bluuebunny/crossref_metadata_2025 huggingface dataset:
Antioxidant Effect of Thioredoxin and Vitamin D3 in Peritoneal Dialysis Patients
Authors: Ileri Sara Yavuz , Batman Adnan , Eraldemir Ceyla , Bakırdogen Serkan , Dervisoglu Erkan
Published: May 2022

Background. Among the chronic diseases, chronic kidney failure is one of diseases that have the most difficulty in coping with oxidative stress due to the deterioration of the antioxidant system balance in the body. Beyond being a vitamin, 1α,25‐dihydroxycholecalciferol (vitamin D3) is a molecule that positively or negatively affects many enzymes which are in protein structures. Thioredoxin (TRX), which has an important role in the antioxidant system, is one of these proteins. By conducting this study, we wanted to emphasize the role of vitamin D3 in reducing the oxidative stress load on patients undergoing peritoneal dialysis (PD) via serum TRX level measurement. Methods. In this study, we evaluated the medical treatments of 69 PD patients who were followed up routinely. The patients were divided into 2 groups according to whether they used vitamin D3 or not. 49 of our patients were using vitamin D3. While requesting routine laboratory tests, we reserved a separate serum sample to measure serum TRX levels by double‐antibody sandwich enzyme‐linked immunosorbent assay for all patients. Results. Only one parameter has a significant statistical relationship with serum TRX level and the treatment protocol. The serum TRX level was significantly higher (211,62 U/l ± 314,46) in the group receiving vitamin D3 compared to the group which is not using Vitamin D3 (101,63 U/l ± 215,03) (p < 0,006). Conclusion. This study highlights the importance of appropriate dose of vitamin D3 replacement especially in PD patients who are under intense oxidative stress compared to healthy individuals.
Application of ERCP Procedures in Choledocholithiasis with Duodenal Stenosis Patients
Authors: Shi Yuyan , Zhong Jinghon , Zhou Jianbo , Song Qifeng
Published: May 2022

Objective. The treatment of choledocholithiasis with duodenal stenosis is a clinical difficult problem. This study aimed to investigate the efficacy and safety of ERCP via gastroscopy in the treatment of choledocholithiasis and duodenal stenosis. Methods. From January 2015 to December 2020, 21 patients with choledocholithiasis with duodenal stenosis who underwent ERCP treatment under gastroscopy in our hospital were enrolled. The patients’ case characteristics, ERCP status, and complication rate were analyzed. Results. Among the 21 patients, 17 cases were successful in ERCP, and a total of 29 times ERCPs were performed, with an average of 1.71 times per patient. Among the failures of ERCP, selective deep intubation of common bile duct was unsuccessful in 4 cases. Six patients underwent multiple lithotomies, after the operation, of which 4 patients underwent secondary ERCP lithotomy and 2 patients underwent triple ERCP lithotomy. All patients successfully completed the balloon dilation without serious complications. Two patients developed mild acute pancreatitis after ERCP, and all recovered after medication. Conclusion. In patients with choledocholithiasis and duodenal stenosis, ERCP treatment by gastroscopy has a higher success rate and does not increase the incidence of complications, but there is a problem of cholecystolithiasis recurrence.
A Novel
N
-Gram-Based Image Classification Model and Its Applications in Diagnosing Thyroid Nodule and Retinal OCT Images
Authors: Wang Guanfang , Chen Xianshan , Tian Geng , Yang Jiasheng
Published: May 2022

Imbalanced classes and dimensional disasters are critical challenges in medical image classification. As a classical machine learning model, the

n

-gram model has shown excellent performance in addressing this issue in text classification. In this study, we proposed an algorithm to classify medical images by extracting their

n

-gram semantic features. This algorithm first converts an image classification problem to a text classification problem by building an

n

-gram corpus for an image. After that, the algorithm was based on the

n

-gram model to classify images. The algorithm was evaluated by two independent public datasets. The first experiment is to diagnose benign and malignant thyroid nodules. The best area under the curve (AUC) is 0.989. The second experiment is to diagnose the type of fundus lesion. The best result is that it correctly identified 86.667% of patients with dry age-related macular degeneration (AMD), 93.333% of patients with diabetic macular edema (DME), and 93.333% of normal individuals.
Effect of Preprocessing Storage Temperature and Time on the Physicochemical Properties of Winter Melon Juice
Authors: Sun Xiuxiu , Baldwin Elizabeth A. , Manthey John , Dorado Christina , Rivera Ted , Bai Jinhe
Published: May 2022

Numerous studies demonstrated that winter melons (Benincasa hispida) have a long storage life at 20°C without quality and flavor degradation in fruit. However, fruit for processing are frequently handled under refrigerated conditions or exposed to a warehouse without air conditioning. Therefore, this research aimed to evaluate whether a short high- and low-temperature storage of fruit, prior to processing, changes the flavor and nutritional profiles of winter melon juice. Weight loss of 1.71% was recorded subsequent to 20 days of 10°C storage, with 5.15% weight loss at 30°C. Sugar content significantly decreased during storage at 10°C and 30°C, while the soluble solids content slightly increased. Several specific phenolic compounds were detected, and the total concentration of phenolics increased over the storage time at both temperatures. The concentration of sulfur compounds, as well as hexanal and total volatiles that are principally responsible for off-flavor reduced significantly during storage and the reduction was greater at 10°C than at 30°C. The results indicate that preprocessing fruit storage at 10 or 30°C for 20 days will not harm the quality and flavor of winter melon juice. However, longer storage time caused water-soaked spots at 10°C and dry rot at 30°C.
An Action Recognition Technology for Badminton Players Using Deep Learning
Authors: Liu Jiatong , Liang Bo
Published: May 2022

