Tissue distribution of sprifermin (recombinant human fibroblast growth factor 18) in the rat following intravenous and intra-articular injection

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Comparison between MRI, microbiology and histology in evaluation of antibiotics in a murine model of thigh infection.

Although in vivo magnetic resonance imaging (MRI) is rapidly becoming a recognised tool in experimental pharmacological research, at the best of our knowledge, scarce application in the field of antibacterial drug research has been reported so far. In this last field, animal models of bacterial infections are used to test the efficacy of novel compounds. In this paper we have explored the potential usefulness of MRI in monitoring the chronological evolution of experimental bacterial infections and the effect of different therapeutic treatments. A murine model of thigh infection induced by Staphylococcus aureus has been used and the efficacy of vancomycin and imipenem/cilastatin has been tested. Three groups of infected animals were studied by microbiology, histology and MRI methods. The results obtained show that in vivo MRI data are highly consistent with microbiological and histological data, allowing, similarly to these commonly used techniques, the efficacy of different antibacterial treatments to be quantified. Our findings suggest that MRI could be used to assess the efficacy of new chemical entities in antibacterial pharmacological research. The advantages of MRI, as a non invasive technique, in comparison with commonly used microbiological and histological methods are discussed

Machine-learning predicted and actual 2-year structural progression in the IMI-APPROACH cohort

In the Innovative Medicine's Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee osteoarthritis (OA) study, machine learning models were trained to predict the probability of structural progression (s-score), predefined as >0.3 mm/year joint space width (JSW) decrease and used as inclusion criterion. The current objective was to evaluate predicted and observed structural progression over 2 years according to different radiographic and magnetic resonance imaging (MRI)-based structural parameters. Radiographs and MRI scans were acquired at baseline and 2-year follow-up. Radiographic (JSW, subchondral bone density, osteophytes), MRI quantitative (cartilage thickness), and MRI semiquantitative [SQ; cartilage damage, bone marrow lesions (BMLs), osteophytes] measurements were obtained. The number of progressors was calculated based on a change exceeding the smallest detectable change (SDC) for quantitative measures or a full SQ-score increase in any feature. Prediction of structural progression based on baseline s-scores and Kellgren-Lawrence (KL) grades was analyzed using logistic regression. Among 237 participants, around 1 in 6 participants was a structural progressor based on the predefined JSW-threshold. The highest progression rate was seen for radiographic bone density (39%), MRI cartilage thickness (38%), and radiographic osteophyte size (35%). Baseline s-scores could only predict JSW progression parameters (most P>0.05), while KL grades could predict progression of most MRI-based and radiographic parameters (P<0.05). In conclusion, between 1/6 and 1/3 of participants showed structural progression during 2-year follow-up. KL scores were observed to outperform the machine-learning-based s-scores as progression predictor. The large amount of data collected, and the wide range of disease stage, can be used for further development of more sensitive and successful (whole joint) prediction models. Trial Registration: Clinicaltrials.gov number NCT03883568.</p

Test-retest precision and longitudinal cartilage thickness loss in the IMI-APPROACH cohort

Objective: To investigate the test-retest precision and to report the longitudinal change in cartilage thickness, the percentage of knees with progression and the predictive value of the machine-learning-estimated structural progression score (s-score) for cartilage thickness loss in the IMI-APPROACH cohort - an exploratory, 5-center, 2-year prospective follow-up cohort. Design: Quantitative cartilage morphology at baseline and at least one follow-up visit was available for 270 of the 297 IMI-APPROACH participants (78% females, age: 66.4 +/- 7.1 years, body mass index (BMI): 28.1 +/- 5.3 kg/m(2), 55% with radiographic knee osteoarthritis (OA)) from 1.5T or 3T MRI. Test-retest precision (root mean square coefficient of variation) was assessed from 34 participants. To define progressor knees, smallest detectable change (SDC) thresholds were computed from 11 participants with longitudinal test-retest scans. Binary logistic regression was used to evaluate the odds of progression in femorotibial cartilage thickness (threshold: similar to 211 mu m) for the quartile with the highest vs the quartile with the lowest s-scores. Results: The test-retest precision was 69 mu m for the entire femorotibial joint. Over 24 months, mean cartilage thickness loss in the entire femorotibial joint reached -174 mu m (95% CI: [-207, -141] mu m, 32.7% with progression). The s-score was not associated with 24-month progression rates by MRI (OR: 1.30, 95% CI: [0.52, 3.28]). Conclusion: IMI-APPROACH successfully enrolled participants with substantial cartilage thickness loss, although the machine-learning-estimated s-score was not observed to be predictive of cartilage thickness loss. IMI-APPROACH data will be used in subsequent analyses to evaluate the impact of clinical, imaging, biomechanical and biochemical biomarkers on cartilage thickness loss and to refine the machine-learning-based s-score. (c) 2022 The Author(s). Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</p