Surface topography induces 3D self-orientation of cells and extracellular matrix resulting in improved tissue function
Bibliographic record
Abstract
The organization of cells and extracellular matrix (ECM) in native tissues plays a crucial role in their functionality. However, in tissue engineering, cells and ECM are randomly distributed within a scaffold. Thus, the production of engineered-tissue with complex 3D organization remains a challenge. In the present study, we used contact guidance to control the interactions between the material topography, the cells and the ECM for three different tissues, namely vascular media, corneal stroma and dermal tissue. Using a specific surface topography on an elastomeric material, we observed the orientation of a first cell layer along the patterns in the material. Orientation of the first cell layer translates into a physical cue that induces the second cell layer to follow a physiologically consistent orientation mimicking the structure of the native tissue. Furthermore, secreted ECM followed cell orientation in every layer, resulting in an oriented self-assembled tissue sheet. These self-assembled tissue sheets were then used to create 3 different structured engineered-tissue: cornea, vascular media and dermis. We showed that functionality of such structured engineered-tissue was increased when compared to the same non-structured tissue. Dermal tissues were used as a negative control in response to surface topography since native dermal fibroblasts are not preferentially oriented in vivo. Non-structured surfaces were also used to produce randomly oriented tissue sheets to evaluate the impact of tissue orientation on functional output. This novel approach for the production of more complex 3D tissues would be useful for clinical purposes and for in vitro physiological tissue model to better understand long standing questions in biology.
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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".