Microfluidic devices for cell based high throughput screening
Bibliographic record
Abstract
Cell based screening assays are increasingly used in drug discovery due to the physiological significance of the results and high content information obtained from them. Miniaturization of this format, currently carried out in microwell plates, is at its limit due to increased unnatural interaction of cells with walls inside micro-wells. In order to overcome this limitation, we present a new format for dynamically controlled, precise, spatial and temporal dosing of a continuous cell culture layer, using microfluidics. The device consists of a micropatterned nanoporous membrane layer that allows specific spatial locations in the continuous gel layer above, to be chemically addressed by external electric field through a microfluidic network below it. We demonstrate that the control of electric field across the nanoporous membrane leads to extremely precise dosing (approximately 50 microg accuracy). Spot sizes of 200 microm to 6 mm in diameter and inter-spot distances of 0.4-10 mm have been obtained. Microarray spot densities of 156 spots/cm(2) were obtained, which is five times higher than the densities used in current cell based assays. The capability of this method in handling small molecules, proteins and drugs is also demonstrated. This format of spatial dosing of continuous cell culture will enable further miniaturization of cell based assays and aid in high-throughput high-content screening.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".