The molecular etiology of idiopathic pulmonary fibrosis (IPF) has been extensively investigated to identify new therapeutic targets. Although anti-inflammatory treatments are not effective for patients with IPF, damaged alveolar epithelial cells play a critical role in lung fibrogenesis. Here, we establish an organoid-based lung fibrosis model using mouse and human lung tissues to assess the direct communication between damaged alveolar type II (AT2)-lineage cells and lung fibroblasts by excluding immune cells. Using this in vitro model and mouse genetics, we demonstrate that bleomycin causes DNA damage and activates p53 signaling in AT2-lineage cells, leading to AT2-to-AT1 transition-like state with a senescence-associated secretory phenotype (SASP). Among SASP-related factors, TGF-β plays an exclusive role in promoting lung fibroblast-to-myofibroblast differentiation. Moreover, the autocrine TGF-β-positive feedback loop in AT2-lineage cells is a critical cellular system in non-inflammatory lung fibrogenesis. These findings provide insights into the mechanism of IPF and potential therapeutic targets.

Radiologic pleuroparenchymal fibroelastosis (PPFE)-like lesion including pulmonary apical cap can be occasionally observed in clinical settings. However, the significance of radiologic PPFE-like lesion is unclear in connective tissue disease (CTD)-related interstitial lung disease (ILD). A total of 113 patients with CTD-related ILD were enrolled and assessed for radiologic PPFE-like lesion, which was defined as bilateral, upper lobe, and subpleural dense consolidations with or without pleural thickening on chest high-resolution computed tomography. The clinical, radiologic, and pathologic characteristics were evaluated. Radiologic PPFE-like lesion was found in 21 patients (19%) and were relatively frequent in those with systemic sclerosis (6/14: 43%) and primary Sjögren's syndrome (4/14: 29%). Patients with PPFE-like lesion were significantly older, had lower body mass index, higher ratio of residual volume to total lung capacity, and higher complication rate of pneumothorax and/or pneumomediastinum than those without. Twelve of the 21 patients were diagnosed radiologically as usual interstitial pneumonia (UIP) or possible UIP pattern. Two of three patients who underwent surgical lung biopsy of the upper lobes showed UIP on histopathology. Another patient was confirmed to have upper lobe PPFE on autopsy. During the clinical course, progression of the radiologic PPFE-like lesions was observed in 13 of 21 patients. Six patients died (mortality rate: 29%) and their PPFE-like lesions were commonly progressive. In the total cohort, our multivariate analysis identified the presence of PPFE-like lesion as a significant risk factor for respiratory death (hazard ratio: 4.10, 95% confidence interval: 1.33–12.65, p = 0.01). In patients with CTD-related ILD, radiologic PPFE-like lesion, which may present as not only PPFE but also apical cap and upper lobe subpleural fibrosis commonly due to UIP, was not uncommon and was associated with poor prognosis. Clinicians should be cautious with this radiologic finding, particularly when it is progressive.

Although differentiation of lung fibroblasts into α-smooth muscle actin (αSMA)-positive myofibroblasts is important in the progression of idiopathic pulmonary fibrosis (IPF), few biomarkers reflecting the fibrotic process have been discovered. We performed microarray analyses between FACS-sorted steady-state fibroblasts (lineage (CD45, TER-119, CD324, CD31, LYVE-1, and CD146)-negative and PDGFRα-positive cells) from untreated mouse lungs and myofibroblasts (lineage-negative, Sca-1-negative, and CD49e-positive cells) from bleomycin-treated mouse lungs. Amongst several genes up-regulated in the FACS-sorted myofibroblasts, we focussed on Ltbp2, the gene encoding latent transforming growth factor-β (TGF-β) binding protein-2 (LTBP2), because of the signal similarity to Acta2, which encodes αSMA, in the clustering analysis. The up-regulation was reproduced at the mRNA and protein levels in human lung myofibroblasts induced by TGF-β1. LTBP2 staining in IPF lungs was broadly positive in the fibrotic interstitium, mainly as an extracellular matrix (ECM) protein; however, some of the αSMA-positive myofibroblasts were also stained. Serum LTBP2 concentrations, evaluated using ELISA, in IPF patients were significantly higher than those in healthy volunteers (mean: 21.4 compared with 12.4 ng/ml) and showed a negative correlation with % predicted forced vital capacity (r = −0.369). The Cox hazard model demonstrated that serum LTBP2 could predict the prognosis of IPF patients (hazard ratio for death by respiratory events: 1.040, 95% confidence interval: 1.026–1.054), which was validated using the bootstrap method with 1000-fold replication. LTBP2 is a potential prognostic blood biomarker that may reflect the level of differentiation of lung fibroblasts into myofibroblasts in IPF.

Deep learning technology has been used in the medical field to produce devices for clinical practice. Deep learning methods in cytology offer the potential to enhance cancer screening while also providing quantitative, objective, and highly reproducible testing. However, constructing high-accuracy deep learning models necessitates a significant amount of manually labeled data, which takes time. To address this issue, we used the Noisy Student Training technique to create a binary classification deep learning model for cervical cytology screening, which reduces the quantity of labeled data necessary. We used 140 whole-slide images from liquid-based cytology specimens, 50 of which were low-grade squamous intraepithelial lesions, 50 were high-grade squamous intraepithelial lesions, and 40 were negative samples. We extracted 56,996 images from the slides and then used them to train and test the model. We trained the EfficientNet using 2,600 manually labeled images to generate additional pseudo labels for the unlabeled data and then self-trained it within a student-teacher framework. Based on the presence or absence of abnormal cells, the created model was used to classify the images as normal or abnormal. The Grad-CAM approach was used to visualize the image components that contributed to the classification. The model achieved an area under the curve of 0.908, accuracy of 0.873, and F1-score of 0.833 with our test data. We also explored the optimal confidence threshold score and optimal augmentation approaches for low-magnification images. Our model efficiently classified normal and abnormal images at low magnification with high reliability, making it a promising screening tool for cervical cytology.