bluuebunny/crossref_metadata_2025 huggingface dataset:
G-Sep: A Deep Learning Algorithm for Detection of Long-Term Sepsis Using Bidirectional Gated Recurrent Unit
Authors: Rajmohan R. , Kumar T. Ananth , Julie E. Golden , Robinson Y. Harold , Vimal S. , Kadry Seifidine , Crespo Ruben Gonzalez
Published: April 2022

Sepsis is a common and deadly condition that must be treated eloquently within 19 hours. Numerous deep learning techniques, including Recurrent Neural Networks, Convolution Neural Networks, Long Short-Term Memory, and Gated Recurrent Units, have been suggested for diagnosing long-term sepsis. Regardless, a sizable portion of them are computationally risky and have precision problems. The primary issue described is that output will degrade, and resource utilization will expand proportionately as the volume of dependencies grows. To overcome these issues, we propose a G-Sep technique utilizing Bidirectional Gated Recurrent Unit Algorithm, which consumes much less resource to detect the disease and in a short time with better accuracy than the existing methods to diagnose the sepsis. AI models could assist with distinguishing potential clinical factors and give better than existing conventional low-execution models. The proposed model is implemented utilizing Conda and Tensorflow Framework using the California Inpatient Severe Sepsis (CISS) Patient Dataset. The comparative simulation of the various existing models and the proposed G-Sep model is done using Conda and Tensor frameworks. The simulation results revealed that the proposed model had outperformed other frameworks in terms of mean average precision (mAP), receiver operating characteristic curve (ROC), and Area under the ROC Curve (AUROC) metrics linearly.
Radial Restricted Boltzmann Machines with Functional Neural Network for Classification of the Fake and Real News Analysis
Authors: Muthu Lakshmi V. , Radhika R. , Kalpana G.
Published: April 2022

News methods are the main source of news for people around the world. However, there was a surprising rise in social media spread in later periods due to the significant increase in technology and updates. Fake news is as old as the news industry - misinformation, hoax, propaganda and satire have been around for a long time. So fake news is untrue and false information without proper evidence. Auto-detection technology is being built and studied on artificial intelligence and deep learning to address fake news’s rise and spread. This method’s efforts have been made to automate the process of fake news detection. This existing machine learning method has less classification accuracy, and multiple combination Machine learning methods have not consumed memory usage; it provides a higher false rate. Overcome this problem by introducing a deep learning method, namely Radial Restricted Boltzmann Machines with Functional Neural Network (R2BM-FNN), to reduce memory usage from a large set of training data. Initially, to apply the Count Victories distribution for data preprocessing to remove the stop words and unrelated text from data sets. In this proposed method neural network to train the neurons with the help of stochastic gradient descent. This proposed GRU-RLU method results in classification accuracy, precision, sensitivity, specificity, and execution time to evaluate system performance.
Research on Key Technology of Edge-Node Resource Scheduling Based on Linear Programming
Authors: Wang Zhen , Yao Nan , Liu Ziquan
Published: April 2022

With the arrival of the 5G era, centralized cloud computing is gradually shifting to edge computing. However, due to the characteristics of distributed multi-deployment of edge computing, it is difficult to ensure that all edge-node clusters have enough resources. Faced with the limited resources of edge-node cluster, how to carry out reasonable resource scheduling and provide users with high-quality services (high quality and low delay) has become the focus of current research. Aiming at this problem, in this paper, the edge-node resource scheduling based on linear programming is the key technology being studied. It is compared with conventional algorithm-based edge-node resource scheduling system. In the five deployments of the edge node resource scheduling system under the conventional algorithm, 8 virtual machines are used. The edge node resource scheduling system under the linear programming algorithm uses 7 virtual machines for 3 deployments and 8 virtual machines for 2 deployments. It can be seen that the results affirm the feasibility of edge node resource scheduling based on linear programming, and make due contributions to current research.
Soft b-Connected Mapping in Soft Topology
Authors: Rajput Alpa Singh , Thakur S. S.
Published: April 2022

In this paper, we introduce the concepts of soft b-connectedness between soft sets and soft set b-connected mapping in soft topological spaces. We showed that the concept of b-connectedness between soft sets is stronger than that of semi-connectedness between soft sets and pre-connectedness between soft sets. Further some of its properties and characterizations of soft b-connectedness between soft sets and soft set b-connected mapping are established.
SERS Immunosensor Based on High-Density “Hotspots” Au@SiO2 Array Substrate and Au-Ag Nanoshells Probes for Ultrasensitive Detection of Dual Biomarkers in the Cervical Cancer Serum
Authors: Ran Menglin , Song Qilong , Liu Yifan , Sun Yue , Cao Xiaowei , Lu Dan
Published: April 2022

Cervical cancer is a common cause of cancer-related death in women worldwide, and quantitative detection of serum tumor markers has practical implications for diagnosing cervical cancer. In this study, a novel surface-enhanced Raman scattering (SERS)-based immunosensor for the hypersensitive simultaneous detection of survivin and osteopontin (OPN) in cervical cancer serum was proposed, which was composed of the AuNPs@SiO2 microspheres (Au@SiO2) array immunosubstrate and Au-Ag nanoshells (Au-AgNSs) immunoprobes. The experimental results demonstrated that the constructed SERS immunosensor exhibited satisfactory selectivity and reproducibility while possessing detection limits as low as 0.908[Formula: see text]pg/mL and 0.813[Formula: see text]pg/mL for survivin and OPN in human serum. Finally, its usefulness in real serum samples (from healthy subjects, cervical intraepithelial neoplasia 1 (CIN 1), CIN 2, CIN 3 and cervical cancer patients) was verified, and the detection data were in good agreement with the enzyme-linked immunosorbent assay results. This immunosensor has excellent performance, which is promising for the early screening of cancer.
2-Distance coloring of planar graphs without triangles and intersecting 4-cycles
Authors: Bu Yuehua , Zhang Zewei , Zhu Hongguo
Published: April 2022

