bluuebunny/crossref_metadata_2025 huggingface dataset:
Decreased Spp1 Expression in Acute Myocardial Infarction after Ischemia and Reperfusion Injury
Authors: Li Ling , Huang Jungang , Zhao Zongkai , Wen Zhuzhi , Li Kang , Ma Tianjiao , Zhang Lisui , Zheng Junmeng , Liang Shi
Published: August 2021

Background. With the progress of shock therapy and the establishment and promotion of methods such as thrombolytic therapy and percutaneous coronary intervention (PCI), many tissues and organs have been reperfused after ischemia which may cause even worse disorder called ischemia-reperfusion injury (IRI). mRNAs have been found to have significant impacts on ischemia-reperfusion through various mechanisms. In view of the accessibility of mRNAs from blood, we aimed to find the association between mRNA and ischemia-reperfusion. Methods. We used the GSE83472 dataset from the Gene Expression Omnibus (GEO) database to find differential RNA expression between ischemia-reperfusion tissue and control samples. In addition, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to find the biological property of 449 RNAs from GSE83472 via the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Besides, Gene Set Enrichment Analysis (GSEA), a tool to find the pathway orientation of a gene set, was used for further study in the four most significant KEGG pathways. Furthermore, we constructed a protein-protein interaction (PPI) network. In the end, we used quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and western blotting to measure and compare the expression of Spp1 in patients who accepted percutaneous coronary intervention. Results. The bioinformatics analyses suggested that Spp1 was a hub gene in reperfusion after ischemia. The qRT-PCR result showed that the Spp1 expression was significantly downregulated in ischemia-reperfusion cells after PCI compared with normal samples and so as the western blotting. Conclusion. Spp1 might play an essential role in acute myocardial infarction after ischemia and reperfusion injury.
Wang-Bi Tablet Ameliorates DMM-Induced Knee Osteoarthritis through Suppressing the Activation of p38-MAPK and NF-κB Signaling Pathways in Mice
Authors: Li Hui , You Yan , Jiang Bing , Li Haidong , Li Xiang , Wu Wei , Cao Hong , Shen Xiaoyan , Zou Jun
Published: August 2021

Background. Traditional Chinese medicine (TCM) exhibits outstanding therapeutic effects on the treatment of osteoarthritis (OA). Wang-Bi tablets (WBTs) have been used in clinics to treat knee osteoarthritis (KOA) by alleviating joint swelling and paining, and thus, the quality of life in patients with KOA was improved. However, its underlying molecular mechanism of anti-inflammatory response remains unclear. Therefore, further investigation is required. Purpose. This study aimed to explore the function of WBT in KOA mice and uncover the possible molecular mechanisms. Study Design. A KOA model was constructed by destabilizing the medial meniscus (DMM). IL-1β-treated chondrocytes were used to investigate the precise mechanism in vitro. Methods. (1) C57BL/6 male mice (8-week-old) were divided into Model, Sham, WBT-L, WBT-M, and WBT-H groups. After intragastric administration of 0.5% CMC-Na or WBT for 4 weeks, inflammation and pathological change were analyzed by ELISA, RT-qPCR, hematoxylin and eosin (H & E) and safranine O staining. (2) Isolated chondrocytes were stimulated with IL-1β followed by WBT-containing serum treatment, and then, the expression of inflammatory cytokines was analyzed by ELISA and RT-qPCR. (3) The effects of WBT on inflammatory signaling cascades in mice knee joint and chondrocytes were detected by WB. Results. The results indicated that WBT could alleviate inflammation and prevent cartilage injury in KOA mice. Compared with 0.5% CMC-Na-treated mice, the serum glycosaminoglycans (GAG) level in WBT-treated mice was notably increased, while the proinflammatory cytokine interleukin- (IL-) 6 level was decreased. In addition, WBT treatment suppressed the activation of NF-κB and p38 signaling pathways both in vivo and in vitro. Conclusion. WBT can effectively inhibit articular cartilage injury and inflammatory response in KOA mice. The protective role of WBT in mice KOA was a result of the downregulation of NF-κB and p38-MAPK signal pathways.
Comparative Studies on Trade and Value‐Added Trade along the “Belt and Road”: A Network Analysis
Authors: Sui Guangjun , Zou Jialing , Wu Shuang , Tang Danling
Published: August 2021

With the deepening of regional industrial transfer, traditional trade data cannot fully explain the real level of trade in a region. This paper aims to reveal the changing pattern of real trade along the “Belt and Road” by establishing a value‐added trade (VAT) network of the “Belt and Road” countries and comparing it with the trade network. Applying a network method, we analyze and compare the structures, characteristics, evolutions, and underlying dynamisms of both networks. With a thorough interpretation and visualization of the network density, network centrality, trade communities, and influencing factors of both networks in the three time sections of 2005, 2010, and 2015, we come to the following findings: (1) the connectivity of both networks has been greatly enhanced, reflecting a more integrated regional economy. (2) The center‐external structure of both networks has been further strengthened and the polarization of the VAT network is more obvious than that of the trade network. (3) The organizations of trade communities and VAT communities are largely different. Specifically, China rapidly increased its core position in the VAT network and incorporated more and more countries into its VAT community. (4) The underlying factors have similar impacts on both networks. While the growth of regional economic size and free trade agreements will enhance both trade and VAT, the economic gaps and population differences among the “Belt and Road” countries will prohibit regional economic ties. Based on these findings, we propose suggestions on further regional economic cooperation by taking advantage of China’s core position to promote regional VAT, construct broad trade channels, and enhance trade governance.
A Novel Deep Convolutional Neural Network Based on ResNet‐18 and Transfer Learning for Detection of Wood Knot Defects
Authors: Gao Mingyu , Song Peng , Wang Fei , Liu Junyan , Mandelis Andreas , Qi DaWei
Published: August 2021

Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves wood utilization. Traditional neural network techniques have not yet been employed for wood defect detection due to long training time, low recognition accuracy, and nonautomatical extraction of defect image features. In this work, a model (so‐called ReSENet‐18) for wood knot defect detection that combined deep learning and transfer learning is proposed. The “squeeze‐and‐excitation” (SE) module is firstly embedded into the “residual basic block” structure for a “SE‐Basic‐Block” module construction. This model has the advantages of the features that are extracted in the channel dimension, and it is fused in multiscale with original features. Instantaneously, the fully connected layer is replaced with a global average pooling; consequently, the model parameters could be reduced effectively. The experimental results show that the accuracy has reached 99.02%, meanwhile the training time is also reduced. It shows that the proposed deep convolutional neural network based on ReSENet‐18 combined with transfer learning can improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.
Evaluation of Failure Probability in Series System of Three‐Axle Trucks under Strong Crosswind
Authors: Hu Yahui , Guo Yingshi , Fu Rui , Xu Qingjin
Published: August 2021

The probability of wind‐induced failure accidents in three‐axle trucks under pulsating strong crosswinds and the corresponding critical safe speed are investigated in this study. Reliability theory and random fuzzy methods are utilized to establish the membership function of the failure probability in the series system (FPSS) composed of rollover, side‐slip, and rotation failure accidents. The Kaman spectrum is used to realistically simulate the fluctuating wind time history curves of different average speeds. Four factors affecting the six‐component force coefficient of the three‐axle truck and the crosswind load are considered: fluctuating average wind speed, wind direction (angle), truck driving speed, and road adhesion coefficient. A three‐axle truck nonlinear model is established accordingly. The model is used to obtain the dynamic response of the three‐axle truck under strong crosswind conditions as per the time‐varying curves of the vertical load of the truck, the time‐varying curves of the lateral displacement of the center of mass, and the time‐varying curves of the heading angle. An advanced Monte Carlo simulation algorithm based on importance sampling is used to determine the probability of a three‐axle truck with FPSS under strong crosswinds; the given acceptable probability of failure (accident) is used to obtain the critical safety speed. The sensitivity analysis of random variables reveals that the possibility of three truck failures of the three‐axle truck in strong crosswinds is, from largest to smallest, rollover, side‐slip, and rotation. This research may provide useful guidance for exploring the probability of wind‐induced accidents and the critical safety speeds of vehicles, as well as useful general information for road transportation management departments.
Ameliorative Effects and Mechanism of Buyang Huanwu Decoction on Pulmonary Vascular Remodeling: Network and Experimental Analyses
Authors: Chen Yucai , Cui Lidan , Wang Can , Liu Jianing , Guo Jian
Published: August 2021

Pulmonary hypertension (PH) is a severe and progressive cardiovascular disease. Its pathological mechanism is complex, and the common pathological feature is pulmonary vascular remodeling. The efficacy of existing therapeutic agents is limited. Traditional Chinese medicine (TCM) has its unique advantages in the prevention and treatment of complex diseases. In this study, the approaches of network pharmacology combined with biological verification are employed to explore the role of Buyang huanwu decoction (BYHWD) in the treatment of PH. The active ingredients in BYHWD were first screened based on the ADME properties of the compounds. In turn, the mean of data mining was utilized to analyze the potential targets of BYHWD for the treatment of PH. On this basis, a series of interaction networks were constructed for searching the core targets. The genes including AKT1, MMP9, NOS3/eNOS, and EGFR were found to be possible key targets in BYHWD. The results of enrichment analysis showed that the targets of BYHWD focused on smooth muscle cell proliferation, migration, and apoptosis, which are classic biological processes involved in pulmonary vascular remodeling and are closely related to the PI3K‐Akt‐eNOS pathway. The methods of biological experiments were adopted to verify the above results. The present study elucidated the mechanism of BYHWD in the treatment of PH and provided new ideas for the clinical use of TCM in the treatment of PH.
Acute Myocardial Infarction of the Left Main Coronary Artery Presenting with Cardiogenic Shock and Pulmonary Edema during Noncardiac Surgery
Authors: Takenaka Sakae , Konishi Takao , Sato Tomoya , Tada Atsushi , Koizumi Takuya , Mizuguchi Yoshifumi , Kadosaka Takahide , Motoi Ko , Kobayashi Yuta , Komoriyama Hirokazu , Kato Yoshiya , Sarashina Miwa , Omote Kazunori , Tsujinaga Shingo , Sato Takuma , Kamada Rui , Kamiya Kiwamu , Iwano Hiroyuki , Nagai Toshiyuki , Orimo Tatsuya , Kamachi Hirofumi , Taketomi Akinobu , Anzai Toshihisa
Published: August 2021

