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Record W4401715798 · doi:10.1080/19490976.2024.2390135

Microbiota and plasticity of antigen-presenting ILC3s: impact on antitumor immune response

2024· review· en· W4401715798 on OpenAlexfundno aff
Alessia Calabrò, Fabiana Drommi, Claudia De Pasquale, Giuseppe Navarra, Paolo Carrega, Irene Bonaccorsi, Guido Ferlazzo, Stefania Campana

Bibliographic record

VenueGut Microbes · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsnot available
FundersMinistry of Health, British ColumbiaMinistero dell’Istruzione, dell’Università e della RicercaMinistero della SaluteMinistry of Education
KeywordsBiologyInnate lymphoid cellImmune systemImmunologyMajor histocompatibility complexCrosstalkAcquired immune systemContext (archaeology)Cancer immunotherapyAntigen presentationAntigenAntigen-presenting cellGut floraImmunotherapyT cell

Abstract

fetched live from OpenAlex

Growing evidence highlights the pivotal role of RORγt-innate lymphoid cells (ILCs) in the establishment of antitumor immune response and in enhancing tumor sensitivity to immunotherapy. Noteworthy, type 3 ILCs (ILC3s) have been recently acknowledged as an important class of antigen-presenting cells (APCs) in the context of host-microorganism interactions shaping the adaptive immune response in the intestinal mucosa. Although a broad range of mouse models has led to significant progress in untangling the role of ILC3s as APCs, the outcome of major histocompatibility complex (MHC)-dependent ILC-T cell crosstalk in colorectal cancer (CRC) remains underexplored in human. Moreover, expression of MHCII is confined to ILC3 subset, endowed with lymphoid tissue-inducing properties, that adopts tissue-specific fates and functions. Intestinal microbiota could dictate the plasticity of antigen-presenting ILC3s and we here summarize our current understanding of the functions of these cells in both mouse and human CRC discussing the role of microbiota as a key modulator of their tumor-suppressive activity.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.031
GPT teacher head0.324
Teacher spread0.294 · 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.

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

Citations8
Published2024
Admission routes1
Has abstractyes

Explore more

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