bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction and Analysis of Dynamic Changes of College Students’ Ideological and Political Changes Based on Multiple Regression
Authors: Wang Minhong
Published: July 2022

The construction of a harmonious society requires college students to coordinate their ideological, political, and moral qualities with social development and the needs of the times. Through the investigation and analysis of the ideological, political, and moral qualities of college students, on the one hand, we can see that the ideological, political, and moral qualities of college students are generally positive and healthy. On the other hand, it also exposes the outstanding problems in the ideological and political aspects of college students and the shortcomings of the ideological and political work in colleges and universities. This paper analyzes the dynamic changes of college students’ ideological and political changes and further studies the relationship between various indicators and students’ ideological and moral qualities through multiple linear regression analysis.
Risk Assessment of Operator’s Big Data Internet of Things Credit Financial Management Based on Machine Learning
Authors: Bi Wentai , Liang Yuan
Published: August 2022

Credit risk evaluation innovation is of incredible importance to monetary establishments. AI innovation can fundamentally work on the precision and versatility of credit risk evaluation. This paper aims to study the risk assessment of operator big data Internet of Things credit financial management based on machine learning. It proposes machine learning-related algorithms, including the introduction of logistic model and decision tree model, as well as related concepts of credit financial management risk. This paper proposes that big data can be better used to reduce financial risk management problems and proposes specific actions based on the actual situation of the company. This paper selects company A for financial risk management evaluation through case analysis and compares it with three major e-commerce companies. The experimental results show that the earnings per share of company A is between −0.99 and 0. Company A is still in a state of loss in recent years, and there are certain debt risks, operational risks, and capital risks.
[Retracted] Music Classification Method Using Big Data Feature Extraction and Neural Networks
Authors: Li Xiabin , Li Jin
Published: July 2022

From the cassette era to the CD era to the digital music era, the quantity of music has grown rapidly. People cannot easily search for the desired music without classifying enormous music resources and developing a successful music retrieval system. By examining users’ historical listening patterns for personalised recommendations, the music recommendation algorithm can lessen message fatigue for users and enhance user experience. Relying on manual labelling is how traditional music is classified. It would be inefficient and unrealistic to attempt to classify music using manual labelling in the age of big data. Feature extraction and neural networks are the tools employed in this paper. The model’s parameters can be trained using conventional gradient descent techniques, and the model’s trained convolution neural network can learn the image’s features and finish the extraction and classification of the features. This algorithm is 12 percent superior to the conventional algorithm, according to the research in this paper. It has strong ability and is appropriate for widespread implementation with the same number of iterations.
A Fault Identification Method for Electric Submersible Pumps Based on DAE-SVM
Authors: Yang Peihao , Chen Jiarui , Zhang Hairong , Li Sheng
Published: July 2022

The purpose of this study was to investigate how to detect abnormalities in electric submersible pumps (ESPs) in advance and how to classify the faults by monitoring the production data before pumps break down. Additionally, a new method based on the denoising autoencoder (DAE) and support vector machine (SVM) is proposed. Firstly, the ESP production data were processed and fault-related features were screened using the random forest (RF) algorithm. Secondly, input data were randomly damaged by the addition of noise, a DAE network structure was constructed, and the optimal learning rate, noise reduction coefficient, and other parameters were set. Thirdly, the real-time status of the production data of ESP was monitored with reconstruction errors to detect the point when an abnormality occurs signifying a pending fault. Finally, SVM was used to distinguish the type of fault. Compared with existing fault diagnosis methods, our method not only has the advantages of easy extraction of effective data features, higher accuracy, and strong generalization ability but can also detect an abnormal state indicating a coming fault and identify its type, hence enabling the preparation of an appropriate advance solution.
[Retracted] Effect of Intervention of Probiotics in Advance on Treg/Th17 in Premature Mice
Authors: Liu Mengmeng , Mao Juanjuan , Zhang Shifa
Published: July 2022

To preliminarily understand the differentiation characteristics of regulatory T cells (Tregs) and Th17 at a different time in preterm mice, the impacts of probiotics on immune function progression, as well as the correlation of probiotics with Tregs and Th17. On embryonic day 18 of gestation, a mouse model of preterm birth was built using mifepristone (RU486). Following IPI of RU486, newborn mice were randomized to probiotics or NS gavage administration. Full‐term newborn mice were given the same dose of NS gavage administration. Phenotypic analysis of peripheral immune cell frequency was performed using flow cytometry. Cytokine measurements were phenotyped by enzyme‐linked immunosorbent assays. On the 14th and 21st days after birth, the highest and lowest expressions of Foxp3, the Treg transcription factor, were observed in full‐term mice and premature mice by NS gavage administration, respectively, while the opposite trend was found in the Th17 transcription factor IL‐17.IL‐2, IL‐6, and TGF‐β rose with age but showed different trends among the three groups. IL‐2 is the highest in full‐term mice and the lowest in premature mice. IL‐6 and TGF‐β is the lowest in full‐term mice and the highest in premature mice. Probiotics are beneficial to the development and maturation of the immune system, which may play a role in regulating the ratio of Treg/Th17. Probiotic preintervention can effectively promote the differentiation of Treg and inhibit the differentiation of Th17 in premature mice. Its mechanism of action may play a biological role by regulating cytokine (IL‐2, IL‐6, and TGF‐β) secretions.
Curcumin Elevates microRNA‐183‐5p via Cathepsin B‐Mediated Phosphatidylinositol 3‐Kinase/AKT Pathway to Strengthen Lipopolysaccharide‐Stimulated Immune Function of Sepsis Mice
Authors: Liu Yanan , Feng LiZhi , Hou Guo , Yao Lan
Published: July 2022

