Clinical Impact of Percentage of Natural Killer Cells and Natural Killer-Like T Cell Population in Acute Myeloid Leukemia
Bibliographic record
Abstract
BACKGROUND: Natural killer (NK) function defects have been seen in many hematological malignancies, including acute myeloid leukemia (AML). AML is associated with deficient human leukocyte antigen (HLA) expression on leukemia blasts which become targets for killing by NK and natural killer-like T (NKT) cells. However, NK and NKT cells are not effective in killing autologous leukemia blasts, maybe due to number or functional abnormalities. The aim of the work was to detect the number and percentage of NK and NKT cells in patients with AML and the impact of their percentage on the prognosis, response to treatment and survival. METHODS: Bone marrow and peripheral blood samples were collected from 50 adult patients diagnosed as de novo AML who presented to the Hematology Unit in the Oncology Center Mansoura University (OCMU) at time of diagnosis. NK and NKT cells were detected by using immunophenotyping by expression of cell surface and cytoplasmic markers (anti-CD3 fluorescein isothiocyanate (FITC), anti-CD16/56 phycoerythrin (PE)). RESULTS: We observed significant reduction in the median values of NK and NKT cells in AML patients in comparison to normal values. There was an insignificant correlation to response to induction treatment. While a significant correlation to overall survival (OS) (P = 0.03) was observed. The correlation to risk stratification was significant with NK cells (P < 0.001), but not with NKT cells (P = 0.23). CONCLUSION: We concluded that the number and percentage of NK and NKT cells decreased significantly in AML patients and the frequency of NK and NKT cells is inversely proportionate with prognosis and OS in studied AML patients. We recommend correlating both number and function of NK and NKT cells in future studies to help provide a wide field of interest for possibility of demonstrating novel therapies using NK cells for curing AML.
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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".