A fucosyltransferase inhibition assay using image-analysis and digital microfluidics
Bibliographic record
Abstract
Sialyl-LewisX and LewisX are cell-surface glycans that influence cell-cell adhesion behaviors. These glycans are assembled by α(1,3)-fucosyltransferase enzymes. Their increased expression plays a role in inflammatory disease, viral and microbial infections, and cancer. Efficient screens for specific glycan modifications such as those catalyzed by fucosyltransferases are tended toward costly materials and large instrumentation. We demonstrate for the first time a fucosylation inhibition assay on a digital microfluidic system with the integration of image-based techniques. Specifically, we report a novel lab-on-a-chip approach to perform a fluorescence-based inhibition assay for the fucosylation of a labeled synthetic disaccharide, 4-methylumbelliferyl β-N-acetyllactosaminide. As a proof-of-concept, guanosine 5′-diphosphate has been used to inhibit Helicobacter pylori α(1,3)-fucosyltransferase. An electrode shape (termed “skewed wave”) is designed to minimize electrode density and improve droplet movement compared to conventional square-based electrodes. The device is used to generate a 10 000-fold serial dilution of the inhibitor and to perform fucosylation reactions in aqueous droplets surrounded by an oil shell. Using an image-based method of calculating dilutions, referred to as “pixel count,” inhibition curves along with IC50 values are obtained on-device. We propose the combination of integrating image analysis and digital microfluidics is suitable for automating a wide range of enzymatic assays.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".