syedzayyan/paper_mill_benchmark huggingface dataset:
Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm

This research was aimed at discussing the application value of different machine learning algorithms in the prediction of early alzheimer’s disease (ad), which was based on hippocampal volume changes in magnetic resonance imaging (mri).In the research, the 84 cases in american alzheimer’s disease neuroimaging initiative (adni) database were selected as the research data.Based on the scoring results of cognitive function, all cases were divided into three groups, including cognitive function normal (normal group), early mild cognitive impairment (e-mci group), and later mild cognitive impairment (l-mci group) groups.Each group included 28 cases.The features of hippocampal volume changes in mri images of the patients in different groups were extracted.The samples of training set and test set were established.Besides, the established support vector machine (svm), decision tree (dt), and random forest (rf) prediction models were used to predict e-mci.Metalinear regression was utilized to analyze mri feature data, and the predictive accuracy, sensitivity, and specificity of different models were calculated.The result showed that the volumes of hippocampal left ca1, left ca2-3, left ca4-dg, left presubiculum, left tail, right ca2-3, right ca4-dg, right presubiculum, and right tail in e-mci group were all smaller than those in normal group ( p &lt; 0.01 ).The corresponding volume of hippocampal subregions in l-mci group was remarkably reduced compared with that in normal group ( p &lt; 0.001 ).The volumes of regions left ca1, left ca2-3, left ca4-dg, right ca2-3, right ca4-dg, and right presubiculum were all positively correlated with logical memory test-delay recall (lmt-dr) score ( r 2 = 0.1702 , 0.3779, 0.1607, 0.1620, 0.0426, and 0.1309; p &lt; 0.001 ).The predictive accuracy of training set sample by dt, svm, and rf was 86.67%, 93.33%, and 98.33%, respectively.Based on the changes in the volumes of left ca4-dg, right ca2-3, and right ca4-dg, the predictive accuracy of e-mci and l-mci by rf model was both higher than those by dt model ( p &lt; 0.01 ).Besides, the predictive accuracy, sensitivity, and specificity of e-mci by rf model was all notably higher than those by dt model ( p &lt; 0.01 ).The above results demonstrated that the effective early ad prediction models were established by the volume changes in hippocampal subregions, which was based on rf in the research.The establishment of early ad prediction models offered certain reference basis to the diagnosis and treatment of ad patients.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Prediction of Conversion from CIS to Clinically Definite Multiple Sclerosis Using Convolutional Neural Networks

Multiple sclerosis (ms) is a chronic neurological disease of the central nervous system (cns).Early diagnosis of ms is highly desirable as treatments are more effective in preventing ms-related disability when given in the early stages of the disease.The main aim of this research is to predict the occurrence of a second ms-related clinical event, which indicates the conversion of clinically isolated syndrome (cis) to clinically definite ms (cdms).In this study, we apply a branch of artificial intelligence known as deep learning and develop a fully automated algorithm primed with convolutional neural network (cnn) that has the ability to learn from mri scan features.The basic architecture of our algorithm is that of the vgg16 cnn model, but amended such that it can handle mri dicom images.A dataset comprised of scans acquired using two different scanners was used for the purposes of verification of the algorithm.A group of 49 patients had volumetric mri scans taken at onset of the disease and then again one year later using one of the two scanners.In total, this yielded 7360 images which were then used for training, validation, and testing of the algorithm.Initially, these raw images were taken through 4 steps of preprocessing.In order to boost the efficiency of the process, we pretrained our algorithm using the publicly available adni dataset used to classify alzheimer’s disease.Finally, we used our preprocessed dataset to train and test the algorithm.Clinical evaluation conducted a year after the first time point revealed that 26 of the 49 patients had converted to cdms, while the remaining 23 had not.Results of testing showed that our algorithm was able to predict the clinical results with an accuracy of 88.8% and with an area under the curve (auc) of 91%.A highly accurate algorithm was developed using cnn approach to reliably predict conversion of patients with cis to cdms using mri data from two different scanners.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
The Influence of the Development of the Internet of Things Industry on the Optimization of the High- and New-Tech Industry Structure under the Wireless Mobile Network

