Creating a More Perfect Union: Modeling Intestinal Bacteria-Epithelial Interactions Using Organoids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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 it