Acute myocardial infarction (AMI) caused by severe stenosis of left main coronary artery (LMCA) presenting with cardiogenic shock and pulmonary edema during noncardiac surgery is uncommon, but a catastrophic event. A 77-year-old male with cholangiocarcinoma underwent hepatectomy. During the surgery, he presented with cardiogenic shock, which did not respond to infusion administration or vasopressor. A transesophageal echocardiogram revealed anterior, septal, and lateral severe hypokinesia and impaired left ventricular function. Emergent coronary angiogram showed severe stenosis of LMCA. The patient underwent primary percutaneous coronary intervention (PCI) under the support of intra-aortic balloon pump, followed by extracorporeal membrane oxygenation. The chest roentgenogram showed pulmonary edema. Two days after PCI, he successfully underwent hepatectomy and bile duct resection. Early identification of the cause of hemodynamic instability during noncardiac surgery and invasive strategy are important for minimizing the myocardial injury and improving clinical outcomes in AMI of LMCA.
Screening Prognosis-Related lncRNAs Based on WGCNA to Establish a New Risk Score for Predicting Prognosis in Patients with Hepatocellular Carcinoma
Authors: Zhou Xueliang , Dou Mengmeng , Liu Zaoqu , Jiao Dechao , Li Zhaonan , Chen Jianjian , Li Jing , Yao Yuan , Li Lifeng , Li Yahua , Han Xinwei
Published: August 2021

Background. Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered. Methods. In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso-penalized Cox regression analysis and nomogram model were used to establish a new risk scoring system and predict the prognosis of patients with liver cancer. The expression of survival-related DE lncRNAs was verified by qRT-PCR. Results. A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs, and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso-penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Finally, the correlation between the risk score and immune cell infiltration and gene set enrichment analysis were determined. Conclusions. In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC and the establishment of the new risk scoring system and nomogram model provides the new perspective for predicting the prognosis of HCC.
Diffuse-Type Tenosynovial Giant Cell Tumor of the Knee with Concurrent Polymicrobial Infection (Klebsiella oxytoca and Group B Streptococcus)
Authors: Marshall J. Hunter , Skedros John G. , Campana Chris F. , Seibert Allan M.
Published: August 2021

Tenosynovial giant cell tumors (TGCT) are a rare class of benign proliferative tumors that are classified according to their presentation: localized-type (L-TGCT) or diffuse-type (D-TGCT). TGCT is synonymous with pigmented villonodular synovitis (PVNS). We describe the unique case of a 56-year-old obese male with type 2 diabetes who had polymicrobial septic arthritis of his left knee joint with concurrent D-TGCT in the same knee. While on a vacation, he noticed spontaneous left knee pain and swelling with an acute onset of fever. He was diagnosed with septic arthritis that was attributed to hematogenous spread from a leg laceration. The septic arthritis was treated with arthroscopic lavage and debridement, including simultaneous excision of the D-TGCT lesions, followed by intravenous ceftriaxone. Cultures of the synovial tissue that were obtained during arthroscopy grew Klebsiella oxytoca and beta-hemolytic (group B) Streptococcus agalactiae. We were not able to find another reported case of any joint with (1) a polymicrobial bacterial infection that included Klebsiella oxytoca and (2) concurrent bacterial septic arthritis and TGCT.
Pharmacovigilance with Transformers: A Framework to Detect Adverse Drug Reactions Using BERT Fine-Tuned with FARM
Authors: Hussain Sajid , Afzal Hammad , Saeed Ramsha , Iltaf Naima , Umair Mir Yasir
Published: August 2021

Adverse drug reactions (ADRs) are the undesirable effects associated with the use of a drug due to some pharmacological action of the drug. During the last few years, social media has become a popular platform where people discuss their health problems and, therefore, has become a popular source to share information related to ADR in the natural language. This paper presents an end-to-end system for modelling ADR detection from the given text by fine-tuning BERT with a highly modular Framework for Adapting Representation Models (FARM). BERT overcame the predominant neural networks bringing remarkable performance gains. However, training BERT is a computationally expensive task which limits its usage for production environments and makes it difficult to determine the most important hyperparameters for the downstream task. Furthermore, developing an end-to-end ADR extraction system comprising two downstream tasks, i.e., text classification for filtering text containing ADRs and extracting ADR mentions from the classified text, is also challenging. The framework used in this work, FARM-BERT, provides support for multitask learning by combining multiple prediction heads which makes training of the end-to-end systems easier and computationally faster. In the proposed model, one prediction head is used for text classification and the other is used for ADR sequence labeling. Experiments are performed on Twitter, PubMed, TwiMed-Twitter, and TwiMed-PubMed datasets. The proposed model is compared with the baseline models and state-of-the-art techniques, and it is shown that it yields better results for the given task with the

