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Record W1973899346 · doi:10.1039/b812443d

An integrated microfluidic chip for chromosome enumeration using fluorescence in situ hybridization

2008· article· en· W1973899346 on OpenAlexaff
Vincent J. Sieben, Carina Debes-Marun, Linda M. Pilarski, C. Backhouse

Bibliographic record

VenueLab on a Chip · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicrofluidic chipFluorescence in situ hybridizationFish <Actinopterygii>MicrofluidicsChipIn situReagentIn situ hybridizationLab-on-a-chipFluorescenceChromosomeComputer scienceComputer hardwareNanotechnologyChemistryBiologyMaterials scienceGeneticsFisheryGeneGene expressionPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Fluorescence in situ hybridization (FISH) is a powerful technique for probing the genetic content of individual cells at the chromosomal scale. Conventional FISH techniques provide a sensitive diagnostic tool for the detection of chromosomal alterations on a cell-by-cell basis; however, the cost-per-test in terms of reagent and highly qualified labour has prevented its wide-spread utilization in clinical settings. Here, we address the inefficient use of labour with the first integrated and automated on-chip FISH implementation, one that requires only minutes of setup time from the technician. Our microfluidic chip has lowered the reagent use by 20-fold, decreased the labour time by 10-fold, and substantially reduced the amount of support equipment needed. We believe this cost-effective platform will make sensitive FISH techniques more accessible for routine clinical usage.

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 score0.020
Threshold uncertainty score0.748

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 head0.015
GPT teacher head0.229
Teacher spread0.215 · 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

Citations53
Published2008
Admission routes1
Has abstractyes

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