bluuebunny/crossref_metadata_2025 huggingface dataset:
Challenging Obstetrical Management in Generalized Peritonitis during Pregnancy
Authors: Oliveira Susana Patrícia Lima , Sousa Ana Isabel , Martins Nuno Nogueira
Published: April 2022

Acute abdomen in pregnancy represents a diagnostic and therapeutic challenge, despite the current advances in modern medicine, since the typical symptoms and altered laboratory parameters mimic normal pregnancy. Acute appendicitis is the most common nonobstetric surgical emergency during pregnancy, with an incidence of 1 per 500-2000 pregnancies. Delayed diagnosis and reluctance to operate on a pregnant woman predispose to adverse maternal and fetal outcomes. The elective termination of pregnancy or interventions to prolong it in the presence of appendicitis is controversial. We present a case of a 38-year-old Caucasian woman, G2P0, admitted to the Obstetric Emergency Department at 13 4/7 weeks of gestation with a primary complaint of severe nausea and vomiting associated with progressive diffuse abdominal pain which had started 7 days before. After the difficulty of inherent differential diagnosis, she was diagnosed with generalized peritonitis due to acute perforated appendicitis. Prompt exploratory laparotomy with appendectomy and drainage of multiple abscesses were performed. Conservative obstetrical management was assumed, with subsequent periodic monitoring of the fetal focus. Due to abdominal compartment syndrome, the abdomen was left open for 4 days. After 7 days in the intensive care unit, recovery was favorable, pregnancy remained uneventful, and a healthy full-term baby was born 27 weeks later. This case represents a successful example of how the cooperation of the obstetrics and general surgery teams and the decision of conservative obstetrical management in the surgical environment contributed to optimizing maternal health, achieving the best obstetrical outcome.
Optimization of Site Selection for Emergency Medical Facilities considering the SEIR Model
Authors: Liu Jingkuang , Cao Liying , Zhang Dongyu , Chen Zibo , Lian Xiaotong , Li Ying , Zhang Yingyi
Published: April 2022

Since the outbreak of COVID-19, the rapid construction and operation of Wuhan Vulcan Mountain Hospital and Raytheon Hospital have attracted positive responses from local and international observers. At the same time, it has also highlighted the urgency for the construction of emergency medical facilities for public health emergencies. Before construction, the practical location of medical facilities is the basis for improving the city’s emergency management ability. Based on the classic susceptible, exposed, infected, and recovered (SEIR) epidemic model and epidemic data in Guangzhou, we established a multi-stage time-delay SEIR epidemic model that is suitable for epidemic research in Guangzhou. According to the results of the model, the five areas with the highest number of infected patients were identified, which included Baiyun District, Panyu District, Haizhu District, Tianhe District, and Zengcheng District. We then centralized infected individuals at five demand points. Based on the distribution of these points and by combining the characteristics of the emergency medical facilities, we built and solved the set covering location decision model, and considered the economy, society, and environment as the starting points to optimize the site location. Finally, based on simulations, we concluded that appropriate site selection can increase the time required to reach the maximum number of patients and reduce the proportion of infected and exposed people by 11.3% and 1.11%, respectively. This is indicative of the effectiveness of the site selection model and the rational selection of facility points in this study. It solves the optimization problem of the location decision of emergency medical facilities for public health emergencies in China, and also provides some valuable references for site selection decisions of emergency medical facilities in other areas.
Optimization of English Machine Translation by Deep Neural Network under Artificial Intelligence
Authors: Guo Xiaohua
Published: April 2022

To improve the function of machine translation to adapt to global language translation, the work takes deep neural network (DNN) as the basic theory, carries out transfer learning and neural network translation modeling, and optimizes the word alignment function in machine translation performance. First, the work implements a deep learning translation network model for English translation. On this basis, the neural machine translation model is designed under transfer learning. The random shielding method is introduced to implement the language training model, and the machine translation is slightly adjusted as the goal of transfer learning, thereby improving the semantic understanding ability in translation performance. Meanwhile, the work design introduces the method of word alignment optimization and optimizes the performance of word alignment in the transformer system by using word corpus. The experimental results show that the proposed method reduces the average alignment error rate by 8.1%, 24.4%, and 22.1% in EnRo (English-Roman), EnGe (English-German), and EnFr (English-French), respectively, compared with the previous algorithms. Compared with the designed optimization method, the word alignment error rate is lower than that of traditional methods. The modeling and optimization method is feasible, which can effectively solve the problems of insufficient information utilization, large parameter scale, and difficult storage in the process of machine translation. Additionally, it provides a feasible idea and direction for the optimization and improvement in neural machine translation (NMT) system.
m6A RNA Methylation Regulators Contribute to Predict and as a Therapy Target of Pulmonary Fibrosis
Authors: Deng Meng-Sheng , Chen Kui-Jun , zhang Dong-Dong , Li Guan-Hua , Weng Chang-Mei , Wang Jian-Min
Published: April 2022

