Electrochemical Analysis of Circulating Nucleic Acids for Liquid Biopsy
Bibliographic record
Abstract
To put disease-related biomarkers to work in the clinic, new high-performance technologies are needed to enable rapid and sensitive analysis of clinical specimens. Electrochemical methods providing low cost and direct biomarker readout have attracted a great deal of attention for this application. We exploit controlled nanostructuring of electrode surfaces to enhance biomolecular capture rates and efficiencies to solve this long-standing problem, and showed that the nanoscale morphologies of electrode surfaces control their sensitivities. An electrocatalytic reporter system that leverages a pair of redox reagents is used to readout the presence of specific nucleic acids and other analytes bound to electrode surfaces. Recently, we have developed assays that are able to detect nucleic acids, proteins and small molecules, with universally high sensitivity levels. This presentation will highlight how electrodeposited metals can be used to create high performance sensors that can be applied to the analysis of circulating tumor DNA (Das et al, JACS (2016)) and RNA (Das et al, Nature Chemistry (2015) for the realization of liquid biopsy as an alternative to invasive methods.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".