Anti-diabetic effects of bioactive peptides: recent advances and clinical implications
Bibliographic record
Abstract
Diabetes mellitus, particularly type 2 diabetes, is a major global health issue, the prevalence of which seems to be on the rise worldwide. Interventions such as healthy diet, physical activity, maintaining a healthy weight, and medication (for those with a diagnosis of diabetes) are among the most effective strategies to prevent and control diabetes. Three-quarters of patients diagnosed with diabetes are in countries with poor financial infrastructure, nutritional awareness and health care systems. Concomitantly, the cost involved in managing diabetes through the intake of antidiabetic drugs makes it prohibitive for majority of patients. Food protein-derived bioactive peptides have the potential of being formulated as nutraceuticals and drugs in combating the pathogenesis and pathophysiology of metabolic disorders with little or "no known" complications in humans. Coupled with lifestyle modifications, the potential of bioactive peptides to maintain normoglycemic range is actualized by influencing the activities of incretins, DPP-IV, α-amylase, and α-glucosidase enzymes. This article discusses the biofunctionality and clinical implications of anti-diabetic bioactive peptides in controlling the global burden of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".