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Record W4321596105 · doi:10.3389/fimmu.2023.1128023

Understanding fibroblast-immune cell interactions via co-culture models and their role in asthma pathogenesis

2023· review· en· W4321596105 on OpenAlexafffund
Fama Thiam, Safiya Al Yazeedi, Kang-ni Feng, Sakshi Phogat, E. Demirsoy, J. Brussow, Filsan Ahmed Abokor, Emmanuel T. Osei

Bibliographic record

VenueFrontiers in Immunology · 2023
Typereview
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersMitacsCanada Foundation for InnovationProvidence Health Care
KeywordsPathogenesisFibroblastAsthmaImmunologyImmune systemInflammationFibrosisLungMedicineEffectorIn vitroBiologyPathologyGeneticsInternal medicine

Abstract

fetched live from OpenAlex

Asthma is a chronic lung disease involving airway inflammation and fibrosis. Fibroblasts are the main effector cells important for lung tissue production which becomes abnormal in asthmatics and is one of the main contributors to airway fibrosis. Although fibroblasts were traditionally viewed solely as structural cells, they have been discovered to be highly active, and involved in lung inflammatory and fibrotic processes in asthma. In line with this, using 2D and 3D in vitro co-culture models, a complex interaction between lung fibroblasts and various immune cells important for the pathogenesis of asthma have been recently uncovered. Hence, in this review, we provide the first-ever summary of various studies that used 2D and 3D in vitro co-culture models to assess the nature of aberrant immune cell-fibroblast interactions and their contributions to chronic inflammation and fibrotic mechanisms in asthma pathogenesis.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.065
GPT teacher head0.298
Teacher spread0.234 · 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

Citations31
Published2023
Admission routes2
Has abstractyes

Explore more

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