1972—2016年江苏省启东市胃癌死亡流行特征分析

目的分析江苏省启东市1972—2016年胃癌死亡流行特征。方法收集启东市1972—2016年恶性肿瘤死亡登记数据库及历年人口资料, 计算死亡率、中国人口标化率(中标率)、世界人口标化率(世标率)、35～64岁截缩率、0～74岁累积死亡率、累积死亡风险、变化百分比、死亡率年均变化百分比。结果 1972—2016年启东市胃癌死亡例数为15 863例, 占全部恶性肿瘤死亡例数的16.04%, 胃癌死亡率为31.37/10万, 中标率为12.97/10万, 世标率为21.39/10万, 35～64岁截缩死亡率为28.86/10万, 0～74岁累积死亡率为2.54%, 胃癌死亡累积风险为2.51%。男性死亡10 114例, 男性死亡率、中标率、世标率分别为40.53/10万、17.98/10万和30.13/10万;女性死亡5 749例, 女性死亡率、中标率、世标率分别为22.45/10万、8.52/10万和13.92/10万。25岁以下各年龄组的死亡率<1/10万, 死亡率随年龄的增长而升高, 50～岁组达到并超过人群的平均死亡率水平, 80～岁组达到死亡高峰。1972—2016年间胃癌死...     

What Should We Focus on in Sepsis Fluid Resuscitation?--A Research Based on Scientometrics and Visual Analysis

Sepsis is a life-threatening condition and a global disease burden.Heterogeneous syndrome is defined as severe organ dysfunction caused by a dysregulated host response to infection,with renewed emphasis on the immune pathophysiology.Researchers worldwide constantly update the diagnostic criteria of sepsis and have introduced concepts such as“sepsis-3”“Surviving Sepsis Campaign(SSC)”“Early Goal-Directed Therapy(EGDT)”,the 3-h and 6-h bundles to an hour-1 bundle[1],“limited ventilation”“the best PEEP[2]”,and“Lung Protective-Ventilation”.Despite all efforts of experimental and clinical research during the last three decades,the ability to positively influence the course and outcome of the syndrome remains limited.     

早孕期监测子宫动脉多普勒参数在预测低危人群不良妊娠结局的价值

目的 建立妊娠11~13+6周子宫动脉多普勒参数在低危人群中的正常参考值，同时评估其对不良妊娠结局的预测价值。方法 收集2019年6月至2021年6月于我院行产前超声检查的妊娠11~13+6周孕妇，根据妊娠结局分组。收集两侧子宫动脉多普勒指标，包括搏动指数（PI）、阻力指数（RI）、舒张早期是否有切迹，以及孕妇基本临床资料和胎儿出生信息，将以上相关参数进行统计学分析。结果 最终纳入800例孕妇，包括正常妊娠结局组740例和不良妊娠结局组60例。两组孕妇体质量指数（BMI）、分娩孕周和胎儿出生体质量比较，差异均有统计学意义（均P＜0.05）。随着孕周的增加，子宫动脉两侧平均搏动指数（mPI）、平均阻力指数（mRI）和两侧舒张早期切迹检出率均呈逐渐下降的趋势。ROC曲线分析显示，mPI、mRI及两侧舒张早期切迹预测妊娠结局的曲线下面积（AUC）分别为0.542、0.574、0.521，三者联合预测妊娠结局的AUC为0.648；孕妇BMI、年龄mPI、mRI及两侧舒张早期切迹预测妊娠结局的AUC为0.751。结论 建立了低危人群在妊娠11~13+6周子宫动脉多普勒参数的正常参考值范围。在妊娠11~13+6周单纯应用子宫动脉多普勒参数预测妊娠结局的价值有限，将子宫动脉参数与临床相关指标结合可提高对不良妊娠结局的预测价值。     

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.