Biomedical Applications of polymeric micelles in the treatment of diabetes mellitus: Current success and future approaches
Bibliographic record
Abstract
INTRODUCTION: Diabetes mellitus (DM) is the most common metabolic disease and multifactorial, harming patients worldwide. Extensive research has been carried out in the search for novel drug delivery systems offering reliable control of glucose levels for diabetics, aiming at efficient management of DM. AREAS COVERED: Polymeric micelles (PMs) as smart drug delivery nanocarriers are discussed, focusing on oral drug delivery applications for the management of hyperglycemia. The most recent approaches used for the preparation of smart PMs employ molecular features of amphiphilic block copolymers (ABCs), such as stimulus sensitivity, ligand conjugation, and as a more specific example the ability to inhibit islet amyloidosis. EXPERT OPINION: PMs provide a unique platform for self-regulated or spatiotemporal drug delivery, mimicking the working mode of pancreatic islets to maintain glucose homeostasis for prolonged periods. This unique characteristic is achieved by tailoring the functional chemistry of ABCs considering the physicochemical traits of PMs, including sensing capabilities, hydrophobicity, etc. In addition, the application of ABCs for the inhibition of conformational changes in islet amyloid polypeptide garnered attention as one of the root causes of DM. However, research in this field is limited and further studies at the clinical level are required.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".