bluuebunny/crossref_metadata_2025 huggingface dataset:
The Positive and Negative Effects of Calcium Supplementation on Mortality in Septic ICU Patients Depend on Disease Severity: A Retrospective Study from the MIMIC-III
Authors: He Wencheng , Huang Lei , Luo Hua , Chen Jingying , Li Weijia , Zhang Yiming , An Youzhong , Zhang Weixing
Published: June 2022

Background. Calcium administration in septic patients with hypocalcemia is a controversial issue. The present study preliminarily investigated the effects of calcium supplementation on the length of hospitalization and mortality in septic ICU patients with different severities of hypocalcemia and disease. Method. A total of 5761 eligible septic patients, including 2689 who received calcium supplementation and 3072 who did not receive calcium supplementation, were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The cofounding covariates between the calcium supplement and nonsupplement groups were balanced using the propensity score matching model. We compared the length of stay (LOS) in the ICU and hospital with 28-day and hospital mortality and stratified the analysis according to the sequential organ failure assessment (SOFA) score and ionized calcium (iCa) at the first ICU admission in the matched groups. Results. The results showed that iCa at the first ICU admission was associated with mortality in sepsis patients (HR: 0.421; 95% CI: 0.211∼0.837), but the lowest mortality rate was observed in patients with mild hypocalcemia. A total of 993 paired patients were included in the analysis after propensity score matching. Regardless of the SOFA score or presence of iCa, the LOS in the ICU was higher in the calcium supplement group than in the nonsupplement group. The survival analysis was stratified by the SOFA score and showed that calcium supplementation reduced mortality when the patient’s SOFA score was ≥8 (

p
=
0.002

), and it worsened the outcome when the patient’s SOFA score was ≤4 (

p
=
0.010

). It had no significant effect on patients with SOFA scores ranging from 5 to 7 (

p
=
0.911

). Conclusion. Our results showed that mild hypocalcemia may be protective in septic patients, and calcium supplementation may have positive and negative effects on mortality depending on disease severity. The SOFA score may be a valuable clinical index for decisions regarding calcium administration.
Effects of Modified Sang ju-Yin Decoction Combined with IFNαlb Nebulization on IL-1β and HBD2 in Children with Asthmatic Bronchitis
Authors: Zhang Jun , Wang You lan , Liu Ying ting , Yuan Min , Jin Jian guo
Published: June 2022

Background. Breathing disease swelling of the lung tubes caused by viral infection is more and more likely to develop into related to the lung tubes breathing disease, especially repeating breathing loudly. Objective. To investigate the effect of modified Sang ju-Yin Decoction combined with Interferon (IFN)αlb nebulization on children with asthmatic bronchitis and the effects of IL-1β and β-defensin 2 (HBD2). Materials and Methods. The clinical data of 80 children with asthmatic bronchitis who were diagnosed and treated in our hospital from May 2019 to May 2021 were selected as the research objects and divided into the control group and the observation group with 40 cases in each group according to different treatment methods. Among them, the control group was nebulized with IFNαlb, and the observation group was given addition and subtraction of Sang ju-Yin Decoction based on the control group. The clinical symptoms, pulmonary function indexes, adverse reactions, and effects on serum inflammatory indexes were observed and compared between the two groups. Results. There was no significant difference in the scores of symptoms and signs between the two groups before treatment (

P
>
0.05

), while the scores of shortness of breath, cough, stridor, lung rales, and signs after treatment of observation group were better than those of the control group (

P
<
0.05

). There was no significant difference in serum inflammatory indexes and pulmonary function indexes between the two groups before treatment (

P
>
0.05

), while the differences in IL-6, IL-10, TNF-α, IL-1β, and HBD2 after treatment were significant between the two groups (

P
<
0.05

). After treatment, the RR, TV, and PEF indexes of the two groups of children were significantly improved (

P
<
0.05

). After treatment, the adverse reaction rate of liver function damage, dry throat discomfort, rash, nausea, and vomiting in the observation group was 7.5%, which was significantly lower than 27.5% in the control group (

P
<
0.05

). Conclusion. Modified Sang ju-Yin Decoction combined with IFNαlb nebulization can improve symptoms and promote the recovery of patients in the treatment of acute bronchitis.
A Combined Approach for Multi-Label Text Data Classification
Authors: Štrimaitis Rokas , Stefanovič Pavel , Ramanauskaitė Simona , Slotkienė Asta
Published: June 2022

