Objective: To explore in a cross-sectional fashion if overweight individuals with knee osteoarthritis (OA) and intraarticular calcium crystal (CaC) deposits experience more knee joint inflammation and knee pain compared with individuals without CaC deposits.
Subjects And Methods: We used pre-randomization imaging data from an RCT, the LOSE-IT trial. Participants with knee OA (clinical diagnosis of knee OA and KLG 1-3) had CT and 3 T MRI of the index knee.
The research field of epistemic justice in healthcare has gained traction in the last decade. However, the importation of Miranda Fricker's original philosophical framework to medicine raises several interrelated issues that have largely escaped attention. Instead of pushing forward, crafting new concepts or exploring other medical conditions, we suggest that it is time to take stock, reconsider, and articulate some fundamental issues that confront the field of epistemic injustice in healthcare.
View Article and Find Full Text PDFKnee Surg Sports Traumatol Arthrosc
February 2025
Purpose: Anterior cruciate ligament injury increases the risk of knee osteoarthritis, possibly via early onset of knee pain and changes in musculoskeletal function. This study compared knee muscle strength and movement biomechanics during walking and forward lunge between individuals with and without knee pain after anterior cruciate ligament reconstruction.
Methods: Cross-sectional study including participants at least 3 years post anterior cruciate ligament reconstruction, aged 18-40 at the time of surgery, and body mass index ≤30.
Polymeric membranes offer an appealing solution for sustainable CO capture, with potential for large-scale deployment. However, balancing high permeability and selectivity is an inherent challenge for pristine membranes. To address this challenge, the development of mixed matrix membranes (MMMs) is a promising strategy.
View Article and Find Full Text PDFSpectrochim Acta A Mol Biomol Spectrosc
May 2025
Non-halogenated flame-retardant Melamine (MEL), an alternative to toxic halogenated flame retardants, has been recognized as a substance of very high concern by the ECHA. Therefore, identifying MEL in plastic waste is essential for ensuring safe handling and recycling. This study presents industrial in-line quantitative identification techniques for MEL in low-density polyethylene (LDPE) and polypropylene (PP) via short-waved infrared (SWIR) hyperspectral imaging combined with machine learning.
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