Immunohistochemical expression of PTTG in brain tumors
Bibliographic record
Abstract
Pituitary tumor transforming gene (PTTG) plays a role in many cellular processes. PTTG overexpression is seen in several tumor types and correlates with survival time/recurrence. We evaluated PTTG expression in various types of brain tumors (n=94). Immunohistochemistry was performed using a monoclonal PTTG antibody (DCS‐280, Abcam;Cambridge, MA) and the streptavidin‐biotin‐peroxidase complex method. The intensity of PTTG immunoreactivity was evaluated semi‐quantitatively on a 0‐4 scale. PTTG expression was evident in most tumor cells and was predominantly nuclear. In glial tumors, PTTG immunoreactivity was higher in glioblastomas (IV), anaplastic oligoastrocytomas (III), anaplastic oligodendrogliomas (III), oligoastrocytomas (II), oligodendrogliomas (II), and pilocytic astrocytomas (I) (range: 3.1–3.5), whereas notably lower PTTG was seen in myxopapillary ependymomas (I) and ependymomas (II) (1.5 & 1.6). In non‐glial tumors, hemangiopericytomas and schwannomas had higher PTTG score (3.4), than meningiomas (2.3). Thus, it appears that PTTG expression is not associated with tumor grade, but rather with tumor type, the most striking difference being between ependymomas and other glial tumors. PTTG may be a valuable therapy target in some brain tumors. Acknowledgments Authors thank the Jarislowsky Foundation and the Lloyd‐Carr‐Harris Foundation for their generous support.
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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) | 0.002 | 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".