Automated data analysis solutions are very dependent on data and its quality. The possibility of assigning more than one class to the same data item is one of the specificities that need to be taken into account. There are no solutions, dedicated to Lithuanian text data classification that helps to assign more than one class to data item. In this paper, a new combined approach has been proposed for multilabel text data classification for text analysis. The main aim of the proposed approach is to improve the accuracy of traditional classification algorithms by incorporating the results obtained using similarity measures. The experimental investigation has been performed using the financial news multilabel text data in the Lithuanian language. Data have been collected from four public websites and classified by experts into ten classes manually, where each of the data items has no more than two classes. The results of five commonly used algorithms have been compared for dataset classification: the support vector machine, multinomial naive Bayes, k-nearest neighbours, decision trees, linear and discriminant analysis. In addition, two similarity measures have been compared: the cosine distance and the dice coefficient. Research has shown that the best results have been obtained using the cosine similarity distance and the multinomial naive Bayes classifier. The proposed approach combines the results of these two methods. Research on different cases of the proposed approach indicated the peculiarities of its application. At the same time, the combined approach allowed us to obtain a statistically significant increase in global accuracy.
Consider the Consequences: A Risk Assessment Approach for Industrial Control Systems
Authors: Kim Aram , Oh Junhyoung , Kwon Kookheui , Lee Kyungho
Published: June 2022

The development of information and communication technologies extended the application of digitalized industrial control systems (ICSs) to critical infrastructure. With this circumstance, emerging sophisticated cyberattacks by adversaries, including nation-backed terrorists, target ICSs due to their strategic value that critical infrastructure can cause severe consequences to equipment, people, and the environment due to the cyberattacks on ICSs. Therefore, critical infrastructure owners should provide high assurance to those involved, such as neighboring residents and governments, that the facility is adequately protected against cyberattacks. The risk assessment that identifies, estimates, and prioritizes risks is vital to provide high assurance. This study proposes a framework for evaluating risks by quantifying the likelihood of cyber exploitation and the consequences of cyberattacks. The quantification of the likelihood of cyber exploitation is inspired by research on Bayesian attack graphs (BAGs), allowing probability evaluation that considers the causal relationship between ICSs and multistage attacks. For the cyberattack consequences quantification, we propose a methodology to evaluate how far an impact will spread and thus how many functions will be influenced when an ICS is exploited. The methodology is conducted by ICS experts identifying and listing functional dependencies and essential function goals among ICSs that they are already familiar with and do not require in-depth cybersecurity knowledge. Through experiments, we demonstrated how to apply our framework to assess the risks of the plant protection system, which is a safety-grade digital system used in nuclear power plants. The result shows that risk can be multidimensionally assessed than previous literature, such as discovering that components that were not considered important have high risk due to their functional connectivity.
Analysis of the Therapeutic Effect of Changyanning on Intestinal Flora in Inflammatory Bowel Disease
Authors: Hu Xinyu , Yang Fuyuan
Published: June 2022

Research Purposes. Inflammatory bowel disease (IBD) is an autoimmune disease coinduced by genes, environment, and immune response, mainly including ulcerative colitis (UC) and Crohn’s disease (CD). There are a large number and variety of intestinal bacteria in the human intestinal tract. These bacteria maintain a balance with the human environment and participate in the normal physiological processes of the human body. They play a unique role in defending against pathogen invasion, maintaining the homeostasis of the human immune system and metabolizing substances. Intestinal flora imbalance may be one of the pathogenic factors of IBD, and restoring the disturbed intestinal flora has become a research hotspot in the prevention and treatment of IBD. Changyanning is mainly composed of Dijincao grass, yellow hairy ear grass, camphor tree roots, maple leaves, and so on. Clinical studies have shown that Changyanning alone or in combination has a significant effect on ulcerative colitis, but the treatment mechanism is not yet clear. In this study, we established an IBD animal model to explore the therapeutic mechanism of Changyanning on inflammatory bowel disease and its effect on intestinal flora. Research Methods. Male C57BL/6 SPF mice were given free access to 4% dextran sulfate sodium (DSS) solution for 7 days to establish an ulcerative colitis model. After the model was established, different doses of Changyanning tablets, Changyanning granules, and sulfasalazine were given by gavage for 7 days. The relieving effects of the above drugs on the symptoms of inflammatory bowel disease were evaluated by evaluating the mouse/rat body weight, survival rate, disease activity index, colon length, pathological tissue score, and other indicators. Results. The DSS‐induced IBD mouse model showed significant increases in weight loss, DAI score, and pathological score. Both Changyanning tablets and granules can relieve the weight loss of mice, restore the colon length, and protect the colon tissue structure of mice. In reducing DAI and pathological scores in mice, Changyanning granules had a better effect. In conclusion, Changyanning can significantly improve the quality of life of IBD model animals, relieve intestinal inflammatory response, and relieve colonic edema, ulceration, and necrosis. The results show that Changyanning has a certain therapeutic effect on IBD. This study also provides experimental evidence for the application of Changyanning in the treatment of IBD, which is of great significance to its clinical application. The trail is registered in ChiCTR with number (TRN) ChiCTR2000028830.
Optimization of Deep Eutectic Solvents Extraction of Effective Components from Phellodendron chinense Schneid by Response Surface Methodology
Authors: Yue Xianwen , Cao Yuan , Wang Yuejie , Bao Huiwei , Xu Yang
Published: June 2022

