Tissue Patterning: Translating Design Principles from In Vivo to In Vitro
Bibliographic record
Abstract
Recapitulating the architecture of native tissue remains a significant challenge, impeding the progress of engineering tissues. Imposing appropriate organization is especially challenging in tissues that contain multiple cellular components in complex structural units. One solution is to mimic developmental processes in embryos. In an embryo, cells are organized by tissue patterning, whereby induction of fate-determining genes is spatially controlled to generate patterns of cell differentiation and maturation. Following patterning, the imposed cell organization is further reinforced by implementation of compartment boundaries, which prevent intermingling of cells from distinct phenotypic domains, thereby ensuring preservation of proper cell organization in growing and reorganizing tissues. Both morphogenic processes utilize a conserved set of fundamental principles, the implementation of which leads to highly regulated cell organization. In this article, we review these patterning principles in vivo and reflect on the progress made by tissue engineers in mimicking tissue patterning ex vivo.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".