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Record W2972970598 · doi:10.1016/j.dib.2019.104490

Microarray data and pathway analyses for human microvascular endothelial cells supplemented with low dose vitamin D or niacin during lipotoxicity

2019· article· en· W2972970598 on OpenAlexafffund
Kia M. Peters, Nica M. Borradaile

Bibliographic record

VenueData in Brief · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsWestern University
FundersRobarts Research InstituteAgriculture and Agri-Food CanadaAgricultural Adaptation Council
KeywordsNiacinDNA microarrayBiologyMicroarray analysis techniquesLipotoxicityGene expressionCell biologyMicroarrayBiological pathwayCancer researchGeneEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

Low dose niacin and vitamin D can directly improve human microvascular endothelial cell angiogenic function under lipotoxic conditions Peters et al.,2019. Despite exerting similar benefits on in vitro angiogenic function, these vitamins are known to signal through independent receptors, raising the possibility that differential changes in gene expression may underlie these effects. Here we provide data collected using Affymetrix GeneChip microarrays to compare gene expression in human microvascular endothelial cells treated for 16 h with growth medium containing BSA alone, or BSA complexed with the saturated fatty acid palmitate, and supplemented with 10 μM niacin or 10 nM vitamin D (1,25-dihydroxyvitamin D3). Data sets of differential gene expression included many genes involved in cellular stress responses. Pathway analyses of genes specific to vitamin D treatment identified a robust overrepresentation of pathways related to the cell cycle and DNA replication and repair.

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: Dataset · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.284
Teacher spread0.260 · 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
GenreDataset

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
Published2019
Admission routes2
Has abstractyes

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