bluuebunny/crossref_metadata_2025 huggingface dataset:
Isolation and Molecular Characterization of Peste des Petits Ruminants Virus from Outbreaks in Southern Ethiopia, 2020
Authors: Mohammed Abde Aliy , Chibssa Tesfaye Rufael , Terfa Waktole , Aklilu Fasil , Damena Delesa , Belayneh Redeat , Kidane Menbere
Published: May 2022

Peste des petits ruminants (PPR) is one of the most important transboundary diseases of small ruminants. In this study, nasal and oral swabs (n = 24) were collected from sheep (n = 7) and goats (n = 17) with clinical signs in southern Ethiopia in March 2020. PPR virus was isolated on Vero dog cells expressing the signaling lymphocyte activation molecule (VDS) and screened using RT-qPCR. Positive samples were confirmed by conventional RT-PCR followed by sequencing of a partial nucleoprotein (N) gene segment. Results revealed that 54% (n = 13/24) of the tested samples were PPRV-positive Phylogenetic analysis revealed that the viruses belonged to lineage IV and lineage II. The lineage IV viruses were similar, although not identical, to other lineage IV viruses previously reported in Ethiopia and other East African countries while the lineage II viruses have been reported for the first time in Ethiopia showed a high nucleotide identity (99.06%) with the vaccine (Nigeria 75/1) that is currently used in Ethiopia for the prevention of PPR. Further investigations are therefore recommended in order to fully understand the true nature of the lineage II PPRVs in Ethiopia.
Postliver Transplantation Rhino-Orbital Mucormycosis, an Unexpected Cause of a Downhill Course
Authors: Sabhapandit Swapnali , Sharma Mithun , Sekaran Anuradha , Menon Balachandran , Kulkarni Anand , TR Soumya , Nagaraja Rao Padaki , Nageshwar Reddy Duvurr
Published: May 2022

The COVID-19 pandemic has impacted transplantation worldwide in a major way with infections and post-COVID-19 complications contributing to increased mortality and morbidity. We present a case of a 42-year-old lady who developed rhino-orbital mucormycosis in a postliver transplantation period. The initial presentation of the patient was very subtle. She never had overt COVID-19 infection before surgery and in the recovery period. Though cases of mucormycosis in the wound site have been reported, this would be one of the rare cases of rhino-orbital mucormycosis postliver transplantation. This infection leads to a rapid downhill course and the death of the patient. Atypical infections and presentations need to be monitored in postliver transplantation patients who are getting operated on during this pandemic, and a high level of clinical suspicion is required to pick up these cases at an early stage.
Light Weighted Healthcare CNN Model to Detect Prostate Cancer on Multiparametric MRI
Authors: Soni Mukesh , Khan Ihtiram Raza , Babu K. Suresh , Nasrullah Syed , Madduri Abhishek , Rahin Saima Ahmed
Published: May 2022

The SEMRCNN model is proposed for autonomously extracting prostate cancer locations from regions of multiparametric magnetic resonance imaging (MP-MRI). Feature maps are explored in order to provide fine segmentation based on the candidate regions. Two parallel convolutional networks retrieve these maps of apparent diffusion coefficient (ADC) and T2W images, which are then integrated to use the complimentary information in MP-MRI. By utilizing extrusion and excitation blocks, it is feasible to automatically increase the number of relevant features in the fusion feature map. The aim of this study is to study the current scenario of the SE Mask-RCNN and deep convolutional network segmentation model that can automatically identify prostate cancer in the MP-MRI prostatic region. Experiments are conducted using 140 instances. SEMRCNN segmentation of prostate cancer lesions has a Dice coefficient of 0.654, a sensitivity of 0.695, a specificity of 0.970, and a positive predictive value of 0.685. SEMRCNN outperforms other models like as V net, Resnet50-U-net, Mask-RCNN, and U network model for prostate cancer MP-MRI segmentation. This approach accomplishes fine segmentation of lesions by recognizing and finding potential locations of prostate cancer lesions, eliminating interference from surrounding areas, and improving the learning of the lesions’ features.
Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images
Authors: Raghavendra U. , En Wei Joel Koh , Gudigar Anjan , Shetty Akanksha , Samanth Jyothi , Paramasivam Ganesh , Jagadish Sujay , Kadri Nahrizul Adib , Karabatak Murat , Yildirim Özal , Arunkumar N. , Ardakani Ali Abbasian
Published: May 2022

Hypertension (HTN) is a major risk factor for cardiovascular diseases. At least 45% of deaths due to heart disease and 51% of deaths due to stroke are the result of hypertension. According to research on the prevalence and absolute burden of HTN in India, HTN positively correlated with age and was present in 20.6% of men and 20.9% of women. It was estimated that this trend will increase to 22.9% and 23.6% for men and women, respectively, by 2025. Controlling blood pressure is therefore important to lower both morbidity and mortality. Computer‐aided diagnosis (CAD) is a noninvasive technique which can determine subtle myocardial structural changes at an early stage. In this work, we show how a multi‐resolution analysis‐based CAD system can be utilized for the detection of early HTN‐induced left ventricular heart muscle changes with the help of ultrasound imaging. Firstly, features were extracted from the ultrasound imagery, and then the feature dimensions were reduced using a locality sensitive discriminant analysis (LSDA). The decision tree classifier with contourlet and shearlet transform features was later employed for improved performance and maximized accuracy using only two features. The developed model is applicable for the evaluation of cardiac structural alteration in HTN and can be used as a standalone tool in hospitals and polyclinics.
Mucinous Signet-Cell Adenocarcinoma of the Ileum: A Diagnostic Challenge—Case Report and Review of the Literature
Authors: Martins Tiago , Umar Jalaluddin , Groudan Kevin , Bharadwaj Hariharan S. , Desilets David
Published: May 2022

