bluuebunny/crossref_metadata_2025 huggingface dataset:
Predictive Analysis of Serum NO, PGI2, and Ox-LDL Levels on Disease Progression in Patients with Lacunar Cerebral Infarction
Authors: Tao Xiaoxiao , Qiu Chenfeng , Feng Xuewen , Wang Linlin
Published: April 2022

Objective. To analyze and predict the progress of patients with lacunar infarction by analyzing the levels of serum NO, PGI2, and Ox-LDL produced by endothelial cells. Methods. 138 patients with lacunar infarction and 34 healthy people were selected. The selected samples were divided into progressive group, nonprogressive group, and control group for biochemical test and endothelial function test. The levels of serum NO, PGI2, and Ox-LDL were obtained. The observation indexes of different groups were compared for statistical analysis and multivariate logistic regression analysis. Results. The indexes of LI patients in the nonprogression group were different from those in the control group. The content of Ox-LDL in the nonprogression group was higher than that in the control group, while the indexes of serum NO and PGI2 were lower than that in the control group. The level of Ox-LDL in LI patients in the progressive group was much higher than that in healthy people in the control group seven days after admission, while the levels of serum NO and PGI2 were lower, and the difference of serum on was more obvious. The level of Ox-LDL in the progressive group was much higher than that in the nonprogressive group, while the levels of serum NO and PGI2 in the progressive group were lower, and the level of serum NO was significantly different from that in the nonprogressive group.

Ox
−
LDL
>
76.48
 
U
/
L

,

NO
>
55.24
 
ummol
/
L

, and

PGI
2
>
29.78
 
ng
/
L

were independent risk factors for the progression of lacunar infarction. Conclusion. Because the patients with lacunar infarction have endothelium-dependent relaxation disorder, the changes of serum NO, PGI2, and Ox-LDL can be used as the evaluation index of the disease progress of patients with lacunar infarction and can be widely used in clinical detection.
Effects of Dietary Lysine Levels on Growth Performance and Glycolipid Metabolism via the AKT/FoxO1 Pathway in Juvenile Largemouth Bass, Micropterus salmoides
Authors: Huang Dongyu , Liang Hualiang , Ge Xianping , Zhu Jian , Li Songlin , Wang Yongli , Ren Mingchun , Chen Xiaoru
Published: April 2022

A 56-day feeding experiment was conducted to determine the dietary lysine requirement of juvenile largemouth bass (Micropterus salmoides) and investigate the effects of dietary lysine on growth, whole-body composition, and hepatic gene expression related to glycolipid metabolism via the AKT/FoxO1 pathway. The juveniles (

17.34
±
0.02

 g) were fed six graded lysine levels (2.11% (control), 2.56%, 2.92%, 3.33%, 3.68%, and 4.09%, dry diet). The results showed that the 3.33% dietary lysine level significantly increased the final body weight (FBW), weight gain rate (WGR), and specific growth rate (SGR) and improved the feed conversion ratio (FCR) compared with the control group. The whole-body composition was not significantly affected by dietary lysine levels, while lowest hepatic lipid contents were found in the 2.92% and 3.33% dietary lysine groups. Regarding glycolipid metabolism, compared with the control group, 3.33% dietary lysine improved the protein kinase B (AKT) and inhibited the forkhead box O1 (FoxO1), thus upregulated the pyruvate kinase (PK) mRNA levels to enhance glycolysis. Furthermore, sterol-regulatory element binding protein-1c (SREBP1c) and peroxisome proliferator-activated receptor-γ (PPAR-γ) were downregulated by 3.33% dietary lysine, which caused the downregulation of lipid synthesis-related genes acetyl-CoA carboxylase-1 (ACC) and stearyl-CoA desaturase (SCD) mRNA. In addition, 3.33% dietary lysine promoted the expression of the lipolysis-related genes peroxisome proliferator-activated receptor-α (PPAR-α) and carnitine palmitoyl transferase-1 (CPT1). According to the quadratic regression analysis based on the FCR and SGR values, the optimal dietary lysine levels were estimated to be 3.03% and 3.07% of the diet (6.39% and 6.48% of dietary protein), respectively.
Preclinical Rodent Models of Arthritis and Acute Inflammation Indicate Immunomodulatory and Anti-Inflammatory Properties of Juglans regia Extracts
Authors: Mobashar Aisha , Shabbir Arham , Shahzad Muhammad , Gobe Glenda
Published: April 2022

