Developing a microfluidic-microwave platform for real-time, non-invasive and sensitive monitoring of pathogens and antibiotic susceptibility testing
Bibliographic record
Abstract
Microfluidics and microelectromechanical systems (MEMS) are areas of studies that have become highly valued for their point-of-care (POC) and high-throughput potential, particularly in healthcare and biomedical applications. However, lab-on-a-chip devices often require supplementary equipment to operate such as pumps, computers, analyzers, valves, etc. This creates a lab-around-a-chip environment. Therefore, capillary fluidics has been developed to eliminate the need of some external machines such as pumps and valves. By capitalizing upon geometric changes to create a specific hydrodynamic profile, capillary fluidics creates an autonomous fluid delivery system for microfluidics which renders devices simple to use in POC environments. Furthermore, it is easily implemented with various sensory methods, such as optical, electrochemical or microwave sensing. One application in which capillary fluidics can vastly improve the quality of service is infection diagnosis and antibiotic susceptibility testing. Due to issues with infection diagnosis and outdated antibiotic susceptibility testing (AST) methods, physicians are over-prescribing broad-spectrum antibiotics. This coupled with patients’ non-compliance in antibiotic administration gives pathogens the ability to evolve a resistance to antibiotics. The root of the underlying issue creates a critical need to focus on bacterial infection diagnosis and AST, as contemporary practices require up to two weeks, are expensive, labor intensive, and lack the potential for point-of-care testing. Therefore, diagnosis and AST are not performed in most cases leading to broad-spectrum antibiotic prescriptions being overused. For the application of monitoring pathogens and performing AST, microwave sensing was selected for highly sensitive and relatively inexpensive implementation. Microwave resonators generate electrical fields and detect dielectric shifts within their sensing zone to characterize bioassays in a real-time, sensitive and non-invasive manner. Currently, microwave sensors are being optimized with multiple resonators to further optimize sensitivity and selectivity for biomedical applications. This makes microwave sensing an attractive approach to couple with capillary microfluidics for infection diagnosis and AST. This work aims to provide a proof of concept in coupling capillary microfluidics with planar microwave resonators to create a sensing platform to monitor the growth and antibiotic susceptibility of E. coli.
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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".