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author:("Li, haiyuan")
1.  A geometric scaling model for assessing the impact of aneurysm size ratio on hemodynamic characteristics 
The intracranial aneurysm (IA) size has been proved to have impacts on the hemodynamics and can be applied for the prediction of IA rupture risk. Although the relationship between aspect ratio and hemodynamic parameters was investigated using real patients and virtual models, few studies focused on longitudinal experiments of IAs based on patient-specific aneurysm models. We attempted to do longitudinal simulation experiments of IAs by developing a series of scaled models.
In this work, a novel scaling approach was proposed to create IA models with different aneurysm size ratios (ASRs) defined as IA height divided by average neck diameter from a patient-specific aneurysm model and the relationship between the ASR and hemodynamics was explored based on a simulated longitudinal experiment. Wall shear stress, flow patterns and vessel wall displacement were computed from these models. Pearson correlation analysis was performed to elucidate the relationship between the ASR and wall shear stress. The correlation of the ASR and flow velocity was also computed and analyzed.
The experiment results showed that there was a significant increase in IA area exposed to low WSS once the ASR > 0.7, and the flow became slower and the blood was more difficult to flow into the aneurysm as the ASR increased. Meanwhile, the results also indicated that average blood flow velocity and WSS had strongly negative correlations with the ASR (r = −0.938 and −0.925, respectively). A narrower impingement region and a more concentrated inflow jet appeared as the ASR increased, and the large local deformation at aneurysm apex could be found as the ASR >1.7 or 0.7 < the ASR <1.0.
Hemodynamic characteristics varied with the ASR. Besides, it is helpful to further explore the relationship between morphologies and hemodynamics based on a longitudinal simulation by building a series of patient-specific aneurysm scaled models applying our proposed IA scaling algorithm.
PMCID: PMC3930299  PMID: 24528952
Intracranial aneurysm; Scaled models; Aneurysm Size Ratio; Hemodynamics
2.  HLA-DRB1 Shared Epitope-Dependent DR-DQ Haplotypes Are Associated with Both Anti-CCP–Positive and –Negative Rheumatoid Arthritis in Chinese Han 
PLoS ONE  2013;8(8):e71373.
The association between Human Leukocyte Antigen (HLA) class II and rheumatoid arthritis (RA) has been extensively studied, but few reported DR-DQ haplotype. Here we investigated the association of HLA-DRB1, DQA1, DQB1, and DR-DQ haplotypes with RA susceptibility and with anti-CCP antibodies in 281 RA patients and 297 control in Han population. High-resolution genotyping were performed. The HLA-DRB1 shared epitope (SE)-encoding allele *0405 displayed the most significant RA association (P = 1.35×10−6). The grouped DRB1 SE alleles showed great association with RA (P = 3.88×10−13). The DRB1 DRRAA alleles displayed significant protective effects (P = 0.021). The SE-dependent DR-DQ haplotype SE-DQ3/4/5 remained strong association with both anti-CCP -positive (P = 3.71×10−13) and -negative RA (P = 3.89×10−5). Our study revealed that SE alleles and its haplotypes SE-DQ3/4/5 were highly associated with RA susceptibility in Han population. The SE-DQ3/4/5 haplotypes were associated with both anti-CCP positive RA and -negative RA.
PMCID: PMC3741114  PMID: 23951149
3.  An improved level set method for vertebra CT image segmentation 
Clinical diagnosis and therapy for the lumbar disc herniation requires accurate vertebra segmentation. The complex anatomical structure and the degenerative deformations of the vertebrae makes its segmentation challenging.
An improved level set method, namely edge- and region-based level set method (ERBLS), is proposed for vertebra CT images segmentation. By considering the gradient information and local region characteristics of images, the proposed model can efficiently segment images with intensity inhomogeneity and blurry or discontinuous boundaries. To reduce the dependency on manual initialization in many active contour models and for an automatic segmentation, a simple initialization method for the level set function is built, which utilizes the Otsu threshold. In addition, the need of the costly re-initialization procedure is completely eliminated.
Experimental results on both synthetic and real images demonstrated that the proposed ERBLS model is very robust and efficient. Compared with the well-known local binary fitting (LBF) model, our method is much more computationally efficient and much less sensitive to the initial contour. The proposed method has also applied to 56 patient data sets and produced very promising results.
An improved level set method suitable for vertebra CT images segmentation is proposed. It has the flexibility of segmenting the vertebra CT images with blurry or discontinuous edges, internal inhomogeneity and no need of re-initialization.
PMCID: PMC3701568  PMID: 23714300
Level set method; Image segmentation; Vertebra CT images
4.  An automated and simple method for brain MR image extraction 
The extraction of brain tissue from magnetic resonance head images, is an important image processing step for the analyses of neuroimage data. The authors have developed an automated and simple brain extraction method using an improved geometric active contour model.
The method uses an improved geometric active contour model which can not only solve the boundary leakage problem but also is less sensitive to intensity inhomogeneity. The method defines the initial function as a binary level set function to improve computational efficiency. The method is applied to both our data and Internet brain MR data provided by the Internet Brain Segmentation Repository.
The results obtained from our method are compared with manual segmentation results using multiple indices. In addition, the method is compared to two popular methods, Brain extraction tool and Model-based Level Set.
The proposed method can provide automated and accurate brain extraction result with high efficiency.
PMCID: PMC3180437  PMID: 21910906
5.  4-(2-Cyano­ethyl­sulfan­yl)-5′-(pyridin-4-yl)tetra­thia­fulvalene 
In the title compound, C14H10N2S5 [systematic name; 3-({2-[4-(pyridin-4-yl)-2H-1,3-dithiol-2-yl­idene]-2H-1,3-dithiol-4-yl}sul­fan­yl)propane­nitrile], all of the non-H atoms except for the cyano­ethyl­sulfanyl group, are approximately coplanar [maxium deviation = 0.090 (3) Å]. The two five-membered 1,3-dithiole rings are twisted by 2.6 (2)°. Weak inter­molecular S⋯S inter­actions occur [3.586 (4) and 3.530 (4) Å].
PMCID: PMC3120500  PMID: 21754838
6.  Novel Natural Inhibitors of CYP1A2 Identified by in Silico and in Vitro Screening 
Inhibition of cytochrome P450 (CYP) is a major cause of herb–drug interactions. The CYP1A2 enzyme plays a major role in the metabolism of drugs in humans. Its broad substrate specificity, as well as its inhibition by a vast array of structurally diverse herbal active ingredients, has indicated the possibility of metabolic herb–drug interactions. Therefore nowadays searching inhibitors for CYP1A2 from herbal medicines are drawing much more attention by biological, chemical and pharmological scientists. In our work, a pharmacophore model as well as the docking technology is proposed to screen inhibitors from herbal ingredients data. Firstly different pharmaphore models were constructed and then validated and modified by 202 herbal ingredients. Secondly the best pharmaphore model was chosen to virtually screen the herbal data (a curated database of 989 herbal compounds). Then the hits (147 herbal compounds) were continued to be filtered by a docking process, and were tested in vitro successively. Finally, five of eighteen candidate compounds (272, 284, 300, 616 and 817) were found to have inhibition of CYP1A2 activity. The model developed in our study is efficient for in silico screening of large herbal databases in the identification of CYP1A2 inhibitors. It will play an important role to prevent the risk of herb–drug interactions at an early stage of the drug development process.
PMCID: PMC3116189  PMID: 21686183
CYP1A2; pharmacophore; docking; herb–drug interaction

Results 1-6 (6)