Curcumin (Cur), a natural polyphenol compound, has been testified to modulate innate immune responses and also showed anti‐inflammatory properties. Nevertheless, the mechanism was still poorly unknown, especially regarding Cur‐modulated microRNAs (miRNAs) under the inflammatory response. CD39+ regulatory T cells (Tregs) were provided with distinct immunosuppressive action and exerted a critical role in the modulation of immune balance in sepsis. Nevertheless, the impact of Cur on the immune function of sepsis mice has not been reported. In this study, the influence of Cur on the inflammatory response and immune function of sepsis mice via augment of miR‐183‐5p and Cathepsin B (CTSB)‐mediated phosphatidylinositol 3‐kinase (PI3K)/AKT pathway was explored. Adoption of 20 mg/kg Cur was for gavage. In the meantime, injection of plasmid vectors of interference with miR‐183‐5p or CTSB was into the tail vein. Intraperitoneal injection of lipopolysaccharide (10 mg/kg) was to stimulate model of sepsis mice. Histopathological changes of sepsis mice were observed. The contents of tumor necrosis factor‐α and Interleukin (IL)‐1β and IL‐6 in serum of mice were examined. Detection of alanine aminotransferase, aspartate aminotransferase (AST), urea nitrogen (BUN), and creatinine in serum of mice was performed. Test of the percentage of CD39+ Tregs in tail venous blood of mice was implemented. Examination of miR‐183‐5p, CTSB, and PI3K/AKT was performed. The targeting of miR‐183‐5p and CTSB was detected. Cur was available to ameliorate the histological damage, to reduce the content of inflammatory factors, AST, and BUN, and to decline the percentage of CD39+ Tregs in tail venous blood of sepsis mice. Elevated miR‐183‐5p or silenced CTSB was available to further enhance the protection of Cur. Cur was available to accelerate miR‐183‐5p, which negatively modulated CTSB and Cur‐mediated PI3K/AKT pathway via the miR‐183‐5p/CTSB axis to restrain inflammation of sepsis mice and enhance its immune function.
Multi-Tone Piano Transcription Based on Visualization and Knowledge Recognition Algorithm
Authors: Fan Yingjing
Published: July 2022

Scientific computing visualization is also known as visualization. In an academic sense, visualization is a way of computing. It can convert data into intuitive geometric images, making it easier to understand. Visualization is the use of computer graphics and image processing techniques. It is a technical means to convert piles of seemingly disorganized data into graphics or images through special computer methods. Recording is the process of recording sound signals on a medium. Specifically, it is the process of converting sound into electrical signals through microphones and amplifiers and recording them with different materials and techniques. Piano tuning is the adjustment of the strings so that they reach a certain high pitch. This paper aims to study the analysis and evaluation of multi-tone piano transcriptions based on scientific computing visualization and knowledge recognition algorithms. It expects to improve the quality of piano recording with the support of scientific visualization technology and knowledge recognition algorithm. This paper briefly explains the big data, and on this basis, the visual data mining technology is mentioned, and the current situation in the visual data mining is explained. A matrix calculation of the characteristic matrix of piano timbres was carried out to achieve the resynthesis of the 25th harmony in piano timbres electronically. The results of the trials in this article demonstrate that when the Gaussian number changes to 5 during the recording process, the difference that exists of the acoustic voltage intensity and the noise level phase is clearly diminished. When the Gauss figure is 10, there is an inversion of the value of the sound pressure phase.
Gastric Pseudomelanosis: An Uncommon Finding
Authors: Samies John , Shah Ruchit N. , Pramick Michelle , Unzueta Alberto
Published: July 2022

Gastric pseudomelanosis is a benign condition that has been characterized by pigment deposition within subepithelial macrophages inside the stomach wall. Occurrence of the condition is rare, with pseudomelanosis occurring more often within the duodenal mucosa. Few cases have reported pseudomelanosis within the gastric mucosa. We report a case of an 86-year-old female who presented with worsening iron deficiency anemia concerning for gastrointestinal bleeding. Her endoscopic evaluation was remarkable for a speckled pattern of dark pigmentation within the stomach, confirmed to be pseudomelanosis by histologic evaluation with pigment showing positive staining for iron.
An Improved Logistic Regression Method for Assessing the Performance of Track and Field Sports
Authors: Zheng Songling , Man Xi
Published: August 2022

