Mengshu Cao

The etiology and underlying mechanisms of acute exacerbation of idiopathic pulmonary fibrosis (AE‑IPF) remain poorly understood. Although several animal models have been developed to study AE‑IPF, a systematic evaluation and comparison of these models has not yet been reported. In the present study, PF was induced in mice by a single intratracheal administration of bleomycin (BLM). On day 14 after the initial BLM challenge, AE‑PF in mice was induced by intratracheal re‑challenge with replication‑deficient adenoviral vectors (ADV), lipopolysaccharide (LPS) or a second dose of BLM. Micro‑chest computed tomography (CT) was performed on day 20, and blood, bronchoalveolar lavage fluid (BALF) and lung tissue samples were collected after sacrifice on day 21. Compared with mice receiving a single dose of BLM alone, all three AE‑PF groups exhibited significant body weight loss and increased mortality (with the highest mortality in the LPS group), as well as more extensive lung injury on CT. Histopathological scores for inflammation and fibrosis, hydroxyproline content and expression levels of fibrotic markers (fibronectin, collagen I, α‑smooth muscle actin and MMP7) were significantly elevated in the AE‑PF groups. Inflammatory cytokines (IL‑6, IL‑1β and TNF‑α) were markedly increased in both serum and BALF. Furthermore, the AE‑PF models showed downregulation of alveolar epithelial cell markers (E‑cadherin and pro‑surfactant protein C) and a significant increase in apoptotic activity. Notably, fibrosis progression was more severe in the ADV and two‑dose BLM groups than in the LPS group. Taken together, these findings indicate that all three triggers can induce AE‑PF through enhanced apoptosis, inflammation and fibrosis. These results support the use of day 14 re‑challenge as a standardized model for AE‑PF. Among the three models, the ADV‑induced AE‑PF model may serve as a particularly suitable platform for investigating the pathogenesis of acute exacerbations in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease, with unknown pathogenesis and no effective treatment. Identifying the key molecular of IPF in underlying mechanisms is critical for developing targeted therapies. Differentially expressed genes (DEGs) were identified based on GSE53845 data from the Gene Expression Omnibus (GEO) database and the Limma R package, followed by gene set enrichment analysis (GSEA). The key module genes selected by Weighted Gene Co-expression Network Analysis (WGCNA) were integrated with the DEGs. The hub genes were screened using three machine-learning algorithms, with further performance validated through Receiver Operating Characteristic (ROC) curves and nomogram models. In addition, validation was performed using external validation sets, in vitro experiments and human lung tissues. Enrichment analyses were conducted using GeneMANIA and GSEA. Branched chain amino acid transferase 2 (BCAT2) was identified as a central hub gene in IPF by intersecting key module genes with DEGs through WGCNA and machine learning methods. Experimental validation confirmed the significantly downregulation of BCAT2 in the lung tissues of IPF patients and in TGF-β1-treated alveolar epithelial cells (AECs). Moreover, upregulation of BCAT2 attenuated the expression of fibrosis markers in AECs exposed to TGF-β1. Ultimately, Co-expression analysis and GSEA indicated that BCAT2 is closely involved in several key signaling pathways. Collectively, our findings suggest that BCAT2 is a critical protective molecule in the pathogenesis of IPF and represents a potential therapeutic target for modulating the progression of pulmonary fibrosis.

Patients with pre-existing interstitial lung disease (ILD) are highly vulnerable to SARS-CoV-2 infection, yet the long-term radiologic and clinical consequ

Background and Objective: Acute exacerbation (AE) is often the fatal complication of idiopathic pulmonary fibrosis (IPF). Emerging evidence indicates that metabolic reprogramming and dysregulation of lipid metabolism are distinctive characteristics of IPF. However, the lipid metabolic mechanisms that underlie the pathophysiology of AE‐IPF remain elusive.Methods: Serum samples for pilot study were collected from 34 Controls, 37 stable IPF (S‐IPF) cases and 41 AE‐IPF patients. UHPLC–MS/MS was utilized to investigate metabolic variations and identify lipid biomarkers in serum. ELISA, quantitative PCR and western blot were employed to validate the identified biomarkers.Results: There were 32 lipid metabolites and 5 lipid metabolism pathways enriched in all IPF patients compared to Controls. In AE‐IPF versus S‐IPF, 19 lipid metabolites and 12 pathways were identified, with 5‐hydroxyeicosatetraenoic Acid (5‐HETE) significantly elevated in AE‐IPF. Both in internal and external validation cohorts, the serum levels of 5‐HETE were significantly elevated in AE‐IPF patients compared to S‐IPF subjects. Consequently, the indicators related to 5‐HETE in lipid metabolic pathway were significantly changed in AE‐IPF patients compared with S‐IPF cases in the lung tissues. The serum level of 5‐HETE was significantly correlated with the disease severity (CT score and PaO₂/FiO₂ ratio) and survival time. Importantly, the receiver operating characteristic (ROC) curve, Kaplan–Meier analysis and Multivariate Cox regression analysis demonstrated that 5‐HETE represents a promising lipid biomarker for the diagnosis and prognosis of AE‐IPF.Conclusion: Our study highlights lipid reprogramming as a novel therapeutic approach for IPF, and 5‐HETE may be a potential biomarker of AE‐IPF patients.

