In utero exposure to arsenic alters lung development and genes related to immune and mucociliary function in mice
Bibliographic record
Abstract
BACKGROUND: Exposure to arsenic via drinking water is a global environmental health problem. In utero exposure to arsenic via drinking water increases the risk of lower respiratory tract infections during infancy and mortality from bronchiectasis in early adulthood. OBJECTIVES: We aimed to investigate how arsenic exposure in early life alters lung development and pathways involved in innate immunity. METHODS: Pregnant BALB/c, C57BL/6, and C3H/HeARC mice were exposed to 0 (control) or 100 μg/L arsenic via drinking water from gestation day 8 until the birth of their offspring. We measured somatic growth, lung volume, and lung mechanics of mice at 2 weeks of age. We used fixed lungs for structural analysis and collected lung tissue for gene expression analysis by microarray. RESULTS: The response to arsenic was genetically determined, and C57BL/6 mice were the most susceptible. Arsenic-exposed C57BL/6 mice were smaller in size, had smaller lungs, and had impaired lung mechanics compared with controls. Exposure to arsenic in utero up-regulated the expression of genes in the lung involved in mucus production (Clca3, Muc5b, Scgb3a1), innate immunity (Reg3γ, Tff2, Dynlrb2, Lplunc1), and lung morphogenesis (Sox2). Arsenic exposure also induced mucous cell metaplasia and increased expression of CLCA3 protein in the large airways. CONCLUSIONS: Alterations in somatic growth, lung development, and the expression of genes involved in mucociliary clearance and innate immunity in the lung are potential mechanisms through which early life arsenic exposure impacts respiratory health.
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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 | 0.001 |
| 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) | 0.003 | 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".