Defined topologically-complex protein matrices to manipulate cell shape<i>via</i>three-dimensional fiber-like patterns
Bibliographic record
Abstract
Culturing cells in three-dimensional (3D) environments has been shown to significantly influence cell function, and may provide a more physiologically relevant environment within which to study the behavior of specific cell types. 3D tissues typically present a topologically complex fibrous adhesive environment, which is technically challenging to replicate in a controlled manner. Micropatterning technologies have provided significant insights into cell-biomaterial interactions, and can be used to create fiber-like adhesive structures, but are typically limited to flat culture systems; the methods are difficult to apply to topologically-complex surfaces. In this work, we utilize crack formation in multilayered microfabricated materials under applied strain to rapidly generate well-controlled and topologically complex 'fiber-like' adhesive protein patterns, capable of supporting cell culture and controlling cell shape on three-dimensional patterns. We first demonstrate that the features of the generated adhesive environments such as width, spacing and topology can be controlled, and that these factors influence cell morphology. The patterning technique is then applied to examine the influence of fiber structure on the nuclear morphology and actin cytoskeletal structure of cells cultured in a nanofibrous biomaterial matrix.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".