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Record W2143539094 · doi:10.1194/jlr.m500011-jlr200

ESI-MS quantitation of increased sphingomyelin in Niemann-Pick disease type B HDL

2005· article· en· W2143539094 on OpenAlexaff
Ching Yin Lee, Alain Lesimple, Åsmund Larsen, Orval Mamer, Jacques Genest

Bibliographic record

VenueJournal of Lipid Research · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSphingolipid Metabolism and Signaling
Canadian institutionsMcGill University
Fundersnot available
KeywordsSphingomyelinPhospholipidElectrospray ionizationChemistryPhosphatidylcholinePhosphocholineLipidomicsCholesterolBiochemistryMass spectrometryChromatographyMembrane

Abstract

fetched live from OpenAlex

HDLs have been proposed to have antiatherogenic properties because of their role in reverse cholesterol transport as lipid acceptors. To elucidate the phospholipid profile of these particles, we used electrospray ionization mass spectrometry to examine the phosphatidylcholine (PC) and sphingomyelin (SM) composition of HDLs purified from plasma and nascently generated in vitro from fibroblasts. We also quantitatively compared the phospholipids present in these lipoproteins between normal and Niemann-Pick disease type B (NPD-B) subjects characterized by sphingomyelinase (SMase) deficiency. We demonstrated that plasma HDLs from NPD-B were significantly enriched in SM by an average of 28%, particularly the palmitoyl SM (with an increase of 95%), which accounted for approximately 25-44% of total SM molecular species. Similarly, we observed an increase of approximately 63% in total SM levels in nascent HDLs prepared from NPD-B fibroblasts. Although PC levels in nascent HDLs were comparable between control and NPD-B cells, there was a 95% increase in total PC levels similar to that of SM in plasma HDLs extracted from NPD-B subjects. These data provide insight into the structure of HDLs and identify potential new roles for SMase in lipoprotein metabolism.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.343
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 head0.051
GPT teacher head0.380
Teacher spread0.329 · 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

Citations28
Published2005
Admission routes1
Has abstractyes

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