bluuebunny/crossref_metadata_2025 huggingface dataset:
Gandi Capsule Improved Podocyte Lipid Metabolism of Diabetic Nephropathy Mice through SIRT1/AMPK/HNF4A Pathway
Authors: Zhang Ying , Yao Huijuan , Li Chao , Sun Wei , Chen Xiaofei , Cao Yan , Liu Yue , Liu Yan , Chen Jihui , Qi Jia , Zhang Qiqiang , Zhang Hai , Xu Ajing , Zhang Jian
Published: April 2022

Podocyte lipid accumulation is a potential therapeutic target for diabetic nephropathy (DN). This study was aimed at clarifying the mechanism of Gandi capsule (GDC) ameliorating DN by regulating the lipid metabolism of podocytes. Network pharmacology methods were performed to screen the key molecules and potential targets of GDC for constructing the molecular-protein interaction network of GDC and conducting signal pathway enrichment analysis. GDC was predicted to ameliorate DN through SIRT1/AMPK/HNF4A pathway. Our results showed that GDC improved renal function in db/db mice. Besides, GDC exhibited effectiveness in relieving kidney tissue damage and renal lipid accumulation in db/db mice, and same effects were present in GDC-active ingredient baicalin. We further proved the new role of HNF4A in the lipid metabolism of DN mediated by SIRT1 and AMPK signaling pathways. The results suggested decreased expression of SIRT1 and p-AMPKα in the kidney tissue and increased expression of HNF4A of db/db mice compared with the control group. GDC and baicalin could reverse these expression changes. Furthermore, similar expression changes were observed in the murine podocyte cell line (MPC-5) treated with different concentrations of GDC and baicalin. Our research suggested that GDC and its active ingredient baicalin could alleviate the abnormal lipid metabolism in the kidney of db/db mice and might exert renal protection through the SIRT1/AMPK/HNF4A pathway.
The Study of Maslow’s Hierarchy of Needs Theory in the Doctor-Nurse Integration Teaching Method on Clinical Interns
Authors: Liu Zhe , Xiang Junying , Luo Fangjing , Hu Xiaoli , Luo Ping
Published: April 2022

The student’s attitude towards the doctor-nurse integrated teaching model and Maslow’s hierarchy of needs theory is an important topic in clinical teaching. In this study, choosing 134 intern doctors and practice nurses who intern from January to December 2020. 67 students are selected as the control group, and the traditional interns teaching mode is adopted. 67 students are selected as the experimental group and Maslow’s hierarchy of needs theory to apply in the doctor-nurse integrated teaching mode. The results show that the doctor-nurse integrated teaching model are accepted by most students, and the application of Maslow’s hierarchy of needs theory in the clinical interns has advantages over the traditional model.
Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm
Authors: Nagarajan Karthik , Rajagopalan Arul , Angalaeswari S. , Natrayan L. , Mammo Wubishet Degife
Published: April 2022

Electricity can be provided to small-scale communities like commercial areas and villages through microgrid, one of the small-scale, advanced, and independent electricity systems out of the grid. Microgrid is an appropriate choice for specific purposes reducing emission and generation cost and increasing efficiency, reliability, and the utilization of renewable energy sources. The main objective of this paper is to elucidate the combined economic emission dispatch CEED problem in the microgrid to attain optimal generation cost. A combined cost optimization approach is examined to minimize operational cost and emission levels while satisfying the load demand of the microgrid. With this background, the authors proposed a novel improved mayfly algorithm incorporating Levy flight to resolve the combined economic emission dispatch problem encountered in microgrids. The islanded mode microgrid test system considered in this study comprises thermal power, solar-powered, and wind power generating units. The simulation results were considered for 24 hours with varying power demands. The minimization of total cost and emission is attained for four different scenarios. Optimization results obtained for all scenarios using IMA give a comparatively better reduction in system cost than MA and other optimization algorithms considered revealing the efficacy of IMA taken for comparison with the same data. The proposed IMA algorithm can solve the CEED problem in a grid-connected microgrid.
Elderly Patients with Nondistant Metastatic Pancreatic Head Adenocarcinoma Cannot Benefit from More Radical Surgery
Authors: Chen Li , Jia Lanning , Tian Zhigang , Yang Yang , Zhao Ke
Published: April 2022

Background. The incidence of pancreatic cancer continues to rise globally, with pancreatic head cancer accounting for nearly 60–70%. Pancreatic head cancer occurs mainly in people over the age of 60, and its morbidity and mortality increase with age. We investigated whether these elderly patients with nondistant metastases would benefit more from expanded pancreaticoduodenectomy (EPD) compared with standard pancreaticoduodenectomy (SPD). Methods. 3317 elderly patients with pancreatic head cancer from the SEER database were included in the study based on the inclusion and exclusion criteria. These patients were divided into a nonsurgical group and surgical group (including EPD and SPD). Univariate and multivariate Cox proportional hazards models were applied to identify the independent risk factors for cancer-specific survival (CSS). The survival differences between the nonsurgical group and surgical group were compared. Propensity score matching (PSM) methods were applied to balance covariates and reduce the interference of confounding variables. The two groups of patients were matched in a 1 : 1 ratio, and the covariates between the two groups were compared to verify the matching validity. The survival difference in different groups was compared after the matching analysis. Results. 3317 enrolled patients were divided into the surgical group (n = 984) and nonsurgical group (n = 2333). Before PSM, there were significant differences in overall survival (OS) and CSS between the nonsurgical group and surgical group (median OS: 8 months vs. 20 months,

