Microfluidic cell culture systems with integrated sensors for drug screening
Bibliographic record
Abstract
Cell-based testing is a key step in drug screening for cancer treatments. A microfluidic platform can permit more precise control of the cell culture microenvironment, such as gradients in soluble factors. These small-scale devices also permit tracking of low cell numbers. As a new screening paradigm, a microscale system for integrated cell culture and drug screening promises to provide a simple, scalable tool to apply standardized protocols used in cellular response assays. With the ability to dynamically control the microenvironment, we can create temporally varying drug profiles to mimic physiologically measured profiles. In addition, low levels of oxygen in cancerous tumors have been linked with drug resistance and decreased likelihood of successful treatment and patient survival. Our work also integrates a thin-film oxygen sensor with a microfluidic oxygen gradient generator which will in future allow us to create spatial oxygen gradients and study effects of hypoxia on cell response to drug treatment. In future, this technology promises to improve cell-based validation in the drug discovery process, decreasing the cost and increasing the speed in screening large numbers of compounds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".