Aim: The aim of this study was to investigate the possibility of using technetium (Tc)-labeled tirofiban (a reversible antagonist of glycoprotein IIb/IIIa) for detection of deep venous thrombosis (DVT) in rats without causing an antiplatelet effect.
Methods: The ability of in vitro tirofiban to inhibit adenosine 5'-diphosphate (ADP)-induced platelet aggregation was evaluated using optical aggregometer. Binding of Tc-tirofiban to platelets was evaluated. Serum levels of unlabeled (a validated high performance liquid chromatography method) and Tc-tirofiban after single intravenous injection were evaluated in male Wistar rats with or without induced DVT (femoral vein ligation model), and the rats were also subjected to whole body scintigraphy.
Results: Tirofiban in vitro inhibits ADP-induced aggregation of human platelets in a dose- and concentration-dependent manner (10 nM to 2 μM), but only if it is added before ADP and not after ADP. Tc labeling did not affect the ability of tirofiban to bind to either human or rat platelets, nor did it affect tirofiban pharmacokinetics in intact rats or in animals with induced DVT. When Tc-tirofiban was injected to rats after induction of DVT, at a molar dose lower than the one showing only a weak antiaggregatory effect in vitro, whole body scintigraphy indicated localization of Tc-tirofiban around the place of the induced DVT.
Conclusion: Tc labeling of tirofiban does not affect its ability to bind to glycoprotein IIb/IIIa or its in vivo pharmacokinetics in rats, either intact or with DVT. A low, nonantiaggregatory dose of Tc-tirofiban may be used to visualize DVT at an early stage.
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http://dx.doi.org/10.2147/DDDT.S112366 | DOI Listing |
Radiol Med
January 2025
Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Purpose: To develop an artificial intelligence (AI) algorithm for automated measurements of spinopelvic parameters on lateral radiographs and compare its performance to multiple experienced radiologists and surgeons.
Methods: On lateral full-spine radiographs of 295 consecutive patients, a two-staged region-based convolutional neural network (R-CNN) was trained to detect anatomical landmarks and calculate thoracic kyphosis (TK), lumbar lordosis (LL), sacral slope (SS), and sagittal vertical axis (SVA). Performance was evaluated on 65 radiographs not used for training, which were measured independently by 6 readers (3 radiologists, 3 surgeons), and the median per measurement was set as the reference standard.
Sci Rep
January 2025
Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.
View Article and Find Full Text PDFJ Mol Diagn
January 2025
Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; Bavarian Cancer Research Center (BZKF), Erlangen, Germany. Electronic address:
Achieving a stable deep molecular response with the option to discontinue tyrosine kinase inhibitors (TKI) treatment is the new therapeutic goal for patients with chronic myeloid leukemia (CML). Several studies have shown that individuals expressing the BCR::ABL1 e14a2 transcript achieve a major molecular response more rapidly than those with the e13a2 transcript. However, technical issues may have confounded these observations, and data for pediatric patients are limited.
View Article and Find Full Text PDFBiochim Biophys Acta Mol Basis Dis
January 2025
MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics and Computational Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China; Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China; Key Laboratory of Alkene-carbon Fibres-based Technology & Application for Detection of Major Infectious Diseases, Soochow University, Suzhou 215123, China; Jiangsu Key Laboratory of Infection and Immunity, Soochow University, Suzhou 215123, China. Electronic address:
Cancer, a heterogeneous disease, presents significant challenges for drug development due to its complex etiology. Drug repurposing, particularly through network medicine approaches, offers a promising avenue for cancer treatment by analyzing how drugs influence cellular networks on a systemic scale. The advent of large-scale proteomics data provides new opportunities to elucidate regulatory mechanisms specific to cancer subtypes.
View Article and Find Full Text PDFEBioMedicine
January 2025
Institute of Immunology, Hannover Medical School, Hannover, Germany; Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany; German Centre for Infection Research, Partner Site Hannover-Braunschweig, Hannover, Germany. Electronic address:
Background: Aging increases disease susceptibility and reduces vaccine responsiveness, highlighting the need to better understand the aging immune system and its clinical associations. Studying the human immune system, however, remains challenging due to its complexity and significant inter-individual variability.
Methods: We conducted an immune profiling study of 550 elderly participants (≥60 years) and 100 young controls (20-40 years) from the RESIST Senior Individuals (SI) cohort.
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