Development of a novel drug delivery system: chitosan nanoparticles entrapped in alginate microparticles
Bibliographic record
Abstract
A novel carrier using chitosan nanoparticles entrapped into alginate microparticles is proposed for protecting molecules of interest from degradation in the digestive tract. The effects of polymer concentration, sonication, stirring, pH, and processing conditions on the physical characteristics of the carrier were studied. FITC and RBITC were used to localise the polymers within particles using CLSM. Diffusion of amaranth red (AR) from nanoparticles was quantified during dissolution under gastric and intestinal conditions. Under optimal preparation conditions, the size distribution of nanoparticles loaded with AR was uniform (690 nm) with an encapsulation efficacy of 21.9%. Alginate microparticles (285 µm) containing a homogenous distribution of nanoparticles and polymers were obtained. At gastric pH, the carrier released less than 5% of the loaded AR and, at intestinal pH, the release was rapid and complete. The drug carriers developed shows a promising use as a vehicle suitable to protect molecules of interest after oral administration.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".