<i>In vitro</i> anti‐inflammatory properties of fermented pepino (<i>Solanum muricatum</i>) milk by <i>γ</i>‐aminobutyric acid‐producing <i>Lactobacillus brevis</i> and an <i>in vivo</i> animal model for evaluating its effects on hypertension
Bibliographic record
Abstract
BACKGROUND: The objectives of this study were to determine the in vitro anti-inflammatory and in vivo antihypertensive effects of fermented pepino (Solanum muricatum) milk by Lactobacillus brevis with the goal of developing functional healthy products. The inflammatory factors of fermented pepino milk with L. brevis were assessed in RAW 264.7 macrophages, including nitric oxide (NO) production. Inflammatory factor genes of cyclooxygenase (COX)-1 and -2, and tumor necrosis factor (TNF)-α were also assayed by a reverse transcription-polymerase chain reaction (RT-PCR). RESULTS: Results showed that fermented PE inhibited NO production in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells with 150 mg mL(-1) fermented PE completely blocking LPS-induced NO production. The mRNA expressions of COX-1, COX-2, and iNOS were attenuated by treatment with higher concentrations of fermented PE (150 mg/ml). Cells treated with fermented pepino extract (PE) (100 ng mL(-1)) exhibited strikingly decreased LPS-induced expression of TNF-α mRNA. During the feeding trial, rats treated with 10% fermented pepino milk (100 µg 2.5 mL(-1)) and 100% fermented pepino milk (1000 µg 2.5 mL(-1)) exhibited significant decreases in the systolic blood pressure. CONCLUSION: Our results showed that fermented pepino milk has wide potential applications for development as a health food.
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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 | 0.000 | 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".