Track and field is an important part of sports. Track and field athletes are an important reserve force for the development of national sports. An accurate assessment of track and field athletes’ performance can help them develop more appropriate training programs and improve their performance. In order to assess the performance of track and field athletes better, this paper proposes an improved logistic regression method. Firstly, this method uses factor analysis to reduce the data dimensions of the factors that affect the performance of track and field athletes, and uses the principal component analysis to select common factors and their corresponding values. Then, according to the common factors, a binary logistic regression model is established to evaluate the performance of track and field athletes. Experiments show that the method can effectively evaluate the performance of track and field athletes and is suitable for athletes of different track and field sports. It has high accuracy, fast evaluation efficiency, and good universality of performance evaluation. For different numbers of athletes, the proposed method has a lower error evaluation index, higher evaluation accuracy, and better evaluation quality. Compared with the other two methods, the proposed method has the shortest evaluation time and is more effective for the performance evaluation of track and field athletes.
Research on Herbal Therapies for Osteoarthritis in 2004–2022: A Web of Science-Based Cross-Sectional Bibliometric Analysis
Authors: Song Weijiang , Chen Jiaqi , Yang Guoyan , Liao Jiahe , Shen Hongbo , Li Sai , Ding Ning , Li Dong
Published: July 2022

Objective. The extent, range, and nature of available research in the field of herbal therapies for osteoarthritis (OA) have not been systematically analyzed. This study aimed to map the literature available on herbal therapies for OA and identify global hotspots and trends in this field. Methods. Studies on herbal therapies for OA published between 2004 and 2022 were searched from the Web of Science Core Collection. Microsoft Excel, SPSS Statistics, and CiteSpace software were used to analyze and visualize the quantity and citations of publications, and the research hotspots and trends in research on herbal therapies for OA. Results. A total of 1649 publications mainly from 76 countries/regions and 270 institutions were included in this study. From 2004 to 2022, there is an upward trend in the publications of herbal therapies for OA. China ranked first in the number of publications (n = 568, 34.45%), followed by the USA (n = 353, 21.41%), South Korea (n = 187, 11.34%), Germany (n = 85, 5.15%), and England (n = 79, 4.79%). Kyung Hee University (n = 46), Xianxiang Liu (n = 25), and Evidence-Based Complementary and Alternative Medicine (n = 74) were the most prolific affiliation, author, and journal, respectively. Felson DT (n = 185) and Arthritis and Rheumatism (n = 1173) held the record for the most cited papers by an author and journal, respectively. Currently, the hot keywords in the field of herbal therapies for OA include knee OA, traditional Chinese medicine (TCM), differentiation, rosa canina, inflammation, oxidative stress, stem cell, and regenerative medicine. The emerging research trends in herbal therapies for OA are herbal medicinal product, chronic knee pain, mesenchymal stem cell, and clinical pharmacology. Conclusions. Research on herbal therapies for OA is flourishing, but communication among countries/regions should be strengthened. Current research on herbal therapies for OA mainly focuses on knee OA, TCM, differentiation, rosa canina, inflammation, oxidative stress, stem cell, and regenerative medicine. The research frontiers are herbal medicinal product, chronic knee pain, mesenchymal stem cell, and clinical pharmacology.
Effects of Autophagy‐Related Genes on the Prognosis and Immune Microenvironment of Ovarian Cancer
Authors: Zhang Jing , Yan He , Fu Yan
Published: July 2022

Ovarian cancer (OC) is among the most malignant tumors of the female reproductive system. The role of autophagy in cancer is complex, and the functional relationship between autophagy‐related genes and OC remains unclear. Here, the prognostic value of autophagy‐related genes in OC and relationships between autophagy and immune function were evaluated. OC data from The Cancer Genome Atlas and the Human Autophagy Database were obtained to identify autophagy‐related genes. Univariate and multivariate Cox analyses were used to construct a prognostic model based on autophagy‐related genes. Relationships between risk scores and clinical traits were evaluated. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Cytoscape were used to analyze gene functions and their effects on the immune microenvironment. Relationships between autophagy genes and long noncoding RNAs (lncRNAs) were evaluated by Pearson’s correlation coefficients, and lncRNAs corresponding to the autophagy‐related genes associated with OC prognosis were used to construct a model. Relationships between risk scores and survival and prognosis were evaluated. Finally, a gene set enrichment analysis was performed. Seven autophagy‐related genes (CAPN1, CDKN1B, DNAJB1, GNAI3, MTMR14, RHEB, and SIRT2) were identified as independent predictors of prognosis. Three lncRNAs corresponding to autophagy genes independently influenced prognosis. Autophagy genes are closely related to immunity. Fifteen immune cell types showed different levels of infiltration between the high‐ and low‐risk groups. Moreover, immune cell infiltration differed between the high‐ and low‐risk groups based on the model. Our analysis of genes and lncRNAs related to prognosis clarifies the role of autophagy in OC and provides a theoretical basis for further research.
A Study on the Generation Mechanism and Development Process of Piping Based on the Theory of Muddy Water Seepage
Authors: Gao Jun , Dang Jiaote , Han Xiaoke , Fang Jianyin , Ren Jie , Tian Wei
Published: July 2022

