bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Effect of Comprehensive Nursing Interventions on Plaque Control in Patients with Periodontal Disease in the Context of Artificial Intelligence
Authors: Xu Juan , Wang Lingling , Sun Hongxia , Liu Shanshan
Published: March 2022

Plaque is a bacterial biofilm that adheres to each other and exists on the tooth surface, and new plaque can continuously reform after removing it from the tooth surface. The pathogenesis of periodontal disease is related to the bacteria, the host and the environment, with the bacteria and bacterial products in plaque being the main initiators of periodontal disease. The effective control of plaque is an effective method for the treatment and prevention of periodontal disease and is often underappreciated in clinical practice. For the traditional diagnostic method through experience and visual observation, it may lead to misdiagnosis and underdiagnosis. In order to accurately diagnose plaque disease, this study designed a convolutional neural network-based oral dental disease diagnosis system for oral care interventions to improve oral health awareness. Thus motivate patients to implement proper oral health care measures, and continuously and lifelong insist on thorough daily plaque removal to improve patients’ physical health and quality of life in periodontal disease patients.
Natural Language Processing Technology Used in Artificial Intelligence Scene of Law for Human Behavior
Authors: Ning Jin
Published: March 2022

In order to study the application of natural language processing (NLP) technology in artificial intelligence (AI) scene of law, NLP technology is used to construct a legal AI retrieval system and further simulate the system. Then, by inputting the subject matter of the case into the system, the system’s accuracy, recall rate, and error rate and other related indicators are evaluated, to analyze the performance of the legal retrieval system. The results show that in the case analysis of a single theme, the accuracy rate of the case with the theme of “impeding police enforcement” is low, and the accuracy rate of the other theme cases is over 70%, and the highest accuracy rate even reaches 95%. In the case retrieval analysis of multitheme, the accuracy rate of case retrieval is improved, higher than 75%, and the zero-detection rate is significantly reduced with the increase in keywords. In the analysis of network case retrieval, the average correct rate of the overall case retrieval will be nearly 65%. Further tests on its reliability show that during the continuous week of the retrieval test, the system has no faults and passed the reliability test. Therefore, through this study, it is found that the application of NLP technology in the legal AI retrieval system has a reliable accuracy, which meets the expectation of this paper.
Hierarchical Network Security Measurement and Optimal Proactive Defense in Cloud Computing Environments
Authors: Xing Jingyu , Zhang Zheng
Published: March 2022

This paper presents an in-depth study and analysis of hierarchical network security measurement and optimal active defense in the cloud computing environment. All the cloud platform security-related data collected through cloud platform monitoring is collected, and then the relevant security data is summarized and analyzed, so that the specific security posture index of the cloud platform can be derived, thus providing a reference for cloud platform managers to judge the security risks of the cloud platform. It provides a reference for cloud platform managers to judge the security risks of cloud platforms. Through the cloud platform security situation awareness system, we mainly study the construction of cloud platform, the construction of security situation awareness system, and the calculation of security situation value and use, thus greatly improving the stability, security, and reliability of the cloud platform. The application of the method avoids the drawbacks of traditional network security management, which relies entirely on past data and cannot sense changes in the security state of the system in real time. At the same time, the predicted results are added to the input of the fuzzy decision-making system, improving the accuracy of the assessment. The method improves the real-time and effectiveness of network security posture prediction, increases the convergence speed and prediction accuracy of the algorithm, and avoids the occurrence of overfitting. Simulation experiments based on the internet network security posture dataset show that this research method has less prediction error than the traditional machine learning methods and other deep learning methods, has higher learning efficiency, and is more rapid, accurate, and effective in predicting the trend of network security posture in the big data environment in the future period.
Anatomical Engineering and 3D Printing for Surgery and Medical Devices: International Review and Future Exponential Innovations
Authors: Cornejo José , Cornejo-Aguilar Jorge A. , Vargas Mariela , Helguero Carlos G. , Milanezi de Andrade Rafhael , Torres-Montoya Sebastian , Asensio-Salazar Javier , Rivero Calle Alvaro , Martínez Santos Jaime , Damon Aaron , Quiñones-Hinojosa Alfredo , Quintero-Consuegra Miguel D. , Umaña Juan Pablo , Gallo-Bernal Sebastian , Briceño Manolo , Tripodi Paolo , Sebastian Raul , Perales-Villarroel Paul , De la Cruz-Ku Gabriel , Mckenzie Travis , Arruarana Victor Sebastian , Ji Jiakai , Zuluaga Laura , Haehn Daniela A. , Paoli Albit , Villa Jordan C. , Martinez Roxana , Gonzalez Cristians , Grossmann Rafael J. , Escalona Gabriel , Cinelli Ilaria , Russomano Thais
Published: March 2022

