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Record W2042575137 · doi:10.1097/aci.0b013e32834ec6eb

Epithelial–mesenchymal transition in the pathophysiology of airway remodelling in asthma

2012· review· en· W2042575137 on OpenAlexafffund
Tillie‐Louise Hackett

Bibliographic record

VenueCurrent Opinion in Allergy and Clinical Immunology · 2012
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsSt. Paul's Hospital
FundersCanadian Institutes of Health Research
KeywordsRespiratory epitheliumAirwayEpithelial–mesenchymal transitionMedicineEpitheliumAsthmaImmunologyMesenchymal stem cellFibrosisDownregulation and upregulationPathologyBiologyGene

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: We currently understand little about the mechanisms that lead to airway remodeling in asthma. The origin of the mesenchymal cells that contribute to fibrosis of the airway is poorly understood. However, emerging evidence suggests that the airway epithelium could contribute to airway remodeling through the process of epithelial-mesenchymal transition (EMT) following environmental challenge. In this review, we will discuss the mechanistic features of EMT and highlight recent descriptions of EMT in the airway to further define the role of the airway epithelium in the pathogenesis of asthma. RECENT FINDINGS: Growth factors, inflammatory mediators, and matricellular proteins expressed following exposure to environmental insults are known to induce downregulation of epithelial cell-cell adhesions and promote mesenchymal gene expression programs both in vitro and in vivo. These results demonstrate that the plastic and dynamic airway epithelium may contribute to airway remodeling via EMT in asthma. SUMMARY: It is becoming increasingly clear that the airway epithelium orchestrates inflammatory and remodeling responses of the airway. Understanding the regulatory mechanisms involved in epithelial plasticity will be crucial to determine effective therapies to halt the progression of airway remodeling in asthma.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
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 head0.118
GPT teacher head0.417
Teacher spread0.299 · 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 designOther design
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

Citations199
Published2012
Admission routes2
Has abstractyes

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