Fecal Microbial Transplants Reduce Antibiotic-resistant Genes in Patients With Recurrent<i>Clostridium difficile</i>Infection
Bibliographic record
Abstract
BACKGROUND: Recurrent Clostridium difficile infection (RCDI) is associated with repeated antibiotic treatment and the enhanced growth of antibiotic-resistant microbes. This study tested the hypothesis that patients with RCDI would harbor large numbers of antibiotic-resistant microbes and that fecal microbiota transplantation (FMT) would reduce the number of antibiotic-resistant genes. METHODS: In a single center study, patients with RCDI (n = 20) received FMT from universal donors via colonoscopy. Stool samples were collected from donors (n = 3) and patients prior to and following FMT. DNA was extracted and shotgun metagenomics performed. Results as well as assembled libraries from a healthy cohort (n = 87) obtained from the Human Microbiome Project were aligned against the NCBI bacterial taxonomy database and the Comprehensive Antibiotic Resistance Database. Results were corroborated through a DNA microarray containing 354 antibiotic resistance (ABR) genes. RESULTS: RCDI patients had a greater number and diversity of ABR genes compared with donors and healthy controls. Beta-lactam, multidrug efflux pumps, fluoroquinolone, and antibiotic inactivation ABR genes were increased in RCDI patients, although donors primarily had tetracycline resistance. RCDI patients were dominated by Proteobacteria with Escherichia coli and Klebsiella most prevalent. FMT resulted in a resolution of symptoms that correlated directly with a decreased number and diversity of ABR genes and increased Bacteroidetes and Firmicutes with reduced Proteobacteria. ABR gene profiles were maintained in recipients for up to a year following FMT. CONCLUSIONS: RCDI patients have increased numbers of antibiotic-resistant organisms. FMT is effective in the eradication of pathogenic antibiotic-resistant organisms and elimination of ABR genes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".