Fecal microbiota transplantation in pigs: current status and future perspective
Bibliographic record
Abstract
Fecal microbiota transplantation (FMT) is gaining attention as a method to modulate the gut microbiome in pigs, with the goal of enhancing health and production outcomes. While some studies indicate that FMT can enhance growth performance and intestinal health in piglets, others report minimal or even negative effects. This variability highlights the need for standardized protocols and further research to optimize FMT for swine applications. Currently, the use of FMT in pigs is still in its early stages, with limited studies showing considerable methodological differences. Although some evidence supports the effectiveness of FMT, significant gaps remain in our understanding of its approach and underlying mechanisms. Therefore, this review summarizes the role and development of gut microbiota in pigs, analyzes existing FMT research in pigs, emphasizes the varying outcomes, illustrates the potential mechanisms of action based on human and animal studies and discusses the innovative potential of using co-evolved microbial communities as a transplant material. As our understanding of pig gut microbiome advances, FMT and related microbiome-based interventions could become valuable tools in pig production. However, ongoing research is essential to elucidate their mechanisms and develop reliable protocols.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".