Expression of nm23 antimetastatic gene product in parathyroid hyperplasia, adenoma and carcinoma. An immunohistological assessment
Bibliographic record
Abstract
OBJECTIVE: The nm23 gene was initially cloned as a metastasis suppressor gene, but the clinical relevance of nm23 as a metastasis suppressor or prognostic indicator for human cancers remain controversial. To evaluate the role of nm23 protein as a prognostic factor and its role in parathyroid neoplasia, we studied nm23 protein expression by immunohistochemical staining in parathyroid lesions. METHODS: Immunohistochemistry using the avidin-biotin peroxidase complex technique with a polyclonal antibody against the nm23 protein was applied to formalin-fixed, paraffin-embedded tissue specimens obtained from 48 patients. The specimens were collected from 38 patients at the University Health Network, Toronto, Canada and from 10 Saudi patients at the King Abdul-Aziz University Hospital, Jeddah, Kingdom of Saudi Arabia. They included parathyroid carcinomas (5 cases), adenomas (22 cases), hyperplasia (21 cases), and normal parathyroid tissue (10 cases). The immunohistochemistry was completed in 2003 at King Abdul-Aziz University Hospital, Jeddah, KSA and University Health Network, Toronto, Canada. RESULTS: Expression of nm23 protein was noted in adenomas and carcinomas as well as in hyperplastic parathyroid glands and there was no significant statistical difference between these groups. Normal parathyroid glands did not show any intense immunoreactivity. CONCLUSION: The results suggest that expression of nm23 in parathyroid lesions is correlated with tumor proliferation rather than suppression of invasion and metastasis. While our data suggest that nm23 may help in the distinction of normal from proliferative parathyroids, these results do not point to nm23 as a reliable prognostic marker in parathyroid lesions.
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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 | 0.000 |
| 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".