bluuebunny/crossref_metadata_2025 huggingface dataset:
Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning
Authors: Ma Qiufang
Published: March 2022

Deep learning technology has recently played an important role in image, language processing, and feature extraction. In the past disease diagnosis, most medical staff fixed the images together for observation and then combined with their own work experience to judge. The diagnosis results are subjective, time-consuming, and inefficient. In order to improve the efficiency of diagnosis, this paper applies the deep learning algorithm to the online diagnosis and classification of CT images. Based on this, in this paper, the deep learning algorithm is applied to CT image online diagnosis and classification. Based on a brief analysis of the current situation of CT image classification, this paper proposes to use the Internet of things technology to collect CT image information and establishes the Internet of things to collect the CT image model. In view of image classification and diagnosis, the convolution neural network algorithm in the deep learning algorithm is proposed to diagnose and classify CT images, and several factors affecting the accuracy of classification are proposed, including the convolution number and network layer number. Using the CT image of the hospital brain for simulation analysis, the simulation results confirm the effectiveness of the deep learning algorithm. With the increase of convolution and network layer and the decrease of compensation, the accuracy of image classification will decline. Using the maximum pool method, reducing the step size can improve the classification effect. Using relu function as the activation function can improve the classification accuracy. In the process of large data set processing, appropriately adding a network layer can improve classification accuracy. In the diagnosis and analysis of brain CT images, the overall classification accuracy is close to 70%, and in the diagnosis of tumor diseases, the accuracy is higher, up to 80%.
Identification of Nine mRNA Signatures for Sepsis Using Random Forest
Authors: Zhou Jing , Dong Siqing , Wang Ping , Su Xi , Cheng Liang
Published: March 2022

Sepsis has high fatality rates. Early diagnosis could increase its curating rates. There were no reliable molecular biomarkers to distinguish between infected and uninfected patients currently, which limit the treatment of sepsis. To this end, we analyzed gene expression datasets from the GEO database to identify its mRNA signature. First, two gene expression datasets (GSE154918 and GSE131761) were downloaded to identify the differentially expressed genes (DEGs) using Limma package. Totally 384 common DEGs were found in three contrast groups. We found that as the condition worsens, more genes were under disorder condition. Then, random forest model was performed with expression matrix of all genes as feature and disease state as label. After which 279 genes were left. We further analyzed the functions of 279 important DEGs, and their potential biological roles mainly focused on neutrophil threshing, neutrophil activation involved in immune response, neutrophil-mediated immunity, RAGE receptor binding, long-chain fatty acid binding, specific granule, tertiary granule, and secretory granule lumen. Finally, the top nine mRNAs (MCEMP1, PSTPIP2, CD177, GCA, NDUFAF1, CLIC1, UFD1, SEPT9, and UBE2A) associated with sepsis were considered as signatures for distinguishing between sepsis and healthy controls. Based on 5-fold cross-validation and leave-one-out cross-validation, the nine mRNA signature showed very high AUC.
Evaluation and Analysis of College Students’ Mental Health from the Perspective of Deep Learning
Authors: Wang Yuefen , Ma Chaoqun
Published: March 2022

College students are easily affected by the outside world, which leads to mental health problems, so it is particularly important to accurately evaluate and analyze the mental health status of college students. At present, the evaluation and analysis model of college students’ mental health is inaccurate and inefficient, which cannot analyze the mental health problems of college students. In order to evaluate and analyze the mental health problems of college students more accurately, this paper designs an evaluation and analysis model of college students’ mental health from the perspective of in-depth learning. The accuracy of model evaluation and analysis is improved, and a better comparison result is obtained. Firstly, the BP neural network model was compared with the logistic model and ARIMA model, and the results showed that the accuracy of the BP neural network model was more than 70% in five comparisons and was higher than that of the logistic model and ARIMA models. Second, the BP deep learning method is compared with several conventional methods (KNN, MF, NCF, and DMF) in the comparison phase of the model. The RMSE, MAE, and MAPE of the BP method are lower than those of the other four traditional methods. Finally, in the comparative experiment, the precision and AUC of the BP model are improved by 2%, and the three indicators of precision, recall, and F1 are also higher than those of other models. Through the specific evaluation of the five indicators of the four college students, from the five indicators of psychological adaptability, frustration, emotional stability, temperament, and personality, the mental health of the four college students is better.
Neutrophil-to-Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio, and Their Variations as a Basis for a Prediction Model in Advanced NSCLC Patients Receiving Anlotinib
Authors: Chen Tian , Song Chao , Liang Gaofeng , Xu Xiaoyu , Wang Chen , Zhang Zhanchun , Tang Mengqiu
Published: March 2022

