• Journal of Medicine and Life Science
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• v.21 no.2
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• pp.40-48
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• 2024

Understanding the effectiveness of statin treatment is essential for developing tailored stroke prevention strategies. We aimed to evaluate the efficacy of statin treatment in preventing recurrent stroke among patients with various ischemic stroke subtypes. Using data from the Clinical Research Collaboration for Stroke-Korea-National Institute for Health (CRCS-K-NIH) registry, we included patients with acute ischemic stroke admitted between January 2011 and July 2020. To evaluate the differential effects of statin treatment based on the ischemic stroke subtype, we analyzed patients with large artery atherosclerosis (LAA), cardio-embolism (CE), and small vessel occlusion (SVO). The primary outcomes were recurrent ischemic stroke and recurrent stroke events. The hazard ratio for outcomes between statin users and nonusers was compared using a Cox proportional hazards model adjusted for covariates. A total of 46,630 patients who met the inclusion criteria were analyzed. Statins were prescribed to 92%, 93%, and 78% of patients with LAA, SVO, and CE subtypes, respectively. The hazards of recurrent ischemic stroke and recurrent stroke in statin users were reduced to 0.79 (95% confidence interval [CI], 0.63-0.99) and 0.77 (95% CI, 0.62-0.95) in the LAA subtype and 0.63 (95% CI, 0.52-0.76) and 0.63 (95% CI, 0.53-0.75) in CE subtype compared to nonusers. However, the hazards of these outcomes did not significantly decrease in the SVO subtype. The effectiveness of statin treatment in reducing the risk of recurrent stroke in patients with LAA and CE subtypes has been suggested. Nonetheless, no significant effect was observed in the SVO subtype, suggesting a differential effect of statins on different stroke subtypes.

• Economic and Environmental Geology
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• v.46 no.2
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• pp.179-198
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• 2013

The Boziguoer A-type granitoids in Baicheng County, Xinjiang, belong to the northern margin of the Tarim platform as well as the neighboring EW-oriented alkaline intrusive rocks. The rocks comprise an aegirine or arfvedsonite quartz alkali feldspar syenite, an aegirine or arfvedsonite alkali feldspar granite, and a biotite alkali feldspar syenite. The major rock-forming minerals are albite, K-feldspar, quartz, arfvedsonite, aegirine, and siderophyllite. The accessory minerals are mainly zircon, pyrochlore, thorite, fluorite, monazite, bastnaesite, xenotime, and astrophyllite. The chemical composition of the alkaline granitoids show that $SiO_2$ varies from 64.55% to 72.29% with a mean value of 67.32%, $Na_2O+K_2O$ is high (9.85~11.87%) with a mean of 11.14%, $K_2O$ is 2.39%~5.47% (mean = 4.73%), the $K_2O/Na_2O$ ratios are 0.31~0.96, $Al_2O_3$ ranges from 12.58% to 15.44%, and total $FeO^T$ is between 2.35% and 5.65%. CaO, MgO, MnO, and $TiO_2$ are low. The REE content is high and the total ${\sum}REE$ is $(263{\sim}1219){\times}10^{-6}$ (mean = $776{\times}10^{-6}$), showing LREE enrichment HREE depletion with strong negative Eu anomalies. In addition, the chondrite-normalized REE patterns of the alkaline granitoids belong to the "seagull" pattern of the right-type. The Zr content is $(113{\sim}1246){\times}10^{-6}$ (mean = $594{\times}10^{-6}$), Zr+Nb+Ce+Y is between $(478{\sim}2203){\times}10^{-6}$ with a mean of $1362{\times}10^{-6}$. Furthermore, the alkaline granitoids have high HFSE (Ga, Nb, Ta, Zr, and Hf) content and low LILE (Ba, K, and Sr) content. The Nb/Ta ratio varies from 7.23 to 32.59 (mean = 16.59) and the Zr/Hf ratio is 16.69~58.04 (mean = 36.80). The zircons are depleted in LREE and enriched in HREE. The chondrite-normalized REE patterns of the zircons are of the "seagull" pattern of the left-inclined type with strong negative Eu anomaly and without a Ce anomaly. The Boziguoer A-type granitoids share similar features with A1-type granites. The average temperature of the granitic magma was estimated at $832{\sim}839^{\circ}C$. The Boziguoer A-type granitoids show crust-mantle mixing and may have formed in an anorogenic intraplate tectonic setting under high-temperature, anhydrous, and low oxygen fugacity conditions.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.