Purpose Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is the primary cause of death in patients with IPF, characterised by diffuse, bilateral ground-glass opacification on high-resolution CT (HRCT). This study proposes a three-dimensional (3D)-based deep learning algorithm for classifying AE-IPF using HRCT images. Materials and methods A novel 3D-based deep learning algorithm, SlowFast, was developed by applying a database of 306 HRCT scans obtained from two centres. The scans were divided into four separate subsets (training set, n=105; internal validation set, n=26; temporal test set 1, n=79; and geographical test set 2, n=96). The final training data set consisted of 1050 samples with 33 600 images for algorithm training. Algorithm performance was evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve and weighted κ coefficient. Results The accuracy of the algorithm in classifying AE-IPF on the test sets 1 and 2 was 93.9% and 86.5%, respectively. Interobserver agreements between the algorithm and the majority opinion of the radiologists were good (κw=0.90 for test set 1 and κw=0.73 for test set 2, respectively). The ROC accuracy of the algorithm for classifying AE-IPF on the test sets 1 and 2 was 0.96 and 0.92, respectively. The algorithm performance was superior to visual analysis in accurately diagnosing radiological findings. Furthermore, the algorithm’s categorisation was a significant predictor of IPF progression. Conclusions The deep learning algorithm provides high auxiliary diagnostic efficiency in patients with AE-IPF and may serve as a useful clinical aid for diagnosis.

Introduction: Idiopathic pulmonary fibrosis (IPF) is characterized by progressive lung dysfunction due to excessive collagen production and tissue scarring. Despite recent advancements, the molecular mechanisms remain unclear.Methods: RNA sequencing identified 475 differentially expressed genes (DEGs) in the TGF-β1-induced primary lung fibrosis model. Gene expression chips GSE101286 and GSE110147 from NCBI gene expression omnibus (GEO) database were analyzed using GEO2R, revealing 94 DEGs in IPF lung tissue samples. The gene ontology (GO) and pathway enrichment, Protein-protein interaction (PPI) network construction, and Maximal Clique Centrality (MCC) scoring were performed. Experimental validation included RT-qPCR, Immunohistochemistry (IHC), and Western Blot, with siRNA used for gene knockdown. A co-expression network was constructed by GeneMANIA.Results: GO enrichment highlighted significant enrichment of DEGs in TGF-β cellular response, connective tissue development, extracellular matrix components, and signaling pathways such as the AGE-RAGE signaling pathway and ECM-receptor interaction. PPI network analysis identified hub genes, including FN1, COL1A1, POSTN, KIF11, and ECT2. CALD1 (Caldesmon 1), CDH2 (Cadherin 2), and POSTN (Periostin) were identified as dysregulated hub genes in both the RNA sequencing and GEO datasets. Validation experiments confirmed the upregulation of CALD1, CDH2, and POSTN in TGF-β1-treated fibroblasts and IPF lung tissue samples. IHC experiments probed tissue-level expression patterns of these three molecules. Knockdown of CALD1, CDH2, and POSTN attenuated the expression of fibrotic markers (collagen I and α-SMA) in response to TGF-β1 stimulation in primary fibroblasts. Co-expression analysis revealed interactions between hub genes and predicted genes involved in actin cytoskeleton regulation and cell-cell junction organization.Conclusions: CALD1, CDH2, and POSTN, identified as potential contributors to pulmonary fibrosis, present promising therapeutic targets for IPF patients.

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Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive scarring interstitial lung disease with an unknown cause. Some patients may experience acute exacerbations (AE), which result in severe lung damage visible on imaging or through examination of tissue samples, often leading to high mortality rates. However, the etiology and pathogenesis of AE-IPF remain unclear. AE-IPF patients exhibit diffuse lung damage, apoptosis of type II alveolar epithelial cells, and an excessive inflammatory response. Establishing a reliable animal model of AE is critical for investigating the pathogenesis. Recent studies have reported a variety of animal models for AE-IPF, each with its own advantages and disadvantages. These models are usually established in mice with bleomycin-induced pulmonary fibrosis, using viruses, bacteria, small peptides, or specific drugs. In this review, we present an overview of different AE models, hoping to provide a useful resource for exploring the mechanisms and targeted therapies for AE-IPF.

Pulmonary alveolar proteinosis (PAP) is a rare pulmonary disorder that is characterized by the abnormal accumulation of surfactant within the alveoli. Alveolar macrophages (AMs) have been identified as playing a pivotal role in the pathogenesis of PAP. In most of PAP cases, the disease is triggered by impaired cholesterol clearance in AMs that depend on granulocyte-macrophage colony-stimulating factor (GM-CSF), resulting in defective alveolar surfactant clearance and disruption of pulmonary homeostasis. Currently, novel pathogenesis-based therapies are being developed that target the GM-CSF signaling, cholesterol homeostasis, and immune modulation of AMs. In this review, we summarize the origin and functional role of AMs in PAP, as well as the latest therapeutic strategies aimed at addressing this disease. Our goal is to provide new perspectives and insights into the pathogenesis of PAP, and thereby identify promising new treatments for this disease.