Are micropatterned substrates for directed cell organization an effective method to create ordered 3D tissue constructs?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tissue-engineered constructs grown in vitro tend to have random arrangements of cells and extracellular matrix (ECM) and much research effort is aimed at developing long-range organization in tissue-engineered constructs. Contact guidance, which utilizes substrates with topographical patterns of the scale of single cells (0.1-100 microm) to limit cell adhesion to specific locations and to influence cell shape and orientation, is one popular method which has been used to generate order in cell cultures. The use of contact guidance to generate three-dimensional (3D) order relies on the assumption that a newly forming cell or tissue layer will be guided by the organization of the previous layer, which has been organized by the patterned substrate. However, the ability for cellular patterns to be coupled through organized cell layers from a patterned substrate has not been effectively demonstrated. The results of this study demonstrate that, although the patterned substrate induces initial organization and polarization, this organization is not sustained in the successive cell/tissue layers that form above the initial cell layer. This finding suggests that cells must be in direct contact with the patterned substrate to maintain their polarization, orientation and positional organization. Therefore, contact guidance does not appear to be a promising technique to create ordered 3D tissue-engineered constructs. Alternative techniques, in particular those involving the application of mechanical, electrical or flow fields, may be more useful in sustaining organization in multilayered constructs as the organizational influence extends as a field into 3D space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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