Freeze-dried fecal microorganisms as an effective biomaterial for the treatment of calves suffering from diarrhea
Bibliographic record
Abstract
Fecal microbiota transplantation (FMT) is a therapeutic modality for treating neonatal calf diarrhea. Several practical barriers, including donor selection, fecal collection, and a limited timeframe for FMT, are the main constraints to using fresh feces for implementing on-farm FMT. We report the utility of FMT with pretreated ready-to-use frozen (F) or freeze-dried (FD) microorganisms for treating calf diarrhea. In total, 19 FMT (F-FMT, n = 10 and FD-FMT, n = 9) treatments were conducted. Both FMT treatments were 100% clinically effective; however, multi-omics analysis showed that FD-FMT was superior to F-FMT. Machine learning analysis with SourceTracker confirmed that donor microbiota was retained four times better in the recipient calves treated with FD-FMT than F-FMT. A predictive model based on receiver operating characteristic curve analysis and area under the curve showed that FD-FMT was more discriminative than F-FMT of the observed changes in microbiota and metabolites during disease recovery. These results provide new insights into establishing methods for preparing fecal microorganisms to increase the quality of FMT in animals and may contribute to FMT in humans.
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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 | 0.000 |
| 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) | 0.001 | 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".