Polycystic ovary syndrome (PCOS) is a complex and heterogeneous common endocrine disease, and periodontitis is a chronic infectious immune inflammatory disease caused by dental plaque. In recent years, a large number of studies have shown that PCOS and periodontitis may be related, but the pathological mechanism is still unclear. Therefore, this article reviews the correlation between PCOS and periodontitis and the possible biological mechanisms of their mutual influence, in order to provide a theoretical basis for the prevention and treatment of periodontal disease in patients with PCOS.
摘要:目的: 研究尿微量白蛋白與冠心病的相關性。 方法 : 按冠狀動脈造影診斷標準將116例患者分為冠心病組(82人) 與非冠心病組(34人),測定晨尿白蛋白/ 肌酐濃度值(ACR),比較兩組患者尿ACR 并分析ACR與冠脈病變程度的相關性。 結果 : 冠心病組ACR顯著高于非冠心病組的; ACR與冠脈計分呈顯著的直線正相關。 結論 :冠心病患者ACR水平升高,微量白蛋白尿與冠狀動脈病變范圍和程度密切相關, 且對冠狀動脈狹窄程度具有獨立預測價值。Abstract: Objective: To investigate the relationship between microalbuminuria and coronary artery disease(CAD). Methods : According to the diagnostic standard of coronary artery angiography,116 patients were divided into CAD group (82 patients) and nonCAD group (34 patients). The albumin and creatinine concentrationratio ratio(ACR) in morning urine samples from patients of both groups was estimated and compared. The correlation of ACR to the extent of coronary lesions was analyzed. Results : ACR in the CAD group was significantly higher than that in nonCAD group. A distinctly linear positive correlation existed between ACR and the score of the coronary lesions. Conclusion : ACR increase in patients with CHD.Micoalbuminuria was associated with the severity of coronary lesions in patients with CHD and is an independent predictor of CAD.
Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S1, systole, S2 and diastole. 500 heart sound samples were used to test the performance of our algorithm. The average evaluation accuracy score (F1) was 0.933, the average sensitivity was 0.930, and the average accuracy rate was 0.936. Compared with other algorithms, the performance of our algorithm was more superior. It is proved that the algorithm has high robustness and anti-noise performance, which might provide a novel method for the feature extraction and analysis of heart sound signals collected in clinical environments.
ObjectiveThe time relationship between seizure semiology and epileptic discharges during focal epileptic seizures is a crucial predictor for the localization of epileptogenic zone. Low voltage fast activities (LVFA), especially gamma band oscillations, are confirmed to play a central role in ictogenesis and semiology production. In the present study, we focus on the “electro-clinical correlation” between LVFA in agranulo-dysgranular insulo-cingulate cortices and the sign of “Chapeau de gendarme (CDG)” via detailed analysis of ictal video-stereoencephalography (video-SEEG) of focal epileptic seizures. MethodsWe retrospectively analyzed the ictal video-SEEG of the 7 cases in which CDG signs were presented in habitual seizures and intracerebral electrodes were co-implanted in agranulo-dysgranular insular and cingulate cortices. We calculate the latency of LVFA in each of cortical regions of interest, agranulo-dygranular insular cortex, agranulo-dysgranular cingulate cortex, and the latency of CDG signs via visual and spectral analysis of the ictal SEEG. Moreover, Pearson correlation analysis and linear regression were used to test the time relationship between gamma band oscillations in agranulo-dysgranular insulo-cingulate cortices and generation of CDG signs. ResultsThe co-activation of LVFA occurred in agranulo-dysgranular insulo-cingulate cortices always preceded the appearance of CDG sign in all of the 69 seizures. The LVFA were confirmed as gamma band oscillations via spectral analysis of SEEG. A linear relationship between the latencies of CDG signs and the latencies of co-activation of agranulo-dysgranular insulo-cingulate cortices in gamma band was furth confirmed by Pearson correlation analysis and linear regression. ConclusionsThere is a causal relationship between the involvement of agranulo-dysgranular insulo-cingulate cortices and the generation of CDG sign, and thus the CDG sign could be view as semiological marker of activation of emotional insulo-cingulate cortex in focal epilepsy.
Objective Secondary osteoporosis is very common in patients with primary osteoporosis. Diabetes is a known cause of secondary osteoporosis. While type I diabetes has been clearly linked with diabetic osteoporosis, the effect of type II diabetes on bone health is controversial.Methods In the present study, we investigated the associations between type II diabetes and osteoporosis as well as fractures at different skeletal sites in Women’s Health Initiative participants.Results Common risk factors such as age, race, BMI, HRT use, and the history of fractures were significantly associated with osteoporosis and fractures in this study population. Diabetic women appeared to have a decreased risk of osteoporosis although it no longer remained significant after adjusting for other risk factors (crude HR=0.78, 95%CI 0.61 to 0.99; adjusted HR=0.93, 95%CI 0.73 to 1.19). The impact of diabetes on fractures varied at different body sites. There was a significant increase of risk of hip fracture (HR=2.54, 95%CI 1.14 to 5.66), but not spine fracture (HR=1.71, 95%CI 0.81 to 3.60) and arm fracture (HR=0.92, 95%CI 0.48 to 1.76) among the women with diabetes. Although the overall risk of fractures in diabetic women did not differ significantly from non-diabetic women (HR=1.37, 95%CI 0.89 to 2.09), the difference had a two-fold increase and was statistically significant after 2,000 days (HR=2.01, 95%CI 1.21 to 3.35), indicating a different hazard at different stages of diabetes.Conclusion Our findings suggest that type II diabetes may not be clearly associated with osteoporosis, it increases a site-specific fracture risk at least in the hip. In addition, the overall fracture risk appears to increase in a time-dependent manner.
