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Record W2021382398 · doi:10.1002/ppul.20772

The effect of elevated dietary cholesterol on pulmonary surfactant function in adolescent mice

2008· article· en· W2021382398 on OpenAlexaff
K.C. McCrae, B. Weltman, S. Alyward, Raymond A. Shaw, Michael G. Sowa, Helmut Unruh, T. Rand, James A. Thliveris, J. E. Scott

Bibliographic record

VenuePediatric Pulmonology · 2008
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsSaint Mary's UniversitySt. Mary's UniversityResearch ManitobaNational Research Council Institute for BiodiagnosticsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
Fundersnot available
KeywordsCholesterolPulmonary surfactantMedicinePhospholipidInternal medicineEndocrinologyBiochemistryChemistry

Abstract

fetched live from OpenAlex

It has been established that phospholipids and cholesterol interact in films of pulmonary surfactant (PS). Generally it is thought that phospholipids increase film stability whereas cholesterol increases film fluidity. To study this further, we modified dietary cholesterol in mice which received either standard rodent lacking cholesterol (sd), or high cholesterol (2%) diet (hc) for 1 month. Phospholipid stability was investigated by a capillary surfactometer (CS), which measures airflow resistance and patency. PS was collected by bronchiolar lavage and centrifuged to obtain the surface-active film (SAF). Results showed that the hc-SAF had significantly more cholesterol than sd-SAF. CS analyses at 37 degrees C showed no significance differences in airflow resistance between hc-SAF and sd-SAF. However, at 37 degrees C, sd-SAF showed greater ability to maintain patency compared to hc-SAF, whereas at 42 degrees C hc-SAF showed patency ability similar to sd-SAF. The results suggested that increased cholesterol in hc-SAF induced less stability in the SAF possibly due to cholesterol's fluidizing effect on phospholipids at physiological temperatures.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.036
GPT teacher head0.319
Teacher spread0.283 · 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 designObservational
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

Citations18
Published2008
Admission routes1
Has abstractyes

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