With the fast development of sports in recent years, the number of people participating in various sports has increased day by day. Because of the advantages of fewer constraints on the field and ease of learning, badminton became one of the most popular sports among them. Numerous works have been done specifically for the recognition of action of badminton players to improve and popularize it, but the traditional badminton player’s badminton action recognition algorithm employs the method of manually constructing a topology map to model the action sequence contained in multiple video frames. Besides, it learns each video frame in a targeted manner to reflect data changes, which is prone to high computational cost, low network generalization, and catastrophic forgetting. In response to the above problems, this paper proposes a deep learning-based action recognition technology for badminton players, which re-encodes the human hitting action sequence data with multirelational characteristics into relational triples, and learns by decoupling based on long short-term memory network. At the same time, this paper designs and completes a set of badminton action recognition schemes based on acceleration and angular velocity signals. Experimental results show that the proposed method achieves 63%, 84%, and 92%, respectively, recognition accuracy on multiple benchmark datasets, which improves the accuracy of human hitting action recognition. As a result, the evaluation will be useful in future work to improve the structure of current deep learning models for higher results in badminton action recognition.
An Early Control Algorithm of Corporate Financial Risk Using Artificial Neural Networks
Authors: Li Jing
Published: May 2022

Based on DL theory, this paper discusses and studies the early warning of enterprise financial risks in detail. And put forward a new enterprise financial risk early-warning model. The purpose is to enable enterprises to better analyze the changing trend of financial data, make correct decisions by managers and investors of enterprises, and promote the stable development of national economy and enterprises. This model is based on the early-warning theory of enterprises, based on the financial statements, business plans, and other relevant accounting information of enterprises, using accounting, finance, and marketing theories, adopting the methods of ratio analysis, comparative analysis, factor analysis, etc., to warn the financial risks of enterprises. This paper uses a lot of data to train the parameters of the DL financial early-warning model and then verifies the established financial early-warning model. In order to verify the reliability of this model, this model is compared with other two financial early-warning models. The results show that the prediction accuracy of this model is as high as 94%, which is 8~15% higher than that of other models. In this paper, the DL method has been applied to financial risk early warning and achieved good results. It has certain theoretical and practical significance in the field of enterprise financial early warning.
Design of Deep Learning Network Model for Personalized Music Emotional Recommendation
Authors: Chen Chen
Published: May 2022

Music is a way for people to express their inner thoughts, and it is an art form to express their feelings and send their emotions. In modern society, people tend to listen to music more and more as a way of leisure and entertainment, and different types of music hold different feelings of listeners and trigger different emotional resonances. In this study, we propose an algorithmic model based on the two-layer attention mechanism, which includes the processing of textual convolutional neural network for music name and music label text data and the processing of two-layer attention mechanism, where the two-layer attention mechanism refers to the first layer of attention mechanism that learns the user's preference for each music feature from the feature level and the second layer of attention mechanism that learns the user's preference for each piece of music in the history listening list from the item level. The experiments show that the NDCG value of this method is improved by about 0.08, and the overall quality of the recommendation list is improved, which indicates that the user interest model constructed based on the fusion of various dings has good characterization ability and is helpful to alleviate data sparsity.
Mechanism of Fructus Mume Pills Underlying Their Protective Effects in Rats with Acetic Acid-Inducedulcerative Colitis via the Regulation of Inflammatory Cytokines and the VEGF-PI3K/Akt-eNOS Signaling Pathway
Authors: Xu Zongying , Zhang Xueli , Lu Ruimin , Zhang Di , Zou Tianyuan , Chen Meng , Zhang Dongmei
Published: May 2022

Background. Fructus mume pills (FMPs) have been clinically proven to be effective for treating ulcerative colitis (UC). However, the therapeutic and protective mechanisms have not been fully studied. Aim. We aimed to explore the mechanism of FMPs in an acetic acid (AA)-induced ulcerative colitis rat model. Methods. The targets, GO terms, and KEGG pathways for the FMPs and UC were screened and constructed using network pharmacology. A possible mechanism was verified in a 4% AA-induced colitis rat model. Colitis activity and state were evaluated using the disease activity index, and colon ulceration and intestinal mucosal damage were determined by histopathological observation through HE, AB-PAS, and Masson pathological staining. The concentrations of TNF-α, IL-6, IL-8, IL-10, MPO, MMP9, CXCR1, eNOS, and VEGF were measured to evaluate vascular permeability effects. Results. The network pharmacology results showed 108 active compounds, and 139 FMP-related targets were identified. Twenty-nine targets were identified for FMPs against UC, which included MMP9, MMP3, ESR1, PTGS1, PPARA, MPO, and NOS2. A total of 1,536 GO terms and 41 pathways were associated with FMP treatment of UC. The pharmacological evaluation showed that FMPs attenuated inflammation in AA-induced colitis by reducing the serum concentrations of TNF-α, IL-6, IL-8, and IL-10 and the colonic concentrations of MPO, MMP9, and CXCR1. FMPs ameliorated hyperpermeability by reducing the colonic VEGF and eNOS concentrations. FMPs also significantly decreased the VEGFA, VEGFR2, Src, and eNOS protein expressions in colon tissue through the VEGF-PI3K/Akt-eNOS signaling pathway. Conclusion. These results suggest that FMPs control UC inflammation by regulating inflammatory cytokine concentrations. FMPs alleviate AA-induced UC by regulating microvascular permeability through the VEGF-PI3K/Akt-eNOS signaling pathway.
Application of Energy-Concentrated Hydraulic Blasting Technology in Tunnel Construction in China
Authors: Han Feng , Li Jigang , Cai Yuancheng , Wang Qiang , Qiu Junling
Published: May 2022

