bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Different Nutritional Nursing Support on the Nutritional Status and Disease Recovery of Elderly Patients with Gastrointestinal Tumors during the Perioperative Period
Authors: Chen Lingzhi , Zheng Sixin , Xie Qi , Huang Liping , Yin Guang
Published: June 2022

Objective. This study explored the effect of different nutritional nursing support on nutritional status, immune function, postoperative bowel motility, and complications in elderly patients with gastrointestinal tumors during the perioperative period. Methods. 300 patients with gastrointestinal tumors treated in the Department of Gastroenterology and anorectal surgery of Hangzhou First People’s Hospital Affiliated with the Medical College of Zhejiang University from February 2018 to March 2020 were selected as the research objects in this study. Patients were divided into the early enteral nutrition (EEN) and total parenteral nutrition (TPN) groups (150 cases in each group) according to the principle of odd and even admission numbers. The patients in the EEN and TPN groups were given enteral nutrition nursing support and parenteral nutrition nursing support, respectively. The nutritional status, immune function, postoperative bowel motility, and complication rate of the two groups were evaluated 7 days after the operation. Results. The nutritional indexes decreased 3 days after the operation and gradually recovered 7 days after the operation in both groups with different nutritional nursing support. The Hb, TRF, PAB, and ALB indexes in the TPN group were significantly lower than those in the EEN group (

P
<
0.01

). On the 7th day after the operation, the indexes of peripheral blood immunoglobulin (IgG, IgM, and IgA) were significantly lower than those in the TPN group, and T lymphocyte subsets (CD4, CD8, and CD4/CD8) demonstrated that the immunological indexes of patients in the EEN group were significantly higher than those in the TPN group (

P
<
0.01

). In terms of intestinal peristalsis, the time of first exhaust and first defecation in the EEN group was significantly shorter than that in the TPN group (

P
<
0.01

) during the perioperative period. Furthermore, both groups had different degrees of complications, while patients demonstrated a lower complication rate in the EEN group compared to those in the TPN group, suggesting a safer postoperative mode. The results of subgroup analysis showed that the nutritional indexes of the gastric cancer group 7 days after operation were significantly higher than those of the colorectal cancer group under EEN and TPN nutritional support modes. Conclusion. Clinical results have suggested that enteral nutrition nursing support can improve the perioperative nutritional status of elderly patients with gastrointestinal tumors by enhancing the immune function and promoting intestinal peristalsis. Meanwhile, the postoperative EEN mode reduces the rate of complications and demonstrates higher safety. Therefore, it has a high clinical application value.
Research on Teaching Quality Evaluation Model of Higher Education Teachers Based on BP Neural Network and Random Matrix
Authors: Gu Qiongying
Published: June 2022

Improving teaching quality is the first task of higher education, and evaluating teaching quality is an effective measure to improve teaching quality. Combining the advantages of BP neural network and random matrix algorithm, the teaching quality evaluation model of higher education teachers is established. In this paper, the improved BP neural network and the random matrix structure are used to normalize the indicators, evaluate the teaching indicators, and build the teacher teaching quality evaluation system model. Through experimental design, the training data set is input into the model for training. In the training process, the increase and decrease ratio of learning rate, momentum term, and other parameters are adjusted to improve the prediction accuracy and convergence speed of the model. Iteration times, training time, MSE, and prediction accuracy were taken as performance comparison indexes of the model. Experiments show that the model solves the shortcomings of the existing teaching quality evaluation methods and models to a certain extent, and improves the accuracy of evaluation prediction. When the number of iterations is 133, the prediction accuracy is as high as 94.9%, which verifies the effectiveness of the model in the evaluation of teaching quality in colleges and universities. Finally, the evaluation index system of teacher teaching quality is comprehensively analyzed, and the results prove that the evaluation model of teacher teaching quality of A university is suitable for the situation of the school, can highlight the guidance, and is scientific and measurable of evaluation.
Model Optimization Analysis of Customer Churn Prediction Using Machine Learning Algorithms with Focus on Feature Reductions
Authors: Sina Mirabdolbaghi Seyed Mohammad , Amiri Babak
Published: June 2022

Currently, Customers are struggling to retain their business in today’s competitive markets. Thus, the issue of customer churn becomes a significant challenge for the industries. In order to achieve this, it is vital to have an efficient churn prediction system. In this paper, we discuss methods for reducing features using PCA, Autoencoders, LDA, T‐SNE, and Xgboost. In this paper, a model for predicting light GBM churn is proposed. The model consists of five steps. The first step is to preprocess the data so that missing and corrupt values can be handled and the data can be scaled. Secondly, implementing a comprehensive feature reduction system based on popular algorithms reduces the features and selects the most suitable one. In the third step, light GBM’s hyperparameter is tuned using Bayesian hyperparameter optimization and genetic optimization algorithms. Lastly, interpreting the model and evaluating the impact of the features on model outputs by using the SHAP method, and finally ranking the churners by customer lifetime value. Aside from evaluating and choosing the best feature reduction methods, the proposed method is also evaluated using four famous datasets. It outperforms other ensemble and ML algorithms like AdaBoost, SVM, and decision tree on over seven evaluation metrics: accuracy, area under the curve (AUC), Kappa, Mathews correlation coefficient (MCC), Brier score, F1 score, and EMPC. In light of the evaluation metrics, our model shows a significant improvement in handling imbalanced datasets in churn prediction. Finally, in this paper, interpretability and how the features affect the model’s output are presented by the SHAP method. Then CLV ranking is suggested for better decision‐making.
Hispidulin Enhances Temozolomide (TMZ)-Induced Cytotoxicity against Malignant Glioma Cells In Vitro by Inhibiting Autophagy
Authors: Chen Zhihua , Zhu Guofeng , Sheng Chunpeng , Lei Jianwei , Song Sihui , Zhu Jianming
Published: June 2022

