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Results for “biomarker variability”

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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

Longitudinal variability of lipoprotein(a) in youth-onset type 1 diabetes: implications for cardiovascular risk stratification.

BACKGROUND: Lipoprotein(a) [Lp(a)] is a genetically determined and independent cardiovascular risk factor, traditionally considered stable across the lifespan, supporting a single lifetime measurement strategy. However, its longitudinal behaviour during childhood and adolescence remains poorly characterised, particularly in individuals with type 1 diabetes who face a markedly increased lifetime risk of coronary artery disease. We therefore aimed to characterise intra- and inter-individual trajectories of Lp(a) in a paediatric type 1 diabetes cohort and to assess the implications of Lp(a) variability for cardiovascular risk classification. METHODS: We conducted a retrospective single-centre cohort study of children and adolescents with type 1 diabetes attending Geneva University Hospitals between 2012 and 2023. Annual fasting Lp(a) concentrations were analysed longitudinally. Variability was assessed in participants with ≥ 2 measurements. Clinically relevant thresholds were used to evaluate cardiovascular risk reclassification. Paired Wilcoxon tests, Pearson and Kendall correlations, and Holm-adjusted p-values (P < 0.05) were applied. Analyses were conducted in R. RESULTS: A total of 286 participants contributed 1403 Lp(a) measurements, with observation periods varying across individuals (median 6.2 years, IQR 2.9-9.6) and between 1 and 13 measurements per participant. At baseline, 26% had elevated Lp(a) (≥ 300 mg/l). Among participants with serial measurements, 32% showed intraindividual fluctuations exceeding 50% of their individual maximum value. Reclassification across the 300 mg/l cardiovascular risk threshold occurred in 11.9% of participants. Lp(a) concentrations peaked between ages 10 and 13 years and declined thereafter. Modest seasonal variation was observed, with higher concentrations in autumn and winter (P < 0.05). CONCLUSIONS: In youth with type 1 diabetes, Lp(a) is not as stable as previously assumed, exhibiting clinically relevant variability over time. These findings challenge the current paradigm of a single lifetime Lp(a) measurement and suggest that repeated assessment, particularly during adolescence, may improve early cardiovascular risk stratification.

Humans↗

PubMed2026

Genomic and epigenetic regulatory mechanisms in exercise-based rehabilitation processes: Cellular and tissue remodeling, microvascular adaptation, and circulating biomarkers.

While exercise-based rehabilitation is known to positively impact functionally related parameters, the role of genomic and epigenomic responses coordinated with cellular, extracellular matrix (ECM), mitochondrial, and microvascular adaptations remains insufficiently investigated. This narrative review summarizes mechanistic evidence linking exercise-associated mechanical, metabolic, hypoxia-redox, inflammatory, and hemodynamic stimuli with tissue remodeling and clinically relevant biomarkers. Current findings indicate that integrin-focal adhesion kinase (FAK) signaling and Hippo YAP/TAZ pathways contribute to mechanical signal transduction, cytoskeletal regulation, and gene expression, whereas metabolic adaptation, ATP homeostasis, and protein synthesis are regulated through AMPK-PGC-1α, SIRT1, and mTOR-dependent pathways. Epigenetic mechanisms, including DNA methylation, histone modifications, chromatin remodeling, and noncoding RNA regulation, further influence cell-specific responses in myofibers, satellite cells, fibro-adipogenic progenitors, endothelial cells, pericytes, and immune cells. In addition, VEGF-VEGFR2, eNOS-NO, and KLF2/KLF4 signaling, together with extracellular matrix turnover and inflammation resolution, contribute to tissue repair and microvascular adaptation during rehabilitation. Importantly, acute exercise-induced molecular responses should not be interpreted as direct evidence of sustained tissue adaptation. Circulating microRNAs, extracellular vesicles, cell-free DNA, collagen-related markers, and vascular proteins represent promising approaches for monitoring rehabilitation-related changes; however, their clinical translation remains limited by challenges related to tissue specificity, biomarker kinetics, analytical variability, and the need for standardized validation alongside structural and functional outcomes.

