Understanding biofilm development on diverse interfaces and controlled environments using novel microfluidic devices
Bibliographic record
Abstract
Biofilms are ubiquitous structured communities of bacterial cells encased in a selfproduced polymeric matrix that protects the microorganisms from hostile physical and chemical environments that grow and respond to stimuli from internal biological processes and external environmental conditions. Biofilms can attach to different surface types and could be beneficial or detrimental depending on the microbial species and their growth. Among all the environmental factors, the interfaces in which biofilm grows play a critical role that directly affects how they thrive and interact with the environment. While there are many devices available to culture biofilm for in vitro studies, such as the microtiter plate, the Calgary Biofilm Device, the Center for Disease Control reactor, and the rotating disk reactor, most of these platforms are somewhat limited to the modelling of solid-liquid interface (SLI) biofilms, where the biofilms are attached to solid-rigid platforms submerged in liquid. Indeed, the environment in which biofilms thrive is diverse, and they could grow on air-liquid interface (ALI), liquidliquid interface (LLI), solid-air interface (SAI) etc. For example, biofilms found along the respiratory tracts and on the roots or leaves of plants are ALI biofilms, whereas biofilms in the urinary tract and on porous membranes that separate different media, such as those commonly found in wastewater treatment, can be best represented as LLI biofilm. Despite the plethora of scientific studies on biofilm, a detailed description of how biofilm develops on different interfaces in a well-controlled environment remains scarce. Being able to control the environment is important to meaningfully compare the different properties and growth patterns exhibited by the biofilm models developed on different interfaces. A device with an inclusive platform that enables the change in interfaces while allowing for the precise manipulation of growth conditions and environmental factors would also be useful and important to determine the efficacy of biofilm control methods accurately.The studies in this thesis have been broadly categorised into four sections, specifically: (1) the design and development of a microfluidic device that enables the use of different interfaces for biofilm culture, (2) the application of the microfluidic device for the dynamic control of environmental conditions to understand biofilm growth on different interfaces, (3) the design and development of a device that enables the real-time in situ electrochemical monitoring of biofilm growth, and (4) demonstration on how the aforementioned devices can be used to access the efficacy of novel drug formulations for treating biofilm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".