An angiotensin-converting enzyme inhibitor in the combination treatment of rheumatoid arthritis
Bibliographic record
Abstract
Angiotensin-converting enzyme (ACE) inhibitors have anti-inflammatory and antiproliferative properties and can affect the processes of angiogenesis, by reducing the effects of angiotensin II (ATII). The use of ACE inhibitors in the combination therapy of rheumatoid arthritis (RA) can be also effective for monitoring disease activity and for reducing a cardiovascular risk. Objective: to evaluate the efficacy of an ACE inhibitor in the combination therapy of RA. Patients and methods. Eighty-four patients with RA and endothelial dysfunction were examined; the mean age was 40.12±10.2 years; the mean disease duration was 4.22±3.43 years. All the patients had a blood level of ATII of >9 pg/ml. Enzyme immunoassay was used to measure the levels of tumor necrosis factor-α (TNF-α) (Vector-Best, Russia), intercellular adhesion molecules 1 (ICAM-1) (Diaclone, France), vascular endothelial growth factor (VEGF) and ATII (Diagnostic, Canada). Wrist ultrasonography using the Doppler ultrasound apparatus ESAOTE MyLAB40 was carried out to assess synovial vascularization. The patients were divided into two groups. Group 1 included 43 patients who were assigned to receive standard therapy for RA according to the rheumatic disease treatment protocols; Group 2 comprised 41 patients who received the standard therapy plus ACE inhibitors 2.5–5 mg/day. Results. The use of ACE inhibitors in the 12-month combination therapy of RA patients led to an improvement in the endothelial regulation of vascular tone, to a decrease in the blood concentration of ICAM-1, to a reduction in the intensity of synovial angiogenesis and in the blood level of VEGF by 39%, and a more significant drop in the levels of CRP and TNF-? and in DAS28 by 1.2 scores as compared to those in the standard 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) | 0.001 | 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".