Type I interferons differentially modulate maternal host immunity to infection by <i>Listeria monocytogenes</i> and <i>Salmonella enterica</i> serovar Typhimurium during pregnancy
Bibliographic record
Abstract
Problem IFN‐alpha receptor deficiency (IFNAR −/− ) enhances immunity to Listeria monocytogenes (LM) and Salmonella enterica serovar Typhimurium (ST) in the non‐pregnant state by inhibiting pathogen‐induced immune cell death. However, the roles of IFNAR signaling in modulating immunity to infection during pregnancy are not well understood. Method of Study C57BL/6J wild‐type (WT) and IFNAR −/− mice were infected systemically with LM or ST. Bacterial burden in spleen and individual placentas was enumerated at day 3 post‐infection. Immune cell numbers and percentages were quantified in spleen and individual placentas, respectively, through flow cytometry. Cytokine expression in serum, spleen, and individual placentas was measured through cytometric bead array. Results IFNAR −/− mice exhibited decreased splenic monocyte numbers in non‐pregnant and pregnant state, and an altered distribution of placental immune cell types in the non‐infected state. IFNAR −/− mice controlled LM infection more effectively than WT mice even during pregnancy. This correlated with enhanced serum IL‐12 expression, despite reduced splenic monocyte numbers relative to WT controls. In contrast, pregnant IFNAR −/− mice unlike their non‐pregnant counterparts exhibited increased susceptibility to ST infection, which was associated with decreased serum IL‐12 expression. Conclusion Type I IFN responses differentially impact host resistance to LM and ST infection during pregnancy through modulation of immune cell distribution and cytokine responses.
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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 | 0.000 |
| 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".