P
<
0.001

; median CSS: 8 months vs. 22 months,

P
<
0.001

). The multivariate CSS Cox regression analysis demonstrated surgery is an independent risk factor. However, no significant differences were founded between the SPD and EPD groups (median OS: 20 months vs. 22 months,

P
=
0.636

; median CSS: 22 months vs. 22 months,

P
=
0.270

). After PSM, there were also no significant differences in OS and CSS between the SPD and EPD groups (median OS: 23 months vs. 18 months,

P
=
0.415

; median CSS: 26 months vs. 18 months,

P
=
0.329

). Conclusion. This study uses PSM to evaluate the effects of EPD and SPD for elderly patients with nondistant metastatic pancreatic head adenocarcinoma. It found that surgery is an independent prognostic factor, but expanded surgery has no survival advantage for these patients, whereas SPD provides a better survival advantage than EPD. SPD is a reasonable treatment option for these patients.
Dynamics of Positive Frequency Dependent Selection Triggers Selection for Silence
Authors: Hashem I. , De Buck V. , Van Impe J.
Published: April 2022

Positive frequency dependent selection is a natural selection regime where the fitness of a phenotype increases with its frequency in the population. Examples can be typically found in the spread of disease tolerance strategies in a population. A characterizing feature of PFDS is that the focal allele may experience favorable selection only when it becomes more frequent in the population, while being selected against when it is rare. In this paper, by applying a solution concept from evolutionary game theory, we hypothesize that the process of emergence of such alleles triggers selection for silence, where the emergence of alleles that can stay unexpressed for a period of time will be favored by selection forces. We illustrate our hypothesis using a mathematical model of a population of single locus diploid organisms with two alleles where a single mutant of an allele that experiences positive frequency dependent selection appears in a population where the other allele is a dominant. The model results show that the longer the silence interval of the new mutant before its expression, the better its chances of getting fixed in the population. However, this effect will be observable only to a certain limit after which further increase in the interval will not have an effect on the expected fixation rate. Two divergences from the simple scenario are also investigated: the nonsynchronous expression of the focal allele and its emergence in a spatial grid limited by a migration bottleneck. In all cases, it is shown that there is an evolutionary pressure inherent in the dynamics of positive frequency dependent selection on the genes to have a delayed expression. It is hypothesized that the regulation systems of such traits will be under selection for either internal silencing mechanisms or to be triggered by an external environmental condition.
Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals
Authors: Cai Xugang , Pan Jiahui
Published: April 2022

This study proposes a brain-computer interface (BCI)- and Internet of Things (IoT)-based smart ward collaborative system using hybrid signals. The system is divided into hybrid asynchronous electroencephalography (EEG)-, electrooculography (EOG)- and gyro-based BCI control system and an IoT monitoring and management system. The hybrid BCI control system proposes a GUI paradigm with cursor movement. The user uses the gyro to control the cursor area selection and uses blink-related EOG to control the cursor click. Meanwhile, the attention-related EEG signals are classified based on a support-vector machine (SVM) to make the final judgment. The judgment of the cursor area and the judgment of the attention state are reduced, thereby reducing the false operation rate in the hybrid BCI system. The accuracy in the hybrid BCI control system was 96.65 ± 1.44%, and the false operation rate and command response time were 0.89 ± 0.42 events/min and 2.65 ± 0.48 s, respectively. These results show the application potential of the hybrid BCI control system in daily tasks. In addition, we develop an architecture to connect intelligent things in a smart ward based on narrowband Internet of Things (NB-IoT) technology. The results demonstrate that our system provides superior communication transmission quality.
Microbial Characteristics of Common Tongue Coatings in Patients with Precancerous Lesions of the Upper Gastrointestinal Tract
Authors: Kang Xiaoyu , Lu Bin , Xiao Pan , Hua Zhaolai , Shen Rui , Wu Jianping , Wu Juan , Wu Zhenfeng , Cheng Chun , Zhang Junfeng
Published: April 2022

The tongue coating (TC) microbiota, a crucial component of the tongue coating, illustrates a huge microbial percentage of the body that mostly includes actinobacteria, bacteroides, firmicutes, and fusobacteria. The TC microbiota is closely related to the development of upper gastrointestinal malignancies, such as oral, gastric, and esophageal cancer. Nonetheless, the microbiological characteristics of common TCs in individuals with precancerous lesions of the upper gastrointestinal tract are still unclear. Herein, we designed a case-control study, recruiting 153 PLUGT patients with four different types of TCs, including 47 white-thin, 19 white-thick, 47 yellow-thin, and 40 yellow-thick, as well as 47 volunteers as controls. To analyze microbial characteristics, 16S rRNA microbiome approaches were used. An enzyme-linked immunosorbent assay (ELISA) was employed to assess serum IL-17A and total bile acid (TBA). According to the obtained results, Leptotrichia was found to be a promising biomarker for thin as well as thick yellow coatings. In comparison to the control TC microbiota, 39 different genera developed commensal networks in common TCs. Lachnoanaerobaculum and pseudonocardia were the most striking core bacteria. Lachnoanaerobaculum positively correlated with Leptotrichia in W-thin and Y-thick coatings, with actinomyces and methylobacterium in Y-thin coatings, with Campylobacter in Y-thick coatings, and with Bradyrhizobium in W-thick and Y-thick coatings. Serum IL-17A levels were greater in cases with W-thin coating than in controls, and serum IL-17A was positively linked with Parvimonas in patients with W-thick or Y-thin coating. In Y-thin coating, the oral dominating bacteria Streptococcus was negatively linked with serum TBA. Taken together, the promoted bacteria were found to be synergistically proliferative in the TCs of PLUGT patients. The diverse TCs had distinct bacterial commensal networks, whereas the common TCs were linked by specific bacteria to serum IL-17A and TBA.
Effect of Allicin and Artesunate Combination Treatment on Experimental Mice Infected with Plasmodium berghei
Authors: Ounjaijean Sakaewan , Somsak Voravuth
Published: April 2022

