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Record W2316020359 · doi:10.1109/nano.2014.6968061

An automated hand-held CMOS-based instrument for hand-held mutation detection via electrophoresis

2014· article· en· W2316020359 on OpenAlexaff
Gordon H. Hall, Tianchi Ma, Madeline Couse, Stacey Hume, David Sloan, D.G. Elliott, D. Moira Glerum, C. Backhouse

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of AlbertaUniversity of Waterloo
Fundersnot available
KeywordsCMOSLab-on-a-chipChipComputer scienceCMOS sensorComputer hardwareMicrofluidicsEmbedded systemEngineeringNanotechnologyMaterials scienceElectronic engineeringTelecommunications

Abstract

fetched live from OpenAlex

Lab-on-chip technology has tremendous potential for point-of-care and in-field medical diagnostics, but the uptake of such technologies is hampered by the high cost and large size of the instrumentation conventionally required. In the present work we demonstrate an instrument that fully automates sample preparation, enzymatic digestion and electrophoresis to implement DNA sizing and restriction fragment length polymorphism analysis for mutation detection. The instrument is largely based on a single CMOS chip, notably for fluorescence detection and high voltage generation, and can be operated in an off-the-shelf mode using low-cost pre-packaged polymeric microfluidic chips. We demonstrate the automated detection of the C282Y single nucleotide mutation in the HFE gene that underlies hereditary haemochromatosis. The system is currently in a portable form factor and could be implemented in a hand-held format. In higher volumes of manufacture, the remainder of the instrument could be moved to the CMOS chip, enabling a single chip instrument that could be housed within an inexpensive, thumb-sized device.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.216
Teacher spread0.211 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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