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Record W2056939173 · doi:10.3389/fimmu.2013.00292

How Numbers, Nature, and Immune Status of Foxp3+ Regulatory T-Cells Shape the Early Immunological Events in Tumor Development

2013· review· en· W2056939173 on OpenAlexaff
Guillaume Darrasse-Jèze, Katrina Podsypanina

Bibliographic record

VenueFrontiers in Immunology · 2013
Typereview
Languageen
FieldImmunology and Microbiology
TopicT-cell and B-cell Immunology
Canadian institutionsUniversité de MontréalMontreal Clinical Research Institute
Fundersnot available
KeywordsFOXP3Immune systemImmunologyBiologyMedicine

Abstract

fetched live from OpenAlex

The influence of CD4(+)CD25(+)Foxp3(+) regulatory T-cells (Tregs) on cancer progression has been demonstrated in a large number of preclinical models and confirmed in several types of malignancies. Neoplastic processes trigger an increase of Treg numbers in draining lymph nodes, spleen, blood, and tumors, leading to the suppression of anti-tumor responses. Treg-depletion before or early in tumor development may lead to complete tumor eradication and extends survival of mice and humans. However this strategy is ineffective in established tumors, highlighting the critical role of the early Treg-tumor encounters. In this review, after discussing old and new concepts of immunological tumor tolerance, we focus on the nature (thymus-derived vs. peripherally derived) and status (naïve or activated/memory) of the regulatory T-cells at tumor emergence. The recent discoveries in this field suggest that the activation status of Tregs and effector T-cells (Teffs) at the first encounter with the tumor are essential to shape the fate and speed of the immune response across a variety of tumor models. The relative timing of activation/recruitment of anti-tumor cells vs. tolerogenic cells at tumor emergence appears to be crucial in the identification of tumor cells as friend or foe, which has broad implications for the design of cancer immunotherapies.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0040.005
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 head0.013
GPT teacher head0.233
Teacher spread0.219 · 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

Citations44
Published2013
Admission routes1
Has abstractyes

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