Hemispheric asymmetry is a fundamental organizing principle of the human brain. Answering the genetic effects of the asymmetry is a prerequisite for elucidating developmental mechanisms of brain asymmetries. Multi-modal magnetic resonance imaging (MRI) has provided an important tool for comprehensively interpreting human brain asymmetry and its genetic mechanism. By combining MRI data, individual differences in brain structural asymmetry have been investigated with quantitative genetic brain mapping using gene-heritability. Twins provide a useful natural model for studying the effects of genetics and environment on the brain. Studies based on MRI have found that the asymmetry of human brain structure has a genetic basis. From the perspective of quantitative genetic analysis, this article reviews recent findings on the genetic effects of asymmetry and genetic covariance between hemispheres from three aspects: the asymmetry of heritability, the heritability of asymmetry and the genetic correlation. At last, the article shows the limitations and future research directions in this field. The purpose of this systematic review is to quickly guide researchers to understand the origins and genetic mechanism of interhemispheric differences, and provide a genetic basis for further understanding and exploring individual differences in laterized cognitive behavior.
Attention can concentrate our mental resources on processing certain interesting objects, which is an important mental behavior and cognitive process. Recognizing attentional states have great significance in improving human’s performance and reducing errors. However, it still lacks a direct and standardized way to monitor a person’s attentional states. Based on the fact that visual attention can modulate the steady-state visual evoked potential (SSVEP), we designed a go/no-go experimental paradigm with 10 Hz steady state visual stimulation in background to investigate the separability of SSVEP features modulated by different visual attentional states. The experiment recorded the EEG signals of 15 postgraduate volunteers under high and low visual attentional states. High and low visual attentional states are determined by behavioral responses. We analyzed the differences of SSVEP signals between the high and low attentional levels, and applied classification algorithms to recognize such differences. Results showed that the discriminant canonical pattern matching (DCPM) algorithm performed better compared with the linear discrimination analysis (LDA) algorithm and the canonical correlation analysis (CCA) algorithm, which achieved up to 76% in accuracy. Our results show that the SSVEP features modulated by different visual attentional states are separable, which provides a new way to monitor visual attentional states.
ObjectiveTo study the relationship among cholecystectomy/gallbladder disease and bile reflux gastritis.MethodsA retrospective collection of 123 patients with bile reflux gastritis who were diagnosed as outpatients and hospitalized from January 2014 to February 2019 in Shengjing Hospital Affiliated to China Medical University, and 221 patients with non-biliary reflux gastritis at the same period were collected. According to the gallbladder status, the patients were divided into three groups: gallbladder disease, cholecystectomy, and gallbladder disease-free group. The relationship between gallbladder status and bile reflux gastritis was analyzed.ResultsAmong 123 patients with bile reflux gastritis, there were 22 cases (17.89%) with cholecystectomy and 26 cases (21.14%) with gallbladder disease; 221 cases of non-biliary reflux gastritis with cholecystectomy in 7 cases (3.17%) and gallbladder disease in 30 cases (13.57%). Univariate analysis showed that the gallbladder status was different between the bile reflux gastritis group and the non-biliary reflux gastritis group (χ2=21.089, P<0.001). The study showed that the gallbladder status was related to the occurrence of bile reflux gastritis. In contrast, patients with cholecystectomy and gallbladder disease had a higher risk of occurrence than those with no gallbladder disease (OR>1, P<0.012 5). Independent risk factors were considered by logistic multivariate regression analysis, including cholecystectomy, gallbladder disease, and age (P<0.05).ConclusionsThere is a correlation between cholecystectomy/gallbladder disease and bile reflux gastritis. Cholecystectomy and gallbladder disease may be the independent risk factors for bile reflux gastritis.
Heart valve disease (HVD) is one of the common cardiovascular diseases. Heart sound is an important physiological signal for diagnosing HVDs. This paper proposed a model based on combination of basic component features and envelope autocorrelation features to detect early HVDs. Initially, heart sound signals lasting 5 minutes were denoised by empirical mode decomposition (EMD) algorithm and segmented. Then the basic component features and envelope autocorrelation features of heart sound segments were extracted to construct heart sound feature set. Then the max-relevance and min-redundancy (MRMR) algorithm was utilized to select the optimal mixed feature subset. Finally, decision tree, support vector machine (SVM) and k-nearest neighbor (KNN) classifiers were trained to detect the early HVDs from the normal heart sounds and obtained the best accuracy of 99.9% in clinical database. Normal valve, abnormal semilunar valve and abnormal atrioventricular valve heart sounds were classified and the best accuracy was 99.8%. Moreover, normal valve, single-valve abnormal and multi-valve abnormal heart sounds were classified and the best accuracy was 98.2%. In public database, this method also obtained the good overall accuracy. The result demonstrated this proposed method had important value for the clinical diagnosis of early HVDs.
In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCI) systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset Ⅳa from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.
