BMI-1 and survivin expression with clinicopathological correlation and prognostic impact in B and T/NK- cell non-Hodgkin lymphoma
Bibliographic record
Abstract
Background: The pathogenesis of non-Hodgkin lymphoma is a complex process that involves several molecular changes. Alterations in polycomb group proteins as well as Survivin have been described but details are still lacking particularly in T/NK-cell lymphomas. Polycomb proteins have a big role in cell cycle and differentiation. Survivin is another recently recognized player in non-Hodgkin lymphoma.Objective: To study the pattern of Bmi-1 and Survivin in different categories of B- and T/NK- cell non-Hodgkin lymphomas, their association with the clinicopathological parameters, and their impact on the prognosis of non-Hodgkin lymphomas.Material& methods: Immunohistochemical staining was used to study paraffin samples of 267 patients’ biopsies. We used tonsils and reactive lymph node as normal control.Results: Both Bmi-1 and Survivin showed significant upregulation in several subtypes B- (P = .000-.02 for Bmi-1 and .00- .03 forSurvivin) and T/NK cell lymphomas (P= .009-.03 for Bmi-1 and 0.008- 0.009 for Survivin) compared to normal tissue. Significantpositive correlation between Bmi-1 and Survivin was detected in both B- (Co= 0.539**, P = .00) and T - cell lymphomas (Co= 0.560**, P = .000). A statistically significant difference between overall survival and expression of both BMI-1 and Survivin was detected (P = .00 for BMI-1and survivin).Conclusion: Bmi-1 and Survivin show significant upregulation as well correlation with clinicopathological parameters and overall survival of non-Hodgkin lymphomas.
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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 | 0.001 |
| 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".