bluuebunny/crossref_metadata_2025 huggingface dataset:
A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting
Authors: Wan Jing , Huang Jiehui , Liao Zhiyuan , Li Chunquan , Liu Peter X.
Published: May 2022

Short-term wind power forecasting (SWPF) is essential for managing wind power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate multi-view learning technologies with attention mechanisms. In this case, some potential features cannot be fully extracted, degenerating the predictive accuracy and robustness in SWPF. To solve this problem, this paper proposes a multi-view ensemble width-depth neural network (MVEW-DNN) for SWPF. Specifically, MVEW-DNN consists of local and global view learning subnetworks, which can effectively achieve more potential global and local view features of the original wind power data. In MVEW-DNN, the local view learning subnetwork is developed by introducing the deep belief network (DBN) model, which can efficiently extract the local view features. On the other hand, by introducing the attention mechanism, a new deep encoder board learning system (deBLS) is developed as the global view learning subnetwork, which provides more comprehensive global information. Therefore, by rationally learning the effective local and global view features, MVEW-DNN can achieve competitive predictive performance in SWPF. MVEW-DNN is compared with the state-of-the-art models in SWPF. The experiment results indicate that MVEW-DNN can provide competitive predictive accuracy and robustness.
An Equity-Based Optimization Model to Solve the Location Problem for Healthcare Centers Applied to Hospital Beds and COVID-19 Vaccination
Authors: Delgado Erwin J. , Cabezas Xavier , Martin-Barreiro Carlos , Leiva Víctor , Rojas Fernando
Published: May 2022

Governments must consider different issues when deciding on the location of healthcare centers. In addition to the costs of opening such centers, three further elements should be addressed: accessibility, demand, and equity. Such locations must be chosen to meet the corresponding demand, so that they guarantee a socially equitable distribution, and to ensure that they are accessible to a sufficient degree. The location of the centers must be chosen from a set of possible facilities to guarantee certain minimum standards for the operational viability of the centers. Since the set of potential locations does not necessarily cover the demand of all geographical zones, the efficiency criterion must be maximized. However, the efficient distribution of resources does not necessarily meet the equity criterion. Thus, decision-makers must consider the trade-off between these two criteria: efficiency and equity. The described problem corresponds to the challenge that governments face in seeking to minimize the impact of the pandemic on citizens, where healthcare centers may be either public hospitals that care for COVID-19 patients or vaccination points. In this paper, we focus on the problem of a zone-divided region requiring the localization of healthcare centers. We propose a non-linear programming model to solve this problem based on a coverage formula using the Gini index to measure equity and accessibility. Then, we consider an approach using epsilon constraints that makes this problem solvable with mixed integer linear computations at each iteration. A simulation algorithm is also considered to generate problem instances, while computational experiments are carried out to show the potential use of the proposed mathematical programming model. The results show that the spatial distribution influences the coverage level of the healthcare system. Nevertheless, this distribution does not reduce inequity at accessible healthcare centers, as the distribution of the supply of health centers must be incorporated into the decision-making process.
Quantile-Zone Based Approach to Normality Testing
Authors: Avdović Atif , Jevremović Vesna
Published: May 2022

Normality testing remains an important issue for researchers, despite many solutions that have been published and in use for a long time. There is a need for testing normality in many areas of research and application, among them in Quality control, or more precisely, in the investigation of Shewhart-type control charts. We modified some of our previous results concerning control charts by using the empirical distribution function, proper choice of quantiles and a zone function that quantifies the discrepancy from a normal distribution. That was our approach in constructing a new normality test that we present in this paper. Our results show that our test is more powerful than any other known normality test, even in the case of alternatives with small departures from normality and for small sample sizes. Additionally, many test statistics are sensitive to outliers when testing normality, but that is not the case with our test statistic. We provide a detailed distribution of the test statistic for the presented test and comparable power analysis with highly illustrative graphics. The discussion covers both the cases for known and for estimated parameters.
Dynamic Linkages among Carbon, Energy and Financial Markets: Multiplex Recurrence Network Approach
Authors: Wang Minggang , Hua Chenyu , Xu Hua
Published: May 2022

It has become a hot issue to integrate the carbon market, energy market, and financial market into one system and explore the relationship among them. Considering that the carbon market, energy market, and financial market all have chaotic characteristics to varying degrees, this paper proposes a theoretical framework to study the linkage relationship among the three markets on the basis of the method of the Multiplex recurrence network. Firstly, we built a multiplex recurrence network of carbon-energy-financial market. Then, based on the connection relationship among nodes of the recurrence network of each market, the degree distribution of nodes of each market, and the information entropy theory, we put forward several metric indicators to explore the correlativity and mutual guidance relation among carbon market, energy market and financial market from micro and macro perspectives. Using the data generated by the deterministic system, the effectiveness of the defined index was confirmed by numerical simulation. The empirical analysis of the carbon market, energy market, and financial market revealed the evolution process of the increasingly close connection between the three markets, and we found that the carbon market plays an increasingly important role in the world capital market system. Based on the research results, we propose some suggestions for market decision-makers, enterprises, and investors.
Stabilization Control of Underactuated Spring-Coupled Three-Link Horizontal Manipulator Based on Energy Absorption Idea
Authors: Zhang Ancai , Fan Lu , Gong Shuli , Pan Guangyuan , Wu Yinghua
Published: May 2022

