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Inhibition of the CD8 T cell response and cytotoxicity by human intravenous immunoglobulin. (P5023)

2013· article· en· W4313386441 on OpenAlexaff
Patrick Trépanier, Renée Bazin

Bibliographic record

VenueThe Journal of Immunology · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsUniversité LavalHéma-Québec
Fundersnot available
KeywordsCytotoxic T cellCD8In vivoImmunologyAntibodyT cellCytotoxicityCytokineIn vitroImmune systemChemistryMedicinePharmacologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Intravenous immunoglobulin (IVIg) is made of plasma-derived human IgG and exerts anti-inflammatory effects in several autoimmune disorders when administered at high doses. The constant increase in its use exposes providers and patients to considerable risks of shortage. Understanding its complex mechanisms of action is required in order to develop potent substitutes. Our group previously demonstrated that IVIg inhibits the in vitro and in vivo activation of CD4 T cells (Aubin et al, Blood 2010), as well as the in vitro CD8 T cell activation (Trépanier et al, Blood 2012). In the present work, we investigated whether IVIg could also interfere with the CD8 T cell response, which contribute to the persistence and severity of many autoimmune conditions. Bone marrow-derived DC from C57BL/6 mice were treated with IVIg and used to cross-present ovalbumin to OVA-specific OT-I CD8 T cells. Proliferation and cytokine production were measured after 72 hours. Cytotoxicity was measured using SIINFEKL-pulsed EL-4 target cells in presence of therapeutic doses of IVIg. Results showed a reduced (50%) CD8 T cells activation in the presence of IVIg and a concomitant decrease in IL-2 and IFN-gamma secretion. In addition, IVIg-treated CD8 T cells are less cytotoxic, as measured by specific lysis of target cells. These results support a novel immunomodulatory mechanism by which IVIg decrease CD8 T cell response. The importance of these results in vivo are currently being investigated.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.063
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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 teacher head, 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

Citations0
Published2013
Admission routes1
Has abstractyes

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