In the process of piping development, the sediment carried by seepage causes the change of porosity and then causes the change of permeability coefficient. Therefore, piping belongs to muddy water seepage (i.e., unstable seepage). Based on the theory of muddy water seepage, the new control equation of piping model is established. The generation mechanism and development process of piping for white fine sand and sand gravel are explored. The advantages for the new control equation of piping model are discussed. The results show that the new control equation of piping model is not only simpler in form but also easier to solve and can better describe the characteristics of piping generation, development, and stability. Compared with white fine sand (the initial relative density and porosity are smaller, and the initial permeability coefficient and critical gradient are larger), the porosity, permeability coefficient, seepage flow, and sediment volume of sandy gravel increase faster with time. When the porosity is the same, the permeability coefficient, seepage flow, and sediment volume of sand gravel are larger. The smaller the area affected by piping, the faster the increase of porosity, permeability coefficient, seepage flow, and sediment volume. When the porosity and permeability coefficient are the same, the seepage flow and sediment volume are smaller. The smaller the initial porosity and permeability coefficient, the later the generation, and development of piping. It can provide a reference for piping danger forecast and emergency response.
The Relationship between Ovarian Ultrasound Parameters and Endocrine and Metabolic Indicators in Patients with Ovarian Syndrome
Authors: Sheng Changqing , Zhang Jin , Jue Jiang
Published: July 2022

Objective. The aim of this study is to investigate the relationship between the levels of endocrine and metabolic indicators and ovarian ultrasonography indicators in patients with ovarian syndrome (PCOS). Methods. Three hundred and forty patients with PCOS from January 2017 to February 2022 were selected as the observation group, and 340 healthy women of the same age were selected as the control group. A retrospective analysis was performed to observe the levels of endocrine and metabolic indicators and ovarian ultrasound examination indicators in the two groups. Results. The levels of testosterone, dehydroepiandrosterone (DHEA-S), luteinizing hormone (LH), LH/FSH, blood glucose, and insulin were higher in the observation group than those in the control group. The levels of low-density lipoprotein (LDL) and free insulin-like growth factor (IGF-I) were higher in the observation group than those in the control group. However, the level of high-density lipoprotein (HDL) was lower in the observation group than that in the control group. The ovarian interstitial area, total ovarian area, ovarian volume, number of follicles, uterine artery pulsatility index (PI), and resistance index (RI) were higher in the observation group than those in the control group. Pearson correlation analysis concluded that estrone (E1) levels in PCOS patients were correlated with ovarian interstitial area, total ovarian area, and ovarian volume. In addition, E1 levels correlated with LH levels, LH/FSH, testosterone, DHEA-S, and progesterone at

P
<
0.05

. Compared with different treatment methods, the total testosterone, LH, and LH/FSH levels in the two groups were decreased compared with those before treatment, and the degree of decrease in the combined treatment group was more significant than that in the treatment alone group. Conclusion. The levels of endocrine metabolism and ovarian ultrasound in PCOS patients are abnormal and there is a close relationship between the levels of endocrine metabolism and ovarian ultrasound. Attention should be paid to the monitoring and regulation of endocrine metabolism and ovarian ultrasound. Integrated traditional Chinese and western medicine can greatly improve the hormone levels in PCOS patients.
Financial Abnormal Data Detection System Based on Reinforcement Learning
Authors: Hou Xuechen
Published: August 2022

The fraud of financial data has seriously damaged the interests of stakeholders and hindered the healthy development of the economy. Based on the reinforcement learning theory, this paper proposes a model of intelligent analysis of enterprise financial data anomalies, constructs the difference method, and transforms nontemporal indicators into temporal indicators. The proposed model uses a convolution neural network with four hidden layers to classify, evaluate, and analyze the constructed temporal indicators of financial data. The main objective is a more effective and accurate identification of the issue. The test results show that the intelligent analysis method of financial abnormal data based on deep learning has ideal effectiveness and accuracy.
Serum Zinc Level in Patients with Severe Genital Warts: A Case-Control Study in a Dermatology Hospital
Authors: Luong Thuy Nguyen Dac , Nguyen Chuyen Thi Hong , Firas Al-Niaimi , Van Trung The
Published: August 2022

Background. Genital warts are a common sexually transmitted disease (STD), and there is no method that completely prevents its recurrence. Recently, zinc has been used in the treatment of cutaneous warts. Nondestructive action, ease of use, and promising results with low chances of relapse were reflected in the treatment. These effects may arise from the immunomodulatory activity of zinc in the event of a viral infection. Objectives. This study was aimed at identifying the relationship between the serum zinc level and the clinical characteristics of patients with genital warts. Materials and Methods. A case-control study was conducted. Genital warts were diagnosed by clinical examination, and disease severity was demonstrated based on the number of affected sites or the spread of lesions. The serum zinc level was measured using atomic absorption spectrophotometry. Results. A total of 78 patients with genital warts and 78 healthy volunteers were enrolled in the study. The mean serum zinc level in the genital wart group was lower than that in the control group (

81.83
±
13.99

 μg/dL vs.

