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Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package. This innovative tool uses the discovery cohort analysis to select precursors, peptides, and proteins that adhere to established targeted assay criteria. TEAQ was applied to Data-Independent Acquisition MS data from plasma samples acquired on an Orbitrap™ Astral™ MS. Identified precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation from 11-point loading curves under three throughputs, to develop a resource for clinical-grade targeted assays. From a clinical cohort of individuals with inflammatory bowel disease (n=492), TEAQ successfully identified 1116 signature peptides for 327 quantifiable proteins from 1180 identified proteins. Embedding stringent selection criteria adaptable to targeted assay development into the analysis of discovery data will streamline the transition to validation and clinical studies.
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http://dx.doi.org/10.1101/2024.03.20.586018 | DOI Listing |
J Gerontol A Biol Sci Med Sci
September 2025
Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, USA.
Maintenance of organismal function requires tightly regulated biomolecular communication. However, with aging, communication deteriorates, thereby disrupting effective information flow. Using information theory applied to skeletal muscle single cell RNA-seq data from young, middle-aged, and aged animals, we quantified the loss of communication efficiency over time.
View Article and Find Full Text PDFJpn J Ophthalmol
September 2025
Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto City, Kyoto Prefecture, 606-8507, Japan.
Purpose: To identify predictors of the 2-year best-corrected visual acuity (BCVA) after subretinal tissue plasminogen activator (tPA) injection for massive submacular hemorrhage (SMH) complicating neovascular age-related macular degeneration (nAMD).
Study Design: A prospective, observational study.
Methods: This study included consecutive eyes with massive SMH and nAMD that underwent vitrectomy with subretinal tPA injection and follow-up for 2 years.
J Virol
September 2025
Department of Microbiology, Immunology and Molecular Genetics, College of Medicine, University of Kentucky, Lexington, Kentucky, USA.
Arthropod-borne viruses (arboviruses) pose a major threat to global public health, impacting both human and animal health. Genomic characterization is important for arboviruses because it allows for an understanding of their evolution and improves timely outbreak and epidemic response. In this study, we used high-throughput sequencing and computational analyses to characterize the genomes and evolution of 46 previously unsequenced or partially sequenced arbovirus isolates collected across 23 countries between 1954 and 1984.
View Article and Find Full Text PDFBrief Bioinform
August 2025
College of Pharmacy, Chongqing Medical University, No. 1 Yixueyuan Road, Yuzhong District, Chongqing 400016, P. R. China.
Drug-induced hepatotoxicity (DIH), characterized by diverse phenotypes and complex mechanisms, remains a critical challenge in drug discovery. To systematically decode this diversity and complexity, we propose a multi-dimensional computational framework integrating molecular structure analysis with disease pathogenesis exploration, focusing on drug-induced intrahepatic cholestasis (DIIC) as a representative DIH subtype. First, a graph-based modularity maximization algorithm identified DIIC risk genes, forming a DIIC module and eight disease pathogenesis clusters.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
School of Information Science and Automation, Northeastern University, Shenyang, 110819 China.
Accurate prediction of drug-target interactions (DTIs) is crucial for improving the efficiency and success rate of drug development. Despite recent advancements, existing methods often fail to leverage interaction features at multiple granular levels, resulting in suboptimal data utilization and limited predictive performance. To address these challenges, we propose CF-DTI, a coarse-to-fine drug-target interaction model that integrates both coarse-grained and fine-grained features to enhance predictive accuracy.
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