AMPK–PGC-1α signaling↗

PubMed2026

Pregnancy diet based on ancestral patterns increases growth in subcortical fetal brain regions.

Evidence on the biological basis for maternal nutrition effects on fetal and newborn neurodevelopment remains limited. This randomized controlled trial in Ecuador tested a maternal dietary pattern-derived from empirical studies of nutrition in human evolution and adapted locally-on offspring growth and brain development. Pregnant women (n = 215) in their first trimester were randomized to: 1) control (n = 104); or 2) Mikhuna ("nourish" in Kichwa) intervention (n = 111). The intervention, from 12 wk gestation to birth, consisted of a weekly food delivery (8 eggs, 500 g fish, and a variety of sustainably sourced fruits and vegetables) and a behavior change communication strategy encouraging diet diversity and limiting highly processed foods. Longitudinal data collection occurred at 12 wk, 21 wk, 35 wk gestation, and 2 wk postpartum, and included ultrasound imaging of fetal bone and brain parameters, maternal dietary intakes, anthropometry, socioeconomic and demographic variables, and other biomarkers. At close of intervention, a significantly higher percentage of women met the minimum dietary diversity threshold in Mikhuna (74.5%) vs. control groups (55.8%) (P = 0.004). Generalized linear regression models showed significant differences in Mikhuna compared to control for: corpus callosum length 0.19 cm (95% CI [0.02, 0.35]), gangliothalamic ovoid height 0.15 cm (95% CI [0.03 to 0.26]), and femur length -0.10 cm (95% CI [-0.19, -0.02]) from 21 wk to 35 wk; and corpus callosum Z 0.56 (95% CI [0.03, 1.09]) and femur length Z -0.21 (95% CI [-0.42, 0.00]) at 35 wk. The Mikhuna intervention increased the growth of subcortical fetal brain structures, which have established roles in motor control, cognition, and signal transmission.

Female↗

PubMed2025

ToxAssay: a hierarchical model-driven tool for advanced toxicogenomics biomarker discovery.

MOTIVATION: Understanding the genetic basis of drug-induced toxicity is crucial for drug development. In-silico analysis of toxicogenomics datasets facilitates early detection of toxicity biomarkers. However, existing tools struggle with the complex interdependencies among hierarchically structured variables, leading to inaccurate biomarker identification. To address this limitation, we developed a Hierarchical Linear Model (HLM) and implemented it in the R package ToxAssay, offering extensive functionality for comprehensive toxicity assessment. RESULTS: ToxAssay outperforms existing methods by improving biomarker detection and computational efficiency. Applied to glutathione depletion-induced toxicity, it prioritized 71 key genes and identified 26 core genes with high discriminative accuracy (AUC = 0.97) and strong cross-correlation (Pearson's r = 0.88) with external datasets. Additionally, our advance outcome pathway (AOP) analysis algorithm uncovered disease outcomes linked to glutathione depletion. These findings provide precise insights into the molecular mechanisms driving drug-induced toxicity. AVAILABILITY AND IMPLEMENTATION: ToxAssay is available as an open-source R package at https://github.com/Fun-Gene/toxassay.

Biomarkers↗

PubMed2024

Myoferlin: A Potential Marker of Response to Radiation Therapy and Survival in Locally Advanced Rectal Cancer.

