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Record W1965404128 · doi:10.1021/ac049385k

High Resolution for Single-Strand Conformation Polymorphism Analysis by Capillary Electrophoresis

2004· article· en· W1965404128 on OpenAlexaff
S. Ozawa, Kokichi Sugano, Tsuyoshi Sonehara, Shinichi Fukuzono, Akira Ichikawa, Noriko Fukayama, Mariko Taylor, Yuji Miyahara, Takashi Irie

Bibliographic record

VenueAnalytical Chemistry · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCapillary electrophoresisSingle-strand conformation polymorphismChemistryChromatographyAutomationResolution (logic)Computational biologyHigh resolutionElectrophoresisBiochemistryComputer scienceArtificial intelligencePolymerase chain reactionGeneBiology

Abstract

fetched live from OpenAlex

Since the successful completion of the Human Genome Project, increasing concern is being directed toward the polymorphic aspect of the genome and its clinical relevance. A form of single-strand DNA-conformation polymorphism analysis (SSCP) employing nondenaturing slab-gel electrophoresis (SGE) is applicable to the genetic diagnosis of bladder cancer from urine samples. To bring this technique into routine clinical practice, the use of capillary electrophoresis (CE) is naturally favorable in terms of speed and automation. However, the resolving power of SSCP, a prerequisite basis for reliability required in diagnostics, remains as a challenge for CE systems. We thus focused on this topic and conducted studies on CE instruments equipped with a single capillary or an array of multiple capillaries, using the resolution (Rs) as a quantitative scale for the resolving power. Polymer concentration and buffer are shown to be the decisive parameters. High Rs values of >2.5 are achieved for representative SNPs markers under the optimized conditions, without sacrificing such intrinsic advantages of CE over SGE as the 10-fold quicker migration time and operation that is reproducible, continuous, and automatic. The strategies presented broaden the limits of CE in both the current and related applications.

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)0.003
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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.005
GPT teacher head0.190
Teacher spread0.185 · 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

Citations18
Published2004
Admission routes1
Has abstractyes

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