Malaria is still a significant health problem in endemic countries and increases Plasmodium resistance to the available antimalarial drugs. Hence, this study aimed to investigate the antimalarial activity of allicin and its combination with artesunate (ART) against rodent malaria Plasmodium berghei ANKA (PbANKA) infected mice. Allicin was prepared in 20% Tween-80. Balb/c mice were inoculated intraperitoneally with 1×107 PbANKA-infected erythrocytes and orally given by gavage with the chosen doses of 1, 10, 50, and 100 mg/kg of allicin and 1, 5, 10, and 20 mg/kg of ART once a day for 4 consecutive days. Effective dose 50 (ED50) of allicin and ART was subsequently investigated. Moreover, the combination (1 : 1) of allicin and ART at the doses of their respective ED50, ED50 1/2, ED50 1/4, and ED50 1/8 was also carried out. The untreated control was given 20% Tween-80. The results showed that allicin presented a dose-dependent antimalarial activity with significance (

p
<
0.05

). The ED50 values of allicin and ART were about 14 and 5 mg/kg, respectively. For combination, allicin and ART showed a synergistic effect at the combination doses of ED50, ED50 1/2, and ED50 1/4 with significantly (

p
<
0.01

) prevented reduction of packed cell volume, bodyweight loss, rapid dropping of rectal temperature, and markedly prolonged mean survival time, compared with the untreated control and single treatment. It can be concluded that allicin exerted potential antimalarial activity in single and its combination with ART.
Low Serum Albumin Is Associated with Poor Prognosis in Patients Receiving Peritoneal Dialysis Treatment
Authors: Shi Yanan , Cai Jiajie , Shi Chunxia , Liu Conghui , Zhou Jingjing , Li Zhongxin
Published: April 2022

Background. The number of patients receiving dialysis treatment is sustainably increasing, especially peritoneal dialysis. Objectives. It is necessary to find out potential factors that may indicate the prognosis of patients receiving peritoneal dialysis treatment. Methods. This study retrospectively involved 325 patients who received peritoneal dialysis treatment. Results.Low serum albumin (HR = 2.254; 95% CI: 1.534–3.311;

P
<
0.001

) and high FBG (Fasting blood glucose) (HR = 1.474; 95% CI: 1.025–2.120;

P
=
0.037

) were risk factors for death in patients receiving peritoneal dialysis treatment. Serum albumin (AUC = 0.683;

P
<
0.001

) and creatinine (AUC = 0.625;

P
<
0.001

) exhibited value of prognosis prediction. Both high FBG (

P
=
0.005

) and low albumin (

P
<
0.001

) were associated with poor prognosis, and low albumin predicted poorer survival. Conclusions. Low serum albumin and high fasting blood glucose were risk factors and associated with poor prognosis. Low albumin has a potential in predicting the prognosis of patients receiving peritoneal dialysis treatment.
Application of Fuzzy
K
-Means Clustering Algorithm in the Innovation of English Teaching Evaluation Method
Authors: Li HongJie
Published: April 2022

As an important course in colleges, the teaching quality of English courses directly affects the efficiency of talent training. Carrying out English teaching evaluation is conducive to solve the problems existing in English teaching timely, improve the teaching level, and promote the demand of English curriculum teaching reform. However, the traditional English teaching evaluation methods adopted by colleges have some disadvantages, such as inaccurate evaluation results and long evaluation time, which urgently needs to innovate and reform the English teaching evaluation method. With the wide application of modern information technology in English teaching, the fuzzy

K

-means clustering algorithm can be used to construct a new English teaching evaluation model, which can effectively make up for the shortcomings of traditional teaching evaluation methods. This study proposes an English teaching evaluation method based on the fuzzy

K

-means clustering algorithm. This study uses the association rule analysis method under the data mining technology to preprocess the English teaching data, establishes the hierarchical structure model combined with the applying steps of the analytic hierarchy, and constructs the judgment matrix, carries out the hierarchical ranking and consistency test, and defines the specific weight of each index in the teaching evaluation index system, transforms the problem of English teaching evaluation into solving the K-means clustering objective function, realizes the scientific and accurate evaluation of English teaching, and provides a guarantee for improving the quality of English teaching. The results show that the English teaching evaluation method based on the fuzzy