PURPOSE: Patients with locally advanced rectal cancer often require neoadjuvant chemoradiation therapy to downstage the disease, but the response is variable with no predictive biomarkers. We have previously revealed through proteomic profiling that myoferlin is associated with response to radiation therapy. The aims of this study were to further validate this finding and explore the potential for myoferlin to act as a prognostic and/or therapeutic target. METHODS AND MATERIALS: Immunohistochemical analysis of a tissue microarray (TMA) for 111 patients was used to validate the initial proteomic findings. Manipulation of myoferlin was achieved using small interfering RNA, a small molecular inhibitor (wj460), and a CRISPR-Cas9 knockout cell line. Radiosensitization after treatment was assessed using 2-dimensional clonogenic assays, 3-dimensional spheroid models, and patient-derived organoids. Underlying mechanisms were investigated using electrophoresis, immunofluorescence, and immunoblotting. RESULTS: Analysis of both the diagnostic biopsy and tumor resection samples confirmed that low myoferlin expression correlated with a good response to neoadjuvant long-course chemoradiation therapy. High myoferlin expression was associated with spread to local lymph nodes and worse 5-year survival (P = .01; hazard ratio, 3.5; 95% CI, 1.27-10.04). This was externally validated using the Stratification in Colorectal Cancer database. Quantification of myoferlin using immunoblotting in immortalized colorectal cancer cell lines and organoids demonstrated that high myoferlin expression was associated with increased radioresistance. Biological and pharmacologic manipulation of myoferlin resulted in significantly increased radiosensitivity across all cell lines in 2-dimensional and 3-dimensional models. After irradiation, myoferlin knockdown cells had a significantly impaired ability to repair DNA double-strand breaks. This appeared to be mediated via nonhomologous end-joining. CONCLUSIONS: We have confirmed that high expression of myoferlin in rectal cancer is associated with poor response to neoadjuvant therapy and worse long-term survival. Furthermore, the manipulation of myoferlin led to increased radiosensitivity in vitro. This suggests that myoferlin could be targeted to enhance the sensitivity of patients with rectal cancer to radiation therapy, and further work is required.

Humans↗

PubMed2025

Uncovering the genetic architecture of ME/CFS: a precision approach reveals impact of rare monogenic variation.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disabling and heterogeneous disorder lacking validated biomarkers or targeted therapies. Clinical variability and elusive pathophysiology hinder progress toward effective diagnostics and treatment. Core symptoms include persistent fatigue, post-exertional malaise, unrefreshing sleep, cognitive dysfunction, and pain. We tested whether an individualized, “n-of-1” genomic and transcriptomic framework combined with comprehensive, participant-informed phenotyping could reveal molecular signatures unique to each patient. METHODS: Clinical-grade whole-genome sequencing was conducted in 31 affected individuals from 25 families, with RNA-seq performed on a subset (16 affected, 7 unaffected) using blood samples. Machine-learning assisted variant triage, transcript-aware damage prediction, and expert review identified pathogenic or likely pathogenic variants in 8 of 25 probands (32%) and 12 of 31 affected individuals (39%). RESULTS: Findings revealed marked genetic heterogeneity, including large-effect rare and more common variants. Implicated pathways included ATP generation, oxidative phosphorylation, fatty acid oxidation; regulation of glycolysis, amino acid and lipid turnover; ion and solute homeostasis; synaptic signaling, excitability, oxygen transport, and muscle integrity, resilience, and post-exertional recovery; previously implicated processes. Plausible modifiers influencing disease onset, severity, and relapsing–remitting patterns and possibly explaining intrafamilial variability and inconsistent findings across studies, were also identified. Despite gene-level diversity, downstream effects converged on impaired energy production, reduced stress resilience, and vulnerability to post-exertional metabolic failure; disruptions consistent with core ME/CFS symptoms of exertional intolerance, cognitive fog, and fatigue. CONCLUSIONS: Our findings support the hypothesis that at least a subset of ME/CFS cases represent distinct molecular disorders that converge on shared physiological pathways. Validation in larger, more diverse cohorts will be essential to test this hypothesis and establish generalizability, but increase size alone is unlikely to resolve causation in a disorder defined by rarity, heterogeneity, and molecular complexity. We suggest that progress will require experimental designs that integrate individual-level genomic data with deep, participant-informed deep phenotyping, capturing the combined effects of rare and common variants and environmental modifiers on disease expression and progression. We believe that an individualized precision medicine framework will uncover molecular drivers and modifiers of ME/CFS previously obscured by heterogeneity, enabling biologically informed stratification, improved trial design, biomarker discovery, and targeted interventions in this historically neglected condition.

