Automatic on-chip RNA–DNA hybridization assay with integrated phase change microvalves
Bibliographic record
Abstract
An RNA–DNA hybridization assay microfluidic chip integrated with electrothermally actuated phase change microvalves for detecting pathogenic bacteria is presented in this paper. In order to realize the sequential loading and washing processes required in such an assay, gravity-based pressure-driven flow and phase-change microvalves were used in the microfluidic chip. Paraffin wax was used as the phase change material in the valves and thin film heaters were used to electrothermally actuate microvalves. Light absorption measured by a photodetector to determine the concentrations of the samples. The automatic control of the complete assay was implemented by a self-coded LabVIEW program. To examine the performance of this chip, Salmonella was used as a sample pathogen. Significantly, reduction in reagent/sample consumption (up to 20 folds) was achieved by this on-chip assay, compared with using the commercial test kit following the same protocol in conventional labs. The experimental results show that the quantitative detection can be obtained in approximately 26 min, and the detection limit is as low as 10 3 CFU ml −1 . This RNA–DNA hybridization assay microfluidic chip shows an excellent potential in the development of a portable device for point-of-testing applications.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".