bluuebunny/crossref_metadata_2025 huggingface dataset:
A Predictive Model for Large-for-Gestational-Age Infants among Korean Women with Gestational Diabetes Mellitus Using Maternal Characteristics and Fetal Biometric Parameters
Authors: Kim Hee-Sun , Oh Soo-Young , Cho Geum Joon , Choi Suk-Joo , Hong Soon Cheol , Kwon Ja-Young , Kwon Han Sung
Published: August 2022

Background: With increasing incidence of gestational diabetes mellitus (GDM), newborn infants with perinatal morbidity, including large-for-gestational-age (LGA) or macrosomia, are also increasing. The purpose of this study was to develop a prediction model for LGA infants with GDM mothers. Methods: This was a retrospective case-control study of 660 women with GDM and singleton pregnancies in four tertiary care hospitals from 2006 to 2013 in Korea. Biometric parameters were obtained at diagnoses of GDM and within two weeks before delivery. These biometric data were all transformed retrospectively into Z-scores calculated using a reference. Interval changes of values between the two periods were obtained. Multivariable logistic and stepwise backwards regression analyses were performed to develop the most parsimonious predictive model. The prediction model included pre-pregnancy body mass index (BMI), head circumference (HC), Z-score at 24 + 0 to 30 + 6 weeks’ gestation, and abdominal circumference (AC) Z-score at 34 + 0 to 41 + 6 weeks within 2 weeks before delivery. The developed model was then internally validated. Results: Our model’s predictive performance (area under the curve (AUC): 0.925) was higher than estimated fetal weight (EFW) within two weeks before delivery (AUC: 0.744) and the interval change of EFW Z-score between the two periods (AUC: 0.874). It was internally validated (AUC: 0.916). Conclusions: A clinical model was developed and internally validated to predict fetal overgrowth in Korean women with GDM, which showed a relatively good performance.
Three-Dimensional Reconstruction from a Single RGB Image Using Deep Learning: A Review
Authors: Khan Muhammad Saif Ullah , Pagani Alain , Liwicki Marcus , Stricker Didier , Afzal Muhammad Zeshan
Published: August 2022

Performing 3D reconstruction from a single 2D input is a challenging problem that is trending in literature. Until recently, it was an ill-posed optimization problem, but with the advent of learning-based methods, the performance of 3D reconstruction has also significantly improved. Infinitely many different 3D objects can be projected onto the same 2D plane, which makes the reconstruction task very difficult. It is even more difficult for objects with complex deformations or no textures. This paper serves as a review of recent literature on 3D reconstruction from a single view, with a focus on deep learning methods from 2018 to 2021. Due to the lack of standard datasets or 3D shape representation methods, it is hard to compare all reviewed methods directly. However, this paper reviews different approaches for reconstructing 3D shapes as depth maps, surface normals, point clouds, and meshes; along with various loss functions and metrics used to train and evaluate these methods.
Applying a Neural Network-Based Machine Learning to Laser-Welded Spark Plasma Sintered Steel: Predicting Vickers Micro-Hardness
Authors: Olanipekun Ayorinde Tayo , Mashinini Peter Madindwa , Owojaiye Oluwakemi Adejoke , Maledi Nthabiseng Beauty
Published: August 2022

This paper presents an artificial neural network (ANN) approach to the estimation of the Vickers hardness parameter at the weld zone of laser-welded sintered duplex stainless steel. The sintered welded stainless-steel hardness is primarily determined by the sintering conditions and laser welding processing parameters. In the current investigation, the process parameters for both the sintering and welding processes were trained by ANNs machine learning (ML) model using a TensorFlow framework for the microhardness predictions of laser-welded sintered duplex stainless steel (DSS 2507 grade). A neural network is trained using a thorough dataset. The mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and R2 for the train and test data were calculated. The predicted values were in good agreement with the measured hardness values. Based on the results obtained, the ANN method can be effectively used to predict the mechanical properties of materials.
Hydrodynamic Performance of a Catamaran in Shallow Waters
Authors: Ulgen Kayhan , Dhanak Manhar R.
Published: August 2022

The effects of limited water depth on the hydrodynamic performance of a catamaran with the full-scale dimensions and geometry of a WAM-V 16 unmanned surface vehicle operating in shallow waters are investigated using an incompressible URANS-VOF solver in OpenFOAM®. Simulations of the flow associated with the passage of the catamaran in shallow waters have been conducted for a range of vehicle speed and several shallow to intermediate water depths under free trim and sinkage conditions. The effects of water depth on the resistance and the dynamic motion of the catamaran are characterized. The total resistance coefficient of the catamaran is shown to increase by as much as over 40% at transcritical Froude numbers, close to the critical depth-dependent Froude number (Frh=1.0). The wave system associated with the flow is examined and its relationship to observed impacts on resistance, trim and sinkage are discussed. The effect of limited water depth on Kelvin’s wake angle is characterized in terms of both length and depth Froude numbers and is shown to be in good agreement with theory.
Optimized Design of Piled Embankment Using a Multi-Effect Coupling Model on a Coastal Highway
Authors: Zhang Aobo , Liao Jin , Liu Zhen , Zhou Cuiying , Zhang Lihai
Published: August 2022

