Protection against neonatal respiratory viral infection via maternal treatment during pregnancy with the benign immune training agent OM-85
Bibliographic record
Abstract
Abstract Objectives Incomplete maturation of immune regulatory functions at birth are antecedent to the heightened risk for severe respiratory infections during infancy. Our forerunner animal model studies demonstrated that maternal treatment with the benign microbial-derived immune modulating agent OM-85 during pregnancy promotes accelerated maturation of immune regulatory networks in the developing fetal bone marrow. Here, we aimed to establish proof-of-concept that this would enhance resilience to severe early life respiratory viral infection during the neonatal period. Methods Pregnant BALB/c mice were treated orally with OM-85 during gestation and their offspring infected intranasally with a mouse-adapted rhinovirus (vMC 0 ) at postnatal day 2. We then assessed clinical course, lung viral titres and lung immune parameters to determine whether offspring from OM-85 treated mothers demonstrate enhanced immune protection against neonatal vMC 0 infection. Results Offspring from OM-85 treated mothers display enhanced capacity to clear an otherwise lethal respiratory viral infection during the neonatal period, with a concomitant reduction in the exaggerated nature of the ensuing immune response. These treatment effects were associated with accelerated postnatal myeloid cell seeding of neonatal lungs and enhanced expression of microbial sensing receptors in lung tissues, coupled in particular with enhanced capacity to rapidly expand and maintain networks of lung dendritic cells expressing function-associated markers crucial for maintenance of local immune homeostasis in the face of pathogen challenge. Conclusion Maternal OM-85 treatment may represent a novel therapeutic strategy to reduce the burden, and potential long-term sequlae, of severe neonatal respiratory viral infection by accelerating development of innate immune competence.
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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".