A monoclonal antibody against kininogen reduces inflammation in the HLA-B27 transgenic rat
Bibliographic record
Abstract
The human leukocyte antigen B27 (HLA-B27) transgenic rat is a model of human inflammatory bowel disease, rheumatoid arthritis and psoriasis. Studies of chronic inflammation in other rat models have demonstrated activation of the kallikrein-kinin system as well as modulation by a plasma kallikrein inhibitor initiated before the onset of clinicopathologic changes or a deficiency in high-molecular-mass kininogen. Here we study the effects of monoclonal antibody C11C1, an antibody against high-molecular-mass kininogen that inhibits the binding of high-molecular-mass kininogen to leukocytes and endothelial cells in the HLA-B27 rat, which was administered after the onset of the inflammatory changes. Thrice-weekly intraperitoneal injections of monoclonal antibody C11C1 or isotype IgG1 were given to male 23-week-old rats for 16 days. Stool character as a measure of intestinal inflammation, and the rear limbs for clinical signs of arthritis (tarsal joint swelling and erythema) were scored daily. The animals were killed and the histology sections were assigned a numerical score for colonic inflammation, synovitis, and cartilage damage. Administration of monoclonal C11C1 rapidly decreased the clinical scores of pre-existing inflammatory bowel disease (P < 0.005) and arthritis (P < 0.001). Histological analyses confirmed significant reductions in colonic lesions (P = 0.004) and synovitis (P = 0.009). Decreased concentrations of plasma prekallikrein and high-molecular-mass kininogen were found, providing evidence of activation of the kallikrein-kinin system. The levels of these biomarkers were reversed by monoclonal antibody C11C1, which may have therapeutic potential in human inflammatory bowel disease and arthritis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".