bluuebunny/crossref_metadata_2025 huggingface dataset:
Could Fibroblast Activation Protein (FAP)‐Specific Radioligands Be Considered as Pan‐Tumor Agents?
Authors: Roustaei Hessamoddin , Kiamanesh Zahra , Askari Emran , Sadeghi Ramin , Aryana Kamran , Treglia Giorgio
Published: February 2022

Background. Cancer‐associated fibroblasts (CAFs) can strongly modulate the response to therapy of malignant tumor cells, facilitating their continuous proliferation and invading behaviors. In this context, several efforts were made in identifying the fibroblast activation protein (FAP) as a CAF recognizer and in designing FAP‐specific PET radiotracers (as 68Ga‐FAPI) along with FAP‐specific therapeutic radioligands. Herein, we review different clinical studies using the various FAP‐specific radioligands as novel theranostic agents in a wide range of oncologic and nononcologic indications. Methods. A comprehensive systematic search was conducted on the PubMed and Scopus databases to find relevant published articles concerning the FAP‐specific PET imaging as well as the FAP‐specific radionuclide therapy in patients with oncologic and nononcologic indications. The enrolled studies were dichotomized into oncologic and nononcologic categories, and the required data were extracted by precisely reviewing the whole text of each eligible study. A meta‐analysis was also performed comparing the detection rates of 68Ga‐FAPI vs. 18F‐FDG PET/CT using odds ratio (OR) and risk difference as outcome measures. Results. Of the initial 364 relevant papers, 49 eligible articles (1479 patients) and 55 case reports were enrolled in our systematic review. These studies observed high radiolabeled FAPI avidity as early as 10 minutes after administration in primary sites of various malignant tumors. Based on the meta‐analysis which was done on the reported detection rates of the 68Ga‐FAPI and 18F‐FDG PET/CT scans, the highest OR belonged to the primary lesion detection rate of gastrointestinal tumors (OR = 32.079, 95% CI: 4.001–257.212; p = 0.001) with low heterogeneity (I2 = 0%). The corresponding value of the nodal metastases belonged to hepatobiliary tumors (OR = 11.609, 95% CI: 1.888–71.365; p = 0.008) with low heterogeneity (I2 = 0%). For distant metastases, the highest estimated OR belonged to nasopharyngeal carcinomas (OR = 77.451, 95% CI: 7.323–819.201; p < 0.001) with low heterogeneity (I2 = 0%). Conclusions. The outperformance of 68Ga‐FAPI PET/CT over 18F‐FDG PET/CT in identifying certain primary tumors as well as in detecting their metastatic lesions may open indications for evaluation of cases with inconclusive 18F‐FDG PET/CT findings. What needs to be emphasized is that the false‐positive results might be problematic and must be taken into account in 68Ga‐FAPI PET/CT interpretation. More clarification on the role of FAPI radioligands in oncologic imaging, radionuclide therapy, and radiotherapy treatment planning is therefore required.
Method for Evaluation and Application of Production Process Chain Complexity in Sewing Workshops considering Human Factor
Authors: Li Huimin , Kong Fansen , Chen Taibo , Kong Liang
Published: February 2022

Existing methods for evaluating manufacturing process chain complexity consider the number of machines, state of machines, number of parts, operation time, and processing sequence of parts. However, such evaluation methods ignore human factors. To consider human factors, human cognitive decision‐making process factors are considered in the complexity evaluation of production processes. Accordingly, a new objective evaluation method of the human factor complexity is proposed. In the proposed method, sewing operations are taken as an example, and the human factor complexity is classified into perceived and cognitive complexity. Information entropy is used to measure cognitive complexity according to the type and quantity of sewing workers’ cognitive activities. The results show that various methods have significant differences in the evaluation of the complexity level of the production process chain. Specifically, the calculation results of the proposed evaluation method are much greater than those of other methods. This indicates that human cognitive and perceived complexities account for a large proportion. Therefore, human factor complexity cannot be omitted.
Adoption of Blockchain Technology for Privacy and Security in the Context of Industry 4.0
Authors: Joshi Shubham , Pise Anil Audumbar , Shrivastava Manish , Revathy C. , Kumar Harish , Alsetoohy Omar , Akwafo Reynah
Published: February 2022

Compared to centralized and decentralized models, distributed models have the potential to dramatically expand the scalability of existing IoT and Industry 4.0 solutions while maintaining participant organizations’ security and privacy. This is partly because participating firms are not required to rely on or trust other services or third parties to manage the data they gather and transfer, out of concern that these parties could misuse the data or, in the worst-case scenario, share it with mass surveillance programs. However, until blockchain technology (BCT) demonstrates its viability as a means of developing security solutions in decentralized, collaborative, and trustless environments, the vast majority of these use cases will struggle to meet the requirements for integrity, immutability, traceability, and notarization. By utilizing BCT, it is possible to eliminate intermediaries, enabling individuals and devices to manage their data independently of third parties and most significantly to achieve a high level of traceability with information flow harmony. This technology enables transaction, transparency, and traceability by enabling for the interchange of historical data. The fundamentals of blockchain are examined in this research paper, along with an investigation of its operation and a discussion of some of its most fundamental aspects and concepts. A concise overview of smart contracts enables us to completely reimagine how network members create and automate transactions. Finally, several IoT and Industry 4.0 application possibilities that leverage blockchain are investigated, as is the blockchain’s future trajectory.
Nanomaterials: A Promising Therapeutic Approach for Cardiovascular Diseases
Authors: Chopra Hitesh , Bibi Shabana , Mishra Awdhesh Kumar , Tirth Vineet , Yerramsetty Sree Vandana , Murali Sree Varshini , Ahmad Syed Umair , Mohanta Yugal Kishore , Attia Mohamed S. , Algahtani Ali , Islam Fahadul , Hayee Abdul , Islam Saiful , Baig Atif Amin , Emran Talha Bin
Published: February 2022

