Creating a More Perfect Union: Modeling Intestinal Bacteria-Epithelial Interactions Using Organoids
Bibliographic record
Abstract
Intestinal organoids have become indispensable tools for many gastrointestinal researchers, advancing their studies of nontransformed intestinal epithelial cells, and their roles in an array of diseases, including inflammatory bowel disease and colon cancer. In many cases. these diseases, as well as many enteric infections, reflect pathogenic interactions between bacteria and the gut epithelium. The complexity of studying this microbe-epithelial interface in vivo has led to significant focus on modeling this cross-talk using organoid models. Considering how quickly the organoid field is advancing, it can be difficult to keep up to date with the latest techniques, as well as their respective strengths and weaknesses. This review addresses the advantages of using organoids derived from adult stem cells and the recently identified differences that biopsy location and patient age can have on organoids and their interactions with microbes. Several approaches to introducing bacteria in a relevant (apical) manner (ie, microinjecting 3-dimensional spheroids, polarity-reversed organoids, and 2-dimensional monolayers) also are addressed, as are the key readouts that can be obtained using these models. Lastly, the potential for new approaches, such as air-liquid interface, to facilitate studying bacterial interactions with important but understudied epithelial subsets such as goblet cells and their products, is evaluated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".