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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".