Extracellular Heat Shock Proteins as Therapeutic Targets and Biomarkers in Fibrosing Interstitial Lung Diseases
Bibliographic record
Abstract
Interstitial lung diseases (ILDs) include a large number of diseases and causes with variable outcomes often associated with progressive fibrosis. Although each of the individual fibrosing ILDs are rare, collectively, they affect a considerable number of patients, representing a significant burden of disease. Idiopathic pulmonary fibrosis (IPF) is the typical chronic fibrosing ILD associated with progressive decline in lung. Other fibrosing ILDs are often associated with connective tissues diseases, including rheumatoid arthritis-ILD (RA-ILD) and systemic sclerosis-associated ILD (SSc-ILD), or environmental/drug exposure. Given the vast number of progressive fibrosing ILDs and the disparities in clinical patterns and disease features, the course of these diseases is heterogeneous and cannot accurately be predicted for an individual patient. As a consequence, the discovery of novel biomarkers for these types of diseases is a major clinical challenge. Heat shock proteins (HSPs) are molecular chaperons that have been extensively described to be involved in fibrogenesis. Their extracellular forms (eHSPs) have been recently and successfully used as therapeutic targets or circulating biomarkers in cancer. The current review will describe the role of eHSPs in fibrosing ILDs, highlighting the importance of these particular stress proteins to develop new therapeutic strategies and discover potential biomarkers in these diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".