Effect of Burdock Root and the Fermented Product on Alloxan-Induced Mouse Hyperglycemia
Bibliographic record
Abstract
<p><strong>Backgrounds:</strong> We reported that feeding 5% <em>Asperagillus awamori-</em>fermented burdock root diet was effective in preventing mouse hyperglycemia caused by alloxan.</p> <p><strong>Methods:</strong> Diets containing 5% burdock roots were prepared from raw and <em>Asperagillus awamori-</em>fermented burdock root powders. Acatalasemic mice<strong>,</strong> having a quite low catalase activity in blood, and normal mice were fed these diets for 14 weeks, separately. Then, alloxan (200 mg/ kg of body weight) or PBS was intraperitoneally administrated to each mouse. After 5 day from the administration, blood glucose assay and glucose tolerance test were carried out, and then insulin, C-peptide and lipid peroxide in plasma were examined.</p> <p><strong>Results:</strong> Incidences of hyperglycemia in normal mice fed control, raw and fermented burdock root diets were 25, 20 and 11 %, respectively, and these in acatalasemic mice<strong> </strong>were 73, 80 and 27%. Insulin and C-peptide in plasma of mice fed raw burdock root diet or control diet were low compared to mice fed the fermented diet.</p> <p><strong>Conclusions:</strong> Intake of raw burdock root does not suppress the alloxan-induced hyperglycemia but the fermented burdock root does. It is suggested that <em>Asperagillus awamori</em> plays an important role for the prevention.</p>
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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 | 0.001 | 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".