Modulated Protein Delivery to Engineer Tissue Repair
Bibliographic record
Abstract
Targeted protein delivery for stimulating tissue repair has been a core focus of the field of tissue engineering for several decades. While many promising protein therapeutics exist, achieving sustained and localized protein delivery to injured tissues remains a challenge. Over the past 25 years, significant breakthroughs have been made in biomaterial-based strategies to improve targeted protein delivery. Protein delivery vehicles that leverage affinity interactions between proteins and materials present an effective approach for modulating the spatiotemporal release of proteins within sites of tissue injury. Stimuli-responsive polymers also enable protein release to be tailored to respond to cell- and tissue-level changes. In this article, we highlight some of the major recent advances in biomaterial strategies for targeted protein delivery with a focus on affinity-based protein delivery systems. We also discuss the future of protein delivery for tissue repair, in which we envision protein delivery strategies that can be tuned in response to the dynamic microenvironment of injured tissues. Achieving targeted protein delivery to injured tissues is a core focus of the field of tissue engineering and has enormous clinical potential. This article highlights significant advances made in biomaterial-based protein delivery strategies over the last 25 years and how they will influence research in the next 25 years. These advances will enable protein release rates to be tuned with increased flexibility to deliberately address the challenges of the dynamic injury environment and ultimately lead to better solutions for patients.
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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) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".