The k-2-distance coloring of graph G is a mapping [Formula: see text] if any two vertices at distance at most two from each other get different colors. The 2-distance chromatic number is the smallest integer k such that G has a k-2-distance coloring, denoted by [Formula: see text]. In this paper, we prove that every planar graph G without 3-cycles and intersecting 4-cycles and [Formula: see text] has [Formula: see text].
SmartFVM: A Fast, Flexible, and Scalable Hardware-based Virtualization for Commodity Storage Devices
Authors: Kwon Dongup , Lee Wonsik , Kim Dongryeong , Boo Junehyuk , Kim Jangwoo
Published: April 2022

A
computational storage
device incorporating a computation unit inside or near its storage unit is a highly promising technology to maximize a storage server’s performance. However, to apply such computational storage devices and take their full potential in virtualized environments, server architects must resolve a fundamental challenge:
cost-effective virtualization
. This critical challenge can be directly addressed by the following questions: (1) how to virtualize two different hardware units (i.e., computation and storage), and (2) how to integrate them to construct virtual computational storage devices, and (3) how to provide them to users. However, the existing methods for computational storage virtualization severely suffer from their low performance and high costs due to the lack of hardware-assisted virtualization support.

In this work, we propose
SmartFVM-Engine
, an FPGA card designed to maximize the performance and cost-effectiveness of computational storage virtualization. SmartFVM-Engine introduces three key ideas to achieve the design goals. First, it achieves high virtualization performance by applying
hardware-assisted virtualization
to both computation and storage units. Second, it further improves the performance by applying
hardware-assisted resource orchestration
for the virtualized units. Third, it achieves high cost-effectiveness by
dynamically constructing and scheduling
virtual computational storage devices. To the best of our knowledge, this is the first work to implement a hardware-assisted virtualization mechanism for modern computational storage devices.
General-purpose Unsupervised Cyber Anomaly Detection via Non-negative Tensor Factorization
Authors: Eren Maksim E. , Moore Juston S. , Skau Erik , Moore Elisabeth , Bhattarai Manish , Chennupati Gopinath , Alexandrov Boian S.
Published: April 2022

Distinguishing malicious anomalous activities from unusual but benign activities is a fundamental challenge for cyber defenders. Prior studies have shown that statistical user behavior analysis yields accurate detections by learning behavior profiles from observed user activity. These unsupervised models are able to generalize to unseen types of attacks by detecting deviations from normal behavior without knowledge of specific attack signatures. However, approaches proposed to date based on probabilistic matrix factorization are limited by the information conveyed in a two-dimensional space. Non-negative tensor factorization, however, is a powerful unsupervised machine learning method that naturally models multi-dimensional data, capturing complex and multi-faceted details of behavior profiles. Our new unsupervised statistical anomaly detection methodology matches or surpasses state-of-the-art supervised learning baselines across several challenging and diverse cyber application areas, including detection of compromised user credentials, botnets, spam e-mails, and fraudulent credit card transactions.
A Segmented Adaptive Router for Near Energy-Proportional Networks-on-Chip
Authors: France-Pillois Maxime , Gamatié Abdoulaye , Sassatelli Gilles
Published: April 2022

A
Network-on-Chip (NoC)
is an essential component of a
chip multiprocessor (CMP)
which however contributes to a large fraction of system energy. The unpredictability of traffic across a NoC frequently involves an expensive over-sizing of NoC resources which in turn leads to a significant contribution to the CMP power consumption. There exists a body of work addressing this issue, however so far solutions fall short when aiming for power reduction whilst maintaining high NoC performance. This paper proposes to combine router architecture optimizations with smart resource management to overcome this limitation. Based on a fully segmented architecture, we present an online adaptive router adjusting its active routing resources to meet the current traffic demand. This enhanced power-gating strategy significantly decreases both static and dynamic power consumption of the NoC, up to 70% for synthetic traffic patterns and up to 58% for real traffic workloads, while preserving NoC latency and throughput. Thanks to these adaptive power-saving mechanisms the proposed segmented NoC router provides near energy-proportional operation across the range of used benchmarks.
Investigation of Electrochemical Dissolution Behavior of Near-α TA15 Titanium Alloy in NaCl Solution with Low-Frequency Pulse Current
Authors: He Bin , Wang Dengyong , Zhang Jun , Lei Weining
Published: April 2022

