Antibody therapy can enhance AngiotensinII-induced myocardial fibrosis
Bibliographic record
Abstract
BACKGROUND: Myocardial fibrosis is a pathological process that is characterized by disrupted regulation of extracellular matrix proteins resulting in permanent scarring of the heart tissue and eventual diastolic heart failure. Pro-fibrotic molecules including transforming growth factor-β and connective tissue growth factor are expressed early in the AngiotensinII (AngII)-induced and other models of myocardial fibrosis. As such, antibody-based therapies against these and other targets are currently under development. RESULTS: In the present study, C57Bl/6 mice were subcutaneously implanted with a mini-osmotic pump containing either AngII (2.0 μg/kg/min) or saline control for 3 days in combination with mIgG (1 mg/kg/d) injected through the tail vein. Fibrosis was assessed after picosirius red staining of myocardial cross-sections and was significantly increased after AngII exposure compared to saline control (11.37 ± 1.41%, 4.94 ± 1.15%; P <0.05). Non-specific mIgG treatment (1 mg/kg/d) significantly increased the amount of fibrosis (26.34 ± 3.03%; P <0.01). However, when AngII exposed animals were treated with a Fab fragment of the mIgG or mIgM, this exacerbation of fibrosis was no longer observed (14.49 ± 2.23%; not significantly different from AngII alone). CONCLUSIONS: These data suggest that myocardial fibrosis was increased by the addition of exogenous non-specific antibodies in an Fc-mediated manner. These findings could have substantial impact on the future experimental design of antibody-based therapeutics.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".