Juglans regia has been used to treat inflammatory and arthritic disorders in traditional medicine. The present study aimed to investigate the antiarthritic and anti-inflammatory potential of ethanolic leaves extract of J. regia. Arthritis was induced in rodents with Freund’s complete adjuvant. J. regia treatment was started on 8th day of arthritis induction and sustained for 20 days. Acute inflammatory models were developed using carrageenan, histamine, serotonin, and dextran. Qualitative and GC-MS analyses were also performed. Arthritis was determined using an arthritis scoring index and histopathological examination of ankle joints. RT-PCR was performed to determine the expression of pro-inflammatory markers (TNF-α, NF-κB, IL-6, IL-1β, and COX-2) and anti-inflammatory IL-4. PGE2 levels were evaluated using an ELISA. Blood and biochemical parameters were also determined. Paw edema was measured using a digital plethysmometer. Treatment with extracts inhibited arthritic development and attenuated paw edema along with all histopathological parameters. The expression levels of pro-inflammatory cytokines and COX-2 were downregulated, while IL-4 was upregulated. PGE2 levels were also reduced in extract-treated groups. Blood and biochemical parameters were nearly normalized in the treatment groups. Both extracts significantly inhibited carrageenan, histamine, serotonin, and dextran-induced paw edema. Qualitative phytochemical screening and GC-MS analysis confirmed that extracts possessed potential medicinal compounds. In conclusion, ethanol and n-hexane extracts of J. regia leaves have immunomodulatory and anti-inflammatory effects that ameliorate experimentally induced arthritis and edema. The inhibition of autacoids may also be one of the mechanisms inducing the immunomodulatory effect.
Machine Learning Model-Based English Project Learning and Functional Research
Authors: Zhang Xiyue , Chen Guiping
Published: April 2022

Under the background of the rapid development of machine learning and information technology, traditional classroom mode is gradually replaced by media classroom. To tackle abstract and incomprehensible problems and restricted practical teaching in practical teaching, online classroom design principle is utilized to embody the classroom optimization and design principle of English language teaching from the perspective of machine learning based on English project learning. Based on the advantages of digitization, deep learning algorithm is used to establish classroom application model by information input. Besides, English language and relevant language application scenarios are presented from the perspective of machinery. The simulated textbook contents and relevant extended knowledge points are displayed in classrooms by online teaching. The current advantages of Internet communication are combined with machine learning algorithms for field simulations and calculations on the relevant course content of English subjects. The actual operation process in the English learning process is realized through the online form of the network. It ensures the stability and transmission accuracy of online classrooms, reduces information omission and loss during data transmission, and obtains the optimal solution for data simulation in real-time scenarios. Relevant researches demonstrate that machine learning combined with online classroom design breaks through the face-to-face book teaching in traditional classrooms by the dynamic demonstration of life and actual work scenes and entity innovations as well as design. Besides, it stimulates students’ interest in English courses, enhances the overall learning rate, promotes more significant effects of English project learning, and is conducive to the cultivation of comprehensive language talents in the new age.
Study on the Role of College Sports Games and Taekwondo Teaching Based on Information Health
Authors: Zhang Jiaxin , Fang Wei
Published: April 2022

Sports games and taekwondo have hitherto been core facets in colleges and university physical education curricula. This owes to the significant benefits derived from the duo, especially with the increase in sedentary lifestyles, resulting in dreaded repercussions when strenuous exercise is not brought into play. Taekwondo is a type of martial art known to have originated from Korea but has now gained popularity internationally. Some of the known benefits of taekwondo are reducing stress and improving cardiovascular system (CVS) physiology. On the other hand, sports games also help immensely in the physiological and mental health of college and university students. For instance, outdoor games are essential in the pulmonary system as indoor games are for mental health. The purpose of this study is to investigate the role of sports games and taekwondo teaching in colleges and universities. The prospective study was conducted on a sample of 195 students. Both qualitative and quantitative data were collected by random means. The data collected was then analyzed to find the type of distribution. The results indicated a normal distribution with a resultant bell curve. The central tendency and dispersion were calculated. A high level of decision-making, confidence, and fitness was noted in students practicing these activities in their colleges and universities vis-à-vis those who do not. Sports and taekwondo increase the level of training, which enhances the skeletal system to take up and utilize lipids. This process reduces the level of plasma lipids, thus preventing chronic health conditions like obesity and diabetes. In conclusion, sports and taekwondo are essential in maintaining a healthy standard and should always be incorporated into the college and university curriculum.
Improving College English Reading Teaching Efficiency Based on Student Behavior Data Mining and Mobile Edge Computing
Authors: Wan Neng
Published: April 2022

The reading scope of university English reading is constantly expanding, and the teaching content is gradually increasing. Students are unable to grasp the article’s ideas while reading, resulting in an incorrect understanding of the article and the selection of incorrect topics. In English reading, teachers should begin by broadening students’ knowledge, increasing the number of new words they learn on a regular basis, gradually building reading experience, and improving reading efficiency. Data mining (abbreviated as DM) is a method of extracting hidden, unknown, but potentially useful information and knowledge from a large amount of incomplete, noisy, fuzzy, and random practical application data. Based on student behavior DM and mobile edge computing, this paper investigates strategies to improve the efficiency of university English reading instruction. Teachers and students can interact more easily with the help of university English reading teaching based on student behavior DM, and good interpersonal interaction can help students better understand and master the language. It is also beneficial for teachers to provide more tailored guidance for students’ individual university English reading teaching levels and learning abilities, as well as to assist them in developing personalized efficiency improvement strategies. The goal of DM student behavior is to discover knowledge, mine information, and apply rules without making any assumptions, with unknown, useful, and effective results.
Trend Analysis and Prediction on Water Consumption in Southwestern Ethiopia
Authors: Enbeyle Wegayehu , Hamad Abdulsttar Abdullah , Al-Obeidi Ahmed S. , Abebaw Solomon , Belay Assaye , Markos Admasu , Abate Lema , Derebew Bizuwork
Published: April 2022