Malignancies of the small intestine are rare. Signet-ring cell carcinoma (SRCC) is one of the rarest forms of adenocarcinoma that can arise in the small intestines. We present a case of a patient who originally presented with abdominal pain and radiographic findings suggestive of ileal congestion. The ileal biopsy specimens were nonspecific, and the patient began a trial of corticosteroid treatment for suspected Crohn’s disease. A repeat colonoscopy yielded biopsies that were positive for malignancy. The patient then underwent an exploratory laparotomy which led to the diagnosis of SRCC. Given their similar presentations and the extreme rarity of this unusual malignancy, it can be difficult to differentiate between new-onset Crohn’s disease and SRCC. A review of the literature was conducted to provide us with an improved understanding of previously documented cases of SRCC.
Financial Crisis Prediction Based on Long-Term and Short-Term Memory Neural Network
Authors: Ling Tang , Cai Yinying
Published: May 2022

Enterprise financial crisis prediction analysis can predict the business process of enterprises, so that enterprises can take corresponding strategies in time. The financial crisis prediction of listed companies can effectively reflect the business situation, so as to give investors reasonable investment advice. In order to supervise the sustainable management ability of enterprises efficiently and accurately, this paper proposed a financial crisis prediction method based on long-term and short-term memory neural network, so as to provide valuable information for decision-makers. Firstly, the data in the enterprise financial system is analyzed and extracted, and the original data is cleaned and dimensionalized by normalization and feature selection. Then, the long-term and short-term memory neural network is used to build the financial early warning model, and the wolf pack algorithm is used to optimize the initial weight and bias parameters, so as to improve the efficiency of parameter optimization. Finally, the financial data of 20 large- and medium-sized enterprises from 2019 to 2021 are verified and analyzed. The experimental results show that compared with other common machine learning methods, the constructed wolf pack-optimized long-term and short-term memory neural network has the highest prediction performance in terms of root mean square error and goodness of fit, with the goodness of fit reaching 94.2%.
Secure MRI Brain Image Transmission Using IOT Devices Based on Hybrid Autoencoder and Restricted Boltzmann Approach
Authors: Deepthi S. Aruna , Rao E. Sreenivasa , Giriprasad M. N.
Published: May 2022

In recent times, the medical image processing solves several clinical issues by inspecting the visual images, which are generated in the clinical health care units. The main objective of the research is to gain valuable information from the images for better clinical diagnosis. In the biomedical engineering domain, the medical image analysis is an emerging research topic. In the recent decades, the use of medical images is highly growing, which are acquired from different image modalities; so, there is a necessity for data compression for transmission, storage, and management of digital medical image datasets. Hence, the machine learning methods are effective for medical image analysis, where the deep learning models are used in the machine learning tools for automatically learning the feature vectors from the huge medical datasets. The automated deep learning models are effective compared to the conventional handcrafted features. In addition to this, a wireless sensor network (WSN) is used to create a primary health care scheme, which brings patient data together and expands the medical conveniences, whereas the WSN design must comprise of sensor nodes, because it consumes less power and resources at a relatively low cost; so, it is essential for implementing the Raspberry Pi-based WSN nodes. The sensor nodes are important for limited battery capacity and to transmit the vast amount of medical data. The proposed work is broadly classified into two categories such as (i) the medical image compression algorithm is developed using the deep learning model based on autoencoders and restricted Boltzmann machines (RBM) and (ii) implementation of the WSN sensors nodes with Raspberry Pi and Messaging Queue Telemetry Transport (MQTT) Internet of Things (IoT) protocol for secure transmission of the medical images. The experimental results are evaluated using the standard performance metrics like peak signal to noise ratio (PSNR) and presented a Real-Time Linux (RTL) implementation of the design. The proposed model showed 10 dB to 15 dB improvement in the PSNR value, while transmitting the medical images, which is better compared to the existing model.
An Automatic Pronunciation Error Detection and Correction Mechanism in English Teaching Based on an Improved Random Forest Model
Authors: Dai Yuhua
Published: May 2022

