Interplay of physical mechanisms and biofilm processes: review of microfluidic methods
Bibliographic record
Abstract
Bacteria in natural and artificial environments often reside in self-organized, integrated communities known as biofilms. Biofilms are highly structured entities consisting of bacterial cells embedded in a matrix of self-produced extracellular polymeric substances (EPS). The EPS matrix acts like a biological 'glue' enabling microbes to adhere to and colonize a wide range of surfaces. Once integrated into biofilms, bacterial cells can withstand various forms of stress such as antibiotics, hydrodynamic shear and other environmental challenges. Because of this, biofilms of pathogenic bacteria can be a significant health hazard often leading to recurrent infections. Biofilms can also lead to clogging and material degradation; on the other hand they are an integral part of various environmental processes such as carbon sequestration and nitrogen cycles. There are several determinants of biofilm morphology and dynamics, including the genotypic and phenotypic states of constituent cells and various environmental conditions. Here, we present an overview of the role of relevant physical processes in biofilm formation, including propulsion mechanisms, hydrodynamic effects, and transport of quorum sensing signals. We also provide a survey of microfluidic techniques utilized to unravel the associated physical mechanisms. Further, we discuss the future research areas for exploring new ways to extend the scope of the microfluidic approach in biofilm studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".