Aim: To describe a histologic scoring system for murine osteoarthritis (OA) that can be applied universally to instability, enzymatic, transgenic and spontaneous OA models.
Methods: Scientists with expertise in assessing murine OA histopathology reviewed the merits and drawbacks of methods described in the literature. A semi-quantitative scoring system that could reasonably be employed in any basic cartilage histology laboratory was proposed. This scoring system was applied to a set of 10 images of the medial tibial plateau and femoral condyle to yield 20 scores. These images were scored twice by four experienced scorers (CL, SG, MC, TA), with a minimum time interval of 1 week between scores to obtain intra-observer variability. An additional three novice scorers (CR, CL and MM) with no previous experience evaluated the images to determine the ease of use and reproducibility across laboratories.
Results: The semi-quantitative scoring system was relatively easy to apply for both experienced and novice scorers and the results had low inter- and intra-scorer variability. The variation in scores across both the experienced and novice scorers was low for both tibia and femur, with the tibia always having greater consistency.
Conclusions: The semi-quantitative scoring system recommended here is simple to apply and required no specialized equipment. Scoring of the tibial plateaus was highly reproducible and more consistent than that of the femur due to the much thinner femoral cartilage. This scoring system may be a useful tool for both new and experienced scorers to sensitively evaluate models and OA mechanisms, and also provide a common paradigm for comparative evaluation across the many groups performing these analyses.
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http://dx.doi.org/10.1016/j.joca.2010.05.025 | DOI Listing |
Neurogastroenterol Motil
January 2025
Division of Gastroenterology, Rabin Medical Center, Beilinson Campus, Petah Tikva, Israel.
Background: Proton pump inhibitors (PPI) for gastroesophageal reflux disease (GERD) are associated with a high failure rate. Our uncontrolled feasibility study aimed determining the effect of a transcutaneous electrical stimulation system (TESS) on GERD symptoms and acid exposure time (AET).
Methods: Recruited patients with heartburn and regurgitation.
Circ Genom Precis Med
January 2025
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (A.A., L.S.D., E.K.O., R.K.).
Background: While universal screening for Lp(a; lipoprotein[a]) is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a; ARISE), a validated machine learning tool, to health system electronic health records to increase the yield of Lp(a) testing.
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January 2025
Department of Medicine, Division of Cardiology (M.P., N.J.P., N.P.S.), Duke University, Durham, NC.
Background: Established risk models may not be applicable to patients at higher cardiovascular risk with a measured Lp(a) (lipoprotein[a]) level, a causal risk factor for atherosclerotic cardiovascular disease.
Methods: This was a model development study. The data source was the Nashville Biosciences Lp(a) data set, which includes clinical data from the Vanderbilt University Health System.
BJU Int
January 2025
Department of Urology, University of Alabama, Birmingham, AL, USA.
Objectives: To identify associations between 24-h urine abnormalities and clinical risk factors for recurrent stone formers.
Patients And Methods: The Registry for Stones of the Kidney and Ureter was queried for all patients who underwent 24-h urine studies. Patients were categorised by the number of clinical risk factors for recurrent stone disease.
Eur Heart J Digit Health
January 2025
Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA.
Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.
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