A Modular, Lego‐Like Microfluidic Platform for Multimodal Analysis of Biofilm Formation and Antimicrobial Response
Bibliographic record
Abstract
Abstract Biofilms are substantially more resistant compared to free‐floating bacteria, highlighting the need for tools capable of real‐time analysis under physiologically relevant conditions. A modular microfluidic platform with integrated electrodes is presented for dynamic sampling and monitoring of biofilm formation and response. Its architecture enables natural shear stress variations and allows label‐free studies, including optical microscopy and electrical impedance spectroscopy. The platform evaluates biofilm growth, migration, and regrowth and investigates the effect of i) treatment using antibiotics and ii) antimicrobial coatings (i.e., silver nanoparticles) on Pseudomonas aeruginosa biofilms, revealing distinct stages of response and persistence in each case. Distinct biofilm behaviors are identified in response to antibiotic treatment as well as after applying an antibacterial coating (i.e., incomplete vs no biofilm eradication). Furthermore, the platform's modularity allows for biofilm migration and regrowth studies via Lego‐like connected modules, emphasizing the need for strategies addressing biofilm resilience and regrowth. The system provides a robust, scalable, and biologically relevant alternative to traditional methods for biofilm research. In addition to basic assays, this system is well‐suited for clinical environments, where timely and personalized therapeutic decisions are critical for managing biofilm‐related 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".