In vitro Inhibition of Canine Complement-Mediated Hemolysis
Bibliographic record
Abstract
BACKGROUND: Immune-mediated hemolytic anemia (IMHA) is the most common hematologic immune-mediated disease in dogs. Complement fixation on erythrocytes causes hemolysis. Complement inhibition decreases hemolysis in people with the hemolytic disease and also may prove effective in treating IMHA in dogs. HYPOTHESIS/OBJECTIVES: Evaluate the in vitro efficacy of 2 complement inhibitors used in humans against canine complement. METHODS: The inhibitory activity of the C3-inhibitor compstatin and recombinant human C1-esterase inhibitor (C1-INH) was evaluated using an in vitro hemolytic assay and spectrophotometric measurement of released hemoglobin. Dose-response curves for each inhibitor were generated. RESULTS: Compstatin decreased approximately 50% of canine complement-mediated hemolysis in initial experiments. This inhibition largely was lost when a new lot of drug was purchased. C1-INH showed a dose-dependent inhibition. The highest concentration of C1-INH tested (500 μg/mL) decreased >80% of canine complement-mediated hemolysis, and the lowest concentration tested (31.25 μg/mL) decreased hemolysis >60%. CONCLUSIONS AND CLINICAL IMPORTANCE: Human C1-INH is a robust inhibitor of canine complement-mediated hemolysis, whereas compstatin was minimally and variably effective. Human C1-INH may substantially decrease complement-mediated hemolysis in dogs with IMHA and warrants further investigation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".