F

-scores of 89.6%, 97.6%, 84.9%, and 95.9% on Twitter, PubMed, TwiMed-Twitter, and TwiMed-PubMed datasets, respectively. Moreover, training time and testing time of the proposed model are compared with BERT’s, and it is shown that the proposed model is computationally faster than BERT.
On the Topological Dynamics of Dynamical Manifolds and Their Fundamental Group
Authors: Abu-Saleem M. , Almallah Omar
Published: August 2021

The paper aims to deduce the relation between the category of topology and algebra from viewpoint of geometry and dynamical system. We introduce and define a dynamical manifold as a manifold associated with a time parameter. We obtain the induced chain of topological dynamics on the fundamental group from the chain of dynamical maps on a dynamical manifold. For many adjunctions in this context, we deduce the limit topological dynamics and conditional topological dynamics on the fundamental group. We use the category of commutative diagrams as chains of dynamical manifolds to deduce the chains on fundamental groups. Also, we describe how the manifold changes in a dynamical system from the view of the fundamental group.
Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China
Authors: Liu Xiuyu , Zhang Zhen , Jiang Tao , Li Xuehua , Li Yanyi
Published: August 2021

Total suspended matter (TSM) is a core parameter in the quantitative retrieval of ocean color remote sensing and an important indicator for evaluating the quality of the aquatic environment. This study selects part of Nansi Lake in North China as the study area. Researchers used Hyperion remote sensing data and field-measured TSM concentration as data sources. Firstly, the characteristic variables with high correlation were selected based on spectral analysis. Then, seven methods such as linear regression, BP neural network (BP), KNN, random forest (RF), and random forest based on genetic algorithm optimization (GA_RF) are used to construct the inversion model of TSM concentration. The retrieval accuracy of each model shows that the machine learning models are much more accurate than the linear model. Among them, the GA_RF model retrieves the suspended solids concentration with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.98, a root mean square error (RMSE) of 1.715 mg/L, and an average relative error (ARE) of 6.83%. Additionally, the spatial distribution of TSM concentration was inversed by Hyperion remote sensing image. The results showed that the concentration of TSM was lower in the northwest and higher in the southeast, and the concentration distribution was uneven, showing the characteristics of a typical shallow macrophytic lake. This study provides an effective method for monitoring TSM concentration and other water quality parameters in the shallow macrophytic lake and further proves the advantages of machine learning in ocean color inversion. All in all, this research provides some useful methods and suggestions for quantitative inversion of TSM concentration in shallow macrophytic lakes.
Targeting miRNA by Natural Products: A Novel Therapeutic Approach for Nonalcoholic Fatty Liver
Authors: Zobeiri Mehdi , Parvizi Fatemeh , Kalhori Mohammad Reza , Majnooni Mohammad Bagher , Farzaei Mohammad Hosein , Abdollahi Mohammad
Published: August 2021

The increasing prevalence of nonalcoholic fatty liver disease (NAFLD) as multifactorial chronic liver disease and the lack of a specific treatment have begun a new era in its treatment using gene expression changes and microRNAs. This study aimed to investigate the potential therapeutic effects of natural compounds in NAFLD by regulating miRNA expression. MicroRNAs play essential roles in regulating the cell’s biological processes, such as apoptosis, migration, lipid metabolism, insulin resistance, and adipocyte differentiation, by controlling the posttranscriptional gene expression level. The impact of current NAFLD pharmacological management, including drug and biological therapies, is uncertain. In this context, various dietary fruits or medicinal herbal sources have received worldwide attention versus NAFLD development. Natural ingredients such as berberine, lychee pulp, grape seed, and rosemary possess protective and therapeutic effects against NAFLD by modifying the gene’s expression and noncoding RNAs, especially miRNAs.
hucMSC Conditioned Medium Ameliorate Lipopolysaccharide-Induced Acute Lung Injury by Suppressing Oxidative Stress and Inflammation via Nrf2/NF-κB Signaling Pathway
Authors: Tang Yue , Ding Fengxia , Wu Chun , Liu Bo
Published: August 2021

Acute lung injury (ALI) is a common clinical syndrome in the cardiac intensive care unit with a high mortality rate. Inflammation and oxidative stress have been reported to play a crucial role in the development of ALI. Previous studies have shown that human umbilical cord mesenchymal stem cells (hucMSCs) have anti-inflammatory and antioxidative effects in various diseases. However, the anti-inflammatory and antioxidative effects of the hucMSC conditioned medium (CM) on LPS-induced ALI remain unclear. Therefore, in this study, we assessed whether the hucMSC conditioned medium could attenuate LPS-induced ALI and the underlying mechanisms. Mice were randomly divided into four groups: the control group, PBS group, LPS+PBS group, and LPS+CM group. The lung histopathology and bronchoalveolar lavage fluid (BALF) were analyzed after intervention. The Nrf2/NF-κB signaling pathway and its downstream target genes were tested, and the cytokines and growth factors in CM were also measured. The results showed that CM significantly attenuated the histological alterations; decreased the wet/dry weight ratio; reduced the levels of MPO, MDA and ROS; increased SOD and GSH activity; and downregulated the level of proinflammatory cytokines such as IL-1β, IL-6, and TNF-α. Furthermore, CM promoted the expression of Nrf2 and its target genes NQ01, HO-1, and GCLC and inhibited the expression of NF-κB and its target genes IL-6, IL-1β, and TNF-α. These effects may be closely related to the large amounts of cytokines and growth factors in the CM. In conclusion, our results demonstrated that CM could attenuate LPS-induced ALI, probably due to inhibition of inflammation and oxidative stress via the Nrf2/NF-κB signaling pathway.
Design of Deep Learning Model for Task‐Evoked fMRI Data Classification
Authors: Huang Xiaojie , Xiao Jun , Wu Chao
Published: August 2021