A spring-coupled three-link horizontal manipulator (STHM) is an underactuated mechanical system that possesses two control inputs and three degrees of freedom (DOF). This paper discusses the stabilization control problem for this multi-DOF underactuated system. By using an energy-absorbing idea, we design two types of virtual friction controllers: PsD controller and PD controller. Additionally, the stability of the control system is analyzed based on Lyapunov theory and LaSalle’s invariance principle. The design of the stabilizing controller in this paper makes good use of the physical characteristics of the STHM system. The design process of the whole control system is simple. Numerical examples demonstrate the validity and superiority of our developed control strategy.
A Clustering Ensemble Framework with Integration of Data Characteristics and Structure Information: A Graph Neural Networks Approach
Authors: Du Hang-Yuan , Wang Wen-Jian
Published: May 2022

Clustering ensemble is a research hotspot of data mining that aggregates several base clustering results to generate a single output clustering with improved robustness and stability. However, the validity of the ensemble result is usually affected by unreliability in the generation and integration of base clusterings. In order to address this issue, we develop a clustering ensemble framework viewed from graph neural networks that generates an ensemble result by integrating data characteristics and structure information. In this framework, we extract structure information from base clustering results of the data set by using a coupling affinity measure After that, we combine structure information with data characteristics by using a graph neural network (GNN) to learn their joint embeddings in latent space. Then, we employ a Gaussian mixture model (GMM) to predict the final cluster assignment in the latent space. Finally, we construct the GNN and GMM as a unified optimization model to integrate the objectives of graph embedding and consensus clustering. Our framework can not only elegantly combine information in feature space and structure space, but can also achieve suitable representations for final cluster partitioning. Thus, it can produce an outstanding result. Experimental results on six synthetic benchmark data sets and six real world data sets show that the proposed framework yields a better performance compared to 12 reference algorithms that are developed based on either clustering ensemble architecture or a deep clustering strategy.
Collatz Attractors Are Space-Filling
Authors: Aberkane Idriss J.
Published: May 2022

The algebraic topology of Collatz attractors (or “Collatz Feathers”) remains very poorly understood. In particular, when pushed to infinity, is their set of branches discrete or continuous? Here, we introduce a fundamental theorem proving that the latter is true. For any odd x, we first define Axn as the set of all odd numbers with Syr(x) in their Collatz orbit and up to n more digits than x in base 2. We then prove limn→∞|Axn|2n+c≥1 with c>−4 for all x and, in particular, c=0 for x=1, which is a result strictly stronger than that of Tao 2019.
Decoupling Induction and Multi-Order Attention Drop-Out Gating Based Joint Motion Deblurring and Image Super-Resolution
Authors: Chu Yuezhong , Zhang Xuefeng , Liu Heng
Published: May 2022

Resolution decrease and motion blur are two typical image degradation processes that are usually addressed by deep networks, specifically convolutional neural networks (CNNs). However, since real images are usually obtained through multiple degradations, the vast majority of current CNN methods that employ a single degradation process inevitably need to be improved to account for multiple degradation effects. In this work, motivated by degradation decoupling and multiple-order attention drop-out gating, we propose a joint deep recovery model to efficiently address motion blur and resolution reduction simultaneously. Our degradation decoupling style improves the continence and the efficiency of model construction and training. Moreover, the proposed multi-order attention mechanism comprehensively and hierarchically extracts multiple attention features and fuses them properly by drop-out gating. The proposed approach is evaluated using diverse benchmark datasets including natural and synthetic images. The experimental results show that our proposed method can efficiently complete joint motion blur and image super-resolution (SR).
A Comparison Study of the Classical and Modern Results of Semi-Local Convergence of Newton–Kantorovich Iterations-II
Authors: Regmi Samundra , Argyros Ioannis K. , George Santhosh , Argyros Michael I.
Published: May 2022

This article is an independently written continuation of an earlier study with the same title [Mathematics, 2022, 10, 1225] on the Newton Process (NP). This process is applied to solve nonlinear equations. The complementing features are: the smallness of the initial approximation is expressed explicitly in turns of the Lipschitz or Hölder constants and the convergence order 1+p is shown for p∈(0,1]. The first feature becomes attainable by further simplifying proofs of convergence criteria. The second feature is possible by choosing a bit larger upper bound on the smallness of the initial approximation. This way, the completed convergence analysis is finer and can replace the classical one by Kantorovich and others. A two-point boundary value problem (TPBVP) is solved to complement this article.
Monte Carlo Based Isogeometric Stochastic Finite Element Method for Uncertainty Quantization in Vibration Analysis of Piezoelectric Materials
Authors: Xu Yanming , Li Haozhi , Chen Leilei , Zhao Juan , Zhang Xin
Published: May 2022

