syedzayyan/paper_mill_benchmark huggingface dataset:
Analysis and Validation of Differentially Expressed Ferroptosis-Related Genes in Regorafenib-Induced Cardiotoxicity

Although tyrosine kinase inhibitors (tkis) constitute a type of anticancer drugs, the underlying mechanisms of tki-associated cardiotoxicity remain largely unknown.Ferroptosis is a regulated cell death form that implicated in several tumors’ biological processes.Our objective was to probe into the differential expression of ferroptosis-related genes in regorafenib-induced cardiotoxicity through multiple bioinformatics analysis and validation.Methods and materials.Four adult human cardiomyocyte cell lines treated with regorafenib were profiled using gene expression omnibus (geo) (gse146096).Differentially expressed genes (degs) were identified using deseq2 in r (v.3.6.3).Then, gene ontology (go) enrichment analysis, kyoto encyclopedia of genes and genomes (kegg) enrichment analysis, and gene set enrichment analysis (gsea) were used to explore degs’ bioinformatics functions and enriched pathways.We intersected degs with 259 ferroptosis-related genes from the ferr db database.Finally, the m rna levels of differentially expressed ferroptosis-related genes (defrgs) were validated in regorafenib-cultured cardiomyocytes to anticipate the link between defrgs and cardiotoxicity.Results.747,1127,773 and 969 degs were screened out in adult human cardiomyocyte lines a, b, d, and e, respectively.The mechanism by which reg promotes cardiotoxicity associated with ferroptosis may be regulated by pi3k-akt, tgf-beta, and mapk.Gsea demonstrated that reg can promote cardiotoxicity by suppressing genes and pathways encoding extracellular matrix and related proteins, oxidative phosphorylation, or atf-2 transcription factor network.After overlapping degs with ferroptosis-related genes, we got seven defrgs and found that atf3, mt1g, and plin2 were upregulated and ddit4 was downregulated.The roc curve demonstrated that these genes predict regorafenib-induced cardiotoxicity well.Conclusion.We identified four defrgs which may become potential predictors and participate in the regorafenib-induced cardiotoxicity.Our findings provide possibility that targeting these ferroptosis-related genes may be an alternative for clinical prevention and therapy of regorafenib-related cardiotoxicity.

Journal: Oxidative Medicine and Cellular Longevity (2022)
Publication types: Research Article;
An Image Processing Approach for Detection of Prenatal Heart Disease

Prenatal heart disease, generally known as cardiac problems (chds), is a group of ailments that damage the heartbeat and has recently now become top deaths worldwide.It connects a plethora of cardiovascular diseases risks to the urgent in need of accurate, trustworthy, and effective approaches for early recognition.Data preprocessing is a common method for evaluating big quantities of information in the medical business.To help clinicians forecast heart problems, investigators utilize a range of data mining algorithms to examine enormous volumes of intricate medical information.The system is predicated on classification models such as nb, knn, dt, and rf algorithms, so it includes a variety of cardiac disease‐related variables.It takes do with an entire dataset from the medical research database of patients with heart disease.The set has 300 instances and 75 attributes.Considering their relevance in establishing the usefulness of alternate approaches, only 15 of the 75 criteria are examined.The purpose of this research is to predict whether or not a person will develop cardiovascular disease.According to the statistics, naïve bayes classifier has the highest overall accuracy.

Journal: BioMed Research International (2022)
Publication types: Research Article;
An Ideal Approach for Enhancing 5-Fluorouracil Anticancer Efficacy by Nanoemulsion for Cytotoxicity against a Human Hepatoma Cell Line (HepG2 Cells)

The core objectives of the research were to prepare 5‐fluorouracil nanoemulsion (fu‐ne) and to evaluate the physiochemical properties and to study the i n vitro antiproliferation in hep g2 cell lines.The physiochemical parameters determined were compatibility, particle size (ps), polydispersity index (pdi), zeta potential (zp), density, surface tension (st), p h, viscosity, in vitro release of fu, cytotoxicity, and apoptosis study.The prepared fu‐ne3 was stable, sterile, and homogeneous.On the hep g2 (120 μ g.m l -1 ) cells, in vitro cytotoxicity was obtained at ic 50 concentration.Apoptosis examination by ao/eband hoechst staining shows that the majority of cell demise was caused by apoptosis, with a tiny fraction of necrosis.Hence, this investigation concluded that the developed fu‐ne has now desirable characteristics for drug delivery to the cancer cell and may be screened for the in vivo colorectal anticancer activity.

