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Record W2153573421 · doi:10.1101/020511

Single Cells of <i>Pseudomonas aeruginosa</i> Exhibit Electrotaxis and Durotaxis Behaviours

2015· preprint· en· W2153573421 on OpenAlexafffund
Lee Preiss, Suresh Neethirajan

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2015
Typepreprint
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsPseudomonas aeruginosaMotilityBiofilmPilusBiologyMicrofluidicsCystic fibrosisMicrobiologyElectric fieldBiophysicsCell biologyNanotechnologyMaterials scienceBacteriaPhysicsVirulenceBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.187
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2015
Admission routes2
Has abstractyes

Explore more

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