Anti-Thymocyte Globulin Increases Dramatically Natural Killer Cell Interferon Gamma Production While Respecting Other Functionnal Aspects of These Cells.
Bibliographic record
Abstract
Abstract Rational: The beneficial effect of NK cells after clinical hematopoietic transplantation has been seen when anti-thymocyte globulin (ATG) was used within the conditioning regimen. We therefore investigated the effect of ATG on human NK cells. Materials and methods: Percentages of CD3+ and CD3−/CD56+ among PBMC from either normal adult volunteers or cord blood were assessed by flow cytometry. Negatively purified NK cells were cultured for 2–4 days either alone or in the presence of rabbit ATG (Thymoglobuline, Sangstat) and/or IL-15, and then assessed for cell viability and functional properties: apoptosis were investigated by Annexin V staining. Cell proliferation was assessed by CFSE dye dilution. IFN-gamma secretion was quantified by ELISA. Cytotoxicity against K562 cells was studied by a 51Cr release assay. Binding of monoclonal antibodies to NK cells was assessed alone or after competition with whole ATG or ATG-Fab’2 fragments. Results: The percentage of CD3+ cells in PBMC was over 65% before or after culture without ATG. In contrast, no T cell was detected after culture with ATG, whereas the percentage of NK cells increased from less than 15% to more than 50%. Both whole ATG and Fab’2 fragments bound NK cells while rabbit Fc fragments did not. ATG did not modify apoptotic and necrotic NK cells percentages. Proliferation was not affected by ATG. IFN-gamma production was dramatically increased by ATG treatment (figure). ATG respected IL15 induced NK cell cytolytic activity. Competition assays showed that ATG did not alter CD2, CD11a, CD16, CD56, CD57, CD62L, CD94, CD158a, CD158b, NKG2a, NKG2d, HLA-DR expression. CD18 and NKp46 expression was slightly decreased. Conclusion: Our data show that ATG may play a role in the results of KIR mismatch hematopoietic stem cell transplantation. It may be possible that ATG confers functional advantages to NK cells versus T cells. Figure Figure
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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 | 0.001 |
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".