Journal: BioMed Research International (2022)
Publication types: Research Article;
An Exploration: Alzheimer’s Disease Classification Based on Convolutional Neural Network

Alzheimer’s disease (ad) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability.Deep learning models have shown outstanding performance in the diagnosis of ad, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms.Since the 2012 alex net accomplishment, the convolutional neural network (cnn) has been progressively utilized by the medical community to assist practitioners to early diagnose ad.This paper explores the current cutting edge applications of cnn on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of ad.An exhaustive systematic search is conducted on four notable databases: google scholar, ieee xplore, acm digital library, and pub med in june 2021.The objective of this study is to examine the effectiveness of classification approaches on ad to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods.However, cnn has achieved great success in the classification of ad; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field.

Journal: BioMed Research International (2022)
Publication types: Review Article;
BMSC-derived exosomes carrying microRNA-122-5p promote progression of osteoblasts in osteonecrosis of the femoral head

Mesenchymal stem cells (mscs) with multipotential differentiation capacity can differentiate into bone cells under specific conditions and can be used to treat osteonecrosis (on) of the femoral head (onfh) through cell transplantation.The current study aims to explore the role of bone marrow (bm) mscs (bmscs)-derived exosomes carrying micro rna-122-5p (mi r-122-5p) in onfh rabbit models.First, rabbit models with onfh were established.Onfh-related mi rnas were screened using the gene expression omnibus (geo) database.A gain-of-function study was performed to investigate the effect of mi r-122-5p on osteoblasts and bmscs and effects of exosomes carrying mi r-122-5p on onfh.Co-culture experiments for osteoblasts and bmscs were performed to examine the role of exosomal mi r-122-5p in osteoblast proliferation and osteogenesis.The target relationship between mi r-122-5p and sprouty2 (spry2) was tested.Mi r-122, significantly decreased in onfh in the gse89587 expression profile, was screened.Mi r-122-5p negatively regulated spry2 and elevated the activity of receptor tyrosine kinase (rtk), thereby promoting the proliferation and differentiation of osteoblasts.In vivo experiments indicated that bone mineral density (bmd), trabecular bone volume (tbv), and mean trabecular plate thickness (mtpt) of femoral head were increased after over-expressing mi r-122-5p in exosomes.Significant healing of necrotic femoral head was also observed.Exosomes carrying over-expressed mi r-122-5p attenuated onfh development by down-regulating spry2 via the rtk/ras/mitogen-activated protein kinase (mapk) signaling pathway.Findings in the present study may provide mi r-122-5p as a novel biomarker for onfh treatment.

Journal: Clinical Science (2019)
Publication types: Research Article;
Application Research of Fuzzy Control Comprehensive Model in Agricultural Economic Management

Agriculture is the basic industry of the national economy, and the transformation of agricultural economic growth mode is of great significance to ensure the supply of agricultural products and improve the quality of agricultural products.Therefore, it is the only way to solve this problem to change the way of agricultural economic growth through scientific and technological investment, industrial structure adjustment, and other methods.Based on the fuzzy comprehensive evaluation method, this paper analyzes various factors of agricultural economic development and establishes a multilevel fuzzy comprehensive evaluation model of the development status of agricultural economic system.The stochastic frontier production function model of the fuzzy control comprehensive model is applied to decompose the agricultural labor productivity, and the counterfactual thinking and distribution method are used to analyze the influence of these factors on the evolution of provincial agricultural labor productivity differences.The value inequality decomposition method measures the contribution of each factor to the provincial agricultural labor productivity difference as well as the contribution of exogenous explanatory variables to the provincial agricultural technical efficiency difference.It is concluded that the relative contribution rate of nonagricultural activities to the difference in agricultural technical efficiency is the largest, and the average relative contribution rate reaches 74%.The improvement of agricultural labor productivity cannot rely solely on the increase in the input of agricultural material factors, but should rely on science and technology.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Construction of Dynamic Balance Model of Supply and Demand in Labor Market under Flexible Employment

The dynamic balance of supply and demand in the labor market is a key issue at present.The change of employment quality is relatively complex.The current dynamic balance model of supply and demand cannot obtain high-precision evaluation results of employment quality and meet the requirements of practical application.Therefore, a dynamic balance model of labor market supply and demand under flexible employment is constructed.Big data network is used to set data acquisition channels, complete data collection, unify data sample format, formulate data processing flow, and obtain processed data samples.The correlation analysis method in big data analysis technology is used to complete the analysis of college graduation employment.This paper analyzes the relevant research work of employment quality evaluation, establishes the employment quality evaluation index system, collects the index data, normalizes the index data, then uses the grey correlation method to determine the weight value of the employment quality evaluation index, and uses the fuzzy c-means algorithm to establish the dynamic balance model of supply and demand in the labor market.The experimental results show that the designed method can better balance the supply-demand relationship in the labor market and has a good effect.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Application Exploration of Scenario Logistics Ecosystem Based on beyond 5G and IoT Architecture

