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632 results for “Cartilage”
Data for: NFATc1 marks articular cartilage progenitors and negatively determines articular chondrocyte differentiation
<p>The origin and differentiation mechanism of articular chondrocytes remain poorly understood. Broadly, the difference in developmental mechanisms of articular and growth-plate cartilage is still less elucidated. Here, we identified that the nuclear factor of activated T-cells cytoplasmic 1 (NFATc1) is a crucial regulator of articular, but not growth-plate, chondrocyte differentiation during development. At the early stage of mouse knee development (embryonic day 13.5), NFATc1-expressing cells were mainly located in the flanking region of the joint interzone. With development, NFATc1-expressing cells generated almost all articular chondrocytes, but not chondrocytes in limb growth-plate primordium. NFATc1-expressing cells displayed prominent capacities for colony formation and multipotent differentiation. Transcriptome analyses revealed a set of characteristic genes in NFATc1-enriched articular cartilage progenitors. Strikingly, the expression of NFATc1 was diminished with articular chondrocyte differentiation and suppressing NFATc1 expression in articular cartilage progenitors was sufficient to induce spontaneous chondrogenesis while overexpressing NFATc1 suppresses chondrogenesis. Mechanistically, NFATc1 negatively regulated the transcriptional activity of the <em>Col2a1</em> gene. Thus, our results reveal that NFATc1 characterizes articular, but not growth-plate, cartilage progenitors during development and negatively determines articular chondrocyte differentiation at least partly through regulating COL2A1 gene transcription.</p>
Synchrotron CT image of hyaline cartilage lesion in the tibia of a 9 week old male STR/Ort mouse
<p>Figure from: Evans, L. A. E. (2023). <em>Imaging the articular osteochondral interface. </em>PhD Thesis. Royal Veterinary College, University of London, UK.</p> <p>Video of synchrotron CT slices showing hyaline cartilage lesion in the tibia of a 9 week old male STR/Ort mouse. Image obtained on the I13-2 beamline at Diamond Light Source, Oxfordshire, during beamtime MG24786-2. Scanning involved a bespoke cold stage designed by researchers including Dr Peter Rockett, Dr Saurabh Shah and Professor Peter Lee. Cold stage details can be found in a published research article with DOI: 10.1039/c7ra00642j. </p>
Superiority of MACI® Versus Microfracture Treatment in Patients With Symptomatic Articular Cartilage Defects in the Knee
ClinicalTrials.gov study NCT00719576. IPD Sharing: NO. Countries: 7. Publications: 1.
RCT of ChondroCelect® (in an ACI Procedure) vs Microfracture in the Repair of Cartilage Defects of the Knee
ClinicalTrials.gov study NCT00414700. IPD Sharing: Not stated. Countries: 4. Publications: 8.
Spirox Latera™ Implant Support of Lateral Nasal Wall Cartilage (LATERAL-OR) Study
ClinicalTrials.gov study NCT02952313. IPD Sharing: NO. Countries: 1. Publications: 2.
Trial Comparing BST-CarGel and Microfracture in Repair of Articular Cartilage Lesions in the Knee
ClinicalTrials.gov study NCT00314236. IPD Sharing: Not stated. Countries: 3. Publications: 4.
Serum Cartilage Oligomeric Matrix Protein Levels After 12 Weeks of Different Exercises
ClinicalTrials.gov study NCT01576159. IPD Sharing: Not stated. Countries: 1. Publications: 3.
IMPACT: Safety and Feasibility of a Single-stage Procedure for Focal Cartilage Lesions of the Knee.
ClinicalTrials.gov study NCT02037204. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Effectiveness of Autologous Adipose-derived Stem Cells in the Treatment of Knee Cartilage Injury
ClinicalTrials.gov study NCT03955497. IPD Sharing: NO. Countries: 1. Publications: 4.
