Bibliographic record
Abstract
Pituitary tumors, arising from the pituitary gland, can be classified as functioning or non-functioning based on their hormone production. Previous studies demonstrated that impairment of cellular processes, such as autophagy, a crucial cellular recycling mechanism, has been implicated in pituitary tumorigenesis and hormone dysregulation. This review comprehensively examines the intricate relationship between autophagy and pituitary tumors. We explore the multifaceted role of autophagy in cancer, highlighting its dual nature as both a tumor suppressor and a promoter depending on the context. We also discuss the specific mechanisms of autophagy, including macroautophagy, mitophagy, crinophagy, and their relevance to pituitary tumorigenesis and hormone regulation. Furthermore, we analyze the current literature regarding the impact of various therapeutic interventions in pituitary tumor cells, with both autophagy-promoting and autophagy-inhibiting strategies. We address the challenges in interpreting autophagy activity and its complex interplay with hormone production. Current evidence suggests the potential of targeting autophagy as a therapeutic approach for pituitary tumors, emphasizing further research and clinical trials to determine the optimal strategy for individual patients and improve long-term outcomes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".