This study intensively examined the monthly water consumption forecasting performance using advanced time series (ARIMA) models. Thus, this study intends to identify the appropriate ARIMA models to best fit the water consumption data in Southwestern Ethiopia Tepi town and forecast water consumption effectively in the city. The data used for this study was the monthly water consumption in Tepi town from January 2016 to December 2021.The data were converted to returns to enhance their statistical properties and the returns were used to fit a mean equation. The monthly average water consumption in Tepi Town is 77227.8 meters cubic. Both original and transformed data show the trend of water consumption is increasing over time. Several ARIMA models were fitted to the data, and it emerged that the most adequate model for the data was ARIMA (1, 1, 1) based on the model selection criterion. The parameters for ARIMA models were estimated using the Ordinary Least Squares Estimation (OLS) method. The model was used to forecast the consumption for the next ten months and to advise Tepi town Water Company Limited in the city to meet the demand of the people. Conclusion. The consumption of water is increasing from December to September.
Teaching Platform for Physical Training of Track and Field Events in Colleges and Universities Based on Data Mining Technology
Authors: Yu Ao , Cho Hyun Choul , Chen Yujue , Chen Xi , Wei Jie , Feng Yufang
Published: April 2022

To a large extent, track and field sports require strong physical fitness of athletes, and athletes’ physical fitness determines their competition results. With the improvement of people’s living standards, athletes can get better nutritional supplements, but competition in track events has gradually become fierce, and physical fitness is extremely important for athletes. Physical training can improve athletes’ endurance, sports coordination, and sensitivity, but coaches should arrange the training intensity reasonably, not exceeding the athlete’s tolerance, to avoid problems such as overloading training causing athletes to be injured and sports age shortened. Traditional track and field training methods are no longer suitable for the physical development of modern athletes. This paper mainly studies the college track and field sports training teaching platform based on data mining technology. By using data mining technology, this paper constructs a track and field training platform in colleges and universities. Therefore, this paper designs a teaching platform for physical training in track and field events and puts the teaching platform into training teaching. It uses data mining technology to collect athletes’ sports characteristics and analyze athletes. The physical parameters and movement norms of the people develop a personalized training program for them.
Identification of a Novel 15q21.1 Microdeletion in a Family with Marfan Syndrome
Authors: Yang Rencong , Zhang Wu , Lu Hua , Liu Jinlong , Xia Yu , Liao Shengjie , Li Xiaohui , Zhang Xiaoshen , Fan Xiaoping , Wang Chaojie
Published: April 2022

Background. Marfan syndrome (MFS) is a connective tissue disease involving multiple systems, with thoracic aortic aneurysm (TAA) as the most common life-threatening manifestation. Method. A pedigree with TAA was investigated, and peripheral venous blood was extracted from six family members. After whole exome sequencing (WES) and chromosomal microarray analysis (CMA) in these individuals, bioinformatics and inheritance analyses were performed. Result. WES revealed a novel, small, 0.76 Mb microdeletion in 15q21.1, which cosegregated with the disease phenotype in the family and led to the haploinsufficiency of the fibrillin 1 (FBN1) gene, which is associated with MFS. This small copy number variant (CNV) was confirmed by CMA. Conclusion. Our study expands the phenotypic spectrum of the pathogenic CNV associated with MFS, thereby facilitating clinical genetic diagnosis and future genetic counseling for this family.
Clinical Evaluation of Levetiracetam in the Treatment of Epilepsy
Authors: Wu Haohao , Liu Jia , Qian Fang , Yang Junsu , Wang Yue , Guan Shaoyong
Published: April 2022

Objectives. Epilepsy is a chronic neurological disorder that is characterized by episodes of seizure. Methods. In this study, patients with status epilepticus in the Intensive Care Unit of the Department of Neurology of Qujing First People’s Hospital were collected and treated with levetiracetam injection, continuous bedside EEG monitoring (cEEG) technology, and quantitative EEG (qEEG) technique. The inhibitory effects of different doses of levetiracetam injection and sodium valproate on abnormal discharge, the improvement of clinical symptoms, the incidence of adverse reactions, and prognosis were monitored, analyzed, and compared. Results. Compared with the experimental group of sodium valproate, 1000 mg/d levetiracetam group and 1500 mg/d levetiracetam group had a high probability of successful symptom control and a short control time. The patients had a low recurrence rate and a long recurrence time, and the probability of abnormal discharge in EEG was low. Conclusions. The recording results showed that levetiracetam could significantly inhibit the abnormal discharge of patients. Compared with sodium valproate, high-dose levetiracetam is a drug with a rapid effect, good effect, and long action time.
Efficient Energy Utilization Algorithm through Energy Harvesting for Heterogeneous Clustered Wireless Sensor Network
Authors: Getahun Masresha , Azath M. , Sharma Durga Prasad , Tuni Amin , Adane Abel
Published: April 2022

