Bibliographic record
Abstract
The proglucagon gene (gcg) encodes a number of peptide hormones that are of cell-type specifically expressed in the pancreatic islets, the distal ileum and the large intestine, as well as certain brain neuronal cells. These hormones are important in controlling blood glucose homeostasis, intestinal cell proliferation, and satiety. More importantly, the major hormone generated in the pancreas (i.e. glucagon) exerts opposite effects to the ones that are produced in the intestines (i.e. glucagon-like peptide-1 (GLP-1) and GLP-2). To understand the mechanisms underlying cell-type-specific gcg expression may lead to the identification of novel drug targets to control endogenous hormone production for therapeutic purposes. Extensive in vitro examinations have shown that more than a half dozen of homeodomain (HD) proteins are able to interact with the gcg gene promoter and activate its expression. In vivo 'knock-out' mouse studies, however, cannot demonstrate the role of some of them (i.e. Cdx-2, Brn-4, and Nkx6.2) in the development of pancreatic islet alpha-cells, suggesting that these HD proteins may exert some redundant functions in the genesis of gcg-producing cells. Investigations have also revealed that gcg expression is controlled by both protein kinase A and Epac signaling pathways in response to cAMP elevation, and cell-type specifically controlled by insulin and the effectors of the Wnt signaling pathway. This review summarizes our current understanding on the mechanisms underlying gcg transcription and presented my interpretations on how the interactions between different signaling networks regulate gcg expression.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".