Viscosity and degradation controlled injectable hydrogel for esophageal endoscopic submucosal dissection
Bibliographic record
Abstract
Endoscopic submucosal dissection (ESD) is a common procedure to treat early and precancerous gastrointestinal lesions. Via submucosal injection, a liquid cushion is created to lift and separate the lesion and malignant part from the muscular layer where the formed indispensable space is convenient for endoscopic incision. Saline is a most common submucosal injection liquid, but the formed liquid pad lasts only a short time, and thus repeated injections increase the potential risk of adverse events. Hydrogels with high osmotic pressure and high viscosity are used as an alternate; however, with some drawbacks such as tissue damage, excessive injection resistance, and high cost. Here, we reported a nature derived hydrogel of gelatin-oxidized alginate (G-OALG). Based on the rheological analysis and compare to commercial endoscopic mucosal resection (EMR) solution (0.25% hyaluronic acid, HA), a designed G-OALG hydrogel of desired concentration and composition showed higher performances in controllable gelation and injectability, higher viscosity and more stable structures. The G-OALG gel also showed lower propulsion resistance than 0.25% HA in the injection force assessment under standard endoscopic instruments, which eased the surgical operation. In addition, the G-OALG hydrogel showed good in vivo degradability biocompatibility. By comparing the results acquired via ESD to normal saline, the G-OALG shows great histocompatibility and excellent endoscopic injectability, and enables create a longer-lasting submucosal cushion. All the features have been confirmed in the living both pig and rat models. The G-OALG could be a promising submucosal injection agent for esophageal ESD.
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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) | 0.001 | 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".