The construction of traffic network brings opportunities to the development of tunnel construction technology. At present, the construction of rock tunnels is still dominated by drilling and blasting. There are some problems in conventional blasting, such as over and underexcavation, damage of surrounding rock, large blasting vibration, and high content of tunnel dust. The energy-concentrated hydraulic blasting technology (ECHBT) combines the advantages of energy-concentrated blasting technology (ECBT) and hydraulic blasting technology (HBT) and makes up for the defects of conventional blasting. This paper summarizes the principle of ECHBT and its application effect in tunnel construction in China. In addition to solving the problems existing in conventional blasting technology, ECHBT can also improve tunneling efficiency, can be used in special stratum tunnels, and can save construction costs. However, there are also some factors affecting the blasting effect in practical applications, which need to be paid attention to. At present, there is little theoretical research on ECHBT; with the further improvement of optimization research, it can be expected to be more widely used in the field of tunnel construction.
Understanding the Cellular Sources of the Fractional Exhaled Nitric Oxide (FeNO) and Its Role as a Biomarker of Type 2 Inflammation in Asthma
Authors: Escamilla-Gil Jose M. , Fernandez-Nieto Mar , Acevedo Nathalie
Published: May 2022

Fractional exhaled nitric oxide (FeNO) has gained great clinical importance as a biomarker of type 2 inflammation in chronic airway diseases such as asthma. FeNO originates primarily in the bronchial epithelium and is produced in large quantities by the enzyme inducible nitric oxide synthase (iNOS). It should be noted that nitric oxide (NO) produced at femtomolar to picomolar levels is fundamental for respiratory physiology. This basal production is induced in the bronchial epithelium by interferon gamma (IFNγ) via Janus kinases (JAK)/STAT‐1 signaling. However, when there is an increase in the expression of type 2 inflammatory cytokines such as IL‐4 and IL‐13, the STAT‐6 pathway is activated, leading to overexpression of iNOS and consequently to an overproduction of airway NO. Increased NO levels contributes to bronchial hyperreactivity and mucus hypersecretion, increases vascular permeability, reduces ciliary heartbeat, and promotes free radical production, airway inflammation, and tissue damage. In asthmatic patients, FeNO levels usually rise above 25 parts per billion (ppb) and its follow‐up helps to define asthma phenotype and to monitor the effectiveness of corticosteroid treatment and adherence to treatment. FeNO is also very useful to identify those severe asthma patients that might benefit of personalized therapies with monoclonal antibodies. In this review, we revised the cellular and molecular mechanisms of NO production in the airway and its relevance as a biomarker of type 2 inflammation in asthma.
Evolution of Software Development Effort and Cost Estimation Techniques: Five Decades Study Using Automated Text Mining Approach
Authors: Jadhav Anil , Kaur Mandeep , Akter Farzana
Published: May 2022

Software development effort and cost estimation (SDECE) is one of the most important tasks in the field of software engineering. A large number of research papers have been published on this topic in the last five decades. Investigating research trends using a systematic literature review when such a large number of research papers are published is a very tedious and time-consuming task. Therefore, in this research paper, we propose a generic automated text mining framework to investigate research trends by analyzing the title, author’s keywords, and abstract of the research papers. The proposed framework is used to investigate research trends by analyzing the title, keywords, and abstract of select 1015 research papers published on SDECE in the last five decades. We have identified the most popular SDECE techniques in each decade to understand how SDECE has evolved in the past five decades. It is found that artificial neural network, fuzzy logic, regression, analogy-based approach, and COCOMO methods are the most used techniques for SDECE followed by optimization, use case point, machine learning, and function point analysis. The NASA and ISBSG are the most used dataset for SDECE. The MMRE, MRE, and PRED are the most used accuracy measures for SDECE. Results of the proposed framework are validated by comparing it with the outcome of the previously published review work and we found that the results are consistent. We have also carried out a detailed bibliometric analysis and metareview of the review and survey papers published on SDECE. This research study is significant for the development of new models for cost and effort estimations.
CloudConsumerism: A Consumer-Centric Ranking Model for Efficient Service Mapping in Cloud
Authors: Neeraj  , Garg Neha , Navadia Nipun R. , Lakhanpal Anupam , Gupta Indrajeet , Ibrahim Wubshet , Raj Manish
Published: May 2022

