Munc18b Increases Insulin Granule Fusion, Restoring Deficient Insulin Secretion in Type-2 Diabetes Human and Goto-Kakizaki Rat Islets with Improvement in Glucose Homeostasis
Bibliographic record
Abstract
Reduced pancreatic islet levels of Munc18a/SNARE complex proteins have been postulated to contribute to the deficient glucose-stimulated insulin secretion (GSIS) in type-2 diabetes (T2D). Whereas much previous work has purported Munc18a/SNARE complex (Syntaxin-1A/VAMP-2/SNAP25) to be primarily involved in predocked secretory granule (SG) fusion, less is known about newcomer SGs that undergo minimal docking time at the plasma membrane before fusion. Newcomer SG fusion has been postulated to involve a distinct SM/SNARE complex (Munc18b/Syntaxin-3/VAMP8/SNAP25), whose levels we find also reduced in islets of T2D humans and T2D Goto-Kakizaki (GK) rats. Munc18b overexpression by adenovirus infection (Ad-Munc18b), by increasing assembly of Munc18b/SNARE complexes, mediated increased fusion of not only newcomer SGs but also predocked SGs in T2D human and GK rat islets, resulting in rescue of the deficient biphasic GSIS. Infusion of Ad-Munc18b into GK rat pancreas led to sustained improvement in glucose homeostasis. However, Munc18b overexpression in normal islets increased only newcomer SG fusion. Therefore, Munc18b could potentially be deployed in human T2D to rescue the deficient GSIS.
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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) | 0.000 | 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) | 0.002 | 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".