Humans↗

PubMed2026

Reference-Free Microsatellite Instability Detection from Tumor Sequencing Using Intrasample Variability Modeling.

Microsatellite instability (MSI) is a predictive biomarker in several tumor types. However, many next-generation sequencing-based callers require matched normal samples, reference panels, or pretrained models, limiting their portability across assays and sequencing centers. We developed PROMIS (PROfiling of Microsatellite InStability), a tumor-only, reference-free pipeline that uses a discrete mixture model to characterize intrasample repeat-length distributions at predefined microsatellite loci. Locus-level classifications are then aggregated into a continuous MSI score. We benchmarked PROMIS in colorectal (CRC), endometrial (UCEC), and gastric (STAD) cancers from The Cancer Genome Atlas. PROMIS achieved an overall area under the receiver operating characteristic curve (AUC) of 0.995 and cohort-specific AUCs of 1.00 in CRC and stomach adenocarcinoma and 0.999 in uterine corpus endometrial carcinoma, comparable to established tools despite not using matched normals or pretrained models. Subsampling demonstrated robust performance with substantially fewer loci. In silico dilution showed progressively reduced MSI-microsatellite-stable discrimination, with the pooled AUC declining from 0.83 at 10% tumor fraction to 0.53 at 1%. At low tumor fractions, tumor-type-specific baseline microsatellite variability increasingly influenced PROMIS scores. Finally, in prostate and CRC cell-free DNA cohorts, including Illumina TSO500 data and an 18-gene panel, PROMIS yielded MSI scores concordant with orthogonal tissue- and panel-based classifications across the evaluated Illumina-based sequencing contexts. Accordingly, the present validation should be considered limited to Illumina-based sequencing platforms. PROMIS is intended to complement existing genomic profiling workflows by enabling MSI assessment from sequencing data already generated for broader molecular analyses. Prospective clinical validation remains necessary before clinical implementation.

Journal Article↗

PubMed2026

Multiparametric flow cytometry immune profiling of pulmonary and extra-pulmonary tuberculosis reveals distinct blood-based biomarker signatures.

This study investigated immune cell distributions, cell-specific immune markers, and selected biomarker targets in pulmonary tuberculosis (PTB) and extrapulmonary tuberculosis (EPTB) using multiparametric flow cytometry (MFC). Whole blood was collected from 45 individuals, including healthy controls (HC), EPTB, and PTB patients (n = 15/group). Peripheral blood leukocytes were analysed by MFC to characterize CD4+ and CD8+ T cells, natural killer (NK), invariant NKT (iNKT) and NKT cells, classical (CM), intermediate (IM) and non-classical monocytes (NCM), and activated monocytes (AM). Expression of GBP1, CALCOCO2, IFIT3, SNX10, ARG1, PD-1, and PD-L1 was assessed across these immune subsets. Increased frequencies of NK, NKT, and monocytes were observed in PTB and EPTB compared with HC, while CD4+, CD8+, iNKT, and AM were reduced. Monocyte-to-lymphocyte ratios were incrementally elevated in EPTB and PTB compared with HC. Despite variability of expression within groups, median biomarker fold-change expression changes were found between HC, EPTB and PTB groups; (i) (>2.0FC) for ARG1 in CD4, CD8, CM and AM, for CALCOCO2 in AM, GBP1 in CD8 and NCM, PD-1 in CD4, CD8, NK, IM and AM, PD-L1 in CD4, CD8, iNKT and NKT, NK, IM and AM and SNX10 in CD4, CD8, NCM, IM and AM (ii) (<2.0FC) in TB vs HC for CALCOCO2 in iNKT and NKT, IFIT3 in NCM, PD-1 in NK and NCM, PD-L1 in NCM, IM and AM and SNX10 in AM. Statistical significance was achieved for ARG1 (P = 0.017) in CD4 cells. Our findings highlight distinct immune cell and biomarker signatures in PTB and EPTB.

