A simple, organized web-based system improved the transfer efficiency and patient outcomes for endovascular thrombectomy in regional stroke network.

BACKGROUND: Endovascular thrombectomy (EVT) is a time-sensitive treatment for acute ischemic stroke with large vessel occlusion. To optimize transfer efficiency, a web-based platform was introduced in the Tainan Stroke Network (TSN). We assessed its application and effectiveness in regional stroke care. METHOD: This new web-based platform containing a questionnaire-style interface was introduced on October 1, 2021. To assess the transfer efficiency and patient outcomes, acute stroke patients transferred from PSCs to CSC for EVT from April 01, 2020, to December 30, 2022, were enrolled. The patients were classified into the traditional transferal pathway (TTP) group and the new transferal pathway (NTP) group depending on mode of transfer. Patient characteristics, time segments after stroke onset and outcome were compared between groups. RESULT: A total of 104 patients were enrolled, with 77 in the TTP group and 27 in the NTP group. Compared to the TTP group, the NTP group had a significantly shorter onset-to-CSC door time (TTP vs. NTP: 267 vs. 198 min; p = 0.041) and a higher EVT rate (TTP vs. NTP: 18.2% vs. 48.1%, p = 0.002). Among EVT patients, those in the NTP group had a significantly shorter CSC door-to-puncture time (TTP vs. NTP: 131.5 vs. 110 min; p = 0.029). The NTP group had a higher rate of good functional outcomes at 3 months (TTP vs. NTP: 21% vs. 61.5%; p = 0.034). CONCLUSION: This new web-based EVT transfer system provides notable improvements in clinical outcomes, transfer efficiency, and EVT execution for potential EVT candidates without markedly changing the regional stroke care paradigm.

Identification of drug-target interaction from interactome network with 'guilt-by-association' principle and topology features.

MOTIVATION: Identifying drug-target protein interaction is a crucial step in the process of drug research and development. Wet-lab experiment are laborious, time-consuming and expensive. Hence, there is a strong demand for the development of a novel theoretical method to identify potential interaction between drug and target protein. RESULTS: We use all known proteins and drugs to construct a nodes- and edges-weighted biological relevant interactome network. On the basis of the 'guilt-by-association' principle, novel network topology features are proposed to characterize interaction pairs and random forest algorithm is employed to identify potential drug-protein interaction. Accuracy of 92.53% derived from the 10-fold cross-validation is about 10% higher than that of the existing method. We identify 2272 potential drug-target interactions, some of which are associated with diseases, such as Torg-Winchester syndrome and rhabdomyosarcoma. The proposed method can not only accurately predict the interaction between drug molecule and target protein, but also help disease treatment and drug discovery. CONTACTS: zhanchao8052@gmail.com or ceszxy@mail.sysu.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Large-scale identification of potential drug targets based on the topological features of human protein-protein interaction network.

Identifying potential drug target proteins is a crucial step in the process of drug discovery and plays a key role in the study of the molecular mechanisms of disease. Based on the fact that the majority of proteins exert their functions through interacting with each other, we propose a method to recognize target proteins by using the human protein-protein interaction network and graph theory. In the network, vertexes and edges are weighted by using the confidence scores of interactions and descriptors of protein primary structure, respectively. The novel network topological features are defined and employed to characterize protein using existing databases. A widely used minimum redundancy maximum relevance and random forests algorithm are utilized to select the optimal feature subset and construct model for the identification of potential drug target proteins at the proteome scale. The accuracies of training set and test set are 89.55% and 85.23%. Using the constructed model, 2127 potential drug target proteins have been recognized and 156 drug target proteins have been validated in the database of drug target. In addition, some new drug target proteins can be considered as targets for treating diseases of mucopolysaccharidosis, non-arteritic anterior ischemic optic neuropathy, Bernard-Soulier syndrome and pseudo-von Willebrand, etc. It is anticipated that the proposed method may became a powerful high-throughput virtual screening tool of drug target.

[Study on the relations between homodynamic parameters and structure of common carotid artery among the Ethnic She Population, China].

OBJECTIVE: To study the changes of intima-media thickness (IMT) in ambi-common carotid arteries (ambi-CCA) and how they correlated with factors related to quality intima-media thickness (QIMT). METHODS: According to the Chinese Arterial Stiffness Evaluation (CASE) project, the IMT of (ambi-CCA) was measured by QIMT and 2-D ultrasound respectively in 433 She people aged 15 - 87 (mean 49.03 ± 13.54). Difference and tendency were analyzed on age, gender, body mass index (BMI), pulse pressure (PP), total cholesterol (TC), and triglyceride. The whole population was classified into 3 groups by tertiles of pulse pressure. RESULTS: (1) Significant positive correlations were found between ambi-CCA IMT and pulse pressure (P < 0.01). There was no significant difference between tertile1 and tertile 2 of IMT in the left CCA (P > 0.05) found, but with significant difference among the tertile groups, respectively (P < 0.05). There were significant differences among the three groups of IMT in the right CCA, respectively (P < 0.01). (2) The regression factors of IMT in left CCA were age, pulse pressure, weight, LDL-C, blood glucose (BG), TG, and their regression equation was LCC-IMT = 32.61 + 4.29 (age) + 1.77 (PP) + 1.87 (weight) + 16.52 (LDL-C) + 11.77 (BG) - 9.92 (TG), with r = 0.663 and r(2) = 0.44, (P < 0.001). The regression factors of IMT in right CCA were age, PP, height and their regression equation was RCC-IMT = 5.19 (age) + 1.61 (PP) + 2.62 (height) - 219.36, with r = 0.636 and r(2) = 0.41 (P < 0.001). CONCLUSION: There were differences seen on IMT of CCA in the PP and position and were correlated with age, PP, body weight, LDL-C, BG, TC and body height. The difference of ambi-CCA should be called for attention.