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Record W1967985758 · doi:10.1164/rccm.200202-088oc

Mechanical Ventilation–induced Diaphragmatic Atrophy Is Associated with Oxidative Injury and Increased Proteolytic Activity

2002· article· en· W1967985758 on OpenAlexaff
R. Andrew Shanely, Ali Murat Zergeroğlu, Shannon L. Lennon, Takao Sugiura, Tossaporn Yimlamai, Debbie Enns, A. N. Belcastro, Scott K. Powers

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2002
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsWestern University
FundersNational Heart, Lung, and Blood Institute
KeywordsDiaphragmatic breathingAtrophyInternal medicineEndocrinologyMedicineOxidative stressMuscle atrophyMyofibrilPathology

Abstract

fetched live from OpenAlex

Prolonged mechanical ventilation (MV) results in reduced diaphragmatic maximal force production and diaphragmatic atrophy. To investigate the mechanisms responsible for MV-induced diaphragmatic atrophy, we tested the hypothesis that controlled MV results in oxidation of diaphragmatic proteins and increased diaphragmatic proteolysis due to elevated protease activity. Further, we postulated that MV would result in atrophy of all diaphragmatic muscle fiber types. Mechanically ventilated animals were anesthetized, tracheostomized, and ventilated with 21% O2 for 18 hours. MV resulted in a decrease (p < 0.05) in diaphragmatic myofibrillar protein and the cross-sectional area of all muscle fiber types (i.e., I, IIa, IId/x, and IIb). Further, MV promoted an increase (p < 0.05) in diaphragmatic protein degradation along with elevated (p < 0.05) calpain and 20S proteasome activity. Finally, MV was also associated with a rise (p < 0.05) in both protein oxidation and lipid peroxidation. These data support the hypothesis that MV is associated with atrophy of all diaphragmatic fiber types, increased diaphragmatic protease activity, and augmented diaphragmatic oxidative stress.

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)0.001
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 score0.462
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.298
Teacher spread0.273 · 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

Citations356
Published2002
Admission routes1
Has abstractyes

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