Design of a Microfluidic Chip for Magnetic-Activated Sorting of One-Bead-One-Compound Libraries
Bibliographic record
Abstract
Molecular targeting using ligands specific to disease markers has shown great promise for early detection and directed therapy. Bead-based combinatorial libraries have served as powerful tools for the discovery of novel targeting agents. Screening platforms employing magnetic capture have been used to achieve rapid and efficient identification of high-affinity ligands from one-bead-one-compound (OBOC) libraries. Traditional manual methodologies to isolate magnetized "hit" beads are tedious and lack accuracy, and existing instruments to expedite bead sorting tend to be costly and complex. Here, we describe the design and construction of a simple and inexpensive microfluidic magnetic sorting device using standard photolithography and soft lithography approaches to facilitate high-throughput isolation of magnetized positive hit beads from combinatorial libraries. We have demonstrated that the device is able to sort magnetized beads with superior accuracy compared to conventional manual sorting approaches. This chip offers a very convenient yet inexpensive alternative for screening OBOC libraries.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".