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Record W2996497069 · doi:10.1111/all.14157

CARsomes inhibit airway allergic inflammation in mice by inducing antigen‐specific Th2 cell apoptosis

2019· article· en· W2996497069 on OpenAlexaff
Huanping Zhang, Yingxue Sun, Zhi Bin Lin, Gui Yang, Jiang‐Qi Liu, Li‐Hua Mo, Xiao‐Rui Geng, Yan‐Nan Song, Haotao Zeng, Miao Zhao, Guo‐Shun Li, Ping–Chang Yang

Bibliographic record

VenueAllergy · 2019
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsWestern University
Fundersnot available
KeywordsImmunologyInflammationApoptosisAntigenEpitopeMicrovesiclesT cellIn vivoAllergic inflammationMedicineImmune systemBiologymicroRNA

Abstract

fetched live from OpenAlex

BACKGROUND: Skewed T helper (Th)2 response plays a crucial role in the pathogenesis of allergic diseases. The therapeutic efficacy for allergic diseases is unsatisfactory currently. This study aims to regulate the skewed Th2 response with CARsomes. METHODS: The CARsome consisted of an epitope of Dermatophagoides farina-1 (Derf1), a segment of the anti-DEC205 antibody, the scFv, and an open reading frame of perforin. This fusion protein binds to DEC205 molecule on the surface of exosomes derived from dendritic cells (DC). The effects of CARsome on inducing antigen (Ag)-specific Th2 cell apoptosis were assessed both in vivo and in vitro. RESULTS: Exposure to CARsomes in the culture induced Ag-specific Th2 cell apoptosis. Injection of CARsomes through the vein puncture also induced Ag-specific Th2 cell apoptosis in the lungs of sensitized mice. CARsomes could induce Ag-specific regulatory T cells. Administration of CARsomes efficiently inhibited experimental allergic airway inflammation. CONCLUSIONS: The CARsomes can inhibit allergic airway inflammation by inducing Ag-specific Th2 cell apoptosis and induce Ag-specific regulatory T cells. The data suggest that CARsomes have the translational potential to be used to treat allergic airway inflammation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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.

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

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

Citations10
Published2019
Admission routes1
Has abstractyes

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