Renji Hospital

About: Renji Hospital is a healthcare organization based out in Shanghai, China. It is known for research contribution in the topics: Medicine & Biology. The organization has 1112 authors who have published 714 publications receiving 15442 citations. The organization is also known as: Rénjì Yīyuàn.

Dynamic Expression of EpCAM in Primary and Metastatic Lung Cancer Is Controlled by Both Genetic and Epigenetic Mechanisms

Abstract: Although great progress has been achieved in cancer treatment in the past decades, lung cancer remains the leading cause of cancer death, which is partially caused by the fact that most lung cancers are diagnosed at advanced stages. To improve the sensitivity and specificity of lung cancer diagnosis, the underlying mechanisms of current diagnosis methods are in urgent need to be explored. Herein, we find that the expression of EpCAM, the widely used molecular marker for tumor cell characterization and isolation, is strongly upregulated in primary lung tumors, which is caused by both gene amplification and promoter hypomethylation. In contrast, EpCAM expression is severely repressed in metastatic lung tumors, which can be reversed by epigenetic drugs, DNMT inhibitor 5-aza-dC and HDAC inhibitor MS-275. Moreover, tumor-associated macrophages (TAMs) impede EpCAM expression probably through TGFβ-induced EMT signaling. These findings unveil the dynamic expression patterns of EpCAM and differential roles of epigenetic modification in EpCAM expression in primary and metastatic lung tumors, providing novel insights into tumor cell isolation and lung cancer diagnosis.

Validation of NH₃ observations from AIRS and CrIS against aircraft measurements from DISCOVER-AQ and a surface network in the Magic Valley

Abstract: Abstract. Ammonia is a significant precursor of PM2.5 particles and thus contributes to poor air quality in many regions. Furthermore, ammonia concentrations are rising due to the increase of large scale, intensive agricultural activities, which are often accompanied by greater use of fertilizers and concentrated animal feedlots. Ammonia is highly reactive, and thus highly variable and difficult to measure. Satellite based instruments, such as the Atmospheric Infrared Sounder (AIRS), and the Cross-Track Infrared Sounder (CrIS) sensors, have been shown to provide much greater temporal and spatial coverage of ammonia distribution and variability than is possible with in situ networks or aircraft campaigns, but the validation of these data is limited. Here we evaluate ammonia retrievals from AIRS and CrIS against ammonia measurements from aircraft in the California Central Valley and in the Colorado Front Range. The satellite datasets were small and in California were obtained under difficult conditions. We show that the surface values of the retrieved profiles are biased very low in California and slightly high in Colorado, and that the bias appears to be primarily due to smoothing error. We also compare three years of CrIS ammonia against an in situ network in the Magic Valley in Idaho We show that CrIS ammonia captures both the seasonal signal and the spatial variability in the Magic Valley, though it is biased low here also. In summary, analysis adds to the validation record but also points to the need for more validation under different conditions.

Learning Optimal Time-Frequency Representations for Heart Sound: A Comparative Study

Abstract: Computer audition based methods have increasingly attracted efforts among the community of digital health. In particular, heart sound analysis can provide a non-invasive, real-time, and convenient (anywhere and anytime) solution for preliminary diagnosis and/or long-term monitoring of patients who are suffering from cardiovascular diseases. Nevertheless, extracting excellent time-frequency features from the heart sound is not an easy task. On the one hand, heart sound belongs to audio signals, which may be suitable to be analysed by classic audio/speech techniques. On the other hand, this kind of sound generated by our human body should contain some characteristics of physiological signals. To this end, we propose a comprehensive investigation on time-frequency methods for analysing the heart sound, i.e., short-time Fourier transformation, wavelet transformation, Hilbert-Huang transformation, and Log-Mel transformation. The time-frequency representations will be automatically learnt via pre-trained deep convolutional neural networks. Experimental results show that all the investigated methods can reach a mean accuracy higher than 60.0%. Moreover, we find that wavelet transformation can beat other methods by reaching the highest mean accuracy of 75.1% in recognising normal or abnormal heart sounds.

Replantation of Single hand’s Multiplane Severances with 17 Segments

Abstract: An 18-year-old girl suffered a cutting injury in her right hand during work. We found all the fingers and the palm were severed into 17 segments by 6 planes when she arrived at the emergency room of our hospital 3 h after the injury. The thumb was found severing into three segments by nail root, proximal, and distal parts of the thumb proximal segment, respectively. The index finger was severed in the middle and proximal segments, respectively. The middle finger severed in the middle segment, proximal segment, and nail root, respectively. The ring finger severed in the nail root, proximal, and distal part of proximal interphalangeal joint, respectively. The little finger severed in the middle segment and middle part of the proximal segment, respectively. The second to fifth metacarpal bones severed in the distal, middle, and proximal ends, respectively, with sharp edges, among which there was exclusively 2-cm-wide skin are connected at the middle part of the palm (Fig. 5.1).