Taking Phellodendron chinense Schneid (PcS) as the raw material with ultrasonic-assisted eutectic solvent, the effects of various DESs on the extractable content of palmatine and berberine in PcS were investigated. On the basis of the single-factor test, the best DES was determined to be choline chloride and 1,3-propanediol (mole ratio 1 : 2). After optimizing by the response surface method, the optimum extraction conditions were as follows: the solid-liquid ratio was 1 : 30 (w/v), water content was 30% (v/v), vortex time was 7 min, ultrasonic time was 20 min, ultrasonic temperature was 60°C, ultrasonic power was 400 W, and the content of palmatine in PcS was 5.421 ± 0.283 mg/g, and the content of berberine in PcS was 15.573 ± 0.539 mg/g. Therefore, DES prepared from choline chloride and 1,3-propanediol can be used to extract palmatine and berberine from PcS. The optimized process conditions determined by the response surface method are reliable and can provide a reference for the green extraction of effective components from PcS.
Walking Position Data in Football Training Based on Embedded Action Recognition System
Authors: Li Li
Published: June 2022

With the development of image recognition and pattern recognition, action recognition has become a hot research direction in the field of computer actions. Because the embedded platform has the advantages of small size and low power consumption, it is a good choice to use the embedded platform for action recognition. These sensor data are used to overcome the drift error of the gyroscope when the positioning data are collected, and the accuracy and reliability of the data are improved. The system uses two data forwarding nodes to aggregate the data of each sensor and then transmit it to the host computer. The data aggregation node can communicate and upload data by means of Bluetooth or wire. In order to deeply study the relevance of the embedded action recognition system in collecting football training positioning data, this paper uses simulation model establishment method, data collection method, and theory and practice combination method to collect samples, analyzes the embedded action recognition system, and streamlines the algorithm. Using computer technology as a support when building a simulation model, the first thing you need to have is a certain computer technology because when building a simulation model, not all objects can be modeled. At this time, only computer technology can be used as a support to build a simulation model of some objects and create an action recognition system that can record the position data in football training. After establishing the human body simulation recognition model, use MATLAB to extract the K bone information. When collecting, the human body is facing the K device, the number of acquisition frames is 25, 55, 75, 95, 105, and 205, and each frame number is collected 10 times. Take the average. The delay time is 4s. The result shows that the 20 key bone point outputs by K come from the RGB camera on the same side. Further study the actual utility of the compensated model in the presence of occlusion. They are worn on the middle of the thigh and calf, respectively. In a sensor, the measured value of thigh length L1 is 0.6 m, and the value of calf length L2 is 0.4 m. Take the right knee as an example. When the leg is raised, the b-axis coordinate increases by 2%, and the c-axis coordinate decreases by 1.8%. When the leg is lowered, the opposite is true. It can be seen that the compensated coordinate is consistent with the action. It is basically realized that starting from the embedded action recognition system, a model that can support the analysis of football positioning data is designed.
Decision Tree Algorithm in the Performance Evaluation of School-Enterprise Cooperation for Higher Vocational Education
Authors: Yan Yi
Published: June 2022

With the development of China’s economy and the internet, machine learning has become one of the people’s favorite ways of working. This study aims to solve the problems of brain drain, inefficiency, and injustice in the performance appraisal of school-enterprise cooperation. Decision tree technology is used to establish an assessment system. After the assessment index data are segmented, and the fuzzy version of the C4.5 algorithm is used to calculate and count different data segments. Finally, different data types are used to classify and construct decision trees. The school-enterprise cooperation performance appraisal system has been established and perfected for higher vocational education. In this way, the performance appraisal system is optimized in the school-enterprise cooperation. The system improves work efficiency while reducing manual labor and improves the problems in the performance appraisal of other colleges and universities’ cooperation. Facts have proved that the establishment of decision trees can effectively solve the problems of duplication of indicators and complex calculations in performance appraisal. After being optimized, the C4.5 algorithm will increase the calculation accuracy to 95%. The overall speed of establishment is increased by 5% based on the algorithm before optimization. This breaks the traditional performance appraisal system and promotes the performance appraisal of school-enterprise cooperation to be more open, transparent, fair, and equal.
Risk Measurement of Local Government Debt Based on Complex Networks: Taking China’s Urban Investment Bonds as an Example
Authors: Wu Yihu , Zhao Huiqian
Published: June 2022

For many countries, the systemic risk of local government debt is a matter of concern, but at present, there is little analysis in this area. The complex network based on information analysis provides an effective tool for analyzing the system risk between networks. This paper takes China as an example and takes the provincial urban investment bonds as the object for research. The regional network constructed with the interest rate data of urban investment bonds represents the overall systemic risk of these bonds. The main conclusions are as follows: provincial nodes in western and central areas (underdeveloped areas) are more important in the system, and they are more easier to form debt defaults. For an interconnected urban investment bond network, the higher the default risk of an individual, the greater the risk of the entire system. Finally, based on the indicators obtained from the provincial urban investment bond network model, this paper puts forward policy suggestions for the prevention of urban investment bond risks.
HMGA1 Promotes Macrophage Recruitment via Activation of NF-κB-CCL2 Signaling in Hepatocellular Carcinoma
Authors: Chen Junming , Ji Kang , Gu Lingyan , Fang Yu , Pan Ming , Tian Shuxia
Published: June 2022

