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PubMed2026

Spinal meningiomas: histopathological grading using a benchmark radiomics model with notes on disease control.

OBJECTIVE: Spinal meningiomas (SMs) are common primary spinal tumors for which surgery is considered the first-line treatment when safe and feasible. The ability to extrapolate the tumor grade from preoperative imaging may significantly inform early patient expectation-setting regarding recurrence. Building on radiomics studies in cranial meningiomas, the authors aimed to construct a benchmark radiomics model to preoperatively identify the histological grade of SMs. METHODS: Institutional surgical records from May 2012 to November 2025 were queried for pathology-confirmed meningiomas below the foramen magnum, with preoperative contrast-enhanced imaging available for segmentation. SMs were classified as low-grade (WHO grade 1) and high-grade (WHO grade 2 tumors and grade 1 tumors with atypia). Tumors were manually segmented, and features were extracted using the PyRadiomics software package. An ensemble model of k-nearest neighbors, random forest, and support vector machine classifiers was trained using nested cross-validation on a subset of 10 features to differentiate tumor grades. Clinical data for the cohort were also extracted, and disease control in an adjunctive clinical series was assessed. RESULTS: Seventy-four patients were included in radiomics analysis, with an area under the receiver operating characteristic curve of 0.879 and a mean F1 score of 0.748. The model's top 5 features were all texture features that differed significantly (p < 0.05) across low- and high-grade SMs. These included measures of tumor textural and contrast-enhancement heterogeneity, with overlap with features reported in radiomics models for histological grading of intracranial meningiomas. Fifty-five patients with a median radiographic follow-up of 22.2 (range 1.9-86.4) months remained for clinical analysis after exclusion of patients with less than 1 month of follow-up and syndromic meningiomas. Four recurrences occurred at a median of 20.8 (range 1.8-41.8) months. High-grade tumor pathology did not significantly impact progression-free survival (p = 0.682, log-rank test; Cox regression high vs low grade hazard ratio [HR] 0.62, 95% CI 0.06-6.11, p = 0.685). Subtotal resection was associated with poorer progression-free survival than gross-total resection (p = 0.004, log-rank test; Cox regression subtotal vs gross-total resection HR 10.62, 95% CI 1.46-77.05, p = 0.019). These findings remain contextualized within a relatively limited follow-up window and small recurrence event count, suggesting a need to characterize the interplay between tumor grade and extent of resection as drivers of local disease control in SMs. CONCLUSIONS: A preoperative radiomics model can stratify high-grade SMs using open-source tools applied to single-institution data.

Humans↗

PubMed2026

Depression and amyloid-β across CSF, PET, and plasma biomarkers: a systematic review and meta-analysis.

Alzheimer's disease is increasingly defined by biomarker evidence of amyloid-β and tau pathology, sharpening questions about whether late-life depression contributes to, or instead reflects, this pathology. We conducted a systematic review and meta-analysis of studies published between 2000 and 2025 that compared amyloid-β biomarkers in adults with and without depression, with depression defined by validated clinical diagnoses or symptom rating scales. Twenty-four studies were included, spanning three biomarker sources: cerebrospinal fluid, positron emission tomography imaging, and plasma. Across all sources, the pooled difference in amyloid-β burden between depressed and non-depressed individuals was small and clustered near zero, indicating only a weak, statistically non-significant tendency toward higher amyloid in depression. When the three sources were examined separately, each yielded a similar near-null result, although between-study heterogeneity was considerable for cerebrospinal fluid and plasma and moderate for imaging. Importantly, a prespecified subgroup analysis showed that imaging results diverged by quantification method: studies using the simpler standardized uptake value ratio clustered around zero, whereas the smaller group of studies using kinetic distribution volume ratio modelling showed a significant positive association, suggesting that methodological choices critically influence the observed relationship. Taken together, these findings indicate that depression is not consistently accompanied by greater amyloid-β burden across widely used biomarker platforms. The distribution volume ratio signal nonetheless raises the possibility of subtle associations that cruder methods may obscure, and suggests that depression may shape Alzheimer's disease trajectories more by modifying the clinical impact of amyloid than by altering its amount.

Humans↗

PubMed2026

Multi-omics panorama of glaucoma: Pathogenesis, biomarkers, and novel therapeutic strategies.

Glaucoma is a group of irreversible, blinding eye diseases characterized by progressive loss of retinal ganglion cells, leading to gradual visual field defects that severely impact patients' quality of life. Its complex pathophysiological mechanisms remain incompletely understood, limiting the development of early diagnostic and effective therapeutic strategies. Advances in omics technologies have provided new insights into elucidating the pathophysiology of glaucoma. We summarize specific alterations in genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics associated with glaucoma. We emphasize the systematic analysis of disease mechanisms, identification of clinically applicable biomarkers, and discovery of novel therapeutic targets through the integration of these data. This approach paves new pathways for glaucoma subtype diagnosis and personalized treatment, while also outlining future research directions and challenges.

Humans↗

PubMed2026

EZH1/2 inhibition selectively targets SMARCA4/2 co-deficient lung cancer cells by suppressing stemness and proliferation.

