In Brain and in Vein Detection of Antibiotics Using Electrochemical-DNA Biosensors
Bibliographic record
Abstract
Current approaches towards drug dosing rely on venous draws measurements performed on test patients which are laboratory-analyzed and returned the following days. This practice forces physicians to administer potentially toxic or ineffective concentrations of drugs to patients since dosages are determined based on weight and age of this test group even though pharmacokinetics may differ in each individuals. Having a technology that would in contrast allow, direct, continuous, real-time monitoring of drugs in the living body would revolutionize healthcare and allow personalized drug dosage and adjustment while enabling the development of artificial organs responsible of adjusting these levels. Motivated by this goal, we have developed a class of electrochemical aptamer-based (E-AB) sensors[1]. These sensors are comprised of a redox-reporter-modified DNA “probe” that is attached by one terminus to a self-assembled monolayer deposited on an interrogating gold electrode. The binding of an analyte to this probe alters the kinetics with which electrons exchange to/from the redox reporter via binding-induced conformational changes producing an easily measured change in current when the sensor is interrogated using square-wave voltammetry (see Figure) [2]. E-AB sensors are capable of detecting with high specificity their molecular targets in flowing whole blood and directly in the living body. I will present during this presentation some of these sensors deployed in the vein [3] and in the brain of sedated rats to monitor the pharmacokinetics of the antibiotic, vancomycin. The ability of acquiring high frequency measurements of drug plasma levels using these biosensors has also allowed us to develop a technology that improves our ability to deliver them [3]. Due to their small size, these sensors can also be deployed directly in the brain of freely moving animals to monitor molecules with unmatched temporal resolution [4]. All these advancement in developing E-AB sensors are aimed towards developing new analytical tools for personalized medicine while improving our understanding of drug metabolism. [1]: Dauphin Ducharme, P. and Plaxco, K. W. Anal. Chem. 2016 88: 11654–11662. [2]: Li, H., Dauphin Ducharme, P., Ortega, G., Plaxco, K. W. J Am. Chem. Soc. 2017, 139, 11207-11213. [3]: Dauphin-Ducharme, P., Yang, K., Arroyo-Currás, N., Ploense, L. K., Zhang, Y., Gerson, J., Kurnik, M., Kippin, T. E., Stojanovic, M., Plaxco, K. W. ACS Sensors 2019, 4, 2832-2837. [4]: Ploense, K. L., Dauphin-Ducharme, P., Arroyo-Currás, N., Curtis, S. D., Williams, S., Schwarz, N., Kippin, T. E., Plaxco, K. W. 2019 In preparation. Figure 1
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".