Rapid and real-time monitoring of bacterial growth against antibiotics in solid growth medium using a contactless planar microwave resonator sensor
Bibliographic record
Abstract
Abstract Infection diagnosis and antibiotic susceptibility testing (AST) are pertinent clinical microbiology practices that are in dire need of improvement, due to the inadequacy of current standards in early detection of bacterial response to antibiotics and affordability of contemporarily used methods. This paper presents a novel way to conduct AST which hybridizes disk diffusion AST with microwave resonators for rapid, contactless, and non-invasive sensing and monitoring. In this research, the effect of antibiotic (erythromycin) concentrations on test bacterium, Escherichia coli ( E. coli ) cultured on solid agar medium (MH agar) are monitored through employing a microwave split-ring resonator. A one-port microwave resonator operating at a 1.76 GHz resonant frequency, featuring a 5 mm 2 sensitive sensing region, was designed and optimized to perform this. Upon introducing uninhibited growth of the bacteria, the sensor measured 0.005 dB/hr, with a maximum change of 0.07 dB over the course of 15 hours. The amplitude change decreased to negligible values to signify inhibited growth of the bacteria at higher concentrations of antibiotics, such as a change of 0.005 dB in resonant amplitude variation while using 45 µg of antibiotic. Moreover, this sensor demonstrated decisive results of antibiotic susceptibility in under 6 hours and shows great promise to expand automation to the intricate AST workflow in clinical settings, while providing rapid, sensitive, and non-invasive detection capabilities.
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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".