Cellular bias on the microscale: probing the effects of digital microfluidic actuation on mammalian cell health, fitness and phenotype
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The potential benefits of using new technologies such as microfluidics for life science applications are exciting, but it is critical to understand and document potential biases imposed by these technologies on the observed results. Here, we report the first study of genome-level effects on cells manipulated by digital microfluidics. These effects were evaluated using a broad suite of tools: cell-based stress sensors for heat shock activation, single-cell COMET assays to probe changes in DNA integrity, and DNA microarrays and qPCR to evaluate changes in genetic expression. The results lead to two key observations. First, most DMF operating conditions tested, including those that are commonly used in the literature, result in negligible cell-stress or genome-level effects. Second, for DMF devices operated at high driving frequency (18 kHz) and with large driving electrodes (10 mm × 10 mm), there are significant damage to DNA integrity and differential genomic regulation. We hypothesize that these effects are caused by droplet heating. We recommend that for DMF applications involving mammalian cells that driving frequencies be kept low (≤ 10 kHz) and electrode sizes be kept small (≤ 5 mm) to avoid detrimental effects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 it