K

-means clustering algorithm has a good evaluation accuracy, ensuring that the evaluation error is less than 6%, and the evaluation time is only 3.9 seconds, which significantly reduces the evaluation time.
Labelled Dataset on Distributed Denial-of-Service (DDoS) Attacks Based on Internet Control Message Protocol Version 6 (ICMPv6)
Authors: Manickam Selvakumar , AIghuraibawi Adnan Hasan Bdair , Abdullah Rosni , Alyasseri Zaid Abdi Alkareem , Abdulkareem Karrar Hameed , Mohammed Mazin Abed , Alani Ayman
Published: April 2022

The most dangerous attack against IPv6 networks today is a distributed denial-of-service (DDoS) attack using Internet Control Message Protocol version 6 (ICMPv6) messages. Many ICMPv6-DDoS attack detection mechanisms rely on self-created datasets because very few suitable ICMPv6-DDoS attack datasets are publicly available due to privacy and security concerns. When implemented in a real network, however, a detection system that relies on a dataset with incorrect packet or flow representation and contains unqualified features generates a large number of false alerts. The goal of this work is to create a comprehensive ICMPv6-DDoS attack dataset that can be used for tuning, benchmarking, and evaluating any detection systems designed to detect ICMPv6-DDoS attacks. The proposed datasets met the criteria for a good dataset, ensuring their usefulness to other researchers. A GNS3 network simulation tool is used to simulate an IPv6 network and generate ICMPv6 traffic for the dataset. The generated traffic contains both normal and abnormal ICMPv6 traffic, with the abnormal traffic containing ten different ICMPv6-DDoS attacks based on RA and NS message flooding. Five classifiers were chosen, varying in terms of type, classification performance, and the number of features used, and the results were as follows: decision tree 80%, support vector machine 78%, naïve Bayes 80%,

k

-nearest neighbours 81%, and neural networks 81%. The proposed dataset has been shown to accurately represent attack traffic in tests, with a high detection accuracy and a low false-positive rate.
A Multifaceted Strategy to Enhance Glaucoma Knowledge and Reduce Anxiety in the Uyghur Population of Rural China
Authors: Guan Jieying , Zhu Yingting , Huang Wenmin , Wu Heping , Chen Rongxin , Ma Yuncheng , Xie Lingling , Kuang Junting , Li Zixuan , Wumaier Aizezi , Zhuo Yehong
Published: April 2022

Purpose. The aim of the study is to assess the impact of a multifaceted education strategy for Uyghur patients with glaucoma on their anxiety, satisfaction, and comprehension of disease information in rural China. Methods. This study recruited 100 patients who were randomized into two groups: the control group that received a conventional consultation and the intervention group that received a multifaceted education approach. Information comprehension was evaluated using a 15-item questionnaire. A 6-item State-Trait Anxiety Inventory (STAI-6) score was used to assess patient anxiety levels. Satisfaction was evaluated on the day of discharge. Results. After education, patients in the intervention group scored significantly higher than those in the control group on their knowledge of glaucoma (

p
≤
0.001

). Education level, family history of glaucoma, and number of glaucoma clinic visits were independently associated with baseline scores (p = 0.038, 0.002, and 0.017, respectively). The age was negatively correlated with scores (

p
≤
0.001

) after education. The STAI-6 score of the intervention group was 28.50 ± 5.13, which was significantly lower than that of the control group (32.50 ± 6.47, p = 0.001). Income levels were negatively correlated and the duration of glaucoma was positively correlated with STAI-6 scores (

p
≤
0.001

and

p
=
0.003

, respectively). Overall satisfaction was significantly higher in the intervention group (

p
≤
0.001

). The knowledge score was positively correlated with overall satisfaction and follow-up probability (both

p
≤
0.001

). The STAI-6 score was negatively correlated with overall satisfaction and follow-up probability (

p
=
0.017

and 0.006, respectively). Conclusions. This new multifaceted educational strategy can enhance patients’ knowledge of glaucoma, reduce their anxiety, and improve their satisfaction. ClinicalTrials.gov (No. 2100050926)
Energy Transport and Effectiveness of Thermo-Sloutal Time’s Relaxation Theory in Carreau Fluid with Variable Mass Diffusivity
Authors: Irfan Muhammad , Anwar Muhammad Shoaib , Sardar Humara , Khan Masood , Khan Waqar Azeem
Published: April 2022

Two different frames’ temperature creates thermal transport that gives advantage in energy fabrication in the power sector, burning in microscopic devices, and for remedy transport through heat transfer in materials. Here the article scrutinizes the transport of head utilizing the thermo-sloutal time’s relaxation, and aspects of non-Fick’s flux with variable conductivity and mass diffusivity in Carreau fluid have been elaborated. The magnetic aspect is also examined in a bidirectional stretched surface. The numerical procedure of ODEs via bvp4c method has been aimed at the solutions of influential parameters. The portrayal of influential factors is also presented. The intensifying behavior has been noted on concentration and temperature scattering when inconsistent thermal conductivity and variable mass diffusivity boost up. Furthermore, the temperature and concentration relaxation times are incorporated for the better understanding of the flow problem. The assessments of current article with former literature are also presented for the endorsement of outcomes.
Bone Density, Geometry, and Mass by Peripheral Quantitative Computed Tomography and Bone Turnover Markers in Children with Diabetes Mellitus Type 1
Authors: Jaworski Maciej , Wierzbicka Elżbieta , Czekuć-Kryśkiewicz Edyta , Płudowski Paweł , Kobylińska Maria , Szalecki Mieczysaw
Published: April 2022

