Modeling Epithelial Homeostasis and Reactive Epithelial Changes in Human and Murine Three‐Dimensional Esophageal Organoids
Bibliographic record
Abstract
The homeostatic proliferation-differentiation gradient in the esophageal epithelium is perturbed under inflammatory disease conditions such as gastroesophageal reflux disease and eosinophilic esophagitis. Herein we describe the protocols for rapid generation (<14 days) and characterization of single-cell-derived, three-dimensional (3D) esophageal organoids from human subjects and mice with normal esophageal mucosa or inflammatory disease conditions. While 3D organoids recapitulate normal epithelial renewal, proliferation, and differentiation, non-cell autonomous reactive epithelial changes under inflammatory conditions are evaluated in the absence of the inflammatory milieu. Reactive epithelial changes are reconstituted upon exposure to exogenous recombinant cytokines. These changes are modulated pharmacologically or genetically ex vivo. Molecular, structural, and functional changes are characterized by morphology, flow cytometry, biochemistry, and gene expression analyses. Esophageal 3D organoids can be translated for the development of personalized medicine in assessment of individual cytokine sensitivity and molecularly targeted therapeutics in esophagitis patients © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Generation of esophageal organoids from biopsy or murine esophageal epithelial sheets Basic Protocol 2: Propagation and cryopreservation of esophageal organoids Basic Protocol 3: Harvesting of esophageal organoids for RNA isolation, immunohistochemistry, and evaluation of 3D architecture Basic Protocol 4: Modeling of reactive epithelium in esophageal organoids.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".