Tough Adhesive Puncture Sealing Sutures with Swelling Triggered Bioadhesion for Enhanced Wound Closure
Bibliographic record
Abstract
Abstract Surgical sutures are gold‐standard wound closure devices. However, they are unable to form a tight seal with surrounding tissues, raising the risks of body fluid leakage and surgical site infection. Additionally, the use of sutures can result in cracking at suture roots, damage and micro‐trauma to soft tissues due to the slicing and compression of suture fibers after their placement. Bioadhesives capable of mimicking natural biological interfaces are appealing alternatives, but they cannot achieve the same level of strength as conventional surgical sutures. Here, a tough adhesive puncture sealing (TAPS) suture, featuring swelling‐triggered bioadhesion to mend the gap between the suture and the surrounding tissues with a soft yet tough adhesive interface is reported. This unique design principle of TAPS sutures is applicable to diverse soft tissues of various defect sizes and can be controlled by modulating the hydrogel swelling kinetics. The advantages of the TAPS sutures for meniscal tear repair and intestine tissue sealing ex vivo, corroborates their favorable applications in managing mechanically active musculoskeletal and gastrointestinal tissues are demonstrated. The design and performance of the TAPS sutures offer extensive possibilities for redesigning surgical tools and developing next‐generation medical devices for wound management and tissue repair.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".