Machine learning methods have been successfully applied to neuroimaging signals, one of which is to decode specific task states from functional magnetic resonance imaging (fMRI) data. In this paper, we propose a model that simultaneously utilizes characteristics of both spatial and temporal sequential information of fMRI data with deep neural networks to classify the fMRI task states. We designed a convolution network module and a recurrent network module to extract the spatial and temporal features of fMRI data, respectively. In particular, we also add the attention mechanism to the recurrent network module, which more effectively highlights the brain activation state at the moment of reaction. We evaluated the model using task‐evoked fMRI data from the Human Connectome Project (HCP) dataset, the classification accuracy got 94.31%, and the experimental results have shown that the model can effectively distinguish the brain states under different task stimuli.
Nutritional Composition and Sensory Acceptability of Stinging Nettle (Urtica simensis) Flour-Supplemented Unleavened Maize (Zea mays L.) Flatbread (Kitta)
Authors: Diddana Tona Zema , Kelkay Gezahegn Nigusse , Tescha Endale Elisho
Published: August 2021

In Ethiopia, a few studies had been conducted to improve the nutritional values and sensory acceptability of maize-based flatbread. These studies did not address indigenous edible wild green vegetables like stinging nettle (Urtica simensis). Consequently, there was a scientific report gap on the effect of incorporating stinging nettle leaf flour into local staple foods like flatbread. Therefore, this study was intended to investigate the nutritional composition and sensory acceptability of unleavened maize (Zea mays L.) flatbread (Kitta) supplemented with stinging nettle (Urtica simensis) flour. The flatbread was developed from composite flour of germinated maize and nettle leaf in a ratio of 90 : 10, 85 : 15, 80 : 20, and 75 : 25, respectively. Hundred percent (100%) nongerminated maize flour flatbread was used as control. Proximate composition, minerals (Fe, Zn, and Ca), and vitamin C contents were analyzed. The sensory acceptability test was rated by a nine-point hedonic scale. The result revealed that crude protein and fat decreased from 11.02 g to 7.21 g and 1.12 g to 0.48 g, respectively, when the amount of nettle flour supplementation increased from 0% to 25%. On the contrary, total ash, crude fiber, and total carbohydrate slightly increased from 1.84 to 3.81 g, 2.19 to 3.05, and 75.53 to 80.05 g, respectively. The calcium, zinc, and iron content significantly (

p
<
0.05

) increased from 60.51 to 283.74 mg, 5.09 to 9.24 mg, and 1.72 to 3.59 mg when the amount of nettle flour increased from 0% to 25%, respectively. All sensory acceptability tests showed decrement with increasing the amount of nettle flour, but the control group has the highest acceptability.
Common Fixed Points of a Subfamily of a Nonexpansive Periodic Evolution Family on a Strictly Convex Banach Space
Authors: Rahmat Gul , Shah Tariq , Sarwar Muhammad , Aydi Hassen , Alsamir Habes
Published: August 2021

In this study, we establish some results for strong convergence of a sequence to a common fixed point of a subfamily of a nonexpansive and periodic evolution family of bounded linear operators acting on a closed and bounded subset

J

of a strictly convex Banach space

X

. In fact, we generalized the results from semigroups of the operator to an evolution family of operators.
Nardosinone Alleviates Parkinson’s Disease Symptoms in Mice by Regulating Dopamine D2 Receptor
Authors: Bian Li-hua , Yao Zi-wei , Zhao Cheng-bowen , Li Qiu-yu , Shi Jin-li , Guo Jian-you
Published: August 2021