TA15 material is a typical near-α titanium alloy and widely used for the aircraft key load bearing components. Electrochemical machining (ECM) is a cost-effective method to machine difficult-to-cut TA15. Due to the high chemical reactivity of titanium, titanium alloy is prone to passivation, which increases the difficulty of ECM, especially for some common ECM methods with low-frequency pulse currents. To investigate the change of surface characteristics of titanium alloy in pulse ECM, the dissolution behavior of TA15 in NaCl solution under the low frequency pulse current was analyzed by scanning electronic microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and electrochemical impedance spectroscopy (EIS). The results showed that even after removal of initial air-formed oxide film, TA15 would be passivated to form a new passive film during long pulse-off time. Under short pulse-on time, the passive film formed in pulse-off time could not be effectively removed in one pulse-on time, and the passivation effect would be accumulated, resulting in poor machined surface with humps and pits. When the pulse-on time was long enough to completely remove the newly formed passive film, TA15 was dissolved normally to obtain excellent surface with clear crystal structure.
Amorphous Carbon Coatings on Glass for High Voltage Protection
Authors: Baránková H. , Bardos L.
Published: April 2022

Radio frequency Hollow Cathode based hybrid process integrating both Physical Vapor Deposition and Plasma Enhanced Chemical Vapor Deposition was used for deposition of amorphous carbon on glass samples. The films were subjected to high voltage pulses and the performance was compared with uncoated glass samples to test the protection ability of the films, the ability to prevent the deteriorating effects of corona flashovers/arcs. In contrast to the uncoated glass the well adherent carbon films with thicknesses between 3.5 and 17 μm exhibited an excellent protection of the glass substrate against the flashovers/arc damages in both polarities of the electric field with voltages up to 300 kV.
Prenatal stress enhances atherosclerosis and telomere shortening in ApoE knockout mouse offspring
Authors: Ito Tomoaki , Saeki Harumi , Guo Xin , Sysa-Shah Polina , Tamashiro Kellie L. , Lee Richard S. , Ishiyama Shun , Orita Hajime , Sato Koichi , Brock Malcolm V. , Gabrielson Kathleen
Published: April 2022

Children born to women who experience stress during pregnancy have an increased risk of atherosclerosis in later life, but few animal models have explored mechanisms. To study this phenomenon, timed-bred ApoE knockout mice were determined pregnant with ultrasound and randomly assigned on gestation day 8.5 to either a control (no stress) or prenatal stress (PS) group using 2 h of restraint for five consecutive days. PS significantly increased plasma corticosterone levels in pregnant mice. The litters from PS mice showed increased neonatal mortality within the first week of life. Body weights (at euthanasia) of adult offspring at 25 wk from the PS group were significantly increased compared with weights of controls. Adult offspring from these pregnancies were serially imaged with ultrasound to measure plaque thickness and were compared with plaque macroscopic and microscopic pathology. PS groups had increased plaque thickness determined by ultrasound, gross, histological evaluation and increased aortic root and valve macrophage infiltration at 25 wk. Five-week-old mice from PS group had significant decrease in mean arterial pressure, yet blood pressure normalized by 10 wk. As prenatal stress induced increased atherosclerosis, and telomeres are susceptible to stress, aortas from 10-wk-old mice were compared for telomere lengths and were found to be significantly shorter in PS mice compared with control mice. These studies support future investigation of how stress impacts telomere shortening in animal models and human aortas. This model could be further used to investigate the role of prenatal stress, telomere biology, and atherosclerosis pathogenesis in adults.
Astrocytic responses to high glucose impair barrier formation in cerebral microvessel endothelial cells
Authors: Garvin Jodi , Semenikhina Marharyta , Liu Qiuli , Rarick Kevin , Isaeva Elena , Levchenko Vladislav , Staruschenko Alexander , Palygin Oleg , Harder David , Cohen Susan
Published: April 2022

Hyperglycemic conditions are prodromal to blood-brain barrier (BBB) impairment. The BBB comprises cerebral microvessel endothelial cells (CMECs) that are surrounded by astrocytic foot processes. Astrocytes express high levels of gap junction connexin 43 (Cx43), which play an important role in autocrine and paracrine signaling interactions that mediate gliovascular cross talk through secreted products. One of the key factors of the astrocytic “secretome” is vascular endothelial growth factor (VEGF), a potent angiogenic factor that can disrupt BBB integrity. We hypothesize that high-glucose conditions change the astrocytic expression of Cx43 and increase VEGF secretion leading to impairment of CMEC barrier properties in vitro and in vivo. Using coculture of neonatal rat astrocytes and CMEC, we mimic hyperglycemic conditions using high-glucose (HG) feeding media and show a significant decrease in Cx43 expression and the corresponding increase in secreted VEGF. This result was confirmed by the analyses of Cx43 and VEGF protein levels in the brain cortex samples from the type 2 diabetic rat (T2DN). To further characterize inducible changes in BBB, we measured transendothelial cell electrical resistance (TEER) and tight junction protein levels in cocultured conditioned astrocytes with isolated rat CMEC. The coculture monolayer’s integrity and permeability were significantly compromised by HG media exposure, which was indicated by decreased TEER without a change in tight junction protein levels in CMEC. Our study provides insight into gliovascular adaptations to increased glucose levels resulting in impaired cellular cross talk between astrocytes and CMEC, which could be one explanation for cerebral BBB disruption in diabetic conditions.
Identification of Enterprise Financial Risk Based on Clustering Algorithm
Authors: Li Bingxiang , Tao Rui , Li Meng
Published: April 2022

