Multifaceted Design and Emerging Applications of Tissue Adhesives
Bibliographic record
Abstract
Tissue adhesives can form appreciable adhesion with tissues and have found clinical use in a variety of medical settings such as wound closure, surgical sealants, regenerative medicine, and device attachment. The advantages of tissue adhesives include ease of implementation, rapid application, mitigation of tissue damage, and compatibility with minimally invasive procedures. The field of tissue adhesives is rapidly evolving, leading to tissue adhesives with superior mechanical properties and advanced functionality. Such adhesives enable new applications ranging from mobile health to cancer treatment. To provide guidelines for the rational design of tissue adhesives, here, existing strategies for tissue adhesives are synthesized into a multifaceted design, which comprises three design elements: the tissue, the adhesive surface, and the adhesive matrix. The mechanical, chemical, and biological considerations associated with each design element are reviewed. Throughout the report, the limitations of existing tissue adhesives and immediate opportunities for improvement are discussed. The recent progress of tissue adhesives in topical and implantable applications is highlighted, and then future directions toward next-generation tissue adhesives are outlined. The development of tissue adhesives will fuse disciplines and make broad impacts in engineering and medicine.
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.
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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".