Teachers in traditional English classes focus more on writing and grammar instruction, while oral language instruction is neglected. In exam-oriented education, most Chinese students can master English written test skills, but only a few students can communicate effectively in English daily. People are progressively realizing that language is a tool for communication and communication in recent years, as the frequency of international exchanges has increased and that language learning should focus on oral language education. However, there are numerous issues with teaching oral English. When students perform individual oral practice after class, for example, they are unable to determine whether their pronunciation is correct. As a result, a computer-assisted study into automatic pronunciation of spoken English has become a viable solution to these issues. However, the present spoken English pronunciation mistake correction model’s accuracy and stability have not yet reached an optimal level. Based on this background, this work provides an enhanced random forest model and uses it to detect and correct automatic pronunciation errors in English classes. The improved random forest (RF) algorithm is used to classify and detect whether the learner’s pronunciation is correct. Mel cepstral coefficient (MFCC) is used for feature extraction, and principal component analysis (PCA) is used for dimensionality reduction of feature data. The experimental structure demonstrates that by using a combination classification framework based on MFCC, PCA, and RF, the learner’s pronunciation difficulty may be resolved. This allows for different error categories to receive feedback corrections.
Noise-Tolerant Zeroing Neural Dynamics for Solving Hybrid Multilayered Time-Varying Linear Equation System
Authors: Li Jian , Shi Yang , Sun Yange , Fan Jianwei , Mirza Mohammed Aquil
Published: May 2022

Resource allocation problem in a wireless sensor network can be formulated as time-varying optimization, which can be further converted as time-varying linear equation system (TVLES). Hybrid multilayered time-varying linear equation system (HMTVLES) involving hybrid multilayers and time-variation characteristic is a complicated and challenging problem. Recently it has been solved by zeroing neural dynamics (ZND) method under ideal conditions, i.e., without noises. However, noises are ubiquitous, immanent and unavoidable in real-time systems. In this work, we propose a noise-tolerant zeroing neural dynamics (NTZND) model for solving HMTVLES. It can deal with different kinds of noises such as constant noise, linear-increasing noise, and random noise. Theoretical analyses guarantee the precision of NTZND model in the presence of different kinds of noises. In addition, a general NTZND model is proposed based on general activation function. Besides, classical ZND method and gradient neural dynamics (GND) method are also investigated and compared. Numerical experimental results are presented to verify the theoretical results of proposed models.
Application of Motion Capture Based on Digital Filtering Algorithm in Sports Dance Teaching
Authors: Rao Fan
Published: May 2022

In order to improve the teaching effect of sports dance, this paper analyzes the traditional dance teaching motion capture, uses sensor motion perception algorithms to capture sports dance motion perception, and designs an intelligent sensor system that can be used for sports dance motion capture. Moreover, this paper combines the digital filter algorithm to design the hardware system structure of the sports dance motion capture system and builds a motion capture system for sports dance teaching based on the digital filter algorithm according to actual needs. In addition, this paper combines the simulation test to evaluate the performance of the system designed in this paper. The research results show that the motion capture system for sports dance teaching based on the digital filtering algorithm proposed in this paper can play an important role in sports dance teaching and effectively improve the efficiency of sports dance teaching.
Ginsenoside Rh4 Inhibits Colorectal Cancer Cell Proliferation by Inducing Ferroptosis via Autophagy Activation
Authors: Wu Yingchao , Pi Dajin , Chen Yiliu , Zuo Qian , Zhou Shuyao , Ouyang Mingzi
Published: May 2022

Colorectal cancer (CRC) is a severe threat to human health. Ginsenosides such as ginsenoside Rh4 have been widely studied in the antitumor field. Here, we investigated the antiproliferative activity and mechanism of Rh4 against CRC in vivo and in vitro. The CRC xenograft model showed that Rh4 inhibited xenograft tumor growth with few side effects (

p
<
0.05

). As determined by MTT colorimetric assays, Western blotting, and immunohistochemical analysis, Rh4 effectively inhibited CRC cell proliferation through autophagy and ferroptosis (

p
<
0.05

). Rh4 significantly upregulated autophagy and ferroptosis marker expression in CRC cells and xenograft tumor tissues in the present study (

p
<
0.05

). Interestingly, the ferroptosis inhibitor ferrostatin-1 (Fer-1) reversed Rh4-induced ferroptosis (

p
<
0.05

). Moreover, the autophagy inhibitor 3-methyladenine (3-MA) also reversed Rh4-induced ferroptosis (

p
<
0.05

). These results indicate that Rh4-induced ferroptosis is regulated via the autophagy pathway. In addition, Rh4 increased reactive oxygen species (ROS) accumulation, leading to the activation of the ROS/p53 signaling pathway (

p
<
0.05

). Transcriptome sequencing also confirmed this (

p
<
0.05

). Moreover, the ROS scavenger N-acetyl-cysteine (NAC) reversed the inhibitory effect of Rh4 on CRC cells (

p
<
0.05

). Therefore, this study proves that Rh4 inhibits cancer cell proliferation by activating the ROS/p53 signaling pathway and activating autophagy to induce ferroptosis, which provides necessary scientific evidence of the great anticancer potential of Rh4.
Consolidation of a Certain Discrete Probability Distribution with a Subclass of Bi-Univalent Functions Involving Gegenbauer Polynomials
Authors: Amourah Ala , Alomari Mohammad , Yousef Feras , Alsoboh Abdullah
Published: May 2022

