Elevated CD56dimCD16neg NK Cells on Day 28 after HCT: Role in Immunity Against Viral Infections or Residual Tumor Cells?.
Bibliographic record
Abstract
Abstract Natural killer (NK) cells represent a lymphocyte subset important in the immune surveillance against virus infected and malignant cells. In humans, two major NK cell subsets have been defined by CD56 and CD16 staining: Regulatory NK cells (CD56 high CD16 neg), which are potent cytokine (IFNg) producers; and Cytolytic NK cells (CD56 dim CD16 pos) that are involved in target cell lysis. Functions of other NK cell subset are unknown. One of such subset, CD56 dimCD16 neg NK cells have recently been reported to represent cytolytic cells, degranulated after contact with target cells. NK cells are the first lymphocyte subset recovering to normal quantity and function after an allogeneic or autologous HCT, typically by 4 weeks post HCT. In the present investigation, we attempted to determine percentages of following NK cell subsets (Regulatory NK cells, Cytolytic NK cells, CD56dimCD16 neg, CD56 highCD16 pos and CD56neg CD16 high NK cells) at multiple time points (day 28, 56, 84, 180 and 365) after allogeneic (n= 32) and autologous (n= 25) HCT and compared with that of healthy individuals (n=14). Whole blood specimen was incubated with fluorochrome-conjugated monoclonal antibodies (CD3-ECD, CD14-PE, CD56-FITC and CD16-PC5) and analyzed by four color flowcytometry. NK cells were defined as mononuclear cells expressing CD16 or CD56 and not expressing CD3 or CD14. The most striking finding was that of significant increase in percentage of CD56dimCD16−ve cells (among NK cells) on day 28. This was true for both autologous HCT recipient (n=24; median = 1.02% of total NK cells) and allogeneic HCT recipient (n=26; median = 1.1% of total NK cells) in comparison to the healthy individuals (n=14; median = 0.56% of total NK cells). We speculate the rise in CD56dimCD16−ve NK cells at 1 month post HCT may be due to increased degranulation of cytolytic NK cells in response to viral infections or residual malignancy.
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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 | 0.000 |
| 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) | 0.002 | 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".