Background. A phase III randomized multicenter trial (ALTER0303) reported anlotinib to be significantly beneficial to patient survival. An array of inflammatory biomarkers, such as neutrophil lymphocyte ratio (NLR) and platelet lymphocyte ratio (PLR), are associated with the response to treatment in numerous types of cancer. However, we found few studies investigating the predictive value of NLR or PLR in advanced NSCLC treatment with anlotinib. Thus, our objective was to examine the relationship between NLR and PLR and treatment effect, as well as to individuate patient stratification and selection. Methods. NLR and PLR as well as their variations were calculated in 152 advanced NSCLC patients receiving anlotinib as a third or further-line treatment at Ningbo Medical Center Lihuili Hospital between July 2018 and December 2020. The best cut-off values of NLR and PLR for predicting the treatment response were selected. Survival curves were plotted using the Kaplan–Meier method, while univariable and multivariable Cox regression were used to identify and determine dependent and independent predictors of survival. Results, Low disease control rate (DCR) was related with a high pre-NLR (

P
=
0.007

), high pre-PLR (

P
=
0.004

), and elevated post-NLR (

P
=
0.010

). Multivariate analysis determined high pre-PLR (>205.63) and elevated post-NLR to be independently associated with poor PFS or OS. Patients whose risk score was 2 resulting from the prediction model based on pre-PLR and post-NLR had a 4.52 times higher risk of death compared to patients whose risk score was 0 (HR: 4.516, 95% CI: 2.502-8.152,

P
≤
0.001

). Conclusions. Pre-PLR and post-NLR were independent prognostic indicators in patients with advanced NSCLC receiving anlotinib as a third or further-line treatment. Patients whose risk value score was 0 had a higher therapy effectiveness and better survival.
Research Contribution and Comprehensive Review towards the Semantic Segmentation of Aerial Images Using Deep Learning Techniques
Authors: Anilkumar P. , Venugopal P.
Published: March 2022

Semantic segmentation is a significant research topic for decades and has been employed in several applications. In recent years, semantic segmentation has been focused on different deep learning approaches in the area of computer vision, which has aimed for getting superior efficiency while analyzing the aerial and remote-sensing images. The main aim of this review is to provide a clear algorithmic categorization and analysis of the diverse contribution of semantic segmentation of aerial images and expects to give the comprehensive details associated with the recent developments. In addition, the emerged deep learning methods demonstrated much improved performance measures on several public datasets and incredible efforts have been dedicated to advancing pixel-level accuracy. Hence, the analysis on diverse datasets of each contribution is studied, and also, the best performance measures achieved by the existing semantic segmentation models are evaluated. Thus, this survey can facilitate researchers in understanding the development of semantic segmentation in a shorter time, simplify understanding of its latest advancements, research gaps, and challenges to be used as a reference for developing the new semantic image segmentation models in the future.
Protective Effect of Sufentanil on Myocardial Ischemia-Reperfusion Injury in Rats by Inhibiting Endoplasmic Reticulum Stress
Authors: Qiu Liuji , Zhong Guochen , Hou Zhiqi , Lin Jiayin , Sun Laibao
Published: March 2022

Objective. Sufentanil is the most common drug in clinical practice for the treatment of ischemic heart disease. This study is to investigate the protective mechanism of sufentanil on rat myocardial ischemia-reperfusion (I/R) injury. Methods. A rat I/R model was established by ligating the left anterior descending coronary artery. A total of 24 SD male rats were enrolled and divided randomly into the control group, I/R group, sufentanil group (SUF; 3 μg/kg), and diltiazem group (DLZ; 20 mg/kg; positive control). The rat hearts were subjected to 30 min of ischemia followed by 120 min of reperfusion. Subsequently, hemodynamics, pathological changes of myocardial tissue, serum biochemical parameters, oxidative stress factors, the level of serum inducible nitric oxide synthases (iNOS), interleukin-6 (IL-6), and other bioactive factors were analyzed in the rats. Result. Compared with the I/R group, sufentanil significantly improved cardiac action, myocardial fiber, and cardiomyocyte morphology and reduced inflammatory cell infiltration in rats in the SUF group. And the level of creatine kinase isoenzyme (CK-MB), troponin (cTn), lactate dehydrogenase (LDH), malondialdehyde (MDA), iNOS, and IL-6 was significantly declined in the serum of SUF group, while the activities of glutathione peroxidase (GSH-Px) and superoxide dismutase (SOD) were significantly activated in the myocardial tissues. In addition, sufentanil also significantly decreased the protein expression of GRP78, CHOP, Caspase 12, and ATF6 in the myocardial tissue of the SUF group. Conclusion. Sufentanil has a significant protective activity on myocardial I/R injury in rats, the mechanism of which may be associated with the inhibition of endoplasmic reticulum stress and oxidative stress.
hnRNPC Promotes Malignancy in Pancreatic Cancer through Stabilization of IQGAP3
Authors: Yang Nannan , Liu Lin , Liu Xiaoyu , Chen Yingjie , Lu Jian , Wang Zhongmin
Published: March 2022

