Selective molecular biomarkers to predict biologic behavior in pituitary tumors
Bibliographic record
Abstract
INTRODUCTION: To date, several cell proliferation markers, apoptosis, vascular markers, oncogenes, tumor suppressor genes, cell cycle mediators, microRNA (miRNAs), and long noncoding RNAs (lncRNAs) have been identified to be involved in the tumorigenesis, migration, proliferation and invasiveness of pituitary adenomas. There are still no reliable morphologic markers predictive of pituitary adenoma recurrence. Recent scientific research introduced new techniques to enable us to attain new information on the genesis and biologic behavior of pituitary adenomas. Areas covered: This review covers selected, compelling and cumulative information in regards to TACSTD family (EpCAM, TROP2), neuropilin (NRP-1), oncogene-induced senescence (OIS), fascins (FSCN1), invasion-associated genes (CLDN7, CNTNAP2, ITGA6, JAM3, PTPRC and CTNNA1) EZH2, and ENC1 genes and endocan. Expert commentary: Ongoing research provides clinicians, surgeons and researchers with new information not only on diverse pathways in tumorigenesis but also on the clinical aggressive behavior of pituitary adenomas. Newly developed molecular techniques, bioinformatics and new pharmaceutical drug options are helpful tools to widen the perspectives in our understanding of the complex nature of pituitary tumorigenesis. The discovery of new molecular biomarkers can only be accomplished by continuing to investigate pituitary embryogenesis, histogenesis and tumorigenesis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".