Background. Pulmonary fibrosis is difficult to treat. Early diagnosis and finding potential drug therapy targets of pulmonary fibrosis are particularly important. There were still various problems with existing pulmonary fibrosis markers, so it is particularly important to find new biomarkers and drug treatment targets. m6A (N6,2′-O-dimethyladenosine) RNA methylation was the cause of many diseases, and it is regulated by m6A methylation regulators. So, whether RNA methylation regulators can be a diagnostic marker and potential drug therapy target of early pulmonary fibrosis needs to be explored. Materials and Methods. Using GSE110147 and GSE33566 in the GEO database to predict the m6A methylation regulators that may be related to the development of pulmonary fibrosis, we used 10 mg/ml bleomycin to induce mouse pulmonary fibrosis models and human pulmonary fibrosis samples, to confirm whether this indicator can be an early diagnostic marker of pulmonary fibrosis. Results. According to the database prediction results, METTL3 can predict the occurrence and development of pulmonary fibrosis, and the results of MASSON and HE staining show that the fibrosis model of mice is successful, and the fibrosis of human samples is obvious. The results of immunohistochemistry showed that the expression of METTL3 was significantly reduced in pulmonary fibrosis. Conclusions. The m6A methylation regulator METTL3 can be considered as an important biomarker for diagnosing pulmonary fibrosis occurrence, furthermore it could be considered as a drug target because of its low expression in pulmonary fibrosis.
A Quantitative Method to Delineate the Influence Area of Work Zone Considering Route Choice Inertia and Elastic Demand
Authors: Wang Guanfeng , Jia Hongfei , Tian Jingjing , Lin Yu , Wu Ruiyi , Liu Zhendong , Gao Heyao
Published: April 2022

The work zone on the urban road network will affect the surrounding road traffic. To represent the influence area of the work zone, the concept of a subnetwork is proposed in this paper. Delineating a suitable subnetwork quantitatively is a challenging problem. To address this issue, the node synthesized indexes (NSI) are deployed as a variability measure that captures both the change of link flow and origin-destination (OD) demand. The inertia-based stochastic user equilibrium with the elastic demand (ISUEED) model is proposed to accurately provide the data of link flow and OD demand for the network with the work zone. Correspondingly, the data of the network without a work zone can be obtained by the stochastic user equilibrium (SUE) model. According to the value of NSI, the initial range of the subnetwork is determined. Finally, the connectivity and compactness can be guaranteed by the modified L-shell algorithm. To demonstrate the performance of the method, two case studies and sensitivity analyses are conducted based on the Braess network and the local road network in Changchun, China. The proposed method is beneficial to reduce the complexity of the traffic model by substituting the entire network with a subnetwork.
Reliability Prediction for Computer Numerical Control Machine Servo Systems Based on an IPSO-Based RBF Neural Network
Authors: Jiang Zheng , Wang GuangJian , Huang ZuGuang , He Ye , Xue RuiJuan
Published: April 2022

The increasing use of computer numerical control (CNC) machines requires better prediction of the reliability of their servo control systems. A novel reliability prediction model based on radial basis function (RBF) neural network optimized by improved particle swarm optimization (IPSO) was proposed. It can overcome the disadvantages of conventional methods, which are time consuming and resource intensive. The major influences on the reliability of servo system include torque, temperature, current, and complexity. An improved algorithm for predicting the mean time between failure (MTBF) of servo systems based on a particle swarm optimization (PSO) and an RBF neural network algorithm is proposed. Two common problem of the PSO: local minimization and slow convergence were solved by the IPSO. “Zero failure” data preprocessing, data normalization, and small-sample data enhancement were performed on the original data. A homogenized sampling method is proposed to extract training and testing samples. Experimental results show that the improved PSO-based RBF neural network is superior to back propagation (BP) and RBF networks in terms of accuracy in servo system reliability prediction.
Social Networking Security during COVID-19: A Systematic Literature Review
Authors: Abid Rabia , Rizwan Muhammad , Veselý Peter , Basharat Asma , Tariq Usman , Javed Abdul Rehman
Published: April 2022

During the Covid-19 Pandemic, the usage of social media networks increased exponentially. People engage in education, business, shopping, and other social activities (i.e., Twitter, Facebook, WhatsApp, Instagram, YouTube). As social networking expands rapidly, its positive and negative impacts affect human health. All this leads to social crimes and illegal activities like phishing, hacking, ransomware, password attacks, spyware, blackmailing, Middle-man-attack. This research extensively discusses the social networking threats, challenges, online surveys, and future effects. We conduct an online survey using the google forms platform to collect the responses of social networking sites (SNS) users within Pakistan to show how SNS affects health positively and negatively. According to the collected response, we analyzed that 50% of the users use SNS for education purposes, 17.5% use it for shopping purposes, 58.2% use it for entertainment, 37.1% use it for communication, and 9.8% use it for other purposes. According to the response, the excessive use of SNS affects the health that 9.8% users face the physical threat, 42.8% user faces mental health issues due to excessive or inappropriate use of SN, and 50.5% users feel moral threat using Social sites. Finally, we conclude our paper by discussing the open challenges, conclusions, and future directions.
A New Approach to Estimate Concentration Levels with Filtered Neural Nets for Online Learning
Authors: Lee Woodo , Oh Junhyoung , Shim Jaekwoun
Published: April 2022

