Intra-Omental Islet Transplantation Using h-Omental Matrix Islet filliNG (hOMING)
Bibliographic record
Abstract
Regenerative medicine based on cell therapy represents a new hope for curing disease. Current obstacles include proper in vivo validation of the efficiency of the therapy. For transfer to the recipient body, cells often need to be combined with biomaterials, especially hydrogels. However, validation of the efficacy of such a graft requires the right environment, the right hydrogel, and the right recipient site. The omentum might be such a site. Based on the example of islet transplantation, we developed the hOMING (h-Omental Matrix Islet filliNG) technique, which consists of the injection of the graft inside the tissue, in between the omental layers, to improve islet implantation and survival. To achieve this, islets have to be embedded in a hydrogel with a viscosity that enables its injection using an atraumatic needle. Syringes are loaded with a combination of hydrogel and islets. Several injections are performed inside the omental tissue at different entry points, and the deposition of the islet/hydrogel mixture is made along a line. We tested the feasibility of this innovative approach using dextran beads. The beads were well spread throughout the omental tissue, in close proximity to blood vessels. To test the efficacy of the graft, we transplanted islets into diabetic rats and perform a metabolic follow-up over two months. The transplanted islets exhibited a high rate of re-vascularization around and inside islets, and reversed diabetes. The hOMING technique could be applicable for other types of hydrogel or cell therapy, for cells with high metabolic activity.
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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 | 0.001 |
| 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".