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Record W2587492619 · doi:10.1039/c6lc01485b

Automating multi-step paper-based assays using integrated layering of reagents

2017· article· en· W2587492619 on OpenAlexaff
Sana Jahanshahi‐Anbuhi, Balamurali Kannan, Kevin Pennings, M. Monsur Ali, Vincent Leung, Karen Giang, Jingyun Wang, Dawn White, Yingfu Li, Robert Pelton, John D. Brennan, Carlos D. M. Filipe

Bibliographic record

VenueLab on a Chip · 2017
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLayeringComputer scienceComputational biologyReagentEngineeringChemistryBiology

Abstract

fetched live from OpenAlex

We describe a versatile and simple method to perform sequential reactions on paper analytical devices by stacking dry pullulan films on paper, where each film contains one or more reagents or acts as a delay layer. Exposing the films to an aqueous solution of the analyte leads to sequential dissolution of the films in a temporally controlled manner followed by diffusive mixing of the reagents, so that sequential reactions can be performed. The films can be easily arranged for lateral flow assays or for spot tests (reactions take place sequentially in the z-direction). We have tested the general feasibility of the approach using three different model systems to demonstrate different capabilities: 1) pH ramping from low to high and high to low to demonstrate timing control; 2) rapid ready-to-use two-step Simon's assays on paper for detection of drugs of abuse utilizing a 2-layer stack containing two different reagents to demonstrate the ability to perform assays in the z-direction; and 3) sequential cell lysing and colorimetric detection of an intracellular bacterial enzyme, to demonstrate the ability of the method to perform sample preparation and analysis in the form of a spot assay. Overall, these studies demonstrate the potential of stacked pullulan films as useful components to enable multi-step assays on simple paper-based devices.

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 score0.875
Threshold uncertainty score0.413

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 head0.045
GPT teacher head0.277
Teacher spread0.232 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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