国际贸易实务-文献订阅-三峡大学图书馆

摘要： Objective: To examine the utility of decision analysis methods for examining the dynamic nature of impairment-activity interactions following stroke. Design: Decision analyses (Chi-squared Automatic Interaction Detector) of a prospective cohort study. Setting: Community and institutional settings based on the location of participants three months after stroke. Participants: Individuals were recruited from consecutive admissions to a regional academic health center and were assessed three months after stroke (N = 67). Main outcome measures: Neurological impairment was measured with the National Institutes of Health Stroke Scale (NIHSS). Mobility, self-care and instrumental activities of daily living (instrumental ADL) performance were assessed with a performance observation measure, the Performance Assessment of Self-care Skills (PASS). Decision analysis methods were used to examine interactions between neurological impairments and activity outcomes. Results: Unique neurological impairments were associated with each activity outcome (bowel and bladder urgency interacted with mobility;hand function interacted with self-care;mental functions interacted with instrumental ADL), and these findings are supported by previous studies. The predictive validity of mobility and self-care analyses was stronger than the instrumental ADL analyses. Conclusions: Decision analysis methods show promise for understanding dynamic impairment-activity interactions. This understanding may enhance methods for informing rehabilitation decisions.

作为在宫颈癌筛查中3项检验之一的人乳头瘤病毒检查的作用:研究前后的观察

Moss S Gray A Legood R 朱磊

Cancer Screening Evaluation Unit Institute of Cancer Research Sutton Surrey SM2 5NG United Kingdom

来源详细信息

关键词： 宫颈癌筛查人乳头瘤病毒阴道镜检查细胞学异常液基细胞学检查宫颈病变筛查宫颈筛查宫颈涂片

摘要： To assess the effect of introducing testing for human papillomavirus combined with liquid based cytology in women with low grade cytological abnormalities. Design: Observational before and after study. Setting: Three cervical screening laboratories, England. Participants: 5654 women aged 20- 64 with low grade cytological abnormalities found at routine cervical screening in a pilot; 5254 similar women in the period before the pilot. Interventions: Human papillomavirus testing combined with liquid based cytology in the management of women with borderline or mildly dyskaryotic cervical smear results compared with conventional smear tests, with immediate referral to colposcopy of women positive for human papillomavirus. Results: 57.9% (3187/5506) of women tested in the pilot were positive for human papillomavirus. The rate of repeat smears fell by 74% , but the rate of referral to colposcopy for low grade cytological abnormalities more than doubled. The estimated negative predictive value of human papillomavirus testing varied between 93.8% and 99.7% . Conclusion: The addition of testing for human papillomavirus in women with low grade cytological abnormalities resulted in a reduction in the rate of repeat smears, but an increase in rates of referral to colposcopy.

Use of extreme weight control behaviours with and without binge eating in a community sample: Implications for the classification of bulimic-type eating disorders

Mond, J Hay, P Rodgers, B Owen, C Crosby, R Mitchell, J

Neuropsychiat Res Inst Fargo ND 58103 USAJames Cook Univ Sch Med Discipline Psychiat Townsville Qld AustraliaAustralian Natl Univ Natl Ctr Epidemiol & Populat Hlth Canberra ACT AustraliaAustralian Natl Univ Sch Med Med Educ Unit Canberra ACT Australia

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关键词： bulimia nervosa classification functional impairment

摘要： Objective and Method: To inform the classification of bulimic-type eating disorders not meeting formal diagnostic criteria for bulimia nervosa (BN), levels of eating disorder psychopathology and functional impairment associated with subjective and objective bulimic episodes (SBEs and OBEs) and purging and nonpurging methods of weight control were examined in a large community-based sample of women (n = 5,232). Results: Participants who reported recurrent bulimic episodes had significantly higher levels of eating disorder psychopathology and functional impairment than those who did not and this was the case whether the episodes were objective or subjective. Similarly, participants who reported the use of extreme weight control behaviors had higher levels of eating disorder psychopathology and functional impairment than those who did not, and this was the case whether purging or nonpurging behaviors were employed. The combination of bulimic episodes and extreme weight control behaviors was associated with particularly high levels of eating disorder psychopathology and functional impairment. Conclusion: The combination of bulimic episodes, objective or subjective, and extreme weight control behaviors, purging or nonpurging, is significant in terms of impairment in psychosocial functioning among individuals affected by eating disorders not meeting formal diagnostic criteria for BN. The combination of SBEs and extreme weight control behaviors, in particular, warrants further investigation. (c) 2006 by Wiley Periodicals, Inc.

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

Zhang, XG Lu, X Shi, Q Xu, XQ Leung, HCE Harris, LN D Iglehart, J Miron, A Liu, JS Wong, WH

Tsinghua Univ Bioinformat Div TNLIST Beijing 100084 Peoples R ChinaTsinghua Univ Dept Automat Beijing 100084 Peoples R ChinaHarvard Univ Sch Publ Hlth Dept Biostat Boston MA 02115 USADana Farber Canc Inst Dept Canc Biol Boston MA 02115 USAGenome Inst Singapore Med Proteom & Bioanal Sect Singapore SingaporeHarvard Univ Dept Stat Cambridge MA 02138 USAStanford Univ Dept Stat Stanford CA 94305 USA

来源 BioMed Central期刊

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关键词： Feature Selection Informative Gene Recursive Feature Elimination Outlier Sample Feature Selection Step

摘要： Background: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. Results: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5%-similar to 20% improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. Conclusion: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.

学习向量量化神经网络用于胃癌组织样品分类识别的研究

童义平林燕文

韩山师范学院化学系广东潮州521041韩山师范学院生物系广东潮州521041

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