Fecal microbiota transplantation results in bacterial strain displacement in patients with inflammatory bowel diseases
Bibliographic record
Abstract
Fecal microbiota transplantation (FMT), which is thought to have the potential to correct dysbiosis of gut microbiota, has been used to treat inflammatory bowel disease (IBD) for almost a decade. Here, we report an interventional prospective cohort study performed to elucidate the extent of and processes underlying microbiota engraftment in IBD patients after FMT treatment. The cohort included two categories of patients: (a) patients with moderate to severe Crohn's disease (CD) (Harvey-Bradshaw Index ≥ 7, n = 11) and (b) patients with ulcerative colitis (UC) (Montreal classification S2 and S3, n = 4). All patients were treated with a single FMT (via mid-gut, from healthy donors), and follow-up visits were performed at baseline, 3 days, 1 week, and 1 month after FMT (missing time points included). At each follow-up time point, fecal samples and clinical metadata were collected. For comparative analysis, 10 fecal samples from 10 healthy donors were included to represent the diversity level of normal gut microbiota. Additionally, the metagenomic data of 25 fecal samples from five individuals with metabolic syndrome who underwent autologous FMT treatment were downloaded from a previous published paper to represent fluctuations in microbiota induced during FMT. All fecal samples underwent shotgun metagenomic sequencing. We found that 3 days after FMT, 11 out of 15 recipients were in remission (three out of four UC recipients; 8 out of 11 CD recipients). Generally, bacterial colonization was observed to be lower in CD recipients than in UC recipients at both species and strain levels. Furthermore, across species, different strains displayed disease-specific displacement advantages under two-disease status. Finally, most post-FMT species (> 80%) could be properly predicted (area under the curve > 85%) using a random forest classification model, with the gut microbiota composition and clinical parameters of pre-FMT recipients acting as factors that contribute to prediction accuracy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".