A Microfluidic Approach To The Investigation Of Magnetotactic Bacteria Motility Through Viscoelastic Fluids
Bibliographic record
Abstract
Magnetotactic bacteria (MTB), a class of non-pathogenic bacteria that grow magnetic nanoparticles (MNPs) internally, have the potential to be used for targeted drug delivery. The MNPs inside MTB cause them to orient and move in response to magnetic field lines, a phenomenon called magnetotaxis. The self-propulsion capabilities of MTB can allow them to penetrate through tissue, and a magnetic field can be used to direct the MTB to the target tissue or organ. However, when MTB are used in the body, they may encounter fluids of different viscoelasticities, depending on the target location. Thus, an understanding of the capability of directing MTB using a magnetic field through viscoelastic fluids will be essential to their use for targeted drug delivery. In this work, we develop a microfluidic-focused experimental setup to evaluate the motility of MTB in viscoelastic fluids under the influence of different magnetic field strengths. We used polyacrylamide (PAM) dissolved in water as a model viscoelastic fluid. A microfluidic channel is filled with PAM solution for MTB studies. A Brookfield viscometer is used to determine the viscosity average molecular weight of three PAM samples and to obtain the shear rate vs. viscosity plots for various PAM concentrations. Custom 3D-printed Helmholtz coils are used to generate the magnetic field for MTB experiments. The coils are mounted on an Olympus BX51 microscope, and the magnetic field at the center of the coils was characterized through finite element analysis simulations and experimentally. Our microfluidic approach offers the advantage of generating precise magnetic fields along microchannels containing a viscoelastic fluid that can be used on the investigation of bacterial motility on-chip.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".