The COVID‐19 pandemic heavily influenced human life by constricting human social activity. Following the spread of the pandemic, humans did not have a choice but to change their lifestyles. There has been much change in the field of education, which has led to schools hosting online classes as an alternative to face‐to‐face classes. However, the concentration level is lowered in the online learning class, and the student’s learning rate decreases. We devise a framework for recognizing and estimating students’ concentration levels to help lecturers. Previous studies have a limitation in that they classified attention levels using only discrete states. Due to the partial information from discrete states, the concentration levels could not be recognized well. This research aims to estimate more subtle levels as specified states by using a minimum amount of body movement data. The deep neural network is used to continuously recognize the human concentration model, and the concentration levels can be predicted and estimated by the Kalman filter. Using our framework, we successfully extracted the concentration levels, which can aid lecturers and can be expanded to other areas. To implement the framework, we recruited participants to take online classes. Data were collected and preprocessed using pose points, and an accuracy of 90.62 % was calculated by predicting the concentration level using the framework. Furthermore, the concentration level was approximated based on the Kalman filter. We found that webcams can be used to quantitatively measure student concentration when conducting online classes. Our framework is a great help for instructors to measure concentration levels, which can increase the learning efficiency. As a future work of this study, if emotion data and skin thermal data are comprehensively considered, a student’s concentration level can be measured more precisely.
Application of Machine Learning to Badminton Action Decomposition Teaching
Authors: Long Jiahuai , Rong Shuguang
Published: April 2022

The study was aimed at realizing the identification of athletes’ actions in badminton teaching. The teaching process is segmented into many independent actions to help learners standardize their movements in badminton play, improving the national physical quality. First, the principle and advantages of machine vision sensing are introduced. Second, the images and videos about the action decomposition of badminton teaching are collected, and the image data are extracted by Haar-like. Subsequently, badminton players’ actions are recognized and preprocessed, and a dataset is constructed. Furthermore, a new algorithm model is implemented and trained by using Haar-like and Adaptive Boosting (AdaBoost). Finally, the badminton players’ action recognition algorithm is tested and compared with the traditional hidden Markov model (HMM) and support vector machine (SVM). The results show that action images improved by machine vision can process the captured actions effectively, making the computer better identify different badminton teaching actions. The proposed method has a recognition rate of more than 90% for each action, the average recognition accuracy of actions reaches 95%, the average recognition rate of the same person’s actions is 96.5%, and the average recognition rate of different people’s actions is 94.8%. The badminton teaching action recognition model based on Haar-like and AdaBoost can recognize and classify badminton actions and improve the quality of badminton teaching. This study shows that the image processing technology can effectively process the players’ static images, which gives the direction for physical education (PE) under artificial intelligence (AI).
Bioprospecting the Metabolome of Plant Urtica dioica L.: A Fast Dereplication and Annotation Workflow in Plant Metabolomics
Authors: Bhattarai Keshab , Paudel Babita , Dahal Sujan , Yadav Parasmani , Aryal Niraj , Baral Bikash , Bhattarai Hari Datta
Published: April 2022

Plants have a pivotal role in ethnopharmacology, and their preparations are in use globally. However, getting down to the structure requires an effective workflow and mostly requires a time-consuming isolation process. Although bioassay-guided approaches are widely popular, they face a massive problem of rediscovery in recent times, especially in plant metabolomics. Mass spectrometry (MS)-based approach incorporated molecular networking via Global Natural Product Social Molecular Networking (GNPS) is considered here for the benefit of the fast screening of secondary metabolites. This study uses direct crude extracts obtained from various parts of the Urtica dioica plant for the characterization of secondary metabolites. The crude extract of the plant initially displayed promising antioxidant and anti-diabetic activities. Then, we employed mass spectrometry-based dereplication to identify the phytochemical components in the extracts. This led to the discovery of 7 unknown and 17 known secondary metabolites, which were further verified with the SIRIUS 4 platform, a computational tool for the annotation of compounds using tandem MS data. On the other hand, chasing the antioxidant activity of methanolic extract of U. dioica leaves, we employed a bioassay-guided isolation approach. With this method, we isolated and characterized compound 13, a known molecule, which possessed strong antioxidant activity without showing much toxicity in the brine shrimp lethality test at the test concentration of 1 mg/mL. With our results, we advocate the MS-based approach as a good starting point for the dereplication of compounds from the complex crude extracts of plants.
Application of the Multiple Regression Method in Football Tactical Analysis
Authors: Xin Ling , Xu Yan-wei
Published: April 2022

