Multiple-Condition Analysis in a Retrievable Subcutaneous Animal Model for Drug Screening on Full Pancreatic Tissue Digest
Bibliographic record
Abstract
The lack of understanding on how to treat pancreas-related diseases and develop new therapeutics is partly due to the unavailability of appropriate models. In vitro models fail to provide a physiological environment. Testing new drug targets in these models can give rise to bias and misleading results. Therefore, we developed an in vivo model for drug testing on full pancreatic digests, which maintains the interactions between endo- and exocrine tissues and allows retrieving the samples for further analyses. The use of full pancreatic digest eliminates the need to isolate islets, reducing time and cost. In this model, four different conditions can be implanted subcutaneously within the same animal. Each condition consists of full pancreatic tissue digests embedded in alginate beads. All alginate beads in one animal contained full pancreatic digest of the same donor and, after 5-day implantation, were retrieved for analysis focusing on survival, function, and/or organization. Proof-of-principle of the platform was evidenced by showing the effect of hyaluronic acid and vascular endothelial growth factor on the overall function of the full pancreatic digest and on endothelial cells in the pancreatic digest, respectively. Retrieval from identical animals allows direct comparison between conditions. Metabolism (MTT) quantification, dithizone staining, and glucose-stimulated insulin secretion assessment allow to discriminate, using a minimal number of animals, between treatments and validate the system. Because of its simplicity, the model is highly adaptable to specific needs of the user.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".