Cytokine Syntheses by T-Cell Subsets From Chronic Myeloid Leukemia Patients: Relationship Between Pre-Treatment Levels and Response to Imatinib Therapy
Bibliographic record
Abstract
BACKGROUND: Although T-cell cytokine's role in the long-term control of chronic myeloid leukemia (CML) is well established, previous studies showed contradicting results regarding imatinib (IM) effect on the endogenous T-cell function by IM. The purpose of this study was to determine the relation between the endogenous T-cell function prior to therapy and the degree of response to IM therapy in CP CML. In addition, modulation of the endogenous T-cell function during IM therapy was studied. METHODS: We evaluated Th1 (gamma interferon (IFN-γ)), Th2 (interleukin (IL-4)) and tumor necrosis factor (TNF)-α cytokine synthesis by activated T-cell subsets in 20 patients with newly diagnosed CML in chronic phase (CP CML) using flow cytometry before and during IM therapy compared to patients with IM resistance (IM Res) and healthy donors. RESULTS: Patients with optimal response (CML OR) to IM demonstrated a lower pre-treatment Th1 cytokine compared to that of healthy donors, and a higher percentage of Th2 and TNF-α producing T cells compared to that of healthy donors, non-optimal responders (CML nOR) and those with IM Res. A shift from Th2 profile to Th1 profile and initial decline of TNF-α producing T cells was detected early during therapy in optimal responders which was coinciding with complete hematological remission with a significant increase in the percentages of CD4+ve/IFN-γ+ve cells (P = 0.01) and a significant drop of in CD8+ve/IL-4+ve T cells (P = 0.04). CONCLUSION: We believe that pre-treatment levels of IL-4 and/or TNF-α may have a role in identifying CP CML patients who may respond to IM therapy; however, further investigation is needed.
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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 | 0.000 | 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) | 0.001 | 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".