The Interactions of T Cells with Myeloid-Derived Suppressor Cells in Peripheral Blood Stem Cell Grafts
Bibliographic record
Abstract
The interaction of myeloid-derived suppressor cells (MDSCs) with T cells within G-CSF-mobilized peripheral blood stem cell (PBSC) grafts in patients undergoing autologous or allogeneic hematopoietic stem cell transplantation remains to be elucidated. Through studying allo- and auto-PBSC grafts, we observed grafts containing large numbers of T cells and MDSCs with intergraft variability in their percentage and number. T cells from autologous grafts compared to allografts expressed relative higher percentages of inhibitory receptors PD-1, CTLA-4, TIM-3, LAG-3, TIGIT and BTLA. Autograft T cells had decreased cell proliferation and IFN-γ secretion, which supported the possible presence of T cell exhaustion. On the contrary, graft monocytic MDSCs (M-MDSCs) expressed multiple inhibitory receptor ligands, including PD-L1, CD86, Galectin-9, HVEM and CD155. The expression of inhibitory receptor ligands on M-MDSCs was correlated with their corresponding inhibitory receptors on T cells in the grafts. Isolated M-MDSCs had the ability to suppress T cell proliferation and IFN-γ secretion and/or promote Treg expansion. Blocking the PD-L1-PD-1 signaling pathway partially reversed the functions of M-MDSCs. Taken together, our data indicated that T cells and M-MDSCs in PBSC grafts express complementary inhibitory receptor-ligand pairing, which may impact the quality of immune recovery and clinical outcome post transplantation.
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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".