This study presents a multi-effect coupling model to optimize the design of a geosynthetic-reinforced pile-supported embankment (GRPSE) considering the coupling effects of soil arching, membranes, and pile–soil interaction on a coastal highway. The developed model could optimize the design of the GRPSE to fulfill the design and construction requirements at a relatively low project cost. This was achieved by adjusting the critical factors that govern the settlement of GRPSEs, such as pile spacing, tensile stiffness of geosynthetic reinforcement (GR), arrangement of piles, pile cap size, and cushion thickness. The model predictions were validated by a series of field tests using a range of geotechnical sensors. The results show that model predictions agreed with experimental measurements reasonably well. In addition, the results indicate that in comparison to a square arrangement of piles, a triangle net arrangement can decrease the differential settlement of pile soil. Furthermore, this study demonstrates that a change in the GR’s tensile stiffness has little impact on the settlement of GRPSEs. This study can help to improve the stability of roadbeds of coastal highways.
Effect of the Torsion Box Dimensions on Local Stress Distribution and Fatigue Strength Assessment of a Container Ship
Authors: Silva-Campillo Arturo , Pérez-Arribas Francisco
Published: August 2022

The aim of this paper is to estimate the influence of the dimensions of the torsion box (height and width), of a 2400 TEU feeder-class container ship, on local stress distribution and assessment of local fatigue strength by using a numerical approach based on the fatigue limit-state and ultimate limit-state in the midship region. In terms of the fatigue limit-state, the effect of the dimensions of the torsion box is obtained by geometrical modifications, in the connection between the side shell longitudinal stiffeners with the transverse web frame for nine structural details, referring to different arrangements of strengthening elements (brackets and flat bars). The process of comparing the different elements determines the most effective combination. This structural influence on the local stress distribution is assessed, along the longitudinal plates between ordinary stiffeners bounding the perimeter of the torsion box, by calculating the hull girder stresses, local buckling stresses and shear stress distribution induced by vertical and horizontal shear forces, Saint Venant and warping torques, and finally the shear stresses induced by a warping moment. Scantling criteria are obtained allowing for a better design of this very important region in container ships.
New Insights into Methyl Jasmonate Regulation of Triterpenoid Biosynthesis in Medicinal Fungal Species Sanghuangporusbaumii (Pilát) L.W. Zhou & Y.C. Dai
Authors: Liu Zengcai , Liu Ruipeng , Tong Xinyu , Zou Li
Published: August 2022

Triterpenoids are secondary metabolites produced by the fungus Sanghuangporus baumii that have important pharmacological activities. However, the yield of triterpenoids is low and cannot meet market demand. Here, we treated S. baumii with several concentrations of MeJA (methyl jasmonate) and found that the total triterpenoid content was highest (23.31 mg/g) when the MeJA concentration was 250 μmol/L. qRT-PCR was used to quantify the transcription of five key genes involved in triterpenoid biosynthesis. The results showed that the relative transcription of most genes increased with increasing MeJA concentration, indicating that MeJA is a potent inducer of triterpenoid biosynthesis in S. baumii. To further explore whether other terpenoid biosynthesis pathways are also involved in the accumulation of triterpenoids induced by MeJA, we measured the contents of cis-Zeatin (cZ), gibberellins (GAs), and the transcript levels of related biosynthesis genes. We found that MeJA significantly inhibited the biosynthesis of cZ, GAs, and the transcription of related genes. The repressive effects of MeJA on cZ and GA accumulation were further confirmed by growth rate and biomass assays. In conclusion, our study provides an effective method to enhance the triterpenoid content of S. baumii, and also provides novel insights into the mechanism of MeJA-induced triterpenoid biosynthesis.
Use of Machine Learning for Early Detection of Knee Osteoarthritis and Quantifying Effectiveness of Treatment Using Force Platform
Authors: Prabhakar Ashish John , Prabhu Srikanth , Agrawal Aayush , Banerjee Siddhisa , Joshua Abraham M. , Kamat Yogeesh Dattakumar , Nath Gopal , Sengupta Saptarshi
Published: August 2022

