Unraveling Osteoarthritis: Mechanistic Insights and Emerging Therapies Targeting Pain and Inflammation
Bibliographic record
Abstract
Osteoarthritis (OA) is now widely recognized not merely as a cartilage-centric disease but as a multifactorial disorder affecting the entire joint as an organ, including the articular cartilage, subchondral bone, synovium, ligaments, menisci, and the innervating neural elements. This review explores the complex pathophysiology of OA with a focus on the emerging mechanisms of pain and inflammation that extend beyond articular cartilage degradation. Joint inflammation driven by immune activation in response to cellular stress signals promotes the release of pro-inflammatory mediators and catabolic enzymes. Key signaling pathways such as NF-κB, MAPKs, and JAK/STAT amplify these responses, and pain is sustained through peripheral and central sensitization, contributing to exacerbating symptoms even in the absence of visible joint damage. This review also integrates molecular and cellular mechanisms to highlight innovative therapies aimed at modifying both the structural damage and neurosensory drivers of pain. These approaches offer the potential to not only alleviate symptoms but also alter disease progression, signaling a move toward personalized, mechanism-based treatments. Given the intricate interactions among joint tissues, immune activation, and sensory processing, a comprehensive strategy that targets both structural degeneration and neuroinflammation is essential for the future of OA management. Emphasizing the joint as an integrated organ, we advocate for translational research linking molecular pathology with clinically meaningful outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".