Rapid and multiplex detection of Legionella's RNA using digital microfluidics
Bibliographic record
Abstract
Despite recent advances in the miniaturization and automation of biosensors, technologies for on-site monitoring of environmental water are still at an early stage of development. Prevention of outbreaks caused by pathogens such as Legionella pneumophila would be facilitated by the development of sensitive and specific bioanalytical assays that can be easily integrated in miniaturized fluidic handling systems. In this work, we report on the integration of an amplification-free assay in digital microfluidics (DMF) for the detection of Legionella bacteria based on targeting 16s rRNA. We first review the design of the developed DMF devices, which provide the capability to store up to one hundred nL-size droplets simultaneously, and discuss the challenges involved with on-chip integration of the RNA-based assay. By optimizing the various steps of the assay, including magnetic capture, hybridization duration, washing steps, and assay temperature, a limit of detection as low as 1.8 attomoles of synthetic 16s rRNA was obtained, which compares advantageously to other amplification-free detection systems. Finally, we demonstrate the specificity of the developed assay by performing multiplex detection of 16s rRNAs from a pathogenic and a non-pathogenic species of Legionella. We believe the developed DMF devices combined with the proposed detection system offers new prospects for the deployment of rapid and cost-effective technologies for on-site monitoring of pathogenic bacteria.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".