In this study, a Monte Carlo simulation (MCs)-based isogeometric stochastic Finite Element Method (FEM) is proposed for uncertainty quantification in the vibration analysis of piezoelectric materials. In this method, deterministic solutions (natural frequencies) of the coupled eigenvalue problem are obtained via isogeometric analysis (IGA). Moreover, MCs is employed to solve various uncertainty parameters, including separate elastic and piezoelectric constants and their combined cases.
A Study of the Complexity of Problems Posed by Talented Students in Mathematics
Authors: Espinoza Johan , Lupiáñez José Luis , Segovia Isidoro
Published: May 2022

Problem posing and mathematical talent are topics of interest to the community of researchers in Mathematics Education, but few studies reveal talented students’ abilities to solve problem-posing tasks. The data were collected using a problem invention instrument composed of four questionnaires that include free, semi-structured tasks and problem invention structures. The sample consisted of 23 students considered as mathematically talented and 22 students from a standard public school. The results show that the problems posed by the talent group are more complex than those invented by the standard group. The former are longer and show greater diversity of ideas while also requiring more steps to be solved, presenting a higher level of complexity according to the PISA framework, and requiring significant cognitive effort. In conclusion, the problem invention instrument used and the variables defined enabled us to analyze the complexity of the problems posed by the group of talented students. The statistical analysis performed reinforces the differences found in the complexity of the productions by the two groups studied.
Graph-Based Integration of Histone Modification Profiles
Authors: Baccini Federica , Bianchini Monica , Geraci Filippo
Published: May 2022

In this work, we introduce a similarity-network-based approach to explore the role of interacting single-cell histone modification signals in haematopoiesis—the process of differentiation of blood cells. Histones are proteins that provide structural support to chromosomes. They are subject to chemical modifications—acetylation or methylation—that affect the degree of accessibility of genes and, in turn, the formation of different phenotypes. The concentration of histone modifications can be modelled as a continuous signal, which can be used to build single-cell profiles. In the present work, the profiles of cell types involved in haematopoiesis are built based on all the major histone modifications (i.e., H3K27ac, H3K27me3, H3K36me3, H3K4me1, H3K4me3, H3K9me3) by counting the number of peaks in the modification signals; then, the profiles are used to compute modification-specific similarity networks among the considered phenotypes. As histone modifications come as interacting signals, we applied a similarity network fusion technique to integrate these networks in a unique graph, with the aim of studying the simultaneous effect of all the modifications for the determination of different phenotypes. The networks permit defining of a graph-cut-based separation score for evaluating the homogeneity of subgroups of cell types corresponding to the myeloid and lymphoid phenotypes in the classical representation of the haematopoietic tree. Resulting scores show that separation into myeloid and lymphoid phenotypes reflects the actual process of haematopoiesis.
A Fast Galerkin Approach for Solving the Fractional Rayleigh–Stokes Problem via Sixth-Kind Chebyshev Polynomials
Authors: Atta Ahmed Gamal , Abd-Elhameed Waleed Mohamed , Moatimid Galal Mahrous , Youssri Youssri Hassan
Published: May 2022

Herein, a spectral Galerkin method for solving the fractional Rayleigh–Stokes problem involving a nonlinear source term is analyzed. Two kinds of basis functions that are related to the shifted sixth-kind Chebyshev polynomials are selected and utilized in the numerical treatment of the problem. Some specific integer and fractional derivative formulas are used to introduce our proposed numerical algorithm. Moreover, the stability and convergence accuracy are derived in detail. As a final validation of our theoretical results, we present a few numerical examples.
Optimizing the Neural Structure and Hyperparameters of Liquid State Machines Based on Evolutionary Membrane Algorithm
Authors: Liu Chuang , Wang Haojie , Liu Ning , Yuan Zhonghu
Published: May 2022

As one of the important artificial intelligence fields, brain-like computing attempts to give machines a higher intelligence level by studying and simulating the cognitive principles of the human brain. A spiking neural network (SNN) is one of the research directions of brain-like computing, characterized by better biogenesis and stronger computing power than the traditional neural network. A liquid state machine (LSM) is a neural computing model with a recurrent network structure based on SNN. In this paper, a learning algorithm based on an evolutionary membrane algorithm is proposed to optimize the neural structure and hyperparameters of an LSM. First, the object of the proposed algorithm is designed according to the neural structure and hyperparameters of the LSM. Second, the reaction rules of the proposed algorithm are employed to discover the best neural structure and hyperparameters of the LSM. Third, the membrane structure is that the skin membrane contains several elementary membranes to speed up the search of the proposed algorithm. In the simulation experiment, effectiveness verification is carried out on the MNIST and KTH datasets. In terms of the MNIST datasets, the best test results of the proposed algorithm with 500, 1000 and 2000 spiking neurons are 86.8%, 90.6% and 90.8%, respectively. The best test results of the proposed algorithm on KTH with 500, 1000 and 2000 spiking neurons are 82.9%, 85.3% and 86.3%, respectively. The simulation results show that the proposed algorithm has a more competitive advantage than other experimental algorithms.
A Positivity-Preserving Improved Nonstandard Finite Difference Method to Solve the Black-Scholes Equation
Authors: Mehdizadeh Khalsaraei Mohammad , Shokri Ali , Ramos Higinio , Mohammadnia Zahra , Khakzad Pari
Published: May 2022