Nardostachyos Radix et Rhizoma (nardostachys) is the root and rhizome of Nardostachys jatamansi DC. Recent studies have shown that nardostachys may exert an anti-PD effect. In this study, the UHPLC-LTQ-Orbitrap-MS method was used to analyze the brain components of nardostachys in rats. Based on the results of UHPLC-LTQ-Orbitrap-MS analysis, nardosinone was identified to be the most effective anti-PD compound in nardostachys. To further verify this inference, a mouse PD model was established and the effect of nardosinone on PD mice was determined using classic behavioral tests. The results showed that nardosinone was indeed effective for relieving PD symptoms in mice. Moreover, network pharmacology analysis was used to elucidate the mechanism underlying the anti-PD effect of nardosinone. Dopamine receptor D2 (DRD2) was identified as the key target of nardosinone-PD interaction network, which was further verified by molecular docking and Western blotting. The results demonstrated that nardosinone and DRD2 could interact with each other. Furthermore, the expression level of DRD2 was decreased in the brain tissue of PD mice, and nardosinone could restore its expression to a certain extent. In conclusion, our findings suggest that nardosinone may reduce the motor and cognitive symptoms in the animal PD model by regulating DRD2 expression.
The Value of Brain Resting-State Functional Magnetic Resonance Imaging on Image Registration Algorithm in Analyzing Abnormal Changes of Neuronal Activity in Patients with Type 2 Diabetes
Authors: Tong Jiajie , Shan Chunhui , Hu Congcong
Published: August 2021

The aim of this paper was to analyze the application value of resting-state functional magnetic resonance imaging (FMRI) parameters and rigid transformation algorithm in patients with type 2 diabetes (T2DM), which could provide a theoretical basis for the registration application of FMRI. 107 patients confirmed pathologically as T2DM and 51 community medical healthy volunteers were selected and divided into an experimental group and a control group, respectively. Besides, all the subjects were scanned with FMRI. Then, the rigid transformation-principal axis algorithm (RT-PAA), Levenberg–Marquardt iterative closest point (LMICP), and Demons algorithm were applied to magnetic resonance image registration. It was found that RT-PAA was superior to LMICP and Demons in image registration. The amplitude of low-frequency fluctuation (ALFF) values of the left middle temporal gyrus, right middle temporal gyrus, left fusiform gyrus, right inferior occipital gyrus, and left middle occipital gyrus in patients from the experimental group were lower than those of the control group

P
<
0.05

. The Montreal cognitive assessment (MoCA) score was extremely negatively correlated with the ALFF of the left middle temporal gyrus (r = −0.451 and

P
<
0.001

) and highly positively associated with the ALFF of the right posterior cerebellar lobe (r = −0.484 and

P
<
0.001

). In addition, the MoCA score of patients had a dramatically negative correlation with the ALFF of the left middle temporal gyrus (r = −0.602 and

P
<
0.001

) and had a greatly positive correlation with the ALFF of the right posterior cerebellar lobe (r = −0.516 and

P
<
0.001

). The results showed that RT-PAA based on rigid transformation in this study had a good registration effect on magnetic resonance images. Compared with healthy volunteers, the left middle temporal gyrus, right middle temporal gyrus, left fusiform gyrus, right inferior occipital gyrus, and left middle occipital gyrus in patients with T2DM showed abnormal neuronal changes and reduced cognitive function.
Research on Key Technologies of Personalized Intervention for Chronic Diseases Based on Case-Based Reasoning
Authors: Zhang Lin , Qi Ping
Published: August 2021

In recent years, with the acceleration of industrialization, urbanization, and aging process, the number of patients with chronic diseases in the world is increasing year by year. In China, the number of chronic diseases has increased tenfold in 10 years. The percentage of the disease burden in the whole society accounts for 79.4%. Chronic diseases have become the top killer for Chinese people’s health. However, for chronic diseases, prevention is more important than treatment. It is the best way to keep healthy. Therefore, health intervention is the key to prevent chronic diseases. Especially now, with the spread of COVID-19 pandemic, reducing the times of hospital check-ups and treatments for chronic patients is practically significant for releasing the stress on medical staffs and decreasing the rate of transmission and infection of COVID-19. In this paper, case-based reasoning (CBR) technology is used to assist personalized intervention for chronic diseases, and the key technologies of personalized intervention for chronic diseases based on case-based reasoning are proposed. The case organization, case retrieval, and case retention techniques of CBR technology in chronic disease personalized intervention are designed, and the calculation of interclass dispersion is added to the distribution of feature words, which is used to describe the distribution of feature attributes in different categories of cases. It provides an effective method for the establishment of personalized intervention model for chronic disease.
Evaluation of Antibiotic Resistance and Biofilm Production among Clinical Strain Isolated from Medical Devices
Authors: Folliero Veronica , Franci Gianluigi , Dell’Annunziata Federica , Giugliano Rosa , Foglia Francesco , Sperlongano Rossella , De Filippis Anna , Finamore Emiliana , Galdiero Massimiliano
Published: August 2021

