<i>Lactobacillus helveticus</i>and<i>Bifidobacterium longum</i>taken in combination reduce the apoptosis propensity in the limbic system after myocardial infarction in a rat model
Bibliographic record
Abstract
Myocardial infarction (MI) stimulates the release of pro-inflammatory substances that induce apoptosis in the limbic system. Pro-inflammatory cytokines are considered as the root cause of apoptosis, although the mechanism is not fully explained and/or understood at this time. In addition, depression may induce gastrointestinal perturbations that maintain the elevated levels of pro-inflammatory cytokines. It has been shown that some specific probiotic formulations may reduce gastrointestinal problems induced by stress and the pro/anti-inflammatory cytokine ratio. Therefore, we hypothesised that probiotics, when given prophylactically, may diminish the apoptosis propensity in the limbic system following a MI. Male adult Sprague-Dawley rats were given probiotics (Lactobacillus helveticus and Bifidobacterium longum in combination) or placebo in their drinking-water for four consecutive weeks. A MI was then induced in the rats by occluding the left anterior coronary artery for 40 min. Rats were killed following a 72 h reperfusion period. Infarct size was not different in the two groups. Bax/Bcl-2 (pro-apoptotic/anti-apoptotic) ratio and caspase-3 (pro-apoptotic) activity were reduced in the amygdala (lateral and medial), as well as in the dentate gyrus in the probiotics group when compared with the placebo. Akt activity (anti-apoptotic) was increased in these same three regions. No significant difference was observed in Ca1 and Ca3 for the different markers measured. In conclusion, the probiotics L. helveticus and B. longum, given in combination as preventive therapy, reduced the predisposition of apoptosis found in different cerebral regions following a MI.
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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 | 0.000 | 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) | 0.001 | 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".