In this paper, we evaluate and discuss different numerical methods to solve the Black–Scholes equation, including the θ-method, the mixed method, the Richardson method, the Du Fort and Frankel method, and the MADE (modified alternating directional explicit) method. These methods produce numerical drawbacks such as spurious oscillations and negative values in the solution when the volatility is much smaller than the interest rate. The MADE method sacrifices accuracy to obtain stability for the numerical solution of the Black–Scholes equation. In the present work, we improve the MADE scheme by using non-standard finite difference discretization techniques in which we use a non-local approximation for the reaction term (we call it the MMADE method). We will discuss the sufficient conditions to be positive of the new scheme. Also, we show that the proposed method is free of spurious oscillations even in the presence of discontinuous initial conditions. To demonstrate how efficient the new scheme is, some numerical experiments are performed at the end.
Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification
Authors: Zhao Tianna , Zhang Yuanjian , Miao Duoqian
Published: May 2022

Multi-label classification deals with the determination of instance-label associations for unseen instances. Although many margin-based approaches are delicately developed, the uncertainty classifications for those with smaller separation margins remain unsolved. The intuitionistic fuzzy set is an effective tool to characterize the concept of uncertainty, yet it has not been examined for multi-label cases. This paper proposed a novel model called intuitionistic fuzzy three-way label enhancement (IFTWLE) for multi-label classification. The IFTWLE combines label enhancement with an intuitionistic fuzzy set under the framework of three-way decisions. For unseen instances, we generated the pseudo-label for label uncertainty evaluation from a logical label-based model. An intuitionistic fuzzy set-based instance selection principle seamlessly bridges logical label learning and numerical label learning. The principle is hierarchically developed. At the label level, membership and non-membership functions are pair-wisely defined to measure the local uncertainty and generate candidate uncertain instances. After upgrading to the instance level, we select instances from the candidates for label enhancement, whereas they remained unchanged for the remaining. To the best of our knowledge, this is the first attempt to combine logical label learning with numerical label learning into a unified framework for minimizing classification uncertainty. Extensive experiments demonstrate that, with the selectively reconstructed label importance, IFTWLE achieves statistically superior over the state-of-the-art multi-label classification algorithms in terms of classification accuracy. The computational complexity of this algorithm is On2mk, where n, m, and k denote the unseen instances count, label count, and average label-specific feature size, respectively.
Generalized Proportional Caputo Fractional Differential Equations with Delay and Practical Stability by the Razumikhin Method
Authors: Agarwal Ravi , Hristova Snezhana , O’Regan Donal
Published: May 2022

Practical stability properties of generalized proportional Caputo fractional differential equations with bounded delay are studied in this paper. Two types of stability, practical stability and exponential practical stability, are defined and considered, and also some sufficient conditions to guarantee stability are presented. The study is based on the application of Lyapunov like functions and their generalized proportional Caputo fractional derivatives among solutions of the studied system where appropriate Razumikhin like conditions are applied (appropriately modified in connection with the fractional derivative considered). The theory is illustrated with several nonlinear examples.
Tell Me Why I Do Not Like Mondays
Authors: Idilbi-Bayaa Yasmeen , Qadan Mahmoud
Published: May 2022

We conduct a strict and broad analysis of the 30-day expected volatility (VIX) of five very active individual US stocks, three US domestic indices, and that of 10-year US Treasury notes. We find prominent non-random movement patterns mainly on Mondays and Fridays. Furthermore, significant leaps in expected volatility on Monday occur primarily in the first two and the fifth Mondays of the month. We also document that higher values for the 30-day expected volatility on Mondays are more likely when there was a negative change in the volatility on the preceding Fridays. This pattern does not occur on other subsequent days of the week. The results are robust through time and different subsamples and are not triggered by outliers or the week during which the options on the underlying assets expire. Rational and irrational drivers are suggested to explain the findings. Given that, to date, no one has conducted such an examination, our findings are important for investors interested in buying or selling volatility instruments.
The Development of Suitable Inequalities and Their Application to Systems of Logical Equations
Authors: Barotov Dostonjon Numonjonovich , Barotov Ruziboy Numonjonovich , Soloviev Vladimir , Feklin Vadim , Muzafarov Dilshod , Ergashboev Trusunboy , Egamov Khudoyberdi
Published: May 2022

In this paper, two not-difficult inequalities are invented and proved in detail, which adequately describe the behavior of discrete logical functions xor(x1, x2,…, xn) and and(x1, x2,…, xn). Based on these proven inequalities, infinitely differentiable extensions of the logical functions xor(x1, x2,…, xn) and and(x1, x2,…, xn) were defined for the entire ℝn. These suitable extensions were applied to systems of logical equations. Specifically, the system of m logical equations in a constructive way without adding any equations (not field equations and no others) is transformed in ℝn first into an equivalent system of m smooth rational equations (SmSRE) so that the solution of SmSRE can be reduced to the problem minimization of the objective function, and any numerical optimization methods can be applied since the objective function will be infinitely differentiable. Again, we transformed SmSRE into an equivalent system of m polynomial equations (SmPE). This means that any symbolic methods for solving polynomial systems can be used to solve and analyze an equivalent SmPE. The equivalence of these systems has been proved in detail. Based on these proofs and results, in the next paper, we plan to study the practical applicability of numerical optimization methods for SmSRE and symbolic methods for SmPE.
Solution of a Complex Nonlinear Fractional Biochemical Reaction Model
Authors: Rabah Fatima , Abukhaled Marwan , Khuri Suheil A.
Published: May 2022

