bluuebunny/crossref_metadata_2025 huggingface dataset:
New Control Strategy for Heating Portable Fuel Cell Power Systems for Energy-Efficient and Reliable Operation
Authors: Zimprich Sebastian , Dávila-Portals Diego , Matthiesen Sven , Gwosch Thomas
Published: December 2022

Using hydrogen fuel cells for power systems, temperature conditions are important for efficient and reliable operations, especially in low-temperature environments. A heating system with an electrical energy buffer is therefore required for reliable operation. There is a research gap in finding an appropriate control strategy regarding energy efficiency and reliable operations for different environmental conditions. This paper investigates heating strategies for the subfreezing start of a fuel cell for portable applications at an early development stage to enable frontloading in product engineering. The strategies were investigated by simulation and experiment. A prototype for such a system was built and tested for subfreezing start-ups and non-subfreezing start-ups. This was done by heating the fuel cell system with different control strategies to test their efficiency. It was found that operating strategies to heat up the fuel cell system can ensure a more reliable and energy-efficient operation. The heating strategy needs to be adjusted according to the ambient conditions, as this influences the required heating energy, efficiency, and reliable operation of the system. A differentiation in the control strategy between subfreezing and non-subfreezing temperatures is recommended due to reliability reasons.
Design of the Drive Mechanism of a Rotating Feeding Device
Authors: Bottin Matteo , Minto Riccardo , Rosati Giulio
Published: December 2022

Component batching can be a source of time waste in specific industrial applications, such as kitting. Kitting operations are usually performed by hoppers, but other devices can be used to optimize the process. In a previous work, a rotary device has been proved to be more efficient than hoppers; such a device allows the kitting and releasing of the components in a single rotatory movement, while traditional hoppers require at least two movements. In this paper, an improvement of such feeding device is proposed. The movement of the rotary device is driven by a four-bar linkage mechanism which is designed through functional synthesis. Thank to the four-bar linkage mechanism, the alternate motion of the rotary distributor is derived from the constant speed of the motor.
Use of Unmanned Aerial Vehicles for Building a House Risk Index of Mosquito-Borne Viral Diseases
Authors: Muñiz-Sánchez Víctor , Valdez-Delgado Kenia Mayela , Hernandez-Lopez Francisco J. , Moo-Llanes David A. , González-Farías Graciela , Danis-Lozano Rogelio
Published: December 2022

The Vector Control Program in Mexico has developed operational research strategies to identify entomological and sociodemographic parameters associated with dengue transmission in order to direct targeted actions and reduce transmission. However, these strategies have limitations in establishing their relationship with landscape analysis and dengue transmission. This study provides a proof of concept of the use of unmanned aerial vehicle technology as a possible way to collect spatial information of the landscape in real time through multispectral images for the generation of a multivariate predictive model that allows for the establishment of a risk index relating sociodemographic variables with the presence of the vector in its different larval, pupal, and adult stages. With flight times of less than 30 min, RGB orthomosaics were built, where houses, roads, highways, rivers, and trails are observed in detail, as well as in areas with a strong influence of vegetation, detailing the location of the roofs or the infrastructure of the house, grass, bushes, and trees of different dimensions, with a pixel resolution level of 5 centimeters. For the risk index, we developed a methodology based on partial least squares (PLS), which takes into account the different type of variables are involved and the geographic distribution of the houses as well. Results show the spatial pattern of downtown low-risk housing, which increases as we approach the outskirts of the town. The predictive model of dengue transmission risk developed through orthomosaics can help decision makers to plan control and public health activities.
Motor Bearing Damage Induced by Bearing Current: A Review
Authors: Ma Jiaojiao , Xue Yujian , Han Qingkai , Li Xuejun , Yu Changxin
Published: December 2022

The occurrence of the motor shaft voltage and bearing current caused by the inverter will aggravate bearing damage and lead to the premature failure of bearings. Many types of equipment are being shut down due to bearing currents, such as filters, insulated bearings and grounding brushes. Traditional suppression measures cannot eliminate the bearing current and the bearing damage mechanism under the bearing current is not clear. In this paper, the damage caused by the bearing current to bearings is analyzed in detail. The influences of different working conditions on the bearing current and the damage caused are discussed. The source of bearing currents is introduced and the bearing current model under different working conditions is reviewed. An outlook for future studies is proposed, based on the current research status and challenges.
Novel Method of Carbon Precursor Masking to Generate Controlled Perforations in a Carbon Film
Authors: Rouhana Rami , Stommel Markus , Stanko Michael , Muth Markus
Published: December 2022

A patterned carbon film was produced from Linear Low-Density Polyethylene (LLDPE) by the implementation of a novel method named Chemical Masking Perforation (CMP). The following paper describes this procedure, starting with the sulfonation of the precursor polymer LLDPE with Chlorosulphonic acid to stabilize the material, followed by Fourier-transform infrared spectroscopy (FTIR) evaluation to compare the atomic bonds from the stabilized film as well as from the masked sections of the film. To finalize, the cross-linked film was carbonized in an oven at 950 °C. The outcome of this process was a carbon film with a thickness similar to a carbon fiber diameter of 8 µm with controllable size and distribution.
Predicting Women with Postpartum Depression Symptoms Using Machine Learning Techniques
Authors: Gopalakrishnan Abinaya , Venkataraman Revathi , Gururajan Raj , Zhou Xujuan , Zhu Guohun
Published: December 2022

