RESUMO
Electromagnetic spring active isolators have attracted extensive attention in recent years. The standard Bouc-Wen model is widely used to describe hysteretic behavior but cannot accurately describe asymmetric behavior. The standard Bouc-Wen model is improved to better describe the dynamic characteristic of a toothed electromagnetic spring. The hysteresis model of toothed electromagnetic spring is established by adding mass, damping, and asymmetric correction terms with direction. Subsequently, the particle swarm optimization algorithm is used to identify the parameters of the established model, and the results are compared with those obtained from the experiment. The results show that the current has a significant impact on the dynamic curve. When the current increases from 0.5 A to 2.0 A, the electromagnetic force sharply increases from 49 N to 534 N. Under different excitations and currents, the residual points predicted by the model proposed in this work fall basically in the horizontal band region of -20-20 N (for an applied current of 1.0 A) and -40-80 N (for an application of 4.5 mm/s). Furthermore, the maximum relative error of the model is 12.75%. The R2 of the model is higher than 0.98 and the highest value is 0.9993, proving the accuracy of the established model.
RESUMO
Background: Depression is widespread global problem that not only severely impacts individuals' physical and mental health but also imposes a heavy disease burden on nations and societies. The role of inflammation in the pathogenesis and pathophysiology of depression has received much attention, but the precise relationship between the two remains unclear. This study aims to investigate the correlation between depression and inflammation using a network medicine approach. Methods: We utilized a degree-preserving approach to identify the large connected component (LCC) of all depression-related proteins in the human interactome. The LCC was deemed as the disease module for depression. To measure the association between depression and other diseases, we calculated the overlap between these disease protein modules using the Sab algorithm. A smaller Sab value indicates a stronger association between diseases. Building on the results of this analysis, we further explored the correlation between inflammation and depression by conducting enrichment and pathway analyses of critical targets. Finally, we used a network proximity approach to calculate drug-disease proximity to predict the efficacy of drugs for the treatment of depression. We calculated and ranked the distances between depression disease modules and 6,100 drugs. The top-ranked drugs were selected to explore their potential for treating depression based on the hypothesis that their antidepressant effects are related to reducing inflammation. Results: In the human interactome, all depression-related proteins are clustered into a large connected component (LCC) consisting of 202 proteins and multiple small subgraphs. This indicates that depression-related proteins tend to form clusters within the same network. We used the 202 LCC proteins as the key disease module for depression. Next, we investigated the potential relationships between depression and 299 other diseases. Our analysis identified over 18 diseases that exhibited significant overlap with the depression module. Where SAB = -0.075 for the vascular disease and depressive disorders module, SAB = -0.070 for the gastrointestinal disease and depressive disorders module, and SAB = -0.062 for the endocrine system disease and depressive disorders module. The distance between them SAB < 0 implies that the pathogenesis of depression is likely to be related to the pathogenesis of its co-morbidities of depression and that potential therapeutic approaches may be derived from the disease treatment libraries of these co-morbidities. Further, considering that the inflammation is ubiquitous in some disease, we calculate the overlap between the collected inflammation module (236 proteins) and the depression module (202 proteins), finding that they are closely related (Sdi = -0.358) in the human protein interaction network. After enrichment and pathway analysis of key genes, we identified the HIF-1 signaling pathway, PI3K-Akt signaling pathway, Th17 cell differentiation, hepatitis B, and inflammatory bowel disease as key to the inflammatory response in depression. Finally, we calculated the Z-score to determine the proximity of 6,100 drugs to the depression disease module. Among the top three drugs identified by drug-disease proximity analysis were Perphenazine, Clomipramine, and Amitriptyline, all of which had a greater number of targets in the network associated with the depression disease module. Notably, these drugs have been shown to exert both anti-inflammatory and antidepressant effects, suggesting that they may modulate depression through an anti-inflammatory mechanism. These findings demonstrate a correlation between depression and inflammation at the network medicine level, which has important implications for future elucidation of the etiology of depression and improved treatment outcomes. Conclusion: Neuroimmune signaling pathways play an important role in the pathogenesis of depression, and many classes of antidepressants exhibiting anti-inflammatory properties. The pathogenesis of depression is closely related to inflammation.
RESUMO
Hepatitis E virus (HEV) is a zoonotic pathogen of which several species of animal were reported as reservoirs. Swine stands out as the major reservoir for HEV infection in humans, as suggested by the close genetic relationship of swine and human virus. Since 2000, Genotype 4 HEV has become the dominant cause of hepatitis E disease in China. Recent reports showed that genotype 4 HEV is freely transmitted between humans and swine in eastern and southern China. However, the infection status of HEV in human and swine populations in central China is still unclear. This study was conducted in a rural area of central China, where there are many commercial swine farms. A total of 1476 serum and 554 fecal specimens were collected from the general human and swine populations in this area, respectively. The seroepidemiological study was conducted by enzyme-linked immunosorbent assay. Conserved genomic sequences of open reading frame 2 were detected using reverse transcription-PCR. The results indicated that the overall viral burden of the general human subjects was 0.95% (14/1476), while 7.0% (39/554) of the swine excreted HEV in stool. The positive rate of anti-HEV IgG and IgM in the serum samples was 7.9% (117/1476) and 1.6% (24/1476), respectively. Phylogenetic analysis based on the 150 nt partial sequence of the capsid protein gene showed that the 53 swine and human HEV isolates in the current study all belonged to genotype 4, clustering into three major groups. However, the HEV isolates prevalent in the human and swine populations were classified into known distinct subgenotypes, which suggested that no cross-species transmission between swine and humans had taken place in this area. This result was confirmed by cloning and phylogenetic analysis of the complete capsid protein gene sequence of three representative HEV strains in the three major groups. The cross reactivity between anti-HEV IgG from human sera and the two representative strains from swine in central China was confirmed by Dot-blot assay. In conclusion, although all the HEV strains prevalent in central China belonged to genotype 4, there is no evidence of cross-species transmission between human and swine in this area.
