Immunomodulatory bioadhesive technologies
Bibliographic record
Abstract
Bioadhesives have found significant use in medicine and engineering, particularly for wound care, tissue engineering, and surgical applications. Compared to traditional wound closure methods such as sutures and staples, bioadhesives offer advantages, including reduced tissue damage, enhanced healing, and ease of implementation. Recent progress highlights the synergy of bioadhesives and immunoengineering strategies, leading to immunomodulatory bioadhesives capable of modulating immune responses at local sites where bioadhesives are applied. They foster favorable therapeutic outcomes such as reduced inflammation in wounds and implants or enhanced local immune responses to improve cancer therapy efficacy. The dual functionalities of bioadhesion and immunomodulation benefit wound management, tissue regeneration, implantable medical devices, and post-surgical cancer management. This review delves into the interplay between bioadhesion and immunomodulation, highlighting the mechanobiological coupling involved. Key areas of focus include the modulation of immune responses through chemical and physical strategies, as well as the application of these bioadhesives in wound healing and cancer treatment. Discussed are remaining challenges such as achieving long-term stability and effectiveness, necessitating further research to fully harness the clinical potential of immunomodulatory bioadhesives.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".