Deciphering the role of Wnt/β-catenin and miR-214 in knee osteoarthritis: molecular and clinical insights
Bibliographic record
Abstract
Introduction: Understanding the molecular mechanism underlying the pathogenesis of knee osteoarthritis (KOA) may be beneficial in fetching new therapeutics. Our study aims to investigate the implication of Wnt/ β-catenin pathway in development of KOA by detection of the downstream target genes and their crosstalk with miR-214 in patients with KOA and to correlate that with the clinical findings. Methods: Sixty participants were involved in the study. The levels of miR-214, β-catenin, Wnt4, matrix metalloproteinase 3 (MMP3), Bax, caspase 3, and phosphorylated glycogen synthase kinase-3 beta (pGSK3β) were determined. All participants were assessed clinically and radiologically regarding knee joint pain, stiffness, range of motion, and knee medial cartilage thickness. Besides, a correlation between Western Ontario and McMaster Universities (WOMAC) score, clinical, and radiological data, and the measured parameters was conducted. Results and discussion: Patients with KOA showed downregulated miR-214 with upregulated β-catenin, Wnt4, MMP3, Bax, caspase 3, and pGSK3β compared to healthy individuals. Statistically significant positive correlation between WOMAC score, knee joint pain regarding Visual Analogue Scale (VAS) with β-catenin, pGSK3β, Wnt4, MMP3, Bax, and caspase 3, and significant negative relationship between them and knee joint medial cartilage thickness; while there was a statistically significant negative correlation between WOMAC, and clinical findings of osteoarthritis and miR-214 and significant positive relationship between it and knee joint medial cartilage thickness. This study provides valuable insights into involvement of the Wnt/β-catenin and miR-214 in KOA pathogenesis. By targeting these molecular components, future therapeutics may modulate their activity and mitigate chondrocyte apoptosis and matrix degradation, potentially halting KOA progression.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".