Patient-responsive protein biomarkers for cartilage degeneration and repair identified in the infrapatellar fat pad
Bibliographic record
Abstract
OBJECTIVES: Cartilage defects (CDs) are regarded as early manifestation of osteoarthritis (OA). The infrapatellar fat pad (IPFP) is an important mediator in maintaining joint homeostasis, disease progression and tissue repair, with a crucial role of its secreted proteins. Here, we investigate the proteome of the IPFP in relation to clinical status and response to surgical treatment of CDs. METHODS: In order to characterize the proteome of the IPFP, samples from a cohort of 53 patients who received surgical treatment for knee CDs were analyzed with label-free proteomics. Patients were divided based on validated outcome scores for pain and knee function, preoperatively and at 1-year postoperatively, and on MRI assessment of the defect severity, fibrosis and synovitis. RESULTS: Specific proteins were differentially abundant in patients with MRI features and better clinical outcome after CD surgery, including a downregulation of cartilage intermediate layer protein 2 (CILP-2) and microsomal glutathione s-transferase 1 (MGST1), and an upregulation of aggrecan (ACAN), and proteoglycan 4 (PRG4). Pathways related to cell interaction, oxidation and matrix remodeling were altered. CONCLUSION: Proteins in the IPFP that have a function in extracellular matrix, inflammation and immunomodulation were identified as potentially relevant markers for cartilage repair monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".