Design of biomimetic substrates for long-term maintenance of alveolar epithelial cells
Bibliographic record
Abstract
There is a need to establish in vitro lung alveolar epithelial culture models to better understand the fundamental biological mechanisms that drive lung diseases. While primary alveolar epithelial cells (AEC) are a useful option to study mature lung biology, they have limited utility in vitro. Cells that survive demonstrate limited proliferative capacity and loss of phenotype over the first 3-5 days in traditional culture conditions. To address this limitation, we generated a novel physiologically relevant cell culture system for enhanced viability and maintenance of phenotype. Here we describe a method utilizing e-beam lithography, reactive ion etching, and replica molding to generate poly-dimethylsiloxane (PDMS) substrates containing hemispherical cavities that mimic the architecture and size of mouse and human alveoli. Primary AECs grown on these cavity-containing substrates form a monolayer that conforms to the substrate enabling precise control over cell sheet architecture. AECs grown in cavity culture conditions remain viable and maintain their phenotype over one week. Specifically, cells grown on substrates consisting of 50 μm diameter cavities remained 96 ± 4% viable and maintained expression of surfactant protein C (SPC), a marker of type 2 AEC over 7 days. While this report focuses on primary lung alveolar epithelial cells, our culture platform is potentially relevant and useful for growing primary cells from other tissues with similar cavity-like architecture and could be further adapted to other biomimetic shapes or contours.
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.
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 | 0.001 | 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 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".