Prevention of myosin-induced autoimmune myocarditis in mice by anti-L<sub>3</sub>T<sub>4</sub>monoclonal antibody
Bibliographic record
Abstract
This study was aimed at studying the effect of the induction of immune tolerance to swine cardiac myosin from anti-L3T4 monoclonal antibody injection and whether the immune tolerance could protect mice with myosin-induced myocarditis from myocardial injury. Twenty-four Balb/c mice were divided into two groups at random. All of the mice were immunized with swine cardiac myosin on the 1st day, 14th, 28th, 42nd, and 52nd day. Immune tolerance was induced by triplicate injections of 400 microg anti-L3T4 McAb on the 0 day (intravenous), 1st day, and 2nd day (intraperitoneal) in McAb-treated group. In the saline-treated group, saline of the same volume as anti-L3T4 monoclonal antibody was used as a control. The sera and hearts biopsies of all mice were collected on the 58th day. The anti-cardiac myosin antibody was examined with ELISA, and pathological changes of heart were observed by light microscope. It was shown that mice immunized with swine cardiac myosin could produce anti-myosin antibody and the anti-cardiac myosin antibody was positive in most of the saline-treated group but negative in the McAb-treated group. Morphologically, myocardial degeneration, necrosis, and infiltration of inflammatory cells were found in the saline-treated group but not in the McAb-treated group. In conclusion, this study indicated that the immune tolerance to cardiac myosin was induced by the anti-L3T4 monoclonal antibody, and accordingly myocardial injury could be prevented by induction of immune tolerance.
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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 | 0.000 | 0.001 |
| 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".