In cloud, service providers and consumers are primary stakeholders that maintain a business liaison. Cloud service providers (CSPs) offer the services, and consumer uses the services on a payment basis. From a business perspective, the selection of a service based on mutual evaluation benefits both the CSPs and consumers. This paper presents an efficient CloudConsumerism model where the multicriteria decision-making method (MCDM) method, TOPSIS, is used for evaluating the performance of CSPs and consumers. For performance evaluation of CSPs, the performance attributes defined by Cloud Service Measurement Initiative Consortium (CSMIC) are exploited. For evaluating the consumers, this paper is the first approach towards identifying the behavioral attributes for evaluating the cloud consumers analogous to the business models. A service mapping algorithm is proposed for efficient (less overhead and higher robustness) mapping. Extensive simulation experiments are conducted; the results show that the proposed framework can be used for the online cloud-based platform due to limited overhead and high robustness.
Clinical, Serological, and Genetic Characteristics of a Hungarian Myositis‐Scleroderma Overlap Cohort
Authors: Szabó Katalin , Bodoki Levente , Nagy-Vincze Melinda , Béldi Tibor , Vincze Anett , Zilahi Erika , Varga József , Szűcs Gabriella , Dankó Katalin , Griger Zoltán
Published: May 2022

Overlap myositis is a distinct subgroup of idiopathic inflammatory myositis (IIM) with various clinical phenotypes. The aim of this study was to determine the clinical, serological, and genetic features of systemic sclerosis (SSc)‐IIM overlap patients. It was a retrospective study using clinical database of 39 patients, fulfilling both the criteria of SSc and IIM. 56.4% of the patients had limited cutaneous, 43.6% had diffuse cutaneous SSc, whereas 7.7% of the patients had dermatomyositis and 92.3% polymyositis. The two diseases occurred simultaneously in 58.97%, while 10.26% in myositis and 30.77% in scleroderma were initially diagnosed. The frequencies of organ involvement were interstitial lung disease 71.8%, dysphagia 66.7%, cardiac involvement 41%, pulmonary arterial hypertension (PAH) 30.8%, and renal involvement 12.8%, respectively. The presence of human leukocyte antigen (HLA) − DRB1∗03 and DQA1∗051∗01 alleles were significantly higher in the overlap patients than in healthy controls (82.35% vs. 27.54%; p < 0.0001 and 88.24% vs. 30.16; p < 0.0001). Certain clinical parameters, such as fever at diagnosis (41.67% vs. 7.41%, p = 0.0046), cardiac involvement (83.33% vs. 22.22%, p = 0.0008), subcutaneous calcinosis (41.66 vs. 11.11, p = 0.01146), and claw hand deformity (25% vs. 11.11%, p = 0.00016) were significantly associated with the presence of PAH. Upon comparison, the overlap patients and anti‐Jo‐1 positive antisynthetase patients showed similarities in terms of genetic results and major clinical features; however, SSc‐IIM overlap patients could be distinguished with higher erythrocyte sedimentation rate (ESR) level, more frequent presence of Raynaud’s phenomenon (p < 0.0001; OR: 20.00), dysphagia (p < 0.0001; OR: 15.63), and infrequent livedo reticularis (p < 0.01; OR: 0.11). SSc‐IIM overlap myositis is a unique group within IIM‐s possessing characteristic clinical features.
Implementation of a Heart Disease Risk Prediction Model Using Machine Learning
Authors: Karthick K. , Aruna S. K. , Samikannu Ravi , Kuppusamy Ramya , Teekaraman Yuvaraja , Thelkar Amruth Ramesh
Published: May 2022

Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset.
Automatic Scoring of Spoken English Based Principal Component Analysis and Convolutional Neural Network
Authors: Chongya Liu
Published: May 2022

Using computers to help people practice spoken language is a common method, but there are currently some problems. Firstly, because fluency feature is calculated depending on expert knowledge, the key information contained in the original data set may be lost. Secondly, optimize each model’s parameters separately to make the model’s performance in sub-optimal state. In order to solve these problems, a spoken English fluency scoring method based on convolutional neural network is proposed, in order to make the feature extraction consider the short-, medium-, and long-term characteristics of speech signal; three convolution layers are superimposed in this paper, which jointly learns feature extraction and scoring models from the original time-domain signal input. In the feature extraction process, we applied principal component analysis to make useful data extraction of audio features. The experimental results show that the scoring results of the proposed method are more accurate.
Practical Research on the Development of Physical Exercise and Physical Education Training Based on 5G Technology
Authors: Qing Heqin , Liu Bowen
Published: May 2022