Temozolomide (TMZ), an oral alkylating agent, is the widely used first-line chemotherapeutic reagent for glioma in clinical practice. However, TMZ-induced autophagy is another cellular process favoring glioma cell survival. This study aimed to explore whether hispidulin can facilitate TMZ-induced cell death of glioma. The MTT assay showed that coadministration with hispidulin and TMZ could significantly decrease the viability of glioma U87MG cells. Meanwhile, hispidulin administration was also observed to promote TMZ-induced apoptosis. Furthermore, additional hispidulin treatment further elevated TMZ-induced expression of Bax, cleaved-caspase-9, and cleaved-caspase-3 protein but decreased Bcl-2 protein expression in U87MG cells. We also observed that hispidulin suppressed TMZ-induced autophagy to promote apoptosis, as showed by decreased AVOs and LC3B-I/II protein expression. These results collectively suggested that the combination of hispidulin and TMZ could improve the antitumor efficiency of TMZ against malignant gliomas.
Topological Aspects of Certain Covalent Organic Frameworks and Metal Organic Frameworks
Authors: Zhang Guoping , Azeem Mehran , Aslam Adnan , Yousaf Shamaila , Kanwal Salma
Published: June 2022

Topological indices are numerical numbers assigned to molecular graphs and are expected to predict certain of its physical or chemical properties. Metal-organic frameworks (MOFs) are inorganic/organic porous crystalline materials with a regular array of positively charged metal ions which surrounds organic linkers. Covalent organic frameworks (COFs) are a class of organic polymers with highly ordered structure and permanent porosity. In this work, we have computed some degree-based topological indices of naphthalene metal-organic frameworks and thiophene-based covalent triazine framework.
Bioinformatics Analysis Reveals the Biomarker Value and Potential Mechanism of miR-675-3p in Gastric Cancer
Authors: Chen Ruyi , Li Yi , Ouyang Wei , Chen Shaoji
Published: June 2022

Background. Gastric cancer (GC) is still the main challenge for the social and clinical system. Increasing studies have proved that microRNA dysfunction is closely associated with the GC progression. miR-675-3p has been confirmed as the tumor support in multiple tumor cells, while its role in GC remains unclear. Methods. The clinical data in the TCGA database were excavated for analyzing the role of miR-675-3p in pan-cancer and GC. qRT-PCR was applied to detect the abundances of the genes. The Starbase 2.0 was executed to target the prediction of miR-675-3p. Moreover, the enrichment analysis was performed with the DAVID database. The PPI-network analysis of the targets was performed with Cytoscape. Results. miR-675-3p was dramatically upregulated in multiple types of cancer, and elevated miR-675-3p was also found in GC tissues. Moreover, increased miR-675-3p was closely related with the poor survival rates of the patients. The qRT-PCR showed that miR-675-3p was extremely upregulated in GC tissues and cell lines. The enrichment analysis showed that the targets of miR-675-3p were located in the cellular nucleus and associated with the transcriptional misregulation in cancer. The PPI-network showed that three clusters and total of 40 genes were screened as potential hub nodes. Moreover, BRIP1, MYO5B, and PDS5B were related with the prognostic survival of the patients according to the TCGA database and decreased BRIP1, MYO5B, and PDS5B were also found in GC cell lines. Conclusion. This study identified miR-675-3p as a potential biomarker in GC development and revealed the potential regulation network of miR-675-3p.
Investigating the Antimicrobial Activity of Vancomycin-Loaded Soy Protein Nanoparticles
Authors: Zare-Zardini Hadi , Soltaninejad Hossein , Ghorani-Azam Adel , Forouzani-Moghaddam Mohammad Javad , Mozafri Sima , Akhoundi-Meybodi Zohreh , Ferdosian Farzad , Jabinian Fatemeh
Published: June 2022

Developing targeted and slow-release antibiotic delivery systems can effectively reduce drug overdose and side effects. This study aimed to investigate the antimicrobial activity of vancomycin-loaded soy protein nanoparticles (vancomycin-SPNs). For the preparation of SPNs, the desolvation method was applied in different concentrations of vancomycin and soy protein (15:5, 10:15, 6:20, 8:25, and 10:30 of vancomycin:soy protein). Scanning electron microscope (SEM), transmission electron microscopy (TEM), dynamic light scattering (DLS), and FTIR were used for nanoparticle characterization. Antibacterial activity was evaluated by the radial diffusion assay (RDA) and absorbance methods. Proper synthesis was demonstrated by characterization. The best drug loading (% entrapment efficiency = 90.2%), the fastest release rate (% release = 88.2%), and the best antibacterial activity were observed in ratio 10:30 of vancomycin:SPNs. Results showed that SPNs are a potent delivery system for antibiotic loading and slow release to control antibiotic use.
Optimal Control and Cost‐Effectiveness Strategies of Malaria Transmission with Impact of Climate Variability
Authors: Keno Temesgen Duressa , Dano Lemesa Bedjisa , Ganati Gamachu Adugna
Published: June 2022

