Highly Bioactive SDF-1α Delivery from Low-Melting-Point, Biodegradable Polymer Microspheres
Bibliographic record
Abstract
Aliphatic polyester biodegradable microspheres have been extensively studied for controlled and minimally invasive in situ protein delivery. However, they are commonly characterized by protein denaturation via acidic polyester degradation products, whereas their supraphysiologic modulus contributes to the inflammatory response upon implantation. To address these limitations, low-melting-point poly(ε-caprolactone- co -glycolide)- b -poly(ethylene glycol)- b -poly(ε-caprolactone- co -glycolide) (PEG-(PCG) 2 ) copolymers were prepared and characterized for their ability to release bioactive stromal-derived factor-1α (SDF-1α) as a representative therapeutic protein. The PEG molecular weight was chosen such that it would be crystalline at room temperature to promote easy handling of the microspheres, whereas the molecular weight and composition of the hydrophobic PCG blocks were adjusted to ensure the polymer was a viscous amorphous liquid at 37 °C. Microspheres prepared from the triblock copolymers completely degraded within 8 weeks in vitro with a minor decrease in microenvironmental pH. A prolonged release of SDF-1α was observed with its bioactivity highly retained after encapsulation and 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 | 0.000 |
| 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".