检索结果-南通市图书馆

Relationships between clozapine and norclozapine plasma concentrations, clozapine dose, and clinical response in Tunisian patients with schizophrenia-treatment resistance

引用

Open Journal of Psychiatry 2012年第4期2卷 262-268页

作者： aida Taieb Fatma B’chir Roland Molinié José-Edmundo Nava-Saucedo Jaafer Nakhli Marc-André Fliniaux Bechir ben Hadj Ali Saad Saguem Laboratory of Natural Substances National Institute of Research and Physical Chemical Analysis Technopole Sidi Thabet Metabolic Biophysics and Applied Pharmacology Laboratory Department of Biophysics Medicine Faculty of Sousse Sousse Tunisia Psychiatry Department of Central Hospital University (CHU) Sousse Tunisia Unit of Plant Biology and Insect Pests University of Picardy Jules Verne Amiens France

The present study investigated relationships between clozapine dose, clozapine and norclozapine plasma concentrations, and clinical responses to clozapine treatment in Tunisian schizophrenics. Fourteen schizophrenia-treatment resistant patients, recruited for this study, were treated with clozapine for 45 days. Patient health improvement was assessed before and after each cycle of two weeks of clozapine therapy, using the Brief Psychiatric Rating Scale (BPRS). Plasma clozapine and norclozapine concentrations were determined by high-performance liquid chromatography (HPLC). No significant correlations between plasma clozapine and norclozapine concentrations and clinical health improvement among our schizophrenic patients were found. However, a significant correlation was observed between clinical health improvement given by BPRS scores and norclozapine plasma concentration to daily clozapine dose ratio (NCZ/D). Despite the small sample size of our study, our findings suggest that the clozapine therapy response variations observed in our patients may be, in part, explained by the interindividual differences in plasma norclozapine concentration to clozapine dose ratio (NCZ/D). So the NCZ/D parameter could be used as a good indicator for adjusting the clozapine dose-adaptation strategy and consequently for improving the clinical psychopathological state of schizophrenia-treatment resistant patients.

关键词： Clozapine Norclozapine HPLC Tunisian Schizophrenic BPRS

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

引用

Journal of Intelligent Learning Systems and Applications 2024年第2期16卷 53-79页

作者： Aws I. Abu Eid Achraf ben Miled Ahlem Fatnassi Majid A. Nawaz Ashraf F. A. Mahmoud Faroug A. Abdalla Chams Jabnoun aida Dhibi Firas M. Allan Mohammed Ahmed Elhossiny Salem Belhaj Imen ben Mohamed Faculty of Computing Studies Arab Open University Amman Jordan Computer Science Department Science College Northern Border University Arar Kingdom of Saudi Arabia Artificial Intelligence and Data Engineering Laboratory LR21ES23 Faculty of Sciences of Bizerte University of Carthage Tunis Tunisia Applied College Northern Border University Arar Kingdom of Saudi Arabia Faculty of Specific Education Mansoura University Mansoura Egypt

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

关键词： Artificial Intelligence Machine Learning Spark Apache Big Data SAIM

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：