Cardiovascular diseases (CVDs) are a primary cause of death globally. A few classic and hybrid treatments exist to treat CVDs. However, they lack in both safety and effectiveness. Thus, innovative nanomaterials for disease diagnosis and treatment are urgently required. The tiny size of nanomaterials allows them to reach more areas of the heart and arteries, making them ideal for CVDs. Atherosclerosis causes arterial stenosis and reduced blood flow. The most common treatment is medication and surgery to stabilize the disease. Nanotechnologies are crucial in treating vascular disease. Nanomaterials may be able to deliver medications to lesion sites after being infused into the circulation. Newer point‐of‐care devices have also been considered together with nanomaterials. For example, this study will look at the use of nanomaterials in imaging, diagnosing, and treating CVDs.
Charmless Quasi-Two-Body
B
Decays in Perturbative QCD Approach: Taking
B
⟶
K

R
⟶

K

+

K

−

as Examples
Authors: Liu Wen-Feng , Zou Zhi-Tian , Li Ying
Published: February 2022

Three-body

B

decays not only significantly broaden the study of

B

meson decay mechanisms but also provide information of resonant particles. Because of complicate dynamics, it is very hard for us to study the whole phase space in a specific approach. In this review, we take

B
⟶
K

R
⟶

K

+

K

−

decays as examples and show the application of the perturbative QCD (PQCD) approach in studying the quasi-two-body

B

decays, where two particles move collinearly with large energy and the bachelor one recoils back. To describe the dynamics of two collinear particles, the

S

,

P

, and

D

-wave functions of kaon-pair with different waves are introduced. By keeping the transverse momenta, all possible diagrams including the hard spectator diagrams and annihilation ones can be calculated in PQCD approach. Most results are well consistent with the current measurements from BaBar, Belle, and LHCb experiments. Moreover, under the narrow-width approximation, we can extract the branching fractions of the two-body decays involving the resonant states and also predict the branching fractions of the corresponding quasi-two-body decays

B
⟶
K

R
⟶

π

+

π

−

. All predictions are expected to be tested in the ongoing LHCb and Belle-II experiments.
TWIST1-EP300 Expedites Gastric Cancer Cell Resistance to Apatinib by Activating the Expression of COL1A2
Authors: Yu Gang , Chen Wanjing , Li Xianghua , Yu Liang , Xu Yanyan , Ruan Qiang , He Yawei , Wang Yong
Published: February 2022

The association between collagen type I alpha (COL1A) and chemoresistance has been verified in cancers. However, the specific role of COL1A2 in gastric cancer (GC) cell resistance to apatinib, a highly selective small-molecule inhibitor of vascular endothelial growth factor receptor 2, has not been investigated before. The purpose of this study was to explore the potential factors associated with COL1A2 regulation on GC cell apatinib resistance in vitro. With the aid of the Oncomine database and integrated bioinformatics methods, we identified COL1A2 overexpression in GC and its prognostic value. Mechanistically, the COL1A2 promoter has a distinct H3K27ac modification site and that E1A binding protein p300 (EP300) and twist family bHLH transcription factor 1 (TWIST1) can bind to the COL1A2 promoter, which in turn transcriptionally activated COL1A2 expression. In addition, overexpression of COL1A2 significantly promoted resistance to apatinib in GC cells, but knockdown of EP300 or TWIST1 remarkably inhibited COL1A2 expression and promoted sensitivity of GC cells to apatinib. Our findings demonstrated that the combination of EP300 and TWIST1 has a synergistically regulatory effect on COL1A2 expression, thus contributing to apatinib resistance in GC cells.
Analysis on Indoor Ventilation Environment of House Type Based on Architectural Aesthetics
Authors: Xu Geng-Yang , Chen Chang-Bing , Cai Zheng-Qun
Published: February 2022

Architectural aesthetics mainly creates architectural beauty according to the law of beauty and realizes the interaction between creative subject and object, and receptor. Its essence has been soaked and attached to all kinds of materialized carriers and behavioral subjects in the living environment. Through the aesthetic optimization of residential house type, this paper analyzes the ventilation efficiency of representative house type, which affects the prevention and control of community infectious diseases and the physical and mental health of residents. Take multiple measures to strengthen the aesthetic design of residential house type, and promote the healthy development of community ecology and the cultural inheritance of harmonious society. It is helpful for architectural designers to improve their understanding of the effect of natural ventilation and provide reference means for designers to reasonably organize indoor natural ventilation in architectural design.
Cross-Border E-Commerce Intelligent Information Recommendation System Based on Deep Learning
Authors: Li Liuqing
Published: February 2022