Three‐dimensional printing (3DP) has recently gained importance in the medical industry, especially in surgical specialties. It uses different techniques and materials based on patients’ needs, which allows bioprofessionals to design and develop unique pieces using medical imaging provided by computed tomography (CT) and magnetic resonance imaging (MRI). Therefore, the Department of Biology and Medicine and the Department of Physics and Engineering, at the Bioastronautics and Space Mechatronics Research Group, have managed and supervised an international cooperation study, in order to present a general review of the innovative surgical applications, focused on anatomical systems, such as the nervous and craniofacial system, cardiovascular system, digestive system, genitourinary system, and musculoskeletal system. Finally, the integration with augmented, mixed, virtual reality is analyzed to show the advantages of personalized treatments, taking into account the improvements for preoperative, intraoperative planning, and medical training. Also, this article explores the creation of devices and tools for space surgery to get better outcomes under changing gravity conditions.
Efficient Message Dissemination on Curve Road in Vehicular Networks
Authors: Huang Shidong , Huang Chuanhe , Wu Dongfang , Yin Yabo , Ashraf M. Wasim Abbas , Fu Bin
Published: March 2022

Effective emergency message dissemination is a great importance on a specific road in vehicular networks (VN). The existing methods are not most efficient solutions for message dissemination on the curve road, which primarily focus on highway and urban road. In order to improve the efficiency of message dissemination on the curved road, the paper proposed a message dissemination method based on bidirectional relay nodes. The message can be disseminated in two directions simultaneously. The paper designed a relay node selection method based on the neighbor nodes’ coverage length of the road. Different waiting delays are assigned to the neighbor nodes according to the cover capability of the road in which the message has not arrived. Simulation results demonstrated that the efficiency of the proposed method is superior to the common solutions in terms of the contention delay and the propagation velocity.
Effect of Intermittent Fasting Diet on Glucose and Lipid Metabolism and Insulin Resistance in Patients with Impaired Glucose and Lipid Metabolism: A Systematic Review and Meta-Analysis
Authors: Yuan Xiaojie , Wang Jiping , Yang Shuo , Gao Mei , Cao Lingxia , Li Xumei , Hong Dongxu , Tian Suyan , Sun Chenglin
Published: March 2022

The question of whether or not intermittent fasting diets improve the clinical indicators of glycolipid metabolism remains unclear. This study systematically reviewed the relevant clinical trials to evaluate the effects of intermittent fasting diet on glucose and lipid metabolism and insulin sensitivity in patients with metabolic syndrome. To evaluate the effect of intermittent fasting diet intervention on patients with disorders of glucose and lipid metabolism, random-effect or fixed-effect meta-analysis models were used to calculate the average difference before and after intermittent fasting diet intervention and the corresponding 95% confidence intervals (CIs). After intermittent fasting diet intervention, in terms of glucose metabolism, fasting blood glucose reduced by 0.15 mmol/L (95% CI: −0.23; −0.06), glycosylated hemoglobin reduced by 0.08 (95% CIs: −0.25; −0.10), insulin plasma levels reduced by 13.25 uUI (95% CIs: −16.69; −9.82), and HOMA-IR decreased by 0.31 on an average (95% CIs: −0.44; −0.19). In addition, BMI decreased by 0.8 kg/m2 (95% CIs: −1.32; −0.28), body weight reduced by 1.87 kg (95% CIs: −2.67; −1.07), and the waist circumference decreased by 2.08 cm (95% CIs: −3.06; −1.10). Analysis of lipid metabolism showed that intermittent fasting diet intervention effectively reduced the total cholesterol level by 0.32 mmol/L (95% CIs: −0.60; −0.05), low-density lipoprotein level by 0.22 mmol/L (95% CIs: −0.37; −0.07), and triglyceride level by 0.04 mmol/L (95% CIs: −0.15; −0.07). Intermittent fasting diets have certain therapeutic effects on blood glucose and lipids in patients with metabolic syndrome and significantly improve insulin resistance. It may be considered as an auxiliary treatment to prevent the occurrence and development of chronic diseases.
The Role of Osthole on TGF-β-Induced Lung Epithelium Apoptosis Injury and Epithelial-Mesenchymal Transition-Mediated Airway Remodeling in Pediatric Asthma
Authors: Tang Jinghai , Liu Jilong , Zhang Xuehua
Published: March 2022

Osthole, a coumarin compound derived from Fructus Cnidii, exerts anti-inflammatory effects in an asthma model. But the effect of osthole on epithelial injury and epithelial-mesenchymal transition (EMT) in asthma remains unclear. 16HBE cells were incubated with TGF-β1 with or without osthole in vitro. Ovalbumin (OVA)-induced asthmatic mouse model was established in vivo. Cell counting kit-8 was carried out to evaluate the viability of 16HBE cells. The impact of osthole on TGF-β1-evoked cell apoptosis and EMT process was measured by flow cytometry based on Annexin V-FITC/PI staining, transwell assay, immunofluorescence, and Western blot. The regulatory role of osthole in TGF-β1/Smad and p38, ERK1/2, and JNK MAPK signaling was detected via Western blot. Osthole treatment significantly suppressed TGF-β1-induced 16HBE cell apoptosis, verified by a reduced percentage of apoptotic cells, decreased expression of proapoptotic proteins (cleaved-caspase3 and Bax), and enhanced antiapoptotic factor (Bcl-2) expression. In addition, the promotive impact of TGF-β1 on the migration of 16HBE cells was reversed by osthole, accompanied by elevated E-cadherin expression and reduced Snail and N-cadherin expression. The activation of the Smad2/3 and MAPKs pathway evoked by TGF-β1 was inhibited by osthole in 16HBE cells. We also found that osthole mitigated airway epithelium injury and subepithelial fibrosis in OVA-challenged asthmatic mice in vivo. Osthole could mitigate TGF-β1-induced epithelial cell injury and EMT process by suppressing the activation of MAPK and Smad2/3 pathways separately. Our present study showed a new insight into understanding the underlying mechanism of osthole injury on epithelium injury and subepithelial fibrosis in airway remodeling. Asthma, epithelial injury, epithelial-mesenchymal transition, and airway remodeling are the effects of osthole on airway remodeling.
Analysis of the Impact of U.S. Trade Policy Uncertainty on China Based on Bayesian VAR Model
Authors: Yan Huan , Xiao Weiguo , Deng Qi , Xiong Sisi
Published: March 2022