In order to solve the problem that corporate financial risks seriously affect the healthy development of enterprises, credit institutions, securities investors, and even the whole of China, the K-means clustering algorithm, the risk screening process, and the Gaussian mixture clustering algorithm, the risk screening process, are proposed; experiments have shown that although the number of high-risk companies selected by the K-means algorithm is small, only 9% of the full sample, the high-risk cluster can contain nearly 30% of the new “special treatment” companies. If the time period is extended to the next 5 years, this proportion will be higher. Finally we found that if the prediction of “special handling” events is used as the criterion for evaluating high-risk clusters, then K-means clustering can effectively screen out those risky companies that need to be treated with caution by investors. The validity of the experiment is verified.
5G Cognitive Radio Networks Using Reliable Hybrid Deep Learning Based on Spectrum Sensing
Authors: Mohanakurup Vinodkumar , Baghela Vishwadeepak Singh , Kumar Sarvesh , Srivastava Prabhat Kumar , Doohan Nitika Vats , Soni Mukesh , Awal Halifa
Published: April 2022

Spectrum sensing is critical in allowing the cognitive radio network, which will be used in the next generation of wireless communication systems. Several approaches, including cyclostationary process, energy detectors, and matching filters, have been suggested over the course of several decades. These strategies, on the other hand, have a number of disadvantages. Energy detectors have poor performance when the signal-to-noise ratio (SNR) is changing, cyclostationary detectors are very complicated, and matching filters need previous knowledge of the main user (PU) signals. Additionally, these strategies rely on thresholds under particular signal-noise model assumptions in addition to the thresholds, and as a result, the detection effectiveness of these techniques is wholly dependent on the accuracy of the sensor. In this way, one of the most sought-after difficulties among wireless researchers continues to be the development of a reliable and intelligent spectrum sensing technology. In contrast, multilayer learning models are not ideal for dealing with time-series data because of the large computational cost and high rate of misclassification associated with them. For this reason, the authors propose a hybrid combination of long short-term memory (LSTM) and extreme learning machines (ELM) to learn temporal features from spectral data and to exploit other environmental activity statistics such as energy, distance, and duty cycle duration for the improvement of sensing performance. The suggested system has been tested on a Raspberry Pi Model B+ and the GNU-radio experimental testbed, among other platforms.
A Dual Discriminator Adversarial Learning Approach for Dental Occlusal Surface Reconstruction
Authors: Tian Sukun , Huang Renkai , Li Zhenyang , Fiorenza Luca , Dai Ning , Sun Yuchun , Ma Haifeng
Published: April 2022

Objective. Restoring the correct masticatory function of partially edentulous patient is a challenging task primarily due to the complex tooth morphology between individuals. Although some deep learning-based approaches have been proposed for dental restorations, most of them do not consider the influence of dental biological characteristics for the occlusal surface reconstruction. Description. In this article, we propose a novel dual discriminator adversarial learning network to address these challenges. In particular, this network architecture integrates two models: a dilated convolutional-based generative model and a dual global-local discriminative model. While the generative model adopts dilated convolution layers to generate a feature representation that preserves clear tissue structure, the dual discriminative model makes use of two discriminators to jointly distinguish whether the input is real or fake. While the global discriminator focuses on the missing teeth and adjacent teeth to assess whether it is coherent as a whole, the local discriminator aims only at the defective teeth to ensure the local consistency of the generated dental crown. Results. Experiments on 1000 real-world patient dental samples demonstrate the effectiveness of our method. For quantitative comparison, the image quality metrics are used to measure the similarity of the generated occlusal surface, and the root mean square between the generated result and the target crown calculated by our method is 0.114 mm. In qualitative analysis, the proposed approach can generate more reasonable dental biological morphology. Conclusion. The results demonstrate that our method significantly outperforms the state-of-the-art methods in occlusal surface reconstruction. Importantly, the designed occlusal surface has enough anatomical morphology of natural teeth and superior clinical application value.
Study on the Law of Rock Anelastic Recovery and the Characteristics of In Situ Stress Field of 2000 m Deep Stratum in Metal Mines of Coastal Area
Authors: Zhang Yuezheng , Ji Hongguang , Xu Hanhua
Published: April 2022

In situ stress field in deep strata is dominated by self-weight stress and tectonic stress, which is the dynamic source of a series of mining dynamic disasters such as rock burst, mine earthquake, and collapse. To develop deep resources and build deep engineering construction, the distribution characteristics of the in situ stress field must first be ascertained, so as to provide a basic basis for the engineering surrounding rock support design and disaster risk prevention and control. In this paper, taking the Sanshandao Gold Mine in the coastal area as the engineering background, in the early stage of the construction of the 2000 m deep shaft, the anelastic strain recovery method was used to measure the deep in situ stress field. The laws and characteristics of hysteretic elastic recovery of rock at different depths are obtained through experiments, and the effects of temperature, time, and other factors on strain recovery are revealed. The in situ stress test results are basically consistent with the traditional test methods. This method has low operational complexity and better application effect in deep formations. The research has accumulated test experience and basis for carrying out in situ stress measurement in the range of 2000 m and even deeper.
QoS-Aware Smart Phone-Based User Tracking Application to Prevent Outbreak of COVID-19 in Saudi Arabia
Authors: Mashat Arwa , Alabdali Aliaa M.
Published: April 2022

