A digital microfluidic platform for primary cell culture and analysis
Bibliographic record
Abstract
Digital microfluidics (DMF) is a technology that facilitates electrostatic manipulation of discrete nano- and micro-litre droplets across an array of electrodes, which provides the advantages of single sample addressability, automation, and parallelization. There has been considerable interest in recent years in using DMF for cell culture and analysis, but previous studies have used immortalized cell lines. We report here the first digital microfluidic method for primary cell culture and analysis. A new mode of "upside-down" cell culture was implemented by patterning the top plate of a device using a fluorocarbon liftoff technique. This method was useful for culturing three different primary cell types for up to one week, as well as implementing a fixation, permeabilization, and staining procedure for F-actin and nuclei. A multistep assay for monocyte adhesion to endothelial cells (ECs) was performed to evaluate functionality in DMF-cultured primary cells and to demonstrate co-culture using a DMF platform. Monocytes were observed to adhere in significantly greater numbers to ECs exposed to tumor necrosis factor (TNF)-α than those that were not, confirming that ECs cultured in this format maintain in vivo-like properties. The ability to manipulate, maintain, and assay primary cells demonstrates a useful application for DMF in studies involving precious samples of cells from small animals or human patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".