We proposed in this study a deterministic mathematical model of malaria transmission with climate variation factor. In the first place, fundamental properties of the model, such as positivity of solution and boundedness of the biological feasibility of the model, were proved whenever all initial data of the states were nonnegative. The next‐generation matrix method is used to compute a basic reproduction number with respect to the disease‐free equilibrium point. The Jacobian matrix and the Lyapunov function are used to check the local and global stability of disease‐free equilibriums. If the basic reproduction number is less than one, the model’s disease‐free equilibrium points are both locally and globally asymptotically stable; otherwise, an endemic equilibrium occurs. The results of the sensitivity analysis of the basic reproduction numbers were obtained, and its biological interpretation was provided. The existence of bifurcation was discussed, and the model exhibits forward and backward bifurcations with respect to the first and second basic reproduction numbers, respectively. Secondly, using the maximum principle of Pontryagin, the optimal malaria reduction strategies are described with three control measures, namely, treated bed nets, infected human treatment, and indoor residual spraying. Finally, based on numerical simulations of the optimality system, the combination of treatment and indoor spraying is the most efficient and least expensive strategy for malaria eradication.
Influence of the α Angle of Flexible Concrete Expanded-Plate Piles on the Bearing Capacity under Horizontal Load
Authors: Qian Yongmei , Chen Yang , Xu Lina , Wang Ruozhu , Chao Sheng
Published: June 2022

Concrete expanded-plate piles have significant advantages in the bearing capacity, settlement, and construction period. The added bearing expanded plate is the key reason for their many advantages; moreover, the slope angle (α angle: the maximum diameter of the bearing expanded plate and the angle formed on the plate) of the bearing expanded plate is the main factor to control the longitudinal symmetry. However, the concrete expanded-plate piles in practical engineering do not exist in a rigid body state, and elastic deformation will occur under variable engineering conditions. In addition, the research in this paper mainly focuses on a flexible concrete expanded-plate pile (F-CEP pile). The flexible pile mentioned in this paper refers to the bending deformation of the pile body under horizontal load, which produces large pile top displacement. This study uses the two methods of small-scale half-plane pile model test and finite element simulation analysis of ANSYS, after referring to the equipotential pile test at the actual site. In this paper, the variation law of the displacement curve and the failure law of the soil around the pile under the horizontal force when the α angle changes are analyzed, and the calculation model of the bearing capacity of F-CEP pile under the α angle change is put forward. This research aims to verify the reliability of existing research and provides strong support for the application, promotion, design, and research of F-CEP piles in construction engineering.
Pregnancy in a Transgender Male: A Case Report and Review of the Literature
Authors: Hassan Ayesha , Perini Jessica , Khan Amna , Iyer Apoorva
Published: June 2022

Introduction. Pregnancy in transgender men is an area of increasing study due to data showing that pregnancy can occur in this population despite the reduction in fertility that generally accompanies treatment with gender-affirming hormone therapies. Case. In this case, we describe a healthy 21-year-old transgender man who was able to achieve pregnancy without reproductive assistance after stopping his testosterone therapy for 2 months. Discussion. Our case is important as it highlights how little is known in regards to gender-affirming hormone therapy on fertility. While testosterone is known to reduce fertility by inducing anovulation and altering ovarian histology, its long-term effects on conception rates and pregnancy are largely unknown. Some studies demonstrate that transgender men, treated with gender-affirming hormone therapy (GAHT), including testosterone, have similar oocyte quantity and quality, as well as similar ovarian reserve, when compared to cisgender women, suggesting that resumption of fertility may be possible after cessation of GAHT. Long-term outcomes for the pregnancy and the offspring of those who have been treated with GAHT are unknown. Conclusion. Recent studies have shown that pregnancy is possible for transgender men who desire biological children and have received gender-affirming hormonal therapy without fertility-preserving measures. Further research is needed to help determine rates of fertility, the likelihood of recovery of fertility, conception rates, and long-term pregnancy outcomes. Such information would help guide physicians in providing education and counseling to their transgender patients regarding reproductive options.
Regulatory Role of miRNAs and lncRNAs in Gout
Authors: Shu Jianlong , Chen Minhua , Ya Chunse , Yang Ruixia , Li Fengzhen
Published: June 2022

