(Invited) Developing Universal Sensing Strategies-Combining Functional Nucleic Acids with Photoelectrochemical and Electrochemical Signal Transduction
Bibliographic record
Abstract
Biosensors combine biorecognition with signal transduction for analyzing biologically-relevant analytes. Nucleic acids are powerful building blocks for biosensors and are used both for biorecognition and facilitating signal transduction. In this talk, we will show that functional nucleic acids can be specifically engineered for electrochemical signal transduction. We develop both aptamers and DNAzymes that are designed for electrochemical signal transduction. Electrochemical biosensors relying on functional nucleic acids are used herein to detect bacterial and viral infectious diseases in native clinical samples such as saliva and urine. In addition to electrochemical signal transduction, we show that nucleic acids serve as powerful tools in photoelectrochemical signal transduction. We use double stranded DNA for biorecognition and for serving as a nano-ruler for tuning the separation between a photoelectrochemical label and the electrode surface. This allows us to switch the signal transduction capability of the system between signal-on and signal-off. Additionally, engineering functional nucleic acids for photoelectrochemical signal transduction, enabled us to develop a new class of pathogen biosensors. These biosensors are used in direct analysis of bacteria in water.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".