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Record W4311443198 · doi:10.1039/d2an01571d

Smartphone-based microplate reader for high-throughput quantitation of disease markers in serum

2022· article· en· W4311443198 on OpenAlexafffund
Rong Deng, Xiaoxin Chao, Haiqin Li, Xiaochun Li, Zehua Yang, Hua‐Zhong Yu

Bibliographic record

VenueThe Analyst · 2022
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsSimon Fraser University
FundersNatural Science Foundation of Shanxi ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsMicrotiter plateProcalcitoninPoint-of-care testingPlate readerComputer sciencePoint of careChromatographyBiomedical engineeringChemistryMedicinePathologyOpticsImmunologyPhysics

Abstract

fetched live from OpenAlex

Herein, a smartphone-based portable reader with integrated optics for standard microtiter plates (96 wells) has been designed and demonstrated for high-throughput quantitation of validated biomarkers in serum. The customized optical attachment was simply constructed with a convex lens and a light source, by which the transmitted light through a 96-well microtiter plate was converged for imaging with a smartphone, so that accurate and wide-range reading of the plate can be achieved. More importantly, relying on the digitized colorimetric analysis of the obtained images, concentrations of various biomarkers can be determined directly using the customized mobile app. A set of validated biomarkers for inflammation and infection, C-reactive protein (CRP), serum amyloid A (SAA), and procalcitonin (PCT) have been quantitated with this new system; both the response ranges and limits of detection meet the requirement of clinical tests. The consistency with the results obtained using a commercial microplate reader proves its reliability and precision, augments its potential as a point-of-care diagnostic device for on-site testing or resource-limited settings.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.210

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 head0.216
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations21
Published2022
Admission routes2
Has abstractyes

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