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Record W1986671417 · doi:10.1039/b710510j

Development of analytical methods for multiplex bio-assay with inductively coupled plasma mass spectrometry

2008· article· en· W1986671417 on OpenAlexafffund
Olga Ornatsky, Robert Kinach, Dmitry Bandura, Xudong Lou, Scott D. Tanner, Vladimir Baranov, Mark Nitz, Mitchell A. Winnik

Bibliographic record

VenueJournal of Analytical Atomic Spectrometry · 2008
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersNational Institute of General Medical SciencesOntario Genomics InstituteGenome Canada
KeywordsMultiplexInductively coupled plasma mass spectrometryMass spectrometryChemistryChromatographyElemental analysisInductively coupled plasmaReagentAnalytical Chemistry (journal)PlasmaBioinformaticsBiology

Abstract

fetched live from OpenAlex

Advances in the development of highly multiplexed bio-analytical assays with inductively coupled plasma mass spectrometry (ICP-MS) detection are discussed. Use of novel reagents specifically designed for immunological methods utilizing elemental analysis is presented. The major steps of method development, including selection of elements for tags, validation of tagged reagents, and examples of multiplexed assays, are considered in detail. The paper further describes experimental protocols for elemental tagging of antibodies, immunostaining of live and fixed human leukemia cells, and preparation of samples for ICP-MS analysis. Quantitative analysis of surface antigens on model cell lines using a cocktail of seven lanthanide labeled antibodies demonstrated high specificity and concordance with conventional immunophenotyping.

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)0.006
metaresearch head score (Gemma)0.005
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 score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.003

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 head0.034
GPT teacher head0.340
Teacher spread0.306 · 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

Citations127
Published2008
Admission routes2
Has abstractyes

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