Electroceutical Approach for Impairing the Motility of Pathogenic Bacterium Using a Microfluidic Platform
Bibliographic record
Abstract
Electrotaxis, or galvanotaxis, refers to the migration pattern of cells induced in response to electrical potential. Electrotaxis has not been explored in detail in bacterial cells; information regarding the impact of current on pathogenic bacteria is severely lacking. Using microfluidic platforms and optical microscopy, we designed a series of single- and multi-cue experiments to assess the impact of varying electrical currents and acetic acid concentrations on bacterial motility dynamics in pathogenic multi-drug resistant (MDR) strains of Pseudomonas aeruginosa and Escherichia coli. The use of the microfluidic platform allows for single-cue experiments where electrical current is supplied at a range that is biocidal to bacteria and multi-cue experiments where acetic acid is combined with current to enhance disinfection. These strategies may offer substantial therapeutic benefits, specifically for the treatment of biofilm infections, such as those found in the wound environment. Our results showed that an application of current in combination with acetic acid has profound inhibitory effects on MDR strains of P. aeruginosa and E. coli, even with brief applications. Specifically, E. coli motility dynamics and cell survival were significantly impaired starting at a concentration of 0.125 mA of direct current (DC) and 0.31% acetic acid, while P. aeruginosa was impaired at 0.70 mA and 0.31% acetic acid. As these strains are relevant wound pathogens, it is likely that this strategy would be effective against similar strains in vivo and could represent a new approach to hasten wound healing.
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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) | 0.001 | 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".