On-chip bioluminescence biosensor for the detection of microbial surface contamination
Bibliographic record
Abstract
Detection of microbial pathogens is important for food safety reasons, and for monitoring sanitation in laboratory environments and health care settings. Traditional detection methods such as culture-based and nucleic acid-based methods are time-consuming, laborious, and require expensive laboratory equipment. Recently, ATP-based bioluminescence methods were developed to assess surface contamination, with commercial products available. In this study, we introduce a biosensor based on a CMOS image sensor for ATP-mediated chemiluminescence detection. The original lens and IR filter were removed from the CMOS sensor revealing a 12 MP periodic microlens/pixel array on an area of 6.5 mm × 3.6 mm. UltraSnap swabs are used to collect samples from solid surfaces including personal electronic devices, and office and laboratory equipment. Samples mixed with chemiluminescence reagents were placed directly on the surface of the image sensor. Close proximity of the sample to the photodiode array leads to high photon collection efficiency. The population of microorganisms can be assessed and quantified by analyzing the intensity of measured chemiluminescence. We report a linear range and limit of detection for measuring ATP in UltraSnap buffer of 10-1000 nM and 225 fmol, respectively. The performance of the CMOS-based device was compared to a commercial luminometer, and a high correlation with a Pearson's correlation coefficient of 0.98589 was obtained. The Bland-Altman plot showed no significant bias between the results of the two methods. Finally, microbial contamination of different surfaces was analyzed with both methods, and the CMOS biosensor exhibited the same trend as the commercial luminometer.
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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) | 0.000 | 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".