Being pregnant and giving birth are big life stages that occur for women. The physical and mental effects of pregnancy and childbirth, like those of many other fleeting life experiences, have the significant potential to influence a mother’s overall health and well-being. They have also been known to trigger Postpartum Depression (PPD) in many cases. PPD can be exhausting for the mother and it may have a negative impact on her capacity to care for herself and her kid if it is not treated. For this reason, in this study, initially, physiological questionnaire Edinburgh Postnatal Depression Scale (EPDS) data were collected from delivered mothers for one week, the score was evaluated by medical experts, and participants with PDD symptoms were identified. As a part of multistage progress, further, follow-up was carried out by collecting the Patient Health Questionnaire-9 (PHQ-9), Postpartum Depression Screening Scale (PDSS) questionnaires for the above-predicted participants until six weeks. As the second step, correlated risk factors with PPD symptoms were identified using statistical analysis. Finally, data were analyzed and used to train and test machine learning algorithms in order to predict postpartum depression from one to six weeks. The extremely Randomized Trees (XRT) algorithm with (Background Information + PHQ-9 + PDSS) data offers the most accurate and efficient prediction. Pregnant women with these features could be identified and treated properly. Moreover, it reduces prolonged complications and remains cost-effective in future clinical models.
Hierarchical Quantum Information Splitting of an Arbitrary Two-Qubit State Based on a Decision Tree
Authors: Li Dongfen , Zheng Yundan , Liu Xiaofang , Zhou Jie , Tan Yuqiao , Yang Xiaolong , Liu Mingzhe
Published: December 2022

Quantum informatics is a new subject formed by the intersection of quantum mechanics and informatics. Quantum communication is a new way to transmit quantum states through quantum entanglement, quantum teleportation, and quantum information splitting. Based on the research of multiparticle state quantum information splitting, this paper innovatively combines the decision tree algorithm of machine learning with quantum communication to solve the problem of channel particle allocation in quantum communication, and experiments showed that the algorithm can make the optimal allocation scheme. Based on this scheme, we propose a two-particle state hierarchical quantum information splitting scheme based on the multi-particle state. First, Alice measures the Bell states of the particles she owns and tells the result to the receiver through the classical channel. If the receiver is a high-level communicator, he only needs the help of one of the low-level communicators and all the high-level communicators. After performing a single particle measurement on the z-basis, they send the result to the receiver through the classical channel. When the receiver is a low-level communicator, all communicators need to measure the particles they own and tell the receiver the results. Finally, the receiver performs the corresponding unitary operation according to the received results. In this regard, a complete hierarchical quantum information splitting operation is completed. On the basis of theoretical research, we also carried out experimental verification, security analysis, and comparative analysis, which shows that our scheme is reliable and has high security and efficiency.
The Connective Eccentricity Index of Hypergraphs
Authors: Yu Guihai , Wu Renjie , Li Xingfu
Published: December 2022

The connective eccentricity index (CEI) of a hypergraph G is defined as ξce(G)=∑v∈V(G)dG(v)εG(v), where εG(v) and dG(v) denote the eccentricity and the degree of the vertex v, respectively. In this paper, we determine the maximal and minimal values of the connective eccentricity index among all k-uniform hypertrees on n vertices and characterize the corresponding extremal hypertrees. Finally, we establish some relationships between the connective eccentricity index and the eccentric connectivity index of hypergraphs.
Multi-AGV Dynamic Scheduling in an Automated Container Terminal: A Deep Reinforcement Learning Approach
Authors: Zheng Xiyan , Liang Chengji , Wang Yu , Shi Jian , Lim Gino
Published: December 2022

With the rapid development of global trade, ports and terminals are playing an increasingly important role, and automatic guided vehicles (AGVs) have been used as the main carriers performing the loading/unloading operations in automated container terminals. In this paper, we investigate a multi-AGV dynamic scheduling problem to improve the terminal operational efficiency, considering the sophisticated complexity and uncertainty involved in the port terminal operation. We propose to model the dynamic scheduling of AGVs as a Markov decision process (MDP) with mixed decision rules. Then, we develop a novel adaptive learning algorithm based on a deep Q-network (DQN) to generate the optimal policy. The proposed algorithm is trained based on data obtained from interactions with a simulation environment that reflects the real-world operation of an automated in Shanghai, China. The simulation studies show that, compared with conventional scheduling methods using a heuristic algorithm, i.e., genetic algorithm (GA) and rule-based scheduling, terminal the proposed approach performs better in terms of effectiveness and efficiency.
New Coronavirus (2019-nCov) Mathematical Model Using Piecewise Hybrid Fractional Order Derivatives; Numerical Treatments
Authors: Sweilam Nasser H. , AL-Mekhlafi Seham M. , Hassan Saleh M. , Alsenaideh Nehaya R. , Radwan Abdelaziz Elazab
Published: December 2022