The traditional physical education teaching model in colleges and universities is basically based on “teaching” to teach learning, to cultivate the knowledge and skills of college students, and to master some scientific physical exercise methods and skills. This article was aimed at conducting research based on the practice of 5G technology to help physical exercise development and physical education training. This article first discusses the importance of outreach training in physical education. It is feasible to implement expansion training in physical education, and it can have a profound impact. It can have a great impact on students’ personality, psychology, and work attitude and will greatly help them enter the society in the future. At the same time, this article introduces the human movement data monitoring method based on 5G technology. It expounds on the two aspects of human body posture description method and exercise data parameter calculation and analyzes and discusses the exercise time, exercise frequency, exercise intensity, exercise items, and exercise persistence of college students of different genders, disciplines, and grades. The results of the study show that the proportion of college students who take part in physical exercise every time “within 0.5 hours” is the highest, which is 40% of the total sample size. The option of “1.5 hours or more” is the least, only 11% of the total number of people. There is a significant difference between the two, indicating that college students should strengthen physical exercise.
[Retracted] Segmentation of Optic Disc and Cup Using Modified Recurrent Neural Network
Authors: Surendiran J. , Theetchenya S. , Benson Mansingh P. M. , Sekar G. , Dhipa M. , Yuvaraj N. , Arulkarthick V. J. , Suresh C. , Sriram Arram , Srihari K. , Alene Assefa
Published: May 2022

Glaucoma is one of the leading factors of vision loss, where the people tends to lose their vision quickly. The examination of cup‐to‐disc ratio is considered essential in diagnosing glaucoma. It is hence regarded that the segmentation of optic disc and cup is useful in finding the ratio. In this paper, we develop an extraction and segmentation of optic disc and cup from an input eye image using modified recurrent neural networks (mRNN). The mRNN use the combination of recurrent neural network (RNN) with fully convolutional network (FCN) that exploits the intra‐ and interslice contexts. The FCN extracts the contents from an input image by constructing a feature map for the intra‐ and interslice contexts. This is carried out to extract the relevant information, where RNN concentrates more on interslice context. The simulation is conducted to test the efficacy of the model that integrates the contextual information for optimal segmentation of optical cup and disc. The results of simulation show that the proposed method mRNN is efficient in improving the rate of segmentation than the other deep learning models like Drive, STARE, MESSIDOR, ORIGA, and DIARETDB.
Characteristics of Pulse Parameters in Patients with Polycystic Ovary Syndrome Varied at Different Body Mass Index Levels
Authors: Feng Xiao , Feng Lu , Gao Hui , Wang Qing-Sheng , Xia Yu-Mo , Xu Zhao-Xia , Wang Yi-Qin
Published: May 2022

Objective. To analyze the characteristics of pulse graph parameters in patients with polycystic ovary syndrome (PCOS) varied at different body mass index (BMI) levels and to provide pulse diagnosis basis for syndrome differentiation and treatment of PCOS. Methods. Pulse graph parameters of 152 patients with PCOS (26 lean patients, 63 patients with moderate weight, and 63 overweight patients) were measured by a Z-BOX pulse meter, and the pulse graph parameters of patients with PCOS varied at different BMI levels were analyzed. Results. Fine pulse, slippery pulse, and string-like pulse were the most common pulse conditions in patients with PCOS. The common pulse conditions of patients with PCOS varied at different BMI levels. The order of pulse conditions was as follows: lean group: fine pulse > string-like pulse > slippery pulse; moderate group: fine pulse > slippery pulse > string-like pulse; and overweight group: slippery pulse > fine pulse > sunken pulse. Compared to the overweight group, the pulse graph parameters h1, h3, h4, h5, h4/h1, As, and Ad increased in the moderate group (

P
<
0.05

), and the parameters h1, h3, and Ad increased (

P
<
0.05

) and the parameter t1 decreased (

P
<
0.05

) in the lean group. Conclusion. Pulse graph parameters among patients with PCOS varied at different BMI levels, which can probably provide pulse diagnosis basis for syndrome differentiation and treatment of PCOS by traditional Chinese medicine (TCM).
Network Pharmacology Analysis and Experimental Verification Strategies Reveal the Action Mechanism of Danshen Decoction in Treating Ischemic Cardiomyopathy
Authors: Liu Mengnan , Yuan Gang , Luo Gang , Guo Xin , Chen Mingtai , Yang Huayi , He Fan , Yang Tingfu , Zhang Xinyue , Wu Qibiao , Zhou Hua , Yang Sijin
Published: May 2022

Background. Danshen Decoction comprises Salvia miltiorrhiza, Santalum album, and Amomum villosum. It can promote blood circulation and remove blood stasis, and is commonly used in the treatment of gastric and duodenal ulcers, coronary heart disease, angina pectoris, etc. This research is based on network pharmacology and is experimentally verified to explore the potential mechanism of Danshen Decoction in the treatment of ischemic cardiomyopathy (ICM). Methods. The effective components and targets of Danshen Decoction were firstly extracted from Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database and Analysis Platform, the drug-component-target-disease network was then constructed, the protein-protein interaction (PPI) network was constructed, the Gene Ontology (GO) enrichment analysis was carried out, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was analyzed in order to find the potential active components and therapeutic mechanisms. Finally, the in vitro hypoxia/reoxygenation model in H9c2 cells was established to verify the predicted active components and therapeutic mechanisms. Results. The results showed that Danshen Decoction has 67 potential active components and 109 therapeutic targets in treating ICM. These targets were rich in a variety of gene functions and different signaling pathways; the main gene targets include TP53, c-Jun, and Akt1. Go enrichment analysis showed that response to drug, membrane raft, and G protein-coupled amine receiver activity rank first in each process, and the main signaling pathways include PI3K-Akt signaling pathway. Through molecular docking and experimental verification of the major active components and core therapeutic targets, the active components of Danshen Decoction demonstrated an ability to reduce the cell damage caused by hypoxia/reoxygenation in H9c2 cells by regulating the core therapeutic target including Akt1, c-Jun, and TP53. Conclusion. Danshen Decoction has the effect of treating ICM in multiple ways, which is consistent with the results of network pharmacology. This laid a foundation for further study in exploring the active principles and pharmacological mechanism of Danshen Decoction.
The Research and Development Thinking on the Status of Artificial Intelligence in Traditional Chinese Medicine
Authors: Li Nan , Yu Jiarui , Mao Xiaobo , Zhao Yuping , Huang Luqi
Published: May 2022

