Single Cells of <i>Pseudomonas aeruginosa</i> Exhibit Electrotaxis and Durotaxis Behaviours
Bibliographic record
Abstract
Abstract Pseudomonas aeruginosa is frequently associated with nosocomial infections, including polymicrobial wound infections and the complex biofilm communities that reside within the cystic fibrosis lung. P. aeruginosa utilizes flagellum-mediated motility to approach, attach to, and spread across a surface using a combination of swimming, swarming and twitching (type IV pili extension and retraction) motility. We report an innovative approach to study the motility of single P. aeruginosa cells in microfluidic channels possessing different structural geometry, all with the flexibility of being able to manipulate stiffness gradients and electric fields to investigate changes in motility in response to specific stimuli. P. aeruginosa cells exhibit restricted motility in reduced microchannel spaces, with preferential migration toward a stiffer region in a rigidness gradient of substrate medium and preferential migration toward a positive electrode in presence of a pulsed or successive electric field. This single-cue environmental study using microfluidic technology lays the groundwork for multi-cue experimentation to more faithfully mimic conditions in vivo , demonstrating just some of the advantages of this technique. This study is designed to examine the interplay between surface rigidity, mechanical, and electrical cues to pave the way for improvements in the design of anti-fouling surfaces for biomedical applications and to identify new ways to inhibit bacterial biofilm growth through motility restriction.
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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 | 0.000 |
| 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) | 0.001 | 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 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".