nm23 expression in malignant ascitic effusions of serous ovarian adenocarcinoma
Bibliographic record
Abstract
The aim of this study was to evaluate nm23 expression as detected in malignant cells of neoplastic ascites and to verify its relationship with the presence of tissue nm23 and p53 in primitive neoplasia. Using an immunocytochemical assay with a specific anti-nm23 polyclonal antibody, nm23 expression was evaluated in ascitic effusions of 45 patients with ovarian serous adenocarcinoma and ascites in normal and/or hyperplastic mesothelial cells from 37 women with various neoplasms (12 ovarian neoplasms) free of malignant cells as controls. nm23 and p53 tissue expression was also detected in 21 corresponding tumor samples, including 11 bilateral lesions. nm23 was expressed in 57% of malignant effusions compared to 43% in controls: the two groups were not correlated. nm23 in effusions agreed with tissue expression (p=0.02) but a direct correlation was not demonstrated. The incidence of nm23 was more frequent in stage III than in stage IV disease (p=0.08) and was associated with mutated p53 expression (p=0.01). Using the Wilcoxon test for unpaired data, a higher incidence (p=0.05) of p53-positive tumors in bilateral cancers was found while a higher expression of nm23 in effusions of patients with monolateral lesions was observed (p=0.08). The presence of p53 was correlated with that of nm23 in both cytologic (p=0.005) and histological samples (p=0.01). Our findings, together with the diversity in biological behaviour present in various tumors, suggest that nm23 is a family of genes with differing biological functions which act as tumor-specific inhibiting factors within a complex process also involving other genes. Due to the analogies and correlations between nm23 and p53, the role of nm23 as a potential predictive factor of response to chemotherapy and in DNA repair is emphasized.
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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 | 0.001 |
| 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".