In order to improve the effect of cross-border e-commerce intelligent information recommendation, this paper applies deep learning to the intelligent information processing and intelligent recommendation of e-commerce and proposes an improved version of the topic model to solve the problem of feature extraction of the text of the recommendation system. In order to deal with translation problems, this paper proposes an end-to-end sequence-to-sequence learning method. In addition, this study uses the long tail theory to excavate the mass commodities in the niche and recommends these products to users as suggestions. Finally, this paper proposes a niche product recommendation algorithm based on the graph search strategy based on the graph model. The experiment shows that the cross-border e-commerce intelligent information recommendation system based on deep learning proposed in this paper has a good recommendation effect and meets the recommendation needs of cross-border e-commerce.
Oscillatory Behaviour of the Nonlinear Damped Fractional Partial Dynamic Equation
Authors: Ramesh R. , Harikrishnan S. , Prakash P. , Ma Yong-Ki
Published: February 2022

In this paper, a partial dynamic equation of fractional order is considered with Neumann and Dirichlet boundary conditions, and we studied the oscillation properties of the fractional partial dynamic equation on time scales. Riccati transformation technique is used to establish oscillation criteria for the fractional partial dynamic equation. The obtained results are verified with examples.
Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation
Authors: Li Yang , Wang Zheng , You Zhu-Hong , Li Li-Ping , Hu Xuegang
Published: February 2022

Protein-protein interactions (PPIs) play a crucial role in understanding disease pathogenesis, genetic mechanisms, guiding drug design, and other biochemical processes, thus, the identification of PPIs is of great importance. With the rapid development of high-throughput sequencing technology, a large amount of PPIs sequence data has been accumulated. Researchers have designed many experimental methods to detect PPIs by using these sequence data, hence, the prediction of PPIs has become a research hotspot in proteomics. However, since traditional experimental methods are both time-consuming and costly, it is difficult to analyze and predict the massive amount of PPI data quickly and accurately. To address these issues, many computational systems employing machine learning knowledge were widely applied to PPIs prediction, thereby improving the overall recognition rate. In this paper, a novel and efficient computational technology is presented to implement a protein interaction prediction system using only protein sequence information. First, the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST) was employed to generate a position-specific scoring matrix (PSSM) containing protein evolutionary information from the initial protein sequence. Second, we used a novel data processing feature representation scheme, MatFLDA, to extract the essential information of PSSM for protein sequences and obtained five training and five testing datasets by adopting a five-fold cross-validation method. Finally, the random fern (RFs) classifier was employed to infer the interactions among proteins, and a model called MatFLDA_RFs was developed. The proposed MatFLDA_RFs model achieved good prediction performance with 95.03% average accuracy on Yeast dataset and 85.35% average accuracy on H. pylori dataset, which effectively outperformed other existing computational methods. The experimental results indicate that the proposed method is capable of yielding better prediction results of PPIs, which provides an effective tool for the detection of new PPIs and the in-depth study of proteomics. Finally, we also developed a web server for the proposed model to predict protein-protein interactions, which is freely accessible online at http://120.77.11.78:5001/webserver/MatFLDA_RFs.
Enhancing Teaching and Learning through Peer Observation: An Indian Case Study
Authors: Katal Avita , Singh Vijay K. , Choudhury Tanupriya , Imran Faisal
Published: February 2022

Peer observation demands the teaching community to observe each other’s teaching practice followed in classrooms and to learn from it. It focuses on individual requirements and learning by receiving and providing constructive feedback. This study gives detailed information about the purpose and principle of peer observation along with the steps followed in the peer observation cycle. The data gathered are from postobservation forms and meetings. Feedback from peers is used to demonstrate the importance of peer observation in improving the overall teaching experience. The results indicated that there is a significant improvement in the teaching style of the instructors. The category of attaining student attention and engagement grew by 28.8 percent, while the category of students demonstrating passion and motivation climbed by 15.27 percent. Peer observation is seen as a beneficial tool for teachers’ professional development. Teachers stated that peer observation reduced their worry, hesitancy, and pressure throughout the teaching process, making it more dependable and real. However, participants identified time constraints and teachers’ lack of observation expertise as barriers to peer observation.
Proposing Novel Data Analytics Method for Anatomical Landmark Identification from Endoscopic Video Frames
Authors: Ayyoubi Nezhad Shima , Khatibi Toktam , Sohrabi Masoudreza
Published: February 2022

Background. The anatomical landmarks contain the characteristics that are used to guide the gastroenterologists during the endoscopy. The expert can also ensure the completion of examination with the help of the anatomical landmarks. Automatic detection of anatomical landmarks in endoscopic video frames can be helpful for guiding the physicians during screening the gastrointestinal tract (GI). Method. This study presents an automatic novel method for anatomical landmark detection of GI tract from endoscopic video frames based on semisupervised deep convolutional neural network (CNN) and compares the results with supervised CNN model. We consider the anatomical landmarks from Kvasir dataset that includes 500 images for each class of Z-line, pylorus, and cecum. The resolution of these images varies from 750 × 576 up to 1920 × 1072 pixels. Result. Experimental results show that the supervised CNN has highly desirable performance with accuracy of 100%. Also, our proposed semisupervised CNN can compete with a slight difference similar to the CNN model. Our proposed semisupervised model trained using 1, 5, 10, and 20 percent of training data records as labeled training dataset has the average accuracy of 83%, 98%, 99%, and 99%, respectively. Conclusion. The main advantage of our proposed method is achieving the high accuracy with small amount of labeled data without spending time for labeling more data. The strength of our proposed method saves the required labor, cost, and time for data labeling.
Deep Learning-Based Fake Information Detection and Influence Evaluation
Authors: Xiang Ning
Published: February 2022

