EFFECT OF DUAL-TRACK INTERACTIVE NURSING INTERVENTION MODEL ON ANXIETY AND DEPRESSION IN PATIENTS WITH CORONARY HEART DISEASE

Background: Elderly patients with coronary heart disease often suffer adverse psychological reactions, such as anxiety and depression. The dual-track interactive nursing model is a nursing intervention aimed to provide specific and community nursing. For patients with chronic diseases, this model can improve the patients’ self-management and rehabilitation. The effect of this model on the mental health of patients with chronic diseases has been unanimously recognized by researchers. In this study, a dual-track interactive nursing model intervention was conducted on the anxiety and depression in elderly patients with coronary heart disease to verify the psychological effect of the model. Subjects and methods: From June 2018 to June 2019, 136 elderly patients with coronary heart disease (mean age of 63.5±8.26 years) from three communities in Changsha, Hunan Province, China were selected as subjects. A total of 53 and 50 patients were identified in the intervention and the control groups, respectively. The control group underwent routine longitudinal referral, whereas the intervention group was subjected to a two-track interactive nursing model intervention. The Short Form-36 Health Survey (SF-36) and related questionnaires were used to monitor and compare the two groups before and after the intervention and employed for scoring and comparative analysis. Results: After the intervention, the mental health scores of the intervention group in total score, somatization, obsessive–compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, and paranoia are significantly lower than those of the control group (P<0.05). The intervention group has scored significantly higher in coping style but significantly lower in negative coping than the control group (P<0.05). Conclusions: The application of the dual-track interactive nursing model intervention in the management of patients with coronary heart disease can improve the self-management and the mental health of patients with coronary heart disease

Short-Term Truckload Spot Rates\u27 Prediction in Consideration of Temporal and Between-Route Correlations

Truckload spot rate (TSR), defined as a price offered on the spot to transport a certain cargo by using an entire truck on a target transportation line, usually price per kilometer-ton, is a key factor in shaping the freight market. In particular, the prediction of short-term TSR is of great importance to the daily operations of the trucking industry. However, existing predictive practices have been limited largely by the availability of multilateral information, such as detailed intraday TSR information. Fortunately, the emerging online freight exchange (OFEX) platforms provide unique opportunities to access and fuse more data for probing the trucking industry. As such, this paper aims to leverage the high-resolution trucking data from an OFEX platform to forecast short-term TSR. Specifically, a lagged coefficient weighted matrix-based multiple linear regression modeling (Lag-WMR) is proposed, and exogenous variables are selected by the light gradient boosting (LGB) method. This model simultaneously incorporates the dependency between historical and current TSR (temporal correlation) and correlations between the rates on alternative routes (between-route correlation). In addition, the effects of incorporating temporal and between-route correlations, time-lagged correlation and exogenous variable selection in modeling are emphasized and assessed through a case study on short-term TSR in Southwest China. The comparative results show that the proposed Lag-WMR model outperforms autoregressive integrated moving average (ARIMA) model and LGB in terms of model fitting and the quality and stability of predictions. Further research could focus on rates\u27 standardization, to define a practical freight index for the trucking industry. Although our results are specific to the Chinese trucking market, the method of analysis serves as a general model for similar international studies

Effects of nitrogen addition and plant litter manipulation on soil fungal and bacterial communities in a semiarid sandy land