Knee osteoarthritis is one of the most prevalent chronic diseases. It leads to pain, stiffness, decreased participation in activities of daily living and problems with balance recognition. Force platforms have been one of the tools used to analyse balance in patients. However, identification in early stages and assessing the severity of osteoarthritis using parameters derived from a force plate are yet unexplored to the best of our knowledge. Combining artificial intelligence with medical knowledge can provide a faster and more accurate diagnosis. The aim of our study is to present a novel algorithm to classify the occurrence and severity of knee osteoarthritis based on the parameters derived from a force plate. Forty-four sway movements graphs were measured. The different machine learning algorithms, such as K-Nearest Neighbours, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Decision Tree Classifier and Random Forest Classifier, were implemented on the dataset. The proposed method achieves 91% accuracy in detecting sway variation and would help the rehabilitation specialist to objectively identify the patient’s condition in the initial stage and educate the patient about disease progression.
Identifying the Relationship between Livelihoods and Land Ecosystem Services Using a Coupled Model: A Case Study in the “One River and Two Tributaries” Region of Tibet
Authors: Wei Hejie , Zheng Jiaxin , Xue Dong , Dong Xiaobin , Liu Mengxue , Zhang Yali
Published: August 2022

In fragile and impoverished areas, identifying the interrelationship between livelihoods and ecosystem services can help protect the ecological environment and improve human well-being. This study selected the “One River and Two Tributaries” region (ORTTR) in Tibet with a fragile, sensitive ecological environment as the study area. With the years 2000, 2005, 2010, 2015, and 2020 as the research time points, a coupled evaluation model of residents’ livelihood and land ecosystem services was constructed to study the relationship between the two. Results showed that from 2000 to 2020, the coupling degree and coupling coordination degree between the two continued to increase because of the improvement in residents’ livelihood and ecosystem services. The level of coupling coordination gradually changed from a reluctant coordination stage to a moderate coordination stage. The coupling coordination degree showed more revealing results than the coupling degree in time scale. The relative development type between the two was mainly of the type lagging residents’ livelihood. By considering the physical geography and socio-economic characteristics and the relative development types, the counties and districts in the ORTTR are divided into ecological conservation areas, ecological restoration areas, and ecological reconstruction areas. The coupled model can evaluate the relationship between livelihoods and ecosystem services from a systematic integration perspective and provide scientific support for the improvement of regional human well-being.
The Impact of Urbanization on Extreme Climate Indices in the Yangtze River Economic Belt, China
Authors: Yang Wentao , Yan Yining , Lin Zhibin , Zhao Yijiang , Li Chaokui , Zhang Xinchang , Shan Liang
Published: August 2022

Urbanization has been proven to be a critical factor in modifying local or regional climate characteristics. This research aims to examine the impact of urbanization on extreme climate indices in the Yangtze River Economic Belt (YREB), China, by using meteorological observation data from 2000 to 2019. Three main steps are involved. First, a clustered threshold method based on remote-sensing nighttime light data is used to extract urban built-up areas, and urban and rural meteorological stations can be identified based on the boundary of urban built-up areas. Nonparametric statistical tests, namely, the Mann–Kendall test and Sen’s slope, are then applied to measure the trend characteristics of extreme climate indices. Finally, the urbanization contribution rate is employed to quantify the impact of urbanization on extreme climate indices. The results indicate that urbanization has a more serious impact on extreme temperature indices than on extreme precipitation indices in the YREB. For extreme temperature indices, urbanization generally causes more (less) frequent occurrence of warm (cold) events. The impact of urbanization on different extreme temperature indices has heterogeneous characteristics, including the difference in contamination levels and spatial variation of the impacted cities. For extreme precipitation indices, only a few cities impacted by urbanization are detected, but among these cities, urbanization contributes to increasing the trend of all indices.
What Prevents the Adoption of Regenerative Agriculture and What Can We Do about It? Lessons and Narratives from a Participatory Modelling Exercise in Australia
Authors: Kenny Daniel C. , Castilla-Rho Juan
Published: August 2022

Regenerative agriculture (RegenAg) can help landholders attune their agricultural practices to the natural design of the earth’s cycles and support systems. The adoption of RegenAg, however, hinges not only on a good understanding of biophysical processes but perhaps more importantly on deep-seated values and beliefs which can become an obstacle for triggering widespread transitions towards synergistic relationships with the land. We designed and facilitated a Participatory Modelling exercise with RegenAg stakeholders in Australia—the aim was to provide a blueprint of how challenges and opportunities could be collaboratively explored in alignment with landholders’ personal views and perspectives. Fuzzy Cognitive Maps (FCM) were used to unpack and formalise landholder perspectives into a semi-quantitative shared ‘mental model’ of the barriers and enablers for adoption of RegenAg practices and to subsequently identify actions that might close the gap between the two. Five dominant narratives which encode the key drivers and pain points in the system were identified and extracted from the FCM as a way to promote the internalisation of outcomes and lessons from the engagement. The Participatory Modelling exercise revealed some of the key drivers of RegenAg in Australia, highlighting the complex forces at work and the need for coordinated actions at the institutional, social, and individual levels, across long timescales (decades). Such actions are necessary for RegenAg to play a greater role in local and regional economies and to embed balancing relationships within systems currently reliant on conventional agriculture with few internal incentives to change. Our methods and findings are relevant not only for those seeking to promote the adoption of RegenAg in Australia but also for governments and agriculturalists seeking to take a behaviorally attuned stance to engage with landholders on issues of sustainable and resilient agriculture. More broadly, the participatory process reported here demonstrates the use of bespoke virtual elicitation methods that were designed to collaborate with stakeholders under COVID-19 lockdown restrictions.
3-Phenyl-3H-naphtho[1,2-e][1,2,3]oxadiazine
Authors: Aouant Alia D. , Varvounis George
Published: August 2022