With the prevalence of the Internet, a large number of users have participated in OSN (Online Social Networks), which has gradually made it the mainstream way for obtaining news or information from the Internet. However, with the rapid development of the Internet, a large amount of fake information has also been spread on the Internet. Therefore, fake information detection is of great significance at the moment. A multimodal fake information detection method is proposed in this article, which has adopted the textual and visual contents in the piece of information to make the judgments. The textual feature representation vector is firstly obtained through the pretraining of the Bert model, and then the visual feature representation is obtained through the pretraining of the VGG-19 model. From the proposed method, two MCBP (Multimodal Compact Bilinear Pooling) modules are adopted. The first MCBP module is adopted to obtain the visual feature representation vector with attention, and the second MCBP module is adopted to join the visual feature with the attention mechanism and the textual feature vector. Then, the joined vector can be adopted for fake information detection. The proposed method in this article is compared with two baseline methods. The experimental results on the Twitter and Weibo datasets have proved that the proposed method in this article is better than the EANN method and the SpotFake method in terms of accuracy, precision, recall, and F1 score.
A Practical Deep Learning Model in Differentiating Pneumonia-Type Lung Carcinoma from Pneumonia on CT Images: ResNet Added with Attention Mechanism
Authors: Du Wang , Luo Xiaojie , Chen Min
Published: February 2022

Objective. We aim to develop a deep neural network model to differentiate pneumonia-type lung carcinoma from pneumonia based on chest CT scanning and evaluate its performance. Materials and Methods. We retrospectively analyzed 131 patients diagnosed with pneumonia-type lung carcinoma and 171 patients with pneumonia treated in Beijing Hospital from October 2019 to February 2021. The average age was 68 (±15) years old, and the proportion of men (162/302) was slightly more than that of women (140/302). In this study, a deep learning based model UNet was applied to extract lesion areas from chest CT images. Lesion areas were extracted and classified by a designed spatial attention mechanism network. The model AUC and diagnostic accuracy were analyzed based on the results of the model. We analyzed the accuracy rate, sensitivity, and specificity and compared the results of the model to the junior and senior radiologists and radiologists based on the model. Results. The model has a good efficiency in detecting pneumonia-like lesions (6.31 seconds/case). The model accuracy rate, sensitivity, and specificity were 74.20%, 60.37%, and 89.36%, respectively. The junior radiologist’s accuracy rate, sensitivity, and specificity were 61.00%, 48.08%, and 75.00%, respectively. The senior radiologist’s accuracy rate, sensitivity, and specificity were 65.00%, 51.92%, and 79.17%, respectively. The results of junior radiologists based on the model were improved (76.00% for accuracy rate, 62.75% for sensitivity, and 89.80% for specificity). The results of senior radiologists based on the model were also improved (78.00% for accuracy rate, 64.71% for sensitivity, and 91.84% for specificity) and the diagnostic accuracy of which was statistically higher than other groups

P
<
0.05

. Based on the lesion texture diversity and the lesion boundary ambiguity, the algorithm produced false-positive samples (13.51%). Conclusion. This deep learning model could detect pneumonia-type lung carcinoma and differentiate it from pneumonia accurately and efficiently.
Cooccurrence of Talon’s Cusp with Dens Invaginatus in the Maxillary Lateral Incisor: A Case Report with Review of Literature
Authors: Ghosh Snehashish , Dhungel Safal , Subedi Bhawana , Pradhan Subi
Published: February 2022

Morphogenic developmental anomalies are common in maxillary lateral incisors, but simultaneous occurrence of two developmental anomalies in a single tooth is relatively uncommon. In this case report, we present a case of cooccurrence of the talon’s cusp with dens invaginatus in the left lateral incisor tooth. Early diagnosis and prompt treatment of such cases are important to prevent any untoward consequences.
Psychometric Properties of Cognitive Behavioral Scientific Insight Questionnaire
Authors: Aminaei Chatroudi Hengameh , Towhidi Afsaneh , Madadi Mohsen
Published: February 2022

Scientific insight is a cognitive and behavioral task. Although as a concept or a measure that sounds very familiar, so far not even one tool was found to measure the cognitive behavioral scientific insight (CBSI). Due to the lack of a questionnaire in this field, the researchers aimed to design a cognitive behavioral scientific insight questionnaire (CBSIQ) and to determine its validity. Therefore, the researchers designed the CBSIQ with 105 questions on a five-point Likert scale, and then 205 university faculty members were asked to complete the tool. The faculty members were recruited using stratified sampling and within each stratum using simple random sampling methods. The data were analyzed using exploratory factor analysis (EFA) and confirmatory factorial analysis (CFA). Results show that CBSIQ ended with 33 questions constituting three factors: scientific thinking and applying scientific methods, scientific moral and commitment, and speaking in scientific terms. It is concluded that the CBSIQ is valid with relatively good fit indices. The implication of the CBSIQ is that it provides the key factors to diagnose individuals’ thinking obstacles; it can act as a framework to design instructional packages in order to teach people how to think insightfully and how to solve their problems scientifically or, in other words, how to acquire a scientific insight. Therefore, CBSIQ has the capability to function as a useful tool and as a remedy specifically to diagnose and resolve educational problems.
Preliminary Study on Risk Factors for Morbidity of Nonalcoholic Fatty Liver Disease in High-Income Male Population
Authors: Han Li , Zhang Yuting , Yue Cui , Huang Yiqin , Wu Yumin , Chen Jie
Published: February 2022