Background. The type 1 diabetes mellitus (T1DM) is a chronic systemic autoimmune-mediated disease characterised by the insulin deficiency and hyperglycaemia. Its deleterious effect on bones concerns not only bone mass, density, and fracture risk but also may involve the linear growth of long bones. Studies on the lower leg in children with T1DM by pQCT have generated conflicting results, and most of the studies published so far focused only on a selected features of the bone. An additional information about growth, modelling, and remodelling processes can be gathered by the bone turnover marker measurement. The objective of the study was to evaluate bone mineral density, mass, and geometry using peripheral quantitative computed tomography as well as bone turnover markers in the patients with type 1 diabetes mellitus. Material and Methods. Bone mineral density, mass, and geometry on the lower leg using peripheral quantitative computed tomography and serum osteocalcin (OC) and carboxyterminal cross-linked telopeptide of type 1 collagen (CTx) were measured in 35 adolescents with T1DM (15 girls) aged 12.3-17.9 yrs. The results were compared to age- and sex-adjusted reference values for healthy controls. Results. Both sexes reveal lower than zero

Z

-scores for lower leg 66% total cortical bone cross-sectional area to muscle cross-sectional area ratio (

−
0.97
±
1.02

,

p
=
0.002517

and

−
0.98
±
1.40

,

p
=
0.007050

, respectively) while tibia 4% trabecular bone density

Z

-score was lowered in boys (

−
0.67
±
1.20

,

p
=
0.02259

). In boys in Tanner stage 5 bone mass and dimensions were diminished in comparison to Tanner stages 3 and 4, while in girls, such a phenomenon was not observed. Similarly, bone formation and resorption were decreased in boys but not in girls. Consistently, bone turnover markers correlated positively with bone size, dimensions, and strength in boys only. Conclusions. T1DM patients revealed a decreased ratio of cortical bone area/muscle area, reflecting disturbed adaptation of the cortical shaft to the muscle force. When analyzing bone mass and dimensions, boys in Tanner stage 5 diverged from “less-mature” individuals, which may suggest that bone development in these individuals was impaired, affecting all three: mass, size, and strength. Noted in boys, suppressed bone metabolism may result in impairment of bone strength because of inadequate repair of microdamage and accumulation of microfractures.
High Level of Uric Acid Promotes Atherosclerosis by Targeting NRF2-Mediated Autophagy Dysfunction and Ferroptosis
Authors: Yu Wei , Liu Weidong , Xie De , Wang Qiang , Xu Chenxi , Zhao Hairong , Lv Jiaming , He Furong , Chen Bingyang , Yamamoto Tetsuya , Koyama Hidenori , Cheng Jidong
Published: April 2022

Atherosclerotic vascular disease (ASVD) is the leading cause of death worldwide. Hyperuricemia is the fourth risk factor for atherosclerosis after hypertension, diabetes, and hyperlipidemia. The mechanism of hyperuricemia affecting the occurrence and development of atherosclerosis has not been fully elucidated. Mononuclear macrophages play critical roles in all stages of atherosclerosis. Studies have confirmed that both hyperuricemia and ferroptosis promote atherosclerosis, but whether high level of uric acid (HUA) promotes atherosclerosis by regulating ferroptosis in macrophages remains unclear. We found that HUA significantly promoted the development of atherosclerotic plaque and downregulated the protein level of the NRF2/SLC7A11/GPX4 signaling pathway in ApoE−/− mice. Next, we evaluated the effect of HUA and ferroptosis inhibitor ferrostatin-1 (Fer-1) treatment on the formation of macrophage-derived foam cells. HUA promoted the formation of foam cells, decreased cell viability, and increased iron accumulation and lipid peroxidation in macrophages treated with oxidized low-density lipoprotein (oxLDL); these effects were reversed by Fer-1 treatment. Mechanistically, HUA significantly inhibited autophagy and the protein level of the NRF2/SLC7A11/GPX4 signaling pathway. Fer-1 activated autophagy and upregulated the level of ferroptosis-associated proteins. Moreover, an NRF2 inducer (tertbutyl hydroquinone (TBHQ)) and autophagy activator (rapamycin (RAPA)) could reverse the inhibitory effect of HUA on foam cell survival. Our results suggest that HUA-induced ferroptosis of macrophages is involved in the formation of atherosclerotic plaques. More importantly, enhancing autophagy and inhibiting ferroptosis by activating NRF2 may alleviate HUA-induced atherosclerosis. These findings might contribute to a deeper understanding of the role of HUA in the pathogenesis of atherosclerosis and provide a therapeutic target for ASVD associated with hyperuricemia.
Improved Cam Shift Algorithm for Detecting Athletes' Action Targets in Sports Videos
Authors: Yuan Yi
Published: April 2022

The detection and tracking of athletes in sports videos is of great importance, as it helps to automate the analysis of sports videos, thus providing advanced tools and instruments for sports training. The Cam Shift algorithm uses the colour information of objects to achieve tracking of moving targets, so it is very important to choose a suitable colour space when obtaining the colour information of objects. To this end, this paper improves the Cam Shift algorithm by converting the visual system mechanism to perceive the colour characteristics of the image processing, i.e., converting the video image from the RGB colour space to the HSV colour space, using the H (hue) component to model the target object. Experiments show that the improved athlete detection and tracking algorithm is more robust in practical applications. The improved tracking algorithm also has better real-time performance, with a processing speed of 20 fps during the experiments.
Immunotherapy of Neuroblastoma: Facts and Hopes
Authors: Anderson John , Majzner Robbie G. , Sondel Paul M.
Published: April 2022