A new mathematical model of Coronavirus (2019-nCov) using piecewise hybrid fractional order derivatives is given in this paper. Moreover, in order to be consistent with the physical model problem, a new parameter μ is presented. The boundedness, existence, and positivity of the solutions for the proposed model are discussed. Two improved numerical methods are presented in this paper. The Caputo proportional constant nonstandard modified Euler–Maruyama method is introduced to study the fractional stochastic model, and the Grünwald–Letnikov nonstandard finite difference method is presented to study the hybrid fractional order deterministic model. Comparative studies with real data from Spain and Wuhan are presented.
Solving Poisson Equations by the MN-Curve Approach
Authors: Luh Lin-Tian
Published: December 2022

In this paper, we adopt the choice theory of the shape parameters contained in the smooth radial basis functions to solve Poisson equations. Luh’s choice theory, based on harmonic analysis, is mathematically complicated and applies only to function interpolation. Here, we aim at presenting an easily accessible approach to solving differential equations with the choice theory which proves to be very successful, not only by its easy accessibility but also by its striking accuracy and efficiency. Our emphases are on the highly reliable prediction of the optimal value of the shape parameter and the extremely small approximation errors of the numerical solutions to the differential equations. We hope that our approach can be accepted by both mathematicians and non-mathematicians.
Hybrid Heuristic for the Multi-Depot Static Bike Rebalancing and Collection Problem
Authors: Bai Xue , Ma Ning , Chin Kwai-Sang
Published: December 2022

The bike rebalancing problem is one of the major operational challenges in the urban bike-sharing system, which involves the redistribution of bikes among stations to prevent stations from being empty or overloaded. This paper investigates a new bike rebalancing problem, which considers the collection of broken bikes in the multi-depot system. The proposed problem can be classified as a two-commodity vehicle routing problem with pick-up and delivery. An integer programming model is formulated to find the optimal vehicle assignment and visiting sequences with the minimum total working time and fixed cost of vehicles. A hybrid heuristic algorithm integrating variable neighborhood search and dynamic programming is proposed to solve the problem. The computational results show that the proposed method can find 26 best solutions out of 36 instances, while the CPLEX obtains 16 best solutions. Impact of broken bikes collection and distribution of depots is examined. Comparison of different practical strategies indicates that the number of vehicles can be significantly reduced by allowing multiple visits to depots. Allowing vehicles to return to different depots can help reduce the total working time.
On Some Families of Codes Related to the Even Linear Codes Meeting the Grey–Rankin Bound
Authors: Bouyukliev Iliya , Bouyuklieva Stefka , Pashinska-Gadzheva Maria
Published: December 2022

Bounds for the parameters of codes are very important in coding theory. The Grey–Rankin bound refers to the cardinality of a self-complementary binary code. Codes meeting this bound are associated with families of two-weight codes and other combinatorial structures. We study the relations among six infinite families of binary linear codes with two and three nonzero weights that are closely connected to the self-complementary linear codes meeting the Grey–Rankin bound. We give a construction method and partial classification results for such codes. The properties of the codes in the studied families and their relations help us in constructing codes of a higher dimension from codes with a given dimension.
An Image-Based Framework for the Analysis of the Murine Microvasculature: From Tissue Clarification to Computational Hemodynamics
Authors: Mañosas Santiago , Sanz Aritz , Ederra Cristina , Urbiola Ainhoa , Rojas-de-Miguel Elvira , Ostiz Ainhoa , Cortés-Domínguez Iván , Ramírez Natalia , Ortíz-de-Solórzano Carlos , Villanueva Arantxa , Malvè Mauro
Published: December 2022

The blood–brain barrier is a unique physiological structure acting as a filter for every molecule reaching the brain through the blood. For this reason, an effective pharmacologic treatment supplied to a patient by systemic circulation should first be capable of crossing the barrier. Standard cell cultures (or those based on microfluidic devices) and animal models have been used to study the human blood–brain barrier. Unfortunately, these tools have not yet reached a state of maturity because of the complexity of this physiological process aggravated by a high heterogeneity that is not easily recapitulated experimentally. In fact, the extensive research that has been performed and the preclinical trials carried out provided sometimes contradictory results, and the functionality of the barrier function is still not fully understood. In this study, we have combined tissue clarification, advanced microscopy and image analysis to develop a one-dimensional computational model of the microvasculature hemodynamics inside the mouse brain. This model can provide information about the flow regime, the pressure field and the wall shear stress among other fluid dynamics variables inside the barrier. Although it is a simplified model of the cerebral microvasculature, it allows a first insight on into the blood–brain barrier hemodynamics and offers several additional possibilities to systematically study the barrier microcirculatory processes.
A Polynomial Fitting Problem: The Orthogonal Distances Method
Authors: Cantera-Cantera Luis Alberto , Vargas-Jarillo Cristóbal , Palomino-Reséndiz Sergio Isaí , Lozano-Hernández Yair , Montelongo-Vázquez Carlos Manuel
Published: December 2022

