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Immunological perspective of self versus tumor antigens: insights from the RIP‐gp model

2011· review· en· W1894616047 on OpenAlexafffund
Dilan Dissanayake, Matthew A. Gronski, Albert Lin, Alisha R. Elford, Pamela S. Ohashi

Bibliographic record

VenueImmunological Reviews · 2011
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
FundersCanadian Institutes of Health Research
KeywordsBiologyLymphocytic choriomeningitisImmunologyAntigenClonal deletionImmune toleranceImmune systemMajor histocompatibility complexCytotoxic T cellT cellCancer researchT-cell receptorCD8Genetics

Abstract

fetched live from OpenAlex

Self-reactive T cells in the body are controlled by mechanisms of peripheral tolerance that limit their activation and induction of immune pathology. Our understanding of these mechanisms has been advanced by the use of tissue-specific promoters to express neo-self-antigens. Here, we present findings using the RIP-gp (rat insulin promoter-glycoprotein) transgenic mouse, which expresses the lymphocytic choriomeningitis virus glycoprotein (LCMV-gp) specifically in the pancreatic β islet cells. T cells responsive to this antigen remain ignorant of the LCMV-gp expressed by the islets, and breaking tolerance is dependent upon the maturation status of antigen-presenting cells, the avidity of the T-cell receptor ligation, and the level of major histocompatibility complex expression in the pancreas. Furthermore, decreased activity of Casitas B-lineage lymphoma b, a negative regulator of T-cell receptor signaling, can allow recognition and destruction of the pancreatic islets. This review discusses the roles of these factors in the context of anti-tissue responses, both in the setting of autoimmunity and in anti-tumor immunity.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.008

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 head0.126
GPT teacher head0.334
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

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

Citations16
Published2011
Admission routes2
Has abstractyes

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