Heat shock protein: a hot topic in idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive disease of the lung parenchyma, causing significant morbidity and mortality [1, 2]. The therapeutic options in IPF are limited to only two recently approved drugs, pirfenidone and nintedanib, which have been shown to slow progression but are not able to stop or reverse the disease [3, 4]. Better pathophysiological knowledge is needed to develop new therapeutic strategies in IPF. The current understanding of the disease is that fibroblastic foci, characterised by accumulation of myofibroblasts and overlying “activated” epithelium, represent “hot zones” of the disease and drivers of abnormal extracellular matrix (ECM) accumulation [5]. Transforming growth factor (TGF)-β1 is a key cytokine involved in the process of fibrogenesis. TGF-β1 causes myofibroblast proliferation and differentiation and increases the synthesis of collagen, fibronectin and many other ECM components [5]. The TGF-β1 signalling pathways are complex and occur essentially through serine/threonine kinase receptors, TGF-β receptors type I and II (TGF-βRI and TGF-βRII). TGF-βRII is constitutively active and activates TGF-βRI via phosphorylation upon ligand binding [6]. The cytoplasmic proteins Smad2 and Smad3 predominantly mediate signals from activated TGF-β1 receptors. Activation of Smad2 and Smad3 via phosphorylation makes them bind to Smad4, promoting translocation to the nucleus where numerous TGF-β1-responsive genes are activated. TGF-β1 pathways are undoubtedly very promising but also challenging targets to treat fibrosis and in particular IPF. HSP90 inhibition could be an exciting new treatment strategy for IPF
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".