Diverse variants of COVID-19 are repeatedly making everyday living unstable. In reality, the conclusive retort of this highly contagious virus still is in incognito mode. The health experts’ primary guideline on the possible prevention of this disease outbreak, including a list of restrictions and confinements, is insufficient in case of any public congregation. As a result, the demand for precise and upgraded real-time COVID-19 tracking and prevention-based applications increases. However, most of the existing android-based applications face a lack of data security and reliability that cannot satisfy the additional quality of service (QoS) requirements. This paper proposes an easy-to-operate android-based multifunctional application to track individuals’ health situations, allow uploading scanning report by the authorized organization like universities, mosques, school, and hospitals and helps the users to maintain guidelines via manageable steps. This article offers a three-layered QoS aware service-oriented task scheduling model upon multitasking android-based frontend focusing the cognitive-based AI applications in healthcare with a continual learning paradigm. Designed model is competent to optimize heterogeneous service scheduling and can minimize data delivery time, as well as the resource cost.
Green Synthesis of Silver Nanoparticles Incorporated Aromatherapies Utilized for Their Antioxidant and Antimicrobial Activities against Some Clinical Bacterial Isolates
Authors: Abdellatif Ahmed A. H. , Alhathloul Sultan S. , Aljohani Abdullah S. M. , Maswadeh Hamzah , Abdallah Emad M. , Hamid Musa Khalid , El Hamd Mohamed A.
Published: April 2022

There is a need to synthesize eco‐friendly nanoparticles with more effective and potent antibacterial activities. A green and cost‐effective method for the synthesis of silver nanoparticles (AgNPs) using Thymus vulgaris, Mentha piperita, and Zingiber officinale extracts was developed. The analytical instrumentation, namely, UV/Vis, absorption spectroscopy, FTIR, and scanning electron microscopy (SEM), was used to determine the developed AgNPs, confirming the functional groups involved in their reduction. Acidic molybdate, DPPH, and FRAP regents were reacted with AgNPs extract to evaluate their antioxidant, scavenging, and oxidative activities. The agar well diffusion method was used to determine the antibacterial potential of AgNPs extracts using clinical isolates. The developed AgNPs showed peaks at 25 cum\Diff, 50 cum\Diff, and 75 cum\Diff, respectively, of 16.59 ± 0.78, 45.94 ± 1.07, and 81.04 ± 0.98 nm, for Thymus vulgaris, Mentha piperita, and Zingiber officinale. SEM revealed uniform prepared and encapsulated AgNPs by plant extracts matrix. The FTIR shows the involvement of amide (‐CO‐NH2), carbonyl (‐CO), and hydroxyl (‐OH), which resulted in the reduction of AgNPs. The AgNPs extract showed significantly higher TAA, DPPH, and FRAP values than free AgNPs and plant extract (p < 0.05). Antibacterial of AgNPs extracts revealed various degrees of inhibition zones against Escherichia coli, Acinetobacter baumannii, and Staphylococcus aureus. The developed AgNPs extract showed acceptable antioxidant activities and noticeable antibacterial potential. The prepared green synthesized AgNPs showed a promising antibacterial activity against four multidrug‐resistant clinical isolates, Escherichia coli, Acinetobacter baumannii, and Staphylococcus aureus. Further, fractionated extracts other than crude extracts will be utilized in the preparation of AgNPs to get more efficient antibacterial activities for future work.
Effects of Bedaquiline on Antimicrobial Activity and Cytokine Secretion of Macrophages Infected with Multidrug-Resistant Mycobacterium tuberculosis Strains
Authors: Lyu Xia-Li , Lin Ting-Ting , Gao Jing-Tao , Jia Hong-Yan , Zhu Chuan-Zhi , Li Zi-Hui , Dong Jing , Sun Qi , Shu Wei , Wang Sai-Sai , Pan Li-Ping , Huang Hai-Rong , Zhang Zong-De , Li Qi
Published: April 2022

Background. Bedaquiline (Bdq) exerts bactericidal effects against drug-susceptible and drug-resistant Mycobacterium tuberculosis strains, including multidrug-resistant M. tuberculosis strains (MDR-MTBs). However, few reported investigations exist regarding Bdq effects on MDR-MTBs-infected macrophages activities and cytokine secretion. Here, Bdq bactericidal activities against MDR-MTBs and related cellular immune mechanisms were explored. Methods. Macrophages infected with MDR-MTBs or H37Rv received Bdq treatments (4 h/8 h/24 h/48 h) at 1 × the minimum inhibitory concentration (1 × MIC), 10 × MIC and 20 × MIC. Intracellular colony-forming units (CFUs) and culture supernatant IL-12/23 p40, TNF-α, IL-6, and IL-10 were determined using the Luminex® 200TM system. Normally distributed continuous data (mean ± standard deviation) were analyzed using t-test or F-test (SPSS 25.0,

P
<
0.05

deemed statistically significant). Results. (1) 100% of Bdq-treated macrophages (all doses applied over 4–48 h) survived with 0% inhibition of proliferation observed. (2) Intracellular CFUs of Bdq-treated MDR-MTBs-infected macrophages decreased over 4–48 h of treatment, were lower than preadministration and control CFUs, decreased with increasing Bdq dose, and resembled H37Rv-infected group CFUs (48 h). (3) For MDR-MTBs-infected macrophages (various Bdq doses), IL-12/23 p40 levels resembled preadministration group levels and exceeded controls (4 h); TNF-α levels exceeded preadministration group levels (24 h/48 h) and controls (24 h); IL-12/23 p40 and TNF-α levels resembled H37Rv-infected group levels (4 h/8 h/24 h/48 h); IL-6 levels exceeded preadministration and H37Rv-infected group levels (24 h/48 h) and controls (24 h); IL-10 levels resembled preadministration and H37Rv-infected group levels (4 h/8 h/24 h/48 h) and were lower than controls (24 h/48 h); IL-12/23 p40 and IL-10 levels remained unchanged as intracellular CFUs changed, with IL-12/23 p40 levels exceeding controls (4 h) and IL-10 levels remaining lower than controls (24 h/48 h); TNF-α and IL-6 levels increased as intracellular CFUs decreased (24 h/48 h) and exceed controls (24 h). Conclusion. Bdq was strongly bactericidal against intracellular MDR-MTBs and H37Rv in a time-dependent, concentration-dependent manner. Bdq potentially exerted immunomodulatory effects by inducing high-level Th1 cytokine expression (IL-12/23 p40, TNF-α) and low-level Th2 cytokine expression (IL-10).
Investigation of Influencing Factors on the Prevalence of Retinopathy in Diabetic Patients Based on Medical Big Data
Authors: Yang Lei , Qi Qin , Zheng Fangjiang , Wei Yanqiang , Wu Qiaoe
Published: April 2022

