A Multiplexed, Point-of-Care Sensing for Dengue
Bibliographic record
Abstract
There is a growing need for diagnostic technologies which can provide accurate disease detection using sensitive, reliable, and inexpensive methods. A lot of research has been directed towards developing efficient point-of-care devices that enable the parallel detection of multiple analytes, in small-volume samples, with high sensitivity and in a short time. Flow cytometry, used for bead-based immunoassays are expensive and bulky. Using a microfluidic based optical detection system makes the whole process convenient and cheap. This motivates us to explore this platform for multiplexed testing by incorporating microbead-based assays. Hence, we aim to develop a microfluidics based optical detection system, which can measure multiple analytes at the same time for diseases like dengue. Dengue detection using NS1-4 (non-structural protein), which inflates on the first day of virus with the IgG antibody, can give significant information for the treatment. Simultaneous detection of dengue antigen and antibody is crucial for prevention and diagnosis of dengue infection. The outcome of this study generates the first-generation prototype of a universal fluorescence detection device capable of both classifying the microspheres and measuring the amount of specific biomarker.
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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".