Objective To predict the disease burden of kidney cancer and to provide basic information for etiology and control planning.Methods We retrieved incidence data of kidney cancer from 18 urban cancer registries from National Central Cancer Registry during ten years period from 1998 to 2007.Ageperiod-cohort Bayesian model was applied for modeling to predict kidney cancer incidence in urban China in 2008-2015.Results Between 1998 and 2007,the incidence of kidney cancer in urban registration areas kept increasing dramatically.Incidence for male raised from 3.12/100 000 in 1998 to 5.36/100 000 in 2007 and from 1.66/100 000 to 2.67/100 000 for female.Different models showed that the increase was mainly caused by a cohort effect (P ＜ 0.001).The predicted incidence rate of kidney cancer for the year 2015 is 9.93 per 100 000 in male and 4.54 per 100 000 in female.The number of new cases will rise to 52 259 in 2015,including 36 616 men and 15 643 women.Conclusions The burden of kidney cancer in urban areas would increase due to the effect of age and cohort.Kidney cancer will become one of the main cancers threatening people's health in urban areas in China.Etiology research and planning of prevention and control for kidney cancer should be enhanced.

Objective To analyze the correlations between liver lipid level determined by liver 3.0 T 1H-MRS in vivo and influencing factors using multiple linear stepwise regression. Methods The prospective study of liver 1H-MRS was performed with 3.0 T system and eight-channel torso phased-array coils using PRESS sequence. Forty-four volunteers were enrolled in this study. Liver spectra were collected with a TR of 1500 ms ,TE of 30 ms, volume of interest of 2 cm ×2 cm ×2 cm, NSA of 64 times. The acquired raw proton MRS data were processed by using a software program SAGE. For each MRS measurement, using water as the internal reference, the amplitude of the lipid signal was normalized to the sum of the signal from lipid and water to obtain percentage lipid within the liver. The statistical description of height, weight, age and BMI, Line width and water suppression were recorded, and Pearson analysis was applied to test their relationships. Multiple linear stepwise regression was used to set the statistical model for the prediction of Liver lipid content. Results Age (39.1 ± 12. 6) years, body weight (64.4 ± 10. 4) kg,BMI (23.3 ±3.1) kg/m2, linewidth (18.9 ±4.4) and the water suppression (90.7 ±6.5)% had significant correlation with liver lipid content (0.00 to 0.96%, median 0. 02% ), r were 0.11,0. 44,0. 40,0. 52, - 0. 73 respectively(P < 0. 05 ). But only age, BMI, line width, and the water suppression entered into the multiple linear regression equation. Liver lipid content prediction equation was as follows: Y =1.395-(0.021 × water suppression) + (0.022 × BMI) + (0.014 × line width) - ( 0. 064 × age),and the coefficient of determination was 0.613, corrected coefficient of determination was 0.59. Conclusion The regression model fitted well, since the variables of age, BMI, width, and water suppression can explain about 60% of liver lipid content changes.

Objective To explore the effect of body mass and body mass index (BMI) on proton hepatic MRS water suppression at 3.0T. Methods The prospective study of hepatic proton MRS was performed with GE Signa Excite HD 3.0T system and eight-channel torso phased-array coils using PRESS sequence in 44 healthy subjects. Liver spectra were collected with TR of 1500 ms, TE of 30 ms, VOI of 2 cm×2 cm×2 cm and NSA of 64 times. Areas and heights for metabolites resonances were caulculated. Results Group with small mass has lower height ([161.2±8.5] cm vs [167.7±6.2])cm, lower BMI ([20.8±2.3] kg/m~2 vs [25.6±2.6]kg/m~2), better water suppression effect (min-max: 90-96 vs 65-94;median: 94 vs 93), smaller height (min-max: 1.41×10~4-5.76 ×10~5 vs 3.45×10~4-1.75×10~6;median: 9.00×10~4 vs 2.58×10~5) and integrated area (min-max: 4.27×10~4-2.00×10~7 vs 1.24×10~5-5.00×10~7;median: 2.64×10~5 vs 1.19×10~6)of Lip2 than larger weight group. Standardized lipid content (min-max: 0-0.11 vs 0-0.96;median: 0.01 vs 0.04) was less. Group with lower BMI had lower weight ([55.2±8.2]kg vs [71.2±7.8]kg), smaller age ([33.2±11.9]years vs [45.6±9.4]years), better water suppression effect(min-max: 90-96 vs 65-95;median: 94 vs 93) smaller of height (min-max: 1.41×10~4-5.76×10~5 vs 3.45×10~4-1.75×10~6;median: 7.37×10~4 vs 2.11×10~5) and integrated area (min-max: [4.27×10~4-2.00×10~7] vs [1.24×10~5 -5.00×10~7];median: 2.64×10~5 vs 1.19×10~6) of Lip2 than larger weight group. Standardized lipid content (min-max: 0-0.08 vs 0.01-0.96;median: 0.01 vs 0.04) was less. There was significant correlation among water suppression, weight (r=-0.478, P=0.001) and BMI (r=-0.494, P=0.001). Conclusion Lipid accumulation in the liver may be the result of increased fat portion of the body depending on mass and BMI, and hinder to achieve effective water suppression.

