Changes in the Content and α-Glucosidase Inhibition Efficiency of Active Substance in Mulberry Leaves during Natural Fermentation
Bibliographic record
Abstract
Since mulberry leaves are rich in alpha-glucosidase inhibiting ingredients, they have been widely used in the treatment of diabetes since ancient times. Microbial fermentation is an effective way to increase the content and efficacy of active ingredients in medicinal plants. In this paper, the beneficial endophytic bacteria in fresh mulberry leaves were used for solid state fermentation, and the effects of fermentation on flavonoid content and α-glucosidase inhibition in fresh mulberry were investigated. The results showed that the polysaccharide content of fresh mulberry leaves decreased rapidly during natural fermentation. The content of flavonoids in mulberry leaves decreased first and then increased. After 10 days of fermentation, the flavonoid content in mulberry leaves was 3.858±0.193mg/g dried ML, which was 2.14 times that of MLF in unfermented mulberry leaves. The alkaloid content increased significantly, reaching the highest value of 12.407±0.993 mg/g dried ML on day 8 of fermentation, which was about 2.5-folds higher than that of unfermented mulberry leaves. The inhibitory rate of the mulberry leaf extract (MLE<sub>8</sub>) fermented for 8 d was higher than that of the unfernented fresh mulberry extract (MLE<sub>U</sub>) when compared with that of α-glucosidase. At the concentration of 0.05mg/ml, the inhibitory rate of α-glucosidase from MLF-F8 increased to 82.7±4.15%while that from MLF-UF was only 66.8±3.34%.At the concentration of 5 μg/ml, the α-glucosidase inhibitory rate of MLA-F8 was 87.5±6.55%, while MLA-UF was only 71.4±5.57%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".