The Effect of Early-Life Stress on Airway Inflammation in Adult Mice
Bibliographic record
Abstract
BACKGROUND/AIMS: Neonatal stress induces permanent physiological changes that may influence the immune system. Early-life stress increases asthma disease severity in children. We investigated the effects of early-life stress on allergic airway inflammation using a murine model of asthma coupled to maternal separation as an early-life stress stimulus. METHODS: Maternally separated (MS) and unseparated control (CON) mice were sensitized with ovalbumin (OVA) beginning at day 31 after birth. RESULTS: Challenging mice with OVA increased airway hyperresponsiveness (AHR) and the number of inflammatory cells recovered in the bronchoalveolar lavage (BAL), compared to saline-challenged mice. Challenging MS mice with OVA resulted in less total inflammatory cells, eosinophils, interferon-gamma, and interleukin-4 in BAL compared to CON mice. However, MS mice challenged with OVA exhibited AHR similar to CON mice challenged with OVA. In contrast, an enhanced stress protocol (MS+) involving removal of pups from their home cages following the removal of the dam resulted in inflammatory cell accumulation and cytokine levels in the BAL similar to CON mice and higher than MS mice. CONCLUSIONS: These findings indicate that the effect of early-life psychological factors on the development of airway inflammatory diseases such as asthma is very complex and depends on the quality of the psychological stress stimulus.
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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 | 0.000 |
| 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".