Aiming at the unreasonable problem of current football tactical analysis, this paper studies the application method of the multiple regression method in football tactical analysis. Build a football tactical rehearsal model, optimize player information and tactical analysis management functions, and analyze football player status. Using the multiple regression method to choose the appropriate position of the players, make the football attack and defense plan. At the same time, the application of the multiple regression method to the analysis of football tactics can better select the optimal tactical plan, to ensure the rationality of football tactical selection.
A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots
Authors: Tang Qili
Published: April 2022

This paper proposes a personalized tourist interest demand recommendation model based on deep neural network. Firstly, the basic information data and comment text data of tourism service items are obtained by crawling the relevant website data. Furthermore, word segmentation and word vector transformation are carried out through Jieba word segmentation tool and Skip-gram model, the semantic information between different data is deeply characterized, and the problem of very high vector sparsity is solved. Then, the corresponding features are obtained by using the feature extraction ability of DNN’s in-depth learning. On this basis, the user’s score on tourism service items is predicted through the model until a personalized recommendation list is generated. Finally, through simulation experiments, the recommendation accuracy and average reciprocal ranking of the proposed algorithm model and the other two algorithms in three different databases are compared and analyzed. The results show that the overall performance of the proposed algorithm is better than the other two comparison algorithms.
A New Version of Weighted Weibull Distribution: Modelling to COVID‐19 Data
Authors: Abdullah Alahmadi Amani , Alqawba Mohammed , Almutiry Waleed , Shawki A. W. , Alrajhi Sharifah , Al-Marzouki Sanaa , Elgarhy Mohammed
Published: April 2022

In this study, we will look at a new flexible model known as the new double‐weighted Weibull distribution. The new Weibull double‐weighted distribution model is highly versatile because numerous submodels are included. The proposed model is very flexible because its density function has many shapes; it can be right skewness, decreasing, and unimodal. Also, the hazard rate function can be increasing, decreasing, up‐side‐down, and J‐shaped. Diverse features of the novel are computed. These qualities include moments, incomplete moments, and Lorenz and Bonferroni curves and quantiles, as well as entropy and order statistics. The maximum likelihood approach is used to estimate the model's parameters. In order to evaluate the accuracy and performance of maximum likelihood estimators, simulation data are presented. The utility and adaptability of the proposed model are demonstrated by utilizing three significant datasets: daily fatalities confirmed cases of COVID‐19 in Egypt and Georgia and relief times of twenty patients using an analgesic.
Histone Methyltransferase SETDB1 Promotes Immune Evasion in Colorectal Cancer via FOSB-Mediated Downregulation of MicroRNA-22 through BATF3/PD-L1 Pathway
Authors: Tian Jiale , Wang Weiwei , Zhu Jichao , Zhuang Yun , Qi Chunrun , Cai Zhengxin , Yan Wenhui , Lu Wenying , Shang Anquan
Published: April 2022

Tumors may develop a variety of immune evasion mechanisms during the progression of colorectal cancer (CRC). Here, we intended to explore the mechanism of histone methyltransferase SETDB1 in immune evasion in CRC. The expression of SETDB1, microRNA-22 (miR-22), BATF3, PD-L1, and FOSB in CRC tissues and cells was determined with their interactions analyzed also. Gain-of-function and loss-of-function approaches were employed to evaluate the effects of the SETDB1/FOSB/miR-22/BATF3/PD-L1 axis on T cell function, immune cell infiltration, and tumorigenesis. Aberrant high SETDB1 expression in CRC was positively associated with PD-L1 expression. SETDB1 negatively regulated miR-22 expression by downregulating FOSB expression, while miR-22 downregulated PD-L1 expression via targeting BATF3. Furthermore, SETDB1 silencing promoted the T cell-mediated cytotoxicity to tumor cells via the FOSB/miR-22/BATF3/PD-L1 axis and hindered CRC tumor growth in mice while leading to decreased immune cell infiltration. Taken together, SETDB1 could activate the BATF3/PD-L1 axis by inhibiting FOSB-mediated miR-22 and promote immune evasion in CRC, which provides a better understanding of the mechanisms underlying immune evasion in CRC.
Study on Dynamic Stability Prediction Model of Slope in Eastern Tibet Section of Sichuan-Tibet Highway
Authors: Ye Tangjin , Shu Tao , Li Junjie , Zhao Penghui , Wang Ying
Published: April 2022