Due to challenges in early‐stage detection, aggressive behavior, and poor response to systemic therapy, pancreatic cancer is one of the most fatal cancer types globally. The role of RNA‐binding protein (RBP) transcription and translation of cancer cells has been well demonstrated, although their roles in pancreatic cancer is less well understood. In this study, we found that heterogeneous nuclear ribonucleoprotein C (hnRNPC), a RBP, is highly expressed in pancreatic ductal adenocarcinoma (PDAC) tissues and cells. In addition, we discovered that overexpression of hnRNPC in PDAC cells in vitro increased cell proliferation, migration, invasion, and metastasis. The presence of hnRNPC promoted tumorigenesis of pancreatic cells in metastatic in vivo models, which was also validated. In silico analyses revealed that hnRNPC is a strong positive regulator of IQ Motif Containing GTPase Activating Protein 3 (IQGAP3) activity. The experimental confirmation of this association revealed a direct interaction of IQGAP3 and hnRNPC to induce cell growth and invasion in PDAC cells by activating the epithelial‐mesenchymal transition. In light of the findings that hnRNPC accelerates PDAC progression by interfering with IQGAP3, it appears that this technique for diagnosis and treatment of PDAC may have promise.
Import and Export Trade Prediction Algorithm of Belt and Road Countries Based on Hybrid RVM Model
Authors: Zhao Yang , Li Zhiqiang
Published: March 2022

Due to the large fluctuation of import and export volume and many influencing factors, it is difficult for the general prediction algorithm to obtain more accurate prediction results. To solve this problem, a trade forecasting method based on the Particle Swarm Optimization (PSO) optimization hybrid Relevance Vector Machine (RVM) model is proposed. The method firstly finds out the indexes that affect the import and export trade and extracts the principal factors of the indexes as the input data of the model through principal component analysis. Then, based on the single-kernel RVM model training with multiple different kernel functions, the multikernel weighting method was used to construct the hybrid kernel RVM model according to the prediction error of the single-kernel RVM model. Finally, the parameters of the hybrid kernel model were optimized by PSO to improve the prediction accuracy. Close trade will strengthen the dependence between countries, and under this premise, it becomes particularly important to study the frequency of trade between countries. In this paper, the countries along the Belt and Road are classified by the method of high-order directed graph clustering, and the trade transactions between the countries are represented by a small network subgraph. Then, the 65 trading countries are classified by cutting graph and K-means clustering method, and the classification results are obtained by running relevant codes in MATLAB. The results are represented by the lattice graph.
Increased Expression of QPRT in Breast Cancer Infers a Poor Prognosis and Is Correlated to Immunocytes Infiltration
Authors: Zhang Qun , Ding Xinwang , Lu Hongzhao
Published: March 2022

Breast cancer (BRCA) is a class of highly heterogeneous tumors. There is a positive correlation between the overall survival of BRCA and immune infiltration of the tumor microenvironment. QPRT is a rarely reported cancer gene, and the underlying mechanism is poorly understood. Based on TCGA data, the role that QPRT plays in BRCA is evaluated in this study. This study used GEPIA to analyze the expression of QPRT in BRCA and, based on the survival module, assessed the impact of QPRT on the survival of patients with BRCA. Furthermore, this study collected the BRCA data set from TCGA and, through utilizing logistic regression, discussed the relationship between QPRT expression and clinical information. Cox regression analysis was used to obtain clinicopathological features relating to the total survival rate of patients with TCGA. Besides, based on the “correlation” and CIBERSORT module, the relationship between cancer immune infiltration and QPRT was analyzed in GEPIA. Tumor status, pathological staging, and lymph nodes have an obvious correlation with the rise of QPRT expression according to the logistic regression univariate analysis. In this analysis, QPRT is expressed as a categorical-dependent variable (median expression value is 2.5). Furthermore, based on multivariate analysis, independent factors for favorable prognosis include negative pathological stage, increased QPRT expression, and remote metastasis. Among them, CIBERSORT analysis found that the increase in QPRT expression will increase with the growth of the level of immune infiltration of neutrophils, B cells, T cells, and mast cells. In addition, the “correlation” module using GEPIA was used to confirm. Taking all factors into consideration, the rise in QPRT expression is related to a good prognosis and a grown proportion of immune cells in BRCA, such as neutrophils, B cells, mast cells, and T cells. These results suggest that QPRT can be used to be a possible biological indicator to evaluate the immune infiltration level of BRCA and its prognosis.
Research on the Impact and Utility of Rural Revitalization Big Data Service on Farmers Based on Integrated Technology Acceptance Model
Authors: Zhao Shiyuan , Wang Lin , Cai Donghong
Published: March 2022

The integrated technology acceptance model was proposed in 2003 and has been gradually applied in many fields since then. At present, the promotion of agricultural big data has achieved initial results, and a number of agricultural big data practice demonstration projects have been formed. For example, Shandong Province supports the establishment of the “Big Data Platform of Bohai Granary Science and Technology Demonstration Project,” which includes four modules of data collection, mining and analysis, monitoring and early warning, and decision-making service. It is characterized by the diversity of massive data sources, the integration of historical and real-time data, and multifactor comprehensive analysis and decision making. However, farmers, as an important service subject of agricultural big data platform, do not have strong willingness to participate. In the era of big data, it is still difficult for farmers to obtain data information and services, and their ability to obtain, screen, and use data is poor. Therefore, it is of great practical significance to effectively improve farmers’ willingness to adopt and use agricultural big data. Based on the integrated technology acceptance model, this study analyzed the impact of big data service of rural revitalization on farmers. Firstly, the basic content of integration technology acceptance theory was analyzed, and the research hypothesis was proposed based on this theory. After that, the questionnaire survey was used to empirically analyze the impact of big data service on farmers. Reliability and validity test, correlation test, goodness of fit test, and path system test were carried out on the analysis results to prove the factors that affect farmers’ behavior and attitude of using big data.
Innovation of Social Work Model Based on Big Data Analysis of the Internet of Things
Authors: Tan Wenhan
Published: March 2022

