Impact of probiotic supplementation in lactating sows on immune and oxidative stress biomarkers, short-chain fatty acids, piglet performance, and fecal microbiome
Bibliographic record
Abstract
This clinical trial aimed to investigate the effects of probiotic supplementation in lactating sows on their serum immunoglobulin levels, milk yield, serum malondialdehyde (MDA), fecal short-chain fatty acid levels, piglet performance, and fecal microbiome. The research was conducted at a commercial swine breeding herd in central Thailand. The study included 109 Canadian Landrace × Yorkshire sows, with parity numbers ranging from 1 to 7. The sows were divided into two groups based on their parity numbers: control (n = 61) and treatment (n = 48). The control group sows received a conventional commercial lactation diet without any antibiotic supplementation, starting from 109.5 ± 1.6 days of gestation and continuing until weaning at 21 days of lactation. In contrast, the treatment group sows were fed a lactational diet supplemented with probiotics (Bacillus subtilis and Bacillus amyloliquefaciens) mixed in the feed during the same period. Additionally, piglets from the treatment group were provided with the same probiotics as a top dressing starting on the third day after birth and continuing until weaning. Blood samples were taken from the sows at the time of farrowing (n = 49) and from the piglets on days 7 (n = 59) and 14 (n = 60) of age. The concentrations of immunoglobulin (Ig), including IgG, IgA, and IgM, were determined using ELISA. Sow serum MDA quantification was performed. Fecal samples from sows were collected at entry, farrowing, and weaning for microbiome analysis. Fecal samples from piglets were collected at 3, 7, and 21 days of age to monitor changes in short-chain fatty acid levels via GC-FID and to analyze the fecal microbiome. Bacterial DNA from fecal samples was extracted using the QIAamp Power Fecal Pro DNA Kit, and 16S rRNA sequencing targeting the V3-V4 regions was performed by Illumina Miseq. Microbiome bioinformatics analyses were conducted using QIIME2 version 2023.9. Sow reproductive characteristics and piglet performance data were compared between the control and treatment groups. The supplementation of probiotics in sow and piglet feed did not show a difference in sow reproductive characteristics and piglet performance (P > 0.05). The sow serum IgM in the treatment group was higher than in the control group (5.19 ± 0.32 vs. 4.10 ± 0.36 mg/mL, P = 0.031), while there were no significant differences found in other sow immunoglobulins (P > 0.05) and serum MDA levels (P > 0.05) between the groups. Conversely, piglet IgM levels on day 7 of life were lower in the treatment group compared to the control group (0.36 ± 0.07 vs. 0.68 ± 0.07 mg/mL, P = 0.002), while on day 14 of life, there was no difference between the groups (P > 0.05). The analysis of alpha diversity in the sow fecal microbiome, the control group shown significantly higher in richness than treatment groups on days 7 and 28 (P < 0.05). Pielou’s evenness index and Shannon index showed no significant differences between sows in the control and treatment groups at any time point (P > 0.05). In the piglets' fecal microbiome, the treatment group shown significantly higher in richness than control group on day 21 of life (P < 0.05). The Shannon diversity also showed significant higher in treatment group than the control group on day 21 (P < 0.05). Evenness index showed no significant differences between piglets in the control and treatment groups at any time point (P > 0.05). Dominant phyla in both sow and piglet in both control and treatment group at every time point were Firmicutes and Bacteroidota. The sow fecal bacterial genera were predominantly composed of Oscillospiraceae UCG-005, followed by Prevotella and Lactobacillus. The dominant bacterial genera in the piglet fecal microbiome were Bacteroides and Lactobacillus. In conclusion, the supplementation of B. subtilis and B. amyloliquefaciens in the feed of sows and piglets influenced the response of IgM in both sows and piglets, albeit in different ways. The fecal short-chain fatty acids in piglets on day 7 showed a higher concentration in the treatment group than in the control group, while there was no difference at other time points. However, there were no observable effects on other aspects of sow reproductive health or piglet growth performance.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 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".