In this work, we introduce and investigate a new subclass of analytic bi-univalent functions based on subordination conditions between the zero-truncated Poisson distribution and Gegenbauer polynomials. More precisely, we will estimate the first two initial Taylor–Maclaurin coefficients and solve the Fekete–Szegö functional problem for functions belonging to this new subclass.
The Most Promising Biomarkers of Allogeneic Kidney Transplant Rejection
Authors: Rogulska Karolina , Wojciechowska-Koszko Iwona , Dołęgowska Barbara , Kwiatkowska Ewa , Roszkowska Paulina , Kapczuk Patrycja , Kosik-Bogacka Danuta
Published: May 2022

Clinical transplantology is a constantly evolving field of medicine. Kidney transplantation has become standard clinical practice, and it has a significant impact on reducing mortality and improving the quality of life of patients. Allogenic transplantation induces an immune response, which may lead to the rejection of the transplanted organ. The gold standard for evaluating rejection of the transplanted kidney by the recipient’s organism is a biopsy of this organ. However, due to the high invasiveness of this procedure, alternative diagnostic methods are being sought. Therefore, the biomarkers may play an essential predictive role in transplant rejection. A review of the most promising biomarkers for early diagnosis and prognosis prediction of allogenic kidney transplant rejection summarizes novel data on neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), C-X-C motif chemokine 10 (CXCL-10), cystatin C (CysC), osteopontin (OPN), and clusterin (CLU) and analyses the dynamics of changes of the biomarkers mentioned above in kidney diseases and the mechanism of rejection of the transplanted kidney.
An Evolutionary Game Analysis of Contractor’s Green Construction Behavior with Government Supervision and WeMedia’s Influence
Authors: Zhang Yuebin , Yi Xin , Qiu Hui , Chen Jingchuan
Published: May 2022

The promotion of green construction is an important approach to achieve high-quality and sustainable development of China’s construction industry. In China, the government supervises contractors’ green construction behavior. However, due to factors such as high cost and immature construction technology, the contractor often does not have a strong interest to adopt green construction behavior. An evolutionary game model is constructed based on the interactive relationship between government and contractor under bounded rationality conditions. As the best way for public opinion to function, WeMedia mechanism is considered in the evolutionary game model from the perspective of public participation. The evolutionary game process of the two players is simulated by system dynamics. The results show that without the influence of WeMedia, the stability of the system evolution depends on two factors. One is the government’s penalty for contractor’s nongreen construction behavior and the other is government’s supervision performance brought from contractor’s green construction behavior. With the influence of WeMedia, when WeMedia’s willingness to positively propagandize the contractor’s green construction behavior strengthens, it can significantly promote the system to converge to the ideal stable strategy. While WeMedia’s capability to negatively expose the contractor’s nongreen construction behavior strengthens, it can also significantly promote the system to converge to the ideal stable strategy. Even if the government takes excessive penalties for the contractor’s nongreen construction behavior, the system can still converge to the ideal stable strategy, by adjusting the willingness of propaganda or the capability of exposure from WeMedia reasonably. With the influence of WeMedia in the green construction supervision game, this study analyzes how the positive propaganda and negative exposure of WeMedia influence the strategic choice of the game between the two parties, to provide suggestions on how the government can efficiently use the WeMedia to promote contractor’s green construction behavior.
A Neural Network Model of Smart Aging Combining Family Structure Change Factors
Authors: Jin Yin-shi
Published: May 2022

In this paper, we analyze the changes in family structure and explore the changes in detail, based on which we construct a neural network model of smart aging. Based on the gender perspective, the individual growth model in the multilayer linear model is used to examine the effects of family structure changes on the elderly in terms of economic exchange, daily care, and emotional support. The results show that there is no significant gender difference in the family structure changes on the elderly in terms of economic exchange and daily care, but there is a significant gender difference in terms of emotional support. To solve the problem of data imbalance in the daily activity categories of the elderly, this paper resamples the data and uses different neural network models for activity recognition of the sensor data generated from the daily activities of the elderly. In this paper, the daily behavior patterns of the elderly over a while are studied by correlating three conditions of time distance, optimal path, and sensor distance to discover the daily behavior patterns of the elderly, while the abnormal behavior patterns can be well separated by EM clustering algorithm. The daily behavior of the elderly is a coarse-grained representation of their daily activities. It is not limited to a specific activity and does not require the sensor ID, trigger time, and location triggered by the activity to be consistent, but in long-term daily activity data, it abstracts the general behavior rules of the elderly activities. Through the research of this paper, the existing system is improved, and the multifaceted needs of the elderly are fully considered, from housing needs to spiritual needs, to face the current elderly care problems with a positive attitude, create a good social elderly care environment for the elderly, and realize the real elderly care.
[Retracted] Value of CSF‐Cl, CSF‐GS, CSF‐MALB, and CSF‐ADA in Differential Diagnosis of Secondary Hydrocephalus
Authors: Huang Junzhang , Chen Bing
Published: May 2022

