Modulation of piglet’s immune system development with fecal microbial transplantation
Bibliographic record
Abstract
Abstract The aim of the study was to investigate fecal microbial transplantation (FMT) as a novel strategy to modulate the postnatal development of porcine immune system and the establishment of the intestinal microbiota in newborn piglets. Ten litters were used for the experiment. At birth, four piglets were identified in each litter and randomly assigned to the control (CTRL) and the FMT groups. At 3,4,8,9, and 10 days of age, piglets in the FMT group were orally administered with FMT material suspended in 10% bovine colostrum solution. The FMT was prepared with an equal mixture of fecal material harvested from healthy suckling and fully weaned piglets. Control group was inoculated with the vehicle solution. Animals were then euthanized at d 22 to identify the leukocyte subsets in mesenteric lymph nodes (MLN) and blood by flow cytometry and to characterize the microbiota from ileum, caecum and colon by 16S rRNA amplicon sequencing. Results showed that FMT reduced the overall number of T lymphocytes (CD3+) both in blood and MLN (P<0.05). Blood percentages of Th lymphocytes (CD3+CD4+CD8α−) were also reduced in piglets receiving FMT (P<0.05), as well as γδ T lymphocyte (CD3+γδ+) and monocyte/macrophage (SWC3+CD14+) concentrations in the MLN (P<0.05). On the other hand, FMT increased the blood percentage of NK cells (CD3−CD4−CD8α+CD16+) (P<0.01). Results obtained from the microflora analysis confirmed that FMT administration affected piglet’s colon microbiota. In conclusion, although it had a mild impact on hindgut microflora, we observed that FMT has the potential to modulate the immune system development in newborn piglets. Further studies are necessary to fully understand the long-lasting effects of FMT on animal health.
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 | 0.000 | 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 | 0.001 |
| 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".