Challenges and Opportunities for Tissue-Engineering Polarized Epithelium
Bibliographic record
Abstract
The epithelium is one of the most important tissue types in the body and the specific organization of the epithelial cells in these tissues is important for achieving appropriate function. Since many tissues contain an epithelial component, engineering functional epithelium and understanding the factors that control epithelial maturation and organization are important for generating whole artificial organ replacements. Furthermore, disruption of the cellular organization leads to tissue malfunction and disease; therefore, engineered epithelium could provide a valuable in vitro model to study disease phenotypes. Despite the importance of epithelial tissues, a surprisingly limited amount of effort has been focused on organizing epithelial cells into artificial polarized epithelium with an appropriate structure that resembles that seen in vivo. In this review, we provide an overview of epithelial tissue organization and highlight the importance of cell polarization to achieve appropriate epithelium function. We next describe the in vitro models that exist to create polarized epithelium and summarize attempts to engineer artificial epithelium for clinical use. Finally, we highlight the opportunities that exist to translate strategies from tissue engineering other tissues to generate polarized epithelium with a functional structure.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".