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1P1-V05 Application to the Drug-Screening System Using Microfluidic Gradient Generator(Nano/Micro Fluid System)

2012· article· en· W2636556275 on OpenAlex
Yuta Abe, Hirotaka Sasaki, Toshihisa Osaki, Ryuji Kawano, Koki Kamiya, Norihisa Miki, Shoji Takeuchi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2012
Typearticle
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMicrofluidicsATP-binding cassette transporterTransporterSubstrate (aquarium)Drug deliveryConcentration gradientChemistryVesicleDrugChromatographyBiophysicsNanotechnologyMaterials scienceMembraneBiochemistryPharmacologyBiology

Abstract

fetched live from OpenAlex

This paper presents a high-throughput methodology to analyze the function of an ATP binding cassette transporter (ABC transporter) by using a microfluidic device that generates a concentration gradient of substrates. In drug development process, determination of the concentration at which the transport of a drug is inhibited by the other one by 50% (IC_<50>) is an important value for safe and effective use of drugs. To determine IC_<50>, substrate transport by ABC transporters at different concentrations must be evaluated. We applied a microfluidic gradient generator, which makes various concentrations of substrates in a single device, for rapid IC_<50> determination. After incubation of ABC transporters at 5 different substrate concentrations in a gradient generator, we succeeded in detecting the difference of substrate transport revealed by the different fluorescence intensity from ABC transporter vesicles.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.244
Teacher spread0.222 · 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