The classical curve-fitting problem to relate two variables, x and y, deals with polynomials. Generally, this problem is solved by the least squares method (LS), where the minimization function considers the vertical errors from the data points to the fitting curve. Another curve-fitting method is total least squares (TLS), which takes into account errors in both x and y variables. A further method is the orthogonal distances method (OD), which minimizes the sum of the squares of orthogonal distances from the data points to the fitting curve. In this work, we develop the OD method for the polynomial fitting of degree n and compare the TLS and OD methods. The results show that TLS and OD methods are not equivalent in general; however, both methods get the same estimates when a polynomial of degree 1 without an independent coefficient is considered. As examples, we consider the calibration curve-fitting problem of a R-type thermocouple by polynomials of degrees 1 to 4, with and without an independent coefficient, using the LS, TLS and OD methods.
Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks
Authors: Butt Nazia , Shahid Ana , Qureshi Kashif Naseer , Haider Sajjad , Ibrahim Ashraf Osman , Binzagr Faisal , Arshad Noman
Published: December 2022

The Internet of Things (IoT) is a tremendous network based on connected smart devices. These networks sense and transmit data by using advanced communication standards and technologies. The smart home is one of the areas of IoT networks, where home appliances are connected to the internet and smart grids. However, these networks are at high risk in terms of security violations. Different kinds of attacks have been conducted on these networks where the user lost their data. Intrusion detection systems (IDSs) are used to detect and prevent cyberattacks. These systems are based on machine and deep learning techniques and still suffer from fitting or overfitting issues. This paper proposes a novel solution for anomaly-based intrusion detection for smart home networks. The proposed model addresses overfitting/underfitting issues and ensures high performance in terms of hybridization. The proposed solution uses feature selection and hyperparameter tuning and was tested with an existing dataset. The experimental results indicated a significant increase in performance while minimizing misclassification and other limitations as compared to state-of-the-art solutions.
An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II
Authors: Delgoshaei Aidin , Ariffin Mohd Khairol Anuar Bin Mohd , Leman Zulkiflle B.
Published: December 2022

Improving the performance of manufacturing systems is a vital issue in today’s rival market. For this purpose, during the last decade, scientists have considered more than one objective function while scheduling a production line. This paper develops a 4-phased fuzzy framework to identify effective factors, determine their weights on multi-objective functions, and, accordingly, schedule manufacturing systems in a fuzzy environment. The aim is to optimize product completion time and operational and product defect costs in a job-shop-based multi-objective fuzzy scheduling problem. In the first and second phases of the proposed framework, it was shown that the existing uncertainty of the internal factors for the studied cases causes the weights of factors to change up to 44.5%. Then, a fuzzy-weighted NSGA-II is proposed (FW-NSGA-II) to address the developed Non-linear Fuzzy Multi-objective Dual resource-constrained scheduling problem. Comparing the outcomes of the proposed method with other solving algorithms, such as the Sine Cosine Algorithm, Simulated Annealing, Tabu Search, and TLBO heuristic, using seven series of comprehensive computational experiments, indicates the superiority of the proposed framework in scheduling manufacturing systems. The outcomes indicated that using the proposed method for the studied cases saved up to 5% in the objective function for small-scale, 11.2% for medium-scale, and 3.8% for large-scale manufacturing systems. The outcomes of this study can help production planning managers to provide more realistic schedules by considering fuzzy factors in their manufacturing systems. Further investigating the proposed method for dynamic product conditions is another direction for future research.
Data-Mining-Based Real-Time Optimization of the Job Shop Scheduling Problem
Authors: Zhao Anran , Liu Peng , Gao Xiyu , Huang Guotai , Yang Xiuguang , Ma Yuan , Xie Zheyu , Li Yunfeng
Published: December 2022

In the job-shop scheduling field, timely and proper updating of the original scheduling strategy is an effective way to avoid the negative impact of disturbances on manufacturing. In this paper, a pure reactive scheduling method for updating the scheduling strategy is proposed to deal with the disturbance of the uncertainty of the arrival of new jobs in the job shop. The implementation process is as follows: combine data mining, discrete event simulation, and dispatching rules (DRs), take makespan and machine utilization as scheduling criteria, divide the manufacturing system production period into multiple scheduling subperiods, and build a dynamic scheduling model that assigns DRs to subscheduling periods in real-time; the scheduling strategies are generated at the beginning of each scheduling subperiod. The experiments showed that the method proposed enables a reduction in the makespan of 2–17% and an improvement in the machine utilization of 2–21%. The constructed scheduling model can assign the optimal DR to each scheduling subperiod in real-time, which realizes the purpose of locally updating the scheduling strategy and enhancing the overall scheduling effect of the manufacturing system.
TwoViewDensityNet: Two-View Mammographic Breast Density Classification Based on Deep Convolutional Neural Network
Authors: Busaleh Mariam , Hussain Muhammad , Aboalsamh Hatim A. , Fazal-e-Amin  , Al Sultan Sarah A.
Published: December 2022

