Objective To investigate effect of metabolic surgery on type 2 diabetes mellitus (T2DM) patients with body mass index (BMI) 27.5–32.5 kg/m2. Methods The clinical data of 43 T2DM patients with BMI 27.5–32.5 kg/m2 underwent metabolic surgery from October 2014 to October 2016 in the Third Xiangya Hospital of Central South University were analyzed retrospectively. The related indexes such as BMI, blood glucose level, blood lipid level were analyzed before and after metabolic surgery. Results All the patients underwent metabolic surgery successfully. Among them, 35 cases underwent laparoscopic gastric bypass surgery while 8 cases underwent laparoscopic sleeve gastrectomy without related complications after operation. Compared with preoperative indexes, the BMI, fasting blood glucose, HbA1c, triglyceride, and total cholesterol on the postoperative different time were all significantly decreased (P<0.05) except for the HbA1c on the postoperative 1-week, the high density lipoprotein level on the postoperative 12-month was significantly increased (P<0.05). The OGTT 30, 60, and 120 min blood glucose levels on the postoperative 1-week and 3-month, and 60 and 120 min blood glucose levels on the postoperative 6-month and 12-month were all significantly decreased (P<0.05). The OGTT-IRT 60 min insulin level on the postoperative 3-month and the 30 min insulin levels on the postoperative 6- and 12-month were all significantly increased (P<0.05). The levels of OGTT-CRT 30 and 60 min C peptide on the postoperative 6-month and the level of 30 min C peptide on the postoperative 12-month were all significantly increased (P<0.05). Conclusions Metabolic surgery is effective in treatment of T2DM patients with BMI 27.5–32.5 kg/m2, and levels of blood glucose and blood lipids can be improved significantly. Synthesis and release of insulin by islet cells can be ameliorated.
We in the present research proposed a classification method that applied infomax independent component analysis (ICA) to respectively extract single modality features of structural magnetic resonance imaging (sMRI) and positron emission tomography (PET). And then we combined these two features by using a method of weight combination. We found that the present method was able to improve the accurate diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Compared AD to healthy controls (HC): the study achieved a classification accuracy of 93.75%, with a sensitivity of 100% and a specificity of 87.64%. Compared MCI to HC: classification accuracy was 89.35%, with a sensitivity of 81.85% and a specificity of 99.36%. The experimental results showed that the bi-modality method performed better than the individual modality in comparison to classification accuracy.
Objective To analyze the aortic development in patients with mild coarctation of the aorta (CoA) and ventricular septal defect (VSD) after isolated VSD repair and to explore the risk factors affecting postoperative aortic development. Methods A retrospective analysis was conducted on the clinical data of 4231 patients who underwent VSD repair at Guangdong Provincial People’s Hospital from January 2018 to August 2023. Patients with mild CoA were selected as the study subjects. Based on whether CoA progressed postoperatively, patients were divided into a progression group and a non-progression group. Univariate and multivariate analyses were performed, and a logistic regression model was established to analyze the factors affecting postoperative aortic development. Results A total of 231 patients were included, with 142 males and 89 females, and a median age of 223 (105, 635) days. Among the 231 patients, 30 showed varying degrees of mild CoA progression during postoperative follow-up, with an incidence rate of 13.0%. Multivariate logistic regression analysis revealed that higher preoperative pulmonary artery pressure [OR=2.053, 95%CI (1.095, 3.850), P=0.025] and larger VSD [OR=20.200, 95%CI (1.614, 254.440), P=0.020] were risk factors for postoperative CoA progression. Conclusion Most patients with mild CoA and VSD exhibited varying degrees of catch-up growth in the aorta postoperatively. Higher preoperative pulmonary artery pressure and larger VSD size are influencing factors for postoperative CoA progression, necessitating more cautious surgical strategies and closer follow-up for this subset of patients.