The explosion in the number of Internet of Things (IoT) devices simply reflects how the growth of big data overlaps perfectly with the growth of the Internet of Things. With the advent of the era of big data, the continuous development of our country’s economy, and the continuous progress of society, the development of social work is also influenced by economic and social development and continues to go hand in hand, making corresponding contributions to the development of our country’s society. Meanwhile, the gradual imbalance between social development and economic development has put much pressure on social work organizations. The research on the social work development model has aroused the concern and discussion of some experts and scholars in recent years. In our paper, we aim to analyze the characteristics of social work models, the role of social work institutions, and the problems of social work models and combine them with qualitative research and quantitative research to make the questionnaire survey method. The results of the survey show that the effective percentages of “satisfaction” and “very satisfied” for “your enthusiasm for work” and “sense of accomplishment that your work brings you” are 68.0% and 58.7%, respectively. It can be seen from this that social workers have a strong sense of experience in the overall work when performing specific tasks.
A Systematic Review and Meta-Analysis on Comparative Kinematics in the Lumbopelvic Region in the Patients Suffering from Spinal Pain
Authors: Zheng ZhiRui , Wang YouQiang , Wang Tong , Wu Yue , Li YuHui
Published: March 2022

Background. Lumbopelvic kinematics has been observed to include different parameters and directly relate to the movement of the hip spine. In the current scenario, more than 65 million people have been suffering from spinal pain, and 18% of adults experience chronic spinal pain. Methods. This systematic review and meta-analysis selected 9 studies for analysis via electronic databases like EMBASE, MEDLINE, Web of Science, Scopus, CINAHL, and Cochrane (CENTRAL). After collecting the data, the dataset has been systematically analyzed through statistical methodologies using RevMan and Stata. Results. Out of 116 studies initially scrutinized, nine were finally selected for the meta-analysis. When range of motion was studied via meta-analysis, it was noted that a considerable reduced movement was noted in the lumbar region of the spine when people were suffering from lower back pain in comparison to control group people. Hence, reduced lumbar range of motion, no difference in the angle of lordosis, and no significant difference in extension and rotation in people with lower back pain were found. However, variability was noted in people suffering from lower back pain for flexion and lateral flexion. A significant heterogeneity was found between the studies which lacked some details and standardization of the criteria which were used for defining patients with lower back pain or without them (control group). Results show that spinal pain is the main reason behind the limitation of lumbar range of motion. It is clear from the data set of mean and standard deviation, and this is clear to establish the relationship between the causes of pelvic and spinal pain. In flexion-based ROM, the mean difference was found to be −9.77 (95% CI: −21.86, 2.32). Similarly, for lateral flexion, the mean difference was found to be −5.58 (with 95% CI: −10.38, −0.79). Conclusion. It can be concluded that spinal disease is too influential for people; thereby, it affects day-to-day life activities by creating painful and restricted movements. It is concluded that people suffering from lower back pain have reduced proprioception and range of movement in the lumbar region when compared to control groups with no lower back pain, which mainly focus on flexion and lateral flexion.
UCB-Based Route and Power Selection Optimization for SDN-Enabled Industrial IoT in Smart Grid
Authors: Li Xiaoyue , Zhou Chaoqun , Liang Zilong , Yu Qiang , Chen Xiankai , He Zhiyuan
Published: March 2022

As an essential building block for smart grid, the industrial internet of things (IIoT) plays a significant role in providing powerful sensing capability and ubiquitous connectivity for differentiated power services. The rapid development of smart grid imposes higher data monitoring and transmission requirements in terms of delay and energy efficiency. However, due to the severe electromagnetic interference (EMI) caused by massive electrical equipment, the transmission performance of IIoT becomes inferior. The traditional single-hop transmission mode evolves towards a multihop cooperation mode to satisfy differentiated quality of service (QoS) requirements. In this paper, we propose an upper confidence bound- (UCB-) based joint route and power selection optimization algorithm to support multihop cooperation mode evolution, which adopts a software-defined networking- (SDN-) enabled IIoT network framework to simplify network configuration and management. Compared with existing local-side-information-based route selection (LSI-RS) and random route selection (RRS) algorithms, simulation results demonstrate that the proposed algorithm has superior performances in total delay, energy efficiency, and utility.
Naloxegol to Prevent Constipation in ICU Adults Receiving Opioids: A Randomized Double-Blind Placebo-Controlled Pilot Trial
Authors: Duprey Matthew S. , Allison Harmony , Garpestad Erik , Riselli Andrew M. , Faugno Anthony , Anketell Eric , Devlin John W.
Published: March 2022

