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Record W2938090651 · doi:10.1002/cpim.73

Identification of Group 2 Innate Lymphoid Cells in Mouse Lung, Liver, Small Intestine, Bone Marrow, and Mediastinal and Mesenteric Lymph Nodes

2019· article· en· W2938090651 on OpenAlexafffund
Mónica Romera‐Hernández, Laura Mathä, Catherine A. Steer, Maryam Ghaedi, Fumio Takei

Bibliographic record

VenueCurrent Protocols in Immunology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsTerry Fox Research InstituteUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMesenteric lymph nodesBone marrowPathologyLymphLungInnate lymphoid cellLymphatic systemSmall intestineMedicineBiologyImmunologyInnate immune systemSpleenInternal medicineImmune system

Abstract

fetched live from OpenAlex

Innate lymphoid cells (ILCs) are a heterogeneous family of lymphocytes that populate barrier and non-barrier tissues. ILCs regulate immune responses to pathogens and commensals but also sustain metabolic homeostasis, tissue remodeling after injury and establish dialogue with the nervous system. ILCs rapidly become activated in the absence of adaptive antigen receptors by responding to signaling molecules provided by hematopoietic or non-hematopoietic cells. Here we provide protocols designed for processing the lung, liver, small intestine, bone marrow, mediastinal and mesenteric lymph nodes in order to obtain a purified leukocyte fraction of cells, in which ILC2 enrichment is optimized. In addition, we describe in detail the methodologies used to activate ILC2s and the assays necessary for the detection of their effector cytokines. We highlight the differences in ILC2 characterization within distinct tissues that we have recently identified. © 2019 by John Wiley & Sons, Inc.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.018
GPT teacher head0.261
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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
Published2019
Admission routes2
Has abstractyes

Explore more

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