Targeting cartilage EGFR pathway for osteoarthritis treatment
Bibliographic record
Abstract
Osteoarthritis (OA) is a widespread joint disease for which there are no disease-modifying treatments. Previously, we found that mice with cartilage-specific epidermal growth factor receptor (EGFR) deficiency developed accelerated knee OA. To test whether the EGFR pathway can be targeted as a potential OA therapy, we constructed two cartilage-specific EGFR overactivation models in mice by overexpressing heparin binding EGF-like growth factor (HBEGF), an EGFR ligand. Compared to wild type, Col2-Cre HBEGF-overexpressing mice had persistently enlarged articular cartilage from adolescence, due to an expanded pool of chondroprogenitors with elevated proliferation ability, survival rate, and lubricant production. Adult Col2-Cre HBEGF-overexpressing mice and Aggrecan-CreER HBEGF-overexpressing mice were resistant to cartilage degeneration and other signs of OA after surgical destabilization of the medial meniscus (DMM). Treating mice with gefitinib, an EGFR inhibitor, abolished the protective action against OA in HBEGF-overexpressing mice. Polymeric micellar nanoparticles (NPs) conjugated with transforming growth factor-α (TGFα), a potent EGFR ligand, were stable and nontoxic and had long joint retention, high cartilage uptake, and penetration capabilities. Intra-articular delivery of TGFα-NPs effectively attenuated surgery-induced OA cartilage degeneration, subchondral bone plate sclerosis, and joint pain. Genetic or pharmacologic activation of EGFR revealed no obvious side effects in knee joints and major vital organs in mice. Together, our studies demonstrate the feasibility of using nanotechnology to target EGFR signaling for OA treatment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".