Rapid and Sensitive Detection of H1N1/2009 Virus from Aerosol Samples with a Microfluidic Immunosensor
Bibliographic record
Abstract
Influenza A H1N1/2009 is a highly infectious, rapidly spreading airborne disease that needs to be monitored in near real time, preferably in a microfluidic format. However, such demonstration is difficult to find as H1N1 concentration in aerosol samples is extremely low, with interference from dust particles. In this work, we measured Mie scatter intensities from a microfluidic device with optical waveguide channels, where the antibody-conjugated latex beads immunoagglutinated with the target H1N1 antigens. Through careful optimizations of optical parameters, we were able to maximize the Mie scatter increase from the latex immunoagglutinations while minimizing the background scatter from the dust particles. The aerosol samples were collected from a 1:10 mock classroom using a button air sampler, where a nebulizer generated aerosols, simulating human coughing. The detection limits with real aerosol samples were 1 and 10 pg/mL, using a spectrometer or a cell phone camera as an optical detector, respectively. These are several orders of magnitudes more sensitive than the other methods. The microfluidic immunosensor readings are in concordance with the results of reverse transcription polymerase chain reaction. The assay time was 30 s for sampling and 5 min for the microfluidic assay.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".