Bridging wounds: tissue adhesives’ essential mechanisms, synthesis and characterization, bioinspired adhesives and future perspectives
Bibliographic record
Abstract
Bioadhesives act as a bridge in wound closure by forming an effective interface to protect against liquid and gas leakage and aid the stoppage of bleeding. To their credit, tissue adhesives have made an indelible impact on almost all wound-related surgeries. Their unique properties include minimal damage to tissues, low chance of infection, ease of use and short wound-closure time. In contrast, classic closures, like suturing and stapling, exhibit potential additional complications with long operation times and undesirable inflammatory responses. Although tremendous progress has been made in the development of tissue adhesives, they are not yet ideal. Therefore, highlighting and summarizing existing adhesive designs and synthesis, and comparing the different products will contribute to future development. This review first provides a summary of current commercial traditional tissue adhesives. Then, based on adhesion interaction mechanisms, the tissue adhesives are categorized into three main types: adhesive patches that bind molecularly with tissue, tissue-stitching adhesives based on pre-polymer or precursor solutions, and bioinspired or biomimetic tissue adhesives. Their specific adhesion mechanisms, properties and related applications are discussed. The adhesion mechanisms of commercial traditional adhesives as well as their limitations and shortcomings are also reviewed. Finally, we also discuss the future perspectives of tissue adhesives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".