Using a set of Chinese economic data and a Bayesian vector autoregression (BVAR) model, this study empirically analyzes the spillover effects of U.S. trade policy uncertainty on the output, consumption, investment, and net export in China. The results find that U.S. trade policy uncertainty is an important factor influencing China’s real economy. Specifically, an increase in U.S. trade policy uncertainty has a significant negative effect on China’s output, consumption, and net export in the short and long run, but it will have a negative impact on investment in the short run and a positive impact in the medium and long run.
The Relationship between Plasma Pentraxin 3 and Serum Amyloid P Levels and Disease Activity in Ankylosing Spondylitis
Authors: Özdemirel Ali Erhan , Güven Serdar Can , Sunar İsmihan , Sari Sürmeli Zühre , Doğanci Alper , Tutkak Hüseyin , Yalçin Sayin Ayşe Peyman , Ataman Şebnem
Published: March 2022

Background. In clinical practice, it is hard to judge the level of disease activity in some patients with ankylosing spondylitis (AS) who have low traditional acute phase reactant values such as erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) but have considerable pain and inflammation. The aim of this study is to investigate plasma pentraxin 3 (PTX3) and serum amyloid P (SAP) levels in patients with AS who had normal ESR and CRP but high disease activity. Methods. 100 AS patients and 100 gender- and age-matched controls were included. Epidemiological, clinical, and treatment data and plasma levels of CRP, ESR, PTX3, and SAP were evaluated. The Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and the Ankylosing Spondylitis Disease Activity Score (ASDAS)-CRP were used for evaluating disease activity. Plasma levels of PTX3 and SAP were compared between AS patients and controls and also among AS patients with active and inactive disease. Results. AS patients had significantly higher plasma levels of PTX3 and SAP than controls. There were not any significant correlations between PTX3 and SAP with BASDAI, ASDAS-CRP, and ESR. There was a positive correlation between PTX3 and CRP. No significant difference in plasma levels of PTX3 and SAP was observed between patients with active disease and inactive disease, both with normal ESR and CRP levels. Disease duration and treatment did not influence plasma PTX3 levels. Conclusions. In patients with AS, plasma levels of PTX3 and SAP were found to be elevated when compared to healthy controls. No association was observed between these biomarkers and disease activity.
The Anticancer Mechanisms of Scutellaria barbata against Lung Squamous Cell Carcinoma
Authors: Li Feng , Fu Xianxian , Liu Lingli , Wei Xiaobin
Published: March 2022

Objective. Scutellaria barbata (S. barbata) is a Chinese traditional medicinal crop with anti-inflammatory as well as anticancer properties. To explore the anticancer mechanisms of functional monomers of S. barbata against lung squamous cell carcinoma (LUSC), a network pharmacology approach and molecular docking were utilized. Methods. The expression profile of genes encoding functional monomer components in S. barbata was obtained from the Traditional Chinese Medicine Systems Pharmacology platform (TCMSP) database. Expression data of LUSC-related genes were acquired from DisGeNET, GeneCards, OMIM, DrugBank, and TTD databases. The target genes of S. barbata that confer anticancer effects against LUSC were obtained by considering the intersecting genes between S. barbata target genes and LUSC-related genes. The potential regulatory pathways enriched in these intersected genes were identified using the KOBAS database, and Gene Ontology (GO) function enrichment analysis was performed using the online tool DAVID. The relationship network of S. barbata functional monomer components-action targets-disease-pathways was established using Cytoscape 3.8.2, and the protein-protein interaction network of those intersected genes was established using the STRING database. Finally, the hub genes were screened by using CytoNCA, a plug-in of Cytoscape, and hub gene expressions in LUSC were evaluated via the Gene Expression Profiling Interactive Analysis (GEPIA) database. AutoDockTools and PyMOL software were employed to verify the molecular docking on disease target proteins and drug functional molecules. Results. In S. barbata, 104 target genes and 20 hub genes encoding functional components against LUSC were screened out, six of which were significantly differentially expressed between LUSC samples and normal tissue samples in the GEPIA database. Here, GO analysis illustrated the involvement of these genes in the signal transduction and positive regulation of transcription from RNA polymerase II promoter and negative regulation of apoptosis, while KEGG pathway enrichment analysis demonstrated that these genes were mainly involved in several pathways, for instance, AGE-RAGE, PI3K-Akt, p53, and MAPK signaling pathway. There are four main functional components docking with six key target proteins, all of which have strong binding activity. Conclusions. We predicted the molecular mechanisms and signaling pathways of genes encoding functional components in S. barbata against LUSC. These discoveries offer novel understanding for further study, laying a scientific foundation for the production of synthetic monomer components of S. barbata.
Generalized Exponential Fuzzy Entropy Approach for Automatic Segmentation of Chest CT with COVID‐19 Infection
Authors: Alotaibi Saud S. , Elaraby Ahmed
Published: March 2022