This paper discusses a complex nonlinear fractional model of enzyme inhibitor reaction where reaction memory is taken into account. Analytical expressions of the concentrations of enzyme, substrate, inhibitor, product, and other complex intermediate species are derived using Laplace decomposition and differential transformation methods. Since different rate constants, large initial concentrations, and large time domains are unavoidable in biochemical reactions, different dynamics will result; hence, the convergence of the approximate concentrations may be lost. In this case, the proposed analytical methods will be coupled with Padé approximation. The validity and accuracy of the derived analytical solutions will be established by direct comparison with numerical simulations.
Association between Self-Medication for Mild Symptoms and Quality of Life among Older Adults in Rural Japan: A Cross-Sectional Study
Authors: Ohta Ryuichi , Ryu Yoshinori , Sano Chiaki
Published: May 2022

Self-medication, a help-seeking behavior to control individual symptoms, can be promoted to prevent the overuse of medical care and improve self-management among older adults. However, evidence regarding the association between self-medication and quality of life (QOL) is lacking. The purpose of this study is to investigate the association between QOL and the usage of self-medication among rural older adults. This cross-sectional study included participants older than 65 years in rural Japanese communities. Data were collected using a questionnaire regarding self-medication trends, the EQ-5D-5L to assess QOL, and a demographics questionnaire. Participants were divided into exposure and control groups based on their tendencies toward self-medication usage. Differences in the demographics between groups were adjusted using propensity score matching. Results: The health status in the exposure group was statistically significantly better than that in the control group in the dimensions of movement, self-care, and usual activities. Conversely, the pain/discomfort and anxiety/depression dimensions were not statistically significantly different. The quality of self-medication behaviors for mild symptoms can be improved with practical knowledge of and access to home remedies and over-the-counter drugs. Educational interventions and system development for better self-medication for mild symptoms and medical care for critical symptoms in rural contexts can be effective in improving QOL among rural older adults.
Modifications of the Dental Hard Tissues in the Cervical Area of Occlusally Overloaded Teeth Identified Using Optical Coherence Tomography
Authors: Stănuși Andreea , Ionescu Mihaela , Cerbulescu Cristina , Popescu Sanda Mihaela , Osiac Eugen , Mercuț Răzvan , Scrieciu Monica , Pascu Roxana Maria , Stănuși Adrian Ştefan , Mercuț Veronica
Published: May 2022

Background and objectives: Occlusal overloads produce a series of manifestations in teeth, especially attrition and non-carious cervical lesions (NCCL). Optical Coherence Tomography (OCT) can highlight and evaluate tooth lesions. The aim of this study was to examine the changes of dental hard tissues in the cervical area because of occlusal overload by macroscopic examination and using in vitro Swept Source OCT examination. Materials and Methods: The study was performed on 21 extracted teeth with occlusal trauma. After macroscopic and OCT examination, the 2D OCT images were transformed into 3D images using ImageJ software. Statistical analysis of macroscopic and OCT images was performed with Statistical Package for Social Sciences. Results: On 21 teeth, 88 cervical lesions (cracks) were identified. Upper premolars with an occlusal Smith and Knight tooth wear score of 2 had the most NCCL. Statistical analysis revealed significant differences in the median widths of cervical lesions between teeth with score 1 and score 3. Additionally, we obtained statistically significant differences in median widths between the buccal and oral surfaces. Conclusions: These cracks can be considered precursors of NCCL. NCCL can be located on dental surfaces in the cervical area other than the buccal one. A 3D reconstruction of OCT images emphasized that cracks are located especially at enamel level, evolving towards the enamel–dentin junction, with multiple ramifications.
Umbilical Cord Knots: Is the Number Related to Fetal Risk?
Authors: Stabile Guglielmo , Carlucci Stefania , De Bonis Lucia , Sorrentino Felice , Nappi Luigi , Ricci Giuseppe
Published: May 2022

