Biomaterials for protein delivery for complex tissue healing responses
Bibliographic record
Abstract
Tissue repair requires a complex cascade of events mediated by a variety of cells, proteins, and matrix molecules; however, the healing cascade can be easily disrupted by numerous factors, resulting in impaired tissue regeneration. Recent advances in biomaterials for tissue regeneration have increased the ability to tailor the delivery of proteins and other biomolecules to injury sites to restore normal healing cascades and stimulate robust tissue repair. In this review, we discuss the evolution of the field toward creating biomaterials that precisely control protein delivery to stimulate tissue regeneration, with a focus on addressing complex and dynamic injury environments. We highlight biomaterials that leverage different mechanisms to deliver and present proteins involved in healing cascades, tissue targeting and mimicking strategies, materials that can be triggered by environmental cues, and integrated strategies that combine multiple biomaterial properties to improve protein delivery. Improvements in biomaterial design to address complex injury environments will expand our understanding of both normal and aberrant tissue repair processes and ultimately provide a better standard of patient care.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".