The plant and soil microbial communities are influenced by variability in environmental conditions (e.g., nitrogen addition); however, it is unclear how long-term nitrogen addition and litter manipulation affect soil microbial communities in a semiarid sandy grassland. Therefore, we simulated the impact of N addition and litter manipulation (litter removal, litter doubling) on plant and soil microbial communities in Horqin grassland, northern China through an experiment from 2014 to 2019. Our results revealed that in the case of non-nitrogen (N0), litter manipulation significantly reduced vegetation coverage (V) (p &lt; 0.05); soil bacterial communities have higher alpha diversity than that of the fungi, and the beta diversity of soil fungi was higher than that of the bacteria; soil microbial alpha diversity was significantly decreased by nitrogen addition (N10) (p &lt; 0.05); N addition and litter manipulation had significantly interactive influences on soil microbial beta diversity, and litter manipulation (C0 and C2) had significantly decreased soil microbial beta diversity (p &lt; 0.05) in the case of nitrogen addition (N10) (p &lt; 0.05). Moreover, bacteria were mostly dominated by the universal phyla Proteobacteria, Actinobacteria, and Acidobacteria, and fungi were only dominated by Ascomycota. Furthermore, the correlation analysis, redundancy analysis (RDA), and variation partitioning analysis indicated that the soil fungi community was more apt to be influenced by plant community diversity. Our results provide evidence that plant and soil microbial community respond differently to the treatments of the 6-year N addition and litter manipulation in a semiarid sandy land

A study of association between expression of hOGG1, VDAC1, HK-2 and cervical carcinoma

<p>Abstract</p> <p>Background</p> <p>Human 8-oguanine glycosylase 1(hOGG1), voltage-dependent anion channel 1(VDAC1), hexokinase 2(HK-2), represented the process of oxidative DNA damage, cell apoptosis and glycolysis, respectively. This study aims to explore the association between expression of hOGG1, VDAC1, HK-2 and cervical carcinoma.</p> <p>Methods</p> <p>A case-control study was conducted. 65 cervical biopsy samples consist of 20 control and 45 cases. The expression of hOGG1, VDAC1 and HK-2 were examined with immunohistochemistry(IHC), immunolabeling was evaluated with stereological cell counts.</p> <p>Results</p> <p>The data showed that the positive proportion of hOGG1 and HK-2 in the case group was higher than that of the control group (P < 0.05). Further, there was an increasing trend for the positive proportion and expression degree of hOGG1 and HK-2 from Control, Mild cervical carcinoma (MCC), Intermediate cervical carcinoma(ICC) to Severe cervical carcinoma(SCC) in order (P < 0.05). To VDAC1, the significant result was not obtained.</p> <p>Conclusions</p> <p>The results suggested that there was a close association between expression of hOGG1, HK-2 and cervical cancer. hOGG1 and HK-2 might play a key role at the early stage of cervical cancer, and the findings of hOGG1 and HK-2 should be considered as a significant biomarker at the early stage of cervical cancer.</p

Multi-scale fusion visual attention network for facial micro-expression recognition

IntroductionMicro-expressions are facial muscle movements that hide genuine emotions. In response to the challenge of micro-expression low-intensity, recent studies have attempted to locate localized areas of facial muscle movement. However, this ignores the feature redundancy caused by the inaccurate locating of the regions of interest.MethodsThis paper proposes a novel multi-scale fusion visual attention network (MFVAN), which learns multi-scale local attention weights to mask regions of redundancy features. Specifically, this model extracts the multi-scale features of the apex frame in the micro-expression video clips by convolutional neural networks. The attention mechanism focuses on the weights of local region features in the multi-scale feature maps. Then, we mask operate redundancy regions in multi-scale features and fuse local features with high attention weights for micro-expression recognition. The self-supervision and transfer learning reduce the influence of individual identity attributes and increase the robustness of multi-scale feature maps. Finally, the multi-scale classification loss, mask loss, and removing individual identity attributes loss joint to optimize the model.ResultsThe proposed MFVAN method is evaluated on SMIC, CASME II, SAMM, and 3DB-Combined datasets that achieve state-of-the-art performance. The experimental results show that focusing on local at the multi-scale contributes to micro-expression recognition.DiscussionThis paper proposed MFVAN model is the first to combine image generation with visual attention mechanisms to solve the combination challenge problem of individual identity attribute interference and low-intensity facial muscle movements. Meanwhile, the MFVAN model reveal the impact of individual attributes on the localization of local ROIs. The experimental results show that a multi-scale fusion visual attention network contributes to micro-expression recognition