With the continuous expansion of my country's logistics market, the business scale and management scale of logistics enterprises are increasing day by day.Smart logistics enterprises realize the “three streams in one” of the logistics industry through informatization and intelligent management, which greatly improves the service efficiency and quality of logistics enterprises.However, there are still many problems in this intelligent logistics mode.In this context, we conduct research experiments on the logistics ecosystem of io t architecture scenarios and draw conclusions through experiments: (1) logistics development in io t building scenarios.The environment includes natural environment, economic environment, political environment, etc.The economic environment plays an important role in driving logistics demand and promoting logistics growth.The political environment helps to improve the logistics infrastructure and ensure the healthy development of logistics.(2) the most important cost of the logistics industry is that transportation costs account for 67% of the entire logistics cost, followed by cargo storage accounting for 16% of the cost, and smart express cabinets accounting for 13% of the cost.From this, we can see the highest cost out of the logistics system under the internet of things is the transportation cost.As a unity is closely linked to the economy and interdependent, logistics is the driving force to promote the development of economic integration.In order to ensure the sustainable development of the economy, it is necessary to continuously improve the service level of logistics, reduce logistics costs, improve operation efficiency, strengthen the linkage development with other industries, and enhance the competitiveness of logistics.We launched an investigation against this background and researched the logistics system under the scenario logistics ecosystem of beyond 5g and io t architecture.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

In order to deal with the problem that the traditional stage costume artistry analysis method cannot correct the results of big data clustering, which leads to deviations in the extraction of costume artistry features, this paper proposes a clothing artistic modeling method based on big data clustering algorithm.The proposed method provides a database for big data clustering by constructing the attribute set of the big data feature sequence training set and, at the same time, constructing a second-order cone programming model to correct the big data.Aiming at the problem that traditional stage costume art analysis methods cannot correct the clustering results of big data.On this basis, the costume elements of the opera stage are segmented, initialized, and transformed into a binary function.Finally, using the convolutional neural network, combining the element segmentation results and the large data clustering space state vector, a feature extraction model of stage costume art is constructed.Experimental results show that the model has good convergence, short time-consuming, high accuracy, and ideal feature recognition capabilities.

Journal: Security and Communication Networks (2021)
Publication types: Research Article;
Analysis of Artistic Creation and Design Methods in Universities Based on Augmented Reality and 5G Communication Technology

In order to enhance the research of art design in colleges and universities, based on the theory of augmented reality, 5g communication technology is used to conduct targeted analysis of art design and creation in colleges and universities.Through the research of feature parameter extraction, parameter consistency test, and optimization algorithm analysis on the relevant data of the art design in colleges and universities, the deficiencies of art design and creation in colleges and universities are pointed out, and relevant solutions are adopted.The results show that with the increase of distribution distance, the curve of the z value shows a linear change trend.The curve corresponding to a value has an obvious polynomial change rule.Through the analysis, it can be seen that the influence of two parameters on augmented reality needs to be considered comprehensively.As the number of iterative steps increases, the consistency curve of augmented reality shows an obvious linear downward trend.However, the increase of parameter m can improve the data changes of augmented reality consistency, and the research shows that parameter m can further promote the consistency changes of augmented reality.The gradual increase of index pn leads to a gradual decline in data pc, while the parameter px has an opposite effect on data pc.Therefore, in order to obtain targeted data and results of 5g communication technology, it is necessary to comprehensively consider the influence of the two factors.Finally, the optimization model based on augmented reality and 5g communication technology is used to analyze and predict the relevant characteristic data of university art design, so as to verify the accuracy of the model.The research results can provide support for the application of augmented reality and 5g communication technology in other art fields.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
An Optimization Model for Logistics Distribution Network of Cross-Border E-Commerce Based on Personalized Recommendation Algorithm

In order to solve the problems of poor timeliness and high service cost caused by international logistics of cross-border e-commerce and to realize the localization of cross-border trade and improve consumers' shopping experience, much cross-border e-commerce has introduced overseas warehouse business.Because the optimization model for the logistics distribution network of cross-border e-commerce cannot solve the problem of high operation cost, the optimization model for the logistics distribution network of cross-border e-commerce based on a personalized recommendation algorithm is designed.Through the analysis of the logistics distribution network of cross-border e-commerce structure and the construction of logistics distribution network of cross-border e-commerce model and multiobjective optimization of logistics distribution network of cross-border e-commerce, the construction process of the logistics distribution network optimization model is completed.The simulation results verify the effectiveness of the model.By comparison, this model can effectively solve the problems that the original model cannot solve and reduce the operation cost of the logistics distribution network.

