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Record W2165589555 · doi:10.1039/c2lc40759k

Microfluidic diagnostic tool for the developing world: contactless impedance flow cytometry

2012· article· en· W2165589555 on OpenAlexfundno aff
Sam Emaminejad, Mehdi Javanmard, R.W. Dutton, Ronald W. Davis

Bibliographic record

VenueLab on a Chip · 2012
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsnot available
FundersNational Human Genome Research InstituteNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsMicrofabricationMicrofluidicsElectrical impedancePrinted circuit boardElectrodeCytometryElectronicsMaterials scienceNanotechnologyBiomedical engineeringElectrical engineeringFlow cytometryEngineeringFabricationChemistry

Abstract

fetched live from OpenAlex

In this work, we demonstrate a novel and cost-effective approach to implement a disposable microfluidic contactless impedance cytometer. Conventional methods for single cell impedance cytometry use microfabricated electrodes in direct contact with the buffer to measure changes of its electrical impedance when cells pass through the applied electric field. However, this approach requires expensive microfabrication of electrodes, and also, the fabricated electrodes cannot be reused without thorough and time-consuming cleaning process. Here, we introduce a novel approach to allow for single cell impedance cytometry using electrodes that can be reused, without the need for microfabrication of the electrodes. This disposable device can be potentially inserted onto a printed circuit board (PCB) which has a non-disposable, yet inexpensive, electronic reading apparatus. This significantly reduces the manufacturing costs, making it suitable for low resource settings, such as point-of-care testing in the developing countries.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.244
Teacher spread0.224 · 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

Citations65
Published2012
Admission routes1
Has abstractyes

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