86.66
±
17.58

 μg/dL); however, this difference was not statistically significant (

P
>
0.05

). The mean concentrations of serum zinc in patients having more than one affected site,

spread
>
2

 cm2, or ten or more lesions were significantly lower than those of the control group (

P
<
0.05

). Conclusions. The results suggested that severe genital warts may be associated with a low serum zinc level in patients.
Future of Business Culture: An Artificial Intelligence‐Driven Digital Framework for Organization Decision‐Making Process
Authors: Rajagopal Navaneetha Krishnan , Qureshi Naila Iqbal , Durga S. , Ramirez Asis Edwin Hernan , Huerta Soto Rosario Mercedes , Gupta Shashi Kant , Deepak S.
Published: July 2022

Technological efforts are currently being used across a broad array of industries. Through the combination of consumer choice and matching principle, the goal of this paper is to investigate the prospective implications of artificial intelligence systems on businesses’ outcomes. From an entrepreneurship standpoint, the research revealed that artificial intelligence systems can help with better decision‐making. What impact does the introduction of AI‐based decision‐making technologies have on organizational policymaking? The quirks of human and AI‐based policymaking are identified in this research based on five important contextual factors: precision of the choice search area, contribution to the innovation of the policymaking process and result, volume of the replacement collection, policymaking pace, and generalizability. We create a novel paradigm comparative analysis of conventional and automation judgment along these criteria, demonstrating how both judgment modalities can be used to improve organizational judgment efficiency. Furthermore, the research shows that, by involving internal stakeholders, they can manage the correlation among AI technologies and improve decision for businessmen. Furthermore, the research shows that customer preferences and industry norms can moderate the link between AI systems and superior entrepreneurial judgment. The goal of this work is to conduct a thorough literature analysis examining the confluence of AI and marketing philosophy, as well as construct a theoretical model that incorporates concerns based on established studies in the areas. This research shows that, in a setting with artificial intelligence systems, customer expectation, industry standards, and participative management, entrepreneurial strategic decisions are enhanced. This research provides entrepreneurs with technology means for enhancing decision‐making, illustrating the limitless possibilities given by AI systems. A conceptual approach is also formed, which discusses the four factors of profit maximization: relationship of AI tools and IT with corporate objectives; AI, organizational learning, and decision‐making methodology; and AI, service development, and value. This study proposes a way to exploit this innovative innovation without destroying society. We show real‐world examples of each of these frameworks, indicate circumstances in which they are likely to improve decision‐making performance in organizations, and provide actionable implications into their constraints. These observations have a wide variety of implications for establishing new management methods and practices from both academic and conceptual viewpoints.
[Retracted] The Characteristics and Paths of the Dissemination of Intangible Cultural Heritage in the Form of Animation in the New Media Environment
Authors: Luo Jie
Published: July 2022

In recent years, the state and even the society pay more and more attention to the protection of the intangible cultural heritage. How to effectively protect and inherit the intangible cultural heritage is the main work at present. Based on the role and influence of animation in the protection of intangible cultural heritage, this paper systematically discusses the promotion role of animation in the inheritance and dissemination of intangible cultural heritage. First, the role of animation communication in the protection of intangible cultural heritage. Finally, this paper takes the combination of intangible cultural heritage culture and animation as an example to discuss and design the combination of intangible cultural heritage protection animation and specific cases. This paper regards animation as a cultural medium, which is of great significance to the effective protection, dissemination, and inheritance of the national intangible cultural heritage.
Simulation of Multimedia Visual Image Motion Track Marking Based on Artificial Bee Colony Algorithm
Authors: Chen Wei
Published: July 2022

With the rapid development of electronic technology, network technology, and multimedia processing technology, people pay more and more attention to the surrounding environment. Paying attention to and handling emergencies have also become one of the main points of attention. Through the combination of multimedia and monitoring system, the collected image data are processed and integratedly controlled by the computer platform, and an intelligent monitoring system for traffic road safety can be obtained. Due to the advantages of multimedia visual image motion trajectory identification technology in road traffic safety, it can effectively prevent the chaos and influence caused by humans in chaotic situations, making it widely used. By identifying the motion trajectories of different objects, road emergencies can be effectively prevented and countermeasures can be established. Based on this, this study proposes the artificial bee colony (ABC) algorithm to identify the motion trajectory in the visual image, but the standard ABC algorithm is too slow to converge in the later stage of trajectory recognition. Therefore, this study is based on the standard ABC algorithm. The trajectory recognition simulation shows that the optimized algorithm accelerates the convergence speed and the ability of the global optimal solution in the trajectory recognition process. Then, the optimized ABC algorithm is used to identify the multimedia visual image trajectory. The experiment shows that the average correct rate of feature extraction is also about 98%. The data identification results also show that the optimized ABC algorithm fits the real trajectory better in the trajectory identification. The running time of the algorithm is shorter than the running time of other comparison algorithms, which fully shows the accuracy and superiority of the algorithm in this study.
Clinical Efficacy of PD-1 Inhibitors Plus Split-Course Radiotherapy in the First-Line Treatment of Advanced Kidney Cancer: A Randomized Controlled Trial
Authors: Li Weiping , Cao Zhigang , Chang Pengcheng , Zhang Bin , Li Fudong , Chang Dehui
Published: July 2022

