Microfluidic wound model for studying the behaviors of <i>Pseudomonas aeruginosa</i> in polymicrobial biofilms
Bibliographic record
Abstract
Pseudomonas aeruginosa is a particularly problematic opportunistic pathogen due to its capacity to form recalcitrant biofilm structures, while cohabiting with other harmful/pathogenic species and harboring the capability to release toxins that cause tissue necrosis. Although it is now recognized that the majority of biofilm infections are polymicrobial, little is known about the complex interactions that occur within polymicrobial communities and few tools exist for studying these interactions. In this study, we have designed a microfluidic model that mimics the relevant physiological properties of wound microenvironment, while incorporating materials present in the human extracellular matrix/wound environment. Using microfluidics combined with imaging techniques, we have validated the robustness of our model comparing traditional GFP-tagging to new fluorescent staining techniques to visualize/resolve individual species within a polymicrobial habitat. We have also demonstrated that chemotactic stimuli may be incorporated into our model through specialized ports in our chamber. Our system is specifically designed for use with high resolution imaging techniques, allowing for data collection throughout the life of the biofilm and in real-time. Ultimately, this model can be used to investigate the spatio-temporal mechanobiological structures of the wound environment, and the response of the bacteria to the drug transport which will significantly contribute to our understanding of the development and progression of polymicrobial biofilm infections.
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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".