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| 1 | Clinical characterization and diagnosis of cystic fibrosis throughexome sequencing in Chinese infants with Bartter-syndrome-like hypokalemia alkalosis显示文摘Cystic fibrosis (CF) is a fatal autosomal-recessive disease caused by mutations in the CFtransmembrane conductance regulator (CFTR) gene. CF is characterized by recurrent pulmonary infectionwith obstructive pulmonary disease. CF is common in the Caucasian population but is rare in the Chinesepopulation. The symptoms of early-stage CF are often untypical and may sometimes manifest as Bartter syndrome(BS)-like hypokalemic alkalosis. Therefore, the ability of doctors to differentiate CF from BS-like hypokalemicalkalosis in Chinese infants is a great challenge in the timely and accurate diagnosis of CF. In China, sporadic CFhas not been diagnosed in children younger than three years of age to date. Three infants, who were initiallyadmitted to our hospital over the period of June 2013 to September 2014 with BS-like hypokalemic alkalosis, werediagnosed with CF through exome sequencing and sweat chloride measurement. The compound heterozygousmutations of the CFTR gene were detected in two infants, and a homozygous missense mutation was found in oneinfant. Among the six identified mutations, two are novel point mutations (c.1526G 〉 C and c.3062C 〉 T) that arepossibly pathogenic. The three infants are the youngest Chinese patients to have been diagnosed with sporadic CFat a very early stage. Follow-up examination showed that all of the cases remained symptom-free after earlyintervention, indicating the potential benefit of very early diagnosis and timely intervention in children with CF.Our results demonstrate the necessity of distinguishing CF from BS in Chinese infants with hypokalemic alkalosisand the significant diagnostic value of powerful exome sequencing for rare genetic diseases. Furthermore, ourfindings expand the CFTR mutation spectrum associated with CF. | Liru Qiu Fengjie Yang Yonghua He Huiqing Yuan Jianhua Zhou | 2018 | Frontiers of Medicine2018，12，5： | 7 |
| 2 | Experimental study of the wake characteristics of a two-blade horizontal axis wind turbine by time-resolved PIV显示文摘Wind tunnel experiments of the wake characteristics of a two-blade wind turbine, in the downstream region of 0 | ZHANG LiRu XING JiangKuan WANG JianWen YUAN RenYu DONG XueQing MA JianLong LUO Kun QIU KunZan NI MingJiang CEN KeFa | 2017 | Science China(Technological Sciences)2017，60，4： | 4 |
| 3 | A New Childhood Pneumonia Diagnosis Method Based on Fine-Grained Convolutional Neural Network显示文摘Pneumonia is part of the main diseases causing the death of children.It is generally diagnosed through chest Xray images.With the development of Deep Learning(DL),the diagnosis of pneumonia based on DL has received extensive attention.However,due to the small difference between pneumonia and normal images,the performance of DL methods could be improved.This research proposes a new fine-grained Convolutional Neural Network(CNN)for children’s pneumonia diagnosis(FG-CPD).Firstly,the fine-grainedCNNclassificationwhich can handle the slight difference in images is investigated.To obtain the raw images from the real-world chest X-ray data,the YOLOv4 algorithm is trained to detect and position the chest part in the raw images.Secondly,a novel attention network is proposed,named SGNet,which integrates the spatial information and channel information of the images to locate the discriminative parts in the chest image for expanding the difference between pneumonia and normal images.Thirdly,the automatic data augmentation method is adopted to increase the diversity of the images and avoid the overfitting of FG-CPD.The FG-CPD has been tested on the public Chest X-ray 2017 dataset,and the results show that it has achieved great effect.Then,the FG-CPD is tested on the real chest X-ray images from children aged 3–12 years ago from Tongji Hospital.The results show that FG-CPD has achieved up to 96.91%accuracy,which can validate the potential of the FG-CPD. | Yang Zhang Liru Qiu Yongkai Zhu Long Wen Xiaoping Luo | 2022 | Computer Modeling in Engineering & Sciences2022，，12： | 0 |
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