While the adoption of multimodal therapy including surgery, radiation, and aggressive combination chemotherapy has improved outcomes for many children with high-risk neuroblastoma, we appear to have reached a plateau in what can be achieved with cytotoxic therapies alone. Most children with cancer, including high-risk neuroblastoma, do not benefit from treatment with immune checkpoint inhibitors (ICI) that have revolutionized the treatment of many highly immunogenic adult solid tumors. This likely reflects the low tumor mutation burden as well as the downregulated MHC-I that characterizes most high-risk neuroblastomas. For these reasons, neuroblastoma represents an immunotherapeutic challenge that may be a model for the creation of effective immunotherapy for other “cold” tumors in children and adults that do not respond to ICI. The identification of strong expression of the disialoganglioside GD2 on the surface of nearly all neuroblastoma cells provided a target for immune recognition by anti-GD2 mAbs that recruit Fc receptor–expressing innate immune cells that mediate cytotoxicity or phagocytosis. Adoption of anti-GD2 antibodies into both upfront and relapse treatment protocols has dramatically increased survival rates and altered the landscape for children with high-risk neuroblastoma. This review describes how these approaches have been expanded to additional combinations and forms of immunotherapy that have already demonstrated clear clinical benefit. We also describe the efforts to identify additional immune targets for neuroblastoma. Finally, we summarize newer approaches being pursued that may well help both innate and adaptive immune cells, endogenous or genetically engineered, to more effectively destroy neuroblastoma cells, to better induce complete remission and prevent recurrence.
Optimizing frequency synthesizer performance based on passive adder entrenched technique for 4G communication systems
Authors: Khleaf Hussain K. , Nahar Ali Kareem , Jabbar Ansam S.
Published: April 2022

Noise in 4G communication systems is a pressing current problem. There are various ways to reduce phase noise. The sigma-delta passive adder entrenched (PAE) approach was chosen for the WCDMA system because it provides a spurious level, phase noise, and low stabilization time. Therefore, for WCDMA applications, this study proposes a frequency synthesizer. It is then suggested that the addition of a passive adder before the modulator's quantizer to eradicate any distortions created as a result of the quantization stage. The design factors for the suggested second order synthesizer for 4G are chosen based on the analytical results for all unit of the suggested system and in accordance with WCDMA specifications. The suggested PAE frequency synthesizer for the application of WCDMA reduces noise exact effectively, according to simulation findings. With this synthesizer, the in-band phase noise is -75 dBc/Hz. For frequency synthesizer simulation, MATLAB (R2020) is utilized.
Pickup and delivery problem in the collaborative city courier service by using genetic algorithm and nearest distance
Authors: Kusuma Purba Daru , Kallista Meta
Published: April 2022

One problem in collaborative pickup delivery problem (PDP) was excessive outsourced jobs. It happened in many studies on the collaborative PDP. Besides, the revenue sharing in it was unclear although important. This work aimed to propose a novel collaborative PDP model which minimizes total travel distance while maintains low outsourced jobs. It proposed several contributions. First, it prioritized internal jobs first rather than full collaborative model. Second, it proposed new revenue sharing model. It adopted cluster-first route-second and mixed pickup and delivery. It was developed by combining the genetic algorithm and nearest distance algorithm where the genetic algorithm was used in the clustering process and the nearest distance was used in the routing process. The simulation result shows that the proposed model was better than the comparing models: (1) combined K-means and genetic algorithm model (KMGA) and (2) combined simulated annealing and last-in first-out (SNLIFO) model. When the number of orders was high (300 units), the total travel distance of the proposed model was 37 percent lower than the KMGA model and 30 percent lower than the SNLIFO model. In average, the outsourcing rate of the proposed model was 70 percent lower than the previous models.
Prediction of COVID-19 disease severity using machine learning techniques
Authors: Ahmed Alaa H. , Al-Hamadani Mokhaled N. A. , Satam Ihab A.
Published: April 2022

A terrifying spread of COVID-19 (which is also known as severe acute respiratory syndrome coronavirus 2 or SARS-COV-2) led scientists to conduct tremendous efforts to reduce the pandemic effects. COVID-19 has been announced pandemic discovered in 2019 and affected millions of people. Infected people may experience headache, body pain, and sometimes difficulty in breathing. For older people, the symptoms can get worse. Also, it can cause death because of the huge effect on some parts of the human body, particularly for those who have chronic diseases like diabetes. Machine learning algorithms are applied to patients diagnosed with Corona Virus to estimate the severity of the disease depending on their chronic diseases at an early stage. Chronic diseases could raise the severity of COVID-19 and that is what has been proved in this paper. This paper applies different machine learning techniques such as random forest, decision tree, linear regression, binary search, and k-nearest neighbor on Mexican patients’ dataset to find out the impact of lifelong illnesses on increasing the symptoms of the virus in the human body. Besides, the paper demonstrates that in some cases, especially for older people, the virus can cause inevitable death.
Low-voltage bulk-driven flipped voltage follower-based transconductance amplifier
Authors: Rajesh Durgam , Tamil Subramanian , Raj Nikhil , Chourasia Bharti
Published: April 2022