Background. Tumor-associated macrophages (TAMs) are known to generate an immune-suppressive tumor microenvironment (TME) and promote tumor progression. Hepatocellular carcinoma (HCC) is a devastating disease that evolves in the background of chronic inflammatory liver damage. In this study, we aimed to uncover the mechanism by which HCC cells recruit macrophages into the TME. Methods. Bioinformatic analysis was performed to identify differentially expressed genes related to macrophage infiltration. An orthotopic HCC xenograft model was used to determine the role of macrophages in HCC tumor growth. Clodronate liposomes were used to delete macrophages. Western blotting analysis, quantitative real-time PCR, and enzyme-linked immunosorbent assay were performed to determine the underlying mechanisms. Results. The high mobility group A1 (HMGA1) gene was identified as a putative modulator of macrophage infiltration in HCC. Deletion of macrophages with clodronate liposomes significantly abrogated the tumor-promoting effects of HMGA1 on HCC growth. Mechanistically, HMGA1 can regulate the expression of C-C Motif Chemokine Ligand 2 (CCL2), also referred to as monocyte chemoattractant protein 1 (MCP1), which is responsible for macrophage recruitment. Moreover, NF-κB was required for HMGA1-mediated CCL2 expression. Pharmacological or genetic inhibition of NF-κB largely blocked CCL2 levels in HMGA1-overexpressing HCC cells. Conclusions. This study reveals HMGA1 as a crucial regulator of macrophage recruitment by activating NF-κB-CCL2 signaling, proves that HMGA1-induced HCC aggressiveness dependents on the macrophage, and provide an attractive target for therapeutic interventions in HCC.
A Two-Stage Train Delay Prediction Method Based on Data Smoothing and Multimodel Fusion Using Asymmetry Features in Urban Rail Systems
Authors: Wang Hao
Published: June 2022

During the operation of urban rail transit trains, train delays have a great negative impact on subway safety and operation management. It is caused by various external environmental disturbances or internal equipment failures. In order to better grasp the situation of train delays, adjust train operation schedules in time, and improve the quality of rail transit command and transportation services, this paper proposes a two-stage train delay prediction method based on data smoothing and multimodel fusion. In the first stage, the Singular Spectrum Analysis (SSA) method is used to smooth the train operation data, and the smoothed components and residual components are extracted. In the second stage, we use different machine learning methods to train the smoothed data and use the

K

-nearest neighbor (KNN) method to fuse different trainers. Finally, the predicted values of the smoothed and residual components are combined to improve the overall performance of the train delay prediction model, especially under asymmetry features. The research results show that the prediction accuracy of the two-stage train delay prediction method based on KNN multimodel fusion is significantly better than that of the independent machine learning model.
Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)
Authors: Habibzadeh Mohammad , Ameri Mahmoud , Sadat Haghighi Seyede Mojde , Ziari Hassan
Published: June 2022

Traffic accidents occur due to a combination of factors that lead to casualties and injuries. By identifying the most effective factors, it is possible for safety authorities to provide appropriate solutions for decreasing the accident severity and implementing preventive measures. The aim of this research was to present models to predict the accident severity on two-lane two-way (TLTW) rural highways in Iran over a one-year period from 2019 to 2020. Therefore, the occurrence probability of any type of accident was determined by artificial neural network (ANN)-based prediction models using nine independent variables affecting the accident severity. This study developed numerous ANN structures using back-propagation to model the potential nonlinear relationship between the accident severity and accident-related factors. Results indicated that among the models, the multilayer perceptron neural network (MLPNN) model with 6-2-2 partition had the best performance and prediction power. This model was developed using the standardized rescaling method for covariates and batch for training. Also, 9, 5, and 2 units were considered automatically for the input, hidden, and output layers, respectively, and the hyperbolic tangent and softmax were used as an activation function in the hidden and output layers, respectively. This model had the lowest cross-entropy error of 39.6 and the highest correct percentage of 82.5%, and the area under the receiver operating characteristic (ROC) curve was 0.852. Moreover, among all the effective variables, pavement condition index, roadside hazard, shoulder width, and passing zone ratio had the greatest impact on the accident severity. Finally, safety strategies were proposed to increase safety and reduce accidents along these roads.
Computing Resource Allocation Strategy Based on Mobile Edge Computing in Internet of Vehicles Environment
Authors: Gao Deng
Published: June 2022

The rapid growth of the Internet of Vehicles applications leads to unreasonable resource allocation, task computing delay, and large energy consumption. To solve this problem, this paper proposes a computing resource allocation strategy based on mobile edge computing in the Internet of Vehicles environment. Firstly, we analyze the process of mobile edge computing network and resource allocation. Then, it is improved by introducing the Halton sequence into the traditional genetic algorithm, and the difference between populations is reduced by canceling the randomness to generate an initial population with smaller individual differences. Furthermore, by optimizing the parameter setting method of traditional genetic algorithm, the crossover probability and mutation probability that change dynamically with fitness value are given, which improves the accuracy of the algorithm. Finally, the simulation results show that under the premise of the same number of tasks, the average delay and total cost of the proposed strategy are also the smallest. When the number of tasks is 45, the average delay of the proposed method is 0.32 s and the total cost is 0.34, which are better than the comparison method. Simulation results show that the improved algorithm has more advantages in delay and overhead.
Passive Framework of Sparse Region Duplication Detection from Digital Images
Authors: Mehmood Zahid , Chaudhry Hassan Nazeer , Naqvi Rizwan Ali , Kulsoom Farzana , Munshi Asmaa , Bilal Muhammad
Published: June 2022