With the rapid development and application of artificial intelligence (AI) in medical field, the diagnostic ways of human health and the social medical structures have changed. Based on the concept of holism and the theory of syndrome differentiation and treatment, TCM realizes comprehensive informatization and intelligence with the help of AI technology in data mining, intelligent diagnosis and treatment, intelligent learning, and decision-making. Furthermore, the intelligent research of TCM technology will further promote the improvement in TCM diagnosis and treatment rules and the leaping development of TCM intelligent instruments. In this article, we performed a systematic review of scientific literature about TCM and AI. Moreover, the practical problems of TCM intellectualization, the current situation and demand of TCM, and the influence of AI in the TCM field are discussed by searching for literature using TCM scientific databases, reference lists, expert consultation, and targeted websites. Finally, we look forward to the application prospects of AI and propose a possible future direction of intelligent TCM in the current health-care system in China.
Study on the Influence of Suspension Parameters on Longitudinal Impact Comfort
Authors: Li Ning , Jiang Junping , Sun Fulu , Ye Mingrui
Published: May 2022

Ride comfort criteria are a key challenge for vehicle dynamic design and optimization. Currently optional parameter is the vertical impact, and longitudinal impact is neglected. With further requirements for future comfortability, effects of longitudinal impact should be investigated in detail. A longitudinal impact model is firstly proposed to evaluate the ride comfort factors based on the dynamic theory and commercial ADAMS® software. Predictions revealed that the hard points of the suspension and the stiffness of rubber bushing (SORB) are the primary factors. A novelty finding is that travel of rubber bushing (TORB) in the linear region is the most important parameter for ride comfort optimization and suspension factor is the weakest, and experimental validation is performed with better agreements.
Developments in Nanoparticles Enhanced Biofuels and Solar Energy in Malaysian Perspective: A Review of State of the Art
Authors: Fayaz H. , Khan Sher Afghan , Saleel C. Ahamed , Shaik Saboor , Yusuf Abdulfatah Abdu , Veza Ibham , Fattah I. M. Rizwanul , Rawi Nurul Fazita Mohammad , Asyraf M. R. M. , Alarifi Ibrahim M.
Published: May 2022

The rapid rise in global oil prices, the scarcity of petroleum sources, and environmental concerns have all created severe issues. As a result of the country’s rapid expansion and financial affluence, Malaysia’s energy consumption has skyrocketed. Biodiesel and solar power are currently two of the most popular alternatives to fossil fuels in Malaysia. These two types of renewable energy sources appear to be viable options because of their abundant availability together with environmental and performance competence to highly polluting and fast depleting fossil fuels. The purpose of adopting renewable technology is to expand the nation’s accessibility to a reliable and secure power supply. The current review article investigates nonconventional energy sources added with nanosized metal particles called as nanomaterials including biodiesel and solar, as well as readily available renewable energy options. Concerning the nation’s energy policy agenda, the sources of energy demand are also investigated. The article evaluates Malaysia’s existing position in renewable energy industries, such as biodiesel and solar, as well as the impact of nanomaterials. This review article discusses biodiesel production, applications, and government policies in Malaysia, as well as biodiesel consumption and recent developments in the bioenergy sector, such as biodiesel property modifications utilizing nanoparticle additions. In addition, the current review study examines the scope of solar energy, different photovoltaic concentrators, types of solar energy harvesting systems, photovoltaic electricity potential in Malaysia, and the experimental setup of solar flat plate collectors (FPC) with nanotechnology.
Reinforcement Learning-Based Adaptive Switching Scheme for Hybrid Optical-Acoustic AUV Mobile Network
Authors: Luo Hanjiang , Xu Ziyang , Wang Jinglong , Yang Yuting , Ruby Rukhsana , Wu Kaishun
Published: May 2022

In an autonomous underwater vehicles– (AUVs–) based optical-acoustic hybrid network, it is critical to achieve ultra high-speed reliable communications, in order to reap the benefits of the complementary systems and perform high-bandwidth and low-latency operations. However, as the mobile AUVs operate in harsh oceanic environments, it is essential to design an effective switching algorithm to execute flexible hybrid acoustic-optical communications and increase the network throughput. In this paper, we propose a Q-learning-based adaptive switching scheme to maximize the network throughput by capturing the dynamics of the varying channels as well as the mobility of AUVs. In order to address the challenge associated with partial observations of the optical channel and improve the switching efficiency in extreme conditions, a blind optical channel estimation method is designed and implemented with the Extended Kalman Filter (EKF), in which the relationship between the underwater acoustic and optical channels is utilized to improve the channel prediction accuracy. Based on this environmental status, a reinforcement learning approach is leveraged to build a near-optimal switching strategy for the hybrid network. We conduct numerical simulations to verify the performance of the scheme, and the simulation results demonstrate that the proposed switching scheme is effective and robust.
Construction of Regression Equation for Maximum Oxygen Uptake Recognition of Respiration and Heart Rate in Exercise Training
Authors: Su Yang , Zheng Lihui
Published: May 2022

