Integrated Circuits for Rapid Sample Processing and Electrochemical Detection of Biomarkers
Bibliographic record
Abstract
The trade-off between speed and sensitivity of detection is a fundamental challenge in the design of point-of-care diagnostics. As the relevant molecules in many diseases exist natively at extremely low levels, many gold-standard diagnostic tests are designed with high sensitivity at the expense of long incubations needed to amplify the target analytes. The central aim of this thesis is to design new strategies to detect biologically relevant analytes with both high speed and sensitivity. The response time of a biosensor is limited by the ability of the target analyte to accumulate to detectable levels at the sensor surface. We overcome this limitation by designing a range of integrated devices to optimize the flux of the analyte to the sensor by increasing the effective analyte concentration, shortening the required diffusion distance, and confining the analyte in close proximity to the sensor. We couple these devices with novel ultrasensitive electrochemical transduction strategies to convert rare analytes into a detectable signal. We showcase the clinical utility of these approaches with several applications including cancer diagnosis, bacterial identification, and antibiotic susceptibility profiling. We design and optimize a device to isolate rare cancer cells from the bloodstream with near 100% efficiency and 10 000-fold specificity. We analyse pathogen specific nucleic acids by lysing bacteria in close proximity to an electrochemical sensor and find that this approach has 10-fold higher sensitivity than standard lysis in bulk solution. We design an electronic chip to readout the antibiotic susceptibility profile with an hour-long incubation by concentrating bacteria into nanoliter chambers with integrated electrodes. Finally, we report a strategy for ultrasensitive visual readout of nucleic acids as low as 100 fM within 10 minutes using an amplification cascade. The strategies presented could guide the development of fast, sensitive and low-cost diagnostics for diseases not previously detectable at the point-of-care.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".