Currently, digital images are widely communicated by media using social media applications. The general public captures the digital images for preserving the family and personal memories and to share with their friends and family. Digital images have been used extensively in forensic science to present the digital images as proof in the court and law enforcement agencies, which present a loophole for the culprits to forge the digital image and change the proofs and evidence. Copy-move forgery (CMF) is among the most widely employed image manipulation methods. In this method, the area of the image is duplicated to some other part to modify its content by applying different postprocessing operations on images like blurring, color reduction, and scaling which is a challenging research problem in copy-move forgery detection (CMFD). In this paper, an efficient and effective CMFD method is presented to identify the single and multiple altered areas in an image in the presence of postprocessing operations. The proposed CMFD method divides the image into circular blocks. It computes a rotation-invariant feature vector from each circular block of the image by applying local intensity order pattern (LIOP) features. The computed feature vectors are then compared using Euclidean distance to locate the suspected image’s forged areas. The experimental results of the proposed CMFD method are reported on three standard datasets of the CMF, namely, CoMoFoD, KLTCI, and MICC-F220. The experimental analysis of the proposed CMFD method on these datasets indicates that it produces robust performance (detection accuracies of 97.29% on the CoMoFoD dataset, 98.53% on the KLTCI dataset, and 97.57% on the MICC-F220 dataset) as compared with state-of-the-art CMFD methods in terms of the standard performance evaluation parameters of the CMF.
Differential Privacy-Based Double Auction for Data Market in Blockchain-Enhanced Internet of Things
Authors: Zhang Junhua , Zhong Caiming
Published: June 2022

With the rapid development of the Internet of Things (IoT), large amounts of data are collected, which constitute a valuable business resource. Hence, a suitable IoT data market needs to be established, and the provision of safe and effective trading services for multiple buyers and sellers is required. This paper introduces an IoT data market framework supported by blockchain. It focuses on a transaction realization scheme for multiple buyers and sellers. In the scheme, the mechanisms are designed to determine the corresponding data providers and recipients for the buyers and sellers, respectively, and the transaction prices of both parties. When the data market runs, an inference attack will raise bid information leakage issues. We study a transaction scheme that enables differential privacy protection of bids based on an exponential mechanism. This paper theoretically proves the individual rationality, weak budget balance, and truthfulness of the normal transaction scheme and differential privacy-based transaction scheme. This paper also theoretically proves the effectiveness of the differential privacy protection for bids of transaction participants. Furthermore, this paper verifies the performances of the two schemes through digital simulation experiments. From the experiments, we can also prove that these schemes occupy reasonable social welfare and computational overhead.
The Role of Connexin in Ophthalmic Neovascularization and the Interaction between Connexin and Proangiogenic Factors
Authors: Xu Chuyang , Zhang Hong , Zhong Wei , Zhou Hongyan
Published: June 2022

The formation of new blood vessels is an important physiological process that occurs during development. When the body is injured, new blood vessel formation helps the body recuperate by supplying more oxygen and nutrients. However, this mechanism can have a negative effect. In ophthalmologic diseases, such as corneal new blood vessels, neonatal vascular glaucoma, and diabetes retinopathy, the formation of new blood vessels has become a critical component in patient survival. Connexin is a protein that regulates the cellular and molecular material carried by cells. It has been demonstrated that it is widely expressed in vascular endothelial cells, where it forms a slit connection between adjacent cells to promote cell-cell communication via hemichannels, as well as substance exchange into intracellular environments. Numerous studies have demonstrated that connexin in vascular endothelial cells plays an important role in angiogenesis and vascular leakage. The purpose of this study was to investigate the effect between the angiogenesis-associated factor and the connexin. It also reveals the effect of connexin on ophthalmic neovascularization.
Fault Diagnosis and Handling of the Two-Dimensional Tracking Servo System for Space
Authors: Ma Zelong , Zhao Qinglei , Wang Shuxin
Published: June 2022

Aiming at the on-orbit safety problem of the two-dimensional tracking servo system for space, based on ant colony optimization (ACO) and expert systems, this paper proposes a fault diagnosis and handling strategy of the two-dimensional (2D) tracking servo system for space. Specifically, a fault library was established for the tracking servo system according to field observations. The ACO was called to optimize the classification of fault features. In addition, sensitivity and specificity were defined to evaluate the classification performance of the diagnosis rules found by the ant colony, aiming to suppress the redundant information in fault diagnosis, reduce the rows of the in-orbit codes, and improve the accuracy of in-orbit fault diagnosis. Meanwhile, the diagnosis information will be processed by the expert system, such that the servo system can work safely and stably in orbit, and less number of in-orbit computing resources were used. Simulation results show that the proposed strategy achieves highly precise and reliable diagnosis.
Rule Extraction for Screening of COVID-19 Disease Using Granular Computing Approach
Authors: Rozehkhani Seyyed Meysam , Mohammadzad Maryam
Published: June 2022

