EXTRACELLULAR MATRIX REMODELING IN ATOPIC DERMATITIS HARNESSES THE ONSET OF AN ASTHMATIC PHENOTYPE AND IS A POTENTIAL CONTRIBUTOR TO THE ATOPIC MARCH
Bibliographic record
Abstract
Abstract The development of atopic dermatitis (AD) in infancy, and subsequent allergic rhinitis, food allergies, and asthma in later childhood, is known as the atopic march. The mechanism is largely unknown, yet the course of disease indicates the contribution of inter-epithelial crosstalk, through to the onset of inflammation in the skin and progression to another mucosal epithelium. Here, we investigated if and how skin-lung epithelial crosstalk could contribute to the development of the atopic march. First, we emulated this inter-epithelial crosstalk through indirect co-culture of bioengineered atopic-like skin disease models and three-dimensional bronchial epithelial models triggering an asthma-like phenotype in the latter. A subsequent secretome analysis identified throm-bospondin-1, CD44, complement factor C3, fibronectin, and syndecan-4 as potentially relevant skin-derived mediators. As these mediators are extracellular matrix (ECM)-related proteins, we then studied the involvement of the ECM, unveiling distinct proteomic, transcriptomic, and ultrastructural differences in atopic samples. The latter indicated ECM remodeling triggering the release of the above-mentioned mediators. In addition to pro-inflammatory effects in lung tissue, the ECM mediators also exert distinct effects on CD4 + T cells. In vivo mouse data showed that exposure to these mediators over seven days dysregulated activated circadian clock genes which have been previously discussed in the context of atopic diseases and asthma development. We hypothesize the existence of a skin-lung axis that could contribute to the atopic march driven by skin ECM remodeling. One Sentence Summary Atopic skin harbors the progression of atopic diseases to lung tissue through a skin-lung axis that contributes to the atopic march via extracellular matrix remodeling.
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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 | 0.001 |
| 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".