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find Author "GUO Bin" 4 results
  • Evidence-based Practice of Pit and Fissure Sealants

    We searched randomized controlled trials, meta-analysis and systematic reviews from OVID-EBM Reviews which included ACP Journal Club, The Cochrane Library, and MEDLINE(1991 to 2005 ) to evaluate clinical effectiveness of pit and fissure sealants for caries. The resultsshowed that pit and fissure sealants were recommended to prevent caries of the occlusal surface. The effectiveness varied between the two types of sealants, in general, flowable resin composite had a more satisfactory retention than glass ionomer composite. Acid etch was helpful for less microleakage and more satisfactory retention. Mechanical air-abrasion with acid etch may have the best border seal, However, we were not sure of the effectiveness of Er:YAG laser, technique of dental drill preparation and splicing. More high quality clinical trials on pit and fissure sealants are still needed.

    Release date:2016-09-07 02:17 Export PDF Favorites Scan
  • Clincal Aanlysis of 23 Cases with Craniocerebral Injury Combined with Optic Nerve Injury

    目的:探讨颅脑损伤合并视神经损伤的发病机制及治疗.方法:对23例颅脑损伤合并视神经损伤患者的临床资料做回顾性分析。结果:经过积极治疗部分患者视力有不同程度改善。结论:治疗颅脑损伤合并视神经损伤,强调神经外科和眼科协同处理,掌握治疗时机。

    Release date:2016-09-08 10:02 Export PDF Favorites Scan
  • Influence analysis of glenohumeral bone structure on anterior shoulder instability

    Objective To investigate the effect of glenohumeral bone structure on anterior shoulder instability by three-dimensional CT reconstruction. Methods The clinical data of 48 patients with unilateral anterior shoulder dislocation (instability group) and 46 patients without shoulder joint disease (control group) admitted between February 2012 and January 2024 were retrospectively analyzed. There was no significant difference in gender and side between the two groups (P>0.05). The patients were significantly younger in the instability group than in the control group (P<0.05). The glenoid joint morphological parameters such as glenoid height, glenoid width, ratio of glenoid height to width, glenoid inclination, the humeral containing angle, and glenoid version were measured on three-dimensional CT reconstruction of the glenoid. The differences of the above indexes between the two groups were compared, and the differences of the above indexes between the two groups were compared respectively in the male and the female. Random forest model was used to analyze the influencing factors of anterior shoulder instability. ResultsThe comparison between the two groups and the comparison between the two groups in the male and the female showed that the ratio of of the instability group glenoid height to width was larger than that of the control group, the glenoid width and humeral containing angle were smaller than those of the control group, and the differences were significant (P<0.05); there was no significant difference in glenoid height, glenoid inclination, and glenoid version between the two groups (P>0.05). The accuracy of the random forest model was 0.84. The results showed that the top four influencing factors of anterior shoulder instability were ratio of glenoid height to width, the humeral containing angle, age, and glenoid width. Conclusion Ratio of glenoid height to width and the humeral containing angle are important influencing factors of anterior shoulder instability.

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  • Fusion of electroencephalography multi-domain features and functional connectivity for early dementia recognition

    Dementia is a neurodegenerative disease closely related to brain network dysfunction. In this study, we assessed the interdependence between brain regions in patients with early-stage dementia based on phase-lock values, and constructed a functional brain network, selecting network feature parameters for metrics based on complex network analysis methods. At the same time, the entropy information characterizing the EEG signals in time domain, frequency domain and time-frequency domain, as well as the nonlinear dynamics features such as Hjorth and Hurst indexes were extracted, respectively. Based on the statistical analysis, the feature parameters with significant differences between different conditions were screened to construct feature vectors, and finally multiple machine learning algorithms were used to realize the recognition of early categories of dementia patients. The results showed that the fusion of multiple features performed well in the categorization of Alzheimer’s disease, frontotemporal lobe dementia and healthy controls, especially in the identification of Alzheimer’s disease and healthy controls, the accuracy of β-band reached 98%, which showed its effectiveness. This study provides new ideas for the early diagnosis of dementia and computer-assisted diagnostic methods.

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