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Record W1801950864 · doi:10.1039/c5an00646e

An integrated ceramic, micro-fluidic device for the LC/MS/MS analysis of pharmaceuticals in plasma

2015· article· en· W1801950864 on OpenAlexaff
Paul Rainville, James Peter Murphy, Mike Tomany, Ian D. Wilson, Norman W. Smith, Christopher A. Evans, Jonathan Kheler, Chester L. Bowen, Robert S. Plumb, Jeremy K. Nicholson

Bibliographic record

VenueThe Analyst · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsKensington Health
Fundersnot available
KeywordsChromatographyCapillary actionCeramicChemistryAnalyteMicrofluidicsAnalytical Chemistry (journal)Materials scienceNanotechnology

Abstract

fetched live from OpenAlex

An integrated capillary scale (300 μm id) ceramic microfluidic LC system combined with MS/MS has been successfully employed for the quantitative analysis of pharmaceutical compounds in human plasma. The capillary ceramic microfluidic LC/MS/MS system showed an approximate 20-fold (range 11-38-fold) increase in sensitivity compared with a standard 2.1 mm scale UPLC/MS/MS system for a broad range of analytes. The loading capacity of the devices capillary separations channel allowed injection of 2 μL of an aqueous solution, and up to 1.2 μL of a typical protein-precipitated plasma sample, onto the reversed-phase chromatography system. The system also showed excellent chromatographic performance and robustness, with no deleterious effects on the chromatography observed over the course of 1000 injections of protein-precipitated plasma. The ability of the ceramic microfluidic LC/MS/MS system to deliver this level of sensitivity and performance enables the routine quantification of pharmaceutical compounds from small format samples, such as those obtained by dried blood spot or other blood microsampling approaches, to be performed.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.031
GPT teacher head0.293
Teacher spread0.262 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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