Engineered vildagliptin-loaded polymeric nanoparticles via microfluidic and spray drying for enhanced antidiabetic activity
Bibliographic record
Abstract
Abstract Background Vildagliptin (VLG), an antidiabetic agent, presents a potential solution to this widespread affliction. It exhibits notable attributes, such as a high solubility and a shorter elimination half-life. The current study uses a microreactor to fabricate sustained-release VLG-encapsulated cross-linked chitosan–dextran sulfate nanoparticles (VLG-CDNPs). The fabrication was systematically optimized using the design of experiment approach. Results The optimized VLG-CDNPs had an average particle size of 217.4 ± 12.3 nm and an encapsulation efficiency of 78.25 ± 3.0%. Scanning electron microscopy revealed that the nanoparticles had a smooth spherical shape. Spray drying was used for drying, and the reconstitution ability was close to ideal (~ 1.33). In vitro studies revealed sustained VLG release over 12 h, with ~ 58% in acidic and ~ 83% in basic conditions. Cell viability remained at 80% even at 100 μg/mL, and glucose uptake in L6 cells was significantly enhanced with VLG-CDNPs (78.34%) compared to pure VLG (60.91%). VLG-CDNPs also showed moderate inhibitory activity against α-amylase (41.57%) and α-glucosidase (63.48%) compared to pure VLG, which had higher inhibition levels. Conclusion The study’s outcome suggested that the optimized VLG-CDNPs may serve as an effective and promising nanoformulation for managing diabetes mellitus.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".