In this study, we considered the reaction of (E)-1-[(2-phenylhydrazono)methyl]naphthalen-2-ol with iodobenzene diacetate in dichloromethane-produced novel 3-phenyl-3H-naphtho[1,2-e][1,2,3]oxadiazine in 11% yield. By analogy to previously published work, we suggested that the reaction proceeds via the intermediacy of an o-naphthoquinone azomethide that undergoes conjugated 6π-electrocylization to produce the product. 1D, 2D NMR, HRMS, IR, and UV-VIS spectra provided information that supported the structure of the product.
Magneto Elasticity Modeling for Stress Sensors
Authors: Diguet Gildas , Froemel Joerg , Kurita Hiroki , Narita Fumio , Makabe Kei , Ohtaka Koichi
Published: August 2022

In this article, the stress/stress sensing capability of FeSiB thin films is demonstrated and discussed. The sensing relies on the change in permeability by the application of stress, compressive and tensile, and the application of DC magnetic field. This susceptibility/permeability was tested by the exciting field (AC) being in the same direction with the applied stress. The susceptibility was shown to exhibit a maximal value at a given applied stress, the critical stress. Moreover, this maximal amplitude and position was changing with the application of an external DC magnetic field. For the DC field applied in the direction of the exciting field (AC) and longitudinal to the stress, the critical stress was shifted toward negative values and for the DC field applied perpendicularly, the critical stress was shifted toward larger positive values. This was experimentally demonstrated, and a model was constructed for a better understanding.
The Existence and Uniqueness of Riccati Fractional Differential Equation Solution and Its Approximation Applied to an Economic Growth Model
Authors: Johansyah Muhamad Deni , Supriatna Asep Kuswandi , Rusyaman Endang , Saputra Jumadil
Published: August 2022

This work proposes and investigates the existence and uniqueness of solutions of Riccati Fractional Differential Equation (RFDE) with constant coefficients using Banach’s fixed point theorem. This theorem is the uniqueness theorem of a fixed point on a contraction mapping of a norm space. Furthermore, the combined theorem of the Adomian Decomposition Method (ADM) and Kamal’s Integral Transform (KIT) is used to convert the solution of the Fractional Differential Equation (FDE) into an infinite polynomial series. In addition, the terms of an infinite polynomial series can be decomposed using ADM, which assumes that a function can be decomposed into an infinite polynomial series and nonlinear operators can be decomposed into an Adomian polynomial series. The final result of this study is to find a solution of the RFDE approach to the economic growth model with a quadratic cost function using the combined ADM and KIT. The results showed that the RFDE solution on the economic growth model using the combined ADM and KIT showed a very good performance. Furthermore, the numerical solution of RFDE on the economic growth model is presented at the end of this work.
The t-Graphs over Finitely Generated Groups and the Minkowski Metric
Authors: Diaz-Porto Gabriela , Gutierrez Ismael , Torres-Grandisson Armando
Published: August 2022

In this paper, we introduce t-graphs defined on finitely generated groups. We study some general aspects of the t-graphs on two-generator groups, emphasizing establishing necessary conditions for their connectedness. In particular, we investigate properties of t-graphs defined on finite dihedral groups.
Fault Classification in a Reciprocating Compressor and a Centrifugal Pump Using Non-Linear Entropy Features
Authors: Medina Ruben , Cerrada Mariela , Yang Shuai , Cabrera Diego , Estupiñan Edgar , Sánchez René-Vinicio
Published: August 2022

