Real-Time Respiration Changes as a Viability Indicator for Rapid Antibiotic Susceptibility Testing in a Microfluidic Chamber Array
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Rapid identification of a pathogen and the measurement of its antibiotic susceptibility are key elements in the diagnostic process of bacterial infections. Microfluidic technologies offer great control over handling and manipulation of low sample volumes with the possibility to study microbial cultures on the single-cell level. Downscaling the dimensions of cultivation systems directly results in a lower number of bacteria required for antibiotic susceptibility testing (AST) and thus in a reduction of the time to result. The developed platform presented in this work allows the reading of pathogen resistance profiles within 2–3 h based on the changes of dissolved oxygen levels during bacterial cultivation. The platform contains hundreds of individual growth chambers prefilled with a hydrogel containing oxygen-sensing nanoprobes and different concentrations of antibiotic compounds. The performance of the developed platform is tested using quality control Escherichia coli strains (ATCC 25922 and ATCC 35218) in response to clinically relevant antibiotics. The results are in agreement with values given in reference guidelines and independent measurements using a clinical AST protocol. Finally, the platform is successfully used for the AST of an E. coli clinical isolate obtained from a patient blood culture.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".