Accessible dynamic micropatterns in monolayer cultures via modified desktop xurography
Bibliographic record
Abstract
Micropatterned cell cultures provide an important tool to understand dynamic biological processes, but often require specialized equipment and expertise. Here we present subtractive bioscribing (SuBscribing), a readily accessible and inexpensive technique to generate dynamic micropatterns in biomaterial monolayers on-the-fly. We first describe our modifications to a commercially available desktop xurographer and demonstrate the utility and limits of this system in creating micropatterned cultures by mechanically scribing patterns into a brittle, non-adhesive biomaterial layer. Patterns are sufficiently small to influence cell morphology and orientation and can be extended to pattern large areas with complex reproducible shapes. We also demonstrate the use of this system as a dynamic patterning tool for cocultures. Finally, we use this technique to explore and improve upon the well-established epithelial scratch assay, and demonstrate that robotic control of the scratching tool can be used to create custom-shaped wounds in epithelial monolayers, and that the scribing direction leaves trace remnants of matrix molecules that may significantly affect conventional implementations of this common assay.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".