True knots of the umbilical cord (UC) are a rare occurrence and are reported in 0.4–1.2% of deliveries. The compression of true knot of the UC can cause obstruction of the fetal circulation, leading to intra-uterine growth retardation or fetal death. Predisposing factors for the genesis of the true UC knot are numerous and include all the conditions, which lead to a relatively large uterine volume. This situation may predispose to free and excessive fetal movements. Although not all true knots lead to perinatal complications, they have been associated with adverse pregnancy outcomes, including fetal distress, fetal hypoxia, intra-uterine growth restriction (IUGR), long-term neurological damage, caesarean delivery and stillbirth. We present a rare case of operative delivery with vacuum in a multiparous woman at term of pregnancy with a double true knot of the UC. As in most cases, the diagnosis was made after delivery, as there were no fetal symptoms during pregnancy. Some authors assume that 3D power sonography may be useful in the diagnosis of true UC knots. However, 3D power Doppler cannot be considered as a definitive method. There are no specific prenatal indications to induce the physician to look for ultrasound signs suggestive of umbilical true knot. Some studies argue that cases of fetal death and fetal risk are directly related to the number of knots. We also support this thesis, even if further observational and retrospective studies are needed to demonstrate it.
Pulse Wave Velocity, a Predictor of Major Adverse Cardiovascular Events, and Its Correlation with the General Stress Level of Health Care Workers during the COVID-19 Pandemic
Authors: Marin Ioana , Iurciuc Mircea , Popescu Florina Georgeta , Iurciuc Stela , Popoiu Calin Marius , Marin Catalin Nicolae , Ursoniu Sorin , Fira-Mladinescu Corneluta
Published: May 2022

Background and Objectives: In the COVID-19 epidemiological context, the health care workers who were treating patients with COVID-19 were exposed daily to additional stress. Pulse wave velocity (PWV) is a predictive parameter for possible major adverse cardiovascular events. The present study aimed to evaluate the correlation between the general stress levels and PWVs of medical workers during the COVID-19 pandemic. Materials and Methods: The study group was heterogeneous in terms of the medical profession. PWV was measured using a TendioMed arteriograph. Assessment of stress level was performed using a general stress questionnaire with questions grouped on the areas that contribute to stress: lifestyle, environment, symptoms, job, relationships and personality. PWV measurements and stress assessment were performed both during the period with many patients with COVID-19 and during the period with few patients with COVID-19. Results: The stress levels and PWVs of subjects were higher in the period when they cared for patients with COVID-19 than in the period when they did not have patients with COVID-19. Conclusions: The study shows a positive correlation between the PWV of each subject and his/her stress score (the higher the stress score, the higher the PWV).
In Vitro and In Ovo Evaluation of the Potential Hepatoprotective Effect of Metformin
Authors: Cozma Gabriel Veniamin , Apostu Alexandru , Macasoi Ioana , Dehelean Cristina Adriana , Cretu Octavian Marius , Dinu Stefania , Gaiță Dan , Manea Aniko
Published: May 2022

Background and Objectives: Metformin is currently the leading drug of choice for treating type 2 diabetes mellitus, being one of the most widely used drugs worldwide. The beneficial effects of Metformin, however, extend far beyond the reduction of blood glucose. Therefore, this study aimed to evaluate Metformin’s effects both in vitro and in ovo. Materials and Methods: Metformin has been tested in five different concentrations in human hepatocytes —HepaRG, in terms of cell viability, morphology, structure and number of nuclei and mitochondria, as well as the effect on cell migration. Through the application of HET-CAM, the biocompatibility and potential anti-irritant, as well as protective effects on the vascular plexus were also assessed. Results: According to the results obtained, Metformin increases cell viability without causing morphological changes to cells, mitochondria, or nuclei. Metformin displayed an anti-irritant activity rather than causing irritation at the level of the vascular plexus. Conclusions: In conclusion, Metformin enhances cell viability and proliferation and, has a protective effect on the vascular plexus. Nonetheless, more studies are required to clarify the mechanism of hepatoprotective effect of metformin.
Pulmonary Pleomorphic Carcinoma Harboring EGFR Mutation Successfully Treated with Osimertinib: A Case Report
Authors: Kano Yukari , Kataoka Nobutaka , Kunimatsu Yusuke , Tsutsumi Rei , Sato Izumi , Tanimura Mai , Nakano Takayuki , Tanimura Keiko , Takeda Takayuki
Published: May 2022

Pulmonary pleomorphic carcinoma (PPC) is well-known for its aggressive nature that is usually resistant to platinum-based chemotherapy. On the other hand, the efficacy of an immune checkpoint inhibitor-based regimen in PPC has been elucidated. PPCs harboring epidermal growth factor receptor (EGFR) mutations are extremely rare, and the efficacy of EGFR-tyrosine kinase inhibitors in PPC is limited compared to their efficacy in EGFR-mutated adenocarcinoma. A 43-year-old female patient presenting with a lung mass with multiple brain metastases, carcinomatous pericarditis, and multiple bone metastases was referred to our department. Transbronchial biopsy confirmed the diagnosis of PPC harboring an EGFR mutation with exon 19 deletion. Subsequently, she was treated with osimertinib, a third-generation EGFR-tyrosine kinase inhibitor, which resulted in partial response with shrinkage of the primary lesion and brain metastases. This partial response remained durable for 11 months with an ongoing regimen. The current case suggests that osimertinib would show promising effects as a first-line treatment for PPCs harboring EGFR mutations, as well as a reasonable sequence of therapy followed by immune checkpoint inhibitor-based regimens.
Factors Associated with Prolonged RT-PCR SARS-CoV-2 Positive Testing in Patients with Mild and Moderate Forms of COVID-19: A Retrospective Study
Authors: Motoc Nicoleta Stefania , Ruta Victoria-Maria , Man Milena Adina , Ungur Rodica Ana , Ciortea Viorela Mihaela , Irsay Laszlo , Nicola Andrea , Valean Dan , Usatiuc Lia Oxana , Matei Ileana Rodica , Borda Ileana Monica
Published: May 2022