The usefulness of wireless sensor networks has fascinated the world’s attention. Usage of low-power microcontrollers and wireless sensors to handle real-world problems such as environmental, medicinal, and structural monitoring has exploded. Wireless sensor nodes are extremely tiny and are designed for low-duty applications such as recording physical characteristics. Wireless sensor network operations such as sensing, calculations, and communication take extensively more energy than these low-powered sensor nodes. They are used both in attainable and inaccessible areas and are usually powered by batteries. Since the sensor is powered by batteries, replacing and charging the battery after its depletion are challenging. Manual battery replacement is hampered by geographical restrictions, which results in significant reduction of wireless sensor network performance and longevity. As a result, this study addresses the energy-constrained wireless sensor networks by creating a technological model to a heterogeneous clustered wireless sensor network in an outdoor application using solar-enabled energy harvesting photovoltaic cells. Due to lack of energy, this effort was employed to overcome the challenges of relatively restricted processing performance and limited radio frequency transmission bandwidth. The program was created to efficiently utilize the energy produced from solar panels and charge the batteries in a variety of ways. The algorithm also controls the discharging process. According to the results of extensive investigation and experimentation, the sensor is continuously operated even when there is no solar light. The batteries are charged on bright day and discharged at night and on overcast days. The algorithm is used to govern the energy supplement to rechargeable lithium-ion polymer batteries as well as a load (sensor). In the worst scenario (no solar light/cloudy, and sensors reporting data every 30 minutes), the present cluster head sensor with 6 ordinary nodes in the cluster lasts just 16.6667 days. But the life duration of the newly designed model algorithm has been raised to 54.16667 days. The normal sensor node’s life duration has also been enhanced from 50 to 91.66667 days.
Clinical Nursing Experience Sharing of Patients with Severe Lung Injury Caused by Gas Poisoning
Authors: Li Lixia
Published: April 2022

Objective. To study the first aid and nursing methods of patients with coal gas poisoning and explore the nursing effect. Method. The clinical data of 50 patients with carbon monoxide poisoning admitted to the emergency department of our hospital from February 2011 to August 2016 were retrospectively analyzed. The patients with severe toxic pulmonary edema were selected as the sample population, and all patients were equally divided by drawing lots. The two groups were divided into the study group and control group. The patients in the control group were given routine nursing intervention, while the patients in the study group were given routine nursing intervention. After the timely treatment of doctors in our hospital and the careful nursing of nursing staff, 45 patients in 50 patients were cured and discharged; 3 patients died of asphyxia due to long poisoning time. Two patients died of severe complications. Result. The total effective rate and oxygenation index of the study group were significantly different from those of the control group (

P
<
0.05

). Conclusion. In the clinical nursing work of patients with severe carbon monoxide toxic pulmonary edema, the nursing effect of emergency cluster therapy is ideal. Patients with carbon monoxide poisoning have obvious age, gender, poisoning causes, and seasonal characteristics. It is an important means to carry out targeted publicity and education in daily life and take effective preventive measures to prevent carbon monoxide poisoning. Once poisoning occurs, timely treatment should be carried out, and careful nursing should be provided to help patients get rid of the danger.
In Silico Identification and Mechanism Exploration of Active Ingredients against Stroke from An-Gong-Niu-Huang-Wan (AGNHW) Formula
Authors: Song Lei , Wu Qihui , Fu Xiaomei , Wang Wentao , Dai Zhao , Gu Yong , Zhuo Yue , Fang Shuhuan , Zhao Wei , Wang Xiaoyun , Wang Qi , Fang Jiansong
Published: April 2022

An-Gong-Niu-Huang-Wan (AGNHW) is a well-known formula for treating cerebrovascular diseases, with roles including clearing away heat, detoxification, and wake-up consciousness. In recent years, AGNHW has been commonly used for the treatment of ischemic stroke, but the mechanism by which AGNHW relieves stroke has not been clearly elucidated. In the current study, we developed a multiple systems pharmacology-based framework to identify the potential antistroke ingredients in AGNHW and explore the underlying mechanisms of action (MOA) of AGNHW against stroke from a holistic perspective. Specifically, we performed a network-based method to identify the potential antistroke ingredients in AGNHW by integrating the drug-target network and stroke-associated genes. Furthermore, the oxygen-glucose deprivation/reoxygenation (OGD/R) model was used to validate the anti-inflammatory effects of the key ingredients by determining the levels of inflammatory cytokines, including interleukin (IL)-6, IL-1β, and tumor necrosis factor (TNF)-α. The antiapoptotic effects of the key ingredients were also confirmed in vitro. Integrated pathway analysis of AGNHW revealed that it might regulate three biological signaling pathways, including IL-17, TNF, and PI3K-AKT, to play a protective role in stroke. Moreover, 30 key antistroke ingredients in AGNHW were identified via network-based in silico prediction and were confirmed to have known neuroprotective effects. After drug-like property evaluation and pharmacological validation in vitro, scutellarein (SCU) and caprylic acid (CA) were selected for further antistroke investigation. Finally, systems pharmacology-based analysis of CA and SCU indicated that they might exert antistroke effects via the apoptotic signaling pathway and inflammatory response, which was further validated in an in vitro stroke model. Overall, the current study proposes an integrative systems pharmacology approach to identify antistroke ingredients and demonstrate the underlying pharmacological MOA of AGNHW in stroke, which provides an alternative strategy to investigate novel traditional Chinese medicine formulas for complex diseases.
An Intelligent Evaluation Model of Enterprise Financial Data Quality Based on Artificial Neural Network
Authors: Liu Chang
Published: April 2022

