Autophagy in pituitary tumors
Bibliographic record
Abstract
Autophagy or self‐cannibalism, is degradation of cytoplasmic constituents providing an alternative energy source and allowing cell survival during hypoxia, nutrient deprivation and metabolic stress. Formation of membranes engulfing part of the cytoplasm, fusion with lysosomes and degradation can be conclusively seen by electron microscopy. To our knowledge, no information is available to date on the role of autophagy in pituitary tumors. We investigated more than 5000 surgically removed human pituitary tumors by electron microscopy. Our results indicate that autophagy is rare and can be demonstrated in every adenohypophysial tumor type. Autophagy is most frequently noted in long acting somatostatin analog treated GH producing and in dopamine agonist treated PRL producing pituitary adenomas. These two treatment modalities which cause tumor shrinkage, clinical and endocrinological improvement, activate autophagy. Many cytotoxic and anticancer drugs stimulate autophagy and counteract the effect of therapy in many tumor types resulting in tumor cell survival. New drugs are needed which inhibit autophagy. It remains to be seen whether anti‐autophagic drugs would suppress the formation of additional nutrients, eliminate the extra energy source and increase the efficiency of drugs used in the treatment of pituitary tumors. This study was supported by the Jarislowsky and Lloyd Carr‐Harris Foundations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".