Faecal microbiota transplantation: Where did it start? What have studies taught us? Where is it going?
Bibliographic record
Abstract
The composition and activity of microorganisms in the gut, the microbiome, is emerging as an important factor to consider with regard to the treatment of many diseases. Dysbiosis of the normal community has been implicated in inflammatory bowel disease, Crohn’s disease, diabetes and, most notoriously, Clostridium difficile infection. In Canada, the leading treatment strategy for recalcitrant C. difficile infection is to receive faecal material which by nature is filled with microorganisms and their metabolites, from a healthy individual, known as a faecal microbiota transplantation. This influx of bacteria into the gut helps to restore the microbiota to a healthy state, preventing C. difficile from causing further disease. Much of what is known with respect to the microbiota and faecal microbiota transplantation comes from animal studies simulating the human disease. Although these models allow researchers to perform studies that would be difficult in humans, they do not always recapitulate the human microbiome. This makes the translation of these results to humans somewhat questionable. The purpose of this review is to analyse these animal models and discuss the advantages and the disadvantages of them in relation to human translation. By understanding some of the limitation of animal models, we will be better able to design and perform experiments of most relevance to human applications.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".