Fatigue is an exhaustion state caused by prolonged physical work and mental work, which can reduce working efficiency and even cause industrial accidents. Fatigue is a complex concept involving both physiological and psychological factors. Fatigue can cause a decline of concentration and work performance and induce chronic diseases. Prolonged fatigue may endanger life safety. In most of the scenarios, physical and mental workloads co-lead operator into fatigue state. Thus, it is very important to study the interaction influence and its neural mechanisms between physical and mental fatigues. This paper introduces recent progresses on the interaction effects and discusses some research challenges and future development directions. It is believed that mutual influence between physical fatigue and mental fatigue may occur in the central nervous system. Revealing the basal ganglia function and dopamine release may be important to explore the neural mechanisms between physical fatigue and mental fatigue. Future effort is to optimize fatigue models, to evaluate parameters and to explore the neural mechanisms so as to provide scientific basis and theoretical guidance for complex task designs and fatigue monitoring.
Electronic skin has shown great application potential in many fields such as healthcare monitoring and human-machine interaction due to their excellent sensing performance, mechanical properties and biocompatibility. This paper starts from the materials selection and structures design of electronic skin, and summarizes their different applications in the field of healthcare equipment, especially current development status of wearable sensors with different functions, as well as the application of electronic skin in virtual reality. The challenges of electronic skin in the field of wearable devices and healthcare, as well as our corresponding strategies, are discussed to provide a reference for further advancing the research of electronic skin.
ObjectTo observe the clinical efficacy and safety of the combination therapy of atorvastatin and JiangZhi Decoction (ZJD) for primary hyperlipidemia (Tan Zhuo Zu E Zheng) and to analyze the interactions of drugs in hypolipidemic effect. MethodsA 2*2 factorial design, single-blind, stratified randomized controlled trial according to the level of lipid was conducted. Primary hyperlipidemia (Tan Zhuo Zu E Zheng) patients met the inclusion criteria were divided into 5 groups:ATV 10 mg group (group A), ATV 20 mg group (group B), ATV 10 mg+JZD group (group C), ATV 20 mg+JZD group (group D), JZD group (group E). After two weeks treatment, the efficacy and safety among the 5 groups were compared. ResultsA total of 92 patients were included, of which, 20 were in group A, 25 in group B, 21 in group C, 17 in group D, and 9 in group E. The results showed that:(1) There was no significant difference between group C and group B in the reduction of serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) (PTC=0.226, PLDL-C=0.818). (2) The results of 2*2 factorial analysis showed that, there was no significant interaction between TCM factor and western medicine factor (PTC=0.605, PLDL-C=0.843). (3) There were no significant differences in safety outcomes among 5 groups (all P values >0.05). ConclusionATV 10 mg+JZD and ATV 20 mg have a similar efficacy in reducing TC and LDL-C. There is no obvious interaction between JZD and ATV in hypolipidemic effect, and the combination therapy of ATV and JZD is safe.
A new type of testing system used for antithrombotic pressure circulatory equipment has been developed, which realized a new method for the calibration of pressure sensor. Multi-path control and acquisition functions are achieved by this method based on human-computer interaction testing system. The precision of pressure sensor is obtained by polynomial fitting for each test point using linear interpolation method. The result showed that the precision test of pressure sensor could be realized easily and efficiently, using the developed testing system, and the parameters of pressure sensor could be calibrated effectively, so that it could be accurately used in the antithrombotic pressure circulatory equipment. The developed testing system has a prosperous future in the aspects of promotion and application.
ObjectiveTo investigate the correlation between expression of stromal interaction molecule 1 (STIM1) and tumor malignant degree or lymph node metastasis in patients with gastric cancer. MethodsA total of 83 patients with gastric cancer treated in the Affiliated Hospital of Southwest Medical University and Sichuan Mianyang 404 Hospital from October 2018 to April 2021 were collected. The expression of STIM1 protein in the gastric cancer tissues and the corresponding adjacent normal gastric tissues was detected by immunohistochemistry method. Meanwhile the correlation between the expression of STIM1 protein and clinicopathologic features or postoperative lymph node status of the patients with gastric cancer was analyzed. ResultsThe positive rate of STIM1 protein expression in the gastric cancer tissues was 95.2% (79/83), including 62 (74.7%) patients with high expression (STIM1 scoring 5–7) and 21 (25.3%) patients with low expression (STIM1 scoring 2–4), which in the corresponding adjacent normal gastric tissues was 41.0% (34/83), the difference was statistically significant (χ2=58.078, P<0.001). The expression of STIM1 protein was not related to gender, age, and tumor size of the patients with gastric cancer (P>0.05), while the proportions of the patients with high expression of STIM1 protein in the gastric cancer patients with low/undifferentiated tumor, T3+T4 of infiltration depth, TNM stage Ⅲ, and lymph node metastasis were higher than those with high/medium differentiation (χ2=11.052, P=0.001), T1+T2 of infiltration depth (χ2=24.720, P<0.001), TNM stage Ⅰ+Ⅱ (χ2=9.980, P=0.002), and non-lymph node metastasis (χ2=6.097, P=0.014). The expression intensity of STIM1 protein was positively correlated with the number of lymph node metastasis (r=0.552, Z=–3.098, P=0.002) and the rate of lymph node metastasis (r=0.561, Z=–6.387, P<0.001). ConclusionsPositive rate of STIM1 protein expression in gastric cancer tissues is relatively high. STIM1 protein expression in gastric cancer tissue is closely related to tumor malignancy and lymph node metastasis, so it might play an important role in progression of gastric cancer.
