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Matrix-assisted laser desorption/ionization mass spectrometric quantification of the mu opioid receptor agonist DAMGO in ovine plasma

2000· article· en· W1964592368 on OpenAlexaff
Dominic M. Desiderio, Urs Wirth, Jerry L. Lovelace, Genevieve Fridland, Edward S. Umstot, Thi M.‐D. Nguyen, Peter W. Schiller, Hazel Szeto, James F. Clapp

Bibliographic record

VenueJournal of Mass Spectrometry · 2000
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsMontreal Clinical Research Institute
FundersNational Center for Research ResourcesU.S. Public Health ServiceNational Institutes of Health
KeywordsChemistryDAMGOReflectronMass spectrometryChromatographyAnalytical Chemistry (journal)IonizationAgonistOpioid peptideOpioidIonOpioid receptorTime-of-flight mass spectrometryReceptorBiochemistry

Abstract

fetched live from OpenAlex

The synthetic opioid peptide analog Tyr-D-Ala-Gly-N-methyl-Phe-Gly-ol (DAMGO), which is a mu opioid receptor-selective agonist, was quantified in ovine plasma samples with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS), using delayed extraction and a reflectron. The internal standard was pentadeuterated DAMGO. Timed-ion selection was used to select the precursor ion. The analysis of the post-source decay fragments improved the detection sensitivity, and the use of the precursor-product ion relationship optimized the specificity. For plasma samples, the inter-assay variability of this method was 6.4% (n = 79) and the intra-assay variability was 6.0% (n = 10). The variability for controls was 3.4% (n = 43). The profile of DAMGO amount versus time was determined in sheep plasma, and the corresponding pharmacokinetic data were calculated.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.000

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 head0.012
GPT teacher head0.255
Teacher spread0.243 · 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

Citations35
Published2000
Admission routes1
Has abstractyes

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