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Record W2100860175 · doi:10.1039/b925111a

A scalable and modular lab-on-a-chip genetic analysis instrument

2010· article· en· W2100860175 on OpenAlexafffund
Govind V. Kaigala, Moris Behnam, Allison Christel Elizabeth Bidulock, Christopher D. Von Bargen, Robert W. Johnstone, D.G. Elliott, C. Backhouse

Bibliographic record

VenueThe Analyst · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModular designScalabilityInstrumentation (computer programming)Computer scienceLab-on-a-chipChipCapillary electrophoresisEmbedded systemComputer hardwareNanotechnologyMicrofluidicsMaterials scienceChemistryOperating systemTelecommunicationsChromatography

Abstract

fetched live from OpenAlex

We demonstrate a new and extremely inexpensive, multipurpose desktop system for operating lab-on-a-chip (LOC) devices. The system provides all of the infrastructure necessary for genetic amplification and analysis, with orders of magnitude improvement in performance over our previous work. A modular design enables high levels of integration while allowing scalability to lower cost and smaller size. The component cost of this system is ca. $600, yet it could support many diagnostic applications. We demonstrate an implementation of genetic amplification via polymerase chain reaction (PCR), and analysis using capillary electrophoresis (CE). The PCR is able to amplify from single or several copies of target DNA and the CE performance (e.g. sensitivity) is comparable to that of commercial photomultiplier-based confocal lab-on-chip instrumentation. We believe this demonstrates that the cost of infrastructure need no longer be a barrier to the wide-spread application of LOC technologies in healthcare and beyond.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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

Citations27
Published2010
Admission routes2
Has abstractyes

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