Objectives. To investigate the value of cerebrospinal fluid chloride (CSF‐Cl), cerebrospinal fluid glucose (CSF‐GS), cerebrospinal fluid microalbumin (CSF‐MALB), and cerebrospinal fluid adenosine deaminase (CSF‐ADA) in the differential diagnosis of secondary hydrocephalus. Methods. 103 patients with secondary hydrocephalus treated in our hospital from January 2018 to April 2022 were selected. According to different types, it is divided into the hemorrhagic hydrocephalus group and tumor hydrocephalus group. By detecting the levels of inflammatory factors such as CSF‐Cl, CSF‐GS, CSF‐MALB, and CSF‐ADA in the two groups, we can analyze the value of these inflammatory factors in the differential diagnosis of secondary hydrocephalus. Results. The level of CSF‐MALB in the hemorrhagic hydrocephalus group was significantly higher than that in the tumor hydrocephalus group, and the difference between the two groups was statistically significant (P < 0.05). There was no significant difference in the levels of CSF‐Cl, CSF‐GS, and CSF‐ADA between the two groups (P > 0.05). The area under ROC curve (AUC) of CSF‐Cl, CSF‐GS, CSF‐MALB, and CSF‐ADA in the differential diagnosis of secondary hydrocephalus was 0.438, 0.553, 0.750, and 0.542, respectively, sensitivity was 15.1%, 45.3%, 79.2%, and 18.9%, respectively, and specificity was 96.0%, 80.0%, 80.0%, and 94.0%, respectively. Conclusions. The inflammatory reaction of hemorrhagic hydrocephalus was significantly greater than that of tumor hydrocephalus. Moreover, CSF‐MALB is closely related to the pathogenesis of hemorrhagic hydrocephalus. At the same time, CSF‐MALB can be used as a good index for rapid differential diagnosis of secondary hydrocephalus.
An Empirical Study of Integrating Information Technology in English Teaching in Artificial Intelligence Era
Authors: Huang Lu
Published: May 2022

Affordance of information technology in artificial intelligence era is accelerating its increasing integration into English teaching all over the world. However, the implementation of integrating information and communication technology requires the transformation of teaching concepts, the optimization of teaching strategies, and the innovation of teaching methods.This empirical study, which is based on analysis of status quo of college English teaching modes in China, aims to explore pedagogical challeges and motivate English teachers to take initiative to transform their clarssrooms with technology. It further proposes a framework as well as some innovative strategies in implementing the reform in college English classrooms. An exploratory study was conducted using the framework, indicating that integrating information communication technology into education can greaty enhance teaching efficiency, effectively improve learning outcomes, and promote students’ positive attitude towards learning.
Modeling and Simulation of Basketball Players’ Cardiopulmonary Endurance Sensor under Different Intensity Training
Authors: Dai Yang
Published: May 2022

In order to explore the effects of HIT and MICT on aerobic endurance of college basketball players. By analyzing the difference between the cardiorespiratory endurance of basketball-specialized college students and the certain correlation between their athletic ability, it can better serve the practice of basketball training and provide certain theoretical guidance and theoretical guidance for the improvement of basketball-specialized college students’ athletic fitness, with a practical application value. 37 male college basketball players were randomly divided into the HIT group and MICT group. They were trained four times a week for 10 weeks. The special indexes of cardiopulmonary and basketball were measured during and before and after exercise. The experiment shows that the heart rate in HIT fast running stage is significantly higher than that in MICT, and the stimulation to cardiopulmonary function is stronger. HIT and MICT are difficult to improve athletes’ VO2max, but they can significantly improve the utilization rate of VO2max of VAT, and the effect of HIT is better than MICT. HIT and MICT have significant effects on improving the performance of

15
 
m
×
17
×
4

run, and the former has obvious advantages. However, in terms of improving the performance of 3/4 sprint, HIT improved significantly.
Digital Industry Financial Risk Early Warning System Based on Improved K-Means Clustering Algorithm
Authors: Duan Xiao-li , Du Xue-xia , Guo Li-mei
Published: May 2022

Corporate financial risks not only endanger the financial stability of digital industry but also cause huge losses to the macro-economy and social wealth. In order to detect and warn digital industry financial risks in time, this paper proposes an early warning system of digital industry financial risks based on improved K-means clustering algorithm. Aiming to speed up the K-means calculation and find the optimal clustering subspace, a specific transformation matrix is used to project the data. The feature space is divided into clustering space and noise space. The former contains all spatial structure information; the latter does not contain any information. Each iteration of K-means is carried out in the clustering space, and the effect of dimensionality screening is achieved in the iteration process. At the same time, the retained dimensions are fed back to the next iteration. The dimensional information of the cluster space is discovered automatically, so no additional parameters are introduced. Experimental results show that the accuracy of the proposed algorithm is higher than other algorithms in financial risk detection.
Herbal Granules of Heat-Clearing and Detoxifying for Children with Mild Hand, Foot, and Mouth Disease: A Bayesian Network Meta-Analysis
Authors: Sheng Yongcheng , Liu Xueting , Wang Qin , Zhang Yuhui , Huang Litao , Hu Dan , Ren Pengwei , Hong Qi , Kang Deying
Published: May 2022

