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Record W3047030607 · doi:10.1183/16000617.0123-2019

Airway mechanical compression: its role in asthma pathogenesis and progression

2020· review· en· W3047030607 on OpenAlexaff
Punnam Chander Veerati, Jennifer A. Mitchel, Andrew T. Reid, Darryl A. Knight, Nathan W. Bartlett, Jin‐Ah Park, Christopher Grainge

Bibliographic record

VenueEuropean Respiratory Review · 2020
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsProvidence Health Care Research InstituteUniversity of British Columbia
FundersNational Heart, Lung, and Blood Institute
KeywordsAsthmaBronchoconstrictionMedicinePathogenesisAirwayImmunologyPathologicalLungRespiratory epitheliumPathologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

The lung is a mechanically active organ, but uncontrolled or excessive mechanical forces disrupt normal lung function and can contribute to the development of disease. In asthma, bronchoconstriction leads to airway narrowing and airway wall buckling. A growing body of evidence suggests that pathological mechanical forces induced by airway buckling alone can perpetuate disease processes in asthma. Here, we review the data obtained from a variety of experimental models, includingin vitro,ex vivoandin vivoapproaches, which have been used to study the impact of mechanical forces in asthma pathogenesis. We review the evidence showing that mechanical compression alters the biological and biophysical properties of the airway epithelium, including activation of the epidermal growth factor receptor pathway, overproduction of asthma-associated mediators, goblet cell hyperplasia, and a phase transition of epithelium from a static jammed phase to a mobile unjammed phase. We also define questions regarding the impact of mechanical forces on the pathology of asthma, with a focus on known triggers of asthma exacerbations such as viral infection.

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)0.001
metaresearch head score (Gemma)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.056
GPT teacher head0.350
Teacher spread0.294 · 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

Citations37
Published2020
Admission routes1
Has abstractyes

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