Sensitive, Real-time and Non-Intrusive Detection of Concentration and Growth of Pathogenic Bacteria using Microfluidic-Microwave Ring Resonator Biosensor
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Infection diagnosis and antibiotic susceptibility testing (AST) are time-consuming and often laborious clinical practices. This paper presents a microwave-microfluidic biosensor for rapid, contactless and non-invasive device for testing the concentration and growth of Escherichia Coli ( E. Coli ) in medium solutions of different pH to increase the efficacy of clinical microbiology practices. The thin layer interface between the microfluidic channel and the microwave resonator significantly enhanced the detection sensitivity. The microfluidic chip, fabricated using standard soft lithography, was injected with bacterial samples and incorporated with a microwave microstrip ring resonator sensor with an operation frequency of 2.5 GHz and initial quality factor of 83 for detecting the concentration and growth of bacteria. The resonator had a coupling gap area on of 1.5 × 1.5 mm 2 as of its sensitive region. The presence of different concentrations of bacteria in different pH solutions were detected via screening the changes in resonant amplitude and frequency responses of the microwave system. The sensor device demonstrated near immediate response to changes in the concentration of bacteria and maximum sensitivity of 3.4 MHz compared to a logarithm value of bacteria concentration. The minimum prepared optical transparency of bacteria was tested at an OD 600 value of 0.003. The sensor’s resonant frequency and amplitude parameters were utilized to monitor bacteria growth during a 500-minute time frame, which demonstrated a stable response with respect to detecting the bacterial proliferation. A highly linear response was demonstrated for detecting bacteria concentration at various pH values. The growth of bacteria analyzed over the resonator showed an exponential growth curve with respect to time and concurred with the lag-log-stationary-death model of cell growth. This biosensor is one step forward to automate the complex AST workflow of clinical microbiology laboratories for rapid and automated detection of bacteria as well as screening the bacteria proliferation in response to antibiotics.
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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) | 0.000 | 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 it