Objectives. Believed to be a result of metabolic syndrome and unhealthy lifestyle, the incidence of nonalcoholic fatty liver disease (NAFLD) has become a serious public health problem. Among the high-income male population, metabolic syndrome and unhealthy lifestyle are particularly prominent. Therefore, we conducted a survey on 375 high-income male subjects, expecting to understand the risk factors and related factors for morbidity of NAFLD among the high-income male population being physically examined in Shanghai. Methods. A cross-sectional study was applied to 375 high-income male subjects (including 190 patients with NAFLD and 185 non-NAFLD subjects) who were examined in the special needs clinic at Huadong Hospital affiliated to Fudan University. In combination with medical history, physical examination, and laboratory test results and by use of a self-made NAFLD health questionnaire, the basic data of the research objects were collected and the obtained data were subject to a correlation analysis. Results. This study investigated 375 high-income males, and the morbidity rate of NAFLD was 50.67%. The NAFLD group was higher than the non-NAFLD group in terms of body weight, BMI, systolic blood pressure, and diastolic blood pressure (

P
<
0.05

). Hypertension (OR = 2.944), diabetes (OR = 7.278), and hyperuricemia (OR = 1.922) are the risk factors for NAFLD; compared with no metabolic diseases, one (OR = 1.848), two (OR = 2.417), and three metabolic diseases (OR = 14.788) are risk factors for the development of NAFLD. Compared with the non-NAFLD group, the NAFLD group had a higher level of WBC, RBC, Hb, PLT, FPG, HbA1c, ALT, AST, GGT, ALP, TP, and UA (

P
<
0.05

). There was a statistically significant difference in the intake of supper and staple foods between the NAFLD group and the non-NAFLD group, and the highly greasy diet was a risk factor for NAFLD (OR = 2.173) as opposed to the nongreasy diet. Conclusion. High-income male population is a high-risk group of NAFLD. Most of the patients with NAFLD have abnormal biochemical indicators as opposed to the healthy population and are more likely to be complicated with other chronic diseases or abnormal health status. And the occurrence of hypertension, diabetes, and hyperuricemia is the risk factor for the development of NAFLD. At the same time, the number of metabolic diseases complicated is also a risk factor for NAFLD as compared with the absence of complications with such metabolic diseases. Compared with a diet that is not greasy, the fact that high-income male NAFLD patients have a very greasy diet increases the risk of NAFLD.
Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark
Authors: Abdel-Fattah Manal A , Othman Nermin Abdelhakim , Goher Nagwa
Published: February 2022

Chronic kidney disease (CKD) has become a widespread disease among people. It is related to various serious risks like cardiovascular disease, heightened risk, and end-stage renal disease, which can be feasibly avoidable by early detection and treatment of people in danger of this disease. The machine learning algorithm is a source of significant assistance for medical scientists to diagnose the disease accurately in its outset stage. Recently, Big Data platforms are integrated with machine learning algorithms to add value to healthcare. Therefore, this paper proposes hybrid machine learning techniques that include feature selection methods and machine learning classification algorithms based on big data platforms (Apache Spark) that were used to detect chronic kidney disease (CKD). The feature selection techniques, namely, Relief-F and chi-squared feature selection method, were applied to select the important features. Six machine learning classification algorithms were used in this research: decision tree (DT), logistic regression (LR), Naive Bayes (NB), Random Forest (RF), support vector machine (SVM), and Gradient-Boosted Trees (GBT Classifier) as ensemble learning algorithms. Four methods of evaluation, namely, accuracy, precision, recall, and F1-measure, were applied to validate the results. For each algorithm, the results of cross-validation and the testing results have been computed based on full features, the features selected by Relief-F, and the features selected by chi-squared feature selection method. The results showed that SVM, DT, and GBT Classifiers with the selected features had achieved the best performance at 100% accuracy. Overall, Relief-F’s selected features are better than full features and the features selected by chi-square.
The Relationship between Intelligent Image Simulation and Recognition Technology and the Health Literacy and Quality of Life of the Elderly
Authors: Wei Baojian , Li Chunyu , Xu Jiangye
Published: February 2022

In order to explore the relationship between intelligent image recognition technology and the mentality and quality of life of the elderly, this paper combines intelligent image simulation technology to identify the behavior of the elderly, protect the safety of the elderly, and provide timely feedback on the adverse conditions of the elderly. Moreover, this paper improves the traditional intelligent image recognition algorithm, verifies the research method of this paper through experimental research, and puts forward corresponding suggestions. Through investigation and research, we can see that the level of health literacy of elderly patients with chronic diseases is low. Therefore, in the future health education, we should strengthen health education for elderly patients with chronic diseases, use different mass media to propagate health knowledge, and promote the formation of healthy lifestyles and behaviors for elderly patients with chronic diseases. At the same time, the experiment also verified that the intelligent image recognition technology proposed in this paper has a positive effect in improving the mentality and quality of life of the elderly.
High-Throughput Metabolomics and Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency (CRIC) Study
Authors: Zhang Jing , Fuhrer Tobias , Ye Hongping , Kwan Brian , Montemayor Daniel , Tumova Jana , Darshi Manjula , Afshinnia Farsad , Scialla Julia J. , Anderson Amanda , Porter Anna C. , Taliercio Jonathan J. , Rincon-Choles Hernan , Rao Panduranga , Xie Dawei , Feldman Harold , Sauer Uwe , Sharma Kumar , Natarajan Loki
Published: February 2022

