An inexpensive and portable microchip-based platform for integrated RT–PCR and capillary electrophoresis
Bibliographic record
Abstract
We present an inexpensive, portable and integrated microfluidic instrument that is optimized to perform genetic amplification and analysis on a single sample. Biochemical reactions and analytical separations for genetic analysis are performed within tri-layered glass-PDMS microchips. The microchip itself consists of integrated pneumatically-actuated valves and pumps for fluid handling, a thin-film resistive element that acts simultaneously as a heater and a temperature sensor, and channels for capillary electrophoresis (CE). The platform is comprised of high voltage circuitry, an optical assembly consisting of a laser diode and a charged coupled device (CCD) camera, circuitry for thermal control, and mini-pumps to generate vacuum/pressure to operate the on-chip diaphragm-based pumps and valves. Using this microchip and instrument, we demonstrate an integration of reverse transcription (RT), polymerase chain reaction (PCR), and capillary electrophoresis (CE). The novelty of this system lies in the cost-effective integration of microfluidics, optics, and electronics to realize a fully portable and inexpensive system (on the order of $1000 in component costs) for performing both genetic amplification and analysis - the basis of many medical diagnostics. We believe that this combination of portability, cost-effectiveness and performance will enable more accessible healthcare.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".