The stability of the slope along the middle section of Tibet controls the safety and smoothness of the Sichuan-Tibet highway, which is affected by multiple and uncertain factors such as rainfall. The slope dynamic stability is evaluated to the benefit of that salvager can prepare in advance and preserve timely and accurately. Therefore, engineering treatment scheme in different batches, stages, and grades can be proposed prospectively. Random Forest algorithm was used to rank 10 primary factors: precipitation, earthquake, human factors, groundwater, slope height, slope gradient, dense degree, weathering depth, vegetation, and slope shape. Considering precipitation and earthquake as dynamic factors, a wavelet and NARX dynamic neural network were used to predict the trend and quantity of precipitation and earthquake, followed by developing a dynamic stability evaluation model by combining a fuzzy neural network model with other indexes. Results show that (1) the superposition error in rainfall and earthquake prediction is 0.21%, proving that the ranking of influencing factors is reasonable, and (2) the back-judgment and test accuracy of the dynamic evaluation model are 93.98% and 91.67%, respectively, indicating that the model is accurate and applicable. The model can evaluate the dynamic stability of slopes and provide more reasonable engineering protection countermeasures so that Highway Public Works Department can deal with emergencies and disasters timely and precisely.
Processing and Optimizing Industrial Structure Adjustment of Intangible Cultural Heritage by Big Data Technology in the Internet Era
Authors: Lei Yun , Li Xishan , Tsai Sang-Bing
Published: April 2022

This paper conducts an in-depth study and analysis of the preservation of ancient village heritage and the restructuring and optimization of cultural industries in western Guangdong in the context of the current “Internet+.” This study is problem-oriented and aims to improve the common rural environment in the less developed areas of western Guangdong from the perspective of sustainable development. This study conducts an objective investigation and understanding of villages, combining contemporary methods and techniques of quantitative analysis. This study aims to understand the regular characteristics of the evolution of the settlement space itself based on the characteristics of the interaction between the settlement space itself, especially with the local natural ecology and human society, to explore the localized decision making of the village settlement space, and to propose a specific and operable systematic approach from macro to micro, an approach that focuses on the village settlements established. This approach focuses on the optimization of the existing pattern, architecture, and surrounding vernacular landscape of rural settlements and is a critical inheritance based on the existing spatial pattern, emphasizing the coordination and unity of multiple factors related to the settlement space. Combining the background of the era of “Internet+” and the actual situation of the development of tourism in ancient villages in western Guangdong, a marketing 4Ps combination strategy suitable for the development of tourism in ancient villages in western Guangdong was formulated. The flexible and diverse forms of operation also determine the independence of the villagers in the distribution of benefits. The villagers take the family as the unit of self-employment, and the distribution of benefits is distributed by the villagers themselves within the family. The product strategy, product pricing strategy, marketing channels, and promotion strategy of “Internet+ rural tourism” are formulated. A study on the cultural renewal strategy of the Nanchang community in the context of rural revitalization strategy. Based on clarifying the idea of cultural renewal, the principles of cultural renewal are proposed based on the strategic planning of rural revitalization: the principle of overall protection and development, the principle of restoring the old as the old, and the principle of regional linkage renewal. Based on the conservation plan of the Nanchang community, the scope of its conservation and conservation framework is sorted out, and the cultural renewal strategy of the Nanchang community is elaborated in four aspects: optimization of the overall layout of the village, enhancement of public space, improvement of key buildings and living heritage of intangible cultural heritage.
Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image
Authors: Hapsari Rinci Kembang , Miswanto Miswanto , Rulaningtyas Riries , Suprajitno Herry , Seng Gan Hong
Published: April 2022

Iris has specific advantages, which can record all organ conditions, body construction, and psychological disorders. Traces related to the intensity or deviation of organs caused by the disease are recorded systematically and patterned on the iris and its surroundings. The pattern that appears on the iris can be recognized by using image processing techniques. Based on the pattern in the iris image, this paper aims to provide an alternative noninvasive method for the early detection of DM and HC. In this paper, we perform detection based on iris images for two diseases, DM and HC simultaneously, by developing the invariant Haralick feature on quantized images with 256, 128, 64, 32, and 16 gray levels. The feature extraction process does early detection based on iris images. Researchers and scientists have introduced many methods, one of which is the feature extraction of the gray-level co-occurrence matrix (GLCM). Early detection based on the iris is done using the volumetric GLCM development, namely, 3D-GLCM. Based on 3D-GLCM, which is formed at a distance ofd=1and in the direction of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, it is used to calculate Haralick features and develop Haralick features which are invariant to the number of quantization gray levels. The test results show that the invariant feature with a gray level of 256 has the best identification performance. In dataset I, the accuracy value is 97.92, precision is 96.88, and recall is 95.83, while in dataset II, the accuracy value is 95.83, precision is 89.69, and recall is 91.67. The identification of DM and HC trained on invariant features showed higher accuracy than the original features.
A Comprehensive Insight into the Phytochemical, Pharmacological Potential, and Traditional Medicinal Uses of Albizia lebbeck (L.) Benth.
Authors: Balkrishna Acharya , Sakshi  , Chauhan Mayur , Dabas Anurag , Arya Vedpriya
Published: April 2022