This paper describes a comparison of three types of feature sets. The feature sets were intended to classify 13 faults in a centrifugal pump (CP) and 17 valve faults in a reciprocating compressor (RC). The first set comprised 14 non-linear entropy-based features, the second comprised 15 information-based entropy features, and the third comprised 12 statistical features. The classification was performed using random forest (RF) models and support vector machines (SVM). The experimental work showed that the combination of information-based features with non-linear entropy-based features provides a statistically significant accuracy higher than the accuracy provided by the Statistical Features set. Results for classifying the 13 conditions in the CP using non-linear entropy features showed accuracies of up to 99.50%. The same feature set provided a classification accuracy of 97.50% for the classification of the 17 conditions in the RC.
Application of GeoGebra in the Teaching of Descriptive Geometry: Sections of Solids
Authors: Chivai Cacilda H. , Soares Armando A. , Catarino Paula
Published: August 2022

Technologies came to revolutionize the teaching of technical design; they brought resources that enabled 3D and 2D graphic simulation. The objective of this research is to understand how the implementation of the dynamic software GeoGebra on the topic of cylindrical sections can improve 3D spatial visualization in students because of the challenges in understanding the 3D representations and transporting them to the 2D sheet. This is a quasi-experimental case study in a mixed qualitative and quantitative approach which was conducted in a grade 12 class on the subject of descriptive geometry. The instruments used were a pre-test, post-test and Santa Barbara Solids Test. The students of the experimental group used GeoGebra and obtained better results in the post-test than the students in the control group, which suggests an improvement in learning; however, the Santa Barbara Solids Test results showed that the difficulties in visualizing views of the projected solids in a two-dimensional plane persisted. This study is also intended to test software in a classroom with students who have never experienced a 3D technology in their educational practices and direct future research in the complex content of descriptive geometry with the help of software.
Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm
Authors: Shaheen Mohamed A. M. , Hasanien Hany M. , Mekhamer Said F. , Qais Mohammed H. , Alghuwainem Saad , Ullah Zia , Tostado-Véliz Marcos , Turky Rania A. , Jurado Francisco , Elkadeem Mohamed R.
Published: August 2022

This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems.
Dynamics of Eyring–Powell Nanofluids When Bioconvection and Lorentz Forces Are Significant: The Case of a Slender Elastic Sheet of Variable Thickness with Porous Medium
Authors: Manan Abdul , Rehman Saif Ur , Fatima Nageen , Imran Muhammad , Ali Bagh , Shah Nehad Ali , Chung Jae Dong
Published: August 2022

We examine thermal management in the heat exchange of compact density nanoentities in crude base liquids. It demands the study of the heat and flow problem with non-uniform physical properties. This study was conceived to analyze magnetohydrodynamic Eyring–Powell nanofluid transformations due to slender sheets with varying thicknesses. Temperature-dependent thermal conductivity and viscosity prevail. Bioconvection due to motivated and dynamic microorganisms for Eyring–Powell fluid flow is a novel aspect herein. The governing PDEs are transmuted into a nonlinear differential structure of coupled ODEs using a series of viable similarity transformations. An efficient code for the Runge–Kutta method is developed in MATLAB script to attain numeric solutions. These findings are also compared to previous research to ensure that current findings are accurate. Computational activities were carried out with a variation in pertinent parameters to perceive physical insights on the quantities of interest. Representative outcomes for velocity, temperature, nanoparticles concentration, and bioconvection distributions as well as the local thermal transport for different inputs of parameters are portrayed in both graphical and tabular forms. The results show that the fluid’s velocity increases with mixed convection parameters due to growing buoyancy effects and the fluid’s temperature also increased with higher Brownian motion Nb and thermophoretic Nt. The numerical findings might be used to create efficient heat exchangers for increasingly challenging thermo-technical activities in manufacturing, construction, and transportation.
Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model
Authors: Yousif Yosra , Elfaki Faiz , Hrairi Meftah , Adegboye Oyelola
Published: August 2022

Masked issues can emerge when dealing with competing risk data. Such issues are exemplified by the cause of a particular failure not being directly exhibited for all units to observe but only proven to be a subset of possible causes of failure. For assessing the impact of explanatory variables (covariates) on the cumulative incidence function (CIF), a process of Bayesian analysis is discussed in this paper. The symmetry assumption is not imposed on the masking probabilities and independent Dirichlet priors assigned to them. The Markov Chain Monte Carlo (MCMC) technique is utilized to implement the Bayesian analysis. The effectiveness of the developed model is tested via numerical studies, including simulated and real data sets.
Highly Aggressive Osteosarcoma of the Ethmoids and Maxillary Sinus-A Case of Successful Surgery and Proton Beam Radiotherapy in a 65-Year-Old Man
Authors: Astl Jaromír , Belsan Tomas , Michnova Ludmila , Kubeš Jiří , Filipovsky Tomas , Blecha Jiri , Holy Richard
Published: August 2022