Microbial biofilms pose a serious threat to patients requiring medical devices (MDs). Prolonged periods of implantation carry a high risk of device-related infections (DRIs). Patients with DRIs often have negative outcomes following the failure of antibiotic treatment. Resistant DRIs are mainly due to the MDs contamination by bacteria producing biofilm. The present study aimed to detect biofilm formation among MD bacterial isolates and to explore their antibiotic resistance profile. The study was conducted on 76 MDs, collected at University Hospital of Campania “Luigi Vanvitelli,” between October 2019 and September 2020. Identification of isolates and antibiotic susceptibility testing were performed using Matrix Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) and Phoenix Becton Dickinson, respectively. Biofilm-forming abilities were assessed using the tissue culture plate (TCP) method. Among the 94 MDs isolated strains, 42.7% were Gram-positive, 40.3% Gram-negative, and 17% Candida species. Among 78 bacterial strains, 43.6% were non-biofilm producers while 56.4% produced biofilms. All biofilm producing isolates were sensitive to a limited spectrum of antibiotic classes. All moderate and strong biofilm producers and 81% of weak biofilm producers were Multidrug Resistance (MDR) strains. In contrast, among non-biofilm producers, only 11.8% were classified as MDR strains. Our results highlighted that Sulfamides and Glycopeptides for the major Gram-positive strains and Fluoroquinolones, Carbapenems, and Aminoglycosides for the most represented Gram-negative isolates could be the most suitable therapeutic choice for most biofilm-DRIs.
Automatic Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Based on CNN + SVM Networks with End‐to‐End Training
Authors: Huang Zhe , Sun Minglang , Guo Chengan
Published: August 2021

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease, and, at present, once it has been diagnosed, there is no effective curative treatment. Accurate and early diagnosis of Alzheimer’s disease is crucial for improving the condition of patients since effective preventive measures can be taken in advance to delay the onset time of the disease. 18F‐Fluorodeoxyglucose positron emission tomography (18F‐FDG PET : PET) is an effective biomarker of the symptom of AD and has been used as medical imaging data for diagnosing AD. Mild cognitive impairment (MCI) is regarded as an early symptom of AD, and it has been shown that MCI also has a certain biomedical correlation with PET. In this paper, we explore how to use 3D PET images to realize the effective recognition of MCI and thus achieve the early prediction of AD. This problem is then taken as the classification of three categories of PET images, including MCI, AD, and NC (normal controls). In order to get better classification performance, a novel network model is proposed in the paper based on 3D convolution neural networks (CNN) and support vector machines (SVM) by utilizing both the excellent abilities of CNN in feature extraction and SVM in classification. In order to make full use of the optimal property of SVM in solving binary classification problems, the three‐category classification problem is divided into three binary classifications, and each binary classification is being realized with a CNN + SVM network. Then, the outputs of the three CNN + SVM networks are fused into a final three‐category classification result. An end‐to‐end learning algorithm is developed to train the CNN + SVM networks, and a decision fusion algorithm is exploited to realize the fusion of the outputs of three CNN + SVM networks. Experimental results obtained in the work with comparative analyses confirm the effectiveness of the proposed method.
Fast Simulation and Chaos Investigation of a DC‐DC Boost Inverter
Authors: Dhifaou Rachid , Brahmi Houda
Published: August 2021

Intensive and repetitive simulations are required to study static and dynamic behaviours of systems. Particular phenomena such as bifurcation and chaos require long simulation times and analysis. To check the existence of bifurcations and chaos in a dynamic system, a fine‐tuning procedure of a bifurcation parameter is to be carried out. This increases considerably the computing time, and a great amount of patience is needed to obtain adequate results. Because of the high switching frequency of a boost inverter, the integration process of the dynamic model used to describe it uses an integration step that is in general less than one microsecond. This makes the integration process time consuming even for a short simulation. Thus, a fast, but accurate, method is suitable to analyse the dynamic behaviour of the converter. This work contains two topics. First, we develop a like‐discrete integration process that permits precise results in a very fast manner. For one switching period, we compute only two or a maximum of three breaking points depending on whether we treat a continuous conduction mode (CCM) or a discontinuous conduction mode (DCM) of the inductor current. Furthermore, with each segment of the dynamic trajectory, an exact analytic formula is associated. The second goal is to use this result to develop a discrete iterative map formulated as in standard discrete time series models. The Jacobian matrix of the found iterative map is defined and used to compute Lyapunov exponents to prove existence of chaos. Performance of the developed study is positively evaluated by using classical simulations and fine‐tuning a bifurcation parameter to detect chaos. This parameter is the desired reference of the inductor current peak. Results show that the proposed scheme is very fast and accurate. The study can be easily extended to other switching topologies of DC‐DC inverters.
Application of Internet of Things and Naive Bayes in Public Health Environmental Management of Government Institutions in China
Authors: Zhang Zhipeng , Zhang Shuxiang
Published: August 2021

As the builder and maintainer of the city appearance and environment, the city appearance and environmental service industry provides urban residents with a beautiful, livable, and comfortable living environment and is also responsible for providing city appearance and environmental public health services for the economic and social functions carried by the global cities task. Based on this problem, this study has carried out the work of government agencies’ public health environmental management and influencing factors based on the Internet of Things and edge computing. This study analyzes the influencing factors of public health environmental management of government agencies based on traditional research and conducts data analysis through the Naive Bayes algorithm. On this basis, the study combines the Internet of Things technology to construct system architecture for public health environmental management. The proposed management system is used to manage government agencies, which meets security, privacy and efficiency, and data security requirements. Experimental results prove that after algorithm optimization and architecture design, the evaluation level of the public health environment is significantly improved.
Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis
Authors: Li Yang , Shan Biaoan , Li Beiwei , Liu Xiaoju , Pu Yi
Published: August 2021

