Continuous optical detection of small-molecule analytes in complex biomatrices
Bibliographic record
Abstract
Abstract Current technology for measuring specific biomarkers – continuously in complex samples, without sample preparation – is limited to just handful of molecules such as glucose and blood oxygen. In this work, we present the first optical biosensor system that enables continuous detection of a wide range of biomarkers in complex samples, such as human plasma. Our system employs a modular duplex-bubble switch (DBS) architecture that converts aptamers into structure-switching fluorescence probes whose affinity and kinetics can be readily tuned. These DBS constructs are coupled to a fiber-optic detector that measures the fluorescence change only within an evanescent field, thereby minimizing the impact of background autofluorescence and enabling direct detection of analytes at physiologically relevant concentrations even in interferent-rich sample matrices. Using our system, we achieved continuous detection of dopamine in artificial cerebrospinal fluid for >24 hours with sub-second resolution and a limit of detection (LOD) of 1 µM. We subsequently demonstrated the system’s generalizability by configuring it to detect cortisol with nanomolar sensitivity in undiluted human plasma. Both sensors achieved LODs orders of magnitude lower than the K D of the DBS element, highlighting the potential to achieve sensitive detection even when using aptamers with modest affinity.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".