Rapid Antibiotic Susceptibility Testing on Pathogenic Bacteria in Solid Growth Medium Using a Real-Time and 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, as current standards are not able to keep up with the mutations and resistance development of certain bacterial strains. This paper presents a novel way to conduct AST which hybridizes disk diffusion AST with microwave resonators for rapid, contactless, non-invasive and high-throughput testing. This work uses Escherichia coli (E. coli) cultured on solid agar and places bacteria samples on a microwave split-ring resonator along with antibiotic disks (erythromycin) of various doses to demonstrate the viability of this sensing method in a clinical microbiological setting. The microwave resonator, operating at a 1.76 GHz resonant frequency, boasted a 5 mm2 sensitive sensing region. A one-port sensor was designed and optimized for detecting dielectric property variations of lossy dielectric materials accurately. This sensor was calibrated to detect uninhibited growth of the bacteria at 0.005 dB/hr, with a maximum change of 0.07 dB over the course of 15 hrs. The transient resonant amplitude change was subsequently dampened for each increasing dosage of antibiotic tested, with 45 µg of erythromycin showing negligible change indicating complete inhibited growth. This AST sensor demonstrated decisive results of antibiotic susceptibility in under 6 hours and shows great promise to further automate the intricate workflow of AST in clinical settings, while providing rapid, sensitive, non-invasive and high-throughput detection capabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".