Alzheimer's disease (AD) is a typical neurodegenerative disease, which is clinically manifested as amnesia, loss of language ability and self-care ability, and so on. So far, the cause of the disease has still been unclear and the course of the disease is irreversible, and there has been no cure for the disease yet. Hence, early prognosis of AD is important for the development of new drugs and measures to slow the progression of the disease. Mild cognitive impairment (MCI) is a state between AD and healthy controls (HC). Studies have shown that patients with MCI are more likely to develop AD than those without MCI. Therefore, accurate screening of MCI patients has become one of the research hotspots of early prognosis of AD. With the rapid development of neuroimaging techniques and deep learning, more and more researchers employ deep learning methods to analyze brain neuroimaging images, such as magnetic resonance imaging (MRI), for early prognosis of AD. Hence, in this paper, a three-dimensional multi-slice classifiers ensemble based on convolutional neural network (CNN) and ensemble learning for early prognosis of AD has been proposed. Compared with the CNN classification model based on a single slice, the proposed classifiers ensemble based on multiple two-dimensional slices from three dimensions could use more effective information contained in MRI to improve classification accuracy and stability in a parallel computing mode.
In order to solve the problem of early classification of Alzheimer’s disease (AD), the conventional linear feature extraction algorithm is difficult to extract the most discriminative information from the high-dimensional features to effectively classify unlabeled samples. Therefore, in order to reduce the redundant features and improve the recognition accuracy, this paper used the supervised locally linear embedding (SLLE) algorithm to transform multivariate data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions. The 412 individuals were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) including stable mild cognitive impairment (sMCI, n = 93), amnestic mild cognitive impairment (aMCI, n = 96), AD (n = 86) and cognitive normal controls (CN, n = 137). The SLLE algorithm used in this paper is to calculate the nearest neighbors of each sample point by adding the distance correction term, and the locally linear reconstruction weight matrix was obtained from its nearest neighbors, then the low dimensional mapping of the high dimensional data can be calculated. In order to verify the validity of SLLE in the task of classification, the feature extraction algorithms such as principal component analysis (PCA), Neighborhood MinMax Projection (NMMP), locally linear mapping (LLE) and SLLE were respectively combined with support vector machines (SVM) classifier to obtain the accuracy of classification of CN and sMCI, CN and aMCI, CN and AD, sMCI and aMCI, sMCI and AD, and aMCI and AD, respectively. Experimental results showed that our method had improvements (accuracy/sensitivity/specificity: 65.16%/63.33%/67.62%) on the classification of sMCI and aMCI by comparing with the combination algorithm of LLE and SVM (accuracy/sensitivity/specificity: 64.08%/66.14%/62.77%) and SVM (accuracy/sensitivity/specificity: 57.25%/56.28%/58.08%). In detail the accuracy of the combination algorithm of SLLE and SVM is 1.08% higher than the combination algorithm of LLE and SVM, and 7.91% higher than SVM. Thus, the combination of SLLE and SVM is more effective in the early diagnosis of Alzheimer’s disease.
Mild cognitive impairment (MCI) is a clinical transition state between age-related cognitive decline and dementia. Researchers can use neuroimaging and neurophysiological techniques to obtain structural and functional information about the human brain. Using this information researchers can construct the brain network based on complex network theory. The literature on graph theory shows that the large-scale brain network of MCI patient exhibits small-world property, which ranges intermediately between Alzheimer's disease and that in the normal control group. But brain connectivity of MCI patients presents topologically structural disorder. The disorder is significantly correlated to the cognitive functions. This article reviews the recent findings on brain connectivity of MCI patients from the perspective of multimodal data. Specifically, the article focuses on the graph theory evidences of the whole brain structural and functional and the joint covariance network disorders. At last, the article shows the limitations and future research directions in this field.
This study uses mind-control game training to intervene in patients with mild cognitive impairment to improve their cognitive function. In this study, electroencephalogram (EEG) data of 40 participants were collected before and after two training sessions. The continuous complexity of EEG signals was analyzed to assess the status of cognitive function and explore the effect of mind-control game training on the improvement of cognitive function. The results showed that after two training sessions, the continuous complexity of EEG signal of the subject increased (0.012 44 ± 0.000 29, P < 0.05) and amplitude of curve fluctuation decreased gradually, indicating that with increase of training times, the continuous complexity increased significantly, the cognitive function of brain improved significantly and state was stable. The results of this paper may show that mind-control game training can improve the status of the brain cognitive function, which may provide support and help for the future intervention of cognitive dysfunction.