At present, the pressure on china’s economic development is increasing day by day due to the profound changes in the internal and external environment.The global economic pattern is undergoing significant changes in terms of the external environment.Adjusting and optimizing the industrial structure will aid in achieving the goal of facilitating transformation through steady growth in the short term.Meanwhile, it will also accelerate sustainable economic development.In this study, the relevant theories of industrial structure optimization are described based on the impact of wireless mobile networks and the internet of things (io t) industry.Based on the gray correlation degree, the high- and new-tech industries under the development of the io t industry are analyzed and the impact of optimization of the high- and new-tech industry structure is investigated.The results show that the development of the io t industry has driven the development of the high- and new-tech industry.The gray correlation between the development of the io t industry and the high- and new-tech industry obtained is 0.64, indicating a strong correlation.The average output share of the electronic computer and office equipment manufacturing industries is 47.09%.The average output ratio of the industrial structure optimization of the electronics and communication manufacturing industry is 42.55%.Moreover, the proportion of the output of medical manufacturing and medical equipment and instrument manufacturing industrial structure optimization is small, 15.63% and 10.54%, respectively.The results have significant value in the research on the impact of the development of the io t industry on the high- and new-tech industry under the wireless mobile network and the effect of its industrial structure optimization.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
The Index Data System of Agricultural Modernization Development Based on Internet Big Data

With the integration and development of the internet, the internet of things, cloud data, and communication technology, data has grown rapidly.Although science and technology have promoted the upgrade and modernization of the modern agricultural system, it has also brought about the problem of a sharp increase in data, making it difficult to grasp the direction of agricultural development.At the same time, in order to better grasp the development of agricultural modernization, this article has carried out research on the agricultural modernization development index data system based on network big data and raised the problem that agricultural modernization based on network big data can accurately grasp the development direction of agricultural modernization.Experiments show that the index data system studied in this article has a good grasp of the state of agricultural modernization development and is the basis for judging and formulating correct agricultural policies.Among them, the indicator data on the rate of change of the national agricultural policy needs to be maintained between 1.84 and 2.01.The research in this article is of great significance to the development of agricultural modernization.

Journal: Wireless Communications and Mobile Computing (2022)
Publication types: Research Article;
The Construction and Realization of the Precise Funding Platform for Impoverished Students in Colleges and Universities Driven by Computer Intelligence Technology

At present, the state’s financial aid for students from financially disadvantaged families is increasing, and the number of students receiving financial aid is also increasing substantially.Poor students have to run back and forth to submit various materials while busy with their own studies, which causes a lot of inconvenience to students and teachers.The development of the poverty-stricken student precision funding platform frees teachers from tedious and low-end work and engages in core affairs, which improves the overall efficiency of school affairs and the controllability of information.In this context, this paper uses computer intelligence technology to realize the construction of the precise funding platform for impoverished students in colleges and universities (cau) and completes the following tasks: (1) the research status of precise funding for impoverished students in cau at home and abroad is introduced.(2) the ssi frame structure diagram of the funding information management platform is designed and the specific function description and detailed design of the relevant modules are given.(3) the module function test and system function test of the platform designed in this paper are completed through experiments.The results show that the system is very stable, and the functions are perfectly realized.

Journal: Advances in Multimedia (2023)
Publication types: Research Article;
Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences

Adaptive neuro‐fuzzy inference system (anfis) blends advantages of both artificial neural networks (anns) and fuzzy logic (fl) in a single framework.It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships.Anfis has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity.Standard anfis has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions.This paper summarizes that the standard anfis is unsuitable for complex human tasks that require precise handling of machines and systems.The state‐of‐the‐art and practice research questions have been discussed, which primarily focus on the applicability of anfis in the diversifying field of engineering sciences.We conclude that the standard anfis architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature‐inspired algorithms through calibration and tuning of parameters.It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields.

Journal: Computational Intelligence and Neuroscience (2021)
Publication types: Research Article;
The Application of 3D Virtual Technology in the Teaching of Clinical Medicine

The booming development of 3d virtual technology provides the basis for the innovation of teaching mode of clinical medicine.Integrating 3d virtual technology into the clinical education teaching of medical students can help medical students learn clinical expertise more intuitively, enrich practical experience, improve hands-on ability, and make up for the shortcomings of traditional medical teaching.However, the traditional 3d modeling method has the problems of sparse and insufficient accuracy of output results, which greatly limits the specific application of 3d virtual technology in clinical teaching.Therefore, this paper addresses the shortcomings of previous 3d virtual technology in 3d modeling of organs, introduces deep learning, and then proposes a pyramidal shape perception network with the ability to generate samples on point clouds.Experimental results show that the proposed model not only has a large improvement in accuracy but also shows a high degree of confidence in providing real-time feedback of local image attributes.

