ObjectiveTo investigate the association between tumor necrosis factor (TNF)-α gene polymorphism and susceptibility to chronic obstructive pulmonary disease (COPD) in eastern Heilongjiang province.MethodsA total of 347 COPD patients in the Department of Respiratory Medicine, the First Affiliated Hospital of Jiamusi University, were enrolled from January 2016 to January 2017. In the same period, 338 healthy subjects in the hospital physical examination center were selected as controls. The genotype of the two groups was analyzed by high resolution melting (HRM) and gene sequencing. The genotype and allele probability of the two groups were compared and analyzed by the SHEsis genetic imbalance haplotype analysis.ResultsBoth TNF-a –308 G/A co-dominant model and recessive model have significant differences between COPD patients and healthy subjects (P=0.036, OR 1.512, 95%CI 1.023 – 2.234; P=0.027, OR 1.202, 95%CI 1.024 – 1.741). –850G/A co-dominant model (P=0.000, OR 1.781, 95%CI 1.363 – 2.329), dominant model (P=0.000, OR 0.391 7, 95%CI 1.363 – 2.329) and hyper-dominant model (P=0.000, OR 2.680, 95%CI 1.728 – 4.156) in the two groups were statistically different. The haploid analysis and haploid genotype analysis showed statistically significant differences (all P<0.05, OR>1, 95%CI>1) at +489, –308, –850 sites by allele A, G, A, respectively between the two groups. There was a significant difference in the lung function between the –308G/A, –863C/A mutant genome and the wild type (P=0.038, P=0.02) in COPD patients according to the classification of lung function.ConclusionsA allele in TNF-α –308 and G allele in TNF-α –850 locus may be risk factors for COPD in the eastern Heilongjiang Province, and the risk of homozygous genotype is higher. +489A, –308G and –850A respectively may be the predisposing factor of COPD while the three genotypes of AGA patients were at higher risk. TNF-α –308 A allele and –863 A allele are related to lung function deterioration, and the two sites with A allele in patients with COPD indicate poor lung function.
Types of publication bias and its background are introduced in this paper, and publication bias can be investigated and deal with three methods: funnel plot, trim and filling method, and formula method. Those methods can be used to detect publication bias in conducting systematic reviews.
ObjectiveTo explore the application of enhanced funnel plots (EFP) and trial sequential analysis (TSA) in robustness assessment of meta-analysis results.MethodsData were extracted from published meta-analysis. The EFP was used to evaluate the robustness of the significance and heterogeneity of the current meta-analysis. The TSA was used to judge the sufficiency of the cumulative sample size of the current meta-analysis and to assess the robustness of conclusions based on current evidence.ResultsThe EFP showed that the meta-analysis results of low-density lipoprotein (LDL) was robust, and the meta-analysis results of triglyceride (TG), total cholesterol (TC) and high-density lipoprotein (HDL) were not stable. The TSA showed that the cumulative sample size of LDL had reached the required information size (RIS), and the current conclusion was stable. The cumulative Z value of TG, TC and HDL neither reached the RIS nor passed through the TSA monitoring boundary or futility boundary, indicating that current conclusions were not robust.ConclusionsThe combination of EFP and TSA can make a comprehensive judgment on the robustness of current meta-analysis results, and provide methodological support in the robustness assessment of results for future systematic reviews and meta-analyses.
To accurately capture and effectively integrate the spatiotemporal features of electroencephalogram (EEG) signals for the purpose of improving the accuracy of EEG-based emotion recognition, this paper proposes a new method combining independent component analysis-recurrence plot with an improved EfficientNet version 2 (EfficientNetV2). First, independent component analysis is used to extract independent components containing spatial information from key channels of the EEG signals. These components are then converted into two-dimensional images using recurrence plot to better extract emotional features from the temporal information. Finally, the two-dimensional images are input into an improved EfficientNetV2, which incorporates a global attention mechanism and a triplet attention mechanism, and the emotion classification is output by the fully connected layer. To validate the effectiveness of the proposed method, this study conducts comparative experiments, channel selection experiments and ablation experiments based on the Shanghai Jiao Tong University Emotion Electroencephalogram Dataset (SEED). The results demonstrate that the average recognition accuracy of our method is 96.77%, which is significantly superior to existing methods, offering a novel perspective for research on EEG-based emotion recognition.
Predicting the termination of paroxysmal atrial fibrillation (AF) may provide a signal to decide whether there is a need to intervene the AF timely. We proposed a novel RdR RR intervals scatter plot in our study. The abscissa of the RdR scatter plot was set to RR intervals and the ordinate was set as the difference between successive RR intervals. The RdR scatter plot includes information of RR intervals and difference between successive RR intervals, which captures more heart rate variability (HRV) information. By RdR scatter plot analysis of one minute RR intervals for 50 segments with non-terminating AF and immediately terminating AF, it was found that the points in RdR scatter plot of non-terminating AF were more decentralized than the ones of immediately terminating AF. By dividing the RdR scatter plot into uniform grids and counting the number of non-empty grids, non-terminating AF and immediately terminating AF segments were differentiated. By utilizing 49 RR intervals, for 20 segments of learning set, 17 segments were correctly detected, and for 30 segments of test set, 20 segments were detected. While utilizing 66 RR intervals, for 18 segments of learning set, 16 segments were correctly detected, and for 28 segments of test set, 20 segments were detected. The results demonstrated that during the last one minute before the termination of paroxysmal AF, the variance of the RR intervals and the difference of the neighboring two RR intervals became smaller. The termination of paroxysmal AF could be successfully predicted by utilizing the RdR scatter plot, while the predicting accuracy should be further improved.
