A Hypothesis: SNARE-ing the Mechanisms of Regulated Exocytosis and Pathologic Membrane Fusions in the Pancreatic Acinar Cell
Bibliographic record
Abstract
The pancreatic acinar cell has been a classic model to study regulated exocytosis occurring at the apical plasma membrane. The acinar cell is also an excellent model with which to study pathologic membrane fusion events, including aberrant zymogen granule fusion with the lysosome and basolateral exocytosis, which are the earliest cellular events of acute pancreatitis. However, despite much effort, little is known about the precise mechanisms that mediate these physiologic and pathologic membrane fusion events until recently. Over the past 5 years, there has been a major advance in the fundamental understanding of vesicle fusion based on the SNARE hypothesis. A basic tenet of the SNARE hypothesis is that the minimal machinery for membrane fusion is a cognate set of v- and t-SNAREs on opposing membranes. A corollary to this hypothesis is that these SNARE proteins are prevented from spontaneous assembly by clamping proteins. Here, the recent developments in the identification of cognate v- and t-SNAREs and clamping proteins are reviewed, which are strategically located to mediate these physiologic exocytic and pathologic fusion events in the pancreatic acinar cell.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".