MétaCan
Menu
Back to cohort
Record W2781907557 · doi:10.1063/1.5009719

Rapid detection of exosomal microRNA biomarkers by electrokinetic concentration for liquid biopsy on chip

2018· article· en· W2781907557 on OpenAlexfundno aff
Lucia S. Cheung, Xi Wei, Diogo Martins, Yong‐Ak Song

Bibliographic record

VenueBiomicrofluidics · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsnot available
FundersYork UniversityAbu Dhabi Education CouncilNew York University Abu Dhabi
KeywordsElectrokinetic phenomenaMicrofluidicsChain reactionChemistrymicroRNAAnalytical Chemistry (journal)NanotechnologyChromatographyMaterials scienceGeneBiochemistry

Abstract

fetched live from OpenAlex

An ion concentration polarization (ICP)-based electrokinetic concentration device is used for accelerating the surface hybridization reaction between exosomal microRNAs (miRNAs) and morpholinos (MOs) as a synthetic oligo capture probe in the nanomolar concentration range in a microfluidic channel. Compared with standard hybridization at the same concentration, the hybridization time of the miRNA target on MO capture probes could be reduced from ∼24 h to 30 min, with an increase in detection speed by 48 times. This ICP-enhanced hybridization method not only significantly decreases the detection time but also makes workflow simple to use, circumventing use of quantitative reverse transcription polymerase chain reaction or other conventional enzyme-based amplification methods that can cause artifacts.

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 categoriesMeta-epidemiology (narrow)
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 score0.033
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.208
Teacher spread0.198 · 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.

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

Citations10
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueBiomicrofluidicsSame topicMicrofluidic and Bio-sensing TechnologiesFrench-language works237,207