Common Prothrombotic Gene Mutations in Cerebral Venous Sinus Thrombosis in North-West of Iran

Objective: Cerebral venous sinus thrombosis (CVST) is a life-threatening cerebrovascular disease which has high prevalence and mortality rate in Iran. Thrombophilia caused by gene mutation is a common cause of CVST. The present study aimed at assessing the prevalence of thrombophilic gene mutations in Iranian CVST patients and then comparing it with normal population. Materials and methods: In a case-control study, polymerase chain reaction-restriction fragment length polymorphism (PCR_RFLP) and amplification-refractory mutation system (ARMS-PCR) were carried out to detect common thrombophilic mutations in 70 CVST patients. Next, it was compared with 82 sex- and age-matched healthy controls. Results: Factor-V-Leiden, Factor-V-Leiden HR2, Factor prothrombin II, MTHFR (667C/T) and MTHFR (1298A/C) prevalence were significantly high in cases of CVST as compared to the controls (P values: 0.012, 0.019, 0.007 and 0.036, respectively). However, there was no significant difference between the two groups in plasminogen activator inhibitor (PAI), angiotensin-converting enzyme (ACE), beta-fibrinogen (FGB), Factor VIII, Factor XIII, and tissue plasminogen activator (tPA) mutations. Conclusion: The findings of the present study suggest that Factor V-Leiden, Factor-V-Leiden HR2, prothrombin II (G20210A), and MTHFR (667C/T & 1298A/C) mutations are more frequent in CVST. Detection of these mutations may help clinicians to decide on the duration of treatment and referral to genetic counseling for valuable prevention.

An ensemble method for data stream classification in the presence of concept drift

One recent area of interest in computer science is data stream management and processing. By ‘data stream', we refer to continuous and rapidly generated packages of data. Specific features of data streams are immense volume, high production rate, limited data processing time, and data concept drift; these features differentiate the data stream from standard types of data. An issue for the data stream is classification of input data. A novel ensemble classifier is proposed in this paper. The classifier uses base classifiers of two weighting functions under different data input conditions. In addition, a new method is used to determine drift, which emphasizes the precision of the algorithm. Another characteristic of the proposed method is removal of different numbers of the base classifiers based on their quality. Implementation of a weighting mechanism to the base classifiers at the decision-making stage is another advantage of the algorithm. This facilitates adaptability when drifts take place, which leads to classifiers with higher efficiency. Furthermore, the proposed method is tested on a set of standard data and the results confirm higher accuracy compared to available ensemble classifiers and single classifiers. In addition, in some cases the proposed classifier is faster and needs less storage space.