In order to address the problem of traditional dolphin adjuvant therapy such as high cost and its limitation in time and place, this paper introduces a three-dimensional virtual dolphin adjuvant therapy system based on virtual reality technology. By adopting Oculus wearable three-dimensional display, the system combined natural human-computer interaction based on Leap Motion with high-precision gesture recognition and cognitive training, and achieved immersive three-dimensional interactive game for child rehabilitation training purposes. The experimental data showed that the system can effectively improve the cognitive and social abilities of those children with autism spectrum disorder, providing a useful exploration for the rehabilitation of those children.
ObjectiveTo investigate key differentially expressed genes (DEGs) in peripheral blood of idiopathic epilepsy patients, as well as their biological functions, cellular localization, involved signaling pathways, through bioinformatics analysis. So to provide new insights for the pathogenesis and prevention of idiopathic epilepsy.MethodsFirstly, we screened and downloaded microarray data including 6 peripheral blood samples of drug-naive patients with idiopathic epilepsy, 8 peripheral blood samples of responders of idiopathic epilepsy treated with Valproate (VPA), and 10 peripheral blood samples of non-responders of idiopathic epilepsy treated with VPA from Gene Expression Omnibus (GEO) data series GSE143272, which Public in January 2020. Secondly, we identified DEGs via the limma package and others in R software. Then we had gotten 74 DEGs, and subsequently conducted gene ontology and pathway enrichment analysis, PPI network analysis and hub gene analysis, using multiple methods containing DAVID, STRING, and Cytohubba in Cytoscape.ResultsWe had identified significant hub DEGs, including TREML3P, KCNJ15, ORM1, RNA28S5, ELANE, RETN, ARG1, LCN2, SLPI, HP, PGLYRP1, BPI, DEFA4, TCN1, MPO, MMP9, CTSG, CXCL8, RNASE3, RNASE2, S100A12, DEFA1B, DEFA1, DEFA3, CEACAM8, MS4A3, PTGS2, PI3, CCL3. The biological processes involved in these DEGs include immune response, inflammatory response, chemotaxis, etc. While, the molecular function is focused on peroxidase activity, chemokine activity, etc. Moreover, KEGG pathway enrichment analysis shows that DEGs were mainly involved in cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway, chemokine signaling pathway and so on.ConclusionThese important key DEGs may be involved in the onset and development of idiopathic epilepsy through a variety of signaling pathways and complex mechanisms.
Objective To explore the influencing factors of internet game addiction among middle school students. Methods Students from a certain district in Sichuan between September 2022 and March 2023 were included as participants. Basic information such as gender, age, whether the subjects were only children, place of residence, parental education, and subjective economic status were investigated. The nine-item Internet Gaming Disorder Scale-short form was used to investigate whether participants had internet game addiction, and the Berkman-Syme Social Network Index was used to evaluate the participants’ social level. Multiple linear regression analysis was used to conduct multivariate analysis to explore the influencing factors of internet game addiction. Results A total of 594 questionnaires were distributed, and 592 valid questionnaires were ultimately obtained. The detection rate of internet game addiction was 12.0%. Multiple linear regression analysis showed that gender (t=−8.281, P<0.001), age (t=3.211, P=0.001), subjective economic status in the region (t=2.025, P=0.043), and social level (t=−4.239, P<0.001) were the influencing factors of online game addiction. Due to the P value was close to the set test level (0.05), subjective economic status in the region was not considered an influencing factor of internet game addiction. Conclusion Teenagers with male gender, older age, and lower social skills are more likely to develop addiction to internet games.
Speech expression is an important high-level cognitive behavior of human beings. The realization of this behavior is closely related to human brain activity. Both true speech expression and speech imagination can activate part of the same brain area. Therefore, speech imagery becomes a new paradigm of brain-computer interaction. Brain-computer interface (BCI) based on speech imagery has the advantages of spontaneous generation, no training, and friendliness to subjects, so it has attracted the attention of many scholars. However, this interactive technology is not mature in the design of experimental paradigms and the choice of imagination materials, and there are many issues that need to be discussed urgently. Therefore, in response to these problems, this article first expounds the neural mechanism of speech imagery. Then, by reviewing the previous BCI research of speech imagery, the mainstream methods and core technologies of experimental paradigm, imagination materials, data processing and so on are systematically analyzed. Finally, the key problems and main challenges that restrict the development of this type of BCI are discussed. And the future development and application perspective of the speech imaginary BCI system are prospected.
To solve the complex interaction problems of hepatitis disease classification, we proposed a lasso method (least absolute shrinkage and selection operator method) with feature interaction. First, lasso penalized function and hierarchical convex constraint were added to the interactive model which is newly defined. Then the model was solved with the convex optimal method combining Karush-Kuhn-Tucker (KKT) condition with generalized gradient descent. Finally, the sparse solution of the main effect features and interactive features were derived, and the classification model was implemented. The experiments were performed on two liver data sets and proved that features interaction contributed to the classification of liver diseases. The experimental results showed that the feature interaction lasso method was of strong explanatory ability, and its effectiveness and efficiency were superior to those of lasso, of all pair-wise lasso, support vector machine (SVM) method, K nearest neighbor (KNN) method, linear discriminant analysis (LDA) classification method, etc.