The proposed work describes an approach for the segmentation of abnormal lung CT scans of COVID‐19. Lung diseases are the leading killer in both men and women. The pulmonary experts normally make attempts, such as early detection of patients by tomography tests before lung specialists treat patients who are tortured by lung disease. Moreover, lung specialists do their best to detect the presence of lung conditions. X rays or CT scan checks are performed for tomography tests. The finest approach for medical diagnosis and a wide range of uses is computed tomography (CT). This kind of imaging offers elaborate cross‐sectional pictures of skinny slices of the organic structure. However, the preprocessing and denoising methods of Lung CT scans may mask some important image features. To address this challenge, we propose a novel framework involving an optimization technique algorithm to solve a multilevel thresholding problem based on information theory to segment abnormal lung CT scans. The proposed framework will evaluate a sample of CT scan images taken from a well‐known benchmark database. The evaluation results will assess subjectively and objectively to demonstrate the effectiveness of the proposed framework.
Privacy Protection of Task in Crowdsourcing: Policy-Hiding and Attribute Updating Attribute-Based Access Control Based on Blockchain
Authors: Yang Kunwei , Yang Bo , Zhou Yanwei , Wang Tao , Gong Linming
Published: March 2022

Crowdsourcing is a new way to solve complex problems by using human intelligence. However, the tasks and user information privacy concerned in crowdsourcing have not been adequately addressed. It is necessary to design a privacy protection mechanism for tasks that need to be restricted to specific user groups. Ciphertext-policy attribute-based encryption (CP-ABE) is an efficient and feasible cryptographic tool, particularly for crowdsourcing systems. The encryptor can choose the access policy independently, which limits the scope of decryption users. At present, most CP-ABE schemes adopt a centralized management platform, which poses problems such as high trust-building costs, DDoS attacks, and single point of failure. In this paper, we propose a new access control scheme based on CP-ABE and blockchain, which has the properties of policy hiding and attribute updating. To protect the privacy of worker’s attributes, we adopt a test algorithm based on a fully homomorphic cryptosystem to confidentially judge whether the worker’s attribute lists match the hidden attributes policy in ciphertext or not before the decryption. Experiment results and comprehensive comparisons show that our mechanism is more flexible, private, and scalable than existing schemes.
Dietary Phytochemicals Targeting Nrf2 to Enhance the Radiosensitivity of Cancer
Authors: Wang Pinghan , Long Fangyi , Lin Hong , Wang Song , Wang Ting
Published: March 2022

Nowadays, cancer has become the second leading cause of death worldwide. Radiotherapy (RT) is the mainstay in management of carcinoma; however, overcoming radioresistance remains a great challenge to successfully treat cancer. Nrf2 is a key transcription factor that is responsible for maintaining cellular redox homeostasis. Activation of Nrf2 signaling pathway could upregulate multifarious antioxidant and detoxifying enzymes, further scavenging excessive reactive oxygen species (ROS). Despite its cytoprotective roles in normal cells, it could also alleviate oxidative stress and DNA damage caused by RT in cancer cells, thus promoting cancer cell survival. Accumulating evidence indicates that overactivation of Nrf2 is associated with radioresistance; therefore, targeting Nrf2 is a promising strategy to enhance radiosensitivity. Dietary phytochemicals coming from natural products are characterized by low cost, low toxicity, and general availability. Numerous phytochemicals are reported to regulate Nrf2 and intensify the killing capability of RT through diverse mechanisms, including promoting oxidative stress, proapoptosis, and proautophagy as well as inhibiting Nrf2-mediated cytoprotective genes expression. This review summarizes recent advances in radiosensitizing effects of dietary phytochemicals by targeting Nrf2 and discusses the underlying mechanisms, including N6-methyladenosine (m6A) modification of Nrf2 mediated by phytochemicals in cancer.
Nationwide Routine Childhood Vaccination Coverage During the COVID-19 Pandemic in Jordan: Current Situation, Reasons, and Predictors of Vaccination
Authors: Abu-rish Eman Y. , Bustanji Yasser , Abusal Kamel
Published: March 2022

Background. The healthcare system in Jordan faced substantial burden during the 2020 COVID-19 pandemic including disruption of routine childhood vaccination services. Aims. We sought, for the first time, to describe the impact of the 2020 pandemic on vaccination coverage of Jordanian children in Jordan and to identify the key contributing factors. Methods. Nationwide vaccination rates were retrieved from the electronic records at the Ministry of Health (2018–2020) enrolling crude births of 220,057 Jordanian children during 2020. Records of doses administered were compared for each month of 2020 with the baseline of 2018–2019. A cross-sectional survey (March–August 2021) was also conducted enrolling a convenient sample of adults aged ≥18 who were Jordanian caregivers for vaccine-eligible children (0–23 months) between 1 January 2020 and the date of the interview. The survey aimed to address caregivers’ adherence to routine vaccination during 2020–2021 and to describe the determinants of the current and future adherence to vaccination where multiple logistic regression model was utilized. Results. The electronic records revealed a significant decline in vaccination coverage during 2020. The greatest decline was observed during the lockdown period from 21 March 2020 to 21 April 2020 (32.4%–46.8%) followed by the decline observed by the entry of the first wave during September-October 2020 (18.4%–22.8%). A drop of 14–16% was observed for the vaccines recommended under the age of 12 months and of 6–7% for those recommended in 1-2-year-old children. The yearly coverage rates for measles-1 (at 9 months), 2 (at 12 months as part of measles-mumps-rubella (MMR) vaccine), and 3 (at 18 months as part of MMR) were 76%, 90%, and 87%, respectively, and for hexavalent-1, 2, and 3 were 78%, 78%, and 77%, respectively. The results of the survey revealed that the main reason for vaccination delay for at least 1 month from the recommended administration time was the lockdown, followed by child illness and smart lockdowns (regional lockdown/health center closure). Vaccination delay was less likely to be observed in children aged ≥12 months (

