ObjectiveTo investigate the screening value of cervical fluid-based cytology test (TCT), high-risk human papillomavirus (HR-HPV) test, and colposcopy for cervical intraepithelial neoplasia (CIN) and cervical cancer in high-risk populations. MethodsA total of 466 patients between January 2013 and January 2015 with a history of intercourse bleeding were enrolled in this study, and the screening value of TCT, HR-HPV test and colposcopy for CIN and cervical cancer were retrospectively evaluated. ResultsIn the 466 patients, 165 were diagnosed with cervical inflammation, 116 with CIN, 182 with grade 2-3 CIN, and 3 with cervical cancer. The colposcopy had the highest sensitivity (84.1%), the lowest specificity (59.4%), high false positive rate (40.6%), low false negative rate (15.9%), and the highest negative predictive value (67.1%). The TCT had the highest specificity (84.8%) and the lowest false positive rate (15.2%). The indicators of HR-HPV were between those of TCT and colposcopy. There were significant differences in terms of these indicators among the three methods (P < 0.05). And the positive prediction value of HR-HPV was the highest (84.5%), while the negative prediction value of colposcopy was the highest (67.1%). There was a significant difference in the predictive value among the three methods (P < 0.05). The consistency of either TCT or HR-HPV alone with pathological diagnosis was poor (K=0.213, 0.343), while that of colposcopy was moderate (K=0.446). Combination of TCT and HR-HPV could significantly improve the diagnosis sensitivity (93.0%) with a lower false negative rate (7.0%); Youden index was 0.736, and the consistency with pathological examination was high (K=0.748). ConclusionsFor high-risk population with a history of intercourse bleeding or other abnormal cervical disorders, the screening sensitivity of TCT and HR-HPV alone for CIN and cervical cancer is low with a high false negative rate. Colposcopy has a high sensitivity and a low specificity. By combination of TCT and HR-HPV, the validity, reliability and predictive values can be improved significantly, and the sensitivity is high with a low false negative rate and a high consistency with pathological examination.
Sleep apnea syndrome (SAS) is a kind of harmful systemic sleep disorder with high incidence, and the pathological mechanism of it is complicated and the diagnosis and treatment are difficult. Mining the characteristic information of SAS from the single or small physiological signal is a hot topic in the research of sleep disorders in recent years. In our study shown in this paper, the detrended fluctuation analysis (DFA) was used to analyze sleep electroencephalogram (EEG) of SAS patients and normal healthy persons based on the non-stationary and nonlinear characteristics. It was found that in both groups, the scaling exponents increased gradually with the deepening of sleep, and in the rapid eye movement (REM) stage, the scaling exponents decreased. The scaling exponents of SAS group were significantly higher than those of the healthy group. The performance of SAS diagnosis based on scaling exponents was evaluated with receiver operator characteristic (ROC) curve. The optimal threshold value 0.81 for the SAS and normal control were obtained, corresponding to the sensitivity 94.4%, specificity 99.2%, and area under curve (AUC) was 0.994. The results show that DFA scaling exponents have a good discrimination power and accuracy for the SAS, which provide a new theoretical basis for SAS diagnosis.
Sleep apnea syndrome (SAS) is a kind of common and harmful systemic sleep disorder. SAS patients have significant iconography changes in brain structure and function, and electroencephalogram (EEG) is the most intuitive parameter to describe the sleep process which can reflect the electrical activity and function of brain tissues. Based on the non-stationary and nonlinear characteristics of EEG, this paper analyzes the correlation dimension of sleep EEG in patients with SAS. Six SAS patients were classed as SAS group and six healthy persons were classified into a control group. The results showed that the correlation dimension of sleep EEG in the SAS group and the control group decreased gradually with the deepening of sleep, and then increased to the level of awake and light sleep stage with rapid eye movement (REM). The correlation dimension of SAS group was significantly lower than that of control group (P<0.01) throughout all the stages. The results suggested that there were significant nonlinear dynamic differences between the EEG signals of SAS patients and of healthy people, which provided a new direction for the study of the physiological mechanism and automatic detection of SAS.
The research of sleep staging is not only the basis of diagnosing sleep related diseases, but also the precondition of evaluating sleep quality, and has important clinical significance. In recent years, the research of automatic sleep staging based on computer has become a hotspot and made some achievements. Feature extraction and feature classification are two key technologies in automatic sleep staging system. In order to achieve effective automatic sleep staging, we proposed a new automatic sleep staging method which combines the energy features and least squares support vector machines (LS-SVM). Firstly, we used FIR band-pass filter to extract the energy features of Pz-Oz channel sleep electroencephalogram (EEG) signals, and compared them with those from wavelet packet transform method. Then we designed an LS-SVM classifier to realize the automatic sleep stage classification. The research showed that FIR band-pass filter (with the Kaiser window) performed better than wavelet packet transform (WPT) for energy feature extraction just in terms of the data from the Sleep-EDF Database and the LS-SVM classifier (with the RBF Kernel function) designed was good, and the automatic sleep staging method proposed in this paper was better than many similar methods from other studies with an average accuracy of 88.89% and had a very prosperous application future.