Sarcomas in the head and neck area are rare diseases with an incidence of under 1% of all head and neck malignant tumours. Osteosarcomas or osteogenic sarcomas consist of neoplastic cells that produce osteoid bone or immature bone. Sarcomas develop more in the mandible than the maxilla. The exact diagnosis of different types of sarcomas is based on the immunohistochemical investigation. These rare tumours are of mesenchymal origin; osteosarcomas and chondrosarcomas are the most common types—Ewing’s sarcomas. The use of proton beam radiotherapy in the treatment of osteosarcoma of the maxilla is rarely reported in the literature. We present a case of successfully treated (surgery and proton beam radiotherapy) poorly differentiated highly aggressive osteosarcoma in the ethmoids and maxillary sinus and morbidity after the treatment. We were presented with a case of a 65-year-old man with anaesthesia and palsy of the right face. The stomatology department performed the extraction of a tooth. One month later, the wound was still open. The histology showed an osteogenic sarcoma in the area of the wound. The oncologist and maxillofacial surgeons in a catchment hospital recommended a nonsurgical approach. Hence, we performed a radical maxillectomy and ethmoidectomy, after which we continued with proton bean radiotherapy. The patient is now five years after therapy without signs of sarcoma; however, he has blindness in the right eye and reduced vision in the left eye, as well as gliosis of the brain, vertigo, tinnitus, trismus, and ancylostomiases. Head and neck osteosarcomas treatment is considered a complex multidisciplinary task. It is currently argued that there is no clear therapeutic protocol for successful treatment. Innovations in treatment modalities such as proton beam radiotherapy appear to have potential, although their effects on long-term morbidity and survival outcomes are still undetermined. We present a rare case report of an osteosarcoma of the maxilla involving an innovative, successful treatment procedure combining surgical excision followed by proton beam radiotherapy. This treatment approach may enable maximum tumour control. This protocol has not been adequately described in the world literature for this diagnosis.
Knee Diameter and Cross-Section Area Measurements in MRI as New Promising Methods of Chondromalacia Diagnosis-Pilot Study
Authors: Sieroń Dominik , Jabłońska Izabella , Lukoszek Dawid , Szyluk Karol , Meusburger Hugo , Delimpasis Georgios , Kostrzewa Maciej , Platzek Ivan , Christe Andreas
Published: August 2022

Background and Objectives: Chondromalacia often affects the knee joint. Risk factors for the development of cartilage degenerative changes include overweight, female sex and age. The use of radiological parameters to assess the knee joint is rarely reported in the literature. Materials and Methods: The study involved 324 patients, including 159 (49%) women and 165 (51%) men, with an age range between 8–87 years (mean: 45.1 ± 20.9). The studied group had a body mass index (BMI) in the range of 14.3–47.3 (mean: 27.7 ± 5.02). A 1.5 Tesla and 3.0 Tesla (T) MRI scanner was used to assess the cartilage of the knee joint using the Outerbridge scale. The radiological parameters analyzed were the Insall–Salvati index, knee surface area, knee AP (antero-posterior) maximal diameter and knee SD (sinistro-dexter) maximal diameter. Results: Parameters such as the knee surface area, knee AP maximal diameter and knee SD maximal diameter showed a significant correlation with Outerbridge Scale (p < 0.014). The age of the patients showed a significant correlation with each knee parameter (p < 0.004). Results of knee AP and SD maximal diameter measurements strongly depended on BMI level. Conclusions: A significant relationship was found between the knee surface area, knee AP maximal diameter and knee SD maximal diameter and the advancement of chondromalacic changes in the knee joint, age and BMI.
Microbiota Assessment of Pediatric Simple and Complex Acute Appendicitis
Authors: Kakar Mohit , Reinis Aigars , Kroica Juta , Engelis Arnis , Broks Renars , Asare Lasma , Vermeulen Marelize , Senica Simone Oliver , Saxena Amulya , Petersons Aigars
Published: August 2022