Background. Regarding ethical considerations of randomized controlled trials (RCTs) in children, limited evidence for mild hand, foot, and mouth disease (HFMD) is available. Recently, with the increasing but result-conflicting RCTs published around herbal granules of heat-clearing and detoxifying (HGs-HD), a head-to-head comparison is urgently needed to choose a suitable therapy for clinical practice. Materials and Methods. This study was conducted according to the preferred reporting items for systematic review and meta-analysis (PRISMA) extension statement for network meta-analysis (NMA). Eight databases (Medline, Embase, and so on) and two trial registry platforms (https://www.clinicaltrials.gov and https://www.chictr.org.cn) were searched from inception to May 26, 2021. The NMA was performed using a random-effect model. The treatment hierarchy was summarized and reported as the surface under the cumulative ranking curve (SUCRA) probability values. The rankings of each HGs-HD at primary outcomes were estimated by the inverse probability weighting (IPW) approach and averaged, which presents the comprehensive improvement effect. Results. Forty-five RCTs involving 18 interventions were included that studied 5,652 children with mild HFMD. The best performance probability for improving symptoms were respectively presented in terms of fever (Xiao’er Resuqing granules, XRGs, 94.9%), rash (Xiao’er Jinqiao granules, 83.9%), hospitalization (Xiao’er Chiqiao Qingre granules, XCQGs, 92.7%), vesicles (Jinlianhua granules, 91.0%), appetite (Xiao’er Chiqiao Qingre granules, XCQGs, 86.7%), and ulcers (Kouyanqing granules, KouGs, 88.8%). Furthermore, the top 5 rankings for comprehensive improvement effect were Yanning granules (YNGs, 2.256), XCQGs (2.858), XRGs (3.270), KouGs (7.223), and Houerhuan Xiaoyan granules (HXGs, 7.597). Conclusions. This is the first NMA of HGs-HD head-to-head comparisons for children with mild HFMD. Of those, YNGs, XCQGs, XRGs, KouGs, and HXGs could be recommended as potential choices for clinical practice. Of course, the results should be interpreted with caution due to the limited high-quality RCTs.
Construction and Application of Talent Evaluation Model Based on Nonlinear Hierarchical Optimization Neural Network
Authors: Pei Xintian
Published: May 2022

Talent assessment attracts the attention of researchers because of its important influence on business management issues. The traditional talent evaluation model has high sample selection cost and large calculation volume ratio, so it has become an urgent problem to be solved. Based on the nonlinear hierarchical optimization neural network, this article improves the corresponding talent evaluation index system and builds a talent evaluation model on the basis of demonstrating the feasibility of using a nonlinear hierarchical optimization neural network for talent evaluation. This article conducts an empirical analysis on the talent evaluation model of the talent evaluation team members and designs a prototype system of the talent evaluation index system based on the nonlinear hierarchical optimization neural network. In the simulation process, MATLAB software is used to complete the program realization of the model, and the accuracy and feasibility of the model are verified by combining the case. Through the evaluation and research of the implementation personnel of the case enterprise, the enterprise talent evaluation based on the nonlinear hierarchical optimization neural network is demonstrated. The empirical results show that it is feasible and accurate to use the nonlinear hierarchical optimization neural network to evaluate the talent evaluation team. The comprehensive prediction accuracy rate of T-1 year is 88.5%, and the comprehensive prediction accuracy rate of T-2 year was 83.45%, which effectively promoted the evaluation of enterprise talents.
Rapid-Onset Antidepressant-Like Effect of Nelumbinis semen in Social Hierarchy Stress Model of Depression
Authors: Shin Jihwan , Lee Jeonghun , Choi Junhyuk , Ahn Byung-Taek , Jang Sang Chul , You Seung-Won , Koh Do-Yeon , Maeng Sungho , Cha Seung-Yun
Published: May 2022

Depression is a disease with increasing prevalence worldwide, and it is necessary to develop a therapeutic agent with better efficacy than existing antidepressant drugs. Antidepressants that act on the glutamatergic nervous system, such as ketamine, have a rapid-onset antidepressant effect and are effective against treatment-resistant depression. However, because of the addictive potential of ketamine, alternative substances without psychological side effects are recommended. In particular, many natural compounds have been tested for their antidepressant effects. The antidepressant effects of Nelumbinis semen (NS) have been tested in many studies, along with the various actions of NS on the glutamatergic system. Thus, it was expected that NS might have a rapid-onset antidepressant effect. To test the antidepressant potential, despair and anhedonic behaviors were measured after administering NS to mice exposed to social hierarchy stress (SHS), and biochemical changes in the prefrontal cortex and hippocampus were analyzed. NS reduced despair-like responses in the forced swim test and tail suspension test. Mice exposed to SHS showed depression-like responses such as increased despair, reduced hedonia, and an anxiety-like response in the novelty suppressed feeding test. NS, but not fluoxetine, improved those depression-like behaviors after acute treatment, and NBQX, an AMPA receptor blocker, inhibited the antidepressant-like effects of NS. The antidepressant-like effect of NS was related to enhanced phosphorylation of mTOR in the prefrontal cortex and dephosphorylation of GluR1 S845 in the hippocampus. Since NS has shown antidepressant-like potential in a preclinical model, it may be considered as a candidate for the development of antidepressants in the future.
Design of Cloud Computing Platform for Large-Scale Multimedia Communication
Authors: Wu XiuJuan
Published: May 2022