Dense breast tissue is a significant factor that increases the risk of breast cancer. Current mammographic density classification approaches are unable to provide enough classification accuracy. However, it remains a difficult problem to classify breast density. This paper proposes TwoViewDensityNet, an end-to-end deep learning-based method for mammographic breast density classification. The craniocaudal (CC) and mediolateral oblique (MLO) views of screening mammography provide two different views of each breast. As the two views are complementary, and dual-view-based methods have proven efficient, we use two views for breast classification. The loss function plays a key role in training a deep model; we employ the focal loss function because it focuses on learning hard cases. The method was thoroughly evaluated on two public datasets using 5-fold cross-validation, and it achieved an overall performance (F-score of 98.63%, AUC of 99.51%, accuracy of 95.83%) on DDSM and (F-score of 97.14%, AUC of 97.44%, accuracy of 96%) on the INbreast. The comparison shows that the TwoViewDensityNet outperforms the state-of-the-art methods for classifying breast density into BI-RADS class. It aids healthcare providers in providing patients with more accurate information and will help improve the diagnostic accuracy and reliability of mammographic breast density evaluation in clinical care.
A Data-Driven System Based on Deep Learning for Diagnosis Fetal Cavum Septum Pellucidum in Ultrasound Images
Authors: Wu Yuzhou , Peng Cheng , Chen Xuechen , Yao Xin , Chen Zhigang
Published: December 2022

Cavum septum pellucidum (CSP) is one of the most important physiologic structures that should be detected in Ultrasound (US) scanning for the normal development of the fetal central nervous system. However, manual measurement of CSP is still a difficult and time-consuming task due to the high noise of US images, even for experienced sonographers. Especially considering that maternal mortality remains high in many developing countries, a data-driven system with a medical diagnosis can help sonographers and obstetricians make decisions rapidly and improve their work efficiency. In this study, we propose a novel data-driven system based on deep learning for the diagnosis of CSP called CA-Unet, which consists of a channel attention network to segment the CSP and a post-processing module to measure and diagnose the anomalies of CSP. We collected the US data from three hospitals in China from 2012 to 2018 year to validate the effectiveness of our system. Experiments on a fetal US dataset demonstrated that our proposed system is able to help doctors make decisions and has achieved the highest precision of 79.5% and the largest Dice score of 77.5% in the segmentation of CSP.
Evolution for First Eigenvalue of LT,f on an Evolving Riemannian Manifold
Authors: Saha Apurba , Azami Shahroud , Breaz Daniel , Rapeanu Eleonora , Hui Shyamal Kumar
Published: December 2022

In this paper, evolution formulas for the first non-zero eigenvalue of the operator LT,f on a weighted closed Riemannian manifold along the Ricci flow as well as along the Yamabe flow are formulated. Some monotonic quantities are also derived for the normalized Ricci flow on Bianchi classes.
New Lifetime Distribution for Modeling Data on the Unit Interval: Properties, Applications and Quantile Regression
Authors: Nasiru Suleman , Abubakari Abdul Ghaniyyu , Chesneau Christophe
Published: December 2022

Probability distributions are very useful in modeling lifetime datasets. However, no specific distribution is suitable for all kinds of datasets. In this study, the bounded truncated Cauchy power exponential distribution is proposed for modeling datasets on the unit interval. The probability density function exhibits desirable shapes, such as left-skewed, right-skewed, reversed J, and bathtub shapes, whereas the hazard rate function displays J and bathtub shapes. For the purpose of modeling dependence between measures in a dataset, a bivariate extension of the proposed distribution is developed. The bivariate probability density function displays monotonic and non-monotonic shapes, making it suitable for modeling complex bivariate relations. Subsequently, the applications of the distribution are illustrated using COVID-19 data. The results revealed that the new distribution provides a better fit to the datasets compared to other existing distributions. Finally, a new quantile regression model is developed and its application demonstrated. The generated quantile regression model offers a decent fit to the data, according to the residual analysis.
Polypharmacological Cell-Penetrating Peptides from Venomous Marine Animals Based on Immunomodulating, Antimicrobial, and Anticancer Properties
Authors: Hemmati Shiva , Rasekhi Kazerooni Haniyeh
Published: December 2022

Complex pathological diseases, such as cancer, infection, and Alzheimer’s, need to be targeted by multipronged curative. Various omics technologies, with a high rate of data generation, demand artificial intelligence to translate these data into druggable targets. In this study, 82 marine venomous animal species were retrieved, and 3505 cryptic cell-penetrating peptides (CPPs) were identified in their toxins. A total of 279 safe peptides were further analyzed for antimicrobial, anticancer, and immunomodulatory characteristics. Protease-resistant CPPs with endosomal-escape ability in Hydrophis hardwickii, nuclear-localizing peptides in Scorpaena plumieri, and mitochondrial-targeting peptides from Synanceia horrida were suitable for compartmental drug delivery. A broad-spectrum S. horrida-derived antimicrobial peptide with a high binding-affinity to bacterial membranes was an antigen-presenting cell (APC) stimulator that primes cytokine release and naïve T-cell maturation simultaneously. While antibiofilm and wound-healing peptides were detected in Synanceia verrucosa, APC epitopes as universal adjuvants for antiviral vaccination were in Pterois volitans and Conus monile. Conus pennaceus-derived anticancer peptides showed antiangiogenic and IL-2-inducing properties with moderate BBB-permeation and were defined to be a tumor-homing peptide (THP) with the ability to inhibit programmed death ligand-1 (PDL-1). Isoforms of RGD-containing peptides with innate antiangiogenic characteristics were in Conus tessulatus for tumor targeting. Inhibitors of neuropilin-1 in C. pennaceus are proposed for imaging probes or therapeutic delivery. A Conus betulinus cryptic peptide, with BBB-permeation, mitochondrial-targeting, and antioxidant capacity, was a stimulator of anti-inflammatory cytokines and non-inducer of proinflammation proposed for Alzheimer’s. Conclusively, we have considered the dynamic interaction of cells, their microenvironment, and proportional-orchestrating-host- immune pathways by multi-target-directed CPPs resembling single-molecule polypharmacology. This strategy might fill the therapeutic gap in complex resistant disorders and increase the candidates’ clinical-translation chance.
Polysaccharide from Edible Alga Enteromorpha clathrata Improves Ulcerative Colitis in Association with Increased Abundance of Parabacteroides spp. in the Gut Microbiota of Dextran Sulfate Sodium-Fed Mice
Authors: Ma Mingfeng , Fu Tianyu , Wang Yamin , Zhang Aijun , Gao Puyue , Shang Qingsen , Yu Guangli
Published: December 2022

