Distal respiratory tract viral infections in young children trigger a marked increase in alveolar mast cells
Bibliographic record
Abstract
Viral infections predispose to the development of childhood asthma, a disease associated with increased lung mast cells (MCs). This study investigated whether viral lower respiratory tract infections (LRTIs) can already evoke a MC response during childhood. Lung tissue from young children who died following LRTIs were processed for immunohistochemical identification of MCs. Children who died from nonrespiratory causes served as controls. MCs were examined in relation to sensitisation in infant mice exposed to allergen during influenza A infection. Increased numbers of MCs were observed in the alveolar parenchyma of children infected with LRTIs (median (range) 12.5 (0–78) MCs per mm2) compared to controls (0.63 (0–4) MCs per mm2, p=0.0005). The alveolar MC expansion was associated with a higher proportion of CD34+tryptase+progenitors (controls: 0% (0–1%); LRTIs: 0.9% (0–3%) CD34+MCs (p=0.01)) and an increased expression of the vascular cell adhesion molecule (VCAM)-1 (controls: 0.2 (0.07–0.3); LRTIs: 0.3 (0.02–2) VCAM-1 per mm2(p=0.04)). Similarly, infant mice infected with H1N1 alone or together with house dust mite (HDM) developed an increase in alveolar MCs (saline: 0.4 (0.3–0.5); HDM: 0.6 (0.4–0.9); H1N1: 1.4 (0.4–2.0); HDM+H1N1: 2.2 (1.2–4.4) MCs per mm2(p<0.0001)). Alveolar MCs continued to increase and remained significantly higher into adulthood when exposed to H1N1+HDM (day 36: 2.2 (1.2–4.4); day 57: 4.6 (1.6–15) MCs per mm2(p=0.01)) but not when infected with H1N1 alone. Our data demonstrate that distal viral infections in young children evoke a rapid accumulation of alveolar MCs. Apart from revealing a novel immune response to distal infections, our data may have important implications for the link between viral infections during early childhood and subsequent asthma development.
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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) | 0.002 | 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".