P

value < 0.001; OR: 0.18; CI: 0.11–0.29) or children with chronic diseases (

P

value < 0.05; OR: 0.5; CI: 0.33–0.88). Conclusion. The current study demonstrates a decline in vaccination coverage of Jordanian children during the 2020 COVID-19 pandemic. It is important to formulate future strategies to promote catch-up vaccination and to avoid future backsliding of vaccination rates during further waves of the COVID-19 pandemic or other pandemics. These include improving health services, allaying caregivers’ concerns about contracting COVID-19, and arranging vaccination campaigns outside health centers.
On the Fractional Variable Order Thermostat Model: Existence Theory on Cones via Piece-Wise Constant Functions
Authors: Rezapour Shahram , Souid Mohammed Said , Bouazza Zoubida , Hussain Azhar , Etemad Sina
Published: March 2022

In the current manuscript, we intend to investigate the existence, uniqueness, and the stability of positive solution in relation to a fractional version of variable order thermostat model equipped with nonlocal boundary values in the Caputo sense. In fact, we will get help from the constant piece-wise functions for transforming our variable order model into an auxiliary standard model of thermostat. By Guo-Krasnoselskii’s fixed point theorem on cones, we derive the required conditions ensuring the existence property for positive solutions. An example is illustrated to examine the validity of the observed results.
An Account of EFL Learners’ Grammatical Knowledge and Motivation toward Learning in an Online Instructional Environment
Authors: Zainal Anna Gustina , Ismail Sayed M. , Tagibova Asiyat Akhmedovna , Al-Sayyed Sa’ida Walid , Wekke Ismail Suardi , Sofyawati Eva Devi , Islam Md. Zahidul , Haidari Mir Mohammad Farooq
Published: March 2022

The current research was an attempt to compare the impacts of online English language learning and face-to-face English language learning on Iranian EFL learners’ grammatical knowledge and their learning motivation. To this end, the Oxford Quick Placement Test (OQPT) was given to 81 Iranian EFL students, and 50 of them who were at the intermediate level were selected as the participants of the study. Then, they were randomly divided into two groups; one experimental group (EG) (

n
=
25

) and one control group (CG) (

n
=
25

). After that, both groups were pretested by a grammar test and a motivation questionnaire. After pretesting, the participants of the EG received the treatment through online instruction. On the other hand, the participants of the CG were taught the grammatical points through a face-to-face instruction. At the end of the treatment, both groups took the posttest of grammar and motivation. The EG outperformed the CG on both the grammar and motivation posttests, according to the findings of paired and independent samples

t

-tests. Implementing online instruction assisted Iranian EFL learners to enhance their grammatical knowledge and increase their learning motivation. The implications of this research can encourage teachers to use online learning methods in their classes.
Identification of a Twelve Epithelial-Mesenchymal Transition-Related lncRNA Prognostic Signature in Kidney Clear Cell Carcinoma
Authors: Xia Qi-Dong , Zhang Yuan , Li Li-Sha , Lu Jun-Lin , Xun Yang , Sun Jian-Xuan , Xu Jin-Zhou , Liu Chen-Qian , Lu Yu-Chao , He Deng , Wang Shao-Gang
Published: March 2022

Background. Epithelial-mesenchymal transition (EMT) plays a vital role in tumor metastasis and drug resistance. It has been reported that EMT is regulated by several long noncoding RNAs (lncRNAs). We aimed to identify EMT-related lncRNAs and develop an EMT-related lncRNA prognostic signature in kidney renal clear cell carcinoma (KIRC). Materials and Methods. In total, 530 ccRCC patients with 611 transcriptome profiles were included in this study. We first identified differentially expressed EMT-related lncRNAs. Then, all the samples with transcriptional data and clinical survival information were randomly split into training/test sets at a ratio of 1 : 1. Accordingly, we further developed a twelve differentially expressed EMT-related lncRNA prognostic signature in the training set. Following this, risk analysis, survival analysis, subgroup analysis, and the construction of the ROC curves were applied to verify the efficacy of the signature in the training set, test set, and all patients. Besides, we further investigated the differential immune infiltration, immune checkpoint expression, and immune-related functions between high-risk patients. Finally, we explored the different drug responses to targeted therapy (sunitinib and sorafenib) and immunotherapy (anti-PD1 and anti-CTLA4). Results. A twelve differentially expressed EMT-related lncRNA prognostic signature performed superior in predicting the overall survival of KIRC patients. High-risk patients were observed with a significantly higher immune checkpoint expression and showed better responses to the targeted therapy and immunotherapy. Conclusions. Our study demonstrates that the twelve differentially expressed EMT-related lncRNA prognostic signature could act as an efficient prognostic indicator for KIRC, which also contributes to the decision-making of the further treatment.
Effect of Sleep Intervention Programs during Cardiac Rehabilitation on the Sleep Quality of Heart Patients
Authors: Ghane Fatemeh , Ghanbari Firoozabadi Mahdieh , Madadizadeh Farzan , Nasiriani Khadijeh
Published: March 2022

