Encapsulation and Controlled Release of rHu-Erythropoietin from Chitosan Biopolymer Nanoparticles
Bibliographic record
Abstract
The objective of this research project was to develop a drug delivery system for recombinant human erythropoietin (rHu-EPO), a glycoprotein hormone used in the treatment of renal anaemia and chemotherapy induced anaemia, using the biopolymer chitosan as the base component. Two types of chitosan nanoparticles were produced through ionotropic gelation using flush mixing with either tripolyphosphate (TPP) or carrageenan polymer. Chitosan-TPP and chitosan-carrageenan nanoparticles were generated under a variety of conditions to evaluate the effects of chitosan concentration, chitosan to anion mass ratio and solution pH on the nanoparticle characteristics of particle diameter, surface charge and particle size distribution. A statistical method of experimentation design, known as response surface modeling, was applied to allow for accurate manipulation of nanoparticle characteristics and to create nanoparticles with optimized characteristics.\nThe encapsulation and controlled release of rHu-EPO from chitosan nanoparticles was evaluated with chitosan-TPP nanoparticles demonstrating an encapsulation efficiency of 43.45±0.84% and ~68% drug release within two weeks, while chitosan-carrageenan nanoparticles had an encapsulation efficiency of 47.97±4.10% and ~50% drug release within two weeks. Both types of chitosan nanoparticles exhibited improved encapsulation and release of rHu-EPO compared to previous results. Also, the molecular weight of the chitosan used and the surface charge of the nanoparticles were shown to have an effect on the encapsulation and release of rHu-EPO.
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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 | 0.000 | 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 | 0.000 |
| 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 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".