Role of G{alpha}12 and G{alpha}13 as Novel Switches for the Activity of Nrf2, a Key Antioxidative Transcription Factor

G{alpha}12 and G{alpha}13 function as molecular regulators responding to extracellular stimuli. NF-E2-related factor 2 (Nrf2) is involved in a protective adaptive response to oxidative stress. This study investigated the regulation of Nrf2 by G{alpha}12 and G{alpha}13. A deficiency of G{alpha}12, but not of G{alpha}13, enhanced Nrf2 activity and target gene transactivation in embryo fibroblasts. In mice, G{alpha}12 knockout activated Nrf2 and thereby facilitated heme catabolism to bilirubin and its glucuronosyl conjugations. An oligonucleotide microarray demonstrated the transactivation of Nrf2 target genes by G{alpha}12 gene knockout. G{alpha}12 deficiency reduced Jun N-terminal protein kinase (JNK)-dependent Nrf2 ubiquitination required for proteasomal degradation, and so did G{alpha}13 deficiency. The absence of G{alpha}12, but not of G{alpha}13, increased protein kinase C {delta} (PKC {delta}) activation and the PKC {delta}-mediated serine phosphorylation of Nrf2. G{alpha}13 gene knockout or knockdown abrogated the Nrf2 phosphorylation induced by G{alpha}12 deficiency, suggesting that relief from G{alpha}12 repression leads to the G{alpha}13-mediated activation of Nrf2. Constitutive activation of G{alpha}13 promoted Nrf2 activity and target gene induction via Rho-mediated PKC {delta} activation, corroborating positive regulation by G{alpha}13. In summary, G{alpha}12 and G{alpha}13 transmit a JNK-dependent signal for Nrf2 ubiquitination, whereas G{alpha}13 regulates Rho-PKC {delta}-mediated Nrf2 phosphorylation, which is negatively balanced by G{alpha}12

An Evaluation of Relative Damage to the Powertrain System in Tracked Vehicles

The objective of this study was to improve the reliability of the endurance test for the powertrain system of military tracked vehicles. The measurement system that measures the driving duty applied to the powertrain system caused by mobility on roads consists of eight analog channels and two pulse channels, including the propeller shaft output torques for the left and right sides. The data obtained from this measurement system can be used to introduce a new technology that produces the output torque of a torque converter and that can be applied to analyze the revolution counting for the endurance and road mobility in the front unit and represent the relative fatigue damages analysis technique and its results according to the driven roads through a cumulative fatigue method

SECBlock-IIoT : A Secure Blockchain-enabled Edge Computing Framework for Industrial Internet of Things

This work was supported by a National Research Foundation of Korea (NRF) grant funded by Korean Government (MSIT) (No. 2021R1A2C2014333).Postprin

Radiomics signature on 3T dynamic contrast-enhanced magnetic resonance imaging for estrogen receptor-positive invasive breast cancers: Preliminary results for correlation with Oncotype DX recurrence scores

To evaluate the ability of a radiomics signature based on 3T dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to distinguish between low and non-low Oncotype DX (OD) risk groups in estrogen receptor (ER)-positive invasive breast cancers.Between May 2011 and March 2016, 67 women with ER-positive invasive breast cancer who performed preoperative 3T MRI and OD assay were included. We divided the patients into low (OD recurrence score [RS] <18) and non-low risk (RS ≥18) groups. Extracted radiomics features included 8 morphological, 76 histogram-based, and 72 higher-order texture features. A radiomics signature (Rad-score) was generated using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate logistic regression analyses were performed to investigate the association between clinicopathologic factors, MRI findings, and the Rad-score with OD risk groups, and the areas under the receiver operating characteristic curves (AUC) were used to assess classification performance of the Rad-score.The Rad-score was constructed for each tumor by extracting 10 (6.3%) from 158 radiomics features. A higher Rad-score (odds ratio [OR], 65.209; P <.001), Ki-67 expression (OR, 17.462; P = .007), and high p53 (OR = 8.449; P = .077) were associated with non-low OD risk. The Rad-score classified low and non-low OD risk with an AUC of 0.759.The Rad-score showed the potential for discrimination between low and non-low OD risk groups in patients with ER-positive invasive breast cancers. Copyright © 2019 the Author(s)

Classification of the glioma grading using radiomics analysis

Background Grading of gliomas is critical information related to prognosis and survival. We aimed to apply a radiomics approach using various machine learning classifiers to determine the glioma grading. Methods We considered 285 (high grade n = 210, low grade n = 75) cases obtained from the Brain Tumor Segmentation 2017 Challenge. Manual annotations of enhancing tumors, non-enhancing tumors, necrosis, and edema were provided by the database. Each case was multi-modal with T1-weighted, T1-contrast enhanced, T2-weighted, and FLAIR images. A five-fold cross validation was adopted to separate the training and test data. A total of 468 radiomics features were calculated for three types of regions of interest. The minimum redundancy maximum relevance algorithm was used to select features useful for classifying glioma grades in the training cohort. The selected features were used to build three classifier models of logistics, support vector machines, and random forest classifiers. The classification performance of the models was measured in the training cohort using accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve. The trained classifier models were applied to the test cohort. Results Five significant features were selected for the machine learning classifiers and the three classifiers showed an average AUC of 0.9400 for training cohorts and 0.9030 (logistic regression 0.9010, support vector machine 0.8866, and random forest 0.9213) for test cohorts. Discussion Glioma grading could be accurately determined using machine learning and feature selection techniques in conjunction with a radiomics approach. The results of our study might contribute to high-throughput computer aided diagnosis system for gliomas

Synovial Sarcoma of the Thyroid Gland

Primary synovial sarcoma of the thyroid is an extremely rare condition which has only been reported twice in the literature. We herein report a case of highly aggressive and rapidly lethal primary synovial sarcoma of the thyroid. A 72-year-old woman presented with extensive local invasion, rapid progression, and early distant metastasis secondary to primary thyroid synovial sarcoma. The tumor exhibited an atypical histologic and immunohistochemical staining pattern. Detection of SYT/SSX fusion transcript confirmed the diagnosis of synovial sarcoma. Due to the aggressive nature of primary synovial sarcoma of the thyroid gland, early diagnosis and comprehensive treatment including wide resection and postoperative chemoradiation is required

Juvenile Paget's Disease with Paranasal Sinus Aplasia

Juvenile Paget's disease (JPD) is a rare skeletal disorder that's characterized by bone demineralization and elevated levels of serum alkaline phosphatase. JPD involves the paranasal sinuses in extremely rare cases. We report here on a 25-month-old Asian male who was diagnosed of JPD associated with aplasia of the paranasal sinuses, but not the ethmoid sinuses. The patient was successfully treated by surgery and we undertook no medical intervention. This appears to be the first reported case of JPD associated with bilateral paranasal sinus aplasia