Objective: We aimed to examine whether the current users of specific NSAIDs have an increased risk of venous thromboembolism (VTE) among knee OA patients.
Methods: We conducted a population-based case-control study using The Health Improvement Network, a database of patient records from general practices in the UK. For every VTE case, we identified five controls matched on age, sex and calendar year of study enrolment. We used conditional logistic regression to assess the association between current use of specific NSAIDs and risk of VTE relative to remote NSAID users.
Results: Among knee OA patients with at least one NSAID prescription, we identified 4020 incident cases of VTE and 20 059 matched controls. Adjusted odd ratios (ORs) relative to the remote users were 1.38 (95% CI: 1.32, 1.44) for recent users and 1.43 (95% CI: 1.36, 1.49) for current users. Among the current NSAID users, the risk of VTE was increased with diclofenac [OR 1.63 (95% CI: 1.53, 1.74)], ibuprofen [OR = 1.49 (95% CI: 1.38, 1.62)], meloxicam [OR = 1.29 (95% CI: 1.11, 1.50)] and coxibs [celecoxib, OR = 1.30 (95% CI: 1.11, 1.51); rofecoxib, OR = 1.44 (95% CI: 1.18, 1.76)]; naproxen did not increase VTE risk [OR = 1.00 (95% CI: 0.89, 1.12)].
Conclusion: Compared with the remote users of NSAIDs, the risk of VTE increased for current users of diclofenac, ibuprofen, meloxicam, and coxibs, but not for naproxen, in the knee OA population. Clinicians should consider the risk profile for specific NSAIDs when recommending their use.
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http://dx.doi.org/10.1093/rheumatology/kew036 | DOI Listing |
Ann Biomed Eng
January 2025
Department of Mechanical and Industrial Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA.
Purpose: To evaluate the population variation in head-to-helmet contact forces in helmet users.
Methods: Four different size Kevlar composite helmets were instrumented with contact pressure sensors and chinstrap tension meters. A total number of 89 volunteers (25 female and 64 male volunteers) participated in the study.
Int Dent J
January 2025
Research and Graduate Studies Department, Mohammed Bin Rashin University of Medicine and Health Sciences, Dubai, UAE. Electronic address:
Objectives: The use of electronic cigarettes "e-cigarettes," or vaping is growing in popularity, especially among adolescents and young adults. While the effects of cigarette smoking on oral health are well-established, the exact impact that e-cigarettes may have on dental tissues is still uncertain. The aim of the current review was to summarize evidence related to the effect of vaping on the periodontal health status of e-cigarette users.
View Article and Find Full Text PDFNutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Information Technology, University of Tabuk, Tabuk 47731, Saudi Arabia.
Web 3.0 marks the beginning of a new era for the internet, characterized by distributed technology that prioritizes data ownership and value expression. Web 3.
View Article and Find Full Text PDFMolecules
January 2025
Computational Systems Biology Group, National Center for Biotechnology (CNB-CSIC), 28049 Madrid, Spain.
Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty in detecting these residues experimentally, prediction methods are essential to cope with the sequence deluge that is filling databases with uncharacterized protein sequences. Deep learning approaches are especially well suited for this task due to the large amounts of protein sequences for training them, the trivial codification of this sequence data to feed into these systems, and the intrinsic sequential nature of the data that makes them suitable for language models.
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