ER Stress Proteins in Autoimmune and Inflammatory Diseases

Over the past two decades, heat shock proteins (HSPs) have been implicated in inflammatory responses and autoimmunity. HSPs were originally believed to maintain protein quality control in the cytosol. However, they also exist extracellularly and appear to act as inflammatory factors. Recently, a growing body of evidence suggested that the other class of stress proteins such as, endoplasmic reticulum (ER) stress proteins, which originally act as protein quality control factors in the secretory pathway and are induced by ER stress in inflammatory lesions, also participate in inflammation and autoimmunity. The immunoglobulin heavy-chain binding protein (Bip)/glucose-regulated protein 78 (GRP78), calnexin, calreticulin, glucose-regulated protein 94 (GRP94)/gp96, oxygen regulated protein 150 (ORP150)/glucose-regulated protein 170 (GRP170), homocysteine-induced ER protein (Herp) and heat shock protein 47 (hsp47)/Serpin H1, which are expressed not only in the ER but also occasionally at the cell surface play pathophysiological roles in autoimmune and inflammatory diseases as pro- or anti-inflammatory factors. Here we describe the accumulating evidence of the participation of ER stress proteins in autoimmunity and inflammation and discuss the critical differences between the two classes of stress proteins

T1-weighted and T2-weighted MRI image synthesis with convolutional generative adversarial networks

Background: The objective of this study was to propose an optimal input image quality for a conditional generative adversarial network (GAN) in T1-weighted and T2-weighted magnetic resonance imaging (MRI) images. Materials and methods: A total of 2,024 images scanned from 2017 to 2018 in 104 patients were used. The prediction framework of T1-weighted to T2-weighted MRI images and T2-weighted to T1-weighted MRI images were created with GAN. Two image sizes (512 × 512 and 256 × 256) and two grayscale level conversion method (simple and adaptive) were used for the input images. The images were converted from 16-bit to 8-bit by dividing with 256 levels in a simple conversion method. For the adaptive conversion method, the unused levels were eliminated in 16-bit images, which were converted to 8-bit images by dividing with the value obtained after dividing the maximum pixel value with 256. Results: The relative mean absolute error (rMAE ) was 0.15 for T1-weighted to T2-weighted MRI images and 0.17 for T2-weighted to T1-weighted MRI images with an adaptive conversion method, which was the smallest. Moreover, the adaptive conversion method has a smallest mean square error (rMSE) and root mean square error (rRMSE), and the largest peak signal-to-noise ratio (PSNR) and mutual information (MI). The computation time depended on the image size. Conclusions: Input resolution and image size affect the accuracy of prediction. The proposed model and approach of prediction framework can help improve the versatility and quality of multi-contrast MRI tests without the need for prolonged examinations

Image synthesis of monoenergetic CT image in dual-energy CT using kilovoltage CT with deep convolutional generative adversarial networks

Purpose: To synthesize a dual-energy computed tomography (DECT) image from an equivalent kilovoltage computed tomography (kV-CT) image using a deep convolutional adversarial network. Methods: A total of 18,084 images of 28 patients are categorized into training and test datasets. Monoenergetic CT images at 40, 70, and 140 keV and equivalent kVCT images at 120 kVp are reconstructed via DECT and are defined as the reference images. An image prediction framework is created to generate monoenergetic computed tomography (CT) images from kV-CT images. The accuracy of the images generated by the CNN model is determined by evaluating the mean absolute error (MAE), mean square error (MSE), relative root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information between the synthesized and reference monochromatic CT images. Moreover, the pixel values between the synthetic and reference images are measured and compared using a manually drawn region of interest (ROI). Results: The difference in the monoenergetic CT numbers of the ROIs between the synthetic and reference monoenergetic CT images is within the standard deviation values. The MAE, MSE, RMSE, and SSIM are the smallest for the image conversion of 120 kVp to 140 keV. The PSNR is the smallest and the MI is the largest for the synthetic 70 keV image. Conclusions: The proposed model can act as a suitable alternative to the existing methods for the reconstruction of monoenergetic CT images in DECT from single-energy CT images

Development of legal mind in the era of high uncertainty: The approach of systematic connection of social studies between elementary school and junior high school

This study aims to enable students to consider the significance and role of legal education in social life and problems concerning intellectual property right (trademark right), relating them to consumers' rights and duty, and company's social responsibility through lesson practice in elementary and junior high school, and acquire the basic way of thinking such as procedural justice. This experience is considered valuable from the perspective of legal literacy. As the achievement of this study is that considering the actual cases enabled the students to organize their own opinions by comprehending the facts, compare their own opinion with the others and understand the importance of creating a more desirable civil society

Biological dosimetric impact of dose-delivery time for hypoxic tumour with modified microdosimetric kinetic model

