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Record W2123115365 · doi:10.1016/j.bbmt.2010.12.517

Anti-GVHD Effect of Antithymocyte Globulin is Mediated by Antibodies Binding to T Cells and Regulatory NK Cells, and Less So or Not at All by Antibodies Binding to Other Mononuclear Cell Subsets

2011· article· en· W2123115365 on OpenAlexaff
Mette Hoegh-Petersen, Minaa A. Amin, Y. Liu, Alejandra Ugarte-Torres, Tyler Williamson, Peter Podgorny, James A. Russell, Jan Storek

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImmunologyCD8Interleukin 21AntibodyFlow cytometryCytotoxic T cellGraft-versus-host diseaseMedicineT cellBiologyImmune systemTransplantationInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Polyclonal rabbit-anti-human T cell globulin (ATG) may decrease the likelihood of graft-vs-host disease (GVHD) without increasing the likelihood of relapse.ATG is polyclonal and the anti-GVHD effect may be mediated through killing/inhibition of one or several lymphocyte subsets (eg, T cells) or their subsets (eg, na€ ıve T cells).To understand the mechanism of action of ATG on GVHD, we determined levels of which ATG fraction (capable of binding to which cell subset) are associated with subsequent development of GVHD.Patients & Methods: We studied 121 patients whose myeloablative conditioning included 4.5 mg/kg ATG (Thymoglobulin).Using flow cytometry, levels of the following ATG fractions in serum from day 7 were determined: capable of binding to naive or memory B cells; naive, central memory (CM) or effector memory (EM) CD4 and CD8 T cells, EM CD8 T cells not expressing CD45RA (EMRA-); cytolytic, regulatory or CD16 + CD56 -NK cells; monocytes and dendritic cells (DCs).ATG levels in patients with vs without aGVHD or cGVHD were compared using Mann-Whitney test.For each fraction where levels were significantly different (p\0.05),we determined if patients with high fraction level had a significantly lower likelihood of aGVHD or cGVHD using log-binomial regression models.Results: In univariate analyses, significantly lower levels of ATG fractions on the following subsets were found in patients developing aGVHD: binding to naive CD4 and CD8 T cells, EM CD4 T cells, and regulatory NK cells, or cGvHD: binding to naive CD4 and CD8 T cells, CM and EM CD4 T cells, and regulatory NK cells.In multivariate analyses, high levels of the following ATG fractions were associated with a low likelihood of aGVHD: binding to naive CD4 T cells (RR 5 .33,p 5 .001),EM CD4 T cells (RR 5 .30,p \ .001),naive CD8 T cells (RR 5 .33,p 5 .002)and regulatory NK cells (RR 5 .36,p 5 .001)or low likelihood of cGVHD: binding to naive CD4 T cells (RR 5 .59,p 5 .028),CM CD4 T cells (RR 5 .49,p 5 .009),EM CD4 T cells (RR 5 .51,p 5 .006),naive CD8 T cells (RR 5 .46,p 5 .005)and regulatory NK cells (RR 5 .55,p 5 .036).Conclusion: For both aGVHD and cGVHD, the anti-GVHD effect with relapse-neutral effect of ATG appears to be mediated by antibodies to antigens expressed on naive T cells (both CD4 and CD8), EM CD4 T cells and regulatory NK cells.This is the first step towards identifying the antibody(ies) within ATG important for the anti-GVHD effect without impacting relapse.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.011
GPT teacher head0.219
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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