Journal: Advances in Multimedia (2022)
Publication types: Research Article;
The Development Dilemma and Path Choice of Innovation and Entrepreneurship Education Based on Game Theory

Innovation and entrepreneurship education (iee) is a powerful driving force to promote the transformation and development of college education and an effective way to broaden the employment channels.However, iee has been used to train students in classrooms by college managers who do not systematically think of practical training for innovation and entrepreneurship talents.There are still many difficulties in the process of development of iee that managers cannot ignore.The relevant models that could deeply analyze the connotation of iee were established through the game theory that explained the game balance among government, universities, and students in the iee process.A game theory model explained the external game behavior of universities and students and the internal game behavior between students.Another model analyzed the strategies of players pursuing maximum payoffs from the perspective of both sides of the game and the influences of these strategies on the players.The problems in the process of iee development are concluded including imperfect management system, unscientific curriculum design, nonideal talent training and unprofessional teachers.Based on it, the development path of iee is explored through the game concept.A scientific and leading system of management and evaluation is needed to put forward by strengthening the top-level design at the national level, and a win-win game situation between colleges and students will be achieved by the optimized education system.

Journal: Advances in Multimedia (2022)
Publication types: Research Article;
POU Class 2 Associating Factor 1 Exerts a Protective Effect on the Respiratory Syncytial Virus-Induced Acute Bronchiolitis by the NF-κB Pathway

Respiratory syncytial virus (rsv) is the main pathogen causing acute bronchiolitis, which is common in infants and young children.A previous study revealed the possible involvement of pou class 2 associating factor 1 (pou2af1) in rsv‐triggered acute bronchiolitis.We attempted to clarify the specific action mechanism of pou2af1 underlying rsv‐triggered inflammation.Methods .Rt‐q pcr measured pou2af1 levels in rsv‐infected children, mice, and airway epithelial cell lines (hbecs).He staining showed histopathological features in the lung tissue of rsv‐infected mice.Elisa examined the contents of proinflammatory cytokines in rsv‐infected mice.Western blotting evaluated the protein abundance of proinflammatory cytokines in rsv‐infected hbecs and assessed nf‐ κ b pathway‐associated protein expression in rsv‐infected mice and rsv‐treated hbecs.Results .Pou2af1 presented depletion in rsv‐infected children, mice, and hbecs.Rsv‐infected triggered lung injury and inflammatory cell infiltration in the mouse lung tissue, while pou2af1 overexpression rescued these changes.Rsv‐infected induced inflammatory impairment in hbecs, whereas pou2af1 reversed this effect.Pou2af1 suppressed the upregulated nf‐ κ b pathway‐associated protein expression in mice and hbecs under rsv infection.Conclusion .Pou2af1 exerts a protective impact on rsv‐induced acute bronchiolitis in vitro and in vivo through the nf‐ κ b pathway.Our research may provide a novel direction for better therapy of rsv‐triggered acute bronchiolitis.

Journal: Evidence-Based Complementary and Alternative Medicine (2023)
Publication types: Research Article;
Potential Pleiotropic Genes and Shared Biological Pathways in Epilepsy and Depression Based on GWAS Summary Statistics

Current epidemiological and experimental studies have indicated the overlapping genetic foundation of epilepsy and depression.However, the detailed pleiotropic genetic etiology and neurobiological pathways have not been well understood, and there are many variants with underestimated effect on the comorbidity of the two diseases.Utilizing genome-wide association study (gwas) summary statistics of epilepsy (15,212 cases and 29,677 controls) and depression (170,756 cases and 329,443 controls) from large consortia, we assessed the integrated gene-based association with both diseases by multimarker analysis of genomic annotation (magma) and fisher’s meta-analysis.On the one hand, shared genes with significantly altered transcripts in gene expression omnibus (geo) data sets were considered as possible pleiotropic genes.On the other hand, the pathway enrichment analysis was conducted based on the gene lists with nominal significance in the gene-based association test of each disease.We identified a total of two pleiotropic genes (cd3g and slco3a1) with gene expression analysis validated and interpreted twenty-five common biological process supported with literature mining.This study indicates the potentially shared genes associated with both epilepsy and depression based on gene expression, meta-data analysis, and pathway enrichment strategy along with traditional gwas and provides insights into the possible intersecting pathways that were not previously reported.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Meta-Analysis;
Potential Molecular Mechanism of Guishen Huoxue Decoction against Intrauterine Adhesion Based on Network Pharmacology

