Nanomedicine in Diabetes: Using Nanotechnology in Prevention and Management of Diabetes Mellitus
Bibliographic record
Abstract
In this review, we provide our view of the state and direction of nanotechnology research in diabetes mellitus, focusing mainly on the last 5 years. Select milestone or historical papers are mentioned in the review as appropriate, but insulin-related nano research is intentionally omitted as it has been recently extensively reviewed. Full onset diabetes is a disease with diverse etiology, increasing prevalence, and no cure. Its successful management relies almost entirely on regular glucose monitoring, pharmacotherapy, dietary compliance, and exercise. The early detection of the disease/disease-risk is especially important in dealing with this serious condition. Here, we review select advances (focusing mainly on the last 5 years) in the (i) nano-based glucose sensors, (ii) role of nanotechnology in genomics and its relevance to diabetes, (iii) antidiabetic nanomedicines, and (iv) novel approaches to the development of an artificial pancreas. Limited milestone or historical papers are mentioned as appropriate, and selected reviews for insulin delivery are provided. It is anticipated that the ongoing advances in the application of nanoscience and nanotechnology in diabetes will lead to continued improvement in the detection, prevention, and management of the disease. Limited milestone or historical papers are mentioned as appropriate, but in depth discussion of insulin delivery is intentionally omitted (recent reviews provided as a reference).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".