In the epidemic status of an unknown virus called Coronavirus, one of the main problems is inadequate access to treatment centers. Statistics show that many people are infected with the virus through unseasonable visits to medical centers immediately after noticing the initial symptoms similar to those reported for Coronavirus. Besides, unnecessary congestion at health centers reduces the quality of service to patients in urgent need of care. Since any external factor, including the virus, appears to have some symptoms after the onset of activity in the affected person, early diagnosis is possible. This paper presents an approach to classifying patients and diagnosing disease by symptoms, based on granular computing. One of the vital features of this method is the extraction of correct rules with zero entropy. This process is done based on a predefined classification of training datasets collected by experts. Granular computing has been a helpful approach in rule extraction and variety in recent years. Experimental results show that the proposed method can successfully detect COVID-19 disease according to its observed symptoms.
Profile of Male Breast Cancer in Makkah Region of Saudi Arabia: A 4-Year Retrospective Analysis of Radiology and Histopathology
Authors: Saeed Muhammad , Abdulshakour Bothaina Mohammed , Bantan Najwa Abdalkabeer A. , Falemban Afnan Hisham , Abdulla Munir , Melibary Ehab M. , Mufti Ahmad H. , Taher Mohiuddin M.
Published: June 2022

Background. Mammography is a method widely used for the diagnosis of breast disorders in women and may help detect breast cancer in its early stages. Male breast cancer often remains undiagnosed or is poorly controlled until serious complications arise; therefore, the use of screening methods is needed to help with early diagnosis. Methods. From a total of 1,667 registered mammography cases screened, 17 male breast disease cases were included in this study. Mammography and ultrasound data were analyzed by Statistical Package of Social Sciences v.22 (SPSS). Diagnosis was made following biopsy in suspicious cases, and histopathological and immunological findings of all such patients were obtained for final diagnosis. Results. The mean age of the patients was 35 years (range, 14-70 years); 17.6% of the cases were aged 37 yrs, and 2 cases were aged 51 and 52 yrs. Of the 17 cases, 11 had breast lesions, and skin thickening was observed in only 1 case. The different patterns of lesions detected were asymmetry of the parenchyma, mastitis, and hamartoma (n=1each), malignant lesions (n=2), and gynecomastia (n=6). According to the BI-RADS categorization, 8 cases were benign, one case was probably benign, and 2 cases were likely malignant. In the 2 cases with malignant lesions, pathological diagnosis was made after hematoxylin and eosin and immunocytochemistry examination as invasive ductal carcinoma (IDC) of no special type (NST), grade II and grade III. Conclusions. Most breast lesions in this study population were benign, while IDC was the most common malignancy encountered. Mammography is currently the most accurate and cost-effective method for detecting breast lesions. The findings of our study may help increase awareness of male breast cancer and encourage Saudi men at risk to perform self-breast exam and undergo routine breast screening.
[Retracted] Construction of Teaching Supervision System of Applied Undergraduate Colleges and Universities under the Perspective of “Internet+”
Authors: Zhang Songfeng , Zhao Fucha
Published: June 2022

With the wide application of “Internet +” in education and the transformation and development of applied colleges and universities, teaching supervision is an important part of colleges and universities’ teaching quality assurance system. However, there are still the following problems in the actual teaching supervision. The first is emphasizing “theory” teaching rather than “practice” teaching; the second is unstable supervisory team; the third is emphasizing “supervision” rather than “guidance”; the fourth is single ways and methods of teaching supervision; the fifth is lack of integration of teaching evaluation information. In order to ensure that the teaching quality of applied undergraduate colleges and universities can meet the requirements of local social development, teaching supervision work must be rooted in reality and focused on the future to build an effective teaching supervision system. Therefore, six suggestions are put forward. The first is to attach importance to the practicality of teaching, that is, application. The second is to insist on the part‐time subjectivity of the supervision team. The third is to form a supervision culture of “guidance as to the main focus, supervision as the supplement, combination of supervision, and guidance to promote teaching quality through feedback.” The fourth is to implement a triple superposition of regular supervision, inspection supervision, and special supervision. The fifth is to strengthen the synergy and innovation of supervision methods. The sixth is to establish an information‐based teaching supervision and management platform so as to solve the deficiencies of the teaching supervision system in application‐oriented colleges and universities in function, organization, culture, methods, methods, technology, and so on.
Patients with Retinitis Pigmentosa May Have a Higher Risk of Developing Open-Angle Glaucoma
Authors: Hung Man-Chen , Chen Yu-Yen
Published: June 2022

