ABSTRACT
<p><b>BACKGROUND</b>Age-related macular degeneration (AMD) is a major cause of irreversible blindness, and awareness of this disease is important in the prevention of blindness. However, lack of public awareness of AMD was shown in previous studies, and there was no report of AMD awareness in the Mainland of China. Therefore, the aim of our study was to assess the awareness of AMD and its risk factors among Beijing residents in China.</p><p><b>METHODS</b>A cross-sectional, computer-assisted, telephone investigation was conducted to measure the awareness of AMD among Beijing residents. All the contacts of potential respondents were randomly generated by computer. Only those above 18 years of age and willing to participate in the study were included. The questionnaire for the study was modified from the AMD Alliance International Global Report. Pearson's Chi-square test and binary logistic regression analysis were used to identify the factors that affected the knowledge of AMD.</p><p><b>RESULTS</b>Among 385 Beijing residents who agreed to participate, the awareness of AMD was 6.8%, far below than that of cataract and glaucoma. Participants who were above 30 years of age (odds ratio [OR] 6.17, confidence interval [CI] 1.44-26.57), with experience of health-related work (OR 8.11, CI 3.25-20.27), and whose relatives/friends or themselves suffering from AMD (OR 32.18, CI 11.29-91.68) had better AMD awareness. Among those familiar with AMD, only 35% of them identified smoking as a risk factor, and only 23.1% of the residents believed that smoking could lead to blindness.</p><p><b>CONCLUSIONS</b>The sample of Chinese population had limited knowledge of AMD. Educational programs need to be carried out to raise public awareness of AMD.</p>
Subject(s)
Adolescent , Adult , Female , Humans , Male , Middle Aged , Young Adult , Beijing , China , Cross-Sectional Studies , Health Knowledge, Attitudes, Practice , Logistic Models , Macular Degeneration , Psychology , Odds Ratio , Patient Education as Topic , Risk Factors , Smoking , Surveys and QuestionnairesABSTRACT
Objective: To analysis the main chemical ingredients of Forsythiae Fructus by LC-MS technology in the positive and negative ions mode. Methods: The chromatographic fingerprint was obtained with Diamonsil II C18 column (250 mm×4.6 mm, 5 μm) and gradient elution with 0.05% H2O-formic acid (A)-acetonitrile (B), and the flow rate was 1.0 mL/min. The column temperature was maintained at 35℃. The detection wavelength was 200-600 nm. Positive and negative ions MS information of Bruker Daltonics 1200 HPLC-Q-TOF was coupled with elemental analysis and compared with literature data to analyze the compounds information. Results: Combined with the accurately relative molecular mass of compounds provided by HPLC-Q-TOF-MS, 24 compounds were identified from the ethanolic extraction of Forsythia Fructus, which include 12 phenylethanoid glycosides, seven lignan ingredients, and five flavonoids. And 3,4-dihydroxyphenylethanoid glycoside (1), 2-methoxy-3,4,5-trihydroxyphenylethanoid glycoside (2), β-hydroxyforsythoside H (7), dihydroquercetagetin (24), and dihydromyricetin (25) were first identified in Forsythiae fructus. Conclusion: The method provides the technical support for the quality control of Forsythiae Fructus and contributes the reference data to elucidating the potential basis of Forsythiae Fructus.
ABSTRACT
A method of medical image segmentation based on support vector machine (SVM) for density estimation is presented. We used this estimator to construct a prior model of the image intensity and curvature profile of the structure from training images. When segmenting a novel image similar to the training images, the technique of narrow level set method is used. The higher dimensional surface evolution metric is defined by the prior model instead of by energy minimization function. This method offers several advantages. First, SVM for density estimation is consistent and its solution is sparse. Second, compared to the traditional level set methods, this method incorporates shape information on the object to be segmented into the segmentation process. Segmentation results are demonstrated on synthetic images, MR images and ultrasonic images.