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Record W4310567029 · doi:10.1021/acs.analchem.2c03315

Sequential Reagent Release from a Layered Tablet for Multistep Diagnostic Assays

2022· article· en· W4310567029 on OpenAlexafffund
Vanessa Y. C. Li, Buddhisha Udugama, Pranav Kadhiresan, Warren C. W. Chan

Bibliographic record

VenueAnalytical Chemistry · 2022
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchTemerty Faculty of Medicine, University of TorontoNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsReagentChemistryMicrocrystalline celluloseChromatographyCelluloseSample preparationHydroxypropyl celluloseCombinatorial chemistryPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Diagnostic assays are commonly performed in multiple steps, where reagents are added at specific times and concentrations into a reaction chamber. The reagents require storage, preparation, and addition in the correct sequence and amount. These steps rely on trained technicians and instrumentation to perform each task. The reliance on such resources hinders the use of these diagnostic assays by lay users. We developed a tablet that can sequentially introduce prequantified lyophilized diagnostic reagents at specific time points for a multistep assay. We designed the tablet to have multiple layers using cellulose-grade polymers, such as microcrystalline cellulose and hydroxypropyl cellulose. Our formulation allows each layer to dissolve at a controlled rate to introduce reagents into the solution sequentially. The release rate is controlled by modulating the compression force or chemical formulation of the layer. Controlling the reagent release time is important because different assays have specific times when reagents need to be added. As proof of concept, we demonstrated two different assays with our tablet system. Our tablet detected nucleic acid target (tpp47 gene from Treponema pallidum) and nitrite ions in an aqueous sample without user intervention. Our multilayer tablets can simplify multistep assay processes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.999

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)0.0020.000

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 head0.013
GPT teacher head0.218
Teacher spread0.204 · 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 designSimulation or modeling
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

Citations7
Published2022
Admission routes2
Has abstractyes

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