Polysaccharide from the edible alga Enteromorpha clathrata has been demonstrated to exert beneficial effects on human health. However, what effect it has on inflammatory bowel diseases has not been investigated. Here, using a mouse model of dextran sulfate sodium (DSS)-induced ulcerative colitis, we illustrate that Enteromorpha clathrata polysaccharide (ECP) could alleviate body weight loss, reduce incidences of colonic bleeding, improve stool consistency and ameliorate mucosal damage in diseased mice. 16S rRNA high-throughput sequencing and bioinformatic analysis indicated that ECP significantly changed the structure of the gut microbiota and increased the abundance of Parabacteroides spp. in DSS-fed mice. In vitro fermentation studies further confirmed that ECP could promote the growth of Parabacteroides distasonis F1-28, a next-generation probiotic bacterium isolated from the human gut, and increase its production of short-chain fatty acids. Additionally, Parabacteroides distasonis F1-28 was also found to have anti-ulcerative colitis effects in DSS-fed mice. Altogether, our study demonstrates for the first time a beneficial effect of ECP on ulcerative colitis and provides a possible basis for understanding its therapeutic mechanisms from the perspective of symbiotic gut bacteria Parabacteroides distasonis.
Factors Influencing Polish Women’s Preference for the Mode of Delivery and Shared-Decision Making: Has Anything Changed over the Last Decade?
Authors: Walędziak Maciej , Jodzis Agnieszka , Różańska-Walędziak Anna
Published: December 2022

Background and Objectives: Shared-decision making has become an important trend in the problem of women’s preference for the way of delivery. There are different factors influencing women, including obstetric history, culture, religion, family and social influences. Materials and Methods: The study was designed as an online survey with the aim of acquiring information about women’s knowledge, opinions and preferences about the mode of delivery and the decision-making process. Data were collected from 1175 women in 2010 and 1033 in 2020. Results: A significant increase in the proportion of women who prefer vaginal delivery (VD) was found to be present with an increasing level of education, with the lowest rate in the group with primary education (66.0% in 2010 and 33.3% in 2020) and highest with medical education—86.3% in 2010 and 69.3% in 2020 (p < 0.05). This trend existed both in 2010 and 2020; however, the proportion of women who preferred VD has decreased over the last decade in all groups, and even two-fold in the primary education group. No significant correlation was found between a history of previous delivery and the preference of the way of delivery, decision-making or paid cesarean delivery on maternal request (CDMR). A history of VD significantly reduced the preference for having a cesarean delivery, with only 6.9% of women in 2010 with a history of VD, and 8.9% in 2020 having preferred a cesarean delivery. In 2010, 34.9% of women with a history of cesarean section (CS) only, compared to 6.9% of women with a history VD only, had preference for CS with, respectively, 36.4% vs. 5.8% in 2020. Conclusions: As the proportion of women who prefer cesarean delivery has significantly increased over the last decade, we should emphasize the importance of educating women about the advantages and disadvantages of vaginal and cesarean delivery. The patient’s preference should always be discussed with the obstetrician and the medical indications explained.
Gastrectomy for Cancer: A 15-Year Analysis of Real-World Data from the University of Athens
Authors: Schizas Dimitrios , Mylonas Konstantinos S. , Syllaios Athanasios , Kapetanakis Emmanouil I. , Hasemaki Natasha , Ntomi Vasileia , Michalinos Adamantios , Theochari Nikoletta A. , Theochari Christina A. , Krivan Sylvia , Mpoura Maria , Bakopoulos Anargyros , Karavokyros Ioannis , Liakakos Theodoros
Published: December 2022

