Fermented soybean meal extract improves oxidative stress factors in the lung of inflammation/infection animal model
Bibliographic record
Abstract
Abstract Context Fermented soybean products have been used in various ways, and more research is being conducted on them to reveal their benefit. Objective The objective of this study was to evaluate the antioxidative activity of fermented soybean meal extract by Lactobacillus plantarum in vitro and in vivo tests. Materials and methods A Lactobacillus plantarum strain RM10 was selected through plate and fermentation experiment, which increased the degree of protein hydrolysis (1.015 μg/mL) and antioxidant activity in soybean meal fermented by selected bacteria (FSBM). In vivo study was done on septic rats as an inflammation/infection model, and then the trial groups were treated with different concentrations of fermented soybean meal extracts (FSBM, 5, 10, and 20%). Results DPPH radical-scavenging and ferrozine ion-chelating activity enhanced (P < 0.05) after fermentation of soybean meal compared to control group. Reduced (P < 0.05) expression of inflammatory genes and enzymes was detected in the lungs of rats treated with fermented soybean meal extract. Discussion and conclusions These results demonstrated that a diet containing fermented soybean meal extract improved extreme inflammatory response in an infectious disease like sepsis by reducing inflammatory factors.
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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) | 0.001 | 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".