Application of gene therapy in osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) is a leading cause of pain and disability globally,characterized by progressive cartilage degeneration, subchondralbone remodeling, and synovial inflammation. Current treatmentsprimarily offer symptomatic relief without addressing the underlyingdisease mechanisms or halting progression. Gene therapy hasemerged as a promising strategy to target the molecular drivers ofOA by modulating key pathways involved in inflammation, tissuedegeneration, and pain. This review summarizes recent advancesin OA gene therapy, including anti-inflammatory approachestargeting IL-1β and IL-10, as well as regenerative strategiesleveraging TGF-β1 and FGF-18. Preclinical and early clinicalstudies have shown encouraging results in both symptom reliefand cartilage preservation. However, significant challengesremain, including vector safety, immune responses, and thecomplex, heterogeneous nature of OA that complicates treatmentresponse. The integration of precision medicine with improved genedelivery platforms and combinatorial therapeutic strategies holdsstrong potential to overcome these limitations. Collectively, theseinnovations may accelerate the development of disease-modifyingosteoarthritis drugs (DMOADs) and provide long-term, effectivetherapeutic options for patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".