Effective Anti-TNF-α Therapy Can Induce Rapid Resolution and Sustained Decrease of Gastroduodenal Mucosal Amyloid Deposits in Reactive Amyloidosis Associated with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of anti-tumor necrosis factor-alpha (anti-TNF) therapy in patients with reactive AA amyloidosis associated with rheumatoid arthritis (RA). METHODS: Fourteen patients with reactive AA amyloidosis associated with RA were prospectively evaluated. Four patients were treated with infliximab and 10 with etanercept. The mean period of anti-TNF therapy was 20.1 +/- 13.8 months. Laboratory findings and renal function were examined before and after initiation of anti-TNF therapy. In 9 patients the area of amyloid deposits in serial gastroduodenal mucosal biopsy specimens was examined and image analysis was performed. RESULTS: C-reactive protein and serum amyloid A protein levels were significantly reduced after initiation of anti-TNF therapy. Twenty-four hour creatinine clearance improved in 4 patients, did not change in 5, and deteriorated in 3. Twenty-four hour urinary protein excretion was significantly decreased in 3 patients, not exacerbated in 6, and increased in 3 after initiation of anti-TNF therapy. The biopsy specimens from the 9 patients who underwent serial gastroduodenal biopsies showed significant decreases in the area of amyloid deposits, from 8.8% +/- 6.4% to 1.6% +/- 0.6% (p = 0.003) after initiation of anti-TNF therapy. Four patients showed a sustained decrease in the areas of amyloid deposits in their third biopsy specimens, and amyloid deposits were not detectable in 2. CONCLUSION: Our results indicate a striking effect of anti-TNF therapy for rapid removal and sustained disappearance of amyloid deposits in gastric mucosal tissue with amelioration of renal functions in patients with reactive amyloidosis due to RA.
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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 | 0.001 |
| 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".