Microfluidic Device for Rapid (<15 min) Automated Microarray Hybridization
Bibliographic record
Abstract
BACKGROUND: Current hybridization protocols on microarrays are slow and need skilled personnel. Microfluidics is an emerging science that enables the processing of minute volumes of liquids to perform chemical, biochemical, or enzymatic analyzes. The merging of microfluidics and microarray technologies constitutes an elegant solution that will automate and speed up microarray hybridization. METHODS: We developed a microfluidic flow cell consisting of a network of chambers and channels molded into a polydimethylsiloxane substrate. The substrate was aligned and reversibly bound to the microarray printed on a standard glass slide to form a functional microfluidic unit. The microfluidic units were placed on an engraved, disc-shaped support fixed on a rotational device. Centrifugal forces drove the sample and buffers directly onto the microarray surface. RESULTS: This microfluidic system increased the hybridization signal by approximately 10fold compared with a passive system that made use of 10 times more sample. By means of a 15-min automated hybridization process, performed at room temperature, we demonstrated the discrimination of 4 clinically relevant Staphylococcus species that differ by as little as a single-nucleotide polymorphism. This process included hybridization, washing, rinsing, and drying steps and did not require any purification of target nucleic acids. This platform was sensitive enough to detect 10 PCR-amplified bacterial genomes. CONCLUSION: This removable microfluidic system for performing microarray hybridization on glass slides is promising for molecular diagnostics and gene profiling.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".