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Record W2033197679 · doi:10.1016/j.jalz.2014.05.1093

P3‐006: MINING THE NEURAL AND PLASMA PHOSPHOLIPIDOME TO IDENTIFY CRITICAL LIPID DETERMINANTS OF ALZHEIMER'S DISEASE CONVERSION

2014· article· en· W2033197679 on OpenAlexaff
Yun Wang, Hongbin Xu, Alexandre P. Blanchard, Matthew Granger, Graham Mazereeuw, Graeme Taylor, Samantha Sherman, Zhibin Ning, Benjamin Lam, Daniel Figeys, Krista L. Lanctôt, Sandra E. Black, Steffany A. L. Bennett

Bibliographic record

VenueAlzheimer s & Dementia · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreSunnybrook HospitalUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsClinical Dementia RatingLipidomeDementiaHippocampusDepression (economics)PsychologyNeuroscienceAlzheimer's diseaseAmygdalaCognitive impairmentInternal medicineDiseaseCognitionMedicineLipid metabolism

Abstract

fetched live from OpenAlex

Commonly conceptualized as undulating fields of identical molecules, neuronal membranes are, in fact, made up of hundreds of chemically and molecularly diverse lipid species. Here, we asked whether impairment of phosphocholine (PC) membrane can be used to predict transition from a pre-symptomatic to symptomatic state in (1) an experimental mouse model of familial Alzheimer's Disease (AD) and (2) in a clinical pilot study of mild cognitive impairment (MCI) and sporadic AD. The PC lipidome was profiled in hippocampus, temporal cortex, plasma at 2, 4, 6, and 8 months in the TgCRND8 AD mouse model and their NonTg littermates following assessment of learning and memory impairment in the Morris Water Maze. These results were compared to clinical circulating profiles of a pilot cohort of 21 cognitively normal, 17 mild cognitive impairment (MCI) patients, and 32 AD patients. Patients were assessed using the Clinical Dementia Rating (CDR), Structured Clinical Interview for DSM-IV Disorders - Depression scale (SCID-I), Hamilton Rating Scale for Depression [HAM-D], and Mini Mental State Examination (MMSE). PCs were isolated using a modified Bligh & Dyer method adapted for efficient extraction of phosphatidylcholines, alkyacylphosphocholines, plasmenylacylphosphocholines, and acylformylglycerophosphocholines. Profiling was performed by LC-ESI-MS with in both precursor ion mode (discovery) and multiple reaction monitoring mode (quantitation). 499 unique PC lipid species were identified. In NonTg mice, PCs clearly clustered into expected patterns of “stable” critical compositions, structural species being actively mobilized to maintain these critical compositions, and bioactive signaling molecules. The abundance of 24 PC species was significantly altered in 5.5 month old TgCRND8 mice. Significant correlations between the abundance of 13 species in either the hippocampus or temporal cortex were detected that could be predicted by associated changes in the circulating lipidome. Experimental profiles were then compared to clinical profiles as part of a pilot study. Increases in 3 critical species correlated with CDR and/or MMSE scores in MCI and AD and 4 species correlated with HAM-D scores in controls. These data provide proof of principle justifying a larger clinical investigation into the capacity of the circulating phospholipidome to predict disease progression.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.293
Teacher spread0.275 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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