In order to enlarge the scale of multimedia communication, based on the design of cloud computing platform, this paper makes an in-depth study on the design of multimedia communication platform. Firstly, we study from three methods: extracting high-quality content, broadening multimedia profit channels, and enhancing copyright awareness. Among them, the big data diversion system under the cloud computing platform is designed to optimize data clustering and pattern recognition. The traditional big data shunting system under the cloud computing platform adopts the open-source embedded system design method. The shunting system is connected to the switch of the cloud computing platform through the coupling coil, resulting in missing points and wrong points in the data in the shunting system. Using this method, it is concluded that the cloud computing platform cannot achieve the optimal allocation in terms of SLA violation rate, resource consumption, and power consumption. After experiments and research, the current development trend of media shows a deviation from this phenomenon. The development of media itself, that is, the self-appreciation of media, is gradually becoming the main purpose of media development. A power simulation cloud computing platform architecture with dynamic expansion, efficient computing, and convenient resource scheduling should be given to face large-scale multimedia communication.
Ropivacaine and Lidocaine in Double Eyelid Blepharoplasty: A Comparative Analysis
Authors: Liu Shaobo , Chu Zheng
Published: May 2022

Double eyelid blepharoplasty is an operation of changing the structure of the eyelid and reshaping the eyelid to form a new upper eyelid fold. This prospective randomized controlled study aimed to compare and evaluate the effect of ropivacaine and lidocaine in double eyelid blepharoplasty. A total of 120 patients who underwent double eyelid blepharoplasty in the beauty department of our hospital from January 2019 to July 2021 were enrolled and randomized (1 : 1) into the control group (1% lidocaine-epinephrine mixture) and the study group (0.75% ropivacaine-epinephrine mixture) via the random number table method. The 0.75% ropivacaine-epinephrine mixture led to lower Wong-Baker FACES Pain Rating visual analog scale score from 2 h to 1 d after surgery versus 1% lidocaine-epinephrine mixture (all

P
<
0.05

). The operation time, intraoperative anesthetic drug dosage, and intraoperative blood loss were similar in the two groups (all

P
<
0.05

). The 0.75% ropivacaine-epinephrine mixture resulted in a lower postoperative swelling score, but a higher patient satisfaction score versus 1% lidocaine-epinephrine mixture (all

P
<
0.05

). The safety profiles of the two groups were similar (all

P
<
0.05

). Both lidocaine and ropivacaine produce desirable anesthesia effects in double eyelid blepharoplasty. Ropivacaine can significantly reduce postoperative pain and mitigate postoperative swelling, with better satisfaction, and is thus worthy of further promotion.
The Emerging Role of Implementing Machine Learning in Food Recommendation for Chronic Kidney Diseases Using Correlation Analysis
Authors: Gupta Sachin , Garg Neeraj , Sinha Divyanshu , Yadav Babita , Gupta Bhoomi , Miah Shahajan
Published: May 2022

Kidneys are vital organs in the human body, and their effective functioning determines life quality. Chronic kidney illness is a kind of nephrotic syndrome in which the kidneys’ capacity to cope normally steadily deteriorates and remains asymptomatic for a long period as the disease progresses. An early CKD detection would help the patient recover faster and easier. Using an artificial intelligence system that can effectively aid in CKD detection in time and suggest the required food nutrition for its treatment and recovery would reap immense benefits for healthcare professionals as well as the patient. ML is a part of AI technology that has been used for effective medical development. This technology helps physicians in the accurate diagnosis of kidney disease and helps in effective treatment prediction by recommending required nutrition. The present research relates to the use of ML in proper kidney disease diagnosis and food recommendations for treatment accordingly. A correlation analysis has been done in this research to observe the strength of ML using the effective finding for renal malfunctioning and identifying the best food products that could help in its treatment and recovery. IBM SPSS version 26 has been used for this research. The correlation analysis has been done to observe the impact of eight independent variables that are age, gender, blood sugar, serum albumin, creatinine, potassium, bacteria, and pus secretion on the two dependent variables that are the risk of CKD occurrence and ML accuracy. The results have exposed that the autonomous values consist of a strong positive correlation with the dependent variable (

p
<
0.005

). The statistical significance values have proved that the dependent values are statistically significant (0.001). The value of ML accuracy at a 95% confidence level has been observed at 88.85%, and the CKD occurrence value is 86.95%. The results have proved that the ML algorithm detects the risk of CKD occurrence accurately in each stage via analyzing blood sugar, creatinine, and potassium levels. The result also shows that the risk of CKD enhances with an increase in age.
Bayesian versus Classical Econometric Inference to Revisit the Role of Human Capital in Economic Growth
Authors: Akbar Muhammad , Alshamari Abdulmohsen Obied , Tariq Muhammad , Marwan Alhelali , Alsaedi Basim S. O. , Ahmad Ishfaq
Published: May 2022

