Injectable and Thermosensitive Hydrogel Containing Liraglutide as a Long-Acting Antidiabetic System
Bibliographic record
Abstract
Diabetes, a global epidemic, has become a serious threat to public health. The present study is aimed at constructing an injectable thermosensitive PEG-polyester hydrogel formulation of liraglutide (Lira), a "smart" antidiabetic polypeptide, in the long-acting treatment of type 2 diabetes mellitus. A total of three thermosensitive poly(ε-caprolactone-co-glycolic acid)-poly(ethylene glycol)-poly(ε-caprolactone-co-glycolic acid) (PCGA-PEG-PCGA) triblock copolymers with similar molecular weights but different ε-caprolactone-to-glycolide (CL-to-GA) ratios were synthesized. The polymer aqueous solutions exhibited free-flowing sols at room temperature and formed in situ hydrogels at body temperature. While the different bulk morphologies, stabilities of aqueous solutions, and the varying in vivo persistence time of hydrogels in ICR mice were found among the three copolymers, all of the Lira-loaded gel formulations exhibited a sustained drug release manner in vitro regardless of CL-to-GA ratios. The specimen with a powder form in the bulk state, a stable aqueous solution before heating, and an appropriate degradation rate in vivo was selected as the optimal carrier to evaluate the in vivo efficacy. A single injection of the optimal gel formulation showed a remarkable hypoglycemic efficacy up to 1 week in diabetic db/db mice. Furthermore, three successive administrations of this gel formulation within one month significantly lowered glycosylated hemoglobin and protected islets of db/db mice. As a result, a promising once-weekly delivery system of Lira was developed, which not only afforded long-term glycemic control but also significantly improved patient compliance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".