Introduction: Metabolomics could offer novel prognostic biomarkers and elucidate mechanisms of diabetic kidney disease (DKD) progression. Via metabolomic analysis of urine samples from 995 CRIC participants with diabetes and state-of-the-art statistical modeling, we aimed to identify metabolites prognostic to DKD progression. Methods: Urine samples (N = 995) were assayed for relative metabolite abundance by untargeted flow-injection mass spectrometry, and stringent statistical criteria were used to eliminate noisy compounds, resulting in 698 annotated metabolite ions. Utilizing the 698 metabolites’ ion abundance along with clinical data (demographics, blood pressure, HbA1c, eGFR, and albuminuria), we developed univariate and multivariate models for the eGFR slope using penalized (lasso) and random forest models. Final models were tested on time-to-ESKD (end-stage kidney disease) via cross-validated C-statistics. We also conducted pathway enrichment analysis and a targeted analysis of a subset of metabolites. Results: Six eGFR slope models selected 9–30 variables. In the adjusted ESKD model with highest C-statistic, valine (or betaine) and 3-(4-methyl-3-pentenyl)thiophene were associated (p < 0.05) with 44% and 65% higher hazard of ESKD per doubling of metabolite abundance, respectively. Also, 13 (of 15) prognostic amino acids, including valine and betaine, were confirmed in the targeted analysis. Enrichment analysis revealed pathways implicated in kidney and cardiometabolic disease. Conclusions: Using the diverse CRIC sample, a high-throughput untargeted assay, followed by targeted analysis, and rigorous statistical analysis to reduce false discovery, we identified several novel metabolites implicated in DKD progression. If replicated in independent cohorts, our findings could inform risk stratification and treatment strategies for patients with DKD.
Correlation between Increased Monocyte Chemotactic Protein 1 Level and Lung Function in Patients with Asthma
Authors: Zong Weifeng , Mo Yuqing , Liang Yuxia
Published: February 2022

Background: The altered expression of monocyte chemotactic protein 1 (MCP1) in bronchoalveolar fluid was observed in patients and animal models of allergic asthma. However, the correlation between induced sputum MCP1 level and asthma clinical features remains poorly understood. Objective: This study aims to explore the relationship of MCP1 protein expression in induced sputum with Th2 inflammation and asthma clinical features. Methods: MCP1 protein expression in induced sputum and serum was detected by ELISA in patients with asthma, and its correlation with Th2 inflammation and lung function was analyzed. The effect of inhaled corticosteroids (ICSs) on MCP1 expression was also evaluated. Results: The MCP1 level in induced sputum (p = 0.0006) and serum (p = 0.0035) was markedly increased and negatively correlated with forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC)% (r = −0.3674, p = 0.0001) and PC-20 (r = −0.5746, p < 0.0001) in patients with asthma. The area under the curve (AUC) of MCP1 level receiver operating characteristic curve in induced sputum and serum was 0.7134 (p = 0.0007) and 0.7589 (p = 0.0042), respectively. The patients with asthma were grouped into four according to their induced sputum MCP1 level and Th2 signature categories (Th2Hi MCP1Hi, Th2Hi MCP1Low, Th2Low MCP1Hi, and Th2Low MCP1Low). The Th2Low MCP1Hi subgroup had the lowest FEV1/FVC% and histamine concentration required to cause a 20% decline in FEV1. After 4 weeks of ICS treatment, the MCP1 levels in induced sputum and serum were significantly reduced. Conclusions: The MCP1 level in induced sputum and serum increased in patients with asthma but decreased after ICS treatment. The Th2Low MCP1Hi subgroup had the worst lung function and highest airway hyperresponsiveness. The combination of MCP1 level in induced sputum and Th2 inflammation defines a new phenotype that can be used to predict lung function and treatment response in patients with asthma.
The Effect of Earplugs and Eye Masks Usage in the Intensive Care Unit on Sleep Quality: Systematic Review
Authors: Polat Eda , Çavdar İkbal , Şengör Kübra
Published: February 2022

Introduction: Patients in the intensive care unit (ICU) have severe and complex disease characteristics and their sleep problems should not be ignored. Poor-quality sleep is associated with impaired immune function and associated susceptibility to disease and infection, decreased energy levels, delirium, delays in recovery. This study is conducted to examine the results of studies examining sleep quality using earplugs and eye masks in the ICU. Methods: PubMed, Science Direct, Google Scholar, and Medline databases were scanned using “Earplugs, Eye masks, Sleep quality, Intensive care units” as keywords. For the search strategy, a query in a patient-intervention-compare-result (PICO) format was used. P: Patients in intensive care; I: Earplugs and eye mask; C: Noise, Light, and Sleep Quality; O: Using earplugs and eye masks improves sleep quality. Results: We included the 17 most eligible studies meeting defined inclusion/exclusion criteria involving 1,372 participants. Randomized controlled trial was used mostly as study design. The interventions within the scope of the studies were earplugs, eye masks, relaxing music, and quiet time protocol. Richard Campbell Sleep Questionnaire and Verran and Snydern Halpern Sleep Scale were the most used scale. Most of the studies reviewed found that the use of non-pharmacological interventions showed a significant improvement in sleep quality. Earplugs and eye masks were found to have potential positive effects on sleep quality and delirium incidence in ICU patients. Conclusion: The use of earplugs and eye masks is a noninvasive, economical, and effective way to improve sleep quality in adult ICU patients.
Globe Ruptures: Outcomes and Prognostic Analysis of Severe Ocular Trauma
Authors: Coelho João , Ferreira André , Kuhn Ferenc , Meireles Angelina
Published: February 2022

