Objective To analyze the features of cutaneous lesions and age of onset of tuberous sclerosis(TS).Methods A total of 54 TS patients were recruited;their clinical data were analysed.Results The occurance of facial angiofibromas,periungual fibromas,hypomelanotic macules and shark macules was 72.5%,26%,70%and 48%,respectively,and the median age of onset of these lesions was 7,14,0.2 and 6 years,respectively,with a peak at 1-9,10-19,＜1 and 1-9 years,respectively.Conclusions Cutaneous lesions are commonly seen in TS,and most of them develop early in life.

Objective To evaluate the effect of exercise on the relationship between body mass index (BMI) and health-related quality of life (HRQOL).Methods A total of 1 114 participants were recruited from the Physical Examination Center of two hospitals.SF-36 was used to measure HRQOL,and the classification of sports level was done according to the subjective evaluation of participants.Results The total score of HRQOL,physical component summary and mental component summary were 76.26± 16.10,75.97 ± 16.67 and 76.55± 18.33 respectively.The results of correlation analysis were as following:the positive correlation between BMI and general health (r=0.109),vitality (r=0.116),social functioning (r=0.092),role emotional (r=0.122),mental health (r =0.145) were significant (P＜ 0.01).The negative correlation between BMI and PF(r=-0.07,P=0.019)was significant.The mediation effects of exercise in was relationship between BMI and general health,vitality,mental health were significant,and the mediating effect were 5.84％,9.14％ and 6.58％.Conclusion This study demonstrates that mediation effects of the exercise are significant in some dimensions of HRQOL,but the direct influence of BMI on the HRQOL plays a leading role.And BMI is a protective factor under certain conditions.

Objective To determine the frequency of Th22 cells and expression of their related cytokines in peripheral blood of patients with alopecia areata(AA), to investigate their significance, and to explore the role of T lymphocytes in the occurrence of AA. Methods A total of 38 patients with AA were enrolled from Central Hospital of Minhang District in Shanghai between January 2015 and May 2016, and served as the case group. At the same time, 38 healthy people served as the control group. Peripheral blood samples were obtained from these patients and controls. Flow cytometry was performed to determine the percentage of Th22 cells, and enzyme?linked immunosorbent assay(ELISA)to measure serum levels of interleukin?22(IL?22)and IL?17. Results Compared with the control group, the case group showed significantly increased percentages of Th22 cells, Th17 cells, IL?17+IL?22+CD4+T cells and IFN?γ+IL?22+CD4+ T cells(all P < 0.01), as well as serum levels of IL?22 and IL?17(both P < 0.05). A significant increase was observed in percentages of Th22 cells, Th17 cells, IL?17+IL?22+CD4+T cells and IFN?γ+IL?22+CD4+T cells, as well as serum levels of IL?22 and IL?17, in patients with severe AA compared with those with mild AA, and in patients with active AA compared with those with stable AA (all P < 0.05). Conclusion Th22 cells and their related cytokines may participate in the occurrence, development and prognosis of AA.

OBJECTIVE: Identifying Atrial Ventricular Hypertrophy Electrocardiogram (AVH ECG)and diagnosing the classification of theirs automatically. METHODS: The ECG data used in this experiment was collected from the First Affiliated Hospital of China Medical University. CNN are combined with conventional methods and a 10 layers of one dimensional CNN are created in this experiment to extract the features of ECG signals automatically and achieve the function of classifying. ROC, sensitivity and F1-score are used here to evaluate the effects of the model. RESULTS: In the experiment of identifying AVH ECG, the AUC of test dataset is 0.991, while in the experiment of classifying AVH ECG, the maximal F1-score can reach 0.992. CONCLUSIONS The CNN model created in this experiment can achieve the auxiliary diagnosis of AVH ECG.
China ; Electrocardiography ; Heart Atria/pathology* ; Humans ; Hypertrophy ; Neural Networks, Computer