AAV GCG-EGFP, a new tool to identify glucagon-secreting α-cells
Bibliographic record
Abstract
Abstract The study of primary glucagon-secreting α-cells is hampered by their low abundance and scattered distribution in rodent pancreatic islets. We have designed a double-stranded adeno-associated virus containing a rat proglucagon promoter (700 bp) driving enhanced green fluorescent protein (AAV GCG-EGFP), to specifically identify α-cells. The administration of AAV GCG-EGFP by intraperitoneal or intraductal injection led to EGFP expression selectively in the α-cell population. AAV GCG-EGFP delivery to mice followed by islet isolation, dispersion and separation by FACS for EGFP resulted in an 86% pure population of α-cells. Furthermore, the administration of AAV GCG-EGFP at various doses to adult wild type mice did not significantly alter body weight, blood glucose, plasma insulin or glucagon levels, glucose tolerance or arginine tolerance. In vitro experiments in transgene positive α-cells demonstrated that EGFP expression did not alter the intracellular Ca2+ pattern in response to glucose or adrenaline. This approach may be useful for studying purified primary α-cells and for the in vivo delivery of other genes selectively to α-cells to further probe their function or to manipulate them for therapeutic purposes.
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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 | 0.001 |
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".