ArticleMedical science monitor : international medical journal of experimental and clinical research2020
Proteomics and Bioinformatics Analysis of Cartilage in Post-Traumatic Osteoarthritis in a Mini-Pig Model of Anterior Cruciate Ligament Repair.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Meta-analysis Integrated With Multi-omics Data Analysis to Elucidate Pathogenic Mechanisms of Age-Related Knee Osteoarthritis in Mice.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022Pooled it
- Omics Studies in Osteoarthritis: A Review of Molecular Mechanisms and Candidate Molecules.Biomolecules · 2026Review
- Omics in mini-livestock: a genomic perspective on the future of sustainable food systems.Frontiers in genetics · 2025Review
- Article
- Animal Models of Osteoarthritis: Updated Models and Outcome Measures 2016-2023.Regenerative engineering and translational medicine · 2024Article
- Screening and identification of osteoarthritis related differential genes and construction of a risk prognosis model based on bioinformatics analysis.Annals of translational medicine · 2022Article
- Tissue Renin-Angiotensin System (tRAS) Induce Intervertebral Disc Degeneration by Activating Oxidative Stress and Inflammatory Reaction.Oxidative medicine and cellular longevity · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND Although osteoarthritis (OA) is a degenerative disease that is increasingly common with age, the pathogenesis of post-traumatic OA (PTOA) is poorly understood. This study aimed to undertake proteomics and bioinformatics analysis of cartilage in PTOA in a mini-pig model of anterior cruciate ligament repair (ACLR). MATERIAL AND METHODS The mini-pig model of PTOA involved autologous orthotopic ACLR. Screening and identification of differentially expressed proteins in the knee joint cartilage in the OA cartilage group were compared with the control group using tandem mass tag (TMT)-labeling liquid chromatography with tandem mass spectrometry (LC-MS-MS). A protein expression level >1.2 fold-change represented protein upregulation and <0.83 fold-change represented protein down-regulation Bioinformatics analysis included Gene Ontology (GO) functional annotation and the Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis to determine the biological functions and pathways of proteins showing altered expression profiles associated with OA. RESULTS There were 2,950 proteins screened from the knee cartilage tissues of the OA model group using quantitative TMT-labeling LC-MS-MS. There were 491 proteins identified with altered expression profiles, 198 proteins were upregulated and 293 proteins were down-regulated in the OA cartilage group. GO function and KEGG pathway enrichment analysis of the 491 proteins identified their functions in cellular processes, metabolic processes, and biological regulation. CONCLUSIONS Proteomics and bioinformatics analysis of cartilage in PTOA in a mini-pig model of ACLR identified OA-related proteins.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.