Rapid Real-Time Antimicrobial Susceptibility Testing with Electrical Sensing on Plastic Microchips with Printed Electrodes
Bibliographic record
Abstract
Rapid antimicrobial susceptibility testing is important for efficient and timely therapeutic decision making. Due to globally spread bacterial resistance, the efficacy of antibiotics is increasingly being impeded. Conventional antibiotic tests rely on bacterial culture, which is time-consuming and can lead to potentially inappropriate antibiotic prescription and up-front broad range of antibiotic use. There is an urgent need to develop point-of-care platform technologies to rapidly detect pathogens, identify the right antibiotics, and monitor mutations to help adjust therapy. Here, we report a biosensor for rapid (<90 min), real time, and label-free bacteria isolation from whole blood and antibiotic susceptibility testing. Target bacteria are captured on flexible plastic-based microchips with printed electrodes using antibodies (30 min), and its electrical response is monitored in the presence and absence of antibiotics over an hour of incubation time. We evaluated the microchip with Escherichia coli and methicillin-resistant Staphylococcus aureus (MRSA) as clinical models with ampicillin, ciprofloxacin, erythromycin, daptomycin, gentamicin, and methicillin antibiotics. The results are compared with the current standard methods, i.e. bacteria viability and conventional antibiogram assays. The technology presented here has the potential to provide precise and rapid bacteria screening and guidance in clinical therapies by identifying the correct antibiotics for pathogens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".