Effect of Process Parameters on the Initial Burst Release of Protein-Loaded Alginate Nanospheres
Bibliographic record
Abstract
The controlled release or delivery of proteins encapsulated in micro/nanospheres is an emerging strategy in regenerative medicine. For this, micro/nanospheres made from alginate have drawn considerable attention for the use as a protein delivery device because of their mild fabrication process, inert nature, non-toxicity and biocompatibility. Though promising, one key issue associated with using alginate micro/nanospheres is the burst release of encapsulated protein at the beginning of the release, which may be responsible for exerting toxic side effects and poor efficiency of the delivery device. To address this issue, this study aimed to investigate the effect of process parameters of fabricating protein-loaded alginate nanospheres on the initial burst release. The alginate nanospheres were prepared via a combination of water-in-oil emulsification and the external gelation method and loaded with bovine serum albumin (BSA) as a model protein. The examined process parameters included alginate concentration, ionic cross-linking time and drying time. Once fabricated, the nanospheres were then subjected to the examination of BSA release, as well as the characterization of their morphology, size, and encapsulation efficiency. Our results revealed that by properly adjusting the process parameters, the initial burst release can be reduced by 13%. Taken together, our study demonstrates that regulating process parameters of fabricating alginate nanospheres is a possible means to reduce the initial burst release.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".