The emergence of machine learning (ML) and blockchain (BC) technology has greatly enriched the functions and services of healthcare, giving birth to the new field of “smart healthcare.” This study aims to review the application of ML and BC technology in the smart medical industry by Web of Science (WOS) using bibliometric visualization. Through our research, we identify the countries with the greatest output, the major research subjects, funding funds, and the research hotspots in this field. We also find out the key themes and future research areas in application of ML and BC technology in healthcare area. We reveal the different aspects of research under the two technologies and how they relate to each other around five themes.
Reactive Oxygen Species (ROS) Regulates Different Types of Cell Death by Acting as a Rheostat
Authors: Villalpando-Rodriguez Gloria E. , Gibson Spencer B.
Published: August 2021

Reactive oxygen species (ROS) are essential for cellular signaling and response to stress. The level of ROS and the type of ROS determine the ability of cells to undergo cell death. Furthermore, dysregulation of the antioxidant pathways is associated with many diseases. It has become apparent that cell death can occur through different mechanisms leading to the classifications of different types of cell death such as apoptosis, ferroptosis, and necroptosis. ROS play essential roles in all forms of cell death, but it is only now coming into focus that ROS control and determine the type of cell death that occurs in any given cell. Indeed, ROS may act as a rheostat allowing different cell death mechanisms to be engaged and crosstalk with different cell death types. In this review, we will describe the ROS regulatory pathways and how they control different types of cell death under normal and disease states. We will also propose how ROS could provide a mechanism of crosstalk between cell death mechanisms and act as a rheostat determining the type of cell death.
TMIGD1 Inhibited Abdominal Adhesion Formation by Alleviating Oxidative Stress in the Mitochondria of Peritoneal Mesothelial Cells
Authors: Wu Yunhua , Li Enmeng , Wang Zijun , Shen Tianli , Shen Cong , Liu Dong , Gao Qiuying , Li Xuqi , Wei Guangbing
Published: August 2021

Background. Postoperative abdominal adhesion remains one of the frequent complications after abdominal surgery and lacks effective intervention. Peritoneal mesothelial cell injury and healing play crucial roles in the process of adhesion formation, and identifying this mechanism might provide new insight into possible new therapeutic strategies for this disease. Transmembrane and immunoglobulin domain‐containing 1 (TMIGD1) has been proven to protect renal epithelial cells from injury induced by oxidative stress and has also been identified as a novel adhesion molecule. Here, we investigated the role of TMIGD1 and its possible mechanism in adhesion formation. Materials and Methods. Immunohistochemistry (IHC), qPCR, and immunofluorescence (IHF) were used to detect the expression of TMIGD1. The grade and tenacity score of adhesion were used to evaluate the adhesion formation conditions. A TMIGD1‐overexpressing HMrSV5 cell line was established. MTT assay, Western blotting, Annexin V apoptosis analysis, and CK19 staining were used to measure mesothelial cell viability, apoptosis, and completeness. ROS and MDA detection were used to measure mesothelial cell oxidative stress levels. JC‐1 staining, IHF, and transmission electron microscopy were performed to assess mitochondrial function. Scratch‐wound and adhesion assays were used to evaluate the adhesion ability of mesothelial cells. Results. First, we showed that TMIGD1 was decreased in mouse abdominal adhesion tissue and peritoneal mesothelial cells. Second, TMIGD1 overexpression inhibited adhesion formation. Third, TMIGD1 overexpression protected mesothelial cells from hydrogen peroxide‐ (H2O2‐) induced oxidative stress injury. Fourth, TMIGD1 overexpression alleviated oxidative stress by protecting the mitochondrial function of mesothelial cells. In addition, TMIGD1 overexpression enhanced mesothelial cell adhesion. Conclusion. Our findings suggest that TMIGD1 protects mesothelial cells from oxidative stress injury by protecting their mitochondrial function, which is decreased in regular abdominal adhesion tissue. In addition, TMIGD1 enhances peritoneal mesothelial cell adhesion to promote healing.
Boyer Moore string-match framework for a hybrid short message service spam filtering technique
Authors: Ojugo Arnold Adimabua , Oyemade David Ademola
Published: August 2021

Advances in technology and the proliferation of mobile device have continued to advance the ubiquitous nature of computing alongside their many prowess and improved features it brings as a disruptive technology to aid information sharing amongst many online users. This popularity, usage and adoption ease, mobility, and portability of the mobile smartphone devices have allowed for its acceptability and popularity. Mobile smartphones continue to adopt the use of short messages services accompanied with a scenario for spamming to thrive. Spams are unsolicited message or inappropriate contents. An effective spam filter studies are limited as short-text message service (SMS) are 140bytes, 160-characters, and rippled with abbreviation and slangs that further inhibits the effective training of models. The study proposes a string match algorithm used as deep learning ensemble on a hybrid spam filtering technique to normalize noisy features, expand text and use semantic dictionaries of disambiguation to train underlying learning heuristics and effectively classify SMS into legitimate and spam classes. Study uses a profile hidden Markov network to select and train the network structure and employs the deep neural network as a classifier network structure. Model achieves an accuracy of 97% with an error rate of 1.2%.