With the progress of information technology and the rapid development of economy, the importance of financial data quality evaluation in social and economic activities is increasing day by day. Based on the present situation of enterprise development and machine learning method, this paper constructs an intelligent evaluation model of enterprise financial data quality. This paper expounds the research methods of financial quality evaluation, including the evaluation method based on financial indicators and the evaluation method based on nonfinancial indicators. This paper introduces the basic theory of financial quality evaluation. And make use of the company’s report data and annual report text data, respectively. In this paper, the evaluation subject has changed from a specialized agency to a public evaluation group, the evaluation object should change from conformity quality to practical quality, and the evaluation method should also change from rule-based expert inference algorithm to cognitive-based social calculation method. The realization of these changes needs the support of intelligent evaluation mode, which consists of three main links: identification and classification management of evaluation subjects, measurement of evaluation objects, and use and standardization of intelligent technologies. Finally, the financial quality evaluation is summarized and prospected, hoping to contribute to the research of the company’s financial quality evaluation.
Daily Physical Activity of College Students Based on Embedded Accelerometer
Authors: Yang Yang , Ma Jianxun
Published: April 2022

With the development of microelectronics and sensor technology, sensors can be widely integrated into mobile phones and mobile devices, and the use of accelerator sensors to monitor human movement has a wide range of application prospects. The human body’s behavior in daily activities is closely related to the individual’s physical and mental health. Therefore, scientifically preparing and monitoring the daily exercise of the human body is very important for fitness planning and improving physical health. This article introduces an embedded acceleration sensor to investigate the daily physical activities of college students. The article lists several accelerometers commonly used to detect human physical activity, confirms the basic status of the human body motion database, and obtains the database construction process. This article selects 100 students from a certain city who participate in resources and use acceleration sensors to detect their daily physical activities for a week. The test result is that among the students surveyed, the wearing time of computer students is 5228.1 minutes for boys and 5557.3 minutes for girls; sports health science students wear 5028.9 minutes for boys and 4878.3 minutes for girls; police school students wear 6598.7 minutes for boys and 6524.1 minutes for girls.
A Hybrid Model for Leaf Diseases Classification Based on the Modified Deep Transfer Learning and Ensemble Approach for Agricultural AIoT-Based Monitoring
Authors: Saberi Anari Maryam
Published: April 2022

As possible diseases develop on plant leaves, classification is constantly hampered by obstacles such as overfitting and low accuracy. To distinguish healthy products from defective ones, the agricultural industry requires precise and error-free analysis. Deep convolutional neural networks are an efficient model of autonomous feature extraction that has been shown to be fairly effective for detection and classification tasks. However, deep convolutional neural networks often require a large amount of training data, cannot be translated, and need a number of parameters to be specified and tweaked. This paper proposes a highly effective structure that can be applied to classifying multiple leaf diseases of plants and fruits during the feature extraction step. It uses a deep transfer learning model that has been modified to serve this purpose. In summary, we use model engineering (ME) to extract features. Multiple support vector machine (SVM) models are employed to enhance feature discrimination and processing speed. The kernel parameters of the radial basis function (RBF) are determined based on the selected model in the training step. PlantVillage and UCI datasets were used to analyze six leaf image sets containing healthy and diseased leaves of apple, corn, cotton, grape, pepper, and rice. The classification process resulted in approximately 90,000 images. During the experimental implementation phase, the results show the potential of a powerful model in classification operations, which will be beneficial for a variety of future leaf disease diagnostic applications for the agricultural industry.
Synthesis of Benzimidazole-Sulfonyl Derivatives and Their Biological Activities
Authors: Mulugeta Endale , Samuel Yoseph
Published: April 2022

Currently, the synthesis of new compounds with potential bioactivities has become a central issue in the drug discovery arena. Among these new compounds, benzimidazole-sulfonyl scaffolds have vital applications in the fields of pharmaceuticals industries. Benzimidazole and sulfonyl compounds have remarkable biological activities, such as antibacterial, antifungal, anti-inflammatory, antiproliferative, carbonic anhydrase inhibitory, and α-amylase inhibitory activities. Furthermore, recent literature mentions the synthesis and bioactivities of some benzimidazole-sulfonyl hybrids. In this review, we focus on reviewing the synthesis of these hybrid scaffolds and their various types of biological activities of the compounds.
Activation of LRP6 with HLY78 Attenuates Oxidative Stress and Neuronal Apoptosis via GSK3β/Sirt1/PGC-1α Pathway after ICH
Authors: Jin Peng , Qi Dongqing , Cui Yuhui , Lenahan Cameron , Deng Shuixiang , Tao Xiaogen
Published: April 2022

