Telomerase Transduced Osteoarthritis Fibroblast-Like Synoviocytes Display a Distinct Gene Expression Profile
Bibliographic record
Abstract
OBJECTIVE: To examine the differential gene expression in telomerase transduced osteoarthritis fibroblast-like synoviocytes (hTERT-OA 13A FLS) and telomerase transduced rheumatoid arthritis FLS (hTERT-RA 516 FLS) and test the hypothesis that longterm culture of hTERT-OA 13A FLS display a disease-specific gene expression profile. METHODS: Gene expression in passage 8 hTERT-OA 13A FLS and passage 8 hTERT-RA 516 FLS were compared using microarray assays. Differential expression of selected genes was further examined by reverse transcription-polymerase chain reaction (RT-PCR). After continuous expansion in culture for an additional 4 months, gene expression in the longterm cultures of hTERT-OA 13A FLS and hTERT-RA 516 FLS was again examined with microarray and real-time RT-PCR. RESULTS: hTERT-OA 13A FLS displayed a distinct gene expression profile. While hTERT-RA 516 FLS expressedADAMTS1, ADAMTS3, ADAMTS5, and several carboxypeptidases, hTERT-OA 13A FLS expressed matrix metalloproteinase (MMP)1, MMP3, and several cathepsins at higher levels. Numerous genes classified in the immune response, lipid transport/catabolism, and phosphate transport biological processes were also expressed at higher levels in hTERT-OA 13A FLS. In contrast, numerous genes classified in the positive regulation of cell proliferation, anti-apoptosis, and angiogenesis biological processes were expressed at higher levels in hTERT-RA 516 FLS. Further, of the recently proposed 21 candidate synovial biomarkers of OA, 12 (57%) were detected in our study. CONCLUSION: The findings indicate that OA FLS may not be a passive bystander in OA and that telomerase transduced OA FLS offer an alternative tool for the study of synovial disease markers and for the identification of new therapeutic targets for OA therapy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".