|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | A modified particle swarm optimization algorithm for optimal allocation of earthquake emergency shelters显示文摘 | Fuyu Hu Wei Xu Xia Li | 2012 | International Journal of Geographical Information Science2012，，9： | 1 |
| 2 | 不同胆管空肠吻合技术治疗成人Ⅰ型胆总管囊肿的比较分析显示文摘目的:比较囊肿完全切除与不完全切除(残留近端1 cm胆总管囊壁)的Roux-en-Y胆管空肠吻合治疗成人Ⅰ型胆总管囊肿的安全性和可行性。方法:回顾性分析1998年1月至2015年12月间收治的267例成人Ⅰ型胆总管囊肿患者的病例资料,其中171例行囊肿完全切除的Rouxen-Y胆管空肠吻合术(PBD 0-cm组),96例行囊肿不完全切除的Rouxen-Y胆管空肠吻合术(PBD 1-cm组)。比较两组患者术后短期及长期并发症发生情况。结果:两组患者在手术时间和吻合口直径方面的差异均无统计学意义。PBD 1-cm组围手术期并发症发生率显著高于PBD 0-cm组(28.1%vs 14.0%,P=0.005),尤其是术后胆管炎(7.3%vs 1.2%,P=0.021)。两组患者术后吻合口狭窄、反流性胆管炎、肝内胆管结石和胆漏等长期并发症的差异均无统计学意义(均P>0.05)。PBD 1-cm组和PBD 0-cm组各有1例患者分别于术后5月和术后25月出现胰胆管恶变。而PBD 1-cm组有1例患者于术后10月出现吻合口恶变。结论:虽然保留部分囊壁可以降低Ⅰ型胆总管囊肿切除术中的吻合难度,但由于相对较高的术后并发症发生率和恶变概率,该吻合方式值得商榷。对于Ⅰ型胆总管囊肿患者,囊肿完全切除后以健康的近端胆管进行吻合是必要的。 | Wenjie Ma Yongqiong Tan Anuj Shrestha Fuyu Li Rongxing Zhou Junke Wang Haijie Hu Qin Yang | 2018 | Gastroenterology Report2018，6，1： | 0 |
| 3 | Urine biomarkers discovery by metabolomics and machine learning for Parkinson’s disease diagnoses显示文摘Parkinson’s disease(PD)is a complex neurological disorder that typically worsens with age.A wide range of pathologies makes PD a very heterogeneous condition,and there are currently no reliable diagnostic tests for this disease.The application of metabolomics to the study of PD has the potential to identify disease biomarkers through the systematic evaluation of metabolites.In this study,urine metabolic profiles of 215 urine samples from 104 PD patients and 111 healthy individuals were assessed based on liquid chromatography-mass spectrometry.The urine metabolic profile was first evaluated with partial leastsquares discriminant analysis,and then we integrated the metabolomic data with ensemble machine learning techniques using the voting strategy to achieve better predictive performance.A combination of 8-metabolite predictive panel performed well with an accuracy of over 90.7%.Compared to control subjects,PD patients had higher levels of 3-methoxytyramine,N-acetyl-l-tyrosine,orotic acid,uric acid,vanillic acid,and xanthine,and lower levels of 3,3-dimethylglutaric acid and imidazolelactic acid in their urine.The multi-metabolite prediction model developed in this study can serve as an initial point for future clinical studies. | Xiaoxiao Wang Xinran Hao Jie Yan Ji Xu Dandan Hu Fenfen Ji Ting Zeng Fuyue Wang Bolun Wang Jiacheng Fang Jing Ji Hemi Luan Yanjun Hong Yanhao Zhang Jinyao Chen Min Li Zhu Yang Doudou Zhang Wenlan Liu Xiaodong Cai Zongwei Cai | 2023 | Chinese Chemical Letters2023，34，10： | 0 |
| 4 | Relational Topology-based Heterogeneous Network Embedding for Predicting Drug-Target Interactions显示文摘Predicting interactions between drugs and target proteins has become an essential task in the drug discovery process.Although the method of validation via wet-lab experiments has become available,experimental methods for drug-target interaction(DTI)identification remain either time consuming or heavily dependent on domain expertise.Therefore,various computational models have been proposed to predict possible interactions between drugs and target proteins.However,most prediction methods do not consider the topological structures characteristics of the relationship.In this paper,we propose a relational topologybased heterogeneous network embedding method to predict drug-target interactions,abbreviated as RTHNE_DTI.We first construct a heterogeneous information network based on the interaction between different types of nodes,to enhance the ability of association discovery by fully considering the topology of the network.Then drug and target protein nodes can be represented by the other types of nodes.According to the different topological structure of the relationship between the nodes,we divide the relationship in the heterogeneous network into two categories and model them separately.Extensive experiments on the realworld drug datasets,RTHNE_DTI produces high efficiency and outperforms other state-of-the-art methods.RTHNE_DTI can be further used to predict the interaction between unknown interaction drug-target pairs. | Linlin Zhang Chunping Ouyang Fuyu Hu Yongbin Liu Zheng Gao | 2023 | Data Intelligence2023，5，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员