Development of a QCM-D based biosensor for detection of waterborne E. coli O157:H7
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The contamination of drinking water by microbial pathogens is recognized as one of the most pressing water supply problems of our day. To minimize the impact of pathogens and parasites on the environment and public health, accurate methods are needed to evaluate their presence and concentration. Although various techniques exist to detect certain pathogens in water (e.g., immunofluorescence or PCR techniques), these are time- and labor-intensive. A direct, real-time method for detection and quantification of target organisms would thus be very useful for rapid diagnosis of water safety. A quartz crystal microbalance with dissipation monitoring (QCM-D) based biosensor for detection of waterborne pathogens (i.e., Escherichia coli O157:H7) was developed. The detection platform is based on the immobilization of affinity purified antibodies onto gold coated QCM-D quartz crystals via a cysteamine self-assembled monolayer. The results show that the optimal sensor response is the initial slope of the dissipation shift. A highly log-log linear response is obtained for detection of E. coli O157:H7 over a broad range of cell concentration from 3 x 105 to 1 x 109 cells/mL. The prepared biosensor also exhibits a log-log linear working range from 107 to 109 cells/mL for E. coli K12 D21, a non-pathogenic model organism. The biosensor also shows satisfactory selectivity using Bacillus subtilis . To our knowledge, this is the first study demonstrating the use of the slope of the dissipation shift as a sensor response when using QCM-D technology.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it