Objectives: An improved microdosimetric kinetic model (MKM) can address radiobiological effects with prolonged delivery times. However, these do not consider the effects of oxygen. The current study aimed to evaluate the biological dosimetric effects associated with the dose delivery time in hypoxic tumours with improved MKM for photon radiation therapy. Methods: Cell survival was measured under anoxic, hypoxic, and oxic conditions using the Monte Carlo code PHITS. The effect of the dose rate of 0.5–24 Gy/min for the biological dose (Dbio) was estimated using the microdosimetric kinetic model. The dose per fraction and pressure of O2 (pO2) in the tumour varied from 2 to 20 Gy and from 0.01 to 5.0% pO2, respectively. Results: The ratio of the Dbio at 1.0–24 Gy/min to that at 0.5 Gy/min (RDR) was higher at higher doses. The maximum RDR was 1.09 at 1.0 Gy/min, 1.12 at 12 Gy/min, and 1.13 at 24 Gy/min. The ratio of the Dbio at 0.01–2.0% of pO2 to that at 5.0% of pO2 (Roxy) was within 0.1 for 2–20 Gy of physical dose. The maximum Roxy was 0.42 at 0.01% pO2, 0.76 at 0.4% pO2, 0.89 at 1% pO2, and 0.96 at 2% pO2. Conclusion: Our proposed model can estimate the cell killing and biological dose under hypoxia in a clinical and realistic patient. A shorter dose-delivery time with a higher oxygen distribution increased the radiobiological effect. It was more effective at higher doses per fraction than at lower doses

Dose uncertainty due to energy dependence in dual-energy computed tomography

Purpose: To evaluate the absolute dose uncertainty at 2 different energies and for the large and small bowtie filters in dual-energy computed tomography (DECT). Material and methods: Measurements were performed using DECT at 80 kV and 140 kilovoltage peak (kVp), and single-energy computed tomography (CT) at 120 kV. The absolute dose was calculated from the mass-energy absorption obtained from the half-value layer (HVL) of aluminium. Results: The difference in the water-to-air ratio of the mean mass energy-absorption coefficients at 80 kV and 140 kV was 2.0% for the small bow-tie filter and 3.0% for the large bow-tie filter. At lower tube voltages, the difference in the absorbed dose with the large and small bow-tie filters was larger. Conclusions: The absolute dose uncertainty due to energy dependence was 3.0%, which could be reduced with single-energy beams at 120 kV or by using the average effective energy measurement with dual-energy beams

Dose compensation based on biological effectiveness due to interruption time for photon radiation therapy

Objective:To evaluate the biological effectiveness of dose associated with interruption time; and propose the dose compensation method based on biological effectiveness when an interruption occurs during photon radiation therapy. Methods:The lineal energy distribution for human salivary gland tumor was calculated by Monte Carlo simulation using a photon beam. The biological dose (Dbio) was estimated using the microdosimetric kinetic model. The dose compensating factor with the physical dose for the difference of the Dbio with and without interruption (Δ) was derived. The interruption time (τ) was varied to 0.1, 0.2, 0.3, 0.4, 0.5, 1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 75, and 120 min. The dose per fraction and dose rate varied from 2 to 8 Gy and 0.1 to 24 Gy/min, respectively. Results:The maximum Δ with 1 Gy/min occurred when the interruption occurred at half the dose. The Δ with 1 Gy/min at half of the dose was over 3% for τ >= 20 min for 2 Gy, τ = 10 min for 5 Gy, and τ = 10 min for 8 Gy. The maximum difference of the Δ due to the dose rate was within 3% for 2 and 5 Gy, and achieving values of 4.0% for 8 Gy. The dose compensating factor was larger with a high dose per fraction and high-dose rate beams. Conclusion:A loss of biological effectiveness occurs due to interruption. Our proposal method could correct for the unexpected decrease of the biological effectiveness caused by interruption time. Advances in knowledge:For photon radiotherapy, the interruption causes the sublethal damage repair. The current study proposed the dose compensation method for the decrease of the biological effect by the interruption

Formulation of objective indices to quantify machine failure risk analysis for interruptions in radiotherapy

Objectives: To evaluate the effect of interruption in radiotherapy due to machine failure in patients and medical institutions using machine failure risk analysis (MFRA). Material and methods: The risk of machine failure during treatment is assigned to three scores (biological effect, B; occurrence, O; and cost of labor and repair parts, C) for each type of machine failure. The biological patient risk (BPR) and the economic institution risk (EIR) are calculated as the product of B and O (B×O) and C and O (C×O), respectively. The MFRA is performed in two linear accelerators (linacs). Result: The multileaf collimator (MLC) fault has the highest BPR and second highest EIR. In particular, TrueBeam has a higher BPR and EIR for MLC failures. The total EIR in TrueBeam was significantly higher than that in Clinac iX. The minor interlock had the second highest BPR, whereas a smaller EIR. Meanwhile, the EIR for the LaserGuard fault was the highest, and that for the monitor chamber fault was the second highest. These machine failures occurred in TrueBeam. The BPR and EIR should be evaluated for each linac. Further, the sensitivity of the BPR, it decreased with higher T1=2 and α/β values. No relative difference is observed in the BPR for each machine failure when T1=2 and α/β were varied. Conclusion: The risk faced by patients and institutions in machine failure may be reduced using MFRA. Advances in knowledge: For clinical radiotherapy, interruption can occur from unscheduled downtime with machine failures. Interruption causes sublethal damage repair. The current study evaluated the effect of interruption in radiotherapy owing to machine failure on patients and medical institutions using a new method, that is, machine failure risk analysis