Background. Constipation is frequent in critically ill adults receiving opioids. Naloxegol (N), a peripherally acting mu-receptor antagonist (PAMORA), may reduce constipation. The objective of this trial was to evaluate the efficacy and safety of N to prevent constipation in ICU adults receiving opioids. Methods and Patients. In this single-center, double-blind, randomized trial, adults admitted to a medical ICU receiving IV opioids (≥100 mcg fentanyl/day), and not having any of 17 exclusion criteria, were randomized to N (25 mg) or placebo (P) daily randomized to receive N (25mg) or placebo (P) and docusate 100 mg twice daily until ICU discharge, 10 days, or diarrhea (≥3 spontaneous bowel movement (SBM)/24 hours) or a serious adverse event related to study medication. A 4-step laxative protocol was initiated when there was no SBM ≥3 days. Results. Only 318 (20.6%) of the 1542 screened adults during the 1/17–10/19 enrolment period met all inclusion criteria. Of these, only 19/381 (4.9%) met all eligibility criteria. After 7 consent refusals, 12 patients were randomized. The study was stopped early due to enrolment futility. The N (n = 6) and

P

(n = 6) groups were similar. The time to first SBM (N 41.4 ± 31.7 vs. P 32.5 ± 25.4 hours,

P

 = 0.56) was similar. The maximal daily abdominal pressure was significantly lower in the N group (N 10 ± 4 vs. P 13 ± 5,

P

 = 0.002). The median (IQR) daily SOFA scores were higher in N (N 7 (4, 8) vs. P 4 (3, 5),

P

 < 0.001). Laxative protocol use was similar (N 83.3% vs. P 66.6%;

P

 = 0.51). Diarrhea prevalence was high but similar (N 66.6% vs.

P

66.6%;

P

 = 1.0). No patient experienced opioid withdrawal. Conclusions. Important recruitment challenges exist for ICU trials evaluating the use of PAMORAs for constipation prevention. Despite being underpowered, our results suggest time to first SBM with naloxegol, if different than P, may be small. The effect of naloxegol on abdominal pressure, SOFA, and the interaction between the two requires further research.
Evaluation System of English Blended Learning Effect Based on Big Data Technology
Authors: Yang Ling
Published: March 2022

In order to improve the effect of English teaching, this paper combines data mining algorithms to construct an intelligent learning system. Moreover, starting from the actual situation, this paper combines the actual needs of English teaching to construct a blended learning system framework. Blended learning provides a new idea for English teaching. As a result, the purpose of this work is to do some empirical research on how to successfully implement blended learning in college English classes. The findings of the study suggest that blended learning may help students improve their English application skills. Furthermore, this article discovered that students’ independent learning capacity, learning passion, and systematic comprehension of maritime equipment were enhanced to some amount using data mining and cloud classroom blended learning resource construction and teaching practise. Finally, this work integrated theoretical and experimental research to demonstrate that the system developed in this paper has a specific impact.
Discrimination of Breast Cancer Based on Ultrasound Images and Convolutional Neural Network
Authors: Du Rui , Chen Yanwei , Li Tao , Shi Liang , Fei Zhengdong , Li Yuefeng
Published: March 2022

The aim of our study was to establish an artificial intelligence tool for the diagnosis of breast disease base on ultrasound (US) images. A deep learning algorithm Efficient-Det assisted US diagnosis method was developed to determine breast suspicious lesions as benign, malignant, or normal. Totally 1181 US images from 487 patients of our hospital and 694 publicly accessible images were employed for modeling, including 558 benign images, 370 malignant images, and 253 normal tissue images. The actual diagnosis results for the patients were determined by the biopsy or surgery. Efficient-Det was first retrained using an exclusive public breast cancer US dataset with transfer learning techniques. A blind test set consisting of 50 benign, 50 malignant, and 50 normal tissue images was randomly picked from the patients’ images as the independent test set to test its searching ability on suspicious tumor regions. Furthermore, the confusion matrix and classification accuracy were employed as evaluation metrics to select the optimal classification models. Efficient-Det has demonstrated remarkable progress in general image recognition tasks with specific advantages of locating and identifying tumor areas simultaneously. Compared to the manual method (mean accuracy: 95.3% and 60 s per image) and traditional feature engineering method (mean accuracy: 90% and 15 s per image), our Efficient-Det has the capability of providing a competitive (mean accuracy: 92.6%) and fast (0.06 s per image) classification result. The deployment of Efficient-Det in our local breast cancer discrimination task exhibits specific applicability within real clinical workflows.
Application Value of Combined Detection of NLR, PNI, D-Dimer, CD3+ T Lymphocytes, and CEA in Colorectal Cancer Screening
Authors: Ding Rui , Chen Zheng , He Ming , Cen Hong , Liu Zehui , Su Yonghui
Published: March 2022

Objective. To investigate the application value of combined detection of neutrophil-lymphocyte ratio (NLR), prognostic nutrition index (PNI), D-dimer (D-D), CD3+ T lymphocytes (CD3+ T), and carcinoembryonic antigen (CEA) in colorectal cancer screening. Methods. The study cohort comprised 187 colorectal cancer patients and 100 mixed hemorrhoids patients as controls from January 2019 to August 2021 at the Fifth Affiliated Hospital of Sun Yat-sen University. Comparing the levels of NLR, PNI, D-D, CD3+ T, and CEA between the two groups of subjects, drawing receiver operating characteristic (ROC) curve evaluates the efficacy of single and combined detection for colorectal cancer screening. Results. Compared with the control group, the levels of NLR, D-D, and CEA in the colorectal cancer group were significantly increased, while the levels of PNI and CD3+ T were significantly decreased (