Objective. To assess the clinical efficacy of programmed death 1 (PD-1) inhibitors plus split-course radiotherapy in the first-line treatment of advanced kidney cancer. Methods. In this prospective, randomized, single-blinded, controlled trial, 44 patients with advanced kidney cancer initially treated in our hospital from January 2017 to December 2018 were recruited. They were concurrently and randomly assigned at a ratio of 1 : 1 to the control group and the study group, with 22 cases in each group. The control group received PD-1 inhibitor nivolumab combined with ipilimumab, and the study group received split-course radiotherapy plus. The primary endpoint is clinical efficacy, and the secondary endpoints are progression-free survival (PFS), overall survival (OS), and adverse events (AEs). Results. Nivolumab plus split-course radiotherapy was associated with an objective remission rate (ORR) of 59.09% versus nivolumab alone with an ORR of 27.27%. The median PFS was 21.5 months (95% CI: 14.1—NA) after single nivolumab therapy and 28.1 months (95% CI: 24.5—NA) after nivolumab plus split-course radiotherapy, with an HR of 1.875 (95% CI: 0.877–4.011). The median OS was 27.1 months (95% CI: 20.7—NA) after single nivolumab therapy and not reached after nivolumab plus split-course radiotherapy and an HR of 2.56 (95% CI: 1.081–6.06). Nivolumab was associated with significantly better OS plus split-course radiotherapy versus nivolumab alone. Conclusion. Nivolumab plus split-course radiotherapy in patients with advanced renal cell carcinoma significantly improves ORR and prolongs overall survival with a good safety profile.
The Effect of Standing Mats on Biomechanical Characteristics of Lower Limbs and Perceived Exertion for Healthy Individuals during Prolonged Standing
Authors: Zhang Yan , Xu Yining , Gao Zixiang , Yan Hongjun , Li Jianshe , Lu Yichen
Published: July 2022

Objective. To identify the effect of standing mats on biomechanical characteristics of lower limbs and perceived exertion for healthy adult individuals during a prolonged standing task. Methods. 32 healthy college students were recruited in the randomized and cross-over designed trial according to the effect size and statistical power. After collecting the anthropometric data, each participant was asked to finish 2 sessions of 4-hour prolonged standing tasks on standing mats (MS) and hard ground (GS) in a random order and with a 72-hour interval rest. The plantar pressure distribution, foot morphology, and scores of the BESS (balance error scoring system) would be recorded pre- and posteach task. The Borg Rating of Perceived Exertion (RPE) would be collected during the whole task. Paired-samples

t

test was adopted to analyse the before and after difference within group and independent-samples

t

test was adopted to analyse the difference between groups separately. Results. (1) A prolonged standing task on both MS and GS have a negative effect on RPE and balance performance. (2) The negative effect on RPE and balance performance induced by MS is significantly smaller than that induced by GS. (3) Compared to GS, prolonged standing on MS has a lower peak plantar pressure and an implicit decrease in navicular drop and AHI (arch index). Conclusion. Standing mat tends to alleviate the fatigue induced by prolonged standing in lower limbs, optimize the distribution of plantar pressure, and maintain the stability.
Investigation of Diabetes Care in Elder Individuals Using Artificial Intelligence
Authors: Vyas Sonali , Gupta Sachin , Tarar Sandhya , Omarov Batyrkhan , Pant Bhasker , Bangare Jyoti L. , Bhowmik Tapas
Published: August 2022

The term blockchain is mainly regarded as the distributed transaction which is mainly comprised of different blocks, and each set tends to represent the data that are being associated with the previous blocks. The blockchain is mainly managed through peer-to-peer networks which comparatively involves in adhering to the protocol of authenticating various blocks to form the blockchain. The usage of blockchain technology has been increasingly used in different fields, and healthcare services are now using blockchain for better patient delivery, detecting disease, and other aspects. The scope of the proposed study is that this study has exploited the function of a blockchain-enabled big data network to support medical professionals in giving better treatment modalities and delivering better patient care. The application of a new generation of smart block chains such as Ethereum and NEM is now offering better services and features in creating blockchain-based healthcare data management and hence support healthcare centers, medical practitioners, nurses, radiologists, and patients for better healthcare management. The application of blockchain technology in big data networks supports adding more value as it results in enhanced data quality, accessibility, and support in creating better security and safety of data and information, which is highly essential in the medical industry. Blockchain technology enables big data technologies enabled in supporting medical practitioners in addressing various healthcare ailments; one of the major diseases impacting many people around the world is diabetes. Patients with such ailments tend to generate more data and information related to the disease and health-related aspects. Hence, this information requires being maintained and analyzed, so that superior healthcare services can be provided. This study is more involved in the investigation of blockchain technology through a big data network enabled in offering better care for elderly individuals who have been affected due to diabetes, the researchers propose to choose a questionnaire method to collect the data from nearly 169 respondents, and these data were then analyzed using SPSS data package. The analyst used percentage analysis, correlation analysis, and chi-square test to analyze the data which has been collated by the researchers. The results and discussion show in detail the major aspects of blockchain technology in supporting healthcare professionals for better diabetes care management for elderly individuals.
Characteristics of Virulence Genes of Clinically Isolated Staphylococci in Jingzhou Area
Authors: Wang Meng , Zhang Qing
Published: July 2022

