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Record W1984553339 · doi:10.1159/000244250

Effect of Acidosis on Bilirubin-Lipid Extract Surfactant Interaction

2009· article· en· W1984553339 on OpenAlexaff
Maurizio Amato, Samuel Schürch, H Bachofen, P. Burri

Bibliographic record

VenueBiology of the Neonate · 2009
Typearticle
Languageen
FieldMedicine
TopicNeonatal Health and Biochemistry
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPulmonary surfactantAcidosisBilirubinMedicineMetabolic acidosisInternal medicineChemistryBiochemistry

Abstract

fetched live from OpenAlex

The risk of bilirubin toxicity in newborn infants with respiratory distress syndrome and hyperbilirubinemia may depend on many factors including pH of the system. Biophysical activity and inhibition characteristics were studied in vitro for lipid extract surfactant (Curosurf, 0.25 mg/ml phospholipids), bilirubin (1.0 mg/ml dissolved in NaOH) and mixed solutions at different pH ranging from 5.0 to 7.4. It was found that unconjugated bilirubin modifies surface tension behavior of lipid extract surfactant films. Maximum and minimum surface tension levels were significantly higher in mixed solutions compared to experiments using pure Curosurf independently from pH. Film area compression for pure Curosurf was not influenced by pH and varied between 22 +/- 4% at pH 5.0 and 23 +/- 9% at pH 7.4. Adding bilirubin to lipid extract surfactant, area compression to achieve minimum surface tension increased significantly to 83 +/- 4% at pH 5.0 and 85 +/- 4% at pH 7.4 (p < 0.01). Bilirubin alone showed negligible surface activity independently from pH (83 +/- 7% at pH 5.0 and 78 +/- 9% at pH 7.4) (p > 0.5). We conclude that bilirubin has a detrimental effect on the surface tension properties of lipid extract surfactant in vitro and that this interaction is independent from pH. These data suggest no influence of acidosis on alveolar surfactant system in babies with respiratory distress syndrome and hyperbilirubinemia.

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 score0.210
Threshold uncertainty score0.235

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 head0.012
GPT teacher head0.324
Teacher spread0.313 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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