Background and Objectives. The aim of this study is to determine the prevailing microbiota in samples from pediatric patients with acute appendicitis, as well as evaluate the antibacterial sensitivity of the isolated microorganisms, comparing the data obtained with the clinic’s antibacterial therapy guidelines. Materials and Methods. The study group consisted of 93 patients between the ages of 7 and 18. All patients underwent a laparoscopic or conventional appendectomy. The children were hospitalized with signs and symptoms suggestive of acute appendicitis. Microbiological cultures from the appendix and abdominal cavity were collected intraoperatively. Results. E. coli was identified in most cases irrespective of the clinical presentation of acute appendicitis. Most strains were susceptible to ampicillin and amoxicillin/clavulanic acid. Five strains of E. coli produced extended spectrum beta-lactamase (ESBL). Pseudomonas aeruginosa (P. aeruginosa) was the second most commonly isolated causative agent. Furthermore, it was common in cases of acute complex appendicitis. Most strains of P. aeruginosa were resistant to amoxicillin/clavulanic acid, ertapenem, ampicillin and cefotaxime, yet were susceptible to ceftazidime. Regardless of the clinical presentation, the samples yielded mixed isolates. Conclusion. E. coli is the main causative agent of acute appendicitis in the pediatric population displaying susceptibility to various antibiotics. P. aeruginosa was more prevalent in cases of acute complex appendicitis. P. aeruginosa isolates were susceptible to ceftazidime; however, they were resistant to cefotaxime, which should, therefore, be removed from guidelines for empirical antibacterial treatment of acute appendicitis due to phenotypic resistance of P. aeruginosa. We recommend antibiotics with distinct implementation to avoid antibiotic resistance.
Lymphoepithelioma-like Carcinoma of the Breast Synchronous with a High-Grade Invasive Ductal Carcinoma and Ductal Carcinoma in Situ in a Different Quadrant of the Same Breast: A Case Report
Authors: Nanev Vasil , Naneva Silvia , Yordanov Angel , Strashilov Strahil , Konsoulova Assia , Vasileva-Slaveva Mariela , Betova Tatyana , Ivanov Ivan
Published: August 2022

Lymphoepithelioma-like breast carcinoma (LELC) is a rare type of malignant breast tumor that is not included in the current edition of the World Health Organization (WHO) classification of breast tumors. Currently, there are no clearly defined therapeutic strategies, and the general information on breast LELC is based on sporadic clinical cases described in the medical literature. We present a clinical case that describes a 49-year-old woman with a tumor formation in the right breast, histologically verified as LELC, together with a non-palpable, synchronous high-grade invasive ductal carcinoma and ductal carcinoma in situ Grade 2 (DCIS G2) in a different quadrant of the same breast. To our knowledge, this is the first case described in the literature that combines a LELC with a synchronous carcinoma in the same breast.
Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients
Authors: Abdelhamid Sultan S. , Scioscia Jacob , Vodovotz Yoram , Wu Junru , Rosengart Anna , Sung Eunseo , Rahman Syed , Voinchet Robert , Bonaroti Jillian , Li Shimena , Darby Jennifer L. , Kar Upendra K. , Neal Matthew D. , Sperry Jason , Das Jishnu , Billiar Timothy R.
Published: August 2022

Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers (proteomics, metabolomics, and lipidomics) to identify prognostic biomarkers in the circulating compartment for adverse outcomes, including mortality and slow recovery, in severely injured trauma patients. Admission plasma samples from patients (n = 129) enrolled in the Prehospital Air Medical Plasma (PAMPer) trial were analyzed using mass spectrometry (metabolomics and lipidomics) and aptamer-based (proteomics) assays. Biomarkers were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling and machine learning analysis. A combination of five proteins from the proteomic layer was best at discriminating resolvers from non-resolvers from critical illness with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.74, while 26 multi-omic features predicted 30-day survival with an AUC of 0.77. Patients with traumatic brain injury as part of their injury complex had a unique subset of features that predicted 30-day survival. Our findings indicate that multi-omic analyses can identify novel admission-based prognostic biomarkers for outcomes in trauma patients. Unique biomarker discovery also has the potential to provide biologic insights.
Interaction between Plasma Metabolomics and Intestinal Microbiome in db/db Mouse, an Animal Model for Study of Type 2 Diabetes and Diabetic Kidney Disease
Authors: Wu Chenhua , Fei Jingjing , Xu Qing , Tao Yingjun , Zhou Ziqi , Wang Yurong , Wu Jie , Gu Harvest F.
Published: August 2022

Evidence has demonstrated that either metabolites or intestinal microbiota are involved in the pathogenesis of type 2 diabetes (T2D) and diabetic kidney disease (DKD). To explore the interaction between plasma metabolomics and intestinal microbiome in the progress of T2D-DKD, in the current study, we analyzed metabolomics in the plasma of db/db mice with liquid chromatography–mass spectrometry and also examined intestinal prokaryotes and entire gut microbiome dysbiosis at the genus level with both 16S rDNA and metagenomic sequencing techniques. We found that Negativibacillus and Rikenella were upregulated, while Akkermansia, Candidatus, Erysipelatoclostridium and Ileibacterium were downregulated in the colon of db/db mice compared with non-diabetic controls. In parallel, a total of 91 metabolites were upregulated, while 23 were downregulated in the plasma of db/db mice. The top five upregulated metabolites included D-arabinose 5-phosphate, estrone 3-sulfate, L-theanine, 3′-aenylic acid and adenosine 5′-monophosphate, and the five most significantly downregulated metabolites were aurohyocholic acid sodium salt, calcium phosphorylcholine chloride, tauro-alpha-muricholic acid sodium salt, galactinol and phosphocholine. These plasma metabolites were interacted with intestinal microbiomes, which are mainly involved in the pathways related to the biosynthesis of unsaturated fatty acids, fatty acid elongation, steroid biosynthesis, and D-arginine and D-ornithine metabolism. In the differential metabolites, N-acetyl-L-ornithine, ornithine and L-kyn could be metabolized by the correspondingly differential ontology genes in the intestinal metagenome. The current study thereby provides evidence for a gut–metabolism–kidney axis in the metabolism of db/db mice, in which the gut microbiome and circulating metabolomics interact, and suggests that information from this axis may contribute to our understanding of T2D and DKD pathogenesis.
Application of Mass Spectrometry for Determining the Geographic Production Area of Wagyu Beef
Authors: Ueda Shuji , Takashima Yasuharu , Gotou Yunosuke , Sasaki Ryo , Nakabayashi Rio , Suzuki Takeshi , Sasazaki Shinji , Fukuda Ituko , Kebede Biniam , Kadowaki Yuki , Tamura Maiko , Nakanishi Hiroki , Shirai Yasuhito
Published: August 2022