A low voltage high performance design of operational transconductance amplifier is proposed in this paper. The proposed architecture is based on bulk driven quasi-floating gate metal oxide semiconductor field effect transistor (MOSFET) which supports low voltage operation and improves the gain of the amplifier. Besides to this the tail current source requirement of operational transconductance amplifier (OTA) is removed by using the flipped voltage follower structure at the input pair along with bulk driven quasi-floating gate MOSFET. The proposed operational transconductance amplifier shows a five-fold increase in direct current (DC) gain and 3-fold increase in unity gain bandwidth when compared with its conventional bulk driven architecture. The metal oxide semiconductor (MOS) model used for amplifier design is of 0.18 um complementary metal oxide semiconductor (CMOS) technology at supply of  0.5 V.
An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval algorithm
Authors: Slamet Slamet , Mohamed Abdelaziz Izzeldin Ibrahim
Published: April 2022

Currently, many companies use data mining for various implementations. One form of implementation is intrusion detection system (IDS). In IDS, the main problem for nuisance network administrators in detecting attacks is false alerts. Regardless of the methods implemented by this system, eliminating false alerts is still a huge problem. To describe data traffic passing through the network, a database of the network security layer (NSL) knowledge discovery in database (KDD) dataset is used. The massive traffic of data sent over the network contains excessive and duplicated amounts of information. This causes the classifier to be biased, reduce classification accuracy, and increase false alert. To that end, we proposed a model that significantly improve the accuracy of the intrusion detection system by eliminating false alerts, whether they are false negative or false positive negative alerts. The results show that the proposed intelligent exoplanet atmospheric retrieval (INARA) algorithm has improved accuracy and is able to detect new attack types efficiently.
Computer model of Tsunami vulnerability using machine learning and multispectral satellite imagery
Authors: Prasetyo Sri Yulianto Joko , Sulistyo Wiwin , Basuki Prihanto Ngesti , Hartomo Kristoko Dwi , Hasiholan Bistok
Published: April 2022

This research aims to develop a tsunami vulnerability assessment model on land use and land cover using information on NDVI, NDWI, MDWI, MSAVI, and NDBI extracted from sentinel 2 A and ASTER satellite images. The optimization model using algorithms LASSO and linear regression. The validation test is MSE, ME, RMSE and MAE which show that the linear regression has a higher accuracy than the LASSO. The NDWI interpolation values are 0.00 - (-0.35) and MNDWI interpolation values are 0.00 - (-0.40) which are interpreted as the presence of water surfaces along a coast. MSAVI are values (-0.20) - (-0.35) which are interpreted as the presence of no vegetation. The NDBI interpolation values are values 0.15-0.20 which are interpreted as the presence of built-up lands with social and economic activities. While the NDVI interpolation values are 0.20-0.30 which are interpreted as the presence of vegetation densities, biomass growths from the photosynthesis process, and moderate to low levels of vegetation health. The digital elevation model ASTER analysis shows that all areas with high socioeconomic activities, low NDVI, high NDWI/MDWI, high MSAVI and high NDBI are in areas with low elevation (10 meters) so they have a high vulnerability to tsunami waves.
Coordinated COVID-19 vaccination scheduling model by using nearest distance-single course timetabling method
Authors: Kusuma Purba Daru , Nugrahaeni Ratna Astuti
Published: April 2022

This work proposes a new coordinated vaccine scheduling model suitable for the city size COVID-19 vaccination program. It is different from the existing COVID-19 vaccination scheduling mechanism where there is no coordination among endpoint providers. On the other side, the vaccine stock in every provider is limited, so that this mismatch creates many unserved participants. Moreover, studies on the COVID-19 vaccination scheduling problem are hard to find. This work aims to solve this mismatch problem. It is developed by combining the nearest distance and the single course timetabling. It is then optimized by using a cloud theory based-simulated annealing algorithm. The simulation result shows that the proposed model outperforms both the uncoordinated and basic course timetabling models. It can minimize the number of unserved participants, total travel distance, and the number of participants with missed timeslot. It produces zero unserved participants if the total vaccine quantity is at least equal to the total number of participants. The proposed model creates lower total travel distance than the uncoordinated or basic course timetabling adopted model. It is also better than the basic course timetabling model in creating a low number of participants with missed timeslot.
Classification of good and damaged rice using convolutional neural network
Authors: Indra Dolly , Fadlillah Hadyan Mardhi , Kasman Kasman , Ilmawan Lutfi Budi , Lahuddin Harlinda
Published: April 2022

Rice production is massive in Indonesia, therefore maintaining the quality of the product is necessary. Detection and classification of objects have become a very important part in image processing. We performed object detection namely rice. After the object is found, it can be classified into two categories, namely good and damaged rice. We conducted a new study on rice which was carried out per group not per grain to obtain or classify good and damaged rice where we had carried out several steps, namely segmentation process using HSV (hue, saturation, value) color space. HSV is used because of its excellence in representing brightness of the image. We considered evaluating brightness because the tendency of damaged rice is darker or paler compared to good rice. To accomodate environment lighting ambiguity we perform the image acquisition in a controlled environment, so that all the images have the same light intensity. Here we use only channel V of HSV to be used in feature extraction using the gray-level co-occurrence matrix (GLCM) and finally convolutional neural network (CNN) is used for classification. From the test experiments that we have done, we have produced 83% prediction accuracy. Considering how similar the good rice is to the spoiled rice, the results are quite impressive.
Dynamic response investigation of PV based CLCIS fed IMD applications using HC and SMC
Authors: Pazhanimurugan Rajamanickam , Bensraj Ramaiyan , Balamurugan Chinnapettai Ramalingam
Published: April 2022

