Formulation and Characterization of Poly (Acrylic Acid)- Co-Chitosan Nanoparticles as pH-Thermo-Responsive System to Control Delivery
Bibliographic record
Abstract
The present study aims to develop a pH thermosensitive nanocarriers as a drug delivery system to better controll drug release. Nanoparticles was developed by the combination of smart polymers, chitosan and poly(acrylic acid) were chosen as biodegradable vectors to encapsulate and transport the drug. The used method was based on the polymerization of acrylic acid using reticulated chitosan as a template. Analysis of particle size, Zeta potential, and size distribution revealed that most of the resulting nanoparticles had an average diameter less than 100nm, with a high Zeta potentiel about -29.7 mV and a narrow size distribution. In addition, the developed system showed an encapsulation efficiency around 97%. In vitro release test was achieved using different buffer solutions with pH equal to 1.2, 3.6, 4.2, 4.8, 6.8 and 7.4. The release profiles showed that nanoparticles provide drug protection at different pH values. They responded at pH = 3.6 and provided sustained controlled release of up to 62.62% over 8 hours. The results reveal that the prepared nanoparticles can be used as drug delivery carriers. They can improve therapeutic efficiency of the drugs used in the treatment of inflamed tissues where the pH is around 3.6 as in the Crohn disease.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".