Global Deletion of Pannexin 3 Resulting in Accelerated Development of Aging‐Induced Osteoarthritis in Mice
Bibliographic record
Abstract
OBJECTIVE: Osteoarthritis (OA) results in pathologic changes in the joint tissue. The mechanisms driving disease progression remain largely unclear, and thus disease-modifying treatments are lacking. Pannexin 3 (Panx3) was identified as a potential mediator of cartilage degeneration in OA, and our previous study in mice indicated that deletion of the Panx3 gene delayed surgically induced cartilage degeneration. This study was undertaken to examine the role of Panx3 in other OA subtypes, particularly primary OA during aging, in a mouse model of aging-induced OA. METHODS: C57BL/6J (Black-6) mice, ages 18-24 months, were analyzed by micro-computed tomography to investigate bone mineral density and body composition. Joints were harvested from the mice, and histopathologic analysis of the joint tissue for OA development was conducted with a specific focus on changes in articular cartilage, subchondral bone, and synovial tissue. RESULTS: Global loss of Panx3 in aging mice was not associated with increased mortality or changes in body composition. Mice lacking Panx3 had shorter appendicular skeletons than WT mice, but overall the body compositions appeared quite similar. Panx3 deletion dramatically accelerated cartilage degeneration and subchondral bone thickening with aging in both 18-month-old and 24-month-old mice, while promoting synovitis in 18-month-old mice. CONCLUSION: These observations in a mouse model of OA suggest that Panx3 has a protective role against the development of primary aging-associated OA. It appears that Panx3 has opposing context-specific roles in joint health following traumatic injury versus that associated with aging. These data strongly suggest that there are differences in the molecular pathways driving different subtypes of OA, and therefore a detailed understanding of these pathways could directly improve strategies for OA diagnosis, therapy, and research.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".