Influence of Microenvironmental Orchestration on Multicellular Lung Alveolar Organoid Development from Human Induced Pluripotent Stem Cells.
Bibliographic record
Abstract
Induced pluripotent stem cells (iPSCs) have emerged as promising in vitro tools, providing a robust system for disease modelling and facilitating drug screening. Human iPSCs have been successfully differentiated into lung cells and three-dimensional lung spheroids or organoids. The lung is a multicellular complex organ that develops under the symphonic influence of the microenvironment. Here, we hypothesize that the generation of lung organoids in a controlled microenvironment (cmO) (oxygen and pressure) yields multicellular organoids with architectural complexity resembling the lung alveoli. iPSCs were differentiated into mature lung organoids following a stepwise protocol in an oxygen and pressure-controlled microenvironment. The organoids developed in the controlled microenvironment displayed complex alveolar architecture and stained for SFTPC, PDPN, and KRT5, indicating the presence of alveolar epithelial type II and type I cells, as well as basal cells. Moreover, gene and protein expression levels were also increased in the cmO. Furthermore, pathway analysis of proteomics revealed upregulation of lung development-specific pathways in the cmO compared to those growing in normal culture conditions. In summary, by using a controlled microenvironment, we established a complex multicellular lung organoid derived from iPSCs as a novel cellular model to study lung alveolar biology in both lung health and disease.
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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".