A triple-unit microfluidic device (D3-chip) for cell migration research
Bibliographic record
Abstract
Chemotaxis is an important research eld and many research labs are studying the mechanisms of chemotaxis of different cell types and its biomedical applications.During the past 20 years, micro uidic devices have been extensively used for chemotaxis research mainly owing to their advantage in cellular microenvironmental control.However, micro uidic chemotaxis experiments are not easy to set up and often limited by lengthy data analysis and the low-throughout.To address these issues, we developed a new triple-unit gradient-generating micro uidic device \(i.e.D3-Chip).The main features of this D3-Chip include standalone stable gradient generation, docking structure for cell alignment, parallel chemotaxis experiments on a single chip and rapid chemotaxis distance measurement without time-lapse imaging and single cell tracking.This D3-Chip was successfully applied to two recently published chemotaxis studies including the effect of broblast growth factor 23 \(FGF23) on neutrophil chemotaxis and the effect of activin A on neutrophil and human breast cancer cell chemotaxis.We believe this D3-Chip can be broadly useful to other cell migration researchers.In this protocol, we describe detailed fabrication and operation of the D3-Chip for cell migration experiments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".