Background. Oxidative stress and neuronal apoptosis have important roles in the pathogenesis after intracerebral hemorrhage (ICH). Previous studies have reported that low-density lipoprotein receptor-related protein 6 (LRP6) exerts neuroprotection in several neurological diseases. Herein, we investigate the role of LRP6 receptor activation with HLY78 to attenuate oxidative stress and neuronal apoptosis after ICH, as well as the underlying mechanism. Methods. A total of 199 CD1 mice were used. ICH was induced via injection of autologous blood into the right basal ganglia. HLY78 was administered via intranasal injection at 1 h after ICH. To explore the underlying mechanism, LRP6 siRNA and selisistat, a Sirt1 selective antagonist, were injected intracerebroventricularly at 48 h before ICH induction. Neurobehavioral tests, Western blot, and immunofluorescence staining were performed. Results. The expression of endogenous p-LRP6 was gradually increased and expressed on neurons after ICH. HLY78 significantly improved the short- and long-term neurobehavioral deficits after ICH, which was accompanied with decreased oxidative stress and neuronal apoptosis, as well as increased expression of p-GSK3β, Sirt1, and PGC-1α, as well as downregulation of Romo-1 and C-Caspase-3. LRP6 knockdown or Sirt1 inhibition abolished these effects of HLY78 after ICH. Conclusion. Our results suggest that administration of HLY78 attenuated oxidative stress, neuronal apoptosis, and neurobehavioral impairments through the LRP6/GSK3β/Sirt1/PGC-1α signaling pathway after ICH.
Combined Efficacy of Balloon Occlusion and Uterine Artery Embolization on Coagulation Function in Patients with High-Risk Placenta Previa during Cesarean Section
Authors: Xu Xiaoli , Zhu Xiayun
Published: April 2022

Purpose. The present study was performed in order to investigate the conbined effect of balloon occlusion and uterine artery embolization on coagulation function in patients with high-risk placenta previa during cesarean section. Methods. There involved a total of 38 patients with high-risk placenta previa undergoing cesarean section in our hospital from August 2019 to January 2021. The patients enrolled were randomly divided into study group (19 cases, receiving balloon occlusion combined with uterine artery embolization) and control group (19 cases, receiving conventional cesarean section). The operation time, intraoperative blood loss, plasma injection volume and hospital stay of the two groups were recorded. Moreover, the postoperative coagulation function indexes, including thrombin time (TT), fibrinogen (FBI), activated partial thromboplastin time (APTT) and prothrombin time (PT), were monitored and compared. Neonatal Apgar score and postoperative complications of the two groups were regarded as parameters for comparison. Results. The intraoperative blood loss, plasma injection volume and hospital stay of the study group were significantly lower compared with the control group (

P
<
0.05

), whereas the operation time of the two groups was comparable (

P
>
0.05

). Compared with the control group, the levels of TT, APTT and PT were lower while the level of FBI was higher in the study group (

P
<
0.05

). The Apgar 1-min and 5-min scores of newborns were compared between the two groups (

P
>
0.05

). However, the incidence of postoperative complications in the study group showed evidently lower outcomes compared with the control group (

P
<
0.05

). Conclusion. The combined approach of balloon occlusion and uterine artery embolization offered potential for improving the coagulation function of patients with high-risk placenta previa during cesarean section. In addition, the approach reduced the amount of blood loss and plasma injection, shortened the length of hospital stay, which was believed available for wide clinical application.
Aerobic Exercise Fatigue Detection Based on Spatiotemporal Entropy and Label Technology
Authors: Zhang Lei , Qiu Liefeng
Published: April 2022

Excessive exercise can strengthen the body and make people happy, but it can also cause physical injury. To address this issue, this paper proposes the TFD-SE (Three-Frame Difference Spatiotemporal Entropy) algorithm and the LB (Label Propagation) algorithm, which are both based on SE (spatiotemporal entropy) and label technology. The TFD-SE algorithm calculates the difference image using the three-frame difference method, then calculates the SE of pixels in the difference image, and performs morphological filtering and threshold segmentation, allowing it to detect moving objects effectively. The significance value of unlabeled nodes in the image is calculated using the LB algorithm. In both qualitative and quantitative comparisons, the experimental results show that both algorithms outperform other classical algorithms in terms of detection performance.
Analyzing the Effect of Badminton on Physical Health and Emotion Recognition on the account of Smart Sensors
Authors: Chang Chun , Chen Kaihua , Cao Jianjun , Wu Qian , Chen Hemu
Published: April 2022

Emotional ability is an important symbol of human intelligence. Human’s understanding of emotions, from subjective consciousness to continuous or discrete emotional dimensions, and then to physiological separability, has shown a trend of gradually diverging from psychological research to the field of intelligent human-computer interaction. This article is aimed at studying the effects of smart sensor-based emotion recognition technology and badminton on physical health. It proposes a method of using smart sensor technology to recognize badminton movements and emotions during the movement. And the impact of emotion recognition based on smart sensors and badminton sports on physical health is carried out in this article. Experimental results show that the emotion recognition technology based on smart sensors can well recognize the changes in people’s emotions during badminton sports, and the accuracy of emotion recognition is higher than 70%. At the same time, experiments show that badminton can greatly improve people’s physical fitness and strengthen people’s physique.
Spatiotemporal Evolution of Regional Green Economy under Administrative Division Adjustment on Applications of Artificial Intelligence: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area
Authors: Wang Xiaoxiao , Shi Ruiting , Shi Qiulu
Published: April 2022