ACI-C Versus AMIC. A Randomized Trial Comparing Two Methods for Repair of Cartilage Defects in the Knee
ClinicalTrials.gov study NCT01458782. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Comparison of BioCartilage Versus Marrow Stimulating Procedure for Cartilage Defects of the Knee
ClinicalTrials.gov study NCT02203071. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data for: NFATc1 marks articular cartilage progenitors and negatively determines articular chondrocyte differentiation
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Panaxatriol exerts anti-senescence effects and alleviates osteoarthritis and cartilage repair fibrosis by targeting UFL1
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Lin28a induces SOX9 and chondrocyte reprogramming via HMGA2 and blunts cartilage loss in mice
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Knee cartilage segmentation literature - For Hunter Viz 2020
<p>Dataset used for the John Hunter - Excellence in Plotting Context 2020</p>
Evaluation of articular cartilage with quantitative MRI in an equine model of post-traumatic osteoarthritis
<p>This dataset contains raw qMRI data and the corresponding calculated relaxation time maps, reference data and an example MATLAB script demonstrating how to access the data, comprising study:</p> <p><strong>Evaluation of articular cartilage with quantitative MRI in an equine model of post-traumatic osteoarthritis </strong></p> <p>Journal of Orthopaedic Research | DOI: https://doi.org/10.1002/jor.24780</p> <p>Kajabi Abdul Wahed*(1,2), Casula Victor(1,2), Sarin Jaakko K.(3,4), Ketola Juuso H.(1), Nykänen Olli(3), te Moller Nikae C.R.(5), Mancini Irina A.D.(5), Visser Jetze(6), Brommer Harold(5), van Weeren P. René(5), Malda Jos(5,6), Töyräs Juha(3,4,7), Nieminen Miika T.(1,2,8), Nissi Mikko J.*(1,3)</p> <ol> <li>Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland</li> <li>Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland </li> <li>Department of Applied Physics, University of Eastern Finland, Kuopio, Finland</li> <li>Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland</li> <li>Department of Equine Sciences, Faculty of Veterinary Medicine, Utrecht University, The Netherlands</li> <li>Department of Orthopaedics, University Medical Center Utrecht, The Netherlands </li> <li>School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia</li> <li>Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland </li> </ol> <p> </p> <p>*Corresponding authors:<br> Mikko J. Nissi<br> Department of Applied Physics,<br> University of Eastern Finland<br> POB 1627,<br> FI-70211, Kuopio, Finland<br> mikko.nissi@uef.fi<br> +358-50-5955517</p> <p>Abdul Wahed Kajabi<br> Research Unit of Medical Imaging, Physics and Technology,<br> University of Oulu<br> POB 50,<br> FI-90029, Oulu, Finland<br> abdul.kajabi@oulu.fi<br> +358-50-3037425</p> <p>Keywords: cartilage, post-traumatic, osteoarthritis, quantitative MRI, relaxation times</p> <p>The file "Pony_AllData.mat" contains all the data of the study. The main struct variable in the file ("all_data") contains several fields storing all the data per sample. In the following, brief descriptions for the main subfields are given:</p> <ul> <li>name: contains generic name of the sample</li> <li>qMRI_Data: contains qMRI raw data, the corresponding relaxation time maps and normalized cartilage full-thickness ROI profiles</li> <li>calcROI_Data: contains ROIs used to calculate the relaxation time maps</li> <li>analysisROI_Data: ROIs used for the analysis</li> <li>DD_prof and interpDD_prof: Original and interpolated profiles for proteoglycan content (Optical Density)</li> <li>PLM_prof and interpPLM_prof: Original and interpolated profiles for collagen fiber orientation (Polarized Light Microscopy)</li> <li>E_eq and E_dyn: Equilibrium and dynamic moduli of articular cartilage.</li> </ul> <p>The example script, "Analysis_script.m" contains a short demonstration on how the data in the struct can be accessed. The script assumes that Aedes (http://aedes.uef.fi) analysis software is available for matlab.</p> <p>(Abdul Wahed Kajabi, 17 Jun 2020)</p>
Data from: Effect of Relaxation-Dependent Adhesion on Pre-Sliding Response of Cartilage
