Effect of Schlafen 2 on Natural Killer and T cell Development from Common T cell/ Natural Killer Progenitors
Bibliographic record
Abstract
Natural Killer (NK) cells are thought to develop from common lymphoid progenitors in the bone marrow. Even though thymus is not essential for NK cell development, Thymic T‐cell/natural killer–cell (T/NK) precursor, DN2 (CD44+CD25+) when cultured on an OP9 stroma, give rise to some NK1.1+ cells. The objective of this study was to see if the Schlafen 1 (Slfn1) and Schlafen 2 (Slfn2) genes differently affect the maturation of T cell and NK cells from DN2 progenitors. We transduced DN2 progenitors prepared from C57BL/6 (B6) mouse thymus with Slfn1 and Slfn2 genes using retroviral vector and cultured those Slfn1 and Slfn2 transduced progenitors on OP9 and OP9 stroma expressing the Notch ligand Delta‐like 1 (OP9‐DL1) with appropriate cytokines to see if it affects generating NK and T‐cells differently. We got a small number of Slfn1 and Slfn2 expressing mature T and NK cells upon culture of transduced DN progenitors on Op9‐DL1 and Op9 stroma cells. There was no difference between Slfn1 positive and Slfn1 negative population regarding CD3 expression but nearly all mature NK cells were belong to Slfn1 negative population. Slfn2 completely blocked maturation of T cells but not NK cells. The result of this study indicates that both Slfn1 and Slfn2 interfere with maturation of DN2 progenitors but T cell development is more sensitive to Slfn2 expression than NK cell development. This research was supported by Terry Fox Laboratory, British Columbia Cancer Research Center and Department of Pathology and Laboratory Medicine, University of British Columbia 675 West 10th Avenue, Vancouver, British Columbia, Canada.
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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) | 0.001 | 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".