As one of the most important organs of human beings, the eyes can receive external visual information and play an important role in perception. Therefore, the method of maintaining eye health is a problem that people pay attention to. Omental disease is one of the most serious microvascular complications in diabetic patients, and it is also the main cause of blindness in patients. The purpose of this article is to investigate the main factors that influence the prevalence of retinopathy in diabetic patients based on medical big data. In this article, a method for investigating the causes of the incidence of retinopathy in diabetic patients based on medical big data is proposed, and a questionnaire survey method and other methods are used for experimental investigation. Combining the data in the figure in the experiment in this article, it can be seen that among diabetic patients, the prevalence of diabetes in men is 12.4%, and the prevalence of diabetes in women is 8.4%. From the data in the figure, it can be known that the rate of retinopathy caused by various factors is between 5% and 7%, and the total prevalence of retinopathy is 47.5%. There are many factors affecting the prevalence of retinopathy in diabetic patients, such as the duration of diabetes, urinary albumin index, glycosylated hemoglobin index, and fasting blood glucose level; various factors lead to an increase in the prevalence of retinopathy in diabetic patients. The results show that there are many factors affecting the prevalence of retinopathy in diabetic patients, so patients should pay attention to exercise, control their diet, and prevent retinopathy.
Observation of the Auxiliary Treatment Effect of Low-Frequency Nerve Therapy Instrument after Hysteroscopy for Moderate and Severe Intrauterine Adhesions Based on Intelligent Medical Treatment
Authors: Yin Mingyue , Pan Yingzheng
Published: April 2022

Uterine adhesions are mainly manifested as menstrual changes in women of childbearing age and affect fertility. Resection of uterine adhesions can well restore the shape of the patient’s uterine cavity and improve the patient’s menstruation. However, how to promote the growth of the endometrium and prevent the recurrence of adhesions after the operation is still a major problem. This article aims to study the use of traditional treatment methods as a control and use low-frequency nerve therapy device to assist in the treatment of posterior intrauterine adhesions recurrence rate, menstrual recovery effective rate, adverse reaction rate, liver function, etc. to study the low-frequency nerve therapy device auxiliary treatment method to prevent the postoperative effect of intrauterine adhesions. This article proposes an image processing algorithm based on intelligent medical related algorithms such as deep learning, Apriori algorithm, and an improved algorithm that introduces the degree of interest and details of 140 patients diagnosed with moderate to severe intrauterine adhesions who underwent hysteroscopic TCRA surgery in a certain affiliated hospital. The medical records were followed up by hysteroscopy and electric resection, and they were randomly divided into a control group and an observation group, with 70 cases in each group. Both groups of patients were closely monitored postoperatively, followed by postoperative review, and recorded menstrual recovery and pregnancy. At the same time, we performed hysteroscopy for recurrence of endometrial adhesions. The experimental results of this article show that the actual treatment rate of the control group is 65.7%, which is much lower than the 95.7% of the experimental group. The probability of returning to normal after 3 months of menstruation in the control group was 34.0%, much lower than the 69% in the experimental group. Three months after operation, the endometrial thickness of the experimental group was much higher than that of the control group, and the RI was lower than that of the control group. The difference was statistically significant (

P
<
0.05

). The clinical treatment results are satisfactory and worthy of clinical screening.
Protective Effect and Potential Mechanism of the Traditional Chinese Medicine Shaoyao-Gancao Decoction on Ethanol-Induced Gastric Ulcers in Rats
Authors: Jin Yang , Zhang Ming , Wang Yuefei , Lu Yuan , Liu Ting , Yang Guiqian , Song Shuoyao , Liu Wen
Published: April 2022

