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Record W3125196719 · doi:10.3390/mi12020109

A Transit Time-Resolved Microflow Cytometry-Based Agglutination Immunoassay for On-Site C-Reactive Protein Detection

2021· article· en· W3125196719 on OpenAlexaff
Jianxi Qu, Yushan Zhang, Mathieu Chenier, Chang‐Qing Xu, Lan Chen, Yonghong Wan

Bibliographic record

VenueMicromachines · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDetection limitImmunoassayAgglutination (biology)ChromatographyChemistryAntibodyMedicineImmunology

Abstract

fetched live from OpenAlex

An accurate and rapid microflow cytometry-based agglutination immunoassay (MCIA) suitable for on-site antibody or antigen detection was proposed. In this study, quantitative C-reactive protein (CRP) detection was chosen as a model assay in order to demonstrate the detection principle. The average transit time was employed to estimate the extent of the agglutination reaction and improve the detection accuracy as compared to the intensity-dependent methods. The detection time was less than 8 min. and only a 20 µL serum sample was needed for each test. The results showed a linear relationship between the average transit time of aggregates and CRP concentrations ranging from 0 to 1 µg/mL. The R2 of this relationship was 0.99. The detection limit of this technology was 0.12 µg/mL CRP. The system used for CRP detection can be extended to also monitor other clinically relevant molecules.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score1.000

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 head0.006
GPT teacher head0.197
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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

Citations8
Published2021
Admission routes1
Has abstractyes

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