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Record W2168557022 · doi:10.4049/jimmunol.171.3.1272

Blockade of Programmed Death-1 Engagement Accelerates Graft-Versus-Host Disease Lethality by an IFN-γ-Dependent Mechanism

2003· article· en· W2168557022 on OpenAlexaff
Bruce R. Blazar, Beatriz M. Carreno, Angela Panoskaltsis‐Mortari, Laura Carter, Yoshiko Iwai, Hideo Yagita∥, Hiroyuki Nishimura, Patricia A. Taylor

Bibliographic record

VenueThe Journal of Immunology · 2003
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsWomen's Health Research Institute
FundersNational Institute of Allergy and Infectious DiseasesNational Heart, Lung, and Blood Institute
KeywordsBlockadeImmunologyGraft-versus-host diseasePerforinFas ligandAutoimmunityDiseaseLethalityLigationMedicineCancer researchApoptosisBiologyReceptorAntigenProgrammed cell deathImmune systemInternal medicineCD8Genetics

Abstract

fetched live from OpenAlex

Acute graft-vs-host disease (GVHD) is influenced by pathways that can enhance or reduce lethality by providing positive or negative signals to donor T cells. To date, the only reported pathway to inhibit GVHD is the CTLA-4:B7 pathway. Because absence of the programmed death-1 (PD-1) pathway has been implicated in a predisposition to autoimmunity and hence a lack of negative signals, the effect of PD-1 pathway blockade on GVHD was explored using several distinct approaches. In each, GVHD lethality was markedly accelerated. Coblockade of CTLA-4 and PD-1 was additive in augmenting GVHD, indicating that these pathways are not fully redundant. Although neither perforin nor Fas ligand expression was required for GVHD enhancement, donor IFN-gamma production was required for optimal GVHD acceleration in the absence of PD-1 ligation. These data indicate that PD-1 ligation down-regulates GVHD through modulation of IFN-gamma production and suggest a novel therapeutic target for inhibiting GVHD lethality.

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 score0.001
Threshold uncertainty score0.003

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.306
Teacher spread0.262 · 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".

Quick stats

Citations323
Published2003
Admission routes1
Has abstractyes

Explore more

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