Electrochemical Aptamer-Based Biosensors for Measurements in Undiluted Human Saliva
Bibliographic record
Abstract
Although blood remains a gold standard diagnostic fluid for most health exams, it involves an unpleasant and relatively invasive sampling procedure (finger pricking or venous draw). Saliva contains many relevant and useful biomarkers for diagnostic purposes, and its collection, in contrast, is noninvasive and can be obtained with minimal effort. Current saliva analyses are, however, achieved using chromatography or lateral flow assays, which, despite their high accuracy and sensitivity, can demand expensive laboratory-based instruments operated by trained personnel or offer only semiquantitative results. In response, we investigated electrochemical aptamer-based (E-AB) biosensors, a reagentless sensing platform, to allow for continuous and real-time measurements directly in undiluted, unstimulated human whole saliva. As a proof-of-concept study, we developed E-AB biosensors capable of detecting low-molecular-weight analytes (glucose and adenosine monophosphate (AMP)). To our knowledge, we report the first E-AB sensor for glucose, an approach that is inherently independent of its chemical reactivity in contrast to home glucometers. For these three sensors, we evaluated their figures of merits, stability, and reusability over short- and long-term exposure directly in saliva. In doing so, we found that E-AB sensors allow rapid and convenient molecular measurements in whole saliva with unprecedented sensitivities in the pico- to nanomolar regime and could be regenerated and reused up to 7 days when washed and stored in phosphate-buffered saline at room temperature. We envision that salivary molecular measurements using E-AB sensors are a promising alternative to invasive techniques and can be used for improved point-of-care clinical diagnosis and at-home measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".