Mast cells infiltrate the alveolar parenchyma in young children with respiratory viral infections
Bibliographic record
Abstract
Background: Asthma usually develops during childhood and is associated with increased numbers of lung MCs (MCs). Whether viral infections in young children can cause increased MC numbers and contribute to asthma development is unknown. We sought to investigate if lower respiratory tract infections (LRTIs) cause alterations in lung MC populations in children. Methods: Lung tissue from 21 young children who died following LRTIs was processed for immunohistochemical identification of MCs and related mediators. Ten children who died from non-respiratory causes were used as controls. MC changes in relation to sensitization were further examined in infant mice exposed to house dust mite (HDM) during the course of influenza A infection. Results: An increased number of MCs were observed in the alveolar parenchyma of young children infected with LRTIs compared to controls. This was associated with a higher frequency of CD34 + tryptase + MC progenitors and an increased expression of vascular cell adhesion molecule (VCAM)-1. Similar to children with LRTIs, infant mice infected with influenza A had an increased number of alveolar MCs. MCs numbers continued to increase and remained significantly higher at 6 weeks post infection and allergy challenge. Conclusions: The results from our study demonstrate that a viral infection affecting the peripheral lung evokes a rapid accumulation of MCs in the alveolar parenchyma in both infant humans and mice. Since MCs are very long-lived and are likely to remain in the tissue after the infection has been cleared, a notion supported by the animal data, the increased MC numbers might also affect susceptibility towards allergens and asthma development later in life.
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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".