Background and Objectives: Encouraging data have been reported from referral centers following gastrointestinal cancer surgery. Our goal was to retrospectively review patient outcomes following gastrectomy for gastric or gastroesophageal junction (GEJ) cancer at a high-volume unit of the University of Athens. Methods: The enrollment period was from June 2003 to September 2018. Disease-free survival (DFS) and overall survival (OS) were estimated using the Kaplan-Meier method. Cox proportional hazard models were constructed to identify variables independently associated with time-to-event outcomes. Results: A total of 205 patients were analyzed. R0 resection was achieved in 183 (89.3%) patients and was more likely to occur following neoadjuvant chemotherapy (p = 0.008). Recurrence developed in 46.6% of our cohort and the median disease-free survival was 31.2 months. On multivariate analysis, only staging (HR = 2.15; 95% CI: 1.06–4.36) was independently associated with increased risk of recurrence. All-cause mortality was 57.2% and the median time of death was 40.9 months. On multivariate regression, staging (HR: 1.35; 95% CI: 1.11–1.65) and recurrence (HR: 2.87; 95% CI: 1.32–6.22) predicted inferior prognosis. Conclusions: Gastrectomy at the University of Athens has yielded favorable outcomes for patients with GEJ cancer.
Population Risk Factors for Severe Disease and Mortality in COVID-19 in the United States during the Pre-Vaccine Era: A Retrospective Cohort Study of National Inpatient Sample
Authors: Raj Kavin , Yeruva Karthik , Jyotheeswara Pillai Keerthana , Kumar Preetham , Agrawal Ankit , Chandna Sanya , Khuttan Akhilesh , Tripathi Shalini , Akella Ramya , Gudi Thulasi Ram , Watts Abi , Toquica Gahona Christian C , Bhagat Umesh , Aedma Surya Kiran , Jalal Ayesha Tamkinat , Ganti Shyam , Varadarajan Padmini , Pai Ramdas G
Published: December 2022

Background-Previous studies on coronavirus disease 2019 (COVID-19) were limited to specific geographical locations and small sample sizes. Therefore, we used the National Inpatient Sample (NIS) 2020 database to determine the risk factors for severe outcomes and mortality in COVID-19. Methods-We included adult patients with COVID-19. Univariate and multivariate logistic regression was performed to determine the predictors of severe outcomes and mortality in COVID-19. Results-1,608,980 (95% CI 1,570,803–1,647,156) hospitalizations with COVID-19 were included. Severe complications occurred in 78.3% of COVID-19 acute respiratory distress syndrome (ARDS) and 25% of COVID-19 pneumonia patients. The mortality rate for COVID-19 ARDS was 54% and for COVID-19 pneumonia was 16.6%. On multivariate analysis, age > 65 years, male sex, government insurance or no insurance, residence in low-income areas, non-white races, stroke, chronic kidney disease, heart failure, malnutrition, primary immunodeficiency, long-term steroid/immunomodulatory use, complicated diabetes mellitus, and liver disease were associated with COVID-19 related complications and mortality. Cardiac arrest, septic shock, and intubation had the highest odds of mortality. Conclusions-Socioeconomic disparities and medical comorbidities were significant determinants of mortality in the US in the pre-vaccine era. Therefore, aggressive vaccination of high-risk patients and healthcare policies to address socioeconomic disparities are necessary to reduce death rates in future pandemics.
Magnetic Resonance Imaging and Spectroscopy Methods to Study Hepatic Glucose Metabolism and Their Applications in the Healthy and Diabetic Liver
Authors: Gursan Ayhan , Prompers Jeanine J.
Published: December 2022

The liver plays an important role in whole-body glucose homeostasis by taking up glucose from and releasing glucose into the blood circulation. In the postprandial state, excess glucose in the blood circulation is stored in hepatocytes as glycogen. In the postabsorptive state, the liver produces glucose by breaking down glycogen and from noncarbohydrate precursors such as lactate. In metabolic diseases such as diabetes, these processes are dysregulated, resulting in abnormal blood glucose levels. Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) are noninvasive techniques that give unique insight into different aspects of glucose metabolism, such as glycogenesis, glycogenolysis, and gluconeogenesis, in the liver in vivo. Using these techniques, liver glucose metabolism has been studied in regard to a variety of interventions, such as fasting, meal intake, and exercise. Moreover, deviations from normal hepatic glucose metabolism have been investigated in both patients with type 1 and 2 diabetes, as well as the effects of antidiabetic medications. This review provides an overview of current MR techniques to measure hepatic glucose metabolism and the insights obtained by the application of these techniques in the healthy and diabetic liver.
Bacillus Metabolites: Compounds, Identification and Anti-Candida albicans Mechanisms
Authors: Wang Weichen , Zhao Jin , Zhang Zhizi
Published: December 2022

Candida albicans seriously threatens human health, especially for immunosuppressed groups. The antifungal agents mainly include azoles, polyenes and echinocandins. However, the few types of existing antifungal drugs and their resistance make it necessary to develop new antifungal drugs. Bacillus and its metabolites has antifungal activity against pathogenic fungi. This review introduces the application of Bacillus metabolites in the control of C. albicans in recent years. Firstly, several compounds produced by Bacillus spp. are listed. Then the isolation and identification techniques of Bacillus metabolites in recent years are described, including high-precision separation technology and omics technology for the separation of similar components of Bacillus metabolites. The mechanisms of Bacillus metabolites against C. albicans are distinguished from the inhibition of pathogenic fungi and inhibition of the fungal virulence factors. The purpose of this review is to systematically summarize the recent studies on the inhibition of pathogenic fungi by Bacillus metabolites. The review is expected to become the reference for the control of pathogenic fungi such as C. albicans and the application of Bacillus metabolites in the future.
Seasonal Patterns of Zoonotic Cutaneous Leishmaniasis Caused by L. major and Transmitted by Phlebotomus papatasi in the North Africa Region, a Systematic Review and a Meta-Analysis
Authors: Karmaoui Ahmed , Sereno Denis , El Jaafari Samir , Hajji Lhoussain
Published: December 2022

