New Emerging Role of Pitx1 Transcription Factor in Osteoarthritis Pathogenesis
Bibliographic record
Abstract
Osteoarthritis is the most common form of arthritis and the precise etiology of this disease remains unclear. We took a candidate gene-driven strategy approach based on the observation that Pitx1 transcription factor was found during hind limb development in regions giving rise to cartilage joints, long bones and skeletal muscles, while its partial in activation led to a progressive formation of osteoarthritis-like phenotype in aging Pitx1 +/- mice. To determine whether Pitx1 plays a role in osteoarthritis pathogenesis in humans, we performed an expression analysis of the pitx1 gene using RNA prepared from articular chondrocyte cultures derived from knee cartilage of patients with osteoarthritis and age- and gender-matched control subjects. Pitx1 expression was detected in articular chondrocytes derived from matched control subjects, whereas in osteoarthritic articular chondrocytes, Pitx1 expression was barely detectable by reverse transcription-polymerase chain reaction. Immunostaining with anti-Pitx1 antibodies of histologic sections of human osteoarthritic and control cartilage showed Pitx1 proteins only in the cartilage of control subjects, whereas Pitx1 proteins were hardly detected in human osteoarthritic sections. Collectively, our results uncovered an unrecognized role for Pitx1 in osteoarthritis and elucidation of the mechanism turning off its expression will clarify its pathophysiological relevance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".