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Record W2065280467 · doi:10.1021/ac051471c

Calibration-Free Quantitative Analysis of mRNA

2005· article· en· W2065280467 on OpenAlexaff
Azza Al‐Mahrouki, Sergey N. Krylov

Bibliographic record

VenueAnalytical Chemistry · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsYork University
Fundersnot available
KeywordsChemistryMessenger RNACapillary electrophoresisCalibrationDetection limitFluorescenceQuantitative analysis (chemistry)Biological systemMolecular biologyChromatographyBiophysicsComputational biologyBiochemistryGenePhysicsBiology

Abstract

fetched live from OpenAlex

Here we introduce a method for accurate and sensitive quantitative analysis of mRNA, which does not require calibration with mRNA. The method uses a fluorescently labeled hybridization probe as a reference standard. It involves the following: (i) annealing mRNA to the excess of the fluorescently labeled ssDNA hybridization probe, (ii) separation of the mRNA-probe hybrid from the excess of the probe by gel-free capillary electrophoresis mediated by ssDNA-binding protein, (iii) fluorescence detection of the hybrid and the excess probe, and (iv) quantification of mRNA using a simple algebraic formula. The method also overcomes a number of other limitations of conventional methods: the entire procedure currently takes only 2 h and accurately quantifies 10(5) copies of mRNA. With further improvements to the method, the procedure can be potentially shortened to 10 min, and the limit of quantification can be decreased to as few as 100 copies of mRNA. In this work, we prove the principle of the method by quantifying mRNA of green fluorescent protein in the matrix of total cellular RNA. The developed method is quantitative, simple, fast, and highly sensitive. It requires commercially available instrumentation only. The method will be an indispensable tool for molecular and cell biology studies.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.299
Teacher spread0.288 · 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
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

Citations20
Published2005
Admission routes1
Has abstractyes

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