Integrated circuit-based instrumentation for microchip capillary electrophoresis
Bibliographic record
Abstract
Although electrophoresis with laser-induced fluorescence (LIF) detection has tremendous potential in lab on chip-based point-of-care disease diagnostics, the wider use of microchip electrophoresis has been limited by the size and cost of the instrumentation. To address this challenge, the authors designed an integrated circuit (IC, i.e. a microelectronic chip, with total silicon area of <0.25 cm2, less than 5 mmx5 mm, and power consumption of 28 mW), which, with a minimal additional infrastructure, can perform microchip electrophoresis with LIF detection. The present work enables extremely compact and inexpensive portable systems consisting of one or more complementary metal-oxide-semiconductor (CMOS) chips and several other low-cost components. There are, to the authors' knowledge, no other reports of a CMOS-based LIF capillary electrophoresis instrument (i.e. high voltage generation, switching, control and interface circuit combined with LIF detection). This instrument is powered and controlled using a universal serial bus (USB) interface to a laptop computer. The authors demonstrate this IC in various configurations and can readily analyse the DNA produced by a standard medical diagnostic protocol (end-labelled polymerase chain reaction (PCR) product) with a limit of detection of approximately 1 ng/microl (approximately 1 ng of total DNA). The authors believe that this approach may ultimately enable lab-on-a-chip-based electrophoretic instruments that cost on the order of several dollars.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".