• Journal of Dental Anesthesia and Pain Medicine
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• v.19 no.6
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• pp.353-360
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• 2019

Background: Controlled hypotension (CH) provides a better surgical environment and reduces operative time. However, there are some risks related to organ hypoperfusion. The EV1000/FloTrac system can provide continuous cardiac output monitoring without the insertion of pulmonary arterial catheter. The present study investigated the efficacy of this device in double jaw surgery under CH. Methods: We retrospectively reviewed the medical records of patients who underwent double jaw surgery between 2010 and 2015. Patients were administered conventional general anesthesia with desflurane; CH was performed with remifentanil infusion and monitored with an invasive radial arterial pressure monitor or the EV1000/FloTrac system. We allocated the patients into two groups, namely an A-line group and an EV1000 group, according to the monitoring methods used, and the study variables were compared. Results: Eighty-five patients were reviewed. The A-line group reported a higher number of failed CH (P = 0.005). A significant correlation was found between preoperative hemoglobin and intraoperative packed red blood cell transfusion (r = 0.525; P < 0.001). In the EV1000 group, the mean arterial pressure (MAP) was significantly lower 2 h after CH (P = 0.014), and the cardiac index significantly decreased 1 h after CH (P = 0.001) and 2 h after CH (P = 0.007). Moreover, venous oxygen saturation (ScVO₂) decreased significantly at both 1 h (P = 0.002) and 2 h after CH (P = 0.029); however, these values were within normal limits. Conclusion: The EV1000 group reported a lower failure rate of CH than the A-line group. However, EV1000/FloTrac monitoring did not present with any specific advantage over the conventional arterial line monitoring when CH was performed with the same protocol and same mean blood pressure. Preoperative anemia treatment will be helpful to decrease intraoperative transfusion. Furthermore, ScVO₂ monitoring did not present with sufficient benefits over the risk and cost.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• Molecules and Cells
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• v.40 no.5
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• pp.371-377
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• 2017

Despite the importance of the receptor activator of nuclear factor (NF)-kappaB ligand (RANKL)-RANK signaling mechanisms on osteoclast differentiation, little has been studied on how RANK expression is regulated or what regulates its expression during osteoclastogenesis. We show here that insulin signaling increases RANK expression, thus enhancing osteoclast differentiation by RANKL. Insulin stimulation induced RANK gene expression in time- and dose-dependent manners and insulin receptor shRNA completely abolished RANK expression induced by insulin in bone marrow-derived monocyte/macrophage cells (BMMs). Moreover, the addition of insulin in the presence of RANKL promoted RANK expression. The ability of insulin to regulate RANK expression depends on extracellular signal-regulated kinase 1/2 (ERK1/2) since only PD98059, an ERK1/2 inhibitor, specifically inhibited its expression by insulin. However, the RANK expression by RANKL was blocked by all three mitogen-activated protein (MAP) kinases inhibitors. The activation of RANK increased differentiation of BMMs into tartrate-resistant acid phosphatase-positive ($TRAP^+$) osteoclasts as well as the expression of dendritic cell-specific transmembrane protein (DC-STAMP) and d2 isoform of vacuolar ($H^+$) ATPase (v-ATPase) Vo domain (Atp6v0d2), genes critical for osteoclastic cell-cell fusion. Collectively, these results suggest that insulin induces RANK expression via ERK1/2, which contributes to the enhancement of osteoclast differentiation.