Effects of Lactobacillus salivarius isolated from feces of fast-growing pigs on intestinal microbiota and morphology of suckling piglets
Bibliographic record
Abstract
The study determined the effects of Lactobacillus salivarius (LS) administered early in the life of suckling piglets on their growth performance, gut morphology, and gut microbiota. Thirty litters of 3-day-old crossbreed piglets were randomly assigned to one of the three treatments, and treatments were commenced on day 3 after birth. During the whole period of the experiment, the piglets were kept with their mothers and left to suckle ad libitum while being supplemented with a milk formula with or without the bacterial probiotic supplemented. The control group (CON) was not treated with probiotics, the HLS group was treated with LS144 (HLS) screened from feces of fast-growing pigs with high body mass index (BMI) while the NLS group was supplemented with LS160 (NLS) screened from feces obtained from pigs of normal BMI. At the weaning time, a higher abundance of Actinobacteria, Lentisphaerae, and Elusimicrobia phyla were observed in NLS piglets, whereas the abundance of Fibrobacteres phylum was significantly reduced in NLS and HLS piglets compared with the CON. A greater abundance of Lactobacillus was detected in the HLS treatment compared with the CON. The abundance of Bacteroides and Fibrobacter was higher in the CON piglets compared with the HLS and NLS piglets. Compared with the CON group, the oral administration of LS significantly increased the number of Lactobacillus and villus height in the duodenum, jejunum, and ileum. Moreover, the villus height of the duodenum was significantly improved in the HLS treatment compared with the NLS treatment. Based on the findings in the neonatal piglet model, we suggest that oral supplementation of LS, particularly LS isolated from high BMI pigs, could be beneficial by improving the intestinal villus height.
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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) | 0.001 | 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 | 0.001 |
| 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".