Glycoproteins in urine have the potential to provide a rich class of informative molecules for studying human health and disease. Despite this promise, the urine glycoproteome has been largely uncharacterized. Here, we present the analysis of glycoproteins in human urine using LC-MS/MS-based intact glycopeptide analysis, providing both the identification of protein glycosites and characterization of the glycan composition at specific glycosites. Gene enrichment analysis reveals differences in biological processes, cellular components, and molecular functions in the urine glycoproteome versus the urine proteome, as well as differences based on the major glycan class observed on proteins. Meta-heterogeneity of glycosylation is examined on proteins to determine the variation in glycosylation across multiple sites of a given protein with specific examples of individual sites differing from the glycosylation trends in the overall protein. Taken together, this dataset represents a potentially valuable resource as a baseline characterization of glycoproteins in human urine for future urine glycoproteomics studies.
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http://dx.doi.org/10.1038/s41598-024-53299-3 | DOI Listing |
Medicine (Baltimore)
January 2025
The First Medical Center of Chinese PLA General Hospital & Medical School, Beijing, China.
Background: This study investigates the role and efficacy of acupuncture combined with rehabilitation therapy during the recovery phase of patients with traumatic spinal cord injury. Patients hospitalized in the acupuncture department of our center between December 1, 2019, and December 1, 2021, were enrolled.
Methods: Participants were divided into an observation group (acupuncture and rehabilitation therapy) and a control group (rehabilitation therapy alone) based on their treatment sequence.
Mikrochim Acta
January 2025
School of Public Health, Hebei Key Laboratory of Occupational Health and Safety for Coal Industry, North China University of Science and Technology, No. 21 Bohai Road, Caofeidian, Tangshan, 063210, Hebei, China.
Biochars (BCs) derived from waste-branches of apple tree, grape tree, and oak were developed for direct solid-phase extraction (SPE) of five benzodiazepines (BZDs) in crude urine samples prior to liquid chromatography-tandem mass spectrometry (LC-MS/MS) determination. Scanning electron microscopy, elemental analyzer, X-ray diffractometry, N adsorption/desorption experiments, and Fourier transform infrared spectrometry characterizations revealed the existence of their mesoporous structure and numerous oxygen-containing functional groups. The obtained BCs not only possessed high affinity towards BZDs via π-π and hydrogen bond interactions, but also afforded the great biocompatibility of excluding interfering components from undiluted urine samples when using SPE adsorbents.
View Article and Find Full Text PDFXenobiotica
January 2025
Department of Pharmaceutical Sciences, School of Pharmacy, Westcoast University, 590 North Vermont Avenue, Los Angeles, CA 90004.
A four-compartment model is presented that simulates inorganic mercury [Hg(II)] pharmacokinetics in blood, tissue, and excreta over a 70 day period. Simulations are validated against data collected from five human subjects, and previously analyzed (Farris, F.F.
View Article and Find Full Text PDFCells
December 2024
Department of Mechanical Engineering, Tufts University, Medford, MA 02155, USA.
The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, these promising findings were limited by a relatively small patient cohort, resulting in modest statistical significance.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Klick Applied Sciences, Klick Health, Toronto, ON, Canada.
Background: Identifying subtle changes in the menstrual cycle is crucial for effective fertility tracking and understanding reproductive health.
Objective: The aim of the study is to explore how fundamental frequency features vary between menstrual phases using daily voice recordings.
Methods: This study analyzed smartphone-collected voice recordings from 16 naturally cycling female participants, collected every day for 1 full menstrual cycle.
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