Background. Retinitis pigmentosa (RP) is the most common retinal hereditary dystrophy, which can lead to blindness if it progresses. Similarly, open-angle glaucoma (OAG) is a genetic disorder. The similarities in genetic variants and pathophysiology between RP and OAG have been reported. We sought to explore whether patients with RP have a significantly higher risk of OAG development. Methods. We enrolled patients with RP into the RP group through Taiwan’s National Health Insurance Research Database from 2001 to 2013; we included a comparison group of 1 : 4 age- and gender-matched individuals without RP. We performed a Cox regression analysis to estimate the crude and adjusted hazard ratios (HRs) for OAG. We adjusted the following confounders in the Cox regression model: age, gender, diabetes mellitus, hypertension, and chronic kidney disease. Results. We enrolled 6,223 subjects with RP and 24,892 subjects for comparison. The mean age of the cohort was 49.0 ± 18.1 years. The RP group had significantly higher percentages of diabetes mellitus, hypertension, and hyperlipidaemia. The cumulative incidence of OAG in patients with RP was 1.57%; this was significantly higher than that in the comparison group (0.58%,

p
<
0.0001

). On univariate Cox regression analysis, the hazard of OAG development was significantly greater in the RP group than in the comparison group with an unadjusted HR of 2.86 (95% confidence interval, 2.21–3.70). The increased risk persisted after adjusting for confounders (adjusted HR = 2.86; 95% CI, 2.21–3.70). Conclusions. This nationwide population-based cohort study showed that people with RP are at a significantly greater risk of developing OAG than individuals without it.
Treatment Represents a Key Driver of Metastatic Cancer Evolution
Authors: Christensen Ditte S. , Ahrenfeldt Johanne , Sokač Mateo , Kisistók Judit , Thomsen Martin K. , Maretty Lasse , McGranahan Nicholas , Birkbak Nicolai J.
Published: June 2022

Metastasis is the main cause of cancer death, yet the evolutionary processes behind it remain largely unknown. Here, through analysis of large panel-based genomic datasets from the AACR Genomics Evidence Neoplasia Information Exchange project, including 40,979 primary and metastatic tumors across 25 distinct cancer types, we explore how the evolutionary pressure of cancer metastasis shapes the selection of genomic drivers of cancer. The most commonly affected genes were TP53, MYC, and CDKN2A, with no specific pattern associated with metastatic disease. This suggests that, on a driver mutation level, the selective pressure operating in primary and metastatic tumors is similar. The most highly enriched individual driver mutations in metastatic tumors were mutations known to drive resistance to hormone therapies in breast and prostate cancer (ESR1 and AR), anti-EGFR therapy in non–small cell lung cancer (EGFR T790M), and imatinib in gastrointestinal cancer (KIT V654A). Specific mutational signatures were also associated with treatment in three cancer types, supporting clonal selection following anticancer therapy. Overall, this implies that initial acquisition of driver mutations is predominantly shaped by the tissue of origin, where specific mutations define the developing primary tumor and drive growth, immune escape, and tolerance to chromosomal instability. However, acquisition of driver mutations that contribute to metastatic disease is less specific, with the main genomic drivers of metastatic cancer evolution associating with resistance to therapy.

Significance:

This study leverages large datasets to investigate the evolutionary landscape of established cancer genes to shed new light upon the mystery of cancer dissemination and expand the understanding of metastatic cancer biology.
DLGAP1-AS2–Mediated Phosphatidic Acid Synthesis Activates YAP Signaling and Confers Chemoresistance in Squamous Cell Carcinoma
Authors: Nan Yabing , Luo Qingyu , Wu Xiaowei , Liu Shi , Zhao Pengfei , Chang Wan , Zhou Aiping , Liu Zhihua
Published: June 2022

Squamous cell carcinomas (SCC) constitute a group of human malignancies that originate from the squamous epithelium. Most patients with SCC experience treatment failure and relapse and have a poor prognosis due to de novo and acquired resistance to first-line chemotherapeutic agents. To identify chemoresistance mechanisms and to explore novel targets for chemosensitization, we performed whole-transcriptome sequencing of paired resistant and parental SCC cells. We identified DLGAP1 antisense RNA 2 (D-AS2) as a crucial noncoding RNA that contributes to chemoresistance in SCC. Mechanistically, D-AS2 affected chromatin accessibility around the histone mark H3K27ac of FAM3 metabolism regulating signaling molecule D (FAM3D), reducing FAM3D mRNA transcription and extracellular protein secretion. FAM3D interacted with the Gαi-coupled G protein–coupled receptors formyl peptide receptor 1 (FPR1) and FPR2 to suppress phospholipase D (PLD) activity, and reduced FAM3D increased PLD signaling. Moreover, activated PLD promoted phosphatidic acid (PA) production and subsequent nuclear translocation of yes-associated protein (YAP). Accordingly, in vivo administration of a D-AS2–targeting antisense oligonucleotide sensitized SCC to cisplatin treatment. In summary, this study shows that D-AS2/FAM3D-mediated PLD/PA lipid signaling is essential for SCC chemoresistance, suggesting D-AS2 can be targeted to sensitize SCC to cytotoxic chemotherapeutic agents.

