Identification of Proteins with Increased Expression in Rheumatoid Arthritis Synovial Tissues
Bibliographic record
Abstract
OBJECTIVE: A proteomic approach was applied to discover novel rheumatoid arthritis (RA)-specific proteins by comparing the expression profiles of synovial membranes from patients with RA, osteoarthritis (OA), and ankylosing spondylitis (AS). METHODS: Synovial tissues were collected from patients with RA (n = 10), OA (n = 10), or AS (n = 6), and healthy controls matched for age and sex. Proteins were separated by 2-dimensional polyacrylamide gel electrophoresis, and the proteins with significantly increased expression in the RA samples were subject to matrix-assisted laser adsorption-ionization time-of-flight spectrometry. Results were verified using Western blot and immunohistochemistry. Levels of the candidate proteins were measured within plasma and synovial fluids from the RA patients (n = 30), who had disease duration of 3-7 years, using ELISA. Levels were also measured within plasma from unmedicated RA patients (n = 41), who had disease duration of 1-6 months. RESULTS: Compared with the OA and AS tissue samples, the proteins Ig-kappa light-chain C region, PRDX4, SOD2, TPI, and TXNDC5 were found with increased expression in synovial tissues of RA patients. PRDX4, SOD2, TPI, and TXNDC5 had 2-fold or more increase in expression in some of the early RA plasma samples (58.55%, 31.7%, 26.8%, and 36.6%, respectively) as compared with the early OA samples and control samples. TXNDC5 had 2-fold or more increase in expression in 53.3% of blood samples and 73.3% of synovial fluid samples from patients with long disease duration of RA as compared with samples from OA and AS patients. CONCLUSION: Functional classification indicated that these identified proteins were related with cell differentiation, glycol metabolism, immunoactivation, and endogenous antioxidant reaction.
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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 | 0.001 |
| 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".