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Record W2549078849 · doi:10.1111/all.13089

Chemokine receptors in allergic diseases

2016· review· en· W2549078849 on OpenAlexfundno aff
Laure Castan, A. Magnan, Grégory Bouchaud

Bibliographic record

VenueAllergy · 2016
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsnot available
FundersSeventh Framework ProgrammeUniversité de NantesOntario Ministry of Research, Innovation and ScienceEuropean CommissionFondation pour la Recherche MédicaleFondation du Souffle
KeywordsChemokine receptorImmunologyChemokineImmune systemAllergyAtopic dermatitisCCL13CCR10Allergic inflammationReceptorCCR1CCR3CCL21InflammationCC chemokine receptorsChemokine receptor CCR5CCL18MedicineBiologyInternal medicine

Abstract

fetched live from OpenAlex

Under homeostatic conditions, as well as in various diseases, leukocyte migration is a crucial issue for the immune system that is mainly organized through the activation of bone marrow-derived cells in various tissues. Immune cell trafficking is orchestrated by a family of small proteins called chemokines. Leukocytes express cell-surface receptors that bind to chemokines and trigger transendothelial migration. Most allergic diseases, such as asthma, rhinitis, food allergies, and atopic dermatitis, are generally classified by the tissue rather than the type of inflammation, making the chemokine/chemokine receptor system a key point of the immune response. Moreover, because small antagonists can easily block such receptors, various molecules have been developed to suppress the recruitment of immune cells during allergic reactions, representing potential new drugs for allergies. We review the chemokines and chemokine receptors that are important in asthma, food allergies, and atopic dermatitis and their respectively developed antagonists.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.025
GPT teacher head0.317
Teacher spread0.292 · 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 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

Citations81
Published2016
Admission routes1
Has abstractyes

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