Viscosity and degradation controlled injectable hydrogel for esophageal endoscopic submucosal dissection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it