The impact of omics research on our understanding of osteoarthritis and future treatments
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review recent studies using 'Omics' approaches (genomics, proteomics, metabolomics, single cell analyses) in patient populations and animal models of osteoarthritis (OA), with the goal of identifying disease-modifying mechanisms that could serve as therapeutic and diagnostic targets. RECENT FINDINGS: The number of genes, pathways and molecules with potential roles in OA pathogenesis has grown substantially over the last 18 months. Studies have expanded from their traditional focus on cartilage and gene expression to other joint tissues, proteins and metabolites. Single cell approaches provide unprecedented resolution and exciting insights into the heterogeneity of cellular activities in OA. Functional validation and investigation of underlying mechanisms in animal models of OA, in particular genetically engineered mice, link Omics findings to pathophysiology and potential therapeutic applications. SUMMARY: Although great progress has been made in the use of Omics approaches to OA, in both animal models and patient samples, much work remains to be done. In addition to filling gaps in data sets not yet existing, integration of data from the various approaches, mechanistic investigations, and linkage of Omics data to patient stratification remain significant challenges.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".