Bead-based multiplex detection of dengue biomarkers in a portable imaging device
Bibliographic record
Abstract
Dengue is one of the most rapidly spreading mosquito-borne viral diseases in the world. Differential diagnosis is a crucial step for the management of the disease and its epidemiology. Point-of-care testing of blood-borne dengue biomarkers provides an advantageous approach in many health care settings, and the ability to follow more than one biomarker at once could significantly improve the management of the disease. Bead-based multiplex technologies (suspension array) can measure multiple biomarker targets simultaneously by using recognition molecules immobilized on microsphere beads. The overarching objective of our work is to develop a portable detection device for the simultaneous measurement of multiple biomarkers important in dengue diagnosis, monitoring and treatment. Here, we present a bead-based assay for the detection of one of the four serotypes of dengue virus non-structural protein (DENV-NS1) as well as its cognate human IgG. In this system, the fluorescent microspheres containing the classification fluorophore and detection fluorophore are imaged through a microfluidic chip using an infinity-corrected microscope system. Calibration curves were plotted for median fluorescence intensity against known concentrations of DENV-NS1 protein and anti-NS1 human IgG. The limit of quantitation was 7.8 ng/mL and 15.6 ng/mL, respectively. The results of this study demonstrate the feasibility of the multiplex detection of dengue biomarkers and present its analytical performance parameters. The proposed imaging device holds potential for point-of-care testing of biomarkers on a highly portable system, and it may facilitate the diagnosis and prevention of dengue as well as other infectious diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".