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Record W2161706178 · doi:10.1021/ac901416s

Polymerase Chain Reaction and Immunoassay−Matrix Assisted Laser Desorption Mass Spectrometry Using Tag-Mass Technology: New Tools to Break Down Quantification Limits and Multiplexes

2009· article· en· W2161706178 on OpenAlexaff
Jonathan Stauber, Mohamed El Ayed, Maxence Wisztorski, R. Day, Isabelle Fournier, Michel Salzet

Bibliographic record

VenueAnalytical Chemistry · 2009
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversité de Sherbrooke
FundersCentre National de la Recherche ScientifiqueAgence Nationale de la Recherche
KeywordsChemistryMass spectrometryImmunoassayOligonucleotideMass spectrometry imagingChromatographyComputational biologyDNAAntibodyBiochemistry

Abstract

fetched live from OpenAlex

We present a new development of the Tag-Mass concept based on a photocleavable linker with tagged molecules for polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) quantification coupled to mass spectrometry. PCR-MS and immunosorbent assay-MS with tagged oligonucleotides, bases, and antibodies will allow the acquisition of multiplexed information from genomic, transcriptomic, and proteomic experiments. This is a novel application of Tag-Mass from tissue imaging to fluid quantification and will open doors to several clinical applications ranging from biomarker-driven gene modulation to use at the patient's bedside following treatment.

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: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.002

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 head0.025
GPT teacher head0.300
Teacher spread0.274 · 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
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
Published2009
Admission routes1
Has abstractyes

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