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Record W2111269206 · doi:10.4049/jimmunol.1101592

Human CD90 Identifies Th17/Tc17 T Cell Subsets That Are Depleted in HIV-Infected Patients

2011· article· en· W2111269206 on OpenAlexfundno aff
Maude Guillot‐Delost, Sabine Le Gouvello, Mariana Mesel‐Lemoine, Mustapha Cheraï, Claude Baillou, Anne E. Simon, Yves Lévy, Laurence Weiss, Samy Louafi, Nathalie Chaput, François Berrehar, Stéphane Kerbrat, David Klatzmann, François M. Lemoine

Bibliographic record

VenueThe Journal of Immunology · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsnot available
FundersCentre hospitalier universitaire Sainte-Justine
KeywordsCD90Human immunodeficiency virus (HIV)VirologyImmunologyBiologyMicrobiologyCellGenetics

Abstract

fetched live from OpenAlex

By revisiting CD90, a GPI-anchored glycoprotein, we show that CD90 is expressed by a subset of CD4(+) and CD8(+) human T cells. CD4(+)CD90(+) cells share similarities with Th17 cells because they express the Th17-specific transcription factor RORC2 and produce IL-17A. CD4(+)CD90(+) cells are activated memory T cells that express the gut mucosal markers CCR6, CD161, and the α(4) and β(7) integrins. Compared with CD90-depleted CCR6(+) memory Th17 cells, CD4(+)CD90(+) cells express higher levels of IL-22 and proinflammatory cytokines (IL-6, TNF-α and GM-CSF), but they produce lower levels of IL-21 and no IL-9. Analyses of CD8(+)CD90(+) cells reveal that they express RORC2 and are able to produce higher levels of IL-17A, IL-22, and CCL20 compared with CD90-depleted CD8(+) cells. These data show that CD90 identifies Th17 and Tc17 cells with a peculiar cytokine profile. Studies of circulating CD90(+) cells in HIV patients show that CD90(+) cells are decreased with an imbalance of the CD4(+)CD90(+)/regulatory T cell ratio in nontreated patients compared with treated patients and healthy donors. Overall, human CD90 identifies a subset of Th17 and Tc17 cells within CD4(+) and CD8(+) T cells, respectively, which are depleted during HIV infection.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.221
Teacher spread0.195 · 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 designObservational
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

Citations47
Published2011
Admission routes1
Has abstractyes

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