Objective To characterize the effect of the ~1H-MRS scan parametem, including the type of coil, TE,NSA and VOI, on shimming, water suppression, spectral signal to noise ratio(SNR)and the stability of the baseline of liver in vivo. Methods ~1H-MRS of liver in vivo was performed prospectively on GE Signa Excite HD 3.0 T system in 46 volunteers. Point-resolred spectroscopy(PRESS)sequence with built-in body coil and eight-channel torso phased-array coils was applied. After the localized scan,the first PRESS sequence with a TR of 1500 ms,TE of 30 ms. VOI of 2 cm×2 cm×2 cm and NSA of 64 times was acquired using eight-channel torso phased-array coils.(The first PRESS sequence parametem was deemed as A).Then,the sequence was repeated with alteration of the three parameters including the type of coil,TE and size of VOI.(Changed parameters deem as B).The data were analyzed with the Wilcoxon matched pairs signed test.0 mark:A is similar to B,1 mark:A better than B,-1 mark:A worse than B.Results SNR(-1 mark 0 pair,0 mark 1 pair,1 mark 10 pair,Z=-3.162,P=0.002)was better in data(n=11)with eight-channel torso phased-array coils(A)than that with the built-in body coil(B),but the autoshimming line width with eight-channel torso phased-array coils were inferior to those with built-in body coil (-1 mark 8 pair,0 mark 2 pair,1 mark 1 pair,Z=-2.511,P=0.012).SNR was better in data(n=13)with TE of 30 ms(A)than that at the sequence with TE of 90 ms(B)(-1 mark 2 pair,0 mark 0 pair,1 mark 11 pair,Z=-2.496,P=0.013).whereas baseline stability was,poorer in the former(-1 mark 10 pair,0 mark 2 pair,1 mark 1 pair,Z=-2.333,P=0.020).SNR at the sequence(n=10)with VOI of 2 cm×2 cm×3 cm(B)was better(-1 mark 6 pair,0 mark 4 pair,1 mark 0 pair,Z=-2.449,P=0.014)than that at the sequence(n=29)with VOI of 2 cm ×2 cm × 2 cm(A),but poorer(-1 mark 0 pair,0 mark 5 pair,1 mark 5 pair,Z=-2.041,P=0.041)auto-shimming line width was shown. By comparison the sequences with NSA of 128 times(B)and NSA of 64 times(A),the former could provide better spectrum SNR(-1 mark 21 pair,0 mark 7 pair,1 mark 1 pair,Z=-4.264,P=0.000).Conclusion It is more easy to achieve a homogeneous bo magnetic field using a small size of VOI and builtin body coli.The sequence with VOI of 2 cm ×2 cm ×3 cm.NSA of 128 times is recommended for clinical use. Increase VOI and NSA are helpful to improve SNR. Longer TE is helpful to improve baseline stability.

