Degradable Thermoresponsive Nanogels for Protein Encapsulation and Controlled Release
Bibliographic record
Abstract
Reversible addition-fragmentation chain transfer (RAFT) polymerization technique was used for the fabrication of stable core cross-linked micelles (CCL) with thermoresponsive and degradable cores. Well-defined poly(2-methacryloyloxyethyl phosphorylcholine), poly(MPC) macroRAFT agent, was first synthesized with narrow molecular weight distribution via the RAFT process. These CCL micelles (termed as nanogels) with hydrophilic poly(MPC) shell and thermoresponsive core consisting of poly(methoxydiethylene glycol methacrylate) (poly(MeODEGM) and poly(2-aminoethyl methacrylamide hydrochloride) (poly(AEMA) were then obtained in a one-pot process by RAFT polymerization in the presence of an acid degradable cross-linker. These acid degradable nanogels were efficiently synthesized with tunable sizes and low polydispersities. The encapsulation efficiencies of the nanogels with different proteins such as insulin, BSA, and β-galactosidase were studied and found to be dependent of the cross-linker concentration, size of protein, and the cationic character of the nanogels imparted by the presence of AEMA in the core. The thermoresponsive nature of the synthesized nanogels plays a vital role in protein encapsulation: the hydrophilic core and shell of the nanogels at low temperature allow easy diffusion of the proteins inside out and, with an increase in temperature, the core becomes hydrophobic and the nanogels are easily separated out with entrapped protein. The release profile of insulin from nanogels at low pH was studied and results were analyzed using bicinchoninic assay (BCA). Controlled release of protein was observed over 48 h.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".