Hypoglycemic and Hypolipidemic Effects of Glucomannan Extracted from Konjac on Type 2 Diabetic Rats
Bibliographic record
Abstract
Diabetes and its complications are one of the most concerned metabolic diseases worldwide and threaten human health severely. Hypoglycemic and hypolipidemic effects of glucomannan extracted from konjac on high-fat diet and streptozocin-induced type 2 diabetic rats were evaluated in this study. Administration of konjac glucomannan significantly decreased the levels of fasting blood glucose, serum insulin, glucagon-like peptide 1, and glycated serum protein. The concentrations of serum lipids, including total cholesterol, triacylglycerols, low-density lipoprotein cholesterol, and non-esterified fatty acid, were notably reduced by konjac glucomannan treatment. In addition, antioxidant capacity, pancreatic injury, and adipose cell hypertrophy were ameliorated by konjac glucomannan administration in type 2 diabetic rats. Besides, ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based lipidomics analysis was used to explore the improvement of lipid metabolic by konjac glucomannan treatment. The disturbance of glycerolipid (diacylglycerol, monoacylglycerol, and triacylglycerol), fatty acyl (acylcarnitine and hydroxyl fatty acid), sphingolipid (ceramide and sphingomyelin), and glycerophospholipid (phosphatidylcholine) metabolism were attenuated by the glucomannan treatment. This study provided new insights for investigating the anti-diabetic effects of konjac glucomannan and suggests that konjac glucomannan may be a promising nutraceutical for treating type 2 diabetes.
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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".