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Record W4391972647 · doi:10.2460/ajvr.23.11.0267

Low levels of microRNA-21 in neutrophil-derived exosomes may contribute to airway smooth muscle hyperproliferation in horses with severe asthma

2024· article· en· W4391972647 on OpenAlexafffund
Amandine Vargas, Sophie Mainguy‐Seers, Roxane Boivin, Jean‐Pierre Lavoie

Bibliographic record

VenueAmerican Journal of Veterinary Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsMicrovesiclesmicroRNAImmunologyInflammationLipopolysaccharideBiologyExosomeCell growthAsthmaTransfectionMedicineCell biologyCancer researchCell cultureGene

Abstract

fetched live from OpenAlex

OBJECTIVE: Neutrophilic inflammation is associated with the degree of airway obstruction in severe equine asthma (SEA), but the contribution of these leukocytes to bronchial remodeling remains ill defined. Neutrophils could cause structural alterations of the airways by the release of exosomes, a type of cell-derived nanoparticles that can modify the biology of local and distant cells. Neutrophil-derived exosomes have been shown to increase airway smooth muscle (ASM) cell proliferation in humans and horses. Therefore, this study aimed to identify neutrophil exosomal microRNAs (miRs) implicated in the regulation of ASM biology in SEA. ANIMALS: 6 horses with SEA and 6 healthy controls. METHODS: The expression of selected miRs in exosomes from peripheral neutrophils was studied by quantitative PCR. The effects of miR-21 transfection in ASM cells were evaluated by gene expression analysis and proliferation studies. RESULTS: The miR-21 was downregulated in neutrophil exosomes from SEA horses, and it attenuated the proliferation of ASM cells stimulated with lipopolysaccharide. CLINICAL RELEVANCE: The lower level of miR-21 in neutrophil-derived exosomes could contribute to ASM hyperproliferation, which could, in turn, promote the thickening of the bronchial wall in SEA.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.338
Teacher spread0.307 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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