Background and Objective. Patients with cardiovascular problems experience sleep disorders. Due to the importance of adequate sleep and rest for the growth and repair of damaged cells, it is necessary to use appropriate interventions to improve it. This study determined the effect of sleep intervention programs during cardiac rehabilitation on patients’ sleep quality. Materials and Methods. In this quasi-experimental study with unequal control group design, 35 individuals participated in the cardiac rehabilitation program as the experimental group and 35 served as the control group. The program included 12 weeks of exercise, 3 sessions per week, 3 sessions of training programs each lasting for 45 minutes, and a special two-session sleep improvement program. Data were collected using the Pittsburgh Sleep Quality Index and analysed with descriptive and inferential statistical methods. Results. There were not any significant differences between the two groups in age, sex, marital status, smoking, and indication for cardiac rehabilitation (

P
>
0.05

). The scores of sleep quality of patients were

9.2
±
1.58

before and

4.40
±
1.14

after intervention in the experimental group and

9.02
±
2.56

before and

7.48
±
1.86

after intervention in the control group. There was no significant difference between the two groups before intervention (

P
=
0.73

). yet there was a significant difference after intervention (

P
=
0.0001

). In addition, scores of sleep quality of patients were significantly different in the experimental and control groups before and after intervention (

P
=
0.0001

). Conclusion. Findings indicated that the quality of sleep of cardiac patients improved after the sleep intervention program during the cardiac rehabilitation program. Therefore, it is suggested to implement sleep improvement programs for cardiac patient care as an effective, easy, and feasible technique. In addition, it is necessary to pay more attention to the sleep improvement program in cardiac rehabilitation. Trial Registration. The trial was retrospectively registered on https://en.irct.ir/trial/50799 on 14 September 2020 (14.09.2020) with registration number IRCT20140307016870N6.
Clinical Efficacy of Yiqi Yangyin Decoction Combined with Docetaxel on Advanced Ovarian Cancer and the Effect on the Levels of Serum Markers VEGF, HE4, and CA125
Authors: Wang Ning , Xiao Fengying , Shao Hua , Shi Shuai , Zhou Yanling
Published: March 2022

Objective. The study is designed to investigate the therapeutic effect of Yiqi Yangyin Decoction combined with docetaxel on advanced ovarian cancer (OC) patients and the effects on serum markers VEGF, HE4, and CA125. Methods. 92 patients with advanced OC were grouped into the study group and control group. The control group was given the treatment of basic chemotherapy combined with docetaxel. The study group was added the treatment of Yiqi Yangyin Decoction on the basis of the control group. The short-term efficacy, adverse reactions, tumor markers, quality of life, 3-year survival, and T cell subsets of the two groups were observed. Results. Compared to the control group, the study group’s incidence of adverse reactions was lower. VEGF, HE4, and CA125 in the study group were decreased more obviously. The levels of CD3+, CD4+, and CD4+/CD8+ were sharply higher in the study group, while CD8+ was notably reduced. After treatment, the scores of physical health, social function, and mental health in the study group were notably higher than those in the control group. Compared with the control group, the 3-year survival rate of the study group was notably higher, and the therapeutic effect of the study group was obviously better. Conclusion. The combination of Yiqi Yangyin Decoction and docetaxel can improve the body immunity and the therapeutic effect of advanced OC, decrease the incidence of adverse reactions, and prolong the survival time, with good safety and effectiveness.
Multimodal Biometrics Fusion Algorithm Using Deep Reinforcement Learning
Authors: Huang Quan
Published: March 2022

Multimodal biometrics fusion plays an important role in the field of biometrics. Therefore, this paper presents a multimodal biometrics fusion algorithm using deep reinforcement learning. In order to reduce the influence of user behavior, user’s personal characteristics, and environmental light on image data quality, data preprocessing is realized through data transformation and single-mode biometric image region segmentation. A two-dimensional Gobar filter was used to analyze the texture of local sub-blocks, qualitatively describe the similarity between the filter and the sub-blocks and extract the phase information and local amplitude information of multimodal biometrics features. Deep reinforcement learning was used to construct the classifier of different modal biometrics, and the weighted sum fusion of different modal biometrics was implemented by fractional information. The multimodal biometrics fusion algorithm was designed. The Casia-iris-interval-v4 and NFBS datasets were used to test the performance of the proposed algorithm. The results show that the fused image quality is better, the feature extraction accuracy is between 84% and 93%, the average accuracy of feature classification is 97%, the multimodal biometric classification time is only 110 ms, the multimodal biometric fusion time is only 550 ms, the effect is good, and the practicability is strong.
Prostate Cancer Susceptibility Loci Identified in GATA2 and ZMIZ1 in Chinese Population
Authors: Zhang Hui-Jing , Liu Zhongyuan , Kan Liang
Published: March 2022

