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Record W2135791655 · doi:10.4049/jimmunol.0900200

Expression of a CD200 Transgene Is Necessary for Induction but Not Maintenance of Tolerance to Cardiac and Skin Allografts

2009· article· en· W2135791655 on OpenAlexaff
Reginald M. Gorczynski, Zhiqi Chen, William He, Ismat Khatri, Yang Sun, Kai Yu, Ivo Boudakov

Bibliographic record

VenueThe Journal of Immunology · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsDoxycyclineTransgeneBiologyReceptorFOXP3Genetically modified mouseImmunosuppressionEctopic expressionEndogenyImmunologyCell biologyGeneEndocrinologyImmune systemGenetics

Abstract

fetched live from OpenAlex

CD200, a type 2 transmembrane molecule of the Ig supergene family, can induce immunosuppression in a number of biological systems, as well as promote increased graft acceptance, following binding to its receptors (CD200Rs). Skin and cardiac allograft acceptance are readily induced in transgenic mice overexpressing CD200 under control of a doxycycline-inducible promoter, both of which are associated with increased intragraft expression of mRNAs for a number of genes associated with altered T cell subset differentiation, including GATA-3, type 2 cytokines (IL-4, IL-13), GITR, and Foxp3. Interestingly, some 12-15 days after grafting, induction of transgenic CD200 expression can be stopped (by doxycycline withdrawal), without obvious significant effect on graft survival. However, neutralization of all CD200 expression (including endogenous CD200 expression) by anti-CD200 mAb caused graft loss, as did introduction of an acute inflammatory stimulus (LPS, 10 microg/mouse, delivered by i.p. injection). We conclude that even with apparently stably accepted tissue allografts, disruption of the immunoregulatory balance by an intense inflammatory stimulus can cause graft loss.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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

Citations46
Published2009
Admission routes1
Has abstractyes

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