Dysbiotic microbiota contributes to the extent of acute myocardial infarction in rats
Bibliographic record
Abstract
Increasing evidence suggests that the intestinal microbiota composition could play a role in specific pathologies such as hypertension, obesity and diabetes. This study aims to demonstrate that the intestinal microbiota modulated by a diet creating dysbiosis increased the size of the myocardial infarction and that probiotics could attenuate this effect. To do this, microbiota transplants from rats fed a dysbiotic or non-dysbiotic diet in the presence or absence of probiotics were performed for 10 days on rats whose microbiota had been previously suppressed by antibiotic therapy. Then, the anterior coronary artery of the transplanted rats was occluded for 30 min. Infarct size was measured after 24 h of reperfusion, while signaling pathways were evaluated after 15 min of reperfusion. Intestinal resistance, plasma concentration of LPS (lipopolysaccharides), activation of NF-κB and Akt and composition of the microbiota were also measured. Our results demonstrate a larger infarct size in animals transplanted with the dysbiotic microbiota without probiotics compared to the other groups, including those that received the dysbiotic microbiota with probiotics. This increase in infarct size correlates with a higher firmicutes/bacteroidetes ratio, NF-kB phosphorylation and plasma LPS concentration, and a decrease in intestinal barrier resistance and Akt. These results indicate that dysbiotic microbiota promotes an increase in infarct size, an effect that probiotics can attenuate.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".