Objective. To explore the regulatory functions of ceRNA networks in the nosogenesis of gout and search for potential therapeutic targets. Methods. We searched the GEO database and downloaded the lncRNA microarray chipset GSE160170. This matrix series was analyzed to yield differentially expressed lncRNAs and mRNAs. Then, the correlations between lncRNAs and miRNAs were obtained by comparing the highly conserved miRNA families. The predicted miRNA-regulating mRNAs were matched to the differentially expressed mRNAs from the chipset analyses to obtain miRNA–mRNA interactions. Next, we used the Cytoscape software to model ceRNA networks and the STRING database to determine their protein–protein interactions. The R software was used to algorithmically screen the functional pathways of key PPI modules in the ceRNA networks. Results. A total of 354 lncRNAs (140 downregulated and 214 upregulated) and 693 mRNAs (399 downregulated and 294 upregulated) were differentially expressed between the gout group and the healthy group. The ceRNA network of differentially expressed lncRNAs contained 86 lncRNAs (35 downregulated and 51 upregulated), 29 miRNAs, and 57 mRNAs. The processes identified in the GO enrichment analysis included gene transcription, RNA polymerase II transcription, and the regulation of cell growth and apoptosis. The pathways identified in the KEGG enrichment analysis included IL-17, TNF, and MAPK signaling. Nine lncRNAs (AC104024, AC084082, AC083843, FAM182A, AC022819, FAM215B, AP000525, TTTY10, and ZNF346-IT1), eleven miRNAs (hsa-miR-1297, hsa-miR-17-5p, hsa-miR-429, hsa-miR-139-5p, hsa-miR-449c-5p, hsa-miR-125a-5p, hsa-miR-125b-5p, hsa-miR-23b-3p, hsa-miR-217, hsa-miR-363-3p, and hsa-miR-20b-5p), and nine mRNAs (JUN, CASP2, PMAIP1, FOS, TNFAIP3, MAP3K8, BTG2, NR4A2, and DUSP2) were identified in the exploration of the key modules. Conclusion. Characterization of ceRNA networks could be a promising approach for better understanding the pathogenesis of gout, with the TTTY10/hsa-miR-139-5p/AP-1 axis likely to be of clinical significance.
Application of Deep Learning Model in the Avoidance of Investment Risk of Multinational Enterprises
Authors: Yan Yan , Ye Guangyu , Feng Fei
Published: June 2022

With the continuous improvement and development of the socialist market economic system, China's economic development has full momentum, but the domestic market is no longer sufficient to meet the needs of enterprise development. China has always focused on peaceful diplomacy, and the world market has a strong demand for Chinese products. This work aims to improve the accuracy of exchange rate forecasting. The risk factors that may be encountered in the investment process of multinational enterprises can be effectively avoided. Combining the advantages of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), the LSTM-CNN (Long Short-Term Memory-Convolutional Neural Network) model is proposed to predict the volatility trend of stocks. Firstly, the investment risk of multinational enterprises is analyzed, and, secondly, the principles of the used CNN and LSTM are expounded. Finally, the performance of the proposed model is verified by setting experiments. The experimental results demonstrate that when predicting the 10 selected stocks, the proposed LSTM-CNN model has the highest accuracy in predicting the volatility of stocks, with an average accuracy of 60.1%, while the average accuracy of the rest of the models is all below 60%. It can be found that the stock category does not have a great impact on the prediction accuracy of the model. The average prediction accuracy of the CNN model is 0.578, which is lower than that of the Convolutional Neural Network-Relevance model, and the prediction accuracy of the LSTM model is 0.592, which is better than that of the Long Short-Term Memory-Relevance model. The designed model can be used to predict the stock market to guide investors to make effective investments and reduce investment risks based on relevant cases. The research makes a certain contribution to improving the company's income and stabilizing the national economic development.
Analyzing Similarity Solution of Modified Fisher Equation
Authors: Hanaç Duruk Esen , Koksal Mehmet Emir , Jiwari Ram
Published: June 2022

In this paper, we first examine the type of structure of the solutions to the modified form of a nonlinear Fisher’s reaction‐diffusion equation. The existence of the traveling wave solution to the equation in the long term is observed by using dynamical system theory and exhibiting a phase space analysis of its stable points. In parallel, we represent radial basis functions (RBFs)‐based differential quadrature methods (DQMs) to close the solution of the equation. The stability analysis of the recommended method is demonstrated. Some initial‐boundary value problems are considered test problems. The numerical results indicate extremely exact and stable initial and boundary conditions in the same domain with dissimilar time ranges.
Identification of Pathogens and Laboratory Activity Test of Kiwifruit Rot Disease in Guizhou Province, China
Authors: Wang Tao , Ren Yanling , Zhao Jinyu , Jiang Yingjie , Tang Jian , Liu Yao , Liu Chao , Wang Juan , Ji Xiaolei , Wang Mingyan
Published: June 2022

Kiwifruit (Actinidia spp.) postharvest decay is common in China, which can cause serious economic losses to kiwifruit industry. In order to further clarify the pathogen of kiwifruit rot disease in Guizhou Province, the rotten fruits of kiwifruit (cultivar “Jinyan”) were collected, and the pathogenic fungi were identified by isolation and purification, pathogenicity test, morphological characteristics, and analysis of rDNA-ITS sequences. The results showed that the pathogenic fungi of kiwifruit rot disease were Diaporthe phaseolorum and Fusarium tricinctum. Meanwhile, the results showed that all the tested agents had a certain inhibitory effect on Diaporthe phaseolorum and Fusarium tricinctum. Among them, 33.5% quinolone SC had the best inhibitory effect on Diaporthe phaseolorum with an EC50 value of 9.67 mg/L, and 25% fludioxonil SC had the best inhibitory effect on Fusarium tridentatus with the EC50 value of 13.13 mg/L. The results will provide a reference for the control of kiwifruit rot disease.
The Integrated Sustainable Development Mechanism of Sports Industry Based on Recombination Fusion Mode
Authors: Zhao Bo , Chen Jianmin , Song Tao
Published: June 2022

