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The Role of a High Cholesterol Diet on Surfactant Biophysics during Acute Lung Injury

2016· article· en· W4389024188 on OpenAlexafffundabout
Scott Milos, Josh Qua Hiansen, Cory Yamashita, Ruud A. W. Veldhuizen

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsPulmonary surfactantCholesterolPhospholipidLungChemistryPulmonary complianceInternal medicineRespiratory systemMedicineEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

Background Pulmonary surfactant is mixture of phospholipid (85%), surfactant proteins (~10%), and neutral lipids, predominately cholesterol (~5%), vital for normal respiratory function. Surfactant's biophysical function is to reduce lung surface tension, especially during exhalation (compression) where surface tensions reach near 0mN/m values, thereby helping to maintain normal pulmonary compliance. However, in acute lung injury, characterized by reduced arterial oxygenation, dysfunction of the surfactant system contributes to impaired respiratory function. Recent studies have shown that supra‐physiological levels of cholesterol in surfactant contributes to impaired surface tension reduction of surfactant from injured lungs. It is unknown if elevated serum cholesterol levels can contribute to elevated cholesterol in surfactant and thereby play a predisposing role in the development of more severe acute lung injury through surfactant biophysical impairment. It was hypothesized that rats fed a high cholesterol diet would have more severely impaired surfactant biophysical function, due to increased cholesterol incorporation into newly secreted surfactant, compared to standard diet counterparts. Methods Rats were randomized to receive either a standard diet or a high cholesterol diet for 17 – 20 days. A blood sample was taken to confirm serum cholesterol levels attributed to each diet. Subsequently, rats were repeatedly lavaged to remove surfactant, inducing acute lung injury and fresh surfactant secretion. Following 2 hours of mechanical ventilation, during which arterial oxygenation was monitored, extracellular surfactant was isolated by lavage and surfactant phospholipid and cholesterol content was quantified. Surfactant was concentrated to 2mg/ml and biophysical function during 20 dynamic compression/expansion cycles was assessed using a constrained sessile drop surfactometer. Minimum surface tension during each compression cycle was the primary indicator of surfactant biophysical function. Results The results showed no difference in arterial oxygenation following surfactant depleted lung injury and ventilation between rats fed a standard diet and rats fed a high cholesterol diet. Additionally there was no difference in phospholipid or cholesterol content in isolated surfactant from rats fed either diet. Interestingly, surfactant isolated from rats fed a high cholesterol diet did have a significantly decreased ability to reach low surface tensions during compression compared to rats fed a standard diet. This was observed particularly at latter compression cycles which are more representative of the repeated compression/expansion environment surfactant is exposed to in the lung. Conclusion In conclusion, contrary to the hypothesis, a high cholesterol diet did not predispose to more severe lung injury, or increased surfactant cholesterol content during surfactant depleted lung injury. However the high cholesterol diet did produce a significant impairment in minimum surface tension reduction, potentially due to other diet induced changes to surfactant such as alterations to other lipid species composing surfactant. Support or Funding Information Sources of Funding : Canadian Institutes of Health Research

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.319
Teacher spread0.303 · 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 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

Citations2
Published2016
Admission routes3
Has abstractyes

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