Humans↗

PubMed2026

Myeloid-Mediated Immunoregulation and Resistance to Immune Checkpoint Inhibitor Therapy Across Squamous Cell Carcinomas: Mechanisms and Reprogramming Strategies.

Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 have improved outcomes across squamous cell carcinomas (SCCs) of the head and neck, lung, esophagus, and skin, yet durable responses remain confined to a subset of patients in every subtype. Objective response rates vary substantially across SCCs despite overlapping genomic alterations, comparable tumor mutational burden, and high PD-L1 expression, indicating that tumor-intrinsic biomarkers alone do not explain this variability. Growing evidence points to the tumor immune microenvironment, and in particular the myeloid compartment, as a critical determinant of immunotherapy responsiveness. In this review, we synthesize current evidence on myeloid-mediated immune regulation across SCC subtypes, focusing on tumor-associated macrophages, myeloid-derived suppressor cells/tumor-associated neutrophils, and dendritic cells, and the mechanisms by which these populations impair antigen presentation, restrict T cell infiltration, and sustain immunologically "cold" tumor states. We further examine therapeutic strategies aimed at reprogramming rather than simply depleting suppressive myeloid populations, including radiation therapy, STING agonism, and myeloid-targeted agents (CSF1R, PI3Kγ, and CXCR2 inhibition), each of which has shown encouraging preclinical and early clinical activity in combination with ICI. Collectively, this evidence supports a model in which the myeloid compartment functions as an actionable, convergent determinant of ICI resistance across SCC subtypes, rather than merely a passive biomarker. We propose that through the integration of spatial and single-cell profiling of myeloid states with clinical history it will be possible to predict response to immune checkpoint therapy and personalize myeloid-directed combination strategies, though the specific biomarkers needed to match individual patients to a given myeloid-targeted approach remain to be defined. We further discuss the toxicity considerations associated with both immune checkpoint blockade and radiation-based combination approaches, the early-phase status of most myeloid-targeted agents currently in clinical development, and the extent to which mechanistic insight, derived predominantly from HNSCC, generalizes to squamous cell carcinomas arising at other anatomic sites.

dendritic cells↗

PubMed2025

Methylation-based droplet digital polymerase chain reaction shows high concordance with chronic lymphocytic leukemia IGHV somatic mutation status.

OBJECTIVE: Somatic hypermutation at immunoglobulin heavy chain variable (IGHV) genes, an established prognostic and predictive biomarker for chronic lymphocytic leukemia (CLL), is assessed by gene sequencing. We developed a single methylation-specific droplet digital polymerase chain reaction (methyl-ddPCR) to predict IGHV status in patients with CLL. METHODS: The CLL methylation array and IGHV data from the International Cancer Genome Consortium (ICGC) were used for biomarker discovery. Top-ranked candidate regions were manually screened for PCR primer and probe binding sites. A single methyl-ddPCR was evaluated on an internal cohort of CLLs with mutated (M), unmutated (U), and inconclusive IGHV results originally determined by next-generation sequencing (NGS). RESULTS: Analysis of ICGC data identified array probe cg23844018 as a candidate for the PCR. The corresponding CpG site showed high methylation levels in U-CLL and lower levels in M-CLL. On the internal cohort, a single optimal cutoff correctly classified 104 of 115 U- and M-CLLs (90.4%; area under the curve = 0.96). The PCR data correlated with some prognostic fluorescence in situ hybridization and CLL subset groupings. Limited analysis suggests that the PCR may be able to stratify some patients with CLL who have inconclusive results on IGHV NGS testing. CONCLUSIONS: The methyl-ddPCR showed high concordance with CLL IGHV status in an internal cohort.

