Nasal Colonization by Staphylococci and Severity of Atopic Dermatitis
Bibliographic record
Abstract
BACKGROUND: Skin colonization by Staphylococcus aureus (SA) correlates with increased severity of atopic dermatitis (AD). The role of nasal SA carriage and coagulase-negative staphylococci (CNSs) in AD is unclear. OBJECTIVE: The aim of this study was to assess the influence of colonization of lesional/nonlesional skin and the anterior nares by SA and CNSs on AD severity in 63 adult patients. METHODS: Disease severity was assessed with SCORAD index. The total immunoglobulin E (IgE) concentration was determined using the enzyme-linked immunosorbent assay method. The prevalence and abundance of staphylococci were assessed with the combination of bacterial culture and mass spectrometry. RESULTS: The prevalence values of SA within the skin (lesional/nonlesional) and anterior nares were 79.4%/61.9% and 69.8%, respectively (vs 5.6% and 13.9%, respectively in controls, P < 0.0001). The carriage of CNSs in all niches was associated with lower mean IgE concentration (1164.66 ± 1010.36 vs 1762.99 ± 1059.15, P < 0.0213; 1166.9 ± 1006.4 vs 2152.7 ± 759.2, P < 0.0063; 1022 ± 1100 vs 1925 ± 880.8, P < 0.0044, respectively). The abundance of SA correlated with the extent of skin lesions and total IgE (ρ = 0.42, P = 0.0007; ρ = 0.488, P < 0.0001; ρ = 0.312, P < 0.2; and ρ = 0.402, P = 0.0007; ρ = 0.403, P < 0.002; ρ = 0.287, P < 0.03, respectively). CONCLUSIONS: Atopic dermatitis severity correlates with both cutaneous and nasal colonization by SA. Staphylococcus aureus seems to promote TH2-type response, whereas CNS probably limits this process. Preventive measures within the anterior nares should be considered for AD patients.
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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 | 0.001 |
| 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".