Purpose. The aim of this study was to further understand the distribution characteristics of staphylococcal virulence genes in the Jingzhou area, in order to provide a basis for clinically effective treatments and prevention and control measures. Methods. A total of 181 strains of staphylococci were collected from Jingzhou Hospital Affiliated to Yangtze University from April 2013 to April 2021, which were divided into the methicillin‐resistant Staphylococcus aureus (MRSA) strains and the methicillin‐sensitive Staphylococcus aureus (MSSA) strains and coagulase‐negative staphylococci (CoNS) by an antimicrobial susceptibility test and PCR method. The 73 MRSA strains were classified by staphylococcal cassette chromosome mec (SCCmec) and Staphylococcus protein A (Spa). Sea, sec, seh, sek, seb, seq, sep, Tsst‐1, clfA, clfB, fnbA, hla, hld, hlg, lukE, bbp, cna, eap, ebpS, sdrC, sdrD, sdrE, and Pvl genes were also detected in all strains. The χ2 test was used for statistical analysis for comparison between groups. Results. The 181 strains of staphylococci were divided into 97 strains of MRSA, 54 strains of MSSA, and 30 strains of CoNS. 73 MRSA strains were derived from clinical specimens such as lower respiratory tract, secretions, sepsis secreted by tissue infection, urine, and hydrothorax. There were 70 strains that can be identified including SCCmec types and 15 Spa types of all strains, while the most popular types were SCCmecIII‐t030 and SCCmecIV‐t437 from lower respiratory tract specimens. There were four virulence genes that were detected including seb, seq, clfB, and hld in CoNS strains, while the detection rates of these four virulence genes in Staphylococcus aureus were higher than that of CoNS, and the differences were statistically significant, P < 0.05(P = 0.004, P = 0.001, P = 0.001, P = 0.001). 23 virulence genes were detected in 151 strains of Staphylococcus aureus, among which the detection rate of the Tsst‐1 gene was the lowest and that of the clfB gene was the highest, and the other genes were 4.6%∼98.0%. The detection rates of sea, sek, seb, seq, sep, cna, eap, ebpS, sdrC, and sdrE virulence genes in MRSA were higher than that of MSSA, and the differences were statistically significant, P < 0.05 (P = 0.001, P = 0.001, P = 0.001, P = 0.001, P = 0.009, P = 0.019, P = 0.001, P = 0.001, P = 0.001, P = 0.003). Conclusion. The mainly prevalent type of MRSA strains in Jingzhou is SCCmecIII‐t030 in lower respiratory tract specimens. Virulence genes of Staphylococcus aureus and antibiotic drug resistance rates are also different from other regions. In this experiment, virulence genes were also detected in CoNS, suggesting that more attention should be paid to the prevention and treatment of these strains clinically.
Research on B2C Online Marketing Mode based on Multimodel Fusion and Intelligent Big Data Analysis Method
Authors: Wei Jianhua
Published: July 2022

B2C online marketing mode is the development trend of future marketing. The key to improve the efficiency of such a mode is to make efficient use of the current large-scale data and mining the corresponding potential value. To control the cost of B2C online marketing mode, this paper analyzes the UJRP model and proposes a hybrid bat difference algorithm (BADE). Experimental results are verified the effectiveness of the proposed method in diversity and cost control. Furthermore, we utilize multimodel fusion strategy (linear weighted fusion) to achieve better performances in B2C online marketing on cost than BADE method. Finally, in the random and diverse online marketing environment, such an improved method can provide the decision-makers of B2C online marketing mode with more flexible choices.
The Relationship Marketing Performance Evaluating in Financial Services Sector of Project Management
Authors: Rostami Mohammad Reza , Mirshahi Hamideh
Published: July 2022

In any economy, the financial sector is interpreted as the driving force in the process of achieving growth through the appropriate and optimal allocation of resources to productive economic sectors. Financial institutions are defined as customer service providers. The generalization of the obtained results to the whole statistical population is the last step in this research. The research is a survey that can provide data on attitudes, feelings, beliefs, past behaviors, recorded behaviors, and the recognition of the acquisition of personal characteristics. Considering that the services available in commercial banks are relatively the same and it is difficult for most banks to differentiate these services from competitors, therefore, many banks around the world have tended to use the relationship marketing approach. The concept of relationship marketing was first mentioned in the American marketing literature in a 1983 article by Barry, who considered relationship marketing as a strategy for attracting, retaining, and strengthening customer relationships. This means that if the bank’s reciprocal relationships increase by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.663 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of the bank’s reciprocal relationships and customer satisfaction with financial services is 8.471 at the 95% confidence level, which is more than 1.96, indicating the importance of the bank’s reciprocal relationships on customer satisfaction with financial services. To evaluate the sixth hypothesis that “the bank’s reciprocal relationships affect customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that the bank’s reciprocal relationships with the value of 0.663 affect customer satisfaction with financial services.
Specific Emitter Identification Based on Multichannel Depth Feature Fusion
Authors: Tong Lin , Fang Mengqing , Xu Yulu , Peng Zhengcheng , Zhu Weijie , Li Ke
Published: July 2022