Purpose: Eye trauma is an important cause of monocular visual loss worldwide. This study aimed to describe the structural and functional characteristics and outcomes of 281 globe ruptures. Methods: Medical records of all patients undergoing surgery for a ruptured globe at Centro Hospitalar Universitário do Porto in the last 17 years were retrospectively reviewed. Results: 281 eyes of 277 patients were included. The majority of them were males (71%) with a mean age of 59.7 ± 20.5. In 38.8% of the cases, the cause was a fall. At the time of admission, 29.5% of the eyes had a visual acuity (VA) greater than 20/200 and 12.5% had no light perception. The median time between trauma and primary repair was less than 8 h (interquartile range 5–12 h). A primary reconstruction was always attempted. Primary evisceration was performed in 14 cases (5%). In multivariable analysis, independent predictors of vision survival were male gender, shorter wound size, and absence of vitreous hemorrhage. Conclusion: In this large, retrospective study on globe ruptures, we identified male gender, short wound length, and the absence of vitreous hemorrhage or retinal detachment as predictors of improved functional outcome. We found a correlation between the presenting and final VAs. The Ocular Trauma Score predicted the outcome well. Our determination to reconstruct the traumatized eyes as early and as completely as possible was justified by the functional results: despite the severity of the injuries, one out of four eyes reached a final VA greater than 20/200.
Max Aub: alemão, francês, espanhol, mexicano? Judeu.
Authors: Kirschbaum Saul
Published: February 2022

Max Aub was a Spanish dramatist, fictionist and essayist, who fought in the Spanish Civil War, was held prisoner in several concentration camps for his participation in the Republican side, and finally moved to Mexico, where he lived and published for the rest of his lite. But he was of Jewish origin. Being a Jew, he experienced painful conflicts with his own jewishness, his repudiated Jewish identity, mainly when he stayed in Israel during the Six Days War, in 1967. After the war, he published Imposible Sinai (Impossible Sinai). This article analyzes Aub's dilemmas of being and not willing to be a Jew, feelingsclose to what is called self-hate, and often expressed in his Diaries.
Effects of Atrial Fibrillation on the Human Ventricle
Authors: Pabel Steffen , Knierim Maria , Stehle Thea , Alebrand Felix , Paulus Michael , Sieme Marcel , Herwig Melissa , Barsch Friedrich , Körtl Thomas , Pöppl Arnold , Wenner Brisca , Ljubojevic-Holzer Senka , Molina Cristina E. , Dybkova Nataliya , Camboni Daniele , Fischer Thomas H. , Sedej Simon , Scherr Daniel , Schmid Christof , Brochhausen Christoph , Hasenfuß Gerd , Maier Lars S. , Hamdani Nazha , Streckfuss-Bömeke Katrin , Sossalla Samuel
Published: February 2022

Rationale:

Atrial fibrillation (AF) and heart failure often coexist, but their interaction is poorly understood. Clinical data indicate that the arrhythmic component of AF may contribute to left ventricular (LV) dysfunction.

Objective:

This study investigates the effects and molecular mechanisms of AF on the human LV.

Methods and Results:

Ventricular myocardium from patients with aortic stenosis and preserved LV function with sinus rhythm or rate-controlled AF was studied. LV myocardium from patients with sinus rhythm and patients with AF showed no differences in fibrosis. In functional studies, systolic Ca
2+
transient amplitude of LV cardiomyocytes was reduced in patients with AF, while diastolic Ca
2+
levels and Ca
2+
transient kinetics were not statistically different. These results were confirmed in LV cardiomyocytes from nonfailing donors with sinus rhythm or AF. Moreover, normofrequent AF was simulated in vitro using arrhythmic or rhythmic pacing (both at 60 bpm). After 24 hours of AF-simulation, human LV cardiomyocytes from nonfailing donors showed an impaired Ca
2+
transient amplitude. For a standardized investigation of AF-simulation, human iPSC-cardiomyocytes were tested. Seven days of AF-simulation caused reduced systolic Ca
2+
transient amplitude and sarcoplasmic reticulum Ca
2+
load likely because of an increased diastolic sarcoplasmic reticulum Ca
2+
leak. Moreover, cytosolic Na
+
concentration was elevated and action potential duration was prolonged after AF-simulation. We detected an increased late Na
+
current as a potential trigger for the detrimentally altered Ca
2+
/Na
+
-interplay. Mechanistically, reactive oxygen species were higher in the LV of patients with AF. CaMKII (Ca
2+
/calmodulin-dependent protein kinase IIδc) was found to be more oxidized at Met281/282 in the LV of patients with AF leading to an increased CaMKII activity and consequent increased RyR2 phosphorylation. CaMKII inhibition and ROS scavenging ameliorated impaired systolic Ca
2+
handling after AF-simulation.

