North American Ginseng ( <i>Panax quinquefolius</i> ) Prevents Hyperglycemia and Associated Pancreatic Abnormalities in Diabetes
Bibliographic record
Abstract
North American ginseng (NAG) has received increasing attention as an alternative medicine for the treatment of diabetes. Extract of the NAG root has been reported to possess antidiabetic properties, but the underlying mechanisms for such effects have not been identified. Here we investigated the effects of NAG root extract on type 1 and 2 diabetes and the underlying mechanisms involved for such effects. Type 1 [C57BL/6 mice with streptozotocin (STZ)-induction] and type 2 (db/db) diabetic models were examined. Groups of diabetic mice (both type 1 and 2) were treated with alcoholic extract of the NAG root (200 mg/kg BW/day, oral gavage) for 1 or 2 months following onset of diabetes. Ginseng treatment significantly increased the body weight in type 1 diabetic animals in contrast to the type 2 model, where it caused diminution of body weight. Blood glucose and glycated hemoglobin levels diminished in the diabetic groups of both models with NAG treatment. Interestingly, plasma insulin and C-peptide levels were significantly increased in the STZ-diabetic mice, whereas they were reduced in the db/db mice following NAG treatment. Histological and morphometric analyses (islet/pancreas ratio) of the pancreas revealed an increase in the islet area following the treatment compared to both the untreated diabetic groups. These data indicate that NAG possibly causes regeneration of β-cells resulting in enhanced insulin secretion. On the other hand, in type 2 diabetes, the additional effects of NAG on body weight might have also resulted in improved glucose control.
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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".