Regular competitions on a bigger size and level are referred to as sports events. The World Cup, the Olympics, Formula, the NBA, and many intercontinental sports events and World Championships of various individual sporting organizations, among others, are among the world’s largest and most significant sporting events. In China, there are also specific live sporting event websites that broadcast weekly sporting events. The purpose of this paper is to study the integrated development mechanism for the sustainable development of natural ecotourism and sports events. For this reason, this paper proposes research and analysis on its industrial integration method and industrial integration path, and it has an in-depth understanding of the sustainable development model. At the same time, relevant experiments are designed in the experimental part to explore the impact of sports events and natural ecotourism in the development process. The experimental results of this paper show that after the improvement, the attractiveness of tourists has increased by 47.48%, which has effectively promoted the development of the related tourism industry.
Human Behavior Recognition in Outdoor Sports Based on the Local Error Model and Convolutional Neural Network
Authors: Hua Xia , Han Lei , Jiang Yang
Published: June 2022

With the rapid development of the Internet, various electronic products based on computer vision play an increasingly important role in people’s daily lives. As one of the important topics of computer vision, human action recognition has become the main research hotspot in this field in recent years. The human motion recognition algorithm based on the convolutional neural network can realize the automatic extraction and learning of human motion features and achieve good classification performance. However, deep convolutional neural networks usually have a large number of layers, a large number of parameters, and a large memory footprint, while embedded wearable devices have limited memory space. Based on the traditional cross-entropy error-based training mode, the parameters of all hidden layers must be kept in memory and cannot be released until the end of forward and reverse error propagation. As a result, the memory used to store the parameters of the hidden layer cannot be released and reused, and the memory utilization efficiency is low, which leads to the backhaul locking problem, limiting the deployment and execution of deep convolutional neural networks on wearable sensor devices. Based on this, this topic designs a local error convolutional neural network model for human motion recognition tasks. Compared with the traditional global error, the local error constructed in this paper can train the convolutional neural network layer by layer, and the parameters of each layer can be trained independently according to the local error and does not depend on the gradient propagation of adjacent upper and lower layers. As a result, the memory used to store all hidden layer parameters can be released in advance without waiting for the end of forward and backward propagation, avoiding the problem of backhaul locking, and improving the memory utilization of convolutional neural networks deployed on embedded wearable devices.
Induction of Resistance of Antagonistic Bacterium Burkholderia contaminans to Postharvest Botrytis cinerea in Rosa vinifera
Authors: Shen Fengying , Liu Zigang , Du Chenyang , Yuan Junhai , Wu Weigang , Wei Dong
Published: June 2022

In order to study the problem that grapes are vulnerable to microbial infection and decay during storage, a method based on antagonistic Burkholderia contaminans against postharvest Botrytis cinerea of Rosa vinifera was proposed in this paper. The method tested the resistance induction mechanism of Botrytis cinerea after harvest and determined the fruit decay rate treated by antagonistic Burkholderia contaminans. The results showed that the antagonistic bacterium B-1 had bacteriostatic effect on many common pathogens of fruits and vegetables to a certain extent, and the bacteriostatic range was wide. Among them, the inhibition rate of Fusarium moniliforme was 75.5% and that of Botrytis cinerea was 51.2%. After testing, it can be found that antagonistic bacteria have an inhibitory effect on pathogenic fungi and have an effect on phenylpropane metabolic pathway, reactive oxygen species metabolic pathway, and the activities of other resistance-related enzymes. Through comparison, it can be found that the antagonistic Burkholderia contaminans has a strong antibacterial mechanism against Botrytis cinerea of rose grape after harvest. The fruit treated with antagonistic B Burkholderia B-1 has significantly reduced the decay rate and increased the activity of antibacterial active protein.
A Risk Model Based on Sorafenib-Response Target Genes Predicts the Prognosis of Patients with HCC
Authors: Liu Xiang , Zeng Jian , Li Huanyu , Li Feng , Jiang Bin , Zhao Ming , Liu Zhuo , Li Ruineng , Ma Tiexiang
Published: June 2022

