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Record W2808278652 · doi:10.1186/s13104-018-3476-5

Evaluation of buccal swabs for pharmacogenetics

2018· article· en· W2808278652 on OpenAlexafffund
J. Sidney Ang, M.N. Aloise, Diana Dawes, Maryn G. Dempster, Robert Fraser, Andrea Paterson, Paul Stanley, Adriana Suarez‐Gonzalez, Martin Dawes, Hagit Katzov-Eckert

Bibliographic record

VenueBMC Research Notes · 2018
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsAXYS Technologies (Canada)College of Pharmacists of British ColumbiaSpinal Cord Injury BCUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsGenotypingBuccal swabMedicineVeterinary medicineGenotypeBiologyGeneticsGene

Abstract

fetched live from OpenAlex

A simple, non-invasive sample collection method is key for the integration of pharmacogenetics into clinical practice. The aim of this study was to gain samples for pharmacogenetic testing and evaluate the variation between dry-flocked and sponge-tipped buccal swabs in yield and quality of DNA isolated. Thirty-one participants collected samples using dry-flocked swabs and sponge-tipped swabs. Samples were assessed for DNA yield, quality and genotyping performance on a qPCR OpenArray platform of 28 pharmacogenetic SNPs and a CYP2D6 TaqMan copy number variant. DNA from sponge-tipped swabs had a significantly greater yield compared to DNA collected with dry-flocked swabs (p = 4.4 × 10 −7 ). Moreover, highest genotyping call rates across all assays and highest CNV confidence scores were observed in DNA samples collected from sponge-tipped swabs (97% vs. 54% dry-flocked swabs; 0.99 vs. 0.88 dry-flocked swabs, respectively). Sample collection using sponge-tipped swabs provides a DNA source of sufficient quantity and quality for pharmacogenetic variant detection using qPCR.

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)0.019
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.232
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.793
GPT teacher head0.671
Teacher spread0.122 · 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.

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

Citations13
Published2018
Admission routes2
Has abstractyes

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