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( Xia Gao ),( Xu Ping Fu ),( Tao Li ),( Jian Zi ),( Yao Luo ),( Qing Wei ),( Er Liang Zeng ),( Yi Xie ),( Yao Li ),( Yu Min Mao ) 생화학분자생물학회 2003 BMB Reports Vol.36 No.6
In microarray data mining, one of the key problems is how to handle weak signals. Based on a bent piecewise linear accumulated distribution generally found in the microarray data, a new detectable threshold finding method is proposed to filter genes with unreliable information in this paper. More reliable and reproducible data is produced for the subsequent data mining.
Gen-Xia Liu,Shu Ma,Yao Li,Yan Yu,Yi-Xiang Zhou,Ya-Die Lu,Lin Jin,Zi-Lu Wang,Jin-Hua Yu 생화학분자생물학회 2018 Experimental and molecular medicine Vol.50 No.-
The putative tumor suppressor microRNA let-7c is extensively associated with the biological properties of cancer cells. However, the potential involvement of let-7c in the differentiation of mesenchymal stem cells has not been fully explored. In this study, we investigated the influence of hsa-let-7c (let-7c) on the proliferation and differentiation of human dental pulp-derived mesenchymal stem cells (DPMSCs) treated with insulin-like growth factor 1 (IGF-1) via flow cytometry, CCK-8 assays, alizarin red staining, real-time RT-PCR, and western blotting. In general, the proliferative capabilities and cell viability of DPMSCs were not significantly affected by the overexpression or deletion of let-7c. However, overexpression of let-7c significantly inhibited the expression of IGF-1 receptor (IGF-1R) and downregulated the osteo/odontogenic differentiation of DPMSCs, as indicated by decreased levels of several osteo/odontogenic markers (osteocalcin, osterix, runt-related transcription factor 2, dentin sialophosphoprotein, dentin sialoprotein, alkaline phosphatase, type 1 collagen, and dentin matrix protein 1) in IGF-1-treated DPMSCs. Inversely, deletion of let- 7c resulted in increased IGF-1R levels and enhanced osteo/odontogenic differentiation. Furthermore, the ERK, JNK, and P38 MAPK pathways were significantly inhibited following the overexpression of let-7c in DPMSCs. Deletion of let-7c promoted the activation of the JNK and P38 MAPK pathways. Our cumulative findings indicate that Let-7c can inhibit the osteo/odontogenic differentiation of IGF-1-treated DPMSCs by targeting IGF-1R via the JNK/P38 MAPK signaling pathways.
Gao, Xia,Fu, Xuping,Li, Tao,Zi, Jian,Luo, Yao,Wei, Qing,Zeng, Erliang,Xie, Yi,Li, Yao,Mao, Yumin 생화학분자생물학회 2003 Journal of biochemistry and molecular biology Vol.36 No.6
In microarray data mining, one of the key problems is how to handle weak signals. Based on a bent piecewise linear accumulated distribution generally found in the microarray data, a new detectable threshold finding method is proposed to filter genes with unreliable information in this paper. More reliable and reproducible data is produced for the subsequent data mining.
Zhou Qing-Qing,Wang Jiashuo,Tang Wen,Hu Zhang-Chun,Xia Zi-Yi,Xue-Song Li,Zhang Rongguo,Yin Xindao,Zhang Bing,Zhang Hong 대한영상의학회 2020 Korean Journal of Radiology Vol.21 No.7
Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists’ workload.