Host response to <i>Neisseria gonorrhoeae</i> infection in female wild type mice is dictated by the dominant sex hormone
Bibliographic record
Abstract
Abstract BACKGROUND Gonorrhea, a sexually transmitted disease (STD) caused by Neisseria gonorrhoeae (GC), causes major sequelae in women. Epidemiology suggests a link between symptomatic gonorrhea and the normal female hormone cycle. The aim of this study was to evaluate the murine gonorrhea-model immune responses to infection during progesterone dominate diestrus, and estrogen dominate estrus. METHODS Hormone cycle stage was determined by cytology of vaginal washes. Mice were infected with 107 bacteria in PBS directly placed into a uterine horn. Serum, upper and lower genitourinary (UGU, LGU respectively) tract tissues were collected at 6 or 18 h post infection. Cytokines levels in these tissues were measured by multiplex immuno assay. Gene transcription in UGU and LGU samples was measured by microarray. RESULTS Infection during estrus induced a moderate cytokine response at the site of infection. Cytokine levels were much greater when infection occurred during diestrus. Microarray analysis of infected diestrus tissues showed significant enrichment of those pathways associated with host immune response when infection occurred in diestrus. CONCLUSIONS GC infection during diestrus was shown to be highly inflammatory. Immune pathways were strongly upregulated as determined by Gene set enrichment analysis (GSEA) of microarray data. The most differentially expressed pathways were associated with pattern-recognition-receptor activation and cytokine response. GSEA analysis of estrus tissue showed very limited enrichment of any immune processes. These data together lead us to conclude that the inflammatory response to GC infection in mice is strongly dependent on the dominant sex hormone at the time of infection.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".