Background and objectives: This article aims to evaluate the number of days necessary for patients with mild and moderate forms of COVID-19 to reach undetectable levels of SARS-CoV-2 RNA in the upper respiratory tract specimens. As a secondary objective, we sought to establish a correlation between different conditions associated with longer viral load as this could result in a longer period of contagion and infectivity. Materials and Methods: It is a retrospective study. A total of 70 patients with confirmed mild and moderate forms of COVID-19 were enrolled in our study. Results: Number of days with traceable viral load was 25.93 (±6.02) days in patients with mild COVID-19 and 26.97 (±8.30) in moderate form (p = 0.72). Age, male gender, and obesity, along with several chronic conditions (cardiac, liver, renal, and neurological disease), were associated with prolonged positive RT-PCR test from the nasal swab (therefore prolonged viral load). These are in general, risk factors for severe forms of COVID-19. Conclusions: There are several conditions associated with prolonged positive RT-PCR in mild and moderate forms of COVID-19. As to why and what is the significance of it remains to be studied.
Relationship between Acute-Phase Symptoms and Immunoglobulin G Seropositivity up to Eight Months after COVID-19
Authors: Štěpánek Ladislav , Janošíková Magdaléna , Nakládalová Marie , Štěpánek Lubomír , Tihelka Antonín , Boriková Alena , Večeřová Renata , Sauer Pavel
Published: May 2022

Background and Objectives: Given the limited knowledge of antibody responses to COVID-19 and their determinants, we analyzed the relationship between the occurrence of acute-phase symptoms and infection-induced immunoglobulin (Ig) G seropositivity up to 8 months post-symptom onset. Materials and Methods: In this cross-sectional study, 661 middle-aged unvaccinated healthcare workers (HCWs) were interviewed about the presence of symptoms during the acute phase of their previously confirmed COVID-19 and were tested for specific IgG, targeting the spike protein (S1 and S2). The dependence of seropositivity on the symptom occurrence was explored through multiple logistic regression, adjusted for the interval between symptom onset and serology testing, and through classification and regression trees. Results: A total of 551 (83.4%) HCWs showed seropositivity and, inversely, 110 (16.6%) HCWs were seronegative. The chance of IgG seropositivity was increased by dyspnea (odds ratio (OR) 1.48, p < 0.001) and anosmia (OR 1.52, p = 0.021). Fever in HCWs with dyspnea resulted in the highest detected seropositivity rate, and anosmia in HCWs without dyspnea significantly increased the proportion of seropositivity. Conclusion: Clinical manifestation of the acute phase of COVID-19 predisposes to the development of infection-induced antibody responses. The findings can be applied for assessing the long-term protection by IgG, and thus, for creating effective surveillance strategies.
The Influence of Maternal Factors on Neonatal Intensive Care Unit Admission and In-Hospital Mortality in Premature Newborns from Western Romania: A Population-Based Study
Authors: Ilyes Stelian-Gabriel , Chiriac Veronica Daniela , Gluhovschi Adrian , Mihaela Valcovici , Dahma George , Mocanu Adelina Geanina , Neamtu Radu , Silaghi Carmen , Radu Daniela , Bernad Elena , Craina Marius
Published: May 2022

Background and Objectives: Neonatal mortality is a global public health issue, disproportionately affecting low- and middle-income nations. Although Romania is a high-income nation, according to the European Union’s most recent demographic data, it had the second-highest infant death rate in 2019. Although significant progress has been made in the last three decades in lowering newborn mortality, more initiatives to accelerate progress are required to meet the 2030 Sustainable Development Goals (SDG) objective. Therefore, we aimed to develop an observational study to determine the influence of maternal factors on in-hospital neonatal intensive care unit admission and mortality in premature infants born in western Romania. While newborn mortality has decreased globally, the pace of decline is far less than what is desired. Materials and Methods: A retrospective study comprising 328 premature patients and 422 full-term newborns, was developed at a tertiary obstetrics and gynecology clinic in western Romania, comprising the period of the last 24 months before the COVID-19 pandemic and the first 24 months of the pandemic. Results: The following variables were identified as statistically significant risk factors for neonatal intensive care unit admission: age > 35 years, OR = 1.59; twin births, OR = 1.14; low gestational age, OR = 1.66; preeclampsia, OR = 2.33; and peripartum infection, OR = 2.25. The same risk factors, with the exception of twin births, were significantly associated with in-hospital neonatal mortality. Except for a longer duration of maternal hospitalization and neonatal therapy with surfactant, steroids, and antibiotics, the COVID-19 pandemic did not cause significant differences in the evolution and outcomes of preterm newborns. Conclusions: The major maternal risk factors for NICU admission were advanced age, twin pregnancy, low gestational age, preeclampsia, and peripartum infection. Additionally, these characteristics contributed to a high likelihood of death, despite adequate access to medical care and advanced life support for the neonates. Understanding the causes of morbidity and death in neonates admitted to the neonatal intensive care unit enables better prioritization and planning of health services, resource reallocation, and care quality improvement.
Hormonal Changes after R-CHOP Chemotherapy in Patients with Diffuse Large B-Cell Lymphoma: A Prospective Study
Authors: Manosroi Worapaka , Chirayucharoensak Jiraporn , Chai-adisaksopha Chatree , Phinyo Phichayut
Published: May 2022

