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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".