In the context of aggravating environmental problems, the concept of green economy, advocated by policies introduced to solve such problems, is being increasingly recognized and emphasized. In addition, digital technologies, represented by the Internet, big data, and artificial intelligence, are constantly integrating with economic and social fields, gradually becoming an important driving force for economic and social development, and also bringing certain positive environmental effects. This study investigated the spatiotemporal evolution characteristics of the green economy of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in the past three decades (1987-2018) under the background of administrative division adjustment and artificial intelligence development. First, the overall green economic development level of the GBA from 1987 to 2018 was analyzed using the superefficiency slack-based measure model. Then, the spatial pattern evolution characteristics and regional differences of green economy in the GBA in this period were assessed by the exploratory spatial data analysis model. According to the results of this study, (1) the green economic development levels of Guangzhou, Shenzhen, Foshan, Zhaoqing, and Hong Kong in the GBA always followed an increasing trend from 1987 to 2018. To be specific, Foshan, Zhongshan, Hong Kong, and Macao ranked among the top areas in the GBA in terms of average green economic development level; moreover, a strong positive correlation was found between economic strength and green economic development. (2) The overall green economic development level of the GBA roughly followed a trend of first decreasing and then increasing, fluctuating around 0.45 on average. (3) Clearly, the green economy of various cities in the GBA showed certain correlations in 1987-2005, but with great fluctuations. However, no spatial correlation was observed after 2006. On the whole, in the GBA, the spatial agglomeration of green economy was weak. (4) There was a general lack of obvious spatial agglomeration in the overall green economic development of the GBA, especially in western Guangdong. In contrast, spatial agglomeration was obvious in eastern Guangdong.
Tissue-Type Plasminogen Activator-Inhibitor Complex as an Early Predictor of Septic Shock: A Retrospective, Single-Center Study
Authors: Zhong Lincui , Dou Jianlin , Lin Qingwei , He Longping , Zeng Qingbo , Song Jingchun
Published: April 2022

Background. Sepsis can progress to septic shock and death, and identifying biomarkers of this progression may permit timely intervention to prevent it. This study explored whether levels of tissue-type plasminogen activator-inhibitor complex (t-PAIC) in serum can predict septic shock early. Methods. We retrospectively analyzed 311 sepsis patients who had been admitted to the intensive care unit (ICU) at our tertiary care hospital between May 2018 and April 2021, and we divided them into those who progressed to septic shock (

n
=
203

) or not (

n
=
108

) based on sepsis-3 definition. After matching patients in the two groups based on propensity scoring, we screened for risk factors of septic shock using logistic regression. We assessed potential predictors of such shock based on the area under the receiver-operating characteristic curve (AUC), Kaplan-Meier survival curves, and correlation analysis. Results. After propensity score matching to generate two equal groups of 108 patients, we found that serum t-PAIC was significantly higher in septic shock patients. Uni- and multivariate logistic regression identified t-PAIC as an independent risk factor for septic shock (OR 1.14, 95% CI 1.09–1.19,

P
<
0.001

) and a biomarker that predicted it with an AUC up to 0.875 (95% CI, 0.829-0.920). Based on the optimal cut-off of

t
‐
PAIC
=
17.9
 
ng
/
mL

, we found that patients at or above this threshold had significantly higher lactate levels and scores on the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA). Such patients also had significantly worse survival (HR 2.4, 95% CI 1.38–4.34,

P
=
0.004

). Spearman’s correlation coefficients were 0.66 between t-PAIC and lactate, and 0.52 between t-PAIC and SOFA. Conclusions. Serum levels of t-PAIC may be an independent risk factor for septic shock, and they may correlate with the severity of such shock.
A Neural Network Approach for Chinese Sports Tourism Demand Based on Knowledge Discovery
Authors: Qi Libin , Tang Yaohan
Published: April 2022

With the vigorous development of the Chinese economy and people’s pursuit of quality, sports activities of people pursuit are no longer limited to simple physical exercise, but a way that pursues higher-quality sports tourism. As a new industry, it cannot guarantee that sports tourism will be accepted by all people, and it will be limited by geographical, economic, time, and other conditions. The participation number of Chinese sports tourism is more concerned by organizers or operators. Predicting the participation number of sports tourism based on the knowledge discovery method is meaningful and economical work. In this paper, a variety of sports tourism data are classified by clustering method, and the categories with similar characteristics are classified. Then, the convolution and long short-term memory hybrid neural network are used to extract the spatial and temporal information of sports tourism characteristics, which completes the prediction of Chinese sports tourism categories. The research results show that the clustering method has high accuracy for the classification of sports tourism categories, and the weights occupied by the categories are relatively uniform. The ConvLSTM neural network also has obvious advantages in predicting Chinese sports tourism methods. The largest error is only 2.89%, and the correlation coefficient also reaches 0.98, which is enough to be trusted for the prediction of Chinese sports tourism categories.
Sidelobe Reduction in NC-OFDM-Based CRNs Using Differential Evolution-Assisted Generalized Sidelobe Canceller
Authors: Ahmed Rashid , Gul Noor , Ahmed Saeed , Khan Muhammad Sajjad , Kim Su Min , Kim Junsu
Published: April 2022

