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A multiphase study was designed to examine the detectability of human growth hormone (GH) use in capillary dried blood spots (DBS). First, 13 subjects self-injected a single, 2-mg dose of somatropin and collected capillary DBS samples for 24 h. Next, nine subjects self-injected 2-mg somatropin, six times over the course of 11 days; DBS were collected intermittently following dosing. Finally, a nondrug, large-scale field study involved DBS collections from an athlete and staff population over 3 years. All DBS samples were self-collected using the Tasso M20 device and were analyzed for the presence of GH using the WADA-approved GH isoforms test. Following the single dose, positive detection within 12 h of dosing was 86% and 56% sensitive on Kits 1 and 2, respectively. In the multidose study, detection within 12 h was 85% and 69% sensitive on Kits 1 and 2, respectively. No positives were detected outside the 12-h window following a single dose, wherein detection was 5.6% sensitive at 24-h in the multidose study. Combining the 12-h windows from both studies, 100% of samples had measurable recombinant (REC) and pituitary (PIT) GH concentrations above the assay LoD, 0.041 ng/ml. Finally, 1213 samples were collected in the large-scale field study: 189 showed REC and PIT concentrations above the LoD; none returned positive results. GH is detectable in capillary DBS using the isoforms method for 12-24 h following use. While detection is short lived, transitioning to a DBS self-collection method can allow more frequent testing and increase deterrence to GH abuse.
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http://dx.doi.org/10.1002/dta.3248 | DOI Listing |
J Chem Theory Comput
September 2025
Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong China.
Coarse-grained (CG) lipid models enable efficient simulations of large-scale membrane events. However, achieving both speed and atomic-level accuracy remains challenging. Graph neural networks (GNNs) trained on all-atom (AA) simulations can serve as CG force fields, which have demonstrated success in CG simulations of proteins.
View Article and Find Full Text PDFJB JS Open Access
September 2025
Department of Orthopaedic Surgery, Mass General Brigham, Harvard Medical School, Boston, Massachusetts.
Background: It is unclear whether the current North Atlantic Treaty Organization (NATO) trauma system will be effective in the setting of Large-Scale Combat Operations (LSCO). We sought to model the efficacy of the NATO trauma system in the setting of LSCO. We also intended to model novel scenarios that could better adapt the current system to LSCO.
View Article and Find Full Text PDFVet World
July 2025
Laboratory of Immunochemistry and Immunobiotechnology, National Center for Biotechnology, 010000, Astana, Kazakhstan.
Background And Aim: Bovine babesiosis, caused by , poses significant economic challenges to Kazakhstan's cattle industry. Early and accurate detection is crucial for interrupting transmission cycles, particularly in regions lacking advanced diagnostic infrastructure. This study aimed to develop a rapid lateral flow immunoassay (LFIA) using a recombinant C-terminal fragment of the recombinant rhoptry-associated protein 1 (rRap1) antigen for the serodiagnosis of bovine babesiosis.
View Article and Find Full Text PDFNoncoding RNA Res
December 2025
Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA.
Purpose: To verify the stability and reliability of circulating microRNA (miRNA) profiles in plasma and serum under different processing and storage conditions to inform future applications to circulating biomarker analyses.
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Nat Comput Sci
September 2025
Department of Chemical Engineering, Tsinghua University, Beijing, China.
With approximately 90% of industrial reactions occurring on surfaces, the role of heterogeneous catalysts is paramount. Currently, accurate surface exposure prediction is vital for heterogeneous catalyst design, but it is hindered by the high costs of experimental and computational methods. Here we introduce a foundation force-field-based model for predicting surface exposure and synthesizability (SurFF) across intermetallic crystals, which are essential materials for heterogeneous catalysts.
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