Clinicopathological evaluation of immunohistochemical Ki-67 and endothelial nitric oxide synthase expression in intracranial ependymoma
Bibliographic record
Abstract
PURPOSE: To analyze the association between Ki-67 and eNOS expression with the pathological grades of patients with intracranial ependymomas, and to determine its value in distinguishing the progression of the disease. METHODS: A clinicopathological study was undertaken in 82 patients with intracranial ependymomas. Tissue samples, obtained by tumour resection, were divided into three groups: low-grade, mid-grade and high-grade ependymomas. Tissue samples obtained from 15 patients with brain contusion were used as control. Immuno-histochemical staining was performed to analyze the association between Ki-67 and eNOS expression with various tumour grades. The cell proliferating marker Ki-67 was assessed by positive cell count. The levels of eNOS positive expression were evaluated as slight, moderate and intense. RESULTS: 48 of 82 cases (58.54%) expressed Ki-67 protein. Expression of Ki-67 and eNOS was negative in all control samples. Positive cell rates were 2.65+/-0.83 % in the low-grade, 9.63+/-0.08 % in the mid-grade, and 28.41+/-0.71 % in the high-grade ependymoma groups. In low-grade ependymomas there were 8 and 12 cases that expressed eNOS slightly or moderately. In the mid-grade ependymoma group eNOS was expressed moderately in 10 cases and intensely in 15. In the high-grade group 20 cases showed intense positive expression of eNOS. The Ki-67 positive cell counts for slight, moderate and intense eNOS expression were 2.20, 6.07 and 22.25, respectively. CONCLUSION: Ki-67 and eNOS expression in intracranial ependymoma tissue was associated with the histopathological grade and malignant degree.
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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 | 0.002 |
| 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) | 0.001 | 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".