Characterization of sex-related differences in allergen house dust mite-challenged airway inflammation, in two different strains of mice
Bibliographic record
Abstract
Biological sex impacts disease prevalence, severity and response to therapy in asthma, however preclinical studies often use only one sex in murine models. Here, we detail sex-related differences in immune responses using a house dust mite (HDM)-challenge model of acute airway inflammation, in adult mice of two different strains (BALB/c and C57BL/6NJ). Female and male mice were challenged (intranasally) with HDM extract (~ 25 μg) for 2 weeks (N = 10 per group). Increase in serum HDM-specific IgE showed a female bias, which was statistically significant in BALB/c mice. We compared naïve and HDM-challenged mice to define immune responses in the lungs by assessing leukocyte accumulation in the bronchoalveolar lavage fluid (BALF), and profiling the abundance of 29 different cytokines in BALF and lung tissue lysates. Our results demonstrate specific sex-related and strain-dependent differences in airway inflammation. For example, HDM-driven accumulation of neutrophils, eosinophils and macrophages were significantly higher in females compared to males, in BALB/c mice. In contrast, HDM-mediated eosinophil accumulation was higher in males compared to females, in C57BL/6NJ mice. Differences in lung cytokine profiles indicated that HDM drives a T-helper (Th)17-biased response with higher IL-17 levels in female BALB/c mice compared to males, whereas female C57BL/6NJ mice elicit a mixed Th1/Th2-skewed response. Male mice of both strains showed higher levels of specific Th2-skewed cytokines, such as IL-21, IL-25 and IL-9, in response to HDM. Overall, this study details sex dimorphism in HDM-mediated airway inflammation in mice, which will be a valuable resource for preclinical studies in allergic airway inflammation and asthma.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".