The main test objective of cardiopulmonary function is the maximum oxygen uptake. In order to identify the maximum oxygen uptake, this study constructs the maximum oxygen uptake recognition regression equation of exercise training respiration and heart rate. Students were selected from high school and randomly assigned to modeling and experimental groups. In the first phase, the general physical characteristics of more than 200 people and whether the vehicle load has increased are directly checked, relevant information is collected, and the indicators, such as age, height, weight, body weight, heart rate, and regression equation, are compared. Predictive model: the results show that the F value in Model 5 is 102.948, much less than 0.000, and much less than 0.001, indicating maximum load, weight, sex, maximum heart rate, BMI, and maximum oxygen uptake. Therefore, the predictive models used to determine body weight, maximum load, sex, maximum heart rate, BMI independent variable, and VO2max as a dependent variable were reliable. The results of the Durbin Watson test are 1,639, indicating that the independence of the terminology of the recognition error in the regression model is good.
Precision Probiotic Medicine to Improve ICB Immunotherapy
Authors: Di Luccia Blanda , Colonna Marco
Published: May 2022

The established impact of gut microbiota- and probiotic-derived metabolites on immune-checkpoint blockade (ICB) has spurred extensive efforts to identify strains and druggable bioactive molecules of microbial origin that can improve tumor immune therapy. In this issue, Kawanabe-Matsuda and colleagues show that the exopolysaccharide EPS-R1 produced by the probiotic strain Lactobacillus delbrueckii subsp. bulgaricus augments the response to ICB therapy by expanding the population of Peyer's patches CCR6+ CD8+ T cells, which can subsequently migrate from the gut into CCL20-expressing tumors to enhance antitumor activity.

See related article by Kawanabe-Matsuda et al., p. 1336 (10).
Treatment Response Monitoring Using a Tumor-Informed Circulating Tumor DNA Test in an Advanced Triple-Negative Breast Cancer Patient: A Case Report
Authors: Azzi Georges , Krinshpun Shifra , Tin Antony , Maninder Minu , Malashevich Allyson Koyen , Malhotra Meenakshi , Vega Ruben Ruiz , Billings Paul R. , Rodriguez Angel , Aleshin Alexey
Published: May 2022

Triple-negative breast cancer (TNBC) is highly aggressive disease that is often refractory to surgery and multiple lines of therapy. Although the repertoire of FDA-approved treatments has expanded, there is an unmet need for biomarkers that can aid in appropriate selection and timing of therapy. We present a case of highly aggressive treatment-resistant TNBC that employed a comprehensive genomic profiling (CGP)-based assay to identify therapeutic targets, followed by longitudinal circulating tumor DNA (ctDNA) testing. For this, a tumor-naïve next-generation sequencing-based targeted panel was used to aid in therapy selection, along with longitudinal personalized and tumor-informed ctDNA testing to monitor tumor response to treatment. Longitudinal ctDNA testing using the tumor-informed assay detected post-surgical molecular residual disease, and rise in ctDNA levels during the surveillance period provided rationale for switching between four lines of therapy. Overall, the combined use of CGP assay with longitudinal ctDNA testing resulted in a potential prolonged survival in this highly aggressive case of TNBC.
Netarsudil-Induced Corneal Flattening in a Child with Secondary Open-Angle Glaucoma
Authors: Ganesh Durga , Coleman Anne L. , Shibayama Vivian P. , Tseng Victoria L.
Published: May 2022

We report a case of a child with secondary open-angle glaucoma who developed 6.5 diopters (D) of corneal flattening upon the addition of Rhopressa (0.02% netarsudil dimesylate solution) eye drops to a preexisting treatment regimen of timolol and latanoprost. This change in corneal power reversed after netarsudil, a rho-kinase inhibitor, was discontinued and replaced with Vyzulta (0.024% latanoprostene bunod ophthalmic solution). The 4-year-old female patient presented with bilateral secondary open-angle glaucoma from Paired Box 6 (PAX6)-related aniridia, aphakia, and persistent fetal vasculature. She was started on netarsudil to treat elevated intraocular pressure (IOP) in her right eye, which was not adequately controlled by latanoprost and timolol. Over 4 months, she developed 6.5D of corneal flattening in her right eye. Netarsudil was stopped and the corneal flattening reversed. There is evidence to support the ability of rho kinase inhibitors to increase the healing of the corneal endothelium in addition to their intended IOP-lowering effects. Rho kinase inhibitors may increase cell proliferation and adhesion within the corneal endothelium, hence decreasing apoptosis and promoting cell preservation. If there was an excess of cell proliferation; however, this might induce stromal cells to abnormally secrete enzymes or proteins, such as TGFβ-induced proteins. This could result in corneal fibrosis, thereby flattening the cornea. Further investigation is required to explore this phenomenon and elucidate its mechanism of action. Corneal flattening may be considered as a potential side effect of the use of netarsudil, particularly in young pediatric patients.
Interpreting the Evidence of Body Mass Index in Relation to Mental Health Status in Community-Dwelling Older Adults
Authors: Lin Wan-Yu , Chang Chin-Kuo , Tung Fu-I , Lin Tung-Yi , Lin Yu-Kai
Published: May 2022

