Variability of RANKL and osteoprotegerin staining in synovial tissue from patients with active rheumatoid arthritis: quantification using color video image analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the interpatient, interbiopsy, and intrabiopsy variability of receptor activator of nuclear factor kB ligand (RANKL) and osteoprotegerin (OPG) immunostaining within synovial tissue from rheumatoid knee joints with active synovitis, using digital image analysis. METHODS: Synovial biopsy specimens were obtained from patients with rheumatoid arthritis (RA) and active synovitis. Immunohistologic analysis was performed on frozen synovial tissue biopsy specimens from 6 patients using a monoclonal antibody (Mab) to detect RANKL (626) or OPG (805 or 8051). Patients with a minimum of 4 synovial biopsies were included in the study. Sections were evaluated by computer assisted image analysis to assess between-patient, between-biopsy, and intra-biopsy variability of OPG and RANKL protein expression. The study was designed to deliberately maximize the variability. RESULTS: Computerized image analysis of staining with Mab to RANKL and OPG revealed variance for each antibody across the 3 components of the total variability. CONCLUSION: Our study shows that variability in synovial immunostaining of RANKL and OPG protein is a significant and complex problem. We discuss methods to reduce this variability and suggest that the auspices of OMERACT may be employed to advance the study of synovium in collaborative international studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".