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Record W6911006232 · doi:10.48620/76400

Influence of Microenvironmental Orchestration on Multicellular Lung Alveolar Organoid Development from Human Induced Pluripotent Stem Cells.

2024· article· en· W6911006232 on OpenAlexaff

Bibliographic record

VenueOpen Access CRIS of the University of Bern · 2024
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsIONICS Mass Spectrometry (Canada)
Fundersnot available
KeywordsOrganoidInduced pluripotent stem cellMulticellular organismLungStem cellA549 cellCellular differentiation

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.352
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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