reduced hepatic XPNPEP3 expression in T2DM+MASLD patients. CONCLUSIONS: Lower plasma XPNPEP3 is independently associated with MASLD in T2DM patients and demonstrates strong diagnostic potential, positioning XPNPEP3 as a promising biomarker for diagnosing MASLD in T2DM patients and a novel target for non-invasive diagnostic tool development.

Humans↗

PubMed2026

Longitudinal Repeated Protein Measurements in a Multiethnic Cohort Identify Novel Diabetes Biomarkers That Reveal Unique Disease Pathways.

There is up to a fourfold increase in diabetes biomarkers identified with longitudinal repeated versus single time point proteomic measurements. The increase in biomarkers identified with longitudinal repeated measurements is supported by a similar proportion being nominated as causal for type 2 diabetes with Mendelian randomization. Proteins unique to the longitudinal repeated analyses highlighted biological pathways (e.g., posttranslational protein modification and cellular structure and cycle regulation) that were distinct from pathways enriched among the shared proteins (e.g., small-molecule metabolic and catabolic processes). Longitudinal protein measurements identify additional novel disease biomarkers and disparate biological pathways compared with single measurement analyses.

Journal Article↗

PubMed2026

Assay-dependent variability in peptide biomarker quantification: experimental evidence from renalase in chronic kidney disease.

BACKGROUND: Renalase is a promising biomarker for kidney disease, but published levels vary widely between studies. We hypothesised that variability in commercial enzyme-linked immunosorbent assays (ELISAs) kits and matrix effects (serum vs plasma) drive these inconsistencies. METHODS: Paired serum and plasma samples from 56 participants (28 chronic kidney disease (CKD) stages 2-5, 28 healthy controls) were tested using three commercial renalase ELISAs (BTLAB, Cloud-Clone, EIAab). We assessed intra-assay precision, inter-assay agreement (Spearman's rank correlation and Bland-Altman analysis on log10-transformed values), matrix effects, and associations with estimated glomerular filtration rate (eGFR). Diagnostic performance was evaluated by Receiver operating characteristic (ROC) analysis. RESULTS: Inter-assay renalase concentrations differed markedly (up to orders of magnitude), with weak inter-assay correlations (r ≤ 0.25). Bland-Altman analyses revealed large, systematic biases between kits. Only the BTLAB assay showed consistent serum/plasma agreement, a significant correlation with eGFR (ρ ≈ 0.32-0.42, p < 0.05), and moderate discriminatory performance for CKD in serum (AUC = 0.70) and plasma (AUC = 0.68). Cloud-Clone and EIAab produced divergent results and strong matrix-dependent biases. CONCLUSIONS: Observed variability among commercial ELISA platforms may compromise comparability between studies. Harmonisation, standardised reference materials, and cross-validation are necessary before renalase assays can be used reliably in clinical practice.

Humans↗

PubMed2026

Dealcoholized muscadine wine improved skin elasticity and oxidative stress biomarkers without affecting gut microbiome in women over 40 in a randomized controlled trial.

Muscadine wine has a unique polyphenol profile distinct from that of common wine, and limited research exists on its health benefits. This study aimed to investigate the effects of intake of dealcoholized muscadine wine (DMW) on skin health, oxidative stress, inflammatory biomarkers, and the gut microbiome. Seventeen healthy women were randomly assigned to consume 300 mL of DMW or a placebo daily for 6 weeks, separated by a 3-week washout period, in a randomized, single-blinded, crossover design. Skin health parameters were measured on the face and forearm. Oxidative stress and inflammatory biomarkers were assessed in plasma. Fecal bacterial DNA was sequenced using shotgun sequencing. DMW did not affect UVB-induced erythema compared to placebo. However, it significantly decreased transepidermal water loss and increased facial gross elasticity. Skin elasticity significantly improved on the forearm, whereas other skin parameters were not affected. DMW significantly decreased plasma levels of matrix metalloproteinase-9 and advanced glycation end products compared with placebo. However, the abundance, diversity, and functions of the gut microbiome were not affected. Polyphenol-rich DMW administered for six weeks improved certain skin health parameters and reduced oxidative and inflammatory stress, without affecting the gut microbiome in healthy women.

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

Translating single-cell RNA sequencing into monocyte direct leukocyte subpopulation-transcript abundance assay ratio-based biomarkers (IFI27/PSAP or IFI27/CTSS) for clinical detection of viral infection.

A rapid method for triaging febrile patients by aetiology (e.g., viral or bacterial infection) using gene expression in peripheral blood (PB) is an intensively researched area. However, gene expression in blood represents a composite sum of gene expression of all the component cell types present in the sample. As a result, numerous genes are measured in most proposed signatures. Herein, we propose a simple ratio-based biomarker (RBB) called direct leukocyte subpopulation-transcript abundance assay (DIRECT LS-TA) that recapitulates gene expressions of a single cell type in PB (i.e., monocytes). Based on single-cell RNA sequencing (scRNAseq) data and bulk expression data, IFI27 and SIGLEC1 are found as interferon-stimulated genes (ISGs) predominantly expressed by monocytes. The DIRECT LS-TA method can use a simple ratio of two genes measured in PB as an RBB to represent the target gene expression in monocytes without the need for monocyte purification. Both scRNAseq and bulk RNA sequencing datasets were used to evaluate the correlation between ISG expression in monocytes and PB, with a particular focus on monocyte expression of IFI27. An iceberg plot of bulk transcriptome data was used to identify genes that were predominantly expressed by monocytes in PB. DIRECT LS-TA RBBs of the three genes (IFI27, IFI44L and SIGLEC1) were evaluated by group-wise comparison, receiver operating characteristic and meta-analysis. In addition, the conventional interferon (IFN) score was evaluated for comparison of diagnostic performance. In viral infection datasets, DIRECT LS-TA of IFI27 (IFI27/PSAP or IFI27/CTSS) was most intensely activated (p value by t test <1e-9) and had the best area under the curve (0.94) among the three potential monocyte ISGs analysed. DIRECT LS-TA SIGLEC1 was also another monocyte biomarker but showed a lower activation (p<9e-5). IFI27/PSAP showed better diagnostic performance than the conventional IFN score. On the other hand, IFI44L was not a predominant monocyte expression gene. DIRECT LS-TA of IFI27 (IFI27/PSAP or IFI27/CTSS) measured in PB was the best biomarker of viral infection and IFN activation among ISGs predominantly expressed by monocytes. It performed even better than the conventional IFN score which required quantification of eight genes. The results suggest that DIRECT LS-TA of IFI27 is a monocyte-informative biomarker which is easy to determine in PB without the need for cell sorting.

Humans↗