Early immune response following<i>Salmonella enterica</i>subspecies<i>enterica</i>serovar Typhimurium infection in porcine jejunal gut loops
Bibliographic record
Abstract
Salmonella enterica subspecies enterica serovar Typhimurium, commonly called S. Typhimurium, can cause intestinal infections in humans and various animal species such as swine. To analyze the host response to Salmonella infection in the pig we used an in vivo gut loop model, which allows the analysis of multiple immune responses within the same animal. Four jejunal gut-loops were each inoculated with 3 x 10(8) cfu of S. Typhimurium in 3 one-month-old piglets and mRNA expressions of various cytokines, chemokines, transcription factors, antimicrobial peptides, toll like and chemokine receptors were assessed by quantitative real-time PCR in the Peyer's patch and the gut wall after 24 h. Several genes such as the newly cloned CCRL1/CCX-CKR were assessed for the first time in the pig at the mRNA level. Pro-inflammatory and T-helper type-1 (Th1) cytokine mRNA were expressed at higher levels in infected compared to non-infected control loops. Similarly, some B cell activation genes, NOD2 and toll like receptor 2 and 4 transcripts were more expressed in both tissues while TLR5 mRNA was down-regulated. Interestingly, CCL25 mRNA expression as well as the mRNA expressions of its receptors CCR9 and CCRL1 were decreased both in the Peyer's patch and gut wall suggesting a potential Salmonella strategy to reduce lymphocyte homing to the intestine. In conclusion, these results provide insight into the porcine innate mucosal immune response to infection with entero-invasive microorganisms such as S. Typhimurium. In the future, this knowledge should help in the development of improved prophylactic and therapeutic approaches against porcine intestinal S. Typhimurium infections.
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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 | 0.001 | 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".