This paper explains a solar photovoltaic (SPV) based cascaded landsman converter inverter structure (CLCIS) which feeds induction motor drive (IMD) using hysteresis controller (HC) and sliding mode controller (SMC). The aim is to create SPV based cascaded landsman converter (CLC) with an enhanced dynamic response. HC and SMC enabled systems are designed and created using MATLAB. The standards of procedure and simulation results are examined. The simulation results of HC and SMC enabled systems are analyzed concerning time-domain parameters and a comparison has been exhibited. The proposed SMC ensures a model reference robust dynamics against changing voltage conditions of photovoltaic (PV) based CLCIS fed IMD system. The consequences reveal that the dynamic response with SMC is better compared to the HC enabled CLCIS-IMD system.
Feature selection for urban land cover classification employing genetic algorithm
Authors: Alzahrani Ali , Bhuiyan Md. Al-Amin
Published: April 2022

Feature selection has attained substantial research interest in image processing, computer vision, pattern recognition and so on due to tremendous dimensional reduction in image analysis. This research addresses a genetic algorithm based feature selection strategy for urban land cover classification. The principal purpose of this research is to monitor the land cover alterations in satellite imagery for urban planning. The method is based on object based classification by detecting the object area of a given image with the knowledge of visual information of the object from remote sensing images. The classification system is organized through a multilayer perceptron with genetic algorithm (MLPGA). Experimental results explicitly indicate that this MLPGA based hybrid feature selection procedure performs classification with sensitivity 94%, specificity 90% and precision 89%, respectively. This MLPGA centered hybrid feature selection scheme attains better performance than the counterpart methods in terms of classification accuracy.
Important factors to remember when constructing a cross-site scripting prevention mechanism
Authors: Maruf Hassan Md. , R. Ahmad Badlishah , Esha Ashrafia , Risha Rafika , S. Hasan Mohammad
Published: April 2022

Web application has become an essential part of daily activities to provide easy accessibility that ensures better performance. It is a platform where sensitive information such as username, password, credit card details, operating system and software version. is stored that attracts intruders to generate most of their attacks. Intruders can steal valuable data by compromising web application security flaws; cross site scripting (XSS) vulnerability is one of these. Several studies have been conducted in order to prevent the XSS vulnerability. In this research, we searched Scopus Indexed articles published in the last 11 years (between 2008 and 2020) using two keywords (“XSS attack prevention” and “XSS prevention”). The purpose of this study was to conduct a literature review on XSS prevention techniques e.g., strengths and weaknesses, including structural issues and real-time deployment location in order to extract valuable information. This review identified 14 articles among the 25 selected articles that provided various suitable prevention techniques for XSS attacks. Seven articles are based on tools that have been implemented and take into account design, coding, testing, and integrating validation processes, six articles are about server site solutions, and one is about automatic mitigation solutions. As a result, this research will be invaluable in guiding the advancement of XSS prevention techniques.
Review on facial expression modeling
Authors: Adibah Najihah Mat Noor Noor , Suaib Norhaida Mohd
Published: April 2022

Facial modeling has been an ongoing research for many years and still shows research trend due to its relevance to current technology. Many applications incorporate facial expression modeling with the help of facial tracking, facial animation and facial recognition. The existing performance of the modeling method faces the challenges to perform well due to many factors. Currently, the use of 2D images and videos as inputs for modeling process are gaining popularity. However, current technologies and development had extended the trends towards acquiring 3D human data. This paper provides an overview on variety of modeling techniques based on human facial model that can lead to future research.
Integration of convolutional neural network and extreme gradient boosting for breast cancer detection
Authors: Sugiharti Endang , Arifudin Riza , Wiyanti Dian Tri , Susilo Arief Broto
Published: April 2022

With the most recent advances in technology, computer programming has reached the capabilities of human brain to decide things for almost all healthcare systems. The implementation of Convolutional Neural Network (CNN) and Extreme Gradient Boosting (XGBoost) is expected to improve the accurateness of breast cancer detection. The aims of this research were to; i) determine the stages of CNN-XGBoost integration in diagnosis of breast cancer and ii) calculate the accuracy of the CNN-XGBoost integration in breast cancer detection. By combining transfer learning and data augmentation, CNN with XGBoost as a classifier was used. After acquiring accuracy results through transfer learning, this reasearch connects the final layer to the XGBoost classifier. Furthermore, the interface design for the evaluation process was established using the Python programming language and the Django platform. The results: i) the stages of CNN-XGBoost integration on histopathology images for breast cancer detection were discovered. ii) Achieved a higher level of accuracy as a result of the CNN-XGBoost integration for breast cancer detection. In conclusion, breast cancer detection was revealed through the integration of CNN-XGBoost through histopathological images. The combination of CNN and XGBoost can enhance the accuracy of breast cancer detection.