On-chip phenotypic investigation of combinatory antibiotic effects by generating orthogonal concentration gradients
Bibliographic record
Abstract
Combinatory therapy using two or more kinds of antibiotics is attracting considerable attention for inhibiting multi-drug resistant pathogenic bacteria. Although the therapy mostly leads to more powerful antimicrobial effects than using a single antibiotic (synergy), interference may arise from certain antibiotic combinations, resulting in the antimicrobial effect being suppressed (antagonism). Here, we present a microfluidic-based phenotypic screening chip to investigate combinatory antibiotic effects by automatically generating two orthogonal concentration gradients on a bacteria-trapping agarose gel. Computational simulations and fluorescence experiments together verify the simultaneous establishment of 121 concentration combinations, facilitating on-chip drug testing with stability and efficiency. Against Gram-negative bacteria, Pseudomonas aeruginosa, our chip allows the measurement of phenotypic growth levels, and enables various types of analyses for all antibiotic pairs to be conducted in 7 h. Furthermore, by providing a specific amount of susceptibility data, our chip enables the two reference models, Loewe additivity and Bliss independence, to be implemented, which classify the antibiotic interaction types into synergy or antagonism. These results suggest the efficacy of our chip as a cell-based drug screening platform for exploring the underlying pharmacological patterns of antibiotic interactions, with potential applications in guidance in clinical therapies and in screening other cell-type agents.
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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".