Objective To understand the new characteristics of clinical symptoms of patients with mild COVID-19 during the prevalence of SARS-CoV-2 Omicron, and provide basis for better prevention and treatment of COVID-19.Methods A cross-sectional retrospective study was conducted with WeChat questionnaire among medical staff with COVID-19 recently, who come from the Third Affiliated Hospital of Chongqing Medical University and The Second Affiliated Hospital of Army Medical University.Results A total of 630 valid questionnaires was received. 99.2% of infected people had been vaccinated against COVID-19. 2.4% of infected persons developed pneumonia and 2.1% were hospitalized. The most common symptoms after infection were coughing (89.7%), fever (83.0%), fatigue (84.1%), headache and dizziness (75.7%), muscle soreness (72.7%), sore throat (62.1%), nasal congestion and runny nose (60.6%), expectoration (71.6%), anorexia (58.0%) and taste loss (40.2%). The incidence of gastrointestinal symptoms and cardiovascular symptoms was relatively low (17.8% and 31.0% respectively). The severity of self-reported symptoms of most infected persons was moderate or severe. The proportion of serious symptoms reported was coughing (23.8%), sore throat (27.0%), headache and dizziness (17.9%). The severity of symptoms reported by young group (<35 years old) was significantly higher than that of older group (>35 years old). Fever was the highest at 38 to 39 ℃ (52.4%). 77.0% of fever sustained for 1 to 3 days. At the time of investigation, the viral detection turned negative in 60.6% of infected people, and the time of turning negative was mostly 7 to 10 days. More than half of the infected persons still had different symptoms, among which cough (43.7%) and fatigue (23.8%) were the most common.Conclusions Most subjects with mild COVID-19 infection have obvious upper respiratory tract and systemic symptoms, the most prominent is the high incidence of cough, which has become a new feature of omicron infection. And most of the infected people have moderate to severe symptoms, and the younger ones have more severer symptoms.
The cognitive impairment of type 2 diabetes patients caused by long-term metabolic disorders has been the current focus of attention. In order to find the related electroencephalogram (EEG) characteristics to the mild cognitive impairment (MCI) of diabetes patients, this study analyses the EEG synchronization with the method of multi-channel synchronization analysis--S estimator based on phase synchronization. The results showed that the S estimator values in each frequency band of diabetes patients with MCI were almost lower than that of control group. Especially, the S estimator values decreased significantly in the delta and alpha band, which indicated the EEG synchronization decrease. The MoCA scores and S value had a significant positive correlation in alpha band.
ObjectiveTo explore the effect of chronic unpredictable mild stress (CUMS) on the reproductive function of mice and provide a suitable animal model for reproduction and stress. MethodsA total of 240 female Kunming mice were feed for 5 days, and then divided randomly into the control group (n=90) and experimental group (n=150). The mice in the experimental group were stressed by 9 chronic mild unpredictable stress factors for 4 weeks and validated by open field test and sucrose consumption test. We administrated pregnant mare serum gonadotropin (PMSG)/human chorionic gonadotropin (HCG) for induction of superovulation and observed the ovarian response and embryo development potential. ResultsAfter 4-week CUMS stimulation, the weight gain, 2% sugar consumption test and open field test were significantly different between the mice in two groups (P>0.05). After PMSG/HCG was administrated, the antra follicles and preovulatory follicles significantly reduced significantly in the experiment group than that in the control group (P<0.05); the number of oocytes, fertilization rate, 2-cell embryos, D4 embryos, blastocysts, high quality embryo rate and D5 bed points were all significantly decreased in the experiment group than those in the control group (P<0.05). ConclusionThe CUMS female Kunming mice model is a kind of emotional stress animal model with low reproductive function, which is effective, operable and repeatable; it could be used for further study on the mechanism of reproductive medicine.