Humans↗

PubMed2026

Proteomic and machine learning analysis predicts treatment response signatures in Myasthenia Gravis.

BACKGROUND: Myasthenia gravis (MG) is a prototypical antibody-mediated autoimmune disease with variable treatment responses with a need for biomarkers to guide therapeutic decision making. Proteomic profiling, coupled with machine learning, offers a hypothesis-free approach to identify multi-protein signatures associated with treatment response. METHODS: We analyzed sera collected at entry (baseline) from participants in a phase 3 trial randomized trial comparing thymectomy plus prednisone versus prednisone alone, along with matched controls using liquid chromatography-mass spectrometry. We derived disease-specific proteomic signatures and evaluated associations between baseline proteins and 6-month clinical outcomes using multiple machine-learning approaches with internal validation. RESULTS: Baseline serum proteomes distinguished MG from controls, with pathway enrichment implicating complement activation, immunoglobulin production, and T-cell receptor signaling. Distinct protein panels predicted 6-month clinical improvement within each treatment arm. In the thymectomy-plus-prednisone group, models captured non-linear relationships of predictive proteins in contrast with the predominant additive patterns observed in the prednisone-alone group. Predictive proteins were enriched for T-cell signaling and leukocyte trafficking functions, providing insight into treatment-specific biology. CONCLUSIONS: Baseline serum proteomics captures core disease characteristics of MG and predicts short-term clinical response in a treatment-specific manner. While our results require validation in independent cohorts, these findings could enable biomarker-guided selection of thymectomy, refine risk stratification, and furnish mechanistic readouts for future MG trials and clinical care. We aim to conduct future studies using -omic approaches to validate these baseline predictive biomarkers and pathways of treatment response in patients with MG.

Adult↗

PubMed2026

Multi-level Transcriptomic and Machine-learning Analyses Identify MZT1 as a Proliferation-associated Prognostic Marker in Lung Adenocarcinoma.

BACKGROUND/AIM: Lung adenocarcinoma (LUAD) exhibits substantial molecular heterogeneity and variable clinical outcomes, highlighting the need for biomarkers that reflect core tumor biological processes. Centrosome-associated proteins regulate mitotic fidelity and genome stability, yet their roles in LUAD remain incompletely defined. In this study, we systematically characterized mitotic spindle organizing protein 1 (MOZART1; MZT1) and related family members in LUAD. MATERIALS AND METHODS: We performed integrated analyses combining bulk transcriptomic datasets, survival modeling, gene set enrichment, immune deconvolution, machine-learning based prognostic modeling, and single-cell RNA sequencing. Expression patterns and clinical associations of MZT family genes were evaluated across pan-cancer and LUAD cohorts. RESULTS: MZT family genes were consistently upregulated in tumor tissues, with MZT1 showing the most robust expression pattern. Elevated MZT1 expression was significantly associated with reduced overall survival. Functional analyses revealed coordinated activation of proliferative and genome maintenance pathways, including G2/M checkpoint regulation, E2F and MYC signaling, and DNA repair. A multivariable analysis indicated that the prognostic association of MZT1 was reduced after adjusting for canonical proliferation markers, suggesting partial overlap with established proliferation signals. The LASSO-based Cox model demonstrated stable time-dependent predictive performance at 1-, 3-, and 5-year survival. Immune analyses indicated associations between MZT1 expression and tumor microenvironmental features. Single-cell analysis showed that MZT1 expression was predominantly enriched in malignant epithelial cells and associated with proliferative cellular states. Protein-level validation supported concordance with transcriptomic findings. CONCLUSION: MZT1 is a proliferation-associated marker that integrates clinical risk, transcriptional programs, cellular heterogeneity, and predictive modeling in LUAD, providing a potential framework for biomarker development and risk stratification.

Humans↗

PubMed1992

Effects of hepatotoxicants on the induction of microsomal monooxygenase activity in sunfish liver by beta-naphthoflavone and benzo[a]pyrene.

