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154. Increased Apelin following Bone Marrow Mononuclear Cell Transplantation Contributes to the Improvement of Cardiac Function in Patients with Severe Heart Failure

Author

Lian Ru Gao, Ru Yi Xu, Ning Kun Zhang, Yu Chen, Zhi Guo Wang, Zhi Ming Zhu, Yu Xing Fei, Yi Cao, Hong Tao Xu, and Ye Yang

Subjects
Medicine
Abstract

We previously reported that intracoronary implantation of bone marrow mononuclear cells (BMMC) into ischemic hearts improved cardiac function after myocardial infarction. However, the mechanisms have not been elucidated. The present study investigates whether apelin, a newly described inotropic peptide with important cardiovascular regulatory properties, contributes to the functional improvement in patients with severe heart failure after cell transplantation. Forty consecutive patients with severe heart failure secondary to myocardial infarction were assigned to the BMMC therapy group or the standard medication group according to each patient's decision on a signed consent document. In 20 patients intracoronary cell infusion was performed, and another 20 patients were matched to receive standard medication as therapeutic controls. An additional 20 healthy subjects were designated as normal controls. Clinical manifestations, echocardiograms, and biochemical assays were recorded. Plasma apelin and brain natriuretic protein (BNP) levels were determined by enzyme immunoassay. Baseline levels of plasma apelin were significantly lower in all heart failure patients compared to normal subjects. In patients who underwent cell transplantation, apelin increased significantly from 3 to 21 days after operation, followed by significant improvement in cardiac function. In parallel, BNP varied inversely with the increase of apelin. In patients receiving standard medical treatment, apelin remained at a lower level. Our findings indicated that increased apelin levels following cell therapy may act as a paracrine mediator produced from BMMCs and play an important role in the treatment of heart failure through autocrine and paracrine mechanisms.

Published
2009
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184. Propagation dynamics of abruptly autofocusing circular Airy-Gaussian vortex beams in the fractional Schr\'odinger equation

Author

He, Shangling, Malomed, Boris A., Mihalache, Dumitru, Peng, Xi, Yu, Xing, He, Yingji, and Deng, Dongmei

Subjects
Nonlinear Sciences - Pattern Formation and Solitons, Physics - Optics
Abstract

We introduce axisymmetric Airy-Gaussian vortex beams in a model of an optical system based on the (2+1)-dimensional fractional Schr\"odinger equation, characterized by its L\'evy index (LI). By means of numerical methods, we explore propagation dynamics of the beams with vorticities from 0 to 4. The propagation leads to abrupt autofocusing, followed by its reversal (rebound from the center). It is shown that LI, the relative width of the Airy and Gaussian factors, and the vorticity determine properties of the autofocusing dynamics, including the focusing distance, radius of the focal light spot, and peak intensity at the focus. A maximum of the peak intensity is attained at intermediate values of LI, close to LI=1.4 . Dynamics of the abrupt autofocusing of Airy-Gaussian beams carrying vortex pairs (split double vortices) is considered too., Comment: to be published in Chaos, Solitons & Fractals

Published
2020

189. COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature

Author

Peng, Yifan, Tang, Yu-Xing, Lee, Sungwon, Zhu, Yingying, Summers, Ronald M., and Lu, Zhiyong

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. We also designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared clinical symptoms and clinical findings of COVID-19 vs. those of influenza to demonstrate the disease differences in the scientific publications. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR., Comment: Accepted by IEEE Transactions on Big Data

Published
2020

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