Biomaterials for protein delivery for complex tissue healing responses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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 it