Mice, Models, Microbiota: How Can We More Accurately Reflect Human Disease?
Bibliographic record
Abstract
With growing evidence that human disease is affected by the microbiota, many researchers have sought to modulate the microbiomes of mice to improve translational research. Altering their microbiomes, which are usually germ-free or specific pathogen-free, might allow mice to more accurately model human disease and hence produce more applicable findings. However, this has been difficult to apply to individual projects due to the disparity of explained methods and results. In this review, we first describe the immunological functions of the gut microbiota and the methods of altering mice microbiota, from transplantation route to age of transplantation to microbiota source. We then present an approach for how the gut microbiota might be considered when modelling human disease in mice. By organizing findings by type of disease - neurological, immunological, chronic inflammatory, and cancer - we propose that mouse models can be improved by considering the source of the microbiota, the presence or absence of certain microbial phyla, and by timing the transplantation during a physiologically relevant stage of development, such as the first five weeks of life.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".