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Record W2974490093 · doi:10.1038/s41378-019-0091-0

A paper-based microfluidic platform with shape-memory-polymer-actuated fluid valves for automated multi-step immunoassays

2019· article· en· W2974490093 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMicrosystems & Nanoengineering · 2019
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsMcGill UniversityUniversity of Toronto
FundersNational Institute on Deafness and Other Communication DisordersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of CanadaU.S. Department of Health and Human Services
KeywordsMicrofluidicsComputer hardwareFluidicsComputer scienceChipEmbedded systemMaterials scienceNanotechnologyEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Smart fluid manipulation with automatically controlled paper valves will enable automated and multi-step immunoassays on paper-based microfluidic devices. In this work, we present an integrated paper-based microfluidic platform with shape-memory polymer (SMP)-actuated fluid valves capable of automated colorimetric enzyme-linked immunosorbent assays (ELISAs). A single-layer microfluidic paper-based analytical device (μPAD) was designed to store all the reagents on the chip, and sequentially transfer reagents to a paper test zone following a specific ELISA protocol through automatic fluidic flow control by the multiple SMP-actuated valves. The actuation of a paper valve was based on the thermally responsive, duel-state shape transformation of a SMP sheet attached to the root of a paper cantilever beam for driving a hydrophilic paper bridge to connect and disconnect two paper channels. A portable colorimetric reader was developed to control the on-chip valve operations, quantify the colorimetric signal output, display the assay result, and wirelessly transmit the data to a smart phone for the application of telemedicine. Reliable operations of the paper valve and the entire μPAD were demonstrated with success rates of 97% and 93%, respectively. A detection mechanism for valve malfunction was designed and confirmed effective to identify any mal-operation of individual valves, thus rendering our platform reliable in real assays. For device calibration, we conducted direct ELISAs of rabbit IgG in phosphate-buffered saline (PBS), and achieved a low limit of detection (LOD) of 27 pM (comparable to that of standard and paper-based ELISAs). In order to demonstrate the clinical application of our multi-step immunoassay platform, we also conducted sandwich ELISAs to quantify the protein level of an inflammatory cytokine, namely tumor necrosis factor (TNF)-α, in surgically injured laryngeal tissues of rats. The protein levels of TNF-α were shown similar between the conventional and μPAD ELISAs.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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