Noncontiguous orthogonal frequency division multiplexing (NC-OFDM) is considered a suitable candidate for the cognitive radio network (CRN) to accomplish efficient data transmission. The NC-OFDM allows secondary users (SUs) to access the primary user (PU) spectrum while being detected idle. However, interference may occur in the adjacent frequency bands of the PU due to sidelobes of the SU transmission. The use of cancellation carriers (CCs) and generalized sidelobe canceller (GSC) is a widely adopted technique to tackle the sidelobes. To this end, this paper presents a differential evolution- (DE-) based GSC (DE-GSC) scheme to suppress unwanted sidelobes. At first, in the DE-GSC1 scheme, the adaptive weight vector is calculated using the DE algorithm while considering the complete samples of the sidelobes for optimization. The optimized weights are then added with the original weights to reduce the sidelobe issue. Next, in the DE-GSC2 scheme, selected elements for the adaptive weight vector near the main NC-OFDM signal are computed using the DE to reduce the search space. The performance of the proposed methods in terms of power spectral density (PSD) is compared with some of the recent techniques employing five different scenarios. Simulation results in the presence of single and multiple spectral hole scenarios validate that the proposed DE-GSC1 and DE-GSC2 methods result in enhanced suppression performance compared with the: original signal, simple CC, simple GSC, DE-based CC (DE-CC), and genetic algorithm- (GA-) based CC (GA-CC) schemes.
T-cell Dysfunction upon Expression of MYC with Altered Phosphorylation at Threonine 58 and Serine 62
Authors: Daniel Colin J. , Pelz Carl , Wang Xiaoyan , Munks Michael W. , Ko Aaron , Murugan Dhaarini , Byers Sarah A. , Juarez Eleonora , Taylor Karyn L. , Fan Guang , Coussens Lisa M. , Link Jason M. , Sears Rosalie C.
Published: April 2022

As a transcription factor that promotes cell growth, proliferation, and apoptosis, c-MYC (MYC) expression in the cell is tightly controlled. Disruption of oncogenic signaling pathways in human cancers can increase MYC protein stability, due to altered phosphorylation ratios at two highly conserved sites, Threonine 58 (T58) and Serine 62 (S62). The T58 to Alanine mutant (T58A) of MYC mimics the stabilized, S62 phosphorylated, and highly oncogenic form of MYC. The S62A mutant is also stabilized, lacks phosphorylation at both Serine 62 and Threonine 58, and has been shown to be nontransforming in vitro. However, several regulatory proteins are reported to associate with MYC lacking phosphorylation at S62 and T58, and the role this form of MYC plays in MYC transcriptional output and in vivo oncogenic function is understudied. We generated conditional c-Myc knock-in mice in which the expression of wild-type MYC (MYCWT), the T58A mutant (MYCT58A), or the S62A mutant (MYCS62A) with or without expression of endogenous Myc is controlled by the T-cell–specific Lck-Cre recombinase. MYCT58A expressing mice developed clonal T-cell lymphomas with 100% penetrance and conditional knock-out of endogenous Myc accelerated this lymphomagenesis. In contrast, MYCS62A mice developed clonal T-cell lymphomas at a much lower penetrance, and the loss of endogenous MYC reduced the penetrance while increasing the appearance of a non-transgene driven B-cell lymphoma with splenomegaly. Together, our study highlights the importance of regulated phosphorylation of MYC at T58 and S62 for T-cell transformation.

Implications:

Dysregulation of phosphorylation at conserved T58 and S62 residues of MYC differentially affects T-cell development and lymphomagenesis.
Analysis of the Clinical Characteristics of Hyponatremia Induced by Trimethoprim/Sulfamethoxazole
Authors: Lei Haibo , Liu Xiang , Zeng Jiang , Fan Zhiqiang , He Yang , Li Zuojun , Wang Chunjiang
Published: April 2022

Background: Trimethoprim-sulfamethoxazole (TMP/SMX) causes hyperkalemia, and hyponatremia caused by TMP/SMX is a challenge for clinicians. We described the clinical features of hyponatremia induced by TMP/SMX after collecting cases. Summary: The median age of the 24 patients (10 males and 14 females) was 67 years (range: 28–90 years). Hyponatremia induced by TMP/SMX manifested as nausea (41.7%) and vomiting (29.2%) or asymptomatic hyponatremia (20.8%). The median duration of hyponatremia was 5 days (range: 3–10 days). The median serum sodium concentration was 118 mmol/L (range: 101–128.1 mmol/L). The serum sodium levels gradually returned to the normal range at 4 days (median; range: 2–14 days) after withdrawing TMP/SMX. Key Messages: TMP/SMX-induced hyponatremia is a rare and serious adverse reaction. Clinicians should be aware of electrolyte disturbances caused by TMP/SMX and should always consider electrolyte monitoring.