Background: In North African countries, zoonotic cutaneous leishmaniasis (ZCL) is a seasonal disease linked to Phlebotomus papatasi, Scopoli, 1786, the primary proven vector of L. major dynamics. Even if the disease is of public health importance, studies of P. papatasi seasonal dynamics are often local and dispersed in space and time. Therefore, a detailed picture of the biology and behavior of the vector linked with climatic factors and the framework of ZCL outbreaks is still lacking at the North African countries’ level. Our study aims to fill this gap via a systematic review and meta-analysis of the seasonal incidence of ZCL and the activity of P. papatasi in North African countries. We address the relationship between the seasonal number of declared ZCL cases, the seasonal dynamic of P. papatasi, and climatic variables at the North African region scale. Methods: We selected 585 publications, dissertations, and archives data published from 1990 to July 2022. The monthly incidence data of ZCL were extracted from 15 documents and those on the seasonal dynamic of P. papatasi from 11 publications from four North African countries. Results: Our analysis disclosed that for most studied sites, the highest ZCL incidence is recorded from October to February (the hibernal season of the vector), while the P. papatasi density peaks primarily during the hot season of June to September. Overall, at the North African region scale, two to four months laps are present before the apparition of the scars reminiscent of infection by L. major. Conclusions: Such analysis is of interest to regional decision-makers for planning control of ZCL in North African countries. They can also be a rationale on which future field studies combining ZCL disease incidence, vector activity, and climatic data can be built.
The Efficacy of Probiotics as Antiviral Agents for the Treatment of Rotavirus Gastrointestinal Infections in Children: An Updated Overview of Literature
Authors: Steyer Andrej , Mičetić-Turk Dušanka , Fijan Sabina
Published: December 2022

Enteric viruses, including the rotavirus, norovirus, and adenoviruses, are the most common cause of acute gastroenteritis. The rotavirus disease is especially prevalent among children, and studies over the past decade have revealed complex interactions between rotaviruses and the gut microbiota. One way to treat and prevent dysbiosis is the use of probiotics as an antiviral agent. This review focuses on the latest scientific evidence on the antiviral properties of probiotics against rotavirus gastroenteric infections in children. A total of 19 studies exhibited a statistically significant antiviral effect of probiotics. The main probiotics that were effective were Saccharomyces cerevisiae var. boulardii, Lacticaseibacillus rhamnosus GG, and various multi-strain probiotics. The underlying mechanism of the probiotics against rotavirus gastroenteric infections in children included immune enhancement and modulation of intestinal microbiota leading to shortening of diarrhoea. However, several clinical studies also found no significant difference in the probiotic group compared to the placebo group even though well-known strains were used, thus showing the importance of correct dosage, duration of treatment, quality of probiotics and the possible influence of other factors, such as the production process of probiotics and the influence of immunisation on the effect of probiotics. Therefore, more robust, well-designed clinical studies addressing all factors are warranted.
Whole Genome Sequencing Reveals Presence of High-Risk Global Clones of Klebsiella pneumoniae Harboring Multiple Antibiotic Resistance Genes in Multiple Plasmids in Mwanza, Tanzania
Authors: Silago Vitus , Mshana Stephen E.
Published: December 2022

Background: Klebsiella pneumoniae is an important multidrug-resistant (MDR) pathogen, causing both community- and healthcare-associated infections. The resistance is due to the continuous accumulation of multiple antibiotic-resistance-genes (ARGs) through spontaneous genomic mutations and the acquisition of conjugative plasmids. This study presents antibiotics resistance genes, plasmids replicons, and virulence genes of K. pneumoniae isolates from clinical specimens in a tertiary hospital, Mwanza, Tanzania. Methods: Whole genome sequencing (WGS) of 34 K. pneumoniae was performed, using an Illumina NextSeq 500, followed by in silco analysis. Results: A total of 34 extended-spectrum beta-lactamase-producing K. pneumoniae, isolated from blood samples from neonatal units were whole-genome sequenced. Of these, 28 (82.4%) had an identified sequence type (ST), with ST14 (39.3%, n = 11) being frequently identified. Moreover, 18 (52.9%) of the bacteria harbored at least one plasmid, from which a total of 25 plasmid replicons were identified with a predominance of IncFIB(K) 48.0% (n = 12). Out of 34 sequenced K. pneumoniae, 32 (94.1%) were harboring acquired antibiotic/biocides-resistance-genes (ARGs) with a predominance of blaCTX-M-15 (90.6%), followed by oqxB (87.5%), oqxA (84.4%), blaTEM-1B (84.4%) and sul2 (84.4%). Interestingly, we observed the ColRNAI plasmid-replicon (n = 1) and qacE gene (n = 4) for the first time in this setting. Conclusion: Global high-risk clones of K. pneumoniae isolates carry multiple ARGs in multiple plasmid-replicons. Findings from this study warrant genomic-based surveillance to monitor high-risk global clones, epidemic plasmids and ARGs in low- and middle-income countries.