Sorafenib is used to treat digestive system tumors in patients who do not respond to or cannot tolerate surgery. However, the roles and inhibitory mechanisms of sorafenib against hepatocellular carcinoma (HCC) are unclear. Differentially expressed genes in tissues from responders and nonresponders to sorafenib were investigated using the HCC GSE109211 data set. Biological functions and mechanisms were studied using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. The expression levels of differential expressed target genes were identified in HCC tissues, using The Cancer Genome Atlas database, and their prognostic and diagnostic values were explored using survival and receiver operating characteristic curve analysis. A nomogram and risk model of sorafenib-response target genes enabled the evaluation of the prognosis of patients with HCC. The relationship between risk scores and levels of infiltrating immune cells was visualized via correlation analysis. We identified 1620 sorafenib-response target genes involved in the PPAR signaling pathway, antigen processing and presentation, and ferroptosis. SLC41A3, SEC61A1, LRP4, PPM1G, and HSP90AA1 were independent risk factors for a poor prognosis for patients with HCC and had diagnostic value. A risk model based on SLC41A3, SEC61A1, LRP4, PPM1G, and HSP90AA1 expression showed that patients with HCC in the high-risk group had a worse prognosis. Consensus-clustering analysis (performed with K set to 2) distinguished two clusters (the cluster 1 and cluster 2 groups). Patients in cluster 1 survived significantly longer than those in cluster 2. The risk score correlated with the levels of T cells, cytotoxic lymphocytes, CD8+ T cells, macrophages, memory B cells, follicular helper T cells, and other immune cells. The high risk based on the sorafenib-response targets SLC41A3, SEC61A1, LRP4, PPM1G, and HSP90AA1 represented the poor prognosis for patients with HCC and significantly correlated with the levels of immune infiltrating cells in HCC.
[Retracted] Synergistic Effect of Conventional Medicinal Herbs against Different Pharmacological Activity
Authors: Ali Huma , Ali Daoud , Almutairi Bader O. , Kumar Gokhlesh , Karga Gizachew Assefa , Masi Chandran , Sundramurthy Venkatesa Prabhu
Published: June 2022

Triticum aestivum (Family: Poaceae), Ocimum sanctum (Family: Lamiaceae), and Tinospora cordifolia (Family: Menispermaceae) are commonly known as wheatgrass, tulsi, and giloy, respectively, which are the plants used as medicines for the treatment of various diseases. All three medicinal plants possess phenolic compounds with other important chemical constituents such as polysaccharides, aliphatic compounds, and alkaloids. The extract of these plants has been prepared and investigated for antioxidant, total phenolic content, total flavonoid content, and antimicrobial study in order to discover potential sources for new pharmaceutical formulations. To determine the antioxidant activity, a free radical scavenging assay for 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) and hydrogen peroxide was performed using ascorbic acid as the standard. The R2 value of the prepared extract was found to be 0.9964 and 0.990 in DPPH and hydrogen peroxide scavenging activity, respectively. The phenolic and flavonoid content was found to be 87.50 μl/ml and 58.00 μl/ml, respectively. The diffusion method was used to screen the antimicrobial activity of the prepared extract sample against various microorganisms. This extract showed better results for antioxidant and antimicrobial activity.
Systemic Therapy for Metastatic Hormone-Sensitive Prostate Cancer: A Randomized Controlled Trial-Based Network Meta-Analysis
Authors: Kang Zhen , Li Wei , Yu Yanhong , Yang Junfeng
Published: June 2022

Objective. To compare the effects of different treatment strategies for metastatic hormone-sensitive prostate cancer (mHSPC) using a network meta-analysis. Methods. English databases (PubMed, Embase, and medRxiv) and Chinese databases (Wanfang and CNKI) were searched for randomized controlled trials (RCTs) on the treatment of mHSPC from inception to June 1, 2021. The overall survival (OS) and failure-free survival (FFS) reported by the included studies were extracted from each study for network meta-analysis. Moreover, the priority ranks of the treatment methods were determined. Results. A total of 18 RCTs with 14,682 patients were included in this study. Androgen-deprivation therapy (ADT) + apalutamide (APA) showed the highest probability of improving the OS (96.2%) and FFS (68.0%). In addition, the patients were stratified into ten subgroups as follows: low/high tumor burden (CHAARTED criteria); Gleason score ≤7/≥8; Eastern Cooperative Oncology Group (ECOG) = 0/≥1; with/without prechemotherapy; and cooperative with/without concomitant radiotherapy. For the improvement of OS, the leading treatments were as follows: (1) ADT + enzalutamide (ENZA) (64.1%)/ADT + abiraterone acetate + prednisone (AAP) (54.3%); (2) ADT + ENZA (41.9%)/ADT + APA (39.2%); (3) ADT + ENZA (39.2%)/ADT + APA (32.1%); (4) ADT + radiotherapy (51%)/ADT + ENZA (76.7%); (5) ADT + AAP (51%)/ADT + AAP (60%). Conclusion. Three endocrine therapy drugs, abiraterone, enzalutamide, and APA, exhibited the best effects in improving the OS and FFS in all patients and subgroups; however, APA had the most prominent treatment effects. Therefore, ADT + APA should be applied as the common treatment for patients with HSPC based on objective and clinical conditions. Trial Registration. This meta-analysis has been registered on the PROSPERO website (Trial number: https://www.crd.york.ac.uk/prospero/CRD42020221062).
Real-World Evidence of Traditional Chinese Medicine (TCM) Treatment on Cancer: A Literature-Based Review
Authors: Peng Linjia , Zhang Ke , Li Yujie , Chen Lianyu , Gao Huifeng , Chen Hao
Published: June 2022

