Microfluidic device for studying cell migration in single or co-existing chemical gradients and electric fields
Bibliographic record
Abstract
Cell migration is involved in physiological processes such as wound healing, host defense, and cancer metastasis. The movement of various cell types can be directed by chemical gradients (i.e., chemotaxis). In addition to chemotaxis, many cell types can respond to direct current electric fields (dcEF) by migrating to either the cathode or the anode of the field (i.e., electrotaxis). In tissues, physiological chemical gradients and dcEF can potentially co-exist and the two guiding mechanisms may direct cell migration in a coordinated manner. Recently, microfluidic devices that can precisely configure chemical gradients or dcEF have been increasingly developed and used for chemotaxis and electrotaxis studies. However, a microfluidic device that can configure controlled co-existing chemical gradients and dcEF that would allow quantitative cell migration analysis in complex electrochemical guiding environments is not available. In this study, we developed a polydimethylsiloxane-based microfluidic device that can generate better controlled single or co-existing chemical gradients and dcEF. Using this device, we showed chemotactic migration of T cells toward a chemokine CCL19 gradient or electrotactic migration toward the cathode of an applied dcEF. Furthermore, T cells migrated more strongly toward the cathode of a dcEF in the presence of a competing CCL19 gradient, suggesting the higher electrotactic attraction. Taken together, the developed microfluidic device offers a new experimental tool for studying chemical and electrical guidance for cell migration, and our current results with T cells provide interesting new insights of immune cell migration in complex guiding environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".