The Role of the Pulmonary Microenvironment in Driving Transition From Systemic Sclerosis to Systemic Sclerosis–Associated Interstitial Lung Disease
Bibliographic record
Abstract
OBJECTIVE: A common complication in systemic sclerosis (SSc) is the development of SSc-associated interstitial lung disease (SSc-ILD), which has poor prognosis and a high mortality rate. The pulmonary microenvironment may include mediators involved in disease pathogenesis that could be targets for new therapies to reduce SSc-to-SSc-ILD transition. Here, we aimed to identify soluble mediators in bronchoalveolar lavage fluid that would differentiate patients with SSc-ILD from those with SSc only through a systematic review. METHODS: Using a preregistered study protocol, 2 databases (Web of Science, PubMed) were screened for publications between 2000 and 2024 in adult patients (keywords "systemic sclerosis AND biomarker AND [lung lavage OR bronchoalveolar lavage]"). Mediators were metaanalyzed (RevMan) and functionally enriched pathways identified (STRING-DB/G:Profiler). RESULTS: Screening identified 20/82 publications for inclusion into the systematic review, with qualitative syntheses (n = 12) and metaanalyses (n = 5). Thirty different mediators were identified, 17 were available for SSc vs SSc-ILD comparison. Mediators showed strong interconnectedness and were clustered into the following 3 groups: (1) those released from tertiary granules (predominately involved in remodeling of extracellular matrix), (2) those with chemokine receptor binding, and (3) those with antioxidant function. CONCLUSION: Due to the limited number of studies, we were unable to perform a metaanalysis on mediators between SSc and SSc-ILD, highlighting the need for further studies. However, our review strongly highlights the involvement of the pulmonary epithelium in SSc-ILD, contributing to positive feedback between injured epithelial cells and fibroblast activation/fibrosis. Further research into the role of the epithelium is needed to identify novel mechanisms leading to SSc-ILD that could serve as novel pharmacological targets.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".