Background. Common genetic risk variants for prostate cancer (PCa) have been identified at approximately 170 loci using genome-wide association studies (GWAS), most of which were identified in European populations. Recently, GWAS were applied to a large Japanese cohort and identified 12 novel susceptibility loci associated with PCa risk. In this study, we aim to investigate PCa susceptibility loci in the Chinese population. The study data will be used to promote PCa risk control in China. Methods. A total of 235 PCa patients and 252 control subjects (all unrelated) were enrolled in this case-control PCa study. Nine single nucleotide polymorphisms (SNPs) were genotyped in GATA2 (rs73862213, rs2335052, and rs10934857), ZMIZ1 (rs704017, rs77911174, and rs3740259), and SUN2 (rs78397383, rs5750680, and rs138705) genes. The associations between the candidate SNPs and PCa were analyzed using multiple-factor logistic regression and haplotype analysis. Results. The allele frequency distributions of rs73862213 and rs2335052 in the GATA2 gene and rs704017 and rs77911174 in the ZMIZ1 gene were found to be significantly different between PCa cases and controls. Haplotype analysis revealed that the G-C-A haplotype of the GATA2 gene (order of SNPs: rs73862213-rs2335052-rs10934857) and the G-G-G haplotype of the ZMIZ1 gene (order of SNPs: rs704017-rs77911174-rs3740259) were associated with increased PCa risk. None of the SUN2 haplotypes were associated with PCa. Conclusions. Our study data indicates that the minor alleles of rs73862213 and rs2335052 in the GATA2 gene and rs704017 and rs77911174 in the ZMIZ1 gene were associated with increased PCa risk. These findings greatly extended our knowledge of the etiology of PCa.
Recognition and Localization of Target Images for Robot Vision Navigation Control
Authors: Chen Muji
Published: March 2022

This paper focuses on a visual navigation control system for mobile robots, recognizing target images and intelligent algorithms for the navigation system’s path tracking and localization techniques. This paper examines the recognition and localization of target images based on the visual navigation control of mobile robots. It proposes an efficient marking line method for recognizing and localization target images. Meanwhile, a fuzzy control method with smooth filtering and high efficiency is designed to improve the stability of robot operation, and the feasibility is verified in different scenarios. The corresponding image acquisition system is developed according to the characteristics of the experimental environment, and the acquired images are preprocessed to obtain corrected grayscale images. Then, target image recognition and linear fitting are performed to obtain target image positioning. The system calculates the angle and distance of the mobile robot, offsetting the target image in real time, adjusting the output signal, and controlling the mobile robot to realize path tracking. The comparison of sensor data and path tracking algorithm results during the experiment shows that the path tracking algorithm achieves good results with an angular deviation of ±1.5°. The application of RANSAC algorithm and improved Hough algorithm was analyzed in visual navigation control, and the two navigation line detection algorithms based on the image characteristics of the target image were improved in the optical detection area of the navigation line for the shortcomings of the two algorithms in visual navigation control, and the algorithms before and after the improvement were compared.
Prediction Model of Ischemic Stroke Recurrence Using PSO-LSTM in Mobile Medical Monitoring System
Authors: Li Qingjiang , Chai Xuejiao , Zhang Chunqing , Wang Xinjia , Ma Wenhui
Published: March 2022

Aiming at the problems of low prediction accuracy and low sensitivity of traditional ischemic stroke recurrence prediction methods, which limits its application range, by introducing an adaptive particle swarm optimization (PSO) algorithm into the Long and Short-Term Memory (LSTM) model, a prediction model of ischemic stroke recurrence using deep learning in mobile medical monitoring system is proposed. First, based on the clustering idea, the particles are divided into local optimal particles and ordinary particles according to the characteristic information and distribution of different particles. By updating the particles with different strategies, the diversity of the population is improved and the problem of local optimal solution is eliminated. Then, by introducing the adaptive PSO algorithm into the LSTM, the PSO-LSTM prediction model is constructed. The optimal super parameters of the model are determined quickly and accurately, and the model is trained combined with the patient’s clinical data. Finally, by using SMOTE method to process the original data, the imbalance of positive and negative sample data is eliminated. Under the same conditions, the proposed PSO-LSTM prediction model is compared with two traditional LSTM models. The results show that the prediction accuracy of PSO-LSTM model is 92.0%, which is better than two comparison models. The effective prediction of ischemic stroke recurrence is realized.
Three-Dimensional Ultrasound Images in the Assessment of Bladder Tumor Health Monitoring under Deep Learning Algorithms
Authors: Shao Changhua , Sun Aichun , Xue Hanwen , Di Xianqiang
Published: March 2022

This study was aimed at exploring the application value of three-dimensional (3D) ultrasound based on deep learning and continued nursing health monitoring (CNHM) mode in promoting the recovery of bladder cancer patients after surgery. 60 patients who underwent muscular noninvasive superficial bladder cancer and bladder perfusion treatment were selected as the research objects. The patients were randomly divided into two groups: an experimental group (30 cases) and a control group (30 cases). Patients in the experimental group adopted a CNHM model during the bladder perfusion treatment. The patients in control group adopted ordinary health monitoring mode. All patients underwent 3D ultrasound examination, and all images were processed using the convolutional neural network (CNN) algorithm. All patients were followed up regularly within 12 after the treatment. The imaging data, quality of life, satisfaction, and complications of the two groups of patients were compared in each time period. The ultrasound image processed by the CNN algorithm was clearer than that processed by the original method, showing higher image quality and more prominent lesion features. After 12 months of health monitoring intervention, the overall health status, scores of various functional areas, and score of functional subscales of the experimental group were greatly higher than those of the control group, and the differences were statistically significant (

