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Record W2955939469 · doi:10.1097/aln.0000000000002837

Oxidants Regulated Diaphragm Proteolysis during Mechanical Ventilation in Rats

2019· article· en· W2955939469 on OpenAlexafffund
Nikolay Moroz, Karen Maes, Jean‐Philippe Leduc‐Gaudet, Peter Goldberg, Basil J. Petrof, Dominique Mayaki, Theodoros Vassilakopoulos, Dilson E. Rassier, Ghislaine Gayan‐Ramirez, Sabah N. A. Hussain

Bibliographic record

VenueAnesthesiology · 2019
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineProteolysisDiaphragm (acoustics)Mechanical ventilationVentilation (architecture)Cell biologyAnesthesiaBiochemistryEnzymeMechanical engineering

Abstract

fetched live from OpenAlex

WHAT WE ALREADY KNOW ABOUT THIS TOPIC: Diaphragm dysfunction and atrophy develop during controlled mechanical ventilation. Although oxidative stress injures muscle during controlled mechanical ventilation, it is unclear whether it causes autophagy or fiber atrophy. WHAT THIS ARTICLE TELLS US THAT IS NEW: Pretreatment of rats undergoing 24 h of mechanical ventilation with N-acetylcysteine prevents decreases in diaphragm contractility, inhibits the autophagy and proteasome pathways, but has no influence on the development of diaphragm fiber atrophy. BACKGROUND: Diaphragm dysfunction and atrophy develop during prolonged controlled mechanical ventilation. Fiber atrophy has been attributed to activation of the proteasome and autophagy proteolytic pathways. Oxidative stress activates the proteasome during controlled mechanical ventilation, but it is unclear whether it also activates autophagy. This study investigated whether pretreatment with the antioxidant N-acetylcysteine affects controlled mechanical ventilation-induced diaphragm contractile dysfunction, fiber atrophy, and proteasomal and autophagic pathway activation. The study also explored whether proteolytic pathway activity during controlled mechanical ventilation is mediated by microRNAs that negatively regulate ubiquitin E3 ligases and autophagy-related genes. METHODS: Three groups of adult male rats were studied (n = 10 per group). The animals in the first group were anesthetized and allowed to spontaneously breathe. Animals in the second group were pretreated with saline before undergoing controlled mechanical ventilation for 24 h. The animals in the third group were pretreated with N-acetylcysteine (150 mg/kg) before undergoing controlled mechanical ventilation for 24 h. Diaphragm contractility and activation of the proteasome and autophagy pathways were measured. Expressions of microRNAs that negatively regulate ubiquitin E3 ligases and autophagy-related genes were measured with quantitative polymerase chain reaction. RESULTS: Controlled mechanical ventilation decreased diaphragm twitch force from 428 ± 104 g/cm (mean ± SD) to 313 ± 50 g/cm and tetanic force from 2,491 ± 411 g/cm to 1,618 ± 177 g/cm. Controlled mechanical ventilation also decreased diaphragm fiber size, increased expression of several autophagy genes, and augmented Atrogin-1, MuRF1, and Nedd4 expressions by 36-, 41-, and 8-fold, respectively. Controlled mechanical ventilation decreased the expressions of six microRNAs (miR-20a, miR-106b, miR-376, miR-101a, miR-204, and miR-93) that regulate autophagy genes. Pretreatment with N-acetylcysteine prevented diaphragm contractile dysfunction, attenuated protein ubiquitination, and downregulated E3 ligase and autophagy gene expression. It also reversed controlled mechanical ventilation-induced microRNA expression decreases. N-Acetylcysteine pretreatment had no affect on fiber atrophy. CONCLUSIONS: Prolonged controlled mechanical ventilation activates the proteasome and autophagy pathways in the diaphragm through oxidative stress. Pathway activation is accomplished, in part, through inhibition of microRNAs that negatively regulate autophagy-related genes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.452

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)0.0000.000

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 head0.010
GPT teacher head0.244
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.

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".

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Citations27
Published2019
Admission routes2
Has abstractyes

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