Real-time, high-sensitivity biosensors are essential for personalized healthcare, enabling early disease diagnosis and continuous monitoring. Organic field-effect transistor (OFET) sensors offer label-free detection, rapid response, and low cost, but noise interference and inherent architectural limitations hinder their use in high-throughput, multi-signal analysis. To address these issues, this study presents a gate-array-based OFET biosensor that improves detection sensitivity, stability, and analytical throughput. The integrated gate-array architecture and signal modulation strategies enable rapid acquisition of multiple transfer curves for efficient concentration analysis of unknown samples. To validate the approach, the sensor detects the thrombotic biomarker D-Dimer with an ultra-low limit of 0.354 ng/mL in PBS, well below clinical thresholds, and performs reliably in human serum and patient samples. This work introduces a robust platform for high-throughput, multi-target detection in complex biological environments, advancing next-generation biomedical diagnostics and personalized healthcare.
Jing Zhang, Qi-Ting Wang, Tian-Tian Song et al.· Talanta: The International J...· 0 citations
Polymyositis (PM), an autoimmune muscle disorder, often leads to interstitial lung disease (ILD), including idiopathic pulmonary fibrosis (IPF). However, causality and mechanisms are unclear. This study aimed to investigate the causal effect of PM on IPF risk, identify potential mediating biomarkers through multi-omics analysis, and explore candidate therapeutic drugs. Two-sample Mendelian randomization (MR) was conducted using publicly available genome-wide association study (GWAS) summary statistics (PM: 244 cases/4,28,965 controls; IPF: 6257 cases/9,47,616 controls). Reverse-causality was evaluated via bidirectional MR. We acknowledge the potential for sample overlap between exposure and outcome datasets when using public GWAS data. To mitigate related biases, we employed robust MR methods, including inverse-variance weighted (IVW) with random effects, MR-Egger, and MR-PRESSO, that are less sensitive to such overlap. Genetic variants with significant missing data or poor imputation quality were excluded as per the quality control standards of the original GWAS. This specific analysis was not preregistered and should be considered exploratory. The analysis proceeded in two sequential phases to identify shared causal mediators: First, MR analyses were conducted with 91 inflammatory proteins, 1400 plasma metabolites, 4907 circulating proteins, and 731 immune cell traits as exposures and IPF as the outcome to identify significant causal biomarkers for IPF. Second, MR analyses were conducted with PM as the exposure and the biomarkers significantly associated with IPF as outcomes to identify those also influenced by PM with consistent effect directions. Identified key proteins underwent functional enrichment analysis. Molecular docking was used to validate interactions between significant mediators and potential drugs. PM significantly increased IPF risk (odds ratio [OR]: 1.06, 95% confidence interval [CI]: 1.02–1.09, P < .01), with no significant reverse causal evidence. Five circulating proteins (AMH, CHCHD10, CRABP2, HERC5, HSPA5) and 5 metabolites mediated this association, enriched in endoplasmic reticulum stress and transforming growth factor (TGF-β) signaling. Three drugs (butein, gambogic acid, tauroursodeoxycholic acid) showed strong binding affinity (ΔG < −7.0 kcal/mol). Genetic evidence supports PM causally increasing IPF risk. Multi-omics revealed key mediating biomarkers and underlying biological pathways, while drug target prioritization identified 3 compounds with therapeutic potential. This informs prevention strategies.
