Nanoengineered Shear-Thinning Hydrogel Barrier for Preventing Postoperative Abdominal Adhesions
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
More than 90% of surgical patients develop postoperative adhesions, and the incidence of hospital re-admissions can be as high as 20%. Current adhesion barriers present limited efficacy due to difficulties in application and incompatibility with minimally invasive interventions. To solve this clinical limitation, we developed an injectable and sprayable shear-thinning hydrogel barrier (STHB) composed of silicate nanoplatelets and poly(ethylene oxide). We optimized this technology to recover mechanical integrity after stress, enabling its delivery though injectable and sprayable methods. We also demonstrated limited cell adhesion and cytotoxicity to STHB compositions in vitro. The STHB was then tested in a rodent model of peritoneal injury to determine its efficacy preventing the formation of postoperative adhesions. After two weeks, the peritoneal adhesion index was used as a scoring method to determine the formation of postoperative adhesions, and STHB formulations presented superior efficacy compared to a commercially available adhesion barrier. Histological and immunohistochemical examination showed reduced adhesion formation and minimal immune infiltration in STHB formulations. Our technology demonstrated increased efficacy, ease of use in complex anatomies, and compatibility with different delivery methods, providing a robust universal platform to prevent postoperative adhesions in a wide range of surgical interventions.
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.
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 | 0.001 |
| 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