Conclusions:

AF causes distinct functional and molecular remodeling of the human LV. This translational study provides the first mechanistic characterization and the potential negative impact of AF in the absence of tachycardia on the human ventricle.
Insulin Prevents Hypercholesterolemia by Suppressing 12α-Hydroxylated Bile Acids
Authors: Semova Ivana , Levenson Amy E. , Krawczyk Joanna , Bullock Kevin , Gearing Mary E. , Ling Alisha V. , Williams Kathryn A. , Miao Ji , Adamson Stuart S. , Shin Dong-Ju , Chahar Satyapal , Graham Mark J. , Crooke Rosanne M. , Hagey Lee R. , Vicent David , de Ferranti Sarah D. , Kidambi Srividya , Clish Clary B. , Biddinger Sudha B.
Published: February 2022

Background:

The risk of cardiovascular disease in type 1 diabetes remains extremely high, despite marked advances in blood glucose control and even the widespread use of cholesterol synthesis inhibitors. Thus, a deeper understanding of insulin regulation of cholesterol metabolism, and its disruption in type 1 diabetes, could reveal better treatment strategies.

Methods:

To define the mechanisms by which insulin controls plasma cholesterol levels, we knocked down the insulin receptor, FoxO1, and the key bile acid synthesis enzyme, CYP8B1. We measured bile acid composition, cholesterol absorption, and plasma cholesterol. In parallel, we measured markers of cholesterol absorption and synthesis in humans with type 1 diabetes treated with ezetimibe and simvastatin in a double-blind crossover study.

Results:

Mice with hepatic deletion of the insulin receptor showed marked increases in 12α-hydroxylated bile acids, cholesterol absorption, and plasma cholesterol. This phenotype was entirely reversed by hepatic deletion of
FoxO1
. FoxO1 is inhibited by insulin and required for the production of 12α-hydroxylated bile acids, which promote intestinal cholesterol absorption and suppress hepatic cholesterol synthesis. Knockdown of
Cyp8b1
normalized 12α-hydroxylated bile acid levels and completely prevented hypercholesterolemia in mice with hepatic deletion of the insulin receptor (n=5–30), as well as mouse models of type 1 diabetes (n=5–22). In parallel, the cholesterol absorption inhibitor, ezetimibe, normalized cholesterol absorption and low-density lipoprotein cholesterol in patients with type 1 diabetes as well as, or better than, the cholesterol synthesis inhibitor, simvastatin (n=20).

Conclusions:

Insulin, by inhibiting FoxO1 in the liver, reduces 12α-hydroxylated bile acids, cholesterol absorption, and plasma cholesterol levels. Thus, type 1 diabetes leads to a unique set of derangements in cholesterol metabolism, with increased absorption rather than synthesis. These derangements are reversed by ezetimibe, but not statins, which are currently the first line of lipid-lowering treatment in type 1 diabetes. Taken together, these data suggest that a personalized approach to lipid lowering in type 1 diabetes may be more effective and highlight the need for further studies specifically in this group of patients.
Cost Analysis From a Randomized Comparison of Immediate Versus Delayed Angiography After Cardiac Arrest
Authors: Camaro Cyril , Bonnes Judith L. , Adang Eddy M. , Spoormans Eva M. , Janssens Gladys N. , van der Hoeven Nina W. , Jewbali Lucia S. , Dubois Eric A. , Meuwissen Martijn , Rijpstra Tom A. , Bosker Hans A. , Blans Michiel J. , Bleeker Gabe B. , Baak Rémon , Vlachojannis George J. , Eikemans Bob J. , van der Harst Pim , van der Horst Iwan C. , Voskuil Michiel , van der Heijden Joris J. , Beishuizen Bert , Stoel Martin , van der Hoeven Hans , Henriques José P. , Vlaar Alexander P. , Vink Maarten A. , van den Bogaard Bas , Heestermans Ton A. , de Ruijter Wouter , Delnoij Thijs S. , Crijns Harry J. , Jessurun Gillian A. , Oemrawsingh Pranobe V. , Gosselink Marcel T. , Plomp Koos , Magro Michael , Elbers Paul W. , van de Ven Peter M. , Lemkes Jorrit S. , van Royen Niels
Published: February 2022

Background

In patients with out‐of‐hospital cardiac arrest without ST‐segment elevation, immediate coronary angiography did not improve clinical outcomes when compared with delayed angiography in the COACT (Coronary Angiography After Cardiac Arrest) trial. Whether 1 of the 2 strategies has benefits in terms of health care resource use and costs is currently unknown. We assess the health care resource use and costs in patients with out‐of‐hospital cardiac arrest.

Methods and Results

A total of 538 patients were randomly assigned to a strategy of either immediate or delayed coronary angiography. Detailed health care resource use and cost‐prices were collected from the initial hospital episode. A generalized linear model and a gamma distribution were performed. Generic quality of life was measured with the RAND‐36 and collected at 12‐month follow‐up. Overall total mean costs were similar between both groups (EUR 33 575±19 612 versus EUR 33 880±21 044;
P
=0.86). Generalized linear model: (β, 0.991; 95% CI, 0.894–1.099;
P
=0.86). Mean procedural costs (coronary angiography and percutaneous coronary intervention, coronary artery bypass graft) were higher in the immediate angiography group (EUR 4384±3447 versus EUR 3028±4220;
P
<0.001). Costs concerning intensive care unit and ward stay did not show any significant difference. The RAND‐36 questionnaire did not differ between both groups.

Conclusions

The mean total costs between patients with out‐of‐hospital cardiac arrest randomly assigned to an immediate angiography or a delayed invasive strategy were similar during the initial hospital stay. With respect to the higher invasive procedure costs in the immediate group, a strategy awaiting neurological recovery followed by coronary angiography and planned revascularization may be considered.

Registration

URL:
https://trialregister.nl
; Unique identifier: NL4857.