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Record W2018369334 · doi:10.1002/lt.23458

Role of regulatory T cells in the promotion of transplant tolerance

2012· review· en· W2018369334 on OpenAlexaff
Itay Shalev, Nazia Selzner, Wendy Huei-Ping Shyu, David Grant, Gary Levy

Bibliographic record

VenueLiver Transplantation · 2012
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineFOXP3ImmunosuppressionLiver transplantationImmune toleranceImmunologyRegulatory T cellTransplantationPeripheral toleranceIL-2 receptorImmune systemT cellInternal medicine

Abstract

fetched live from OpenAlex

Liver transplantation is now recognized as the most effective therapy for patients with end-stage acute and chronic liver failure. Despite outstanding short-term graft and patient survival, liver transplantation continues to face several major challenges, including poor long-term graft survival due to chronic rejection and major side effects of long-term immunosuppressive therapy (which is required for the prevention of rejection). The ability to produce a state of tolerance after transplantation would potentially obviate long-term immunosuppression. Self-tolerance and immune homeostasis involve both central and peripheral immunoregulatory mechanisms. To date, studies have shown that many subsets of regulatory T cells (Tregs) control immune responses to foreign and alloantigens. The identification of Tregs that are positive for CD4, CD25, and the transcription factor forkhead box (Foxp3) has resulted in major advances in our understanding of the immunology of rejection and the development of transplant tolerance. In this article, we focus on the importance of Tregs in tolerance induction in experimental models of liver transplantation. Furthermore, we discuss the therapeutic potential of Tregs for the promotion of tolerance in transplant patients and highlight recent clinical trials of Treg-based therapies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.912
Threshold uncertainty score0.558

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 science0.0000.000
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 head0.023
GPT teacher head0.247
Teacher spread0.224 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations40
Published2012
Admission routes1
Has abstractyes

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