Augmented expression of endothelin‐1, endothelin‐3 and the endothelin‐B receptor in breast carcinoma
Bibliographic record
Abstract
AIMS: Endothelins (ETs) are peptides expressed in many tumours which may stimulate angiogenesis and desmoplasia. Because ETs have not been extensively studied mammary neoplasia, we assessed ET protein and mRNA expression and receptor mRNA expression in normal and neoplastic breast tissues. METHODS AND RESULTS: Tissues from five normal breasts, six fibroadenomas, seven ductal carcinomas in situ (DCIS) and 25 invasive carcinomas were stained with anti-ET-1 and anti-ET-3 antibodies and analysed using a grading system. ET-1, ET-3, ETA and ETB mRNA expression was assessed by quantitative RT-PCR from eight carcinomas and five normals. Weak staining for ET-1 and ET-3 was detected in all normals. Moderate to strong staining was seen in 72% and 64% of carcinomas for ET-1 and ET-3, respectively. Most fibroadenomas showed weak positivity for ET-1 (83%) and ET-3 (67%). ET-1 and ET-3 mRNA levels were upregulated in carcinomas compared with normal breast. No ETA mRNA was not detected in any tissue. ETB mRNA was detected in normal breast and was increased in carcinomas. CONCLUSION: These results suggest that the ET system is altered in breast carcinomas and this may be of importance in the progression from in-situ to invasive carcinoma.
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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".