Morphology, Molecular Regulation and Significance of Apoptosis in Pituitary Adenomas
Bibliographic record
Abstract
Apoptosis represents energy-requiring spontaneous single cell death, with specific morphologic and biochemical features. It is a rapidly processed sequence of events resulting in elimination of damaged cells. Apoptosis occurs in physiological remodeling and proliferative conditions, and also in neoplastic lesions. Several molecules and molecular systems such as bcl-2/bax, Fas/FasL and caspases regulate the apoptotic process. Apoptosis is characterized by a stereotypic pattern of morphologic features, which can be illustrated mostly by electron microscopy. DNA and biochemical assays, based on the specific pattern of nucleosomal fragmentation can detect apoptosis. The in situ labeling techniques are currently used to demonstrate apoptosis in paraffin sections. Several studies of pituitary animal models, cell lines and human pituitaries have been performed during the last 6 years. By electron microscopy, pituitary adenoma cells undergoing apoptosis exhibit a common prototypical pathway of changes. Although the results by the situ labeling techniques are not uniform, apoptosis occurs with low frequency in a subset of pituitary adenomas, in carcinomas and in pituitary hyperplasia. Alternative techniques based on remodeling of cytoskeleton by caspase activity can identify early apoptotic stages. This review presents the principles of apoptosis and summarizes the morphologic and functional changes of apoptosis in pituitary.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".