Engineering temperature‐sensitive poly(<i>N</i>‐isopropylacrylamide) polymers as carriers of therapeutic proteins
Bibliographic record
Abstract
This study was carried out to engineer N-isopropylacrylamide (NiPAM) polymers that contain protein-reactive N-acryloxysuccinimide (NASI) and hydrophobic alkylmethacrylates (AMAs). These thermoreversible, protein-conjugating polymers hold potential for retention of therapeutic proteins at an application site where tissue regeneration is desired. The lower critical solution temperatures (LCST) of the polymers were effectively controlled by the AMA mole content. The AMAs with longer side-chains were more effective in lowering the LCST. Polymers without NASI exhibited a stable LCST in phosphate buffer and in serum over a 10-day study period. The LCST of polymers containing NASI was found to increase over time in phosphate buffer, but not in serum-containing medium. The LCST increase in phosphate buffer was proportional to the AMA content. The feasibility of localizing a therapeutic protein, recombinant human bone morphogenetic protein-2 (rhBMP-2), to a site of application was explored in a rat intramuscular injection model. The results indicated that polymers capable of conjugating to rhBMP-2 were most effective in localizing the protein irrespective of the LCST (13-25 degrees C). For polymers with no NASI groups, a lower LCST resulted in a better rhBMP-2 localization. We conclude that thermosensitive polymers can be engineered for delivery of therapeutic proteins to improve their therapeutic efficacy.
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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".