Possible links between adhesive properties and pre-sliding (static) friction response of cartilage are not fully understood in the literature. The aims of this study are to investigate the relation between adhesion and relaxation time in articular cartilage, and to investigate the effect of relaxation-dependent adhesion on pre-sliding response of cartilage. Adhesion tests were performed to evaluate the work of adhesion of cartilage at different relaxation times. Friction tests were conducted to identify the pre-sliding friction response of cartilage at relaxation times corresponding to adhesion tests. The pre-sliding friction response of cartilage was systematically linked to the work of adhesion and contact conditions by a slip-based failure model. It was found that the work of adhesion increases with relaxation time. Also, the work of adhesion is linearly correlated to the resistance to slip-based failure. In addition, as the work of adhesion increases, the critical adhered area at the moment of failure increases, and the propagation rate of the annular slip (crack) area toward its center increases. These findings offer the mechanistic explanation of the pre-sliding friction behavior and stick-slip response of soft hydrated interfaces such as articular cartilage and hydrogels. Besides, the linear correlation between adhesion and threshold to slip-based failure enables estimation of adhesive strength of such interfaces directly from pre-sliding friction response (e.g., shear wave elastography).
Data from: Automated segmentation of complex patterns in biological tissues: lessons from stingray tessellated cartilage
Introduction - Many biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements. This renders manual image analysis infeasible, in particular when statistical analysis is to be conducted, requiring a larger number of image data to be analyzed. As a consequence, the analysis process needs to be automated to a large degree. In this paper, we describe a multi-step image segmentation pipeline for the automated segmentation of the calcified cartilage into individual tesserae from computed tomography images of skeletal elements of stingrays. Methods - Besides applying state-of-the-art algorithms like anisotropic diffusion smoothing, local thresholding for foreground segmentation, distance map calculation, and hierarchical watershed, we exploit a graph-based representation for fast correction of the segmentation. In addition, we propose a new distance map that is computed only in the plane that locally best approximates the calcified cartilage. This distance map drastically improves the separation of individual tesserae. We apply our segmentation pipeline to hyomandibulae from three individuals of the round stingray (Urobatis halleri), varying both in age and size. Results - Each of the hyomandibula datasets contains approximately 3000 tesserae. To evaluate the quality of the automated segmentation, four expert users manually generated ground truth segmentations of small parts of one hyomandibula. These ground truth segmentations allowed us to compare the segmentation quality w.r.t. individual tesserae. Additionally, to investigate the segmentation quality of whole skeletal elements, landmarks were manually placed on all tesserae and their positions were then compared to the segmented tesserae. With the proposed segmentation pipeline, we sped up the processing of a single skeletal element from days or weeks to a few hours.
FIGURE 6 in Comparative morphology of rostral cartilages in extant mackerel sharks (Chondrichthyes, Lamniformes, Lamnidae) using CT scanning
FIGURE 6. Lamna ditropis Hubbs & Follett, 1947, female, 2340 mm, TL, northeast Pacific Ocean (ERB 0854), 3D reconstructions based on CT scans of the entire head, showing all cartilaginous structures: lateral (A) (image reversed), dorsal (B), ventral (C), anterior (D) and anterolateral (E) views. Abbreviations: see Fig. 2.
FIGURE 5 in Comparative morphology of rostral cartilages in extant mackerel sharks (Chondrichthyes, Lamniformes, Lamnidae) using CT scanning
FIGURE 5. Lamna nasus (Bonnaterre, 1788), male, 1740 mm TL, northeast Atlantic Ocean (ERB 0929), 3D reconstructions based on CT scans of the entire head, showing all cartilaginous structures: lateral (A), dorsal (B), ventral (C), anterior (D) and anterolateral (E) views. Abbreviations: see Fig. 2.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.