While randomized controlled trials (RCTs) are the gold standard for evidence-based medicine, they do not always reflect the real condition of patients in the real-world setting, which limits their generalizability and external validity. Real-world evidence (RWE), generated during routine clinical practice, is increasingly important in determining external effectiveness of the tightly controlled conditions of RCTs and is well recognized as a valuable complement to RCTs by regulatory bodies currently. Since it could provide new ideas and methods for clinical efficacy and safety evaluation of traditional Chinese medicine (TCM) and high-quality evidence support, real-world study (RWS) has received great attention in the field of medicine, especially in the field of TCM. RWS has shown desirable adaptability in the clinical diagnosis and treatment practice of traditional Chinese medicine. Consequently, it is increasingly essential for physicians and researchers to understand how RWE can be used alongside clinical trial data on TCM. Here, we discuss what real-world study is and outline the benefits and limitations of real-world study. Furthermore, using examples from TCM treatment on cancer, including Chinese herbal medicine, acupuncture, moxibustion, integrated TCM and Western medicine treatment, and other treatments, we elaborate how RWE can be used to help inform treatment decisions when doctoring patients with cancer in the clinic.
Salviae miltiorrhizae Liguspyragine Hydrochloride and Glucose Injection Protects against Myocardial Ischemia-Reperfusion Injury and Heart Failure
Authors: Kong Shang , Zhou Dan , Fan Qinghong , Zhao Tingting , Ren Chunguang , Nie Hong
Published: June 2022

Purpose. Myocardial ischemia-reperfusion (MIR) injury is a common stimulus for cardiac diseases like cardiac arrhythmias and heart failure and may cause high mortality rates. Salviae miltiorrhizae liguspyragine hydrochloride and glucose injection (SGI) has been widely used to treat myocardial and cerebral infarctions in China even though its pharmacological mechanisms are not completely clear. Methods. The protective effect and mechanism of SGI on MIR injury and heart failure were investigated through the H9c2 cell model induced by hypoxia/reoxygenation (H/R) and rapamycin, zebrafish model induced by H/R and isoprenaline, and rat MIR model. Results. SGI significantly reduced the infarct size and alleviated the impairment of cardiac functions in the MIR rat model and H/R zebrafish model and promoted cell viability of cardiomyocyte-like H9c2 cells under H/R condition. Consistently, SGI significantly downregulated the serum level of biomarkers for cardiac damage and attenuated the oxidative damage in the MIR and H/R models. We also found that SGI could downregulate the increased autophagy level in those MIR and H/R models since autophagy can contribute to the injurious effects of ischemia-reperfusion in the heart, suggesting that SGI may alleviate MIR injury via regulating the autophagy pathway. In addition, we demonstrated that SGI also played a protective role in the isoproterenol-induced zebrafish heart failure model, and SGI significantly downregulated the increased autophagy and SP1/GATA4 pathways. Conclusion. SGI may exert anti-MIR and heart failure by inhibiting activated autophagy and the SP1/GATA4 pathway.
A Neighborhood Grid Clustering Algorithm for Solving Localization Problem in WSN Using Genetic Algorithm
Authors: Chen Junfeng , Sackey Samson H. , Ansere James Adu , Zhang Xuewu , Ayush Altangerel
Published: June 2022

Finding the location of sensors in wireless sensor networks (WSNs) is a major test, particularly in a wide region. A salient clustering approach is laid out to achieve better performance in such a network using an evolutional algorithm. This paper developed a clustered network called neighborhood grid cluster which has a node assuming the part of a cluster center focused in every grid. Grid-based clustering is less difficult and possesses a lot of benefits compared to other clustering techniques. Besides, we proposed a localization algorithm that centers around assessing the target area by considering the least estimated distance embedded with the genetic algorithm. Performance standards incorporate the energy representation, connectivity stratagem, and distance measure as fitness functions that assess our localization problem to demonstrate its viability. Simulation results confirm that our approach further improves localization accuracy, energy utilization, node lifetime, and localization coverage.
[Retracted] Processing Decision Tree Data Using Internet of Things (IoT) and Artificial Intelligence Technologies with Special Reference to Medical Application
Authors: Al Fryan Latefa Hamad , Shomo Mahasin Ibrahim , Alazzam Malik Bader , Rahman Md Adnan
Published: June 2022

Alternative methods are available for a wide range of medical conditions. Idealistically, doctors would have a tool that would analyse their patients’ symptoms and suggest the most accurate diagnosis and treatment plan. Artificial intelligence uses decision trees to predict and classify large datasets. A decision tree is a versatile prediction model. Its main purpose is to learn from observations and logic. Rule‐based prediction systems represent and categorize events. We discuss the basic properties of decision trees and successful medical alternatives to the classic induction strategy. The study reviews some of the most important medical applications of decision trees (classification). We show researchers and managers how to accurately assess hospital and epidemic management behaviour. Additionally, we discuss decision trees and their applications. The results showed the effectiveness of decision trees in processing medical data by using internet of things (IoT) and artificial intelligence technologies in medical applications. Accordingly, the researchers recommend the use of these technologies in other fields of studies.
Relationship between Investor Sentiment and Price Fluctuation of SSE 50ETF Options
Authors: Wang Yang , Zhang Yinjie , Fu Yuwei
Published: June 2022

In this paper, we have studied the December call-options contract of SSE 50ETF options to put forward the sentiment composite index of options by means of the principal component analysis and to explore the relationship between such index and the fluctuation taking place in option prices. The empirical study has shown that the investor sentiment is correlated with option prices, and option prices prove to be more sensitive to the sentiment, whereas the impact imposed by the sentiment of option investors on option prices is more significant.
Construction of College Chinese Writing Practice Teaching Platform under the Background of Information Technology
Authors: Wu Jingli
Published: June 2022