Intrauterine adhesion (iua) represents an endometrial repair disorder that is associated with menstrual disorders, recurrent pregnancy loss, and infertility.This study aimed to explore the underlying biological mechanisms of guishen huoxue decoction for the treatment of iua based on network pharmacology.Methods.The selection of active compounds for guishen huoxue decoction and prediction of relevant targets were performed by the tcmsp and swiss target prediction databases, respectively.The targets of iua were obtained by three databases, including online mendelian inheritance in man (omim), dis ge net, and gene cards.The drug-disease regulatory network was constructed via cytoscape software, following the acquisition of common genes of active compounds of drug guishen huoxue decoction and disease iua, which was carried out through venny software.Protein-protein interaction (ppi) network and function enrichment analyses were performed.Results.According to the data obtained from tcmsp, a total of 200 potential active compounds of guishen huoxue decoction and their related targets (1068) were screened by the swiss target prediction database.1303 disease targets and 134 common targets were identified.The drug-disease regulatory network showed that 165 active compounds were found to be involved in the treatment of iua.Among 134 common targets, akt1, src, tp53, vegfa, and il-6 were predicted as core genes against iua.Pi3k-akt, rap1, ras, and age-rage were the main signaling pathways that participated in the treatment of guishen huoxue decoction for iua.Conclusion.The active compounds of guishen huoxue decoction confer therapeutic effects against iua by regulating fibrosis, inflammation, and oxidative stress through major signaling pathways such as pi3k-akt and age-rage.

Journal: Evidence-Based Complementary and Alternative Medicine (2022)
Publication types: Research Article;
Polymyxin B, Cefoperazone Sodium-Sulbactam Sodium, and Tigecycline against Multidrug-Resistant Acinetobacter baumannii Pneumonia

The purpose of this study is to investigate the significance of polymyxin b in combination with cefoperazone sodium-sulbactam sodium (csss) and tigecycline for the treatment of multidrug-resistant acinetobacter baumannii- (mdrab-) induced pneumonia on the levels of white blood cell (wbc) count, serum c-reactive protein (crp), and procalcitonin (pct).Methods.Fifty-six patients with mdrab pneumonia admitted to the fifth people’s hospital of wuhu from february 2019 to december 2021 were randomized into the observation group (n = 28) and the experimental group (n = 28) by the random table method.The observation group received intravenous infusion of csss and tigecycline.The experimental group received intravenous infusion of polymyxin b sulfate plus csss and tigecycline.All patients were treated for 14 days.Results.There was no significant difference in the overall response rate between the two groups; the bacterial clearance of the experimental group was significantly higher than that of the observation group; there was no significant difference in the wbc, crp, and pct levels between the two groups prior to the treatment; but after treatment, while the wbc, crp, and pct levels of the two groups decreased, the wbc count, crp, and pct levels of the experimental group were significantly lower than those of the observation group; no significant difference was found in adverse reactions.Conclusion.Polymyxin b-csss-tigecycline has good clinical efficacy in the treatment of mdrab pneumonia.It not only improves the patients’ bacterial clearance rate and effectively reduces the levels of wbc count, serum crp, and pct, but also raises no risk of adverse reactions.Therefore, it is worthy of clinical promotion.

Journal: Evidence-Based Complementary and Alternative Medicine (2022)
Publication types: Clinical Study;
Polymethyl Methacrylate-Based Smart Microfluidic Point-of-Care Testing of Prothrombin Time and International Normalized Ratio through Optical Detection

The mechanical heart valve is a crucial solution for many patients.However, it cannot function on the state of blood as human tissue valves.Thus, people with mechanical valves are put under anticoagulant therapy.A good measurement of the state of blood and how long it takes blood to form clots is the prothrombin time (pt); moreover, it is an indicator of how well the anticoagulant therapy is, and of whether the response of the patient to the drug is as needed.For a more specific standardized measurement of coagulation time, an international normalized ratio (inr) is established.Clinical testing of inr and pt is relatively easy.However, it requires the patient to visit the clinic for evaluation purposes.Many techniques are therefore being developed to provide pt and inr self-testing devices.Unfortunately, those solutions are either inaccurate, complex, or expensive.The present work approaches the design of an anticoagulation self-monitoring device that is easy to use, accurate, and relatively inexpensive.Hence, a two-channel polymethyl methacrylate-based microfluidic point-of-care (poc) smart device has been developed.The arduino based lab-on-a-chip device applies optical properties to a small amount of blood.The achieved accuracy is 96.7%.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;