Background. Shaoyao-Gancao decoction (SGD) is a classic prescription in traditional Chinese medicine. SGD is effective in the treatment of gastric and duodenal ulcers. However, the biological activity and possible mechanisms of SGD in the treatment of gastric ulcers have not been fully elucidated. The purpose of this study was to scientifically evaluate the protective effect and potential mechanism of SGD against ethanol-induced gastric ulcers in rats. Methods. A single gavage of 10 mL/kg of 75% ethanol was used to establish a rat gastric ulcer model. A histopathological examination of the gastric tissue was performed. The levels of TNF-α, EGF, PGE2, SOD, and TBARS in gastric tissue were measured by ELISA. Cellular apoptosis in gastric tissues was assessed by TUNEL assay. The expression levels of caspase-3 and Bcl-2 were determined by immunohistochemistry. The potential mechanism of SGD in treating gastric ulcers was further studied using a network pharmacology research method. Results. The gastric tissue of rats with ethanol-induced gastric ulcers had obvious injury throughout the mucosal layer, which was significantly weakened in rats treated with SGD. Furthermore, treatment with SGD significantly increased the levels of EGF, PGE2, SOD, and Bcl-2 and decreased the levels of TNF-α, TBARS, and caspase-3 in the gastric tissue of rats with ethanol-induced gastric ulcers. SGD reduced ethanol-induced cell apoptosis in gastric tissue from rats with gastric ulcers. A traditional Chinese medicine-based network pharmacology study revealed that SGD exerts its anti-gastric ulcer effect by acting on multiple pathways. Conclusions. The above results indicate that SGD can improve gastric ulcers induced by ethanol. Moreover, this study demonstrated multicomponent, multitarget, and multipathway characteristics of SGD in the treatment of gastric ulcers and provided a foundation for further drug development research.
Tripterygium wilfordii Hook. f. Preparations for Rheumatoid Arthritis: An Overview of Systematic Reviews
Authors: Li Huimin , Hu Ruixue , Xu Simin , Dai Zeqi , Wu Xue , Hu Jing , Liao Xing
Published: April 2022

Objectives. To summarize the quantity and quality of evidence for using Tripterygium wilfordii Hook. f. (TwHF) preparations in patients with rheumatoid arthritis (RA) and to find the reasons of the disparity by comprehensively appraising the related systematic reviews (SRs). Methods. We performed an overview of evidence for the effectiveness and safety of TwHF preparations for patients with RA. We searched seven literature databases from inception to July 15, 2021. We included SRs of TwHF preparations in the treatment of RA. Four tools were used to evaluate the reporting quality, methodological quality, risk of bias, and the certainty of evidence for the included SRs, which are the PRISMA, the AMSTAR-2, the ROBIS, and the GRADE approach. Results. We included 27 SRs (with 385 studies and 33,888 participants) for this overview. The AMSTAR-2 showed that 19 SRs had critically low methodological quality and the remaining 8 had low methodological quality. The rate of overlaps was 68.31% (263/385), and the CCA (corrected covered area) was 0.53, which indicated the degree of overlap is slight. Based on the assessment of ROBIS, all 27 SRs were rated as low risk in phase 1; one SR was rated as low risk in domain 1, 9 SRs were in low risk in domain 2, 16 SRs were in low risk in domain 3, and 16 SRs were in low risk in domain 4 in phase 2; 7 SRs were rated as low risk in phase 3. Among 27 items of PRISMA, 15 items were reported over 70% of compliance, the reporting quality of 16 SRs was rated as “fair,” and 11 were “good.” Using GRADE assessment, moderate quality of evidence was found in 5 outcomes, and 5 outcomes were low quality. Conclusion. The use of TwHF preparations for the treatment of RA may be clinically effective according to the moderate-quality evidence. There are methodological issues, risk of bias, and reporting deficiencies still needed to be improved. SRs with good quality and further randomized clinical trials that focus on clinical important outcomes are needed.
Global Status of Emerging Lophomonas Infection: A Systematic Review of Reported Cases (1993—2020)
Authors: Nakhaei Maryam , Fakhar Mahdi , Sharifpour Ali , Ziaei Hezarjaribi Hajar , Banimostafavi Elham Sadat , Nazar Eisa
Published: April 2022

Purpose. Lophomonas spp., is an emerging protozoan parasite that belongs to the Parabasalids (Parabasalia, lophomonadida) which infects the respiratory tracts of humans. Despite the presence of a few reports of human lophomoniasis, the true burden of Lophomonas infection is unknown. This systematic review aimed to elucidate the latest global status of publications reporting human cases of lophomoniasis as a new emerging protozoal disease. Methods. A comprehensive and systematic search was performed in 10 (five English and five Persian) databases for studies reporting cases of lophomoniasis between 1993 and March 2020 (27 years). Then, the selected articles were carefully reviewed and screened based on the inclusion and exclusion criteria. Results. Overall, 32 eligible publications reporting 307 lophomoniasis cases from around the world are included in this review. The patients were aged between 1 month and 84 (mean age = 23.7 years). The highest number of cases (n = 171; 55.7%,

P
<
0.04

) significantly belonged to the juvenile age group (aged ≤18 years). The male to female ratio of the cases was almost equal, and no statistically significant difference between them was observed. The maximum number of cases (n = 237) was reported from Iran. Most cases (n = 196; 63.85%) had no history of underlying diseases/organ transplantation

P
<
0.001

. Moreover, the BAL specimen was the most commonly used clinical sample to diagnose lophomoniasis

P
<
0.001

. Conclusion. Our findings reveal that the prevalence of lophomoniasis is likely to be markedly underestimated when evaluated based on published case reports. Additionally, our data, at least for the time being, supports the idea that Lophomonas spp. should not be considered as an opportunistic infection. Thus, current work sheds light on some controversial issues regarding the epidemiological aspects of lophomoniasis.
Electroacupuncture Ameliorates Tibial Fracture-Induced Cognitive Dysfunction by Elevating α7nAChR Expression and Suppressing Mast Cell Degranulation in the Hippocampus of Rats
Authors: Zhou Yudi , Hu Cheng , Mao Chenlu , Li Sha , Cui Yaomei , Qian Yanning
Published: April 2022

