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The progressive and generalized loss of skeletal muscle mass and function, also known as sarcopenia, underlies disability, increasing adverse outcomes and poor quality of life in older people. Exercise interventions are commonly recommended as the primary treatment for sarcopenia. Nuclear factor erythroid 2-related factor 2 (Nrf2) plays a vital role in regulating metabolism, mitochondrial function, and the ROS-dependent adaptations of skeletal muscle, as the response to exercise. To investigate the contribution of Nrf2 to the benefits of exercise interventions in older age, aged (∼22 month old) Nrf2 knockout (Nrf2-KO) mice and age-matched wild-type (WT) C57Bl6/J mice were randomly divided into 2 groups (sedentary or exercise group). We found that exercise interventions improved skeletal muscle function and restored the sarcopenia-like phenotype in WT mice, accompanied with the increasing mRNA level of Nrf2. While these alternations were minimal in Nrf2-KO mice after exercise. Further studies indicated that Nrf2 could increase the stability of Drp1 through deubiquitinating and promote Drp1-dependent mitochondrial fission to attenuate mitochondrial disorder. We also observed the effects of sulforaphane (SFN), a Nrf2 activator, in restoring mitochondrial function in senescent C2C12 cells and improving sarcopenia in older WT mice, which were abolished by Nrf2 deficiency. These results indicated that some benefits of exercise intervention to skeletal muscle were Nrf2 mediated, and a future work should focus on Nrf2 signaling to identify a pharmacological treatment for sarcopenia.

The study was aimed at evaluating the correlation between central cervical lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC) patients and ultrasound (US) features, immunohistochemical factors and BRAFV600E mutation. A total of 225 consecutive patients (225 PTCs) who had undergone surgery were included. All PTCs were pre-operatively analysed by US with respect to size, components, echogenicity, shape, margins, microcalcification, multiple cancers or not, internal vascularity and capsule contact or involvement. The presence of four immunohistochemical factors, including cytokeratin 19, human bone marrow endothelial cell 1, galectin-3 and thyroid peroxidase, and BRAFV600E mutation was also evaluated. Univariate and multivariate analyses were performed to identify the risk factors for central CLNM, and a risk model was established. Pathologically, 44% (99/225) of the PTCs had central CLNMs. Multivariate analysis revealed that size ≤10mm, microcalcification, internal vascularity, capsule contact or involvement and BRAFV600E mutation were independent risk factors for central CLNM. The risk score for central CLNM was calculated as follows: risk score = 1.5 × (if lesion size ≤10 mm) + 1.9 × (if microcalcification) + 0.8 × (if internal flow) + 3.0 × (if capsule contact or involvement) + 1.5 × (if BRAFV600E mutation). The rating result was divided into six stages, and the relevant risk rates of central CLNM were 0% (0/1), 0% (0/22), 7.4% (4/54), 48.6% (34/70), 71.2% (42/59) and 100% (19/19), respectively. In conclusion, PTC ≤10mm, microcalcification, internal vascularity, capsule contact or involvement and BRAFV600E mutation are risk factors for central CLNM. The risk model may be useful in treatment planning and management of patients with PTCs.

Ultrasonography is a common modality for diagnosing thyroid nodules. Computer-aided classification of benign and malignant thyroid nodules in the ultrasound images is meaningful. A key challenge in this issue is that a lot of ultrasound images without obvious benign or malignant features are hindering deep learning model to extract effective features. Here, a novel method using two combined classification modules is proposed to separate and classify those images. Firstly, we adopt multi-fold cross-validation based a CNN model to pick out ultrasound images that hard to recognize. Then we add new labels to those images and put them back to the dataset that will be trained with the CNN model to form a tripartite classification module. Finally, a binary classification module will be trained exclusively to discriminate those obscure images. Experimental results on 4148 ultrasound images show that the proposed method lead to excellent performance, with an accuracy of 0.804, which demonstrate the value of this method.

// Bao-Ding Chen 1,2,3,4,5 , Hui-Xiong Xu 1,2,3,4 , Yi-Feng Zhang 1,2,3,4 , Bo-Ji Liu 1,2,3,4 , Le-Hang Guo 1,2,3,4 , Dan-Dan Li 1,2,3,4 , Chong-Ke Zhao 1,2,3,4 , Xiao-Long Li 1,2,3,4 , Dan Wang 1,2,3,4 and Shuang-Shuang Zhao 5 1 Department of Medical Ultrasound, Shanghai Tenth People’s Hospital, Ultrasound Research and Educational Institute, Tongji University School of Medicine, Shanghai, China 2 Department of Medical Ultrasound, Shanghai Tenth People’s Hospital, Clinical College of Nanjing Medical University, Shanghai, China 3 Thyroid Institute, Tongji University School of Medicine, Shanghai, China 4 Shanghai Center of Thyroid Diseases, Shanghai, China 5 Department of Medical Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, China Correspondence to: Hui-Xiong Xu, email: // Keywords : thyroid nodule, calcification, ultrasound, shear-wave elastography, point shear-wave measurement Received : June 30, 2016 Accepted : August 26, 2016 Published : August 31, 2016 Abstract Conventional ultrasound cannot satisfactorily distinguish malignant and benign thyroid nodules. Shear-wave elastography (SWE) can evaluate tissue stiffness and complement conventional ultrasound in diagnosing malignant nodules. However, calcification of nodules may affect the results of SWE. The purposes of this study are to compare the differences of shear-wave speed (SWS) measurement among different calcification groups and compare the diagnostic performance between using a single uniform SWS cutoff value and multiple individual calcification-specific cutoff values using technique of point SWS measurement. We retrospectively identified 517 thyroid nodules (346 benign and 171 malignant nodules) examined by conventional ultrasound and point SWS measurement. There were 177 non-calcified, 159 micro-calcified and 181 macro-calcified nodules. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC) was computed. The mean SWS in malignant nodules more than doubled that of benign nodules (4.81±2.03 m/s vs. 2.29±0.99 m/s, p 2.42 m/s), micro- (SWS >2.88 m/s) and macro-calcification (SWS >3.59 m/s) nodules in the whole group, the AUC was 0.859 (95% confidence interval [CI], 0.826-0.888), which was significantly better than the AUC of 0.816 (95% CI, 0.780-0.848) if a single uniform cutoff value (SWS >2.72 m/s) was applied to all the nodules regardless of calcification status (p=0.011). The cutoff values of SWS for different calcified nodules warrant future prospective validation.

With the increasing incidence rate of thyroid cancer, the diagnosis of thyroid nodules has become an important task. In this paper, we designed a deep neural network (DNN) to classify whether a thyroid nodule is benign or malignant, and proposed a structure which combines local binary pattern (LBP) with deep learning. Our method mitigates the effects of overfitting in medical image diagnosis tasks. With well-designed transfer leaning, we achieve an accuracy of 85% on our own ultrasound thyroid dataset. To ensure the reliability of our experiments, all examples are estimated by experts in Shanghai Tenth People’s Hospital using fine needle analysis (FNA), which is a gold standard for thyroid nodules diagnosis. The experimental results show that combinations of the traditional medial image features can help the deep learning network get more semantic information from low-level inputs.