Parathyroid hormone (PTH) and its related peptide (PTH-related peptide 1–34) are two of the Food and Drug Administration-approved bone-promoting drugs for age-related osteoporosis. Treatment with PTH stimulates bone formation. However, the molecular mechanisms of PTH-mediated osteoblast differentiation and cell proliferation are still not completely understood. In this study, we showed that PTH induced endoplasmic reticulum (ER) stress in osteoblasts through the PKR-like endoplasmic reticulum kinase (PERK)-eukaryotic initiation factor 2α (EIF2α)-activating transcription factor 4 (ATF4)-signaling pathway. After separately blocking PERK-EIF2α-ATF4 signaling with two different inhibitors [AMG’44 and integrated stress response inhibitor (ISRIB)] or specific small interfering RNA for PERK and ATF4, the following targets were all downregulated: expression of osteoblast differentiation markers [runt-related transcription factor 2 (Runx2), alkaline phosphatase (Alp), type I collagen (Col1a1), and osteocalcin (Ocn)], cell proliferation markers (CyclinE, CyclinD, and CDC2), amino acid import (Glyt1), and metabolism-related genes (Asns). Additionally, Alp-positive staining cells, Alp activity, matrix mineralization, Ocn secretion, and cell proliferation indexes were inhibited. Interestingly, we found that salubrinal enhanced PTH-induced osteoblast differentiation and proliferation by maintenance of phosphorylation of EIF2α. Furthermore, we observed that PTH increased the association between heat shock protein 90 (HSP90) and PERK and maintained PERK protein stabilization in the early stages of PTH-induced ER stress. Treatment of MC3T3-E1 cells with geldanamycin, an HSP90 inhibitor, decreased PERK protein expression and inhibited osteoblast differentiation and cell proliferation upon PTH treatment. Taken together, our data demonstrate that PTH regulates osteoblast differentiation and cell proliferation, partly by activating the HSP90-dependent PERK-EIF2α-ATF4 signaling pathway.

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Block matching 3D denoising (BM3D) is an excellent single‐image denoising method. However, it still needs to be improved for solving practical problems. In this study, the authors attempt to improve the method of BM3D. First, one of the problems of BM3D is that some of its references cannot perform self‐adaption when the noise intensity of the images is changed. Therefore, they propose a method using total variation (TV) to calculate the image noise intensity and make the references perform self‐adaption. Second, finding similar blocks in the BM3D method is a time‐consuming procedure. To solve this problem, they analyse the relationship between the numbers of similar blocks and denoising effect, improve the process of searching for similar blocks, and reduce the running time. Third, through the experiment they find that the denoising effect of BM3D method in the domain of complex texture is unsatisfactory. Thus, they proposed a hybrid denoising method for the complex texture area, using the new TV model and BM3D method together to restore the image. Their experimental results show that the improved BM3D method performs better than the original BM3D method.

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