Effects of chemically induced hepatic injury on biotransformation enzymes in fish were studied. Sunfish hybrids (Lepomis macrochirus x L. cyanellus) were dosed per os with allyl formate (ALF) and carbon tetrachloride (CCl4), and the induction of liver EROD (7-ethoxyresorufin O-deethylase) activity was subsequently challenged by injections of beta-naphthoflavone (BNF) and benzo[a]pyrene (B[a]P). Hepatotoxicity of chemical treatments was assessed using blood enzymes (ASAT, ALAT, and LDH) along with other biochemical variables. Both hepatotoxicants partially abolished the induction of EROD (maximally by 76-89%), and the decrease in induction was dose related. The cytosolic activity of glutathione S-transferase (GST) in the liver decreased in parallel with the decrease in EROD induction. Fish receiving high doses of ALF exhibited significantly less microsomal and blood plasma proteins and, occasionally, were jaundiced. These symptoms, however, were less sensitive indicators of hepatotoxicity than alterations in liver EROD and GST. Both ALF and CCl4 increased the activities of hepatic enzymes in the blood plasma, indicating cytotoxicity. In addition B[a]P, unlike BNF, also increased plasma activities of LDH and ALAT at a dose inducing liver EROD, implying simultaneous hepatotoxicity at high sublethal levels of this xenobiotic. These data suggest that hepatotoxic chemicals absorbed by fish may act antagonistically by decreasing the degree of induction of the cytochrome P450 system relative to the inherent capacity of inducing xenobiotic chemicals present in the environment. Therefore, when assessing the toxicological status of water using fish health biomarkers, it is advisable to measure a concert of metabolic and biochemical variables instead of any single biomarker.

Alanine Transaminase↗

PubMed2025

Prenatal organophosphate ester exposure and epigenetic changes at birth: a characterization of the methylome in the ECHO cohort.

BACKGROUND: Prenatal exposure to organophosphate esters (OPEs) affects multiple child health domains. Alterations to the DNA methylome are a plausible mechanism through which these changes occur. This study characterized DNA methylation signatures at birth associated with prenatal OPE biomarkers. METHODS: We included 736 mother-infant pairs from 7 sites in the Environmental influences on Child Health Outcomes (ECHO) Cohort. Five OPE biomarkers were quantified in maternal urine samples collected during the second and third trimesters and modeled as log2-transformed continuous variables. Using covariate-adjusted linear regression, we tested associations between OPE biomarkers and locus-specific, regional, and global cord blood DNA methylation changes measured by Illumina 450 K and EPIC arrays, and gestational epigenetic age measured by the Knight gestational age epigenetic clock generated with measures from the 27 K, 450 K, and EPIC arrays. When feasible, we examined relationships by sex. FINDINGS: Global hypomethylation at multiple regions was associated with BDCPP concentrations (p = 0.003 to 0.02, coef = -0.002). Differentially methylated regions annotated to PCDHGB1 and SLC43A2 were associated with BDCPP and DPHP concentrations, respectively (FDR q < 0.05). In sex-specific analyses, global hypomethylation was associated with prenatal BDCPP (p = 0.006 to 0.03, coef = -0.0003 to -0.0002) and DBUP_DIBP (p = 0.01, coef = -0.0007 to -0.0006) concentrations in females; and global hypermethylation was associated with DBUP_DIBP concentrations in males (p < 0.05, coef = 0.0004). BCETP concentrations were significantly associated with decelerated epigenetic aging at birth in females (p < 0.05, coef = -0.05). INTERPRETATION: Prenatal exposure to OPEs impacts child methylation at birth, suggesting a potential mechanism for the association between prenatal OPE exposure and child health outcomes.

Humans↗

PubMed2026

Organoids in translation: a bench-to-bedside framework for pancreatic cancer precision medicine.

