Micropatterning Strategies to Engineer Controlled Cell and Tissue Architecture in Vitro
Why this work is in the frame
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Bibliographic record
Abstract
Micropatterning strategies, which enable control over cell and tissue architecture in vitro, have emerged as powerful platforms for modelling tissue microenvironments at different scales and complexities. Here, we provide an overview of popular micropatterning techniques, along with detailed descriptions, to guide new users through the decision making process of which micropatterning procedure to use, and how to best obtain desired tissue patterns. Example techniques and the types of biological observations that can be made are provided from the literature. A focus is placed on microcontact printing to obtain co-cultures of patterned, confluent sheets, and the challenges associated with optimizing this protocol. Many issues associated with microcontact printing, however, are relevant to all micropatterning methodologies. Finally, we briefly discuss challenges in addressing key limitations associated with current micropatterning technologies.
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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 it