Konjac-Mannan and American Ginsing: Emerging Alternative Therapies for Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Despite significant achievements in treatment modalities and preventive measures, the prevalence of diabetes has risen exponentially in the last decade. Because of these limitations there is a continued need for new and more effective therapies. An increasing number of people are using dietary and herbal supplements, even though there is a general lack of evidence for their safety and efficacy. Consequently, science based medical and government regulators are calling for more randomized clinical studies to provide evidence of efficacy and safety. Our research group has selected two such promising and functionally complementary therapies for further investigation as potentially emerging alternative therapies for type 2 diabetes: Konjac-mannan (KJM) and American ginseng (AG). We have generated a mounting body of evidence to support the claim that rheologically-selected, highly-viscous KJM, and AG with a specific composition may be useful in improving diabetes control, reducing associated risk factors such as hyperlipidemia and hypertension, and ameliorating insulin resistance. KJM has a demonstrated ability to modulate the rate of absorption of nutrients from the small bowel, whereas AG has post-absorptive effects. Consequently, it appears that KJM and AG are acting through different, yet complementary, mechanisms: KJM by increasing insulin sensitivity and AG likely by enhancing insulin secretion. Before the therapeutic potential of KJM and AG as novel prandial agents for treatment of diabetes can be fully realized, further controlled trials with larger sample sizes and of longer duration are required. A determination of the active ingredients in AG, and the rheology-biology relationship of KJM are also warranted.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".