The Expression of Human Telomerase Reverse Transcriptase Gene and Its Activity in Patients with B-Cell Chronic Lymphocytic Leukemia and Its Impact on Clinical Staging
Bibliographic record
Abstract
BACKGROUND: The B-Cell chronic lymphocytic leukemia is a commonest leukemia in elderly individuals characterized by progressive accumulation of mature lymphocyte in bone marrow and peripheral blood that tend to be immortal. The identification of human telomerase reverse transcriptase (hTERT) has been correlated with disease aggressiveness in malignancies. The previous researchers showed that the hTERT expression could serve as a molecular prognostic marker for B-CLL patients. However, due to the disease differences in prevalence and aggressiveness between the western and eastern countries, there is still a need to see the correlation of hTERT in B-CLL patients of the eastern world.AIM: To test the quantitative assessment of hTERT gene expression in B-CLL patients and its prognostic value in correlation with clinical staging of disease in Iraqi patients.PATIENTS & METHODS: we used the TRAP assay to assess the hTERT gene expression in mononuclear blood cells from 43 B-CLL patients.RESULTS: The hTERT gene expression was detected in 79.1% of B-CLL patients and no positive expression in control group (P=0.001). The hTERT gene expression tends to be significantly higher in advanced B-CLL stage (P=0.0001). Also, the expression was higher among elderly patients, patients with lower hematological parameters, patients with splenomegaly or hepatomegaly, patients with a history of hypertension or diabetes mellitus, and patients with high immunophenotype score. CONCLUSION: Our research suggests that the hTERT expression could serve as a prognostic marker for Iraqi patients with B-CLL as well as western countries.
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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".