P
<
0.05

). ROC curve analysis showed that the combined detection of NLR, PNI, D-D, CD3+ T, and CEA for colorectal cancer screening had an AUCROC of 0.943, a sensitivity of 84.49%, a specificity of 91.00%, and a Youden index of 0.75, and its screening efficacy was significantly superior to that of a single detection (

P
<
0.001

). Conclusion. The combined detection of NLR, PNI, D-D, CD3+ T, and CEA has a high clinical application value for colorectal cancer and can provide a reference for early screening and auxiliary diagnosis of colorectal cancer.
Application of a Nursing Data-Driven Model for Continuous Improvement of PICC Care Quality
Authors: Zhou Juzhen , Wang Lihua
Published: March 2022

A PICC catheter maintenance network was established and managed to monitor the maintenance of catheters in placed patients throughout the process, providing homogeneous PICC catheter continuity of care for patients. Model-driven thinking is an idea for simulation system development. Model-driven architecture (MDA) is a design methodology that implements model-driven thinking and is widely used in simulation system development. Based on the requirements of nursing, the data-driven model is mainly divided into interface layer and functional service layer; this study adopts MDA technology which can detach the functions of the system from the platform, based on domain knowledge, and the metamodel adopts XSD-based data model to generate the PIM model, which is stored in the model library. The results showed that the number of nurses at maintenance sites increased from 79 to 232, the PICC placement rate for oncology patients increased from 35.0% to 76.0%, the nurse maintenance operation pass rate increased from 53.9% to 88.4%, and the maintenance default rate decreased from 40.0% to 10.9%.
A Method for Enterprise Network Innovation Performance Management Based on Deep Learning and Internet of Things
Authors: Luo Bing
Published: March 2022

In the Internet era, traditional performance management has failed to satisfy the modern enterprise development; therefore, enterprises must achieve timely innovation and improvement of performance management. The application of performance management in state-owned firms promotes employee motivation and skill development, as well as the development of innovative thinking. Performance effect, therefore, on the role of management in the state-owned enterprise development is bigger, especially because of the influence of traditional human resources performance management mode. The further development of state-owned enterprise resource management work affected the operation and management of state-owned enterprises. Therefore, state-owned enterprises must further optimize the path of performance management, to achieve its fine development. In view of this, this paper deeply analyzes the problems existing in the current performance management of state-owned enterprises and puts forward optimization methods for the problems, aiming at providing help for the optimization of the performance management of state-owned enterprises. In essence, a value network is a value symbiosis system between firms that involves resource sharing, value cocreation, risk sharing, and benefit win-win situations. The formation and development of value network not only extremely change the industrial structure and mode of production, but also have an important impact on enterprise innovation mechanism. Enterprise innovation under the value network system has become the focus of academic and business circles. With the rapid development of machine learning and internet of things (IOT) technologies, as well as China’s urbanization, China’s agriculture is embracing new development opportunities. Using artificial intelligence (AI) technology to effectively mine agricultural big data and realize effective control and management of intelligent agriculture has become a research hotspot and difficulty. Based on recurrent neural network (RNN) and IOT technology, this paper proposes an enterprise network innovation performance management path optimization system.
Enhanced Image Processing and Fuzzy Logic Approach for Optimizing Driver Drowsiness Detection
Authors: AlKishri Wasin , Abualkishik Abdallah , Al-Bahri Mahmood
Published: March 2022

Driver drowsiness is a severe problem that usually causes traffic accidents, classified as more dangerous. The record of the National Safety Council reported that drowsy driving is caused by 9.5% of all crashes (100,000 cases). Therefore, preventing and minimizing driver fatigue is a significant research area. This study aims to design a nonintrusive real-time drowsiness system based on image processing and fuzzy logic techniques. It is an enhanced approach for Viola–Jones to examine different visual signs to detect the driver's drowsiness level. It extracted eye blink duration and mouth features to detect driver drowsiness based on the desired facial feature image in a specific driver video frame. The size and orientation of the captured features were tracked and handled for determining image features such as brightness, shadows, and clearness. Lastly, the fuzzy control system provides different alert sounds based on the tracked information from the face, eyes, and mouth in separate cases, such as race, wearing glasses or not, gender, and various illumination backgrounds. The experiments’ results show that the proposed approach achieved high accuracy of 94.5% in detecting driver status compared with other studies. Also, the fuzzy logic controller efficiently issued the required alert signal of the drowsy driver status that helps to save the driver's life.
A Meta-Analysis of the Influence of Tumor Necrosis Factor-α-308 Gene Polymorphism on Liver Cirrhosis
Authors: Liu Chang , Yang Songtao
Published: March 2022

