In patients with pulmonary diseases such as idiopathic pulmonary fibrosis and severe acute respiratory distress syndrome, progressive pulmonary fibrosis is caused by dysregulated wound healing via activation of fibroblasts after lung inflammation or severe damage. Migration of fibroblasts toward the fibrotic lesions plays an important role in pulmonary fibrosis. Fibrotic tissue in the lung is much stiffer than normal lung tissue. Emerging evidence supports the hypothesis that the stiffness of the matrix is not only a consequence of fibrosis, but also can induce fibroblast activation. Nevertheless, the effects of substrate rigidity on migration of lung fibroblasts have not been fully elucidated. We evaluated the effects of substrate stiffness on the morphology, α‐smooth muscle actin (α‐SMA) expression, and cell migration of primary human lung fibroblasts by using polyacrylamide hydrogels with stiffnesses ranging from 1 to 50 kPa. Cell motility was assessed by platelet‐derived growth factor (PDGF)‐induced chemotaxis and random walk migration assays. As the stiffness of substrates increased, fibroblasts became spindle‐shaped and spread. Expression of α‐SMA proteins was higher on the stiffer substrates (25 kPa gel and plastic dishes) than on the soft 2 kPa gel. Both PDGF‐induced chemotaxis and random walk migration of fibroblasts precultured on stiff substrates (25 kPa gel and plastic dishes) were significantly higher than those of cells precultured on 2 kPa gel. Transfection of the fibroblasts with short interfering RNA for α‐SMA inhibited cell migration. These findings suggest that fibroblast activation induced by a stiff matrix is involved in mechanisms of the pathophysiology of pulmonary fibrosis.

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Background/AimTo train and validate the prediction performance of the deep learning (DL) model to predict visual field (VF) in central 10° from spectral domain optical coherence tomography (SD-OCT).MethodsThis multicentre, cross-sectional study included paired Humphrey field analyser (HFA) 10-2 VF and SD-OCT measurements from 591 eyes of 347 patients with open-angle glaucoma (OAG) or normal subjects for the training data set. We trained a convolutional neural network (CNN) for predicting VF threshold (TH) sensitivity values from the thickness of the three macular layers: retinal nerve fibre layer, ganglion cell layer+inner plexiform layer and outer segment+retinal pigment epithelium. We implemented pattern-based regularisation on top of CNN to avoid overfitting. Using an external testing data set of 160 eyes of 131 patients with OAG, the prediction performance (absolute error (AE) and R2 between predicted and actual TH values) was calculated for (1) mean TH in whole VF and (2) each TH of 68 points. For comparison, we trained support vector machine (SVM) and multiple linear regression (MLR).ResultsAE of whole VF with CNN was 2.84±2.98 (mean±SD) dB, significantly smaller than those with SVM (5.65±5.12 dB) and MLR (6.96±5.38 dB) (all, p<0.001). Mean of point-wise mean AE with CNN was 5.47±3.05 dB, significantly smaller than those with SVM (7.96±4.63 dB) and MLR (11.71±4.15 dB) (all, p<0.001). R2 with CNN was 0.74 for the mean TH of whole VF, and 0.44±0.24 for the overall 68 points.ConclusionDL model showed considerably accurate prediction of HFA 10-2 VF from SD-OCT.

Loss of MSH6 occurred during the progression from an atypical prolactinoma to a pituitary carcinoma, which may have caused resistance to TMZ treatment. This case suggests that preserving MSH6 function is essential for responsiveness to TMZ treatment in MGMT-negative and p53-mutated atypical pituitary adenoma or pituitary carcinoma.

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