P
<
0.05

). The incidence of adverse reactions in the experimental group was much lower than that in the control group, and the difference was statistically obvious (

P
<
0.05

). The comparison of the recurrence rate between the two groups of patients in each time period was statistically significant. The satisfaction score of the experimental group was much higher than the score of the control group, showing statistically significant difference (

P
<
0.05

). CNN algorithm showed high application value in 3D ultrasound image processing, and the CNHM model was very beneficial to the postoperative recovery of bladder cancer patients.
Potential Lignocellulolytic Microfungi from Pineapple Plantation for Composting Inoculum Additive
Authors: Irawan Bambang , Wahyuningtias Inten , Ayuningtyas Niken , Isky Ola Apriyani , Farisi Salman , Sumardi Sumardi , Afandi Afandi , Hadi Sutopo
Published: March 2022

Pineapple plantations leave a lot of plant biomass after the planting season ends. The abundant residue of pineapple plants causes problems due to the high content of lignocellulose, which is difficult to decompose naturally. This study aimed to isolate and characterize lignocellulolytic microfungi isolates from pineapple plantations. The information of this study was used as data to prepare an inoculum for the induction of pineapple litter composting that was resistant to stress to pineapple plantation habitat. The results showed that there were 11 dominant lignocellulolytic microfungi isolates found from pineapple litter and plantation soil. Using selective media, the selection showed five cellulolytic (Bioggp 3, 6, 9, 11, and 12); five xylanolytic (Bioggp 3, 6, 8, 9, and 12); and two ligninolytic microfungi isolates (Bioggp 2 and 5). Bioggp 3, 6, 9, and 12 are cellulolytic and xylanolytic with Bioggp 3 showing the highest cellulolytic index (4.0) and xylanolytic index (4.20). Testing of ligninolytic microfungi showed that the Bioggp 5 isolate had a stronger lignin indicator (color intensity = 4.0 and zone ratio of 1.47) than the Bioggp 2 isolate. Bioggp 9 had the highest cellulolytic isolate spore productivity at 4.5 × 108 spores/mL with 93.3% spore viability, and Bioggp 3 had the highest xylanolytic isolate spore productivity at 2.5 × 109 spores/mL with 89.3% spore viability. Bioggp 2 had the highest ligninolytic isolate spore productivity at 1.8 × 109 spores/mL, but Bioggp 5 showed the highest spore viability at 98.0%.
Research on Detection and Diagnosis Technology of Subarachnoid Hemorrhage Based on New Association Classification Algorithm
Authors: Chen Long , Zhou Changlong
Published: March 2022

Subarachnoid hemorrhage (SAH) is one of the most common cerebrovascular emergencies, which can lead to serious consequences. Spontaneous subarachnoid hemorrhage accounts for about 15% of acute cerebrovascular accidents. Among them, spontaneous subarachnoid hemorrhage caused by rupture of an intracranial aneurysm or vascular malformation is more common, accounting for about 85%. Therefore, it is very important to detect the early symptoms of subarachnoid hemorrhage through reasonable means and to carry out appropriate clinical intervention and treatment. With the development of imaging technology, computed tomography angiography (CTA) is widely used in clinical practice. However, the accuracy of manual recognition of CT images is not very high, and the efficiency is low. The emergence of data mining technology is gradually solving this problem. In this paper, we introduce and summarize the development of data mining, domestic and foreign research progress, the application status of data mining in the medical field, and the main technologies and methods of data mining. We study the application of association rule extraction technology in data mining in the medical field. The Apriori algorithm for finding frequent item sets in association rule extraction and its series of improved algorithms are studied and finally combined the characteristics of medical CT images, an image mining method of association rules based on the gray-level cooccurrence matrix is proposed. Based on the FP-growth algorithm, the NCFP-growth algorithm based on association rules is proposed and compared with the mining effect of several other algorithms. The proposed algorithm achieves a classification accuracy of above 90%, which is higher than the Apriori algorithm and its improved variations.
SLAM 3D Digital Terrain Mapping with SqueezeNet Driven by Road Traffic Data
Authors: Gu Liuwan , Zhang Hao , Wu Xingjie
Published: March 2022

In order to improve the efficiency of dynamic visualization of large-scale road traffic data in a web environment, this paper proposes a SLAM 3D digital terrain map visualization method using SqueezeNet in a web environment. We propose a hierarchical organization method of the road network, taking into account road attributes and drawing roads at different view heights; a multiroad merging method based on the line segment indexing feature of WebGL (web graphics library) technology, and optimizing the scene by combining view rejection and multithreading technology. The prototype system was developed and case studies were carried out using the national road network data as an example. The experimental results show that the frame rate of large-scale road traffic data visualization in the network environment is above 40 frames/second, which is 20–30 frames/second higher than that of Baidu’s ECharts GL visualization method.