Background. Albizialebbeck is a deciduous tree having tremendous medicinal utilities, for example, respiratory, skin, gastrointestinal, oral disorders, eye, urinary, genital, anorectal, inflammatory, and neurological disorders, and venereal diseases. Several studies have been undertaken on the medicinal and traditional values of A. lebbeck. Objective. The detailed information about its medicinal uses and pharmacological implications is highly scattered and distributed in different data sources. Hence, the study was conducted to supply an inclusive review of its ethnomedicinal uses, phytochemicals, and the available pharmacological attributes supporting its efficiency in traditional medicine. Method. Literature surveys were conducted on this medicinal plant via search engines like Google Scholar, PubMed, and Science Direct, and obtained information up to December 2020 has been assessed and analyzed for this study. Results. Systematic investigation revealed that A. lebbeck consists of various phytochemicals, including major alkaloids, flavonoids, saponins, and terpenoids. Its crude extract, fraction, and bioactive compounds exhibited potent adulticidal, antiallergic, anticancer, anticonvulsant, antidiabetic, antidiarrheal, anti-inflammatory, antimicrobial, antinociceptive, antioxidant, antiparasitic, antipyretic, antivenom, estrogenic, neuroprotective, nootropic, ovicidal, and wound healing activities. Conclusions. This study proposes that A. lebbeck remains a rich source of phytochemicals with various biological activities which possess outstanding therapeutic benefits to humanity across the world. However, studies are required to estimate the potential side effects. Moreover, mechanistic physiognomies of the isolated compounds with known bioactivities are quite limited; thus, forthcoming research needs to focus on the mechanisms of these active phytochemicals to facilitate their potential enrolling for drug discovery.
Research on Active Overbalanced Control Strategy for Lithium-Ion Battery
Authors: Miao Wenqing , Zhang Shuye , Jiang Yunhao , Ding Wenfang , Wang Lujun
Published: April 2022

In order to improve the voltage balancing effect of the lithium-ion power battery pack, a control strategy based on single-cell voltage overbalanced in the battery pack was proposed. In this method, the voltage of each battery is taken as the criterion of energy balance, and the analog switch and the energy storage inductor are used in the energy balance circuit. The PID control strategy of overequalization is proposed, which fully considers the self-recovery of voltage after the end of the charging process. The voltage balanced system of the lithium-ion power battery pack based on the voltage of a single-battery pack is realized, and the voltage maintains a stable equilibrium state after being balanced. The experimental circuit built in this paper verifies the effectiveness of the proposed method and gives the experimental waveform and data of the balanced charging module, which proves the scheme’s feasibility.
Extensive Study on Hematological, Immunological, Inflammatory Markers, and Biochemical Profile to Identify the Risk Factors in COVID-19 Patients
Authors: Ali Eman T. , Sajid Jabbar Azza , Al Ali Hadeel S. , Shaheen Hamadi Saad , Jabir Majid S. , Albukhaty Salim
Published: April 2022

Background. Tissue damage caused by COVID-19 could be detected by several clinical indicators including hematological, immunological, biochemical, and inflammatory markers. This study was to detect these clinical parameters to reveal the correlation between the factors and their roles in the development of COVID-19, to explore the hazard factors in severe cases. Materials and Methods. A total of 200 participants of both sexes were included in the study, with an age range of (25–72) years, categorized into three main groups: 50 healthy individuals, 62 mild infected patients, and 88 severe infected patients with pneumonia. Different hematological and clinical parameters were included in the analysis (Basrah city, Iraq). Serum levels of interleukin-6 (IL-6), ferritin, and high-sensitivity C-reactive protein (hs-CRP) were assessed for all participants using an enzyme-linked immunosorbent assay (ELISA). The liver, renal, and cardiac functions were assessed by clinical chemistry testing. Results. COVID-19 patients had leukocytosis, with an increased number of neutrophils and a decreased lymphocyte count, according to our findings. In regard to inflammatory parameters, both ESR and hs-CRP showed significant differences between the two groups, whereas IL-6 was significantly higher in the total severe group compared to the other two groups. Biochemical results revealed that each lactate dehydrogenase (LDH), ferritin, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) had significant changes in the total severe group. Among pneumonic with an O2 requirement and pneumonic without an O2 requirement, there were significant differences in immunological and inflammatory markers (

p
>
0.05

). The neutrophils-lymphocytes ratio (NLR) was highly elevated in severe who required O2. Moreover, IL-6, lymphocytes, and neutrophils were possible risk factors for COVID-19 infection, with the strongest influence of IL-6 with a high odds ratio (OR: 24.138, 95% CI: 8.437–30.65,

p
<
0.01

). Furthermore, there were significant correlations among the indicators. Conclusion. Each of IL-6, lymphocytes, and neutrophils might represent major factors in the severity of COVID-19 and IL-6 plays the main role in inducing the inflammatory and pathophysiology process that is known as the cytokine storm.
The Mechanism of Osthole in the Treatment of Gastric Cancer Based on Network Pharmacology and Molecular Docking Technology
Authors: Ju Yunjie
Published: April 2022