Significance:

This study identifies D-AS2 as a targetable lipid-related long noncoding RNA that increases phospholipase D activity to promote YAP signaling, triggering chemoresistance in SCC.
Abstract A06: Differential response to autophagy in primary versus melanoma cells
Authors: Morehead Lauren C. , Tackett Alan J. , Miousse Isabelle R.
Published: June 2022

Autophagy is a cellular recycling mechanism triggered by nutrient scarcity. Upregulation of autophagy is associated with lifespan extension, DNA repair, and improved cancer immune recognition. Cancer cells can also utilize this mechanism to cope with nutrient-limited conditions. Chloroquine, an inhibitor of autophagy, has shown promising results alone and in combination with other anticancer agents in various types of cancer. However, several promoters of autophagy have also been shown to display potent anticancer properties. To better understand the relationship between autophagy and cancer, we tested promoters and inhibitors of autophagy in a metastatic melanoma cell line (B16F10) and compared the results with primary human dermal fibroblasts (HDF). After 48h, all tested promoters and inhibitors of autophagy decreased the number of live cells in the B16F10 melanoma cell line compared to untreated controls. In primary cells, promotion of autophagy with methionine restriction (25 μM versus 100 μM in control), complete methionine replacement with homocysteine, and 50 μM of the polyamine spermidine did not affect the number of live cells. Sulforaphane (10 μM) and serum starvation significantly decreased the number of live primary cells. Inhibition of autophagy with 6.25 μM chloroquine did not affect primary cell numbers, while there was a significant decrease in cell numbers associated with a dose of 10 μM of chloroquine and 0.1 μM bafilomycin A1. Cell numbers remained unaffected by longer incubations (96h with replenishment after 48h) of HDF cells with methionine restriction, replacement of methionine with homocysteine, and 6.25 μM chloroquine. These data suggest that cancer cells are more sensitive to both promotion and inhibition of autophagy than primary cells. Interesting compound-specific differences were noted in primary cells. Ongoing preclinical studies evaluate the potential of modulating autophagy as an adjuvant cancer therapy for immune checkpoint inhibitors in metastatic melanoma.

Citation Format: Lauren C. Morehead, Alan J. Tackett, Isabelle R. Miousse. Differential response to autophagy in primary versus melanoma cells [abstract]. In: Proceedings of the AACR Special Conference on Melanoma: From Biology to Target; 2019 Jan 15-18; Houston, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(19 Suppl):Abstract nr A06.
Abstract A18: Ccn1 expression by fibroblasts is required for melanoma metastasis
Authors: Hutchenreuther James , Quensel Katherine , Vincent Krista , Posrovit Lynne-Marie , Leask Andrew
Published: June 2022

Metastatic melanoma is highly fatal. Within the tumor microenvironment, the role of cancer-associated fibroblasts (CAFs) in melanoma metastasis and progression is relatively understudied. Matricellular proteins of the CCN family are emerging targets for cancer therapy. Anti-CCN2 (CTGF) strategies are under clinical development; however, the role of CCN1 (cyr61) in cancers is unclear. Herein, we use bioinformatic analyses to report that the CCN1 is overexpressed, in a fashion independent of BRAF mutational status, in melanoma. In human melanoma patients, CCN1 expression negatively correlates with overall survival, disease-free, and metastasis-free survival (p<0.03). CCN1 expression positively correlates with expression of stroma and neovascularization markers. To assess the role of CAFs in melanoma progression, we used C57BL/6 mice expressing a tamoxifen-dependent cre recombinase expressed under the control of a fibroblast-specific promoter/enhancer (COL1A2) to delete CCN1 postnatally in fibroblasts. Mice deleted or not for CCN1 in fibroblasts were injected subcutaneously with B16-F10 melanoma cells. Loss of CCN1 in CAFs did not result in reduced CAF activation, as detected by staining with anti-α-smooth muscle actin antibodies, but resulted in reduced extracellular matrix surrounding tumors, as detected by trichrome stain, and reduced tumor-induced neovascularization, as detected by staining with anti-CD31 antibodies. CCN1-deficient skin showed reduced expression of mRNAs encoding collagen crosslinking enzymes (PLOD2 and LOX) and impaired formation of collagen fibers, as determined by electron microscopy. Tumor growth was not affected by loss of CCN1 from tumor stroma; however, micrometastasis to the lung was severely impaired (p<0.001). Our results provide new insights into the crosstalk among different cell types in the tumor microenvironment and confirm the essential role of CAFs in metastasis and tumor neovascularization. Our data are consistent with the hypothesis that CAFs are essential for melanoma metastasis and that CCN1 is a therapeutic target for melanoma.

Citation Format: James Hutchenreuther, Katherine Quensel, Krista Vincent, Lynne-Marie Posrovit, Andrew Leask. Ccn1 expression by fibroblasts is required for melanoma metastasis [abstract]. In: Proceedings of the AACR Special Conference on Melanoma: From Biology to Target; 2019 Jan 15-18; Houston, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(19 Suppl):Abstract nr A18.