Asian sand dust aggregate causes atopic dermatitis-like symptoms in Nc/Nga mice
Bibliographic record
Abstract
BACKGROUND: Asian sand dust (ASD) originates from the arid and semiarid areas of China, and epidemiologic studies have shown that ASD exposure is associated with various allergic and respiratory symptoms. However, few studies have been performed to assess the relationship between skin inflammation and ASD exposure. METHODS: Twelve-week-old NC/Nga mice were divided into 6 groups (n = 8 for each group): hydrophilic petrolatum only (control); hydrophilic petrolatum plus ASD (ASD); hydrophilic petrolatum and heat inactivated-ASD (H-ASD); Dermatophagoides farinae extract (Df); Df and ASD (Df + ASD), and; Df and H-ASD (Df + H-ASD). The NC/Nga mice in each group were subjected to treatment twice a week for 4 weeks. We evaluated skin lesions by symptoms, pathologic changes, and serum IgE levels. RESULTS: ASD alone did not induce atopic dermatitis (AD)-like skin symptoms. However, Df alone, Df + H-ASD and Df + ASD all induced AD-like symptoms, and dermatitis scores in the group of Df + ASD group were significantly greater than that of the Df group (P = 0.0011 at day 21; and P = 0.017 at day 28). Mean serum IgE was markedly increased in the Df and Df + ASD groups, compared to the ASD and control groups (P < 0.0001), and serum IgE levels in the Df + ASD group were significantly higher compared to the Df group (P = 0.003). CONCLUSIONS: ASD alone did not cause AD-like symptoms in NC/Nga mice. However, AD-like symptoms induced by Df, a major allergen, were enhanced by adding ASD. Although no epidemiological studies have been conducted for the association between ASD and symptoms of dermatitis, our data suggest that it is likely that ASD may contribute to the exacerbation of not only respiratory symptoms, but also skin diseases, in susceptible individuals.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".