Modulation of Th17 and regulatory T‐cell responses during murine pregnancy contributes to increased maternal susceptibility to <i>Salmonella</i> Typhimurium infection
Bibliographic record
Abstract
Problem Salmonella Typhimurium (S. Tm) infection in pregnant mice results in massive placental infection, fetal loss, and exacerbated systemic infection. The Th17 host response can aid control of S. Tm infection, whereas successful pregnancy correlates to a dampened inflammatory and enhanced regulatory T‐cell (Treg) response. Method of study Mice were infected systemically with S. Tm and tissue bacterial burden, splenic Th17 and Treg cell numbers, and serum cytokines were analyzed. Splenic and/or placental mRNA expression of IL‐17A, RORγ‐t, IL‐10, and TNF was determined. The effects of in vivo CD25+ cell depletion and TLR4 blockade on the course of S. Tm infection and Th17 response were determined. Results Enhanced S. Tm burden in pregnant mice was associated with time‐dependent increased serum inflammatory cytokines. In vivo, TLR4 blockade reduced splenic S. Tm burden, suggesting detrimental TLR4‐mediated inflammation. However, the splenic and placental Th17 response was reduced in S. Tm‐infected pregnant mice relative to non‐pregnant controls. Alternatively, there was an increase in splenic Treg frequency in pregnant mice and depletion of this subset reduced bacterial burden and increased the Th17 response. Conclusion Downregulation of Th17 cell responses by Tregs during pregnancy potentially contributes to exacerbation of S. Tm infection in pregnant mice.
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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 | 0.000 | 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".