Objective. To study the mechanism of osthole in GC based on network pharmacology and molecular docking. Methods. The potential targets of osthole were predicted through the TCM System Pharmacology Analysis Platform, SwissTargetPrediction, and PharmMapper database. Targets related to gastric cancer were obtained through OMIM and GeneCard database. The online tool Venny 2.1.0 was used to screen GC and common target of osthole. The targets after the intersection of drugs and diseases were entered into the STRING database, and the protein interaction network was constructed, and the core targets with high correlation were screened out. The WebGestalt website was used to GO functional enrichment and KEGG pathway analysis and visualization with KOBAS website. The Cytoscape 3.8.2 was employed to draw the C-T-P-D (compound–target–pathway–disease) network visualization diagram. Finally, molecular docking validation was performed using PyMOL 2.5 and Discovery Studio Standalone. Results. Through prediction and screening, 108 corresponding targets were screened from osthole, and 173 targets were obtained after intersecting with gastric cancer targets. Among them, the top ten targets were the core targets of this study, including MAPK3, MAPK1, SRC, AKT1, HSP90AA1, RXRA, ESR1, RELA, MAPK14, and EGFR. The analysis of GO, KEGG, and PPI showed that the mechanism of action of osthole against GC may be closely related to the regulation of the PI3K signaling pathway. The results of molecular docking showed that osthole had a good affinity with MAPK3, which is a crucial part of the PI3K signaling pathway. Conclusion. This study preliminarily revealed the targets and related pathways of osthole in the treatment of gastric cancer and provided a new idea for further exploration of osthole targeted prevention and treatment of gastric cancer.
A Lead‐Lag Relationship and Forecast Research between China’s Crude Oil Futures and Spot Markets
Authors: Zhang Chi , Pan Dandan , Yang Mingyan , Pu Zhengning
Published: April 2022

The integration of the global economy has led to an increasingly strong connection between the futures and spot markets of commodities. First, based on one‐minute high‐frequency prices, this paper applies the thermal optimal path (TOP) method to examine the lead‐lag relationship between Chinese crude oil futures and spot from March 2018 to December 2021. Second, we apply the Mixed Frequency Data Sampling Regression (MIDAS) model and indicators such as deviation degree to test the degree of prediction of high‐frequency prices in the futures market to the spot market. The experimental results show that the futures markets lead the spot market most of the time, but the lead effect reverses when major events occur; 60‐minute futures high‐frequency prices are the most predictive of daily spot data; crude oil futures’ predictive power declined after the Covid‐19 outbreak and is more predictive when night trading is available. This study has important implications, not only to guide investors but also to provide empirical evidence and valid information for policy makers.
A Curious Case of Hemolytic Anemia with Pseudoreticulopenia
Authors: Mayer Scott , Srinivasan Nikhilesh , Nguyen Jenny , Spilman Ryan , Scherbak Dmitriy
Published: April 2022

Herein, we present a unique case of a Coombs-negative, steroid-refractory autoimmune hemolytic anemia (AIHA) complicated by pseudoreticulopenia, describe its clinical presentation, histopathologic findings, and management, and review the salient literature. Coombs-negative, steroid-refractory AIHAs represent fewer than 1% of all AIHAs. Diagnosis of the disease is difficult and often delayed due to the pursuit of alternate diagnoses following a negative Coombs test. However, when suspicion remains high for an autoimmune process, the super-Coombs test may be utilized for the diagnosis of AIHA that the traditional Coombs test fails to detect. A majority of cases respond to rituximab as the indicated second-line therapy, but delays in diagnosis and subsequent treatment may increase morbidity. Reticulopenia may be associated with AIHAs secondary to bone marrow dysfunction, but this patient had a normal function marrow confirmed on biopsy. Indeed, reticulopenia in this case was a diagnostic conundrum that further obscured the diagnosis and delayed treatment. Ultimately, reticulopenia was determined to be pseudoreticulopenia secondary to an alteration in the maturation of the erythroid lineage due to an independent, newly diagnosed pernicious anemia. The interaction of these multiple coexisting disease processes is not previously described in the literature. Increased physician awareness of steroid-refractory, Coombs-negative AIHA, and the development of pseudoreticulopenia as a laboratory finding in pernicious anemia may help to improve patient outcomes.
Coagulation Parameters in Human Immunodeficiency Virus Infected Patients: A Systematic Review and Meta-Analysis
Authors: Getawa Solomon , Adane Tiruneh
Published: April 2022