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with a 5-year survival rate of < 13%. Standard treatments such as FOLFIRINOX or gemcitabine/nab-paclitaxel yield modest response rates, underscoring the urgent need for precision oncology approaches. Patient-derived organoids (PDOs) preserve the genomic, phenotypic, and histopathological features of the source tumor and offer a promising platform for drug screening, biomarker development, and personalized therapy. However, a systematic evaluation of their translational capacities is lacking. METHODS: A systematic review was conducted according to the PRISMA 2020 guidelines (PROSPERO registration pending) using PubMed, EMBASE, and Cochrane CENTRAL (December 10, 2024) to identify English-language PDAC PDO studies that incorporated therapeutic testing. Ninety-five studies met the inclusion criteria. Data extraction captured >75 variables per study, including spanning culture methodology, therapeutic profiling, biomarker integration, and clinical correlation. A 13-domain weighted Translatability Scoring Framework adapted from Wehling et al. assessed predictive validity, biomarker strength, pharmacogenetics, and clinical trial alignment. Scores ranged from 0 to 5 and were categorized as good (>4.0), moderate (3.0-4.0), or low (<3.0) translational potential. RESULTS: Of the 95 studies, 70.5% have been published since 2021, reflecting the rapid growth in this field. The mean PDO generation success rate was 89.7%, with the primary tumor tissue being the predominant source (48.4%). Only 24.8% were directly linked to clinical trials and 5.3% incorporated multi-omic profiling. The median translatability score was 3.13 (range, 1.72-4.59): 45.3% of the studies had low translatability, 50.5% moderate, and only 4.2% had good translational potential. High-scoring studies consistently combine multi-omic biomarker platforms, in vivo validation, clinical outcome correlation, and prospective trial integration. Conversely, the weakest domains were pharmacogenetics, endpoint strategies, and biomarker validation, limiting their overall clinical relevance. CONCLUSIONS: PDOs have demonstrated strong feasibility and in vitro clinical correlation in PDAC; however, their clinical translation remains constrained by limited multi-omic integration, absence of pharmacogenomic modeling, and sparse clinical trial embedding. Standardization of protocols, adoption of harmonized and clinically relevant endpoints, and systematic incorporation of biomarker-driven co-clinical trial frameworks are urgently needed to transition PDOs from promising experimental surrogates to validating precision oncology tools capable of informing therapeutic decision-making in PDAC.

Humans↗

PubMed2026

Efforts towards a precision medicine approach in juvenile idiopathic arthritis.

Juvenile idiopathic arthritis (JIA) is the commonest group of childhood arthritides. Despite the availability of advanced therapeutics, many children and young people (CYP) with JIA experience disease flares, and in some, chronic joint damage. Tailoring treatment based on unique biological profiles would benefit CYP with JIA given their variable clinical presentation and disease course. To date, biomarkers to predict treatment response are lacking. With advances in single cell technologies, we are now able to profile the genes and proteins of target tissues at unprecedented resolution to define the biological basis of disease and guide novel treatment approaches. The complex analyses and combination of biological and clinical outcome data from large datasets across disease phenotypes have become possible with the development of computational and machine learning methods. Here, we summarize the strategies to integrate data through multimodal based approaches to maximize precision medicine and research priorities for CYP with JIA.

Humans↗

PubMed1992

Environmental tobacco smoke: current assessment and future directions.

Scientific information on environmental tobacco smoke (ETS) is critically reviewed. Key areas addressed are: differences in chemical composition between mainstream smoke, sidestream smoke, and ETS; techniques for measurement of ETS; epidemiology; in vitro and in vivo toxicology; and chamber and field studies of perceptual or physiological effects. Questions concerning estimation of ETS exposure, suitability of various biomarkers, calculation of lifetime dose, control of confounding variables, use of meta-analysis, and the relationship between ETS concentrations and human responses all emphasize the need for additional research in order to assess potential effects of ETS on health or comfort.

Animals↗