Compartmentalization of Pancreatic Secretory Zymogen Granules as Revealed by Low-Voltage Transmission Electron Microscopy
Bibliographic record
Abstract
Low-voltage (5-kV) transmission electron microscopy revealed a novel aspect of the pancreatic acinar cell secretory granules not previously detected by conventional (80-kV) transmission electron microscopy. Examination of ultra-thin (30-nm) sections of non-osmicated, stain-free pancreatic tissue sections by low-voltage electron microscopy revealed the existence of granules with non-homogeneous matrix and sub-compartments having circular or oval profiles of different electron densities and sizes. Such partition is completely masked when observing tissues after postfixation with osmium tetroxide by low-voltage transmission electron microscopy at 5 kV and/or when thicker sections (70 nm) are examined at 80 kV. This morphological partition reflects an internal compartmentalization of the granule content that was previously predicted by morphological, physiological, and biochemical means. It corresponds to the segregation of the different secretory proteins inside the granule as demonstrated by high-resolution immunocytochemistry and reflects a well-organized aggregation of the secretory proteins at the time of granule formation in the trans-Golgi. Such partition of the granule matrix undergoes changes under experimental conditions known to alter the secretory process such as stimulation of secretion or diabetes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".