Writing has always been one of the difficulties in teaching Chinese as a foreign language. With the advent of the information society, modern information technology, represented by networking, digitalization, multimedia, and intelligence, has become a creative tool to expand human capabilities, changing people’s traditional way of life, study, and work. Guided by the theory of integration of information technology and curriculum, combined with the characteristics of Chinese writing, this paper puts forward that the goal of integration of information technology and Chinese writing is to change the traditional teaching structure, create an ideal environment for students to learn Chinese and improve their Chinese information literacy. A practical teaching platform for college Chinese writing is constructed, which is designed from the user role and based on user access control. The design includes the roles of graduate students, foreign teachers, administrators, and users. Each user has different operation rights, module rights, and management rights. According to a small amount of online information and the results of a previous course, we can make a good prediction. The results show that this method is effective, and we can predict students’ academic performance through their online behavior.
A Nonlinear Stochastic Matrix Model for the Analysis of the Association between International Trade Growth and Environmental Change
Authors: Zhang Xinmei
Published: June 2022

This study uses a nonlinear stochastic matrix for an in-depth analysis of the association between international trade growth and environmental change. First, for a class of uncertain stochastic nonlinear time-lag systems, this study investigates its time-lag-related robust stochastic stabilization problem. A memory-free state feedback controller for the system is then obtained to ensure the effectiveness of the system performance. This makes the system more general and the corresponding theoretical results less conservative. Finally, numerical examples are given to further verify the validity and feasibility of the results. The “tariff measure” based on the magnitude of adverse changes in trade policy is not an accurate measure of trade policy uncertainty; the stochastic volatility method measures trade policy uncertainty by including tariff fluctuations that have been communicated in advance in the trade policy uncertainty indicator and fail to identify the different commodities that occur during the current trade friction. The trade policy uncertainty index constructed through textual analysis of newspapers can better reflect the trade policy uncertainty during this trade friction between China and the United States, and the rich time-varying nature can also reflect the alternating changes of tension and détente in economic and trade relations over some time. The analysis of the mechanism reveals that exporters have fewer emission reduction facilities than nonexporters but significantly lower energy consumption intensity for coal and water than nonexporters, reasoning that the mechanism by which exporters are more environmentally friendly than nonexporters lies in their cleaner energy use mix. Following the conclusions of this study, relevant policy recommendations are put forward, specifically the use of more efficient energy sources in the production process and more investment in energy efficiency and emission reduction to combat pollution.
ShenKang Injection Attenuates Renal Fibrosis by Inhibiting EMT and Regulating the Wnt/β-Catenin Pathway
Authors: Wei Hui-Ting , Xu Yuan , Tan Xiao-Yang , Jing Hao-Yue , Ma Yue-Rong
Published: June 2022

Shenkang Injection (SKI) is a traditional Chinese medicine injection commonly used in the clinical treatment of chronic kidney disease. Although it has been confirmed that SKI has anti-kidney fibrosis effects, the underlying mechanism remains unclear. To investigate the effects of SKI on epithelial-mesenchymal transition (EMT) and Wnt/β-catenin pathway and explore its potential anti-fibrosis mechanism. A unilateral ureteral obstruction (UUO) model was induced by ligating the left ureter of male SD rats. A total of 24 rats were randomly divided into the following four groups: sham group, model group, SKI group, and benazepril group. The rats in each group were treated for 28 days, and renal function was evaluated by blood urea nitrogen (BUN) and serum creatinine (Scr). The degree of renal fibrosis was assessed by hematoxylin and eosin (HE) and Masson staining. Extracellular matrix (ECM) deposition was evaluated by immunohistochemistry. Real-time fluorescent quantitative PCR (RT-qPCR) and western blotting were used to detect the expression of genes and proteins in the Wnt/β-catenin signaling pathway. Further studies were performed in vitro using HK-2 cells treated with TGF-β1. At 28 days postoperation, the levels of BUN and Scr expression were significantly increased in the UUO group. SKI and benazepril reduced the levels of BUN and Scr, which displayed protective renal effects. Pathological staining showed that compared with the sham operation group, the renal parenchymal structure was severely damaged, the number of glomeruli was reduced, and a large amount of collagen was deposited in the kidney tissue of the UUO group. SKI treatment reduced morphological changes. Immunohistochemistry showed that compared with the sham operation group, the content of collagen I and FN in the kidney tissue of the UUO group were significantly increased, whereas the SKI content was decreased. In addition, compared with the UUO group, the levels of Wnt1, active β-catenin, Snail1, and PAI-1 expression were reduced in the SKI group, suggesting that SKI may reduce renal fibrosis by mediating the Wnt/β-catenin pathway. Further in vitro studies showed that collagen I, FN, and α-SMA levels in HK-2 cells were significantly increased following stimulation with TGF-β1. SKI could significantly reduce the expression of collagen I, FN, and α-SMA. A scratch test showed that SKI could reduce HK-2 migration. In addition, by stimulating TGF-β1, the levels of Wnt1, active β-catenin, snail1, and PAI-1 were significantly upregulated. SKI treatment could inhibit the activity of the Wnt/β-catenin signaling pathway in HK-2 cells. SKI improves kidney function by inhibiting renal fibrosis. The anti-fibrotic effects may be mediated by regulation of the Wnt/β-catenin pathway and EMT inhibition.