Background. Coagulation abnormalities are common complications of human immunodeficiency virus (HIV) infection. Highly active antiretroviral treatment (HAART) decreased the mortality of HIV but increased coagulopathies. HIV-related thrombocytopenia, prolonged prothrombin time (PT), activated partial thromboplastin time (APTT), and high D-dimer level commonly manifested in patients with HIV. Thus, this study is aimed to compare coagulation parameters of HAART-treated and HAART-naïve HIV-infected patients with HIV-seronegative controls. Methods. A systematic literature search was conducted using the databases PubMed/MEDLINE, Embase, Web of Science, and Google Scholar of studies published until July 2021. The primary outcome of interest was determining the pooled mean difference of coagulation parameters between HIV-infected patients and seronegative controls. The Joana Briggs Institute (JBI) critical appraisal tool was used for quality appraisal. Statistical analyses were performed using Stata11.0 software. The statistical results were expressed as the effect measured by standardized mean difference (SMD) with their related 95% confidence interval (CI). Results. A total of 7,498 participants (1,144 HAART-naïve patients and 2,270 HAART-treated HIV-infected patients and 3,584 HIV-seronegative controls) from 18 studies were included. HIV-infected patients (both on HAART and HAART-naive) exhibited significantly higher levels of PT than HIV-seronegative controls (SMD = 0.66; 95% CI: 0.53–0.80 and SMD = 1.13; 95% CI: 0.60–2.0, respectively). The value of APTT was significantly higher in patients with HIV on HAART than in seronegative controls. However, the values of PLT count, APTT, and fibrinogen level were significantly higher in seronegative controls. Besides, the level of fibrinogen was significantly higher in HAART-treated than treatment-naïve patients (SMD = 0.32; 95%CI: 0.08, 0.57). Moreover, the level of APTT and PT had no statistical difference between HAART and HAART-naïve HIV-infected patients. Conclusions. This study identified that HIV-infected patients are more likely to develop coagulation abnormalities than HIV-seronegative controls. Therefore, coagulation parameters should be assessed regularly to prevent and monitor coagulation disorders in HIV-infected patients.
Design Optimization of IoT-Assisted Intelligent Education Soft Robot Based upon Improved GA
Authors: Wu Qun , Ji Gaoqing
Published: April 2022

A soft robot is a kind of robot designed to simulate mollusks. It has the characteristics of degrees of freedom, strong adaptability, and high flexibility and safety. The main purpose of this paper is to study the intelligent education assistance of soft robots and then combine the application of improved genetic algorithm and the Internet of Things technology in soft robots to improve its performance and effect. Therefore, this paper designs the optimal guidance strategy through the NSGA genetic algorithm and then combines the improved genetic algorithm and the application of the Internet of Things technology in the flexible actuator and FEA actuator of the soft robot. In order to optimize the performance of the IoT-assisted intelligent education software robot based on the improved genetic algorithm, the genetic algorithm simulation test experiment, the rolling motion simulation experiment of the bionic software robot, and the inflating and exhausting experiment of the base section of the software robot are designed and analyzed. Through the analysis of the data obtained from the experiment, this paper finally designs a set of controlled experiments to verify its teaching ability. The experimental results show that the students’ evaluation of the IoT intelligent education software robot education method based on the improved genetic algorithm designed in this paper is 16.31 points higher than that of the traditional education method. Compared with the traditional teaching, the scores of the students after the IOT intelligent education software robot teaching based on the improved genetic algorithm is 11.16 points higher.
Systematic Construction and Validation of an Immune‐Related Gene‐Based Model to Predict Prognosis for Ovarian Cancer
Authors: Fu Yihan , Sun Hong
Published: April 2022

Ovarian cancer (OC) is a malignancy with poor prognosis, stubborn resistance, and frequent recurrence. Recently, it has been widely recognized that immune‐related genes (IRGs) have demonstrated their indispensable importance in the occurrence and progression of OC. Given this, this study aimed to identify IRGs with predictive value and build a prognostic model for a more accurate assessment. First, we obtained transcriptome and clinical information of ovarian samples from both TCGA and GTEx databases. After integration, we figured out 10 genes as immune‐related prognostic genes (IRPGs) by performing the univariate Cox regression analysis. Subsequently, we established a TF‐associated network to investigate its internal mechanism. The prognosis model consisting of 5 IRPGs was constructed later by lasso regression analysis. The comparison of the score with the clinical factors validated its independence and superiority in OC’s prognosis. Moreover, the association between the signature and immune cell infiltration demonstrated its ability to image the immune situation of the tumor microenvironment. Finally, the reliability of the risk model was confirmed by the GEO cohort. Together, our study has constructed an independent prognostic model for OC, which may deepen the understanding of the immune microenvironment and help present novel biomarkers or ideas for targeted therapy.
A Rare Case of a Translocation-Associated Perivascular Epithelioid Cell Neoplasm (PEComa)
Authors: Pereira Kimberly , Inamdar Arati A. , Zaveri Aditi , Teitelbaum Jonathan E. , Shertz Wendy , Belitsis Kenneth
Published: April 2022

A perivascular epithelioid cell tumor (PEComa) is a rare mesenchymal neoplasm composed of perivascular epithelioid cells with distinctive histologic, immunohistochemical, and genetic features. PEComas arising from various anatomical sites have been reported, but gastrointestinal PEComas are extremely rare entities. Here, we discuss the clinical and pathological features of a gastrointestinal PEComa with a transcription factor E3 (TFE3) translocation in a 17-year old adolescent male with a clinical presentation of abdominal pain and gastrointestinal bleeding. Our case report provides insight into this rare entity as well as discusses the pathophysiological aspects of TFE3-SFPQ-associated GI PEComas and their management.