Treatment with marine collagen peptides modulates glucose and lipid metabolism in Chinese patients with type 2 diabetes mellitus
Bibliographic record
Abstract
This study was aimed at examining the therapeutic effects of marine collagen peptides (MCPs) from fish hydrolysate in Chinese patients with type 2 diabetes mellitus (T2DM). A total of 100 diabetic patients and 50 healthy controls were recruited. Diabetic patients were randomized into treatment and control groups. The patients in the treatment group received an additional 13 g of MCPs daily for 3 months. Their blood samples were collected before, and 1.5 and 3 months after, treatment to evaluate glucose and lipid metabolism. The levels of serum high-sensitivity C-reactive protein (hs-CRP), nitric oxide (NO), bradykinin, prostacyclin (PGI2), and adipokines were determined. Significantly reduced levels of fasting blood glucose, human glycated hemoglobin A1c (GHbA1c), fasting blood insulin, total triglycerides, total cholesterol, low-density lipoprotein, and free-fatty acids, but increased levels of insulin sensitivity index and HDL were observed in T2DM patients following treatment with MCPs for 1.5 and 3 months. The values of these measures were significantly lower or higher than those of patient controls (p < 0.01), respectively. Interestingly, significantly decreased levels of hs-CRP and NO, but increased levels of bradykinin, PGI2, and adiponectin were detected in MCP-treated T2DM patients (p < 0.01), as compared with their basal values or the levels in patient controls. MCP treatment improved glucose and lipid metabolism in diabetic patients.
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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) | 0.001 | 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) | 0.001 | 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".