MétaCan
Menu
Back to cohort
Record W1976836548 · doi:10.1016/j.jasms.2006.02.012

Three-layer matrix/sample preparation method for MALDI MS analysis of low nanomolar protein samples

2006· article· en· W1976836548 on OpenAlexafffund
Bernd O. Keller, Liang Li

Bibliographic record

VenueJournal of the American Society for Mass Spectrometry · 2006
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryMatrix-assisted laser desorption/ionizationMatrix (chemical analysis)AnalyteSample preparationChromatographyDesorptionMass spectrometryBenzoic acidAnalytical Chemistry (journal)Layer (electronics)Organic chemistryAdsorption

Abstract

fetched live from OpenAlex

A robust and sensitive sample preparation method is presented for matrix-assisted laser desorption ionization (MALDI) mass spectrometric analysis of low nanomolar concentrations of proteins containing high amounts of common salts and buffers. This method involves the production of densely packed sub-micrometer matrix crystals by depositing a matrix solution on top of a matrix seed-layer prepared on a MALDI target. A sub-microliter aliquot of analyte solution is then directly added to the top of the matrix crystals to form a thin-layer. alpha-Cyano-4-hydroxycinnamic acid (4-HCCA) is used as matrix and demonstrated to give better performance than other commonly used matrices, such as 2,5-dihydroxybenzoic acid (DHB), 2-(4-hydroxy-phenylazo) benzoic acid (HABA), or sinapinic acid. This three-layer method is shown to be superior to the other MALDI sample preparation methods, particularly for handling low nanomolar protein solutions containing salts and buffers.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.175
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.315
Teacher spread0.300 · 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
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

Citations44
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Society for Mass SpectrometrySame topicMass Spectrometry Techniques and ApplicationsFrench-language works237,207