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Mechanical ventilation of isolated rat lungs changes the structure and biophysical properties of surfactant

2002· article· en· W2161518794 on OpenAlexaff
Ruud A. W. Veldhuizen, Blayne Welk, Stephen Hearn, Kaushik Nag, Nils O. Petersen, Fred Possmayer

Bibliographic record

VenueJournal of Applied Physiology · 2002
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsLawson Health Research Institute
Fundersnot available
KeywordsPulmonary surfactantMechanical ventilationTidal volumeVentilation (architecture)LungPositive end-expiratory pressureMedicineAnesthesiaPulmonary complianceChemistryRespiratory systemInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Mechanical ventilation is an essential but potentially harmful therapeutic intervention for patients with acute lung injury. The objective of this study was to investigate the effects of mechanical ventilation on large-aggregate surfactant (LA) structure and function. Isolated rat lungs were randomized to either a nonventilated control group, a relatively noninjuriously ventilated group [1 h, 10 ml/kg tidal volume, 3 cmH(2)O positive end-expiratory pressure (PEEP)], or an injuriously ventilated group (1 h, 20 ml/kg tidal volume, 0 cmH(2)O PEEP). Injurious ventilation resulted in significantly decreased lung compliance compared with the other two groups. LA structure, as determined by electron microscopy, revealed that LA from the injurious group had significantly lower amounts of organized lipid-protein structures compared with LA obtained from the other groups. Analysis of the biophysical properties by using a captive bubble surfactometer demonstrated that adsorption and surface tension reduction were significantly impaired with LA from the injuriously ventilated lungs. We conclude that the injurious mechanical ventilation impairs LA function and that this impairment is associated with significant morphological alterations.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.190

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 head0.040
GPT teacher head0.296
Teacher spread0.256 · 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

Citations59
Published2002
Admission routes1
Has abstractyes

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