Journal: Security and Communication Networks (2021)
Publication types: Research Article;
An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images

Cerebellum measures taken from routinely obtained ultrasound (us) images have been frequently employed to determine gestational age and identify developing central nervous system’s anatomical abnormalities.Standardized cerebellar assessments from large‐scale clinical datasets are required to investigate correlations between the growing cerebellum and postnatal neurodevelopmental results.These studies could uncover structural abnormalities that could be employed as indicators to forecast neurodevelopmental and growth consequences.To achieve this, higher‐throughput, precise, and impartial measures must be used to replace the existing human, semiautomatic, and advanced algorithms, which seem to be time‐consuming and inaccurate.In this article, we presented an innovative deep learning (dl) technique for automatic fetal cerebellum segmentation from 2‐dimensional (2d) us brain images.We present re u‐net, a semantic segmentation network tailored to the anatomy of the fetal cerebellum.Moreover, we use u‐net as a foundation models with the incorporation of residual blocks and wiener filter over the last 2 layers to segregate the cerebellum (c) from the noisy us data.590 images for training and 150 images for testing were taken; also, we employed a 5‐fold cross‐assessment method.Our re u‐net scored 91%, 92%, 25.42, 98%, 92%, and 94% for dice score coefficient (dsc), f1‐score, hausdorff distance (hd), accuracy, recall, and precision, correspondingly.The suggested method outperforms the other u‐net predicated techniques by a quantitatively significant margin ( p 0.001).Our presented approach can be used to allow high bandwidth imaging techniques in medical study fetal us images as well as biometric evaluation on a broader scale in fetal us images.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Alpha Protein Kinase 2 Promotes Esophageal Cancer via Integrin Alpha 11

As a common disease around the world, esophageal cancer (ec) primarily includes two subclasses: esophageal adenocarcinoma and esophageal squamous cell carcinoma.Mortality has been rising over the years; hence, exploring the mechanism of ec development has become critical.Among the alpha protein kinases, alpha protein kinase 2 (alpk2) presumably has a connection with ec, but it has never been revealed before.Methods .In this study, ihc analysis was used for alpk2 expression quantification in es tissues.Te‐1 and eca‐109, which are both human ec cell lines, were used for in vitro analysis of cell proliferation, migration, apoptosis, and colony formation.Results .Alpk2 was found to have an abundant expression within ec tissues ( p &lt; 0.001), as well as in the two selected human ec cell lines ( p &lt; 0.05).The data showed that alpk2 depletion suppressed ec cell proliferation, migration, and colony formation, meanwhile stimulating apoptosis ( p &lt; 0.001).The in vivo experiments also displayed inhibitory effects caused by alpk2 depletion on ec tumorigenesis ( p &lt; 0.001).It was further validated that alpk2 depletion made the phosphorylation of akt and m tor, as well as cdk6 and pik3ca levels downregulated ( p &lt; 0.001).Mechanistically, we identified integrin alpha 11 (itga11) as a downstream gene of alpk2 regulating ec.More importantly, we found that itga11 elevation promoted cell proliferation and migration and rescued the suppression effects caused by alpk2 depletion ( p &lt; 0.001).Conclusions .Alpk2 promotes esophageal cancer via integrin its downstream gene alpha 11; alpk2 can potentially act as a target for the treatment of ec.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Allicin Inhibits Osteosarcoma Growth by Promoting Oxidative Stress and Autophagy via the Inactivation of the lncRNA MALAT1-miR-376a-Wnt/ β-Catenin Signaling Pathway

Allicin, an organic sulfur compound extracted from the bulb of allium sativum, can potentially prevent various tumors.Our previous study found that allicin can effectively suppress the proliferation of osteosarcoma cells.However, the molecular mechanisms have not been illustrated.In this study, saos-2 and u2os osteosarcoma cells were used to investigate the underlying mechanisms.A series of experiments were carried out to authenticate the anticancer property of allicin.Knockdown of lnc rna malat1 inhibited the proliferation, invasion and migration and promoted apoptosis of osteosarcoma cells.Knockdown of mi r-376a increased the proliferation, invasion, and migration and dropped apoptosis of osteosarcoma cells.Furthermore, knockdown of mi r-376a reversed the influences of malat1 silencing in osteosarcoma cells.Based on our data, malat1 could downregulate the expression of mi r-376a, subsequently accelerating osteosarcoma.Moreover, oxidative stress and autophagy were identified as the potential key pathway of allicin.Allicin inhibited osteosarcoma growth and promoted oxidative stress and autophagy via malati-mi r-376a.We also found that allicin promotes oxidative stress and autophagy to inhibit osteosarcoma growth by inhibiting the wnt/β-catenin pathway in vivo and in vitro.All data showed that allicin promotes oxidative stress and autophagy of osteosarcoma via the malati-mi r-376a-wnt/β-catenin pathway.

Journal: Oxidative Medicine and Cellular Longevity (2022)
Publication types: Research Article;