An electroadhesive hydrogel interface prolongs porcine gastrointestinal mucosal theranostics
Bibliographic record
Abstract
Establishing a robust and intimate mucosal interface that allows medical devices to remain within lumen-confined organs for extended periods has valuable applications, particularly for gastrointestinal theranostics. Here, we report the development of an electroadhesive hydrogel interface for robust and prolonged mucosal retention after electrical activation (e-GLUE). The e-GLUE device is composed of cationic polymers interpenetrated within a tough hydrogel matrix. An e-GLUE electrode design eliminated the need for invasive submucosal placement of ground electrodes for electrical stimulation during endoscopic delivery. With an electrical stimulation treatment of about 1 minute, the cationic polymers diffuse and interact with polyanionic proteins that have a relatively slow cellular turnover rate in the deep mucosal tissue. This mucosal adhesion mechanism increased the adhesion energy of hydrogels on the mucosa by up to 30-fold and enabled in vivo gastric retention of e-GLUE devices in a pig stomach for up to 30 days. The adhesion strength was modulated by polycationic chain length, electrical stimulation time, gel thickness, cross-linking density, voltage amplitude, polycation concentration, and perimeter-to-area ratio of the electrode assembly. In porcine studies, e-GLUE demonstrated rapid mucosal adhesion in the presence of luminal fluid and mucus exposure. In proof-of-concept studies, we demonstrated e-GLUE applications for mucosal hemostasis, sustained local delivery of therapeutics, and intimate biosensing in the gastrointestinal tract, which is an ongoing clinical challenge for commercially available alternatives, such as endoclips and mucoadhesive. The e-GLUE platform could enable theranostic applications across a range of digestive diseases, including recurrent gastrointestinal bleeding and inflammatory bowel disease.
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".