Specific emitter identification involves extracting the fingerprint features that represent the individual differences of the emitter. This is achieved by processing the radio-frequency signals. Feature extraction and classifier selection are key factors that affect SEI performance. This paper proposes a deep convolutional neural network model based on multisignal feature fusion to identify the emitters. As part of the implementation model, the methods of singular spectrum analysis (SSA), variational mode decomposition (VMD), and intrinsic time-scale decomposition (ITD) are used to extract various signal features of emitter signals. Finally, a multichannel deep learning model is adopted to fuse each signal feature automatically and identify different signal emitters. Experimental results show that the proposed method completely considers the complementarity and independence of varying signal features and excavates hidden deep feature information. Hence, the process is considered reliable and effective.
DNA Polymorphism of the LOXL1 Promoter Region in Exfoliation Syndrome in Uygur Individuals in XinJiang, China
Authors: Ma Yinu , Yang Mengting , Chen Xueyi , Yi Xianglong
Published: July 2022

Purpose. On the basis of our previously reported work, the association of lysyl oxidase-like 1 (LOXL1) promoter region gene polymorphism with exfoliation syndrome (XFS) and exfoliation glaucoma (XFG) in Uygur individuals was examined. Methods. This was a case-control association trial. A total of 242 unrelated XFS/G and 310 control cases were assessed. The genotypes of 6 single nucleotide polymorphisms (SNPs) of the LOXL1 promoter (rs4886761, rs4886467, rs4558370, rs4461027, rs16958477, and rs12914489) were examined via direct sequencing. Results. Each of the above SNPs had significant associations with XFS and XFG. The T allele of rs4886761 (OR (95% CI): 2.204 (1.711–2.838)), G of rs4886467 (OR (95% CI): 1.946 (1.513–2.503)), T of rs4461027 (OR (95% CI): 2.26 (1.773–2.881)), A of rs16958477 (OR (95% CI): 1.792 (1.399–2.297)), and G of rs12914489 (OR (95% CI): 1.103 (0.631–1.929)) independently predicted XFS/G. The genotypes TT and CC of rs4886761 (OR (95% CI): 5.655 (3.000–10.660) and 2.241 (1.473–3.408), respectively), TT and GG of rs4886467 (OR (95% CI): 4.026 (2.162–7.497) and 1.631 (1.08–2.463), respectively), CC and TT of rs4461027 (OR (95% CI): 5.245 (3.037–9.058) and 2.210 (1.37–3.564), respectively), CC and AA of rs16958477 (OR (95% CI): 3.530 (1.968–6.334) and 1.740 (1.145–2.646), respectively) also independently predicted XFS/G. The GGT and GTG haplotypes of rs12914489, rs4886467, and rs4558370 and TC and CT of rs4461027 and rs4886761 showed significant associations with XFS/G. Conclusions. These results confirmed LOXL1 as a susceptibility gene in XFS/XFG among Uygur individuals. The new SNPs of rs4886761, rs4886467, rs4461027, and rs16958477 polymorphisms are involved in the pathogenetic mechanism of XFS/G.
A Prospective Double-Blinded Randomized Controlled Trial Comparing the Intraoperative Injection of Technetium Tc 99m Tilmanocept with Technetium Tc 99m Sulfur Colloid in Breast Cancer Lymphatic Mapping
Authors: Ferez-Pinzon Anyul , Corey Samuel L. , Rochani Haresh , Rehl Elena A. , Burak Jr William E.
Published: July 2022

Introduction. Technetium-labeled sulfur colloid (TSC) is a radiolabeled mapping agent commonly used for sentinel lymph node biopsy (SLNBx). Tilmanocept, a CD206 receptor-targeted mapping agent, has gained recent popularity due to potential advantages of rapid and quick uptake to the SLNs. The objectives of this study were to assess (1) the difference in the number of SLNs harvested using tilmanocept versus TSC and (2) the difference in time to transcutaneous localization when using an intraoperative injection approach. Methods. Patients undergoing breast conservation and SLNBx were consented and randomized to receive either 0.5 mCi of filtered TSC or 0.5 mCi of tilmanocept injected intradermally immediately after induction of anesthesia. Axillary transcutaneous gamma detector probe counts were taken at 1-minute intervals until a hot spot was identified. SLNs were then identified and excised. Additional nodes were excised if their counts per second (cps) were greater than 10% of the cps of the hottest SLN. The number of SLNs was based on both number of nodes collected intraoperatively and the number recorded in the final pathology report. Results. The study population consisted of 86 patients, 48 randomized to tilmanocept and 38 to TSC. There were no significant differences in patient or tumor characteristics between the two groups. Localization rates were 100% for both cohorts. The mean number of SLNs identified and removed was not significantly different (

p
=
0.34

, intraoperatively;

p
=
0.57

, pathology reported). Time to transcutaneous localization was 3.3 ± 2.0 minutes for tilmanocept and 3.9 ± 2.3 minutes for TSC (

p
=
0.19

). The average cps for the hottest node was 2,180.0 ± 2,460.5 in the tilmanocept group compared to 2,679.3 ± 2,687.5 in the TSC group (

p
=
0.94

). Conclusion. There was no significant difference in the number of SLNs harvested or in the time to transcutaneous localization when using tilmanocept versus TSC as the radiolabeled mapping agents for intraoperative injection and mapping. Either agent can be used without any significant difference in performance.