Introduction: Maintaining a better physical and mental health status is an important issue for older adults in their later life. Thus, the study’s purpose was to evaluate the association between body mass index (BMI) and mental health status in older adults aged 65 years old or above residing in communities of Taipei City, Taiwan. Methods: We carried out secondary data analysis with data from a volunteer-based health examination project for older adults >65 years old residing in Taipei City from 2006 to 2010 with a retrospective study design. BMI, calculated by standardized measuring procedures for height and weight, and mental health status, evaluated by 5-item Brief Symptom Rating Scale (BSRS-5), were collected at their first visits of health examination. A BSRS-5 score ≥6 was considered an inferior mental health status for the outcome. In statistical analysis, univariable and multivariable logistic regressions were adopted to estimate the relative risk of inferior mental health status, treating BMI as the major exposure of interest. Results: A total of 90,576 subjects were involved, with a mean age of 73.38 years old (SD = 6.64 years) and 49.21% females. With confounders controlled, compared to normal or overweight (23 ≤ BMI <30), an adjusted OR of 1.23 (95% CI: 1.18, 1.29) on inferior mental health status was detected for the underweight group (BMI <23) significantly. Adjusted OR for those obese (BMI ≧30) was 0.87 (95% CI: 0.79, 0.96). Significantly elevated ORs of underweight were found for both genders, but the significantly protective effect of obese was only detected for females. Conclusion: Keeping an appropriate weight or even being overweighted might be beneficial for older adults dwelling in the community, especially for males.
Lautropia mirabilis: An Exceedingly Rare Cause of Peritoneal Dialysis-Associated Peritonitis
Authors: Calheiros Cruz Gonçalo , Sousa Mariana , Vilela Sara , Teixeira e Costa Fernando , Silva Francisco Jorge
Published: May 2022

Lautropia mirabilis is a gram-negative coccoid bacterium isolated from oral and upper respiratory sites with unclear pathogenic potential. We present an exceedingly rare case of peritoneal dialysis-associated peritonitis due to L. mirabilis in a patient with a recent history of periodontal infection, successfully treated with intraperitoneal antibiotics. We propose that clinicians consider this organism as a potential cause of illness.
Retinal Capillary Abnormalities in Sjögren-Larsson Syndrome Maculopathy
Authors: Staps Pippa , de Breuk Anita , Cruysberg Johannes R.M. , Willemsen Michèl , Theelen Thomas
Published: May 2022

Sjögren-Larsson syndrome (SLS) is a neurometabolic disease with a peculiar crystalline maculopathy. It is yet unclear if vascular abnormalities play a role in SLS maculopathy pathogenesis. We used optical coherence tomography angiography (OCT-A) to search for vessel abnormalities in SLS maculopathy. We performed a cross-sectional study in 4 patients (2 males, 2 females, aged 12–36 years) with various stages of SLS maculopathy. Besides OCT-A imaging, a complete ophthalmological examination and additional retinal imaging by transversal and en face spectral domain (SD) OCT were performed. OCT-A images were qualitatively assessed for vascular abnormalities, and imaging was compared to eight eyes of four healthy controls. On OCT-A, all eyes of patients with SLS showed a reduced capillary density around the fovea, and an enlarged foveal avascular zone (FAZ; SLS patients [n = 6 eyes] mean 0.70 mm2 [SD 0.18]; healthy controls [n = 8 eyes] mean 0.34 mm2 [SD 0.07], p = 0.004). In 2 patients, telangiectatic vessels were seen in the deep capillary layer. In conclusion, OCT angiography showed capillary paucity and morphological vessel abnormalities in these 4 patients with SLS.
Inert and mobile agents navigation interaction using reciprocal velocity obstacles for collisions avoidance
Authors: Juniastuti Susi , Fachri Moch , Nugroho Supeno Mardi Susiki , Hariadi Mochamad
Published: May 2022

Reciprocal velocity obstacles (RVO) is a method used for multiagents navigation that enables collision and oscillation-free avoidance against other mobile agents. Despite its ability in collision avoidance between agents, RVO has a hard time dealing with static obstacle avoidance. This problem has led to a tendency to use RVO only for agents avoidance and use other methods to handle static obstacles avoidance. In this paper, we present our new approach for interaction between mobile agents against static obstacles in the RVO based collision avoidance. We propose a concept called inert agents that interact as static obstacles. This inert agent is stand firm as static obstacles should be, while the inert agent also able to satisfy reactive collision avoidance nature of RVO to produce better avoidance result. We conduct an experiment to compare the performance of avoidance in a certain scenario. Our method shows better results when compared with generic static obstacles.