Nontraditional Therapy of Diabetes and Its Complications
Bibliographic record
Abstract
At present, the most traditional treatment for diabetes is still oral hypoglycaemic drugs and insulin therapy.Nontraditional treatment methods include nondrug therapy, unique treatments, emerging treatments, and application of natural hypoglycaemic drugs and ingredients.Nondrug therapy, such as exercise, nutrition, diet control, and psychotherapy, has achieved certain effects in some patients but has not attracted enough attention.In some countries, such as China and India, there are some effective and unique treatment methods in use, but these are not widely recognized and promoted.These emerging treatment methods include gene therapy, surgical treatment, and gut microbiota regulation therapy.Traditional treatment methods have played an essential role in the treatment of diabetes but, at the same time, have various defects.Alternative and more effective treatments need to be searched, explored, and studied, particularly in nature, where there are many substances that are conducive to the prevention and treatment of diabetes.For example, in recent years, Amelanchier alnifolia, Dillenia indica, and Chrysophyllum albidum have been found to have significant antidiabetic effects, with further research pending.Our special issue focuses on nontraditional treatment and application of diabetes patients and their complications, to improve efficacy, reduce side effects, improve patients' quality of life, and reduce the cost of medical care.In this special issue, we selected multiple original articles and eight reviews which are aimed at exploring the nontraditional therapy of diabetes and its complications from clinical and basic research aspects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".