Cirrhosis is an active hepatic inflammation process of the liver considered as the serious phase of different liver injuries. Epidemiological studies have evaluated the possible association between TNF-α-308G/A gene polymorphism and liver cirrhosis. In this study, we have furthered the study to assess the exact association of TNF-α-308G/A gene polymorphism with liver cirrhosis susceptibility by integrating all available data. Eligible case-control studies were carried out from the establishment of the project to September 2021. Published literature from multiple databases was retrieved, collected, and analyzed by two investigators independently. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for every study. Review Manager 5.2 and Stata 15.0 software were used for meta-analysis and stability was assessed by both subgroup analysis and sensitivity analysis. Begg’s funnel plot and Egger’s regression across the studies were also explored. We examined 22 case-control studies with 2683 cirrhosis patients and 2905 normal controls in four genetic models (GA vs. GG: OR = 0.95, 95%CI: (0.70, 1.30); AA vs. GG: OR = 1.11, 95% CI: (0.66, 1.85), GA + AA vs. GG: OR = 1.00, 95% CI: (0.73, 1.37); AA vs. GA + GG: OR = 1.07, 95%CI: (0.70,1.63)). TNF-α-308G/A gene polymorphism was relatively independent, and the results did not show a significant difference between the two groups. In the subgroup analysis by etiological classification of liver cirrhosis, cirrhosis after HCV infection was positively associated with the risk of TNF-α-308G/A polymorphism (AA vs. GG: OR = 3.02, 95% CI: (1.15, 7.88), AA vs. GA + GG: OR = 2.68, 95% CI: (1.04, 6.95)). The meta-analysis showed TNF-α-308G/A gene polymorphism might not have affected susceptibility for liver cirrhosis. Nevertheless, further and well-designed studies were needed to confirm the findings.
Abstract 101: Cost-Effectiveness and Decision Analysis of Polygenic Risk Scores in Statin Use for Primary Prevention
Authors: Sussman Jeremy , Marrero Wesley , Burke James , Lavieri Mariel , Hayward Rodney A
Published: March 2022

Research Objective:
Polygenic risk scores can improve prediction of first-time cardiovascular disease (CVD). Prediction of future events is the central factor in statin use in current guidelines Therefore, we hypothesized that if polygenic risk scores were added to current risk scores, statin use could become more efficient. We performed a cost-effectiveness analysis to see if genomic testing could be a cost-effective way to prevent CVD.

Study Design:
Decision-analytic model of genomic testing for statin decision-making.

Data sources:
Published literature

Time Horizon:
Lifetime

Perspective:
Third-party payer

Intervention:
Statin decision-making based on current guidelines or based on current guidelines augmented with genetic testing in all Americans ages 40-75 with estimated 10-year CVD risk of 5-20%.

Population Studied:
Primary prevention population in the United States.

Outcome Measures:
Quality-adjusted life-years (QALYs), incremental cost-effectiveness ratios (ICERs), and total 10-year spending.

Principal Findings:
At $200 per genetic test and $144 for a year of medium-strength statin use, full adherence to the current guidelines are estimated to cost $66,155 per QALY saved. Genetic testing would be $263,000 per additional QALY saved. These results are sensitive to improvements in genetic prediction and cost of genetic testing.

Conclusions:
Genomic testing to improve statin use is cost-effective for a very large number of Americans at current prices.
Abstract 102: Impact of Using Older Data on the Accuracy of Cardiovascular Risk Scores
Authors: Sussman Jeremy B , Wiitala Wyndy L , Levine Deborah , Bentley Douglas , Youles Bradley , Hofer Timothy P , Hayward Rodne A
Published: March 2022

Background:
Recent scores to predict atherosclerotic cardiovascular disease, ASCVD, have been found inaccurate, with some concern that risk scores become inaccurate with time, as changing demographics may also change ASCVD risk. A partial solution to the timeliness problem could be creating risk scores using electronic health records (EHR). EHR-based risk scores can be easily updated and tested for changes over time. They can also use more variables than traditional risk scores. This could capture clinical change that would otherwise be missed.

We hypothesized that ASCVD risk scores change over time, but this could be minimized with more robust risk scores. To test this, we looked at change of two EHR-based risk score over three follow-up periods and using different statistical techniques to design the risk scores.

Methods:
Data sources: VA national data linked to Medicare and the National Death Index.

Population: 3 overlapping cohorts from 2002, 2006, and 2009. Each consisted of all active VA patients aged 45-80 who had no documented history of CVD, clinical heart failure or loop diuretic use at baseline.

Prediction models

1. VARS-ASCVD: uses the same variables as traditional risk scores, but all variables were re-calibrated to our population.

2. VARS-EHR: Uses 41 predictor variables and more interaction effects.

Outcome variables: First occurrence of fatal or nonfatal ASCVD during 5 years of follow-up.

Analysis: We looked at the accuracy of risk scores developed in 2002 on patients in 2006 and 2009. The discrimination of the risk scores (the ability to distinguish between those who do and do not develop an event), was evaluated with C-statistisic. The calibration (how closely the predicted probabilities reflect true risk) was evaluated with the Hosmer-Lemeshow Goodness of Fit statistic (GoF).

Results:
Each cohort had at least 1.4 million participants. Between the 3 cohorts the rate of diabetes mellitus increased from 201% to 27% and statin use increased from 25% to 45% of the population.

