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Record W2071134912 · doi:10.1002/bmc.1263

An analytical method for cyclosporine using liquid chromatography–mass spectrometry

2009· article· en· W2071134912 on OpenAlexafffund
Srividya V. Kanduru, Vishwa Somayaji, Afsaneh Lavasanifar, Dion R. Brocks

Bibliographic record

VenueBiomedical Chromatography · 2009
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsChemistryChromatographyMass spectrometryLiquid chromatography–mass spectrometry

Abstract

fetched live from OpenAlex

A liquid chromatographic mass spectrometric (LC-MS) assay has been developed for cyclosporine A (CyA) in rat plasma using amiodarone as internal standard (IS). Rat plasma (100 microL) containing drug and IS were extracted using liquid-liquid extraction with 4 mL of 95:5 ether:methanol. After evaporation of the organic layer the residue was reconstituted with 500 microL of water. Then the aqueous layer was transferred to LC-MS sample vials. A 10 microL volume was injected. The analysis was performed on a C(8) column 3.5 microm (2.1 x 50 mm) heated to 60 degrees C with a mobile phase consisting of acetonitrile:methanol:0.2% NH(4)OH (60:20:20) at an isocratic flow-rate of 0.2 mL/min. The ions used for quantitation of CyA and IS were m/z 1202.8 and 645.9, with retention times of 3.35 and 4.72 min, respectively. Linear relationships (r(2) > 0.99) were achieved between plasma or blood concentration and peak height ratios (drug:IS) over the concentration range 50-5000 ng/mL. The CV% and mean error were <19%. Based on validation data, the lower limit of quantification for the assay was 50 ng/mL. The reported assay method displayed high measures of linearity, sensitivity, reliability and precision, allowing its applicability in pharmacokinetic studies in rat.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.002
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0030.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0100.013

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 head0.028
GPT teacher head0.365
Teacher spread0.337 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2009
Admission routes2
Has abstractyes

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