Background and Objectives: There is a lack of data regarding hormonal metabolic abnormalities resulting from the R-CHOP regimen (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisolone), a commonly used chemotherapy in diffuse large B-cell lymphoma (DLBCL). This study aimed to determine the incidence of hormonal and metabolic changes after R-CHOP therapy. Methods and Methods: This prospective cohort study initially included 17 DLBCL patients. Hormonal tests, including gonadal function, thyroid function, and ACTH stimulation tests for cortisol and metabolic markers, were performed before the 1st and after the 5th cycle of R-CHOP. The paired t-test was used to evaluate the changes. Statistical significance was set at p < 0.05. Results: Out of 17 patients, two died before the last follow-up, and 15 completed the study. LH levels were significantly higher in males after the 5th cycle (p = 0.04), while no significant changes were observed in other hormonal levels. After the 5th cycle, the only trend toward statistical significance was observed with higher FSH in males, higher blood glucose, and cholesterol. After the 5th cycle of chemotherapy, seven patients had at least one hormonal dysfunction, three patients had alterations in their thyroid function tests. Three patients had adrenal insufficiency (AI), one of whom also had secondary hypothyroidism concomitant with hypogonadism and AI. Additionally, two males had hypogonadism, and impaired fasting glucose was observed in three patients. Conclusions: Hormonal and metabolic abnormalities can occur in DLBCL after the 5th R-CHOP cycle. A high level of awareness and careful observation is of value in detecting these abnormalities, as some can be lethal.
KLF10 Functions as an Independent Prognosis Factor for Gastric Cancer
Authors: Lin Yueh-Min , Yeh Kun-Tu , Yeh Chung-Min , Soon Maw-Soan , Hsu Li-Sung
Published: May 2022

Background and Objectives: Krűppel-like factor 10 (KLF10) participates in the tumorigenesis of several human cancers by binding to the GC-rich region within the promoter regions of specific genes. KLF10 is downregulated in human cancers. However, the role of KLF10 in gastric cancer formation remains unclear. Materials and Methods: In this study, we performed immunohistochemical staining for KLF10 expression in 121 gastric cancer sections. Results: The loss of KLF10 expression was correlated with advanced stages and T status. Kaplan–Meier analysis revealed that patients with higher KLF10 levels had longer overall survival than those with lower KLF10 levels. Univariate analysis revealed that in patients with gastric cancer, advanced stages and low KLF10 levels were associated with survival. Multivariate analysis indicated that age, gender, advanced stages, and KLF10 expression were independent prognostic factors of the survival of patients with gastric cancer. After adjusting for age, gender, and stage, KLF10 expression was also found to be an independent prognostic factor in the survival of patients with gastric cancer. Conclusion: Our results collectively suggested that KLF10 may play a critical role in gastric cancer formation and is an independent prognosis factor of gastric cancer.
COVID-19 Induced Taste Dysfunction and Recovery: Association with Smell Dysfunction and Oral Health Behaviour
Authors: Catton Georgia , Gardner Alexander
Published: May 2022

Background and Objectives: Disruption to taste and smell are common symptoms of COVID-19 infection. The current literature overlooks taste symptoms and tends to focus on the sense of smell. Persisting cases (>28 days) of taste dysfunction are increasingly recognised as a major future healthcare challenge. This study focuses on the severity and recovery of COVID-19 induced taste loss and association with olfactory symptoms, lifestyle and oral health factors. Materials and Methods: This study was a cross-sectional survey comparing 182 rapid taste recovery participants (≤28 days) with 47 participants with prolonged taste recovery >28 days. Analyses of taste loss in association with smell loss, age, sex, illness severity, diet, BMI, vitamin-D supplementation, antidepressants, alcohol use, smoking, brushing frequency, flossing, missing teeth, appliances and number of dental restorations were conducted. Differences in the severity of the loss of sour, sweet, salt, bitter and umami tastes were explored. Results: Both the severity and the duration of taste and smell loss were closely correlated (p < 0.001). Salt taste was significantly less affected than all other taste qualities (p < 0.001). Persisting taste loss was associated with older age (mean ± 95% CI = 31.73 ± 1.23 years vs. 36.66 ± 3.59 years, p < 0.001) and reduced likelihood of using floss (odds ratio ± 95% CI = 2.22 (1.15–4.25), p = 0.047). Conclusions: Smell and taste loss in COVID-19 are closely related, although a minority of individuals can experience taste or smell dysfunction in the absence of the other. The taste of salt may be less severely affected than other taste qualities and future work exploring this finding objectively is indicated. The association of flossing with rapid taste recovery adds to the growing evidence of a link between good periodontal health and favourable COVID-19 outcomes.