Application of Bayesian inference to analyze real economic phenomena is rare in the literature on applied economics. This study contributes in two ways. Firstly, it contributes to methodological advancement in the literature on applied economic modeling by estimating a structural model using the classical econometric framework as well as the Bayesian two-stage econometric framework. The performance of the two approaches is compared due to the small sample size and the best model is selected. Secondly, the study is used to get fresh evidence about the impact of human capital upon economic growth in the form of Bayes mean estimates along with their Highest Posterior Density Intervals (HPDIs) which give certain ranges of estimates within which the parameters are likely to lie. Annual data on the Pakistan economy ranging from 1965 to 2019 is used for the estimation of the model. Classical estimates are obtained using the efficient GMM method. Bayes mean estimates are simulated using a Bayesian two-stage procedure assuming multivariate normal-Wishart informative priors. Results show that the Bayesian econometric framework gives more precise parameters’ estimates as compared to the classical econometric framework, and hence, the Bayesian inference may be preferred over classical inference, especially in the case of a small sample size. The Bayes estimates show that a 1% increase in education capital and health capital causes raising economic growth by 0.0091% and 0.1778%, respectively, with a 0.95 probability that the estimates are likely to lie within the intervals 0.0085%–0.0097% and 0.1606%–0.1952%, respectively. Hence, human capital might be considered a vital factor to achieve economic growth in Pakistan. Moreover, health capital shows strong effects as compared to education capital in the process of economic growth.
Electronic Healthcare Data Record Security Using Blockchain and Smart Contract
Authors: Nishi Farjana Khanam , Shams-E-Mofiz Mahizebin , Khan Mohammad Monirujjaman , Alsufyani Abdulmajeed , Bourouis Sami , Gupta Punit , Saini Dinesh Kumar
Published: May 2022

An electronic health record (EHR) is a technology that allows you to keep track of your health information. It keeps computerized records of several healthcare organizations. Records are exchanged via enterprise-wide data systems as well as other networking technologies and exchanges. Patients nowadays expect immediate access to their health information. However, the health sector comes with immediate access to data, and there are worries about the privacy and security of medical records of patients. As a result, a blockchain-based solution can assist in resolving this issue. The blockchain has the potential to beat the conventional centralized system, which suffers from a severe lack of accessibility. This is a decentralized technology that has recently been presented to provide a new viewpoint on data security and system efficiency. This paper presents a blockchain-based system that helps the patient’s data be managed and secured into a single record held by the patient. This system was developed using the Ethereum network using Ganache, as well as programming languages, tools, and techniques such as Solidity and web3.js. The measured approach suggested in this paper uses this platform to store patients’ data and execute functions in a decentralized system using blockchain smart contracts. Transactions are communicated through the smart contract once it has been launched, providing security and privacy features. Furthermore, the transaction’s desired alterations can be verified and transmitted to the entire distributed network. There is also a cryptocurrency wallet (MetaMask) that holds a centrally controlled, private information system in which records can be quickly accessed and secured by authorities. Doctors and patients can access the system through the wallet. Moreover, all the data of the doctor and patient will be secured and managed through this system. This proposed system is aimed at doing things such as the following: blockchain technology allows users to obtain the same data at the same time, increasing efficiency, developing credibility, and reducing barriers. It enables the secure storage of data by setting specific access for users. Additionally, this proposed system facilitates the secure transfer of patient medical records. Finally, this paper describes a health-record system and a new protocol that are quick and secure to use. It allows greater openness and ownership of sensitive data to be recorded and secured and also promotes the healthcare sector with blockchain.
miR-138-5p Inhibits Vascular Mimicry by Targeting the HIF-1α/VEGFA Pathway in Hepatocellular Carcinoma
Authors: Liu Hongwei , Tang Tao , Hu Xiujin , Tan Weihe , Zhou Peng , Zhang Huixian , Liu Yanmei , Chen Chen , Yang Meng , Zhou Meifang , Xuan Shuxia , Cheng Bin , Yin Weiguo , Lin Jinduan
Published: May 2022

Tumour vascular mimicry (VM) is the process by which new blood vessels are formed by tumour cells rather than endothelial cells. An increasing number of studies have revealed that the VM process is associated with cancer progression and metastasis. MiR-138-5p has been reported to act as a tumour suppressor in many cancers. However, the role and underlying mechanism of miR-138-5p in hepatocellular carcinoma (HCC) VM remain unclear. In this study, VM density was detected by CD31/periodic acid-Schiff double staining in HCC clinical specimens. We found that miR-138-5p expression correlated strongly and negatively with microvessel density. Additionally, the miR-138-5p mimic or inhibitor decreased or increased, respectively, tube formation capacity in HepG2 and Hep3B cells. Consistent with this finding, miR-138-5p repressed vessel density in vivo. Moreover, miR-138-5p targeted hypoxia-inducible factor 1α (HIF-1α) and regulated the expression of HIF-1α and vascular endothelial growth factor A (VEGFA), which are established classical master regulators for angiogenesis. Consistent with these findings, the HIF-1α inhibitor CAY10585 effectively blocked HCC cell VM and VEGFA expression. In conclusion, miR-138-5p inhibits HepG2 and Hep3B cell VM by blocking the HIF-1α/VEGFA pathway. Therefore, miR-138-5p may serve as a useful therapeutic target for miRNA-based HCC therapy.