Objective To assess the reliability of auto-shimming line width (LW) and water suppression rate (WS), and the correlation between them. Methods GE Signa Excite HD 3.0T system and eight-channel torso phased-array coils with PRESS sequence were performed in 38 volunteers. Liver spectra were collected with TR of 1500 ms, TE of 30 ms, VOI of 2 cm×2 cm×2 cm, NSA of 64 times. Spectroscopy routine auto-shimming pre-scanning program was executed and the values of LW and WS were recorded. Then another spectroscopy routine auto-shimming pre-scanning program was performed repeatedly and 38 groups of data were obtained totally. Intra-class correlation coefficients (ICC), coefficient of variation (CV) and significance test were conduced on 38 groups of LW and WS data. Spearman rank correlation analysis was used to assess the correlation of LW and WS. Results ①The ICC of LW and WS was 0.862 and 0.961 (both P＜0.0001), respectively, while the value of CV was 0.20, 0.18, 0.09 and 0.08, respectively. Significant difference was not observed; ②The value of correlation coefficient was -0.659, -0.485 (both P＜0.0001), respectively. Conclusion ①The reliability is excellent for in vivo liver 3.0T 1H-MRS and WS appears relatively stable; ②Indexes of LW correlate with WS moderately, and it seems the smaller the value of LW is, the easier to achieve higher WS.

Objective To explore the reproducibility of normal hepatic MRS at 3.0T. Methods The hepatic proton MRS was performed with GE Signa Excite HD 3.0T system and eight-channel torso phased-array coils using PRESS sequence. Thirty-one healthy individuals were enrolled in this study. Liver spectra were collected with TR of 1500 ms, TE of 30 ms, ROI of 2 cm×2 cm×2 cm, NSA of 64 times. The outcomes were statistically analyzed with Wilcoxon signed ranks test and Spearman correlation test.Results There was no significant difference of signal to noise ratio (Z=-0.535,P=0.593), baseline stability (Z=-0.333, P=0.739), linewidth of automatic shimming (Z=-0.305, P=0.761), water suppression (Z=-1.232, P=0.218), height of lipid peak (Z=-0.558,P=0.557), area under the lipid peak (Z=-1.195,P=0.232), height of water peak (Z=-0.647,P=0.518) and area under the warter peak (Z=-0.118, P=0.906) between first examination and second examination. Correlation coefficient of the former and the later measurements of lipid area and water area were 0.784 (P<0.001) and 0.799 (P<0.001), respectively.Conclusion The reproducibility is good for in vivo liver proton MRS at 3.0T.

Objective: To explore the influence of caregiver burden on quality of life among caregivers for patients with lung cancer in Beijing. Methods: From August to October in 2017, 336 caregivers for patients with primary lung cancer from a large tertiary hospital were recruited to this survey by convenient sampling method. The Caregiver Reaction Assessment (CRA) and the WHO Quality of Life-BREF (WHOQOL-BREF) Instruments were used to evaluate the caregiver burden and quality of life. The demographic characteristics were evaluated using Pearson chi-square or Fisher′s exact tests. In addition, using the 4 dimensions of quality of life scale as the dependent variables while the 5 dimensions of caregiver burden and demographic variables as the explanatory variables, we conducted the multiple linear stepwise regression analysis (the defaults were 0.15 for FORWARD and BACKWARD). Results: The scores of family caregivers′ esteem, impact on schedule, impact on finances, impact on health and lack of family support were 4.30±0.47、3.55±0.74、3.10±0.91、2.60±0.78、2.32±0.73, respectively. Regression results showed that demographic factors, including marriage, occupation and relationship with patients and impact on health, caregivers′ esteem and impact on finances dimensions of caregiver burden were the factors which influenced the quality of life of caregivers. Conclusions The caregiver burden in patients with lung cancer is mainly focused on impact on schedule and finances. Impact on health, finances and caregivers′ esteem (seven items were reverse-scored) are significantly negatively correlated with quality of life of caregivers.

In December 2019, novel coronavirus pneumonia epidemic occurred in Wuhan, Hubei Province, and spread rapidly across the country. In the early stages of the epidemic, China adopted the containment strategy and implemented a series of core measures around this strategic point, including social mobilization, strengthening case isolation and close contacts tracking management, blocking epidemic areas and traffic control to reduce personnel movements and increase social distance, environmental measures and personal protection, with a view to controlling the epidemic as soon as possible in limited areas such as Wuhan. This article summarizes the background, key points and core measures in the country and provinces. It sent prospects for future prevention and control strategies.