ObjectiveTo review recent literature on three-dimensional (3-D) plotting as a rapid prototyping method for the manufacturing of patient specific biomaterial scaffolds and tissue engineering constructs. MethodsLiterature review and description of own recent work. ResultsIn contrast to many other rapid prototyping technologies which can be used only for the processing of distinct materials, 3-D plotting can be utilized for all pasty biomaterials and therefore opens up many new options for the manufacturing of bi- or multiphasic scaffolds or even tissue engineering constructs, containing e. g. living cells. Conclusion3-D plotting is a rapid prototyping technology of growing importance which provides flexibility concerning choice of material and allows integration of sensitive biological components.
Objective To detect the single nucleotide polymorphisms ( SNPs) in the upstream promoter region of chemokine like factor ( CKLF) gene and analyze their possible associations with asthma and asthma-related phenotypes. Methods Direct Sequence of the 1553bp upstream promoter region of CKLF gene was performed in 245 Chinese Han human genomic DNAs ( 119 asthmatics and 126 controls) .The frequencies of alleles, genotypes, and haplotypes were determined and the association of these SNPs with asthma were further analyzed. Results Four novel SNPs, SNP88 ( T gt; C) , SNP196 ( T gt; C) , SNP568 ( C gt;G) , and SNP1047 ( C gt; G) were found in the promoter region of CKLF. The frequency of rare allele was 0. 168 ( SNP88C) , 0. 168 ( SNP196C) , 0. 352 ( SNP568G) and 0. 167 ( SNP1047G) , respectively.Haplotypes, their frequencies and the linkage disequilibrium coefficients between SNPs were constructed.Complete linkage disequilibrium( LDs) were observed between SNP88 and SNP196, SNP88 and SNP1047,as well as SNP196 and SNP1047, respectively ( D′=1. 000, r2 = 1. 000) . SNP568 was in partial LD with the other three SNPs ( r2 = 0. 366) . No association between asthma and the SNPs was observed. Conclusions Four SNPs in the regulatory region of CKLF in Chinese Han population were firstly identified. Although no significant correlation with asthma was revealed, the SNP and haplotype information is useful for other disease association studies in the future.
On the basis of Poincare scatter plot and first order difference scatter plot, a novel heart rate variability (HRV) analysis method based on scatter plots of RR intervals and first order difference of RR intervals (namely, RdR) was proposed. The abscissa of the RdR scatter plot, the x-axis, is RR intervals and the ordinate, y-axis, is the difference between successive RR intervals. The RdR scatter plot includes the information of RR intervals and the difference between successive RR intervals, which captures more HRV information. By RdR scatter plot analysis of some records of MIT-BIH arrhythmias database, we found that the scatter plot of uncoupled premature ventricular contraction (PVC), coupled ventricular bigeminy and ventricular trigeminy PVC had specific graphic characteristics. The RdR scatter plot method has higher detecting performance than the Poincare scatter plot method, and simpler and more intuitive than the first order difference method.
Lorenz plot (LP) method which gives a global view of long-time electrocardiogram signals, is an efficient simple visualization tool to analyze cardiac arrhythmias, and the morphologies and positions of the extracted attractors may reveal the underlying mechanisms of the onset and termination of arrhythmias. But automatic diagnosis is still impossible because it is lack of the method of extracting attractors by now. We presented here a methodology of attractor extraction and recognition based upon homogeneously statistical properties of the location parameters of scatter points in three dimensional LP (3DLP), which was constructed by three successive RR intervals as X, Y and Z axis in Cartesian coordinate system. Validation experiments were tested in a group of RR-interval time series and tags data with frequent unifocal premature complexes exported from a 24-hour Holter system. The results showed that this method had excellent effective not only on extraction of attractors, but also on automatic recognition of attractors by the location parameters such as the azimuth of the points peak frequency (APF) of eccentric attractors once stereographic projection of 3DLP along the space diagonal. Besides, APF was still a powerful index of differential diagnosis of atrial and ventricular extrasystole. Additional experiments proved that this method was also available on several other arrhythmias. Moreover, there were extremely relevant relationships between 3DLP and two dimensional LPs which indicate any conventional achievement of LPs could be implanted into 3DLP. It would have a broad application prospect to integrate this method into conventional long-time electrocardiogram monitoring and analysis system.
Extraction and analysis of electroencephalogram (EEG) signal characteristics of patients with autism spectrum disorder (ASD) is of great significance for the diagnosis and treatment of the disease. Based on recurrence quantitative analysis (RQA)method, this study explored the differences in the nonlinear characteristics of EEG signals between ASD children and children with typical development (TD). In the experiment, RQA method was used to extract nonlinear features such as recurrence rate (RR), determinism (DET) and length of average diagonal line (LADL) of EEG signals in different brain regions of subjects, and support vector machine was combined to classify children with ASD and TD. The research results show that for the whole brain area (including parietal lobe, frontal lobe, occipital lobe and temporal lobe), when the three feature combinations of RR, DET and LADL are selected, the maximum classification accuracy rate is 84%, the sensitivity is 76%, the specificity is 92%, and the corresponding area under the curve (AUC) value is 0.875. For parietal lobe and frontal lobe (including parietal lobe, frontal lobe), when the three features of RR, DET and LADL are combined, the maximum classification accuracy rate is 82%, the sensitivity is 72%, and the specificity is 92%, which corresponds to an AUC value of 0.781. The research in this paper shows that the nonlinear characteristics of EEG signals extracted based on RQA method can become an objective indicator to distinguish children with ASD and TD, and combined with machine learning methods, the method can provide auxiliary evaluation indicators for clinical diagnosis. At the same time, the difference in the nonlinear characteristics of EEG signals between ASD children and TD children is statistically significant in the parietal-frontal lobe. This study analyzes the clinical characteristics of children with ASD based on the functions of the brain regions, and provides help for future diagnosis and treatment.