SMARCA4-deficient thoracic malignancies comprise biologically heterogeneous tumors, ranging from conventional non-small cell lung cancer with SMARCA4 alterations to thoracic SMARCA4-deficient undifferentiated tumor (SMARCA4-UT), an aggressive entity frequently associated with concomitant SMARCA2 loss. However, the extent to which SMARCA4-deficient lung cancer cell lines recapitulate SMARCA4-UT-like biology remains incompletely defined. Here, we characterized lung cancer cell lines across distinct SMARCA4 and SMARCA2 states and identified a subgroup with SMARCA4/2 co-deficiency that exhibited reduced expression of epithelial lineage markers and transcriptional similarity to SMARCA4-UT and other SWI/SNF-deficient malignancies. The EZH1/2 inhibitor HM97662 selectively suppressed growth in SMARCA4/2-deficient cells, with limited effects in SMARCA2-proficient cells. EZH1/2 inhibition broadly reduced H3K27me3 and induced derepression of PRC2 targets regardless of drug sensitivity. However, its biological effects were most pronounced in SMARCA4/2-deficient cells, where it promoted apoptosis, reduced stemness marker expression, attenuated the SMARCA4-UT-associated transcriptional signature, and suppressed proliferative and mTORC1-related programs. Chromatin accessibility analysis further revealed cell-line-specific patterns of accessibility loss, with reduced accessibility at stemness-associated transcription factor motif-enriched regions coupled with transcriptional repression of nearby genes in SMARCA4/2-deficient cells. These findings support dual EZH1/2 inhibition as a potential therapeutic vulnerability in SMARCA4/2-deficient, SMARCA4-UT-like lung cancer cells.

Humans↗

PubMed2026

Proteomic profiling reveals that DPP4 overexpression increases cell adhesion, inhibits cell migration, and restores androgen sensitivity in prostate cancer.

Dipeptidyl peptidase-4 (DPP4), a serine protease with both enzymatic and non-enzymatic roles, has emerged as a context-dependent modulator of tumor progression. In the present study, we investigated the expression and function of DPP4 in androgen-sensitive and castration-resistant prostate cancer (CRPC) models. Proteomic analysis of androgen-resistant prostate cells overexpressing DPP4 identified the involvement of the cellular adhesion molecules pathway. In prostate cells, lentiviral-mediated DPP4 overexpression restored androgen receptor signaling, inhibited epithelial-to-mesenchymal transition, and reduced cell migration, whereas DPP4 silencing produced the opposite effects. We demonstrate that DPP4 expression is down-regulated in CRPC cells and that treatment with capsaicin (CAP), a bioactive compound derived from red peppers, restores DPP4 expression. Moreover, DPP4 restoration by CAP suppresses prostate tumorigenesis in the TRAMP mice in vivo model of prostate cancer. Our results suggest that DPP4 could be a new target for CRPC.

Male↗

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

Hypothalamic-Pituitary Axis Involvement in Primary Central Nervous System Lymphoma.

CONTEXT: Primary central nervous system lymphoma (PCNSL) is a rare malignancy that may involve the hypothalamic-pituitary axis (HPA), leading to underrecognized but clinically significant endocrine dysfunction. OBJECTIVE: This work aims to characterize the spectrum and patterns of HPA-related endocrine disturbances in patients with PCNSL. DATA SOURCES: A systematic search was conducted in PubMed, EMBASE, Scopus, and Web of Science, supplemented by gray literature. The search concluded in February 2025. STUDY SELECTION: We included studies reporting adult PCNSL cases with documented dysfunction of at least one hormonal axis. Exclusion criteria were preexisting hypopituitarism or lack of endocrine data. DATA EXTRACTION: Data on demographics, tumor localization, hormonal axes affected, radiological findings, treatment, and outcomes were extracted. Risk of bias was assessed using JBI tools. RESULTS: Ninety-nine cases met the inclusion criteria. Diffuse large B-cell lymphoma accounted for 84% of cases. Endocrine dysfunction included isolated adenohypophyseal involvement (46%), neurohypophyseal (8%), and combined (45%). The most affected pituitary axes were the gonadal and thyroid axes, with 89.7% and 89.2% involvement, respectively. Hypothalamic tumors were strongly associated with combined dysfunction (odds ratio = 9.47; 95% CI, 3.76-23.86; P < .001). Persistent endocrinopathy was more frequent in progressive disease. No direct association was found between endocrine dysfunction and mortality. CONCLUSION: HPA dysfunction in PCNSL is frequent and often underdiagnosed. Hypothalamic involvement is associated broader hormonal impairment. Routine hormonal screening and multidisciplinary management should be standard in PCNSL care to minimize complications and improve outcomes.

Humans↗

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

Understanding specificity in immune-brain pathways: A systematic review of differential associations between individual cytokines and brain structure and function measured through magnetic resonance imaging in humans.

Research shows that cytokines are associated with psychiatric disorders, including major depression, and multiple aspects of brain structure and function. Accumulating data suggest that different cytokines may have unique profiles of biological activity, particularly in their neuromodulatory roles, but it is currently unclear whether they have unique associations with specific neural circuits in humans. In this paper, we systematically review magnetic resonance imaging studies conducted with depressed or healthy control human participants under age 65 that examine associations between peripheral cytokines and brain structure and function, with the goal of evaluating evidence for the specificity of these cytokine-brain associations. We find that across multiple measures of brain structure and function, the majority of studies reviewed reported unique associations between individual cytokines and brain outcomes. A synthesis of findings across studies also suggests a preliminary hypothesis of specific associations of interleukin-6 levels in circulation with the default mode network and tumor necrosis factor-alpha with the salience network, which could be tested in future research. We conclude the review with future directions for research that can strengthen understanding of these associations.

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↗