The research of sleep staging is not only a basis of diagnosing sleep related diseases but also the precondition of evaluating sleep quality, and has important clinical significance. In recent years, the research of automatic sleep staging based on computer has become a hot spot and got some achievements. The basic knowledge of sleep staging and electroencephalogram (EEG) is introduced in this paper. Then, feature extraction and pattern recognition, two key technologies for automatic sleep staging, are discussed in detail. Wavelet transform and Hilbert-Huang transform, two methods for feature extraction, are compared. Artificial neural network and support vector machine (SVM), two methods for pattern recognition are discussed. In the end, the research status of this field is summarized, and development trends of next phase are pointed out.
Denoising methods based on wavelet analysis and empirical mode decomposition cannot essentially track and eliminate noise, which usually cause distortion of heart sounds. Based on this problem, a heart sound denoising method based on improved minimum control recursive average and optimally modified log-spectral amplitude is proposed in this paper. The proposed method uses a short-time window to smoothly and dynamically track and estimate the minimum noise value. The noise estimation results are used to obtain the optimal spectrum gain function, and to minimize the noise by minimizing the difference between the clean heart sound and the estimated clean heart sound. In addition, combined with the subjective analysis of spectrum and the objective analysis of contribution to normal and abnormal heart sound classification system, we propose a more rigorous evaluation mechanism. The experimental results show that the proposed method effectively improves the time-frequency features, and obtains higher scores in the normal and abnormal heart sound classification systems. The proposed method can help medical workers to improve the accuracy of their diagnosis, and also has great reference value for the construction and application of computer-aided diagnosis system.
ObjectiveTo conduct an analysis and identify potential risk factors associated with postoperative complications in patients diagnosed with malignant gastrointestinal tumors who underwent laparoscopic surgery. MethodsFrom January 2023 to October 2023, 500 patients with malignant gastrointestinal tumors who underwent laparoscopic surgery at the Department of General Surgery, the First Medical Center of PLA General Hospital were prospectively selected as the research objects. The incidence of postoperative complications (Clavien-Dindo gradeⅡ and higher) was observed, and then 500 patients were divided into a complication group and a non-complication group. The preoperative physical conditions, operative time and bleeding volume related to the operation were compared and analyzed between the two groups. According to the analysis results and clinical experience, appropriate variables were selected to be included in the multivariate binary logistic regression model for analysis, in order to determine the risk factors for postoperative complications in patients with malignant gastrointestinal tumors. ResultsOf the 500 patients, 453 had no postoperative complications (non-complication group), and 47 had postoperative complications (complication group), with an incidence of 9.4%. Univariate analysis showed that there were significant differences between the complication group and the non-complications group in gender, abdominal girth, preoperative hypoalbuminemia, drinking history, protein diet habits, primary diseases, operative time and intraoperative blood loss (P<0.05), while there were no significant differences between the two groups in age, body mass index, preoperative grip strength, 6 m walking test time, preoperative anemia, hypertension, diabetes, cardiovascular and cerebrovascular diseases, smoking history, education level, exercise habits and preoperative NRS 2002 nutritional score (P>0.05). Multivariate binary logistic regression analysis showed that gender, daily protein diet and exercise frequency, operation time >200 min and intraoperative blood loss >150 mL could be used as independent predictors of postoperative complications in patients with malignant gastrointestinal tumors (P<0.05). ConclusionFor female malignant gastrointestinal tumor patients with low daily protein intake, inadequate physical activity, prolonged operation duration, and massive intraoperative bleeding, perioperative management should be taken in advance and the occurrence of postoperative complications should be vigilant.
目的 探讨强迫症患者失匹配负波(MMN)的特征以及强迫症可能存在的认知功能障碍。 方法 2010年9月-2012年3月将符合纳入标准的21例强迫症患者(OCD)进行耶鲁-布朗强迫症状量表(Y-BOCS)评分,使用日本Nihon Kohden脑诱发电位仪,记录21例OCD患者、21例性别相匹配的正常对照组进行Cz导联MMN潜伏期以及波幅的测定,并将数据进行t检验、相关性分析等处理。 结果 强迫症与正常人组间比较存在MMN潜伏期(t=2.834,P=0.007)延长,波幅增高,但较正常对照组比较无统计学意义,MMN潜伏期与病程以及Y-BOCS评分无相关性。 结论 强迫症患者在大脑处理信息的早期阶段存在认知的自动加工功能的损害,与病程长短以及病情的严重程度无明显相关性。MMN是检测认知功能比较敏感的指标。