DNA Chimeras as Electrochemical Biosensors for Host‐Guest Measurements in Blood
Bibliographic record
Abstract
Few sensing platforms have become ubiquitous to enable rapid and convenient measurements at the point-of-care. Those, however, are "one-off" technologies, meaning that they can only detect a single target and are hardly adaptable. In response, we plan to develop a sensing platform that can be extended to detect other classes of molecules and that affords rapid, convenient, continuous measurements directly in undiluted complex matrices. For this, we decided to rely on a host molecule that presents reversible interactions toward specific guest molecules to develop a new class of sensors that we coined "Electrochemical DNA-host chimeras". As a proof-of-concept for our sensor, we decided to use cyclobis(paraquat-p-phenylene) ("blue box") that we attached on an electrode-bound DNA to allow measurements of electron-rich guests such as dopamine and aspirin. Doing so allows to promote host-guest complex that could be quantified using blue box's electrochemistry. Because of this unique sensor architecture, we achieve, to our knowledge, the first reagentless, continuous and rapid (<5 min) host-guest measurements in undiluted whole blood. We envision that given the library of electroactive host molecules that this will allow the development of a sensing platform for measurements of several classes of molecules in complex matrices 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".