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ObjectiveTo evaluate the effect of Shengmaisan granules on myocardial fibrosis in chronic heart failure patients with Qi-Yin deficiency syndrome by cardiac magnetic resonance （CMR） imaging and serological indicators. MethodSixty-six chronic heart failure patients with Qi-Yin deficiency syndrome who visited the Affiliated Hospital of Integrated Traditional Chinese and Western Medicine， Nanjing University of Chinese Medicine from October 2021 to January 2023 were selected. The patients were assigned into a control group （33 cases） and an observation group （33 cases） by the minimization random method. Both groups received standardized Western medicine treatment for heart failure. In addition， the control group was treated with placebo granules， and the observation group with Shengmaisan granules for a course of 6 months. The baseline data， clinical efficacy， TCM symptom scores， serological indicators ［high-sensitivity C-reactive protein （hs-CRP）， soluble growth stimulation expressed gene 2 protein （sST2）， pro-collagen Ⅲ N-terminal peptide （PⅢNP）， interleukin （IL）-6， IL-11， transforming growth factor-β1 （TGF-β1）］， echocardiography ［Left atrial diameter （LAD）， left ventricular end systolic diameter （LVEDs）， left ventricular end diastolic diameter （LVEDd）］ and CMR indicators ［left ventricular ejection fraction （LVEF）， myocardial extracellular volume fraction （ECV）， and longitudinal relaxation time （T1）］ were compared between the two groups. ResultFinally， 31 patients in the control group and 30 patients in the observation group were included. There was no significant difference in baseline data or indicators between the two groups before treatment. Compared with those before treatment， the scores of TCM symptoms （shortness of breath， fatigue， palpitations， spontaneous or night sweats， thirst/dry throat， feverish feeling in palms and soles， and edema in lower limbs）， total score of TCM symptoms， ECV， T1， inflammation/fibrosis indicators （hs-CRP， sST2， PⅢNP， IL-6， IL-11， and TGF-β1） in observation group decreased （P<0.05， P<0.01）， and the scores of TCM symptoms （except feverish feeling in palms and soles）， T1， and inflammation/fibrosis indicators in the control group decreased （P<0.05， P<0.01）. After treatment， the observation group had lower scores of TCM symptoms （except feverish feeling in palms and soles and edema in lower limbs）， ECV， T1， and inflammation/fibrosis indicators than the control group （P<0.05， P<0.01）. After treatment， the total response rate in the observation group was 93.33% （28/30）， which was higher than that （80.65%， 25/31） in the control group （Z=2.976， P<0.01）. There was no significant difference in adverse reactions between the two groups during treatment. ConclusionFor patients with chronic heart failure with Qi-Yin deficiency syndrome， Shengmaisan Granules can alleviate the TCM symptoms， reduce inflammation， and inhibit myocardial fibrosis by regulating the TGF-β1/IL-11 signaling axis.
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Objective:To analyze the distribution and composition characteristics of jellyfish stings in various coastal baths in Qinhuangdao City from 2017 to 2019, and to provide scientific basis for the prevention, control and early warning of jellyfish stings.Methods:Statistics and analysis of the age, gender, time of stings, location of injury, first symptoms, and playing time in the sea at the time of the sting, etc. of people with jellyfish stings in various bathing beaches along the coast of Qinhuangdao from 2017 to 2019 (July to August) were conducted.Results:The number of jellyfish stings in the coastal bathing beaches of Qinhuangdao City in 2017, 2018, and 2019 was decreasing year by year, with 1 890, 492, and 171 cases respectively. Among them, Qianshuiwan Bathing Beach and Dongshan Bathing Beach had more stings (60.90% and 35.08% respectively in 2017, 24.39% and 64.23% respectively in 2018, 16.96% and 16.42% respectively in 2019). There was no significant change in the gender and age distribution of jellyfish stings each year [57.99% males in 2017, with a median age of 13 (8, 31) years old; 63.21% males in 2018, with a median age of 25 (8, 29) years old; and 59.65% males in 2019, with a median age of 12 (7, 31) years old]. Stings were mainly located at the lower limbs (the proportion of lower limb injuries: 46.54% in 2018, 45.61% in 2019), followed by upper limbs (upper arm, elbow, forearm), trunk, etc. The first symptom was mainly pain (89.43% in 2018, 38.29% in 2019), followed by rash (64.43% in 2018, 59.43% in 2019), numbness, blisters, etc. Sting incidents mainly occurred from 13:00 to 17:59 (the proportion of sting incidents in this time period in 2018 and 2019 were 68.09% and 52.63%, respectively).Conclusions:Jellyfish stings in coastal baths in Qinhuangdao City are mainly distributed in Qianshuiwan Baths and Dongshan Baths. The management of these sea areas should be strengthened, and scientific publicity and medical rescue should be strengthened to prevent jellyfish stings in peak hours and related baths.
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BackgroundA novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Materials and MethodsWe collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19. ResultsThe experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019. ConclusionsThe established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.
