Aligned nanofibrous collagen membranes from fish swim bladder as a tough and acid-resistant suture for pH-regulated stomach perforation and tendon rupture
Bibliographic record
Abstract
BACKGROUND: Wound closure in the complex body environment places higher requirements on suture's mechanical and biological performance. In the scenario of frequent mechanical gastric motility and extremely low pH, single functional sutures have limitations in dealing with stomach bleeding trauma where the normal healing will get deteriorated in acid. It necessitates to advance suture, which can regulate wounds, resist acid and intelligently sense stomach pH. METHODS: Based on fish swim bladder, a double-stranded drug-loaded suture was fabricated. Its cytotoxicity, histocompatibility, mechanical properties, acid resistance and multiple functions were verified. Also, suture's performance suturing gastric wounds and Achilles tendon was verified in an in vivo model. RESULTS: By investigating the swim bladder's multi-scale structure, the aligned tough collagen fibrous membrane can resist high hydrostatic pressure. We report that the multi-functional sutures on the twisted and aligned collagen fibers have acid resistance and low tissue reaction. Working with an implantable "capsule robot", the smart suture can inhibit gastric acid secretion, curb the prolonged stomach bleeding and monitor real-time pH changes in rabbits and pigs. The suture can promote stomach healing and is strong enough to stitch the fractured Achilles tendon. CONCLUSIONS: As a drug-loaded absorbable suture, the suture shows excellent performance and good application prospect in clinical work.
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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) | 0.000 | 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) | 0.000 | 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".