Loss of p75 neurotrophin receptor expression accompanies malignant progression to human and murine retinoblastoma
Bibliographic record
Abstract
We studied the expression of pro-apoptotic neurotrophin receptor p75 (p75(NTR)) in human and murine retinoblastoma, compared to normal retina, and examined changes in p75(NTR) expression with the onset of apoptosis in the course of murine retinoblastoma progression, using immunohistochemistry and quantitative real-time RT-PCR. The murine retinoblastoma is induced by retinal specific expression of SV40 T-antigen (TAg), which blocks the function of the retinoblastoma protein (pRB) and related proteins, and is a well-studied model that closely simulates human retinoblastoma. The majority of human retinoblastoma either lacked or expressed decreased levels of p75(NTR) mRNA, compared to human retina. Moreover, p75(NTR) protein was not detected in any tumor studied, unlike normal retina. Like human retinoblastoma, advanced murine retinoblastoma did not express p75(NTR). However, before tumors emerged, small clusters of TAg-positive cells coexpressed p75(NTR) and activated caspase-3, a marker of apoptosis. Furthermore, in three rare human eyes containing retinoblastoma adjacent to regions resembling the benign retinal tumor retinoma, both normal retina and retinoma-like tissue expressed p75(NTR) protein, while the retinoblastoma did not. We suggest that p75(NTR) loss accompanies progression from retinoma to retinoblastoma.
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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 | 0.000 | 0.000 |
| 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".