Droplet Microfluidic System with On-Demand Trapping and Releasing of Droplet for Drug Screening Applications
Bibliographic record
Abstract
96-Well plate has been the traditional method used for screening drug compounds libraries for potential bioactivity. Although this method has been proven successful in testing dose-response analysis, the microliter consumption of expensive reagents and hours of reaction and analysis time call for innovative methods for improvements. This work demonstrates a droplet microfluidic platform that has the potential to significantly reduce the reagent consumption and shorten the reaction and analysis time by utilizing nanoliter-sized droplets as a replacement of wells. This platform is evaluated by applying it to screen drug compounds that inhibit the tau-peptide aggregation, a phenomena related to Alzheimer's disease. In this platform, sample reagents are first dispersed into nanolitre-sized droplets by an immiscible carrier oil and then these droplets are trapped on-demand in the downstream of the microfluidic device. The relative decrease in fluorescence through drug inhibition is characterized using an inverted epifluorescence microscope. Finally, the trapped droplets are released on-demand after each test by manipulating the applied pressures to the channel network which allows continuous processing. The testing results agree well with that obtained from 96-well plates with much lower sample consumption (∼200 times lower than 96-well plate) and reduced reaction time due to increased surface volume ratio (2.5 min vs 2 h).
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".