Digital Microfluidic Platform for the Detection of Rubella Infection and Immunity: A Proof of Concept
Bibliographic record
Abstract
BACKGROUND: Whereas disease surveillance for infectious diseases such as rubella is important, it is critical to identify pregnant women at risk of passing rubella to their offspring, which can be fatal and can result in congenital rubella syndrome (CRS). The traditional centralized model for diagnosing rubella is cost-prohibitive in resource-limited settings, representing a major obstacle to the prevention of CRS. As a step toward decentralized diagnostic systems, we developed a proof-of-concept digital microfluidic (DMF) diagnostic platform that possesses the flexibility and performance of automated immunoassay platforms used in central facilities, but with a form factor the size of a shoebox. METHODS: DMF immunoassays were developed with integrated sample preparation for the detection of rubella virus (RV) IgG and IgM. The performance (sensitivity and specificity) of the assays was evaluated with serum and plasma samples from a commercial antirubella mixed-titer performance panel. RESULTS: The new platform performed the essential processing steps, including sample aliquoting for 4 parallel assays, sample dilution, and IgG blocking. Testing of performance panel samples yielded diagnostic sensitivity and specificity of 100% and 100% for both RV IgG and RV IgM. With 1.8 μL sample per assay, 4 parallel assays were performed in approximately 30 min with <10% mean CV. CONCLUSIONS: This proof of concept establishes DMF-powered immunoassays as being potentially useful for the diagnosis of infectious disease.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".