Portable Saliva Sensor Based on Dual Recognition Elements for Detection of Caries Pathogenic Bacteria
Bibliographic record
Abstract
Dental caries is one of the most common diseases affecting more than 2 billion people’s health worldwide. In a clinical setting, it is challenging to predict and proactively guard against dental cavities prior to receiving a confirmed diagnosis. Streptococcus mutans ( S. mutans ) in saliva has been recognized as the main causative bacterial agent that causes dental caries. High sensitivity, good selectivity, and a wide detection range are incredibly important factors to affect S. mutans detection in practical applications. In this study, we present a portable saliva biosensor designed for the early detection of S. mutans with the potential to predict the occurrence of dental cavities. The biosensor was fabricated using a S. mutans -specific DNA aptamer and S. mutans -imprinted polymers. Methylene blue was utilized as a redox probe in the sensor to generate current signals for analysis. When S. mutans enters complementarily S. mutans cavities, it blocks electron transfer between methylene blue and the electrode, resulting in decreases in the reduction current signal. The signal variations are associated with S. mutans concentrations that are useful for quantitative analysis. The linear detection range of S. mutans is 10 2 –10 9 cfu mL –1, which covers the critical concentration of high caries risk. The biosensor exhibited excellent selectivity toward S. mutans in the presence of other common oral bacteria. The biosensor’s wide detection range, excellent selectivity, and low limit of detection (2.6 cfu mL –1 ) are attributed to the synergistic effect of aptamer and S. mutans -imprinted polymers. The sensor demonstrates the potential to prevent dental caries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".