The VARS-ASCVD risk scores for men developed in 2002 had the same discrimination of 0.67 in 2006 and 2009, but in women fell from 0.77 to 0.72 then increased to 0.74. The goodness of fit worsened. Using the VARS-EHR model, discrimination stayed similar in men and women. The GOF worsened, but by substantially less.

Conclusions:
ASCVD risk prediction tools become poorly calibrated over fairly short time periods. For effective use, they must be updated regularly.
Abstract 110: Prevalence of Neurological Illness, Stroke and Associated Mortality on a Ugandan Neurology Ward
Authors: Diaz Monica , Hu Xin , Fenton Brenda , Sico Jason
Published: March 2022

Introduction:
While the burden of non-communicable diseases is increasing worldwide in developing countries, less is known regarding the true prevalence of such neurologic illness as cerebrovascular disease throughout sub-Saharan Africa. We sought to understand the prevalence of stroke and other neurologic conditions at Mulago Hospital, Uganda’s largest tertiary care referral center, and their association with post-event mortality.

Methods:
Data was prospectively collected on 335 patients admitted to the neurology wards of Mulago Hospital from January 2009 to May 2011. Data were collected from the date of admission until discharge, including: demographic information, village of residence, admission/discharge diagnoses, HIV status, discharge disposition, and mortality data, without the benefit of an electronic medical record. Kaplan-Meier survival curves and multivariate COX proportional hazard model were used for unadjusted and adjusted analysis to predict survival.

Results:
A total of 307 patients were included in the final analysis with median age is 47 years old (Range=0.9-95). Half were women (50.5%) and 53.7% resided in Kampala. Stroke (27.9%), diabetes (21.3%), head trauma (19.3%), and seizure (13.7%), were the most common diagnoses. A total of 56 (18.2%) patients died during the index hospitalization; patients with a new diagnosis of stroke had the highest mortality (53.3%) compared to patients with other diagnoses. Adjusted regression analysis showed that those unemployed/retired (Hazard Ratio [HR]=4.33, p=.024) and those discharged with stroke (HR=2.77, p=.029) or infectious conditions (HR=5.40, p=.001) had significantly worse survival (Figure).

Conclusions:
Among patients admitted to a Ugandan neurology ward, stroke was the most common diagnosis and the most frequently associated with mortality. Given that such non-communicable diseases as cerebrovascular disease is becoming increasingly more important causes of death in resource-limited settings, future research should seek to understand how mortality relates to other factors during hospitalizations and whether interventions to improve delivery of care to patients at higher risk of mortality may affect patient outcomes.
Abstract 127: Risk Factors for Cardiovascular Disease in Heterozygous Familial Hypercholesterolemia: A Systematic Review and Meta-analysis
Authors: Akioyamen Leo , Genest Jacques , Chu Anna , Inibhunu Happy , Tu Jack V
Published: March 2022

Objectives:
To quantify the association between risk factors and cardiovascular disease (CVD) in heterozygous familial hypercholesterolemia (FH).

Design:
Systematic review, meta-analysis, and meta-regression.

Data sources:
MEDLINE, EMBASE, Global Health, the Cochrane Library, and PubMed for full-text articles published in English from 1990 - 2018.

Methods:
Studies reporting adjusted associations between: (1) traditional cardiovascular risk factors; (2) personal health practices; or (3) clinical characteristics and CVD with ≥ 100 participants (50 participants with and 50 without CVD) were included. A sole reviewer extracted data on study characteristics, risk factors, and outcomes of interest, with independent verification by a second reviewer. Odds ratios with 95% confidence intervals (CIs) were calculated for selected risk factors with random effects meta-analysis, from which values for population-attributable fraction and case impact number were derived. We also performed heterogeneity and publication bias investigations, five sensitivity analyses (by study size, design, adjustment, country and year of publication), and meta-regression.

Results:
We identified 25 studies representing 52,034 participants and 9,751 CVD events. Age [OR: 1.06; 95% CI: 1.05, 1.10], male sex [OR: 1.79; 95% CI: 1.47, 2.18], hypertension [OR: 1.76; 95% CI: 1.41, 2.21], diabetes [OR: 1.60; 95% CI: 1.18, 2.16], body mass index [OR: 1.04; 95% CI: 1.03, 1.05], smoking [OR: 1.69; 95% CI: 1.45, 1.98], lipoprotein a [OR: 1.78; 95% CI: 1.22, 2.60], high-density lipoprotein cholesterol [OR: 1.39; 95% CI: 1.20, 1.61], low-density lipoprotein (p = 0.045) and total cholesterol (p < 0.001) were associated with increased CVD. Smoking, hypertension, and diabetes were associated with 1 in 8, 1 in 10, and 1 in 38 cases of CVD, respectively. However, they accounted for a lower proportion of CVD risk in FH individuals than reported in the general population. Results were broadly consistent in sensitivity analyses.

Conclusions:
Several clinical risk factors are significantly and independently associated with CVD risk in patients with FH and should be targeted for risk factor modification. These data can also inform the selection of variables for prediction models to aid in risk stratifying patients.