Intracerebral neuroinflammation, closely related to brain mast cell (MC) activation, performs an integral function in the pathogenic process of postoperative cognitive dysfunction (POCD). In addition to regulating cognitive activities, the alpha-7-nicotinic acetylcholine receptor (α7nAChR) engages in the progression of cognitive deficiency. In this research, we aimed to investigate how electroacupuncture (EA) affects the cognitive function in rats after tibial fracture surgery to determine whether the underlying mechanism involves the inhibition of hippocampal MC degranulation via α7nAChR. A rat model of tibial fracture surgery for inducing POCD was developed and subjected to treatment with EA or the α7nAChR antagonist α-bungarotoxin (α-BGT) and the α7nAChR agonist PHA-543613. The spatial memory tasks in the Morris Water Maze (MWM) test showed that both EA and PHA-543613-treated rats performed significantly better than untreated rats, with reduced escape latency and increased frequency of passage through the platform. However, EA and PHA-543613 intervention decreased the protein and mRNA levels of High-mobility group box-1(HMGB-1) and proinflammatory cytokines tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β) in the serum and hippocampus, respectively, by upregulating α7nAChR in the hippocampus. Furthermore, EA and PHA-543613 pretreatment reduced the number of activated MCs and suppressed neuronal apoptosis after tibial fracture surgery in the hippocampal CA1 regions, which was reversed by α-BGT. The findings indicated that EA pretreatment ameliorated POCD after tibial fracture surgery in rats by inhibiting brain MC activation and neuroinflammation mediated by the α7nAChR-dependent cholinergic anti-inflammatory system.
[Retracted] Linguistic Analysis of Hindi‐English Mixed Tweets for Depression Detection
Authors: M J Carmel Mary Belinda , S Ravikumar , Arif Muhammad , Kumar Dhilip , K Antony Kumar , G Arulkumaran
Published: April 2022

According to recent studies, young adults in India faced mental health issues due to closures of universities and loss of income, low self‐esteem, distress, and reported symptoms of anxiety and/or depressive disorder (43%). This makes it a high time to come up with a solution. A new classifier proposed to find those individuals who might be having depression based on their tweets from the social media platform Twitter. The proposed model is based on linguistic analysis and text classification by calculating probability using the TF∗IDF (term frequency‐inverse document frequency). Indians tend to tweet predominantly using English, Hindi, or a mix of these two languages (colloquially known as Hinglish). In this proposed approach, data has been collected from Twitter and screened via passing them through a classifier built using the multinomial Naive Bayes algorithm and grid search, the latter being used for hyperparameter optimization. Each tweet is classified as depressed or not depressed. The entire architecture works over English and Hindi languages, which shall help in implementation globally and across multiple platforms and help in putting a stop to the ever‐increasing depression rates in a methodical and automated manner. In the proposed model pipeline, composed techniques are used to get the better results, as 96.15% accuracy and 0.914 as the F1 score have been attained.
MCT-TTE: Travel Time Estimation Based on Transformer and Convolution Neural Networks
Authors: Liu Fengkai , Yang Jianhua , Li Mu , Wang Kuo
Published: April 2022

In this paper, we propose a new travel time estimation framework based on transformer and convolution neural networks (CNN) to improve the accuracy of travel time estimation. We design a traffic information fusion component, which fuses the GPS trajectory, real road network, and external attributes, to fully consider the influence of road network topological characteristics as well as the traffic temporal characteristics on travel time estimation. Moreover, we provide a multiview CNN transformer component to capture the spatial information of each trajectory point at multiple regional scales. Extensive experiments on Chengdu and Beijing datasets show that the mean absolute percent error (MAPE) of our MCT-TTE is 11.25% and 11.78%, which is competitive with the state-of-the-arts baselines.
Inhibition of PDE10A-Rescued TBI-Induced Neuroinflammation and Apoptosis through the cAMP/PKA/NLRP3 Pathway
Authors: Huang Jin , Tang Dang , Cao Yiqiang , Wang Yonggang , Long Jiang , Wei Lin , Song Hai
Published: April 2022

Phosphodiesterase 10A (PDE10A) is a dual-substrate phosphodiesterase that is highly expressed in the striatal complex. PDE10A is an important target for the treatment of ganglion dysfunction and neuroinflammation-related diseases, but its possible impact on traumatic brain injury (TBI) is still unclear. This study aims to investigate the protective effects of inhibiting PDE10A on neuroinflammation post-TBI injury and its possible molecular mechanism. The expression of PDE10A in rats and HT22 cells was determined by Western blotting. The neurological dysfunction of these rats was detected by Nissl staining, hematoxylin-eosin (HE) staining, and Morris water maze test. The activity of HT22 cells was measured by MTT. The findings of this study suggest that PDE10A is highly expressed in the brain tissue of TBI rats and HT22 cells induced by mechanical injury. Inhibition of PDE10A reduces the expression of interleukin-1β (IL-1β) and interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-α) in HT22 cells induced by mechanical injury to inhibit cell apoptosis. Simultaneously, inhibition of PDE10A in TBI rats reduces the time to find a visible platform in the same pool, while cAMP/PKA activator treatment alleviates all of the abovementioned phenomena. Additionally, it is further confirmed that inhibition of PDE10A activates the cAMP/PKA pathway and downregulates the expression of NRLP3. These findings demonstrate that inhibition of PDE10A exerts neuroprotection by inhibiting apoptosis and inflammation following TBI, at least partially by the cAMP/PKA/NLRP3 pathway.