High-Throughput Microfluidic Method To Study Biofilm Formation and Host-Pathogen Interactions in Pathogenic Escherichia coli
Bibliographic record
Abstract
Biofilm formation and host-pathogen interactions are frequently studied using multiwell plates; however, these closed systems lack shear force, which is present at several sites in the host, such as the intestinal and urinary tracts. Recently, microfluidic systems that incorporate shear force and very small volumes have been developed to provide cell biology models that resemble in vivo conditions. Therefore, the objective of this study was to determine if the BioFlux 200 microfluidic system could be used to study host-pathogen interactions and biofilm formation by pathogenic Escherichia coli. Strains of various pathotypes were selected to establish the growth conditions for the formation of biofilms in the BioFlux 200 system on abiotic (glass) or biotic (eukaryotic-cell) surfaces. Biofilm formation on glass was observed for the majority of strains when they were grown in M9 medium at 30 °C but not in RPMI medium at 37 °C. In contrast, HRT-18 cell monolayers enhanced binding and, in most cases, biofilm formation by pathogenic E. coli in RPMI medium at 37 °C. As a proof of principle, the biofilm-forming ability of a diffusely adherent E. coli mutant strain lacking AIDA-I, a known mediator of attachment, was assessed in our models. In contrast to the parental strain, which formed a strong biofilm, the mutant formed a thin biofilm on glass or isolated clusters on HRT-18 monolayers. In conclusion, we describe a microfluidic method for high-throughput screening that could be used to identify novel factors involved in E. coli biofilm formation and host-pathogen interactions under shear force.
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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 | 0.001 |
| 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".