Japanese Black cattle (Japanese Wagyu) beef is attracting attention for its aroma and marbling, and its handling is increasing worldwide. Here, we focused on the origin discrimination of Wagyu beef and analyzed the nutritional components of Japanese Wagyu (produced in multiple prefectures of Japan), Hybrid Wagyu (a cross between Angus and Wagyu cattle born in Australia and transported to Japan), and Australian Wagyu beef using mass spectrometry (MS). Triple-quadrupole liquid chromatography–MS was used to clarify the molecular species of lipids in Wagyu beef. Fourteen classes of lipids were separated, and 128 different triacylglycerides (TGs) were detected. A simple comparative analysis of these TGs using high-performance liquid chromatography revealed significantly higher levels of triolein (C18:1/C18:1/C18:1; abbreviated OOO) and C18:1/C18:1/C16:1 (OOPo) in Japanese Wagyu. Wagyu elements beef were comprehensively analyzed using inductively coupled plasma (ICP)–MS and ICP–optical emission spectrometry. We found significant differences in the rubidium, cesium, and lithium levels of Japanese and Australian Wagyu beef. On comparing metabolites using gas chromatography–MS, we identified significant differences in the levels of amino acids and other components of the Japanese and Australian Wagyu beef. These results suggest the possibility of determining the origin of Wagyu cattle breeds using MS and genetic discrimination.
Clinical Potential of Microbial Strains, Used in Fermentation for Probiotic Food, Beverages and in Synbiotic Supplements, as Psychobiotics for Cognitive Treatment through Gut–Brain Signaling
Authors: Dahiya Divakar , Nigam Poonam Singh
Published: August 2022

Pure and viable strains of microorganisms identified and characterized as probiotic strains are used in the fermentation process to prepare probiotic food and beverages. These products are sources of nutrition and help in the maintenance of gut microflora. The intake of food products prepared with the use of probiotic microorganisms and containing their metabolites and whole microbial cells can be considered as a natural formulation of synbiotic products with prebiotic substrates and culture. Other than through the intake of fermented food and beverages, probiotic microorganisms can be taken through a supplement, which is a complementary form prepared by combining separate sources of prebiotic substrates and specific probiotic cultures. Whether a fermented solid food or beverage, both the components in the product are in a synergistic relationship and contribute to several health benefits at a lower cost. The aim of this article is to review the relevant literature and present the outcomes of recent studies which have been conducted to explore the clinical potential of probiotic strains and their effect on psychological conditions. Studies have shown the relationship between gut microbiota and the brain, and their interaction through signaling. The studies have concluded that the gut–brain axis can be manipulated with the intake of probiotic foods or synbiotic supplements containing specific probiotic strains accompanied with their complementary prebiotics for the enhanced sustainability of healthy GIT microflora.
An Ionic-Liquid-Imprinted Nanocomposite Adsorbent: Simulation, Kinetics and Thermodynamic Studies of Triclosan Endocrine Disturbing Water Contaminant Removal
Authors: Ali Imran , Imanova Gunel T. , Albishri Hassan M. , Alshitari Wael Hamad , Locatelli Marcello , Siddiqui Mohammad Nahid , Hameed Ahmed M.
Published: August 2022

The presence of triclosan in water is toxic to human beings, hazardous to the environment and creates side effects and problems because this is an endocrine-disturbing water pollutant. Therefore, there is a great need for the separation of this notorious water pollutant at an effective, economic and eco-friendly level. The interface sorption was achieved on synthesized ionic liquid-based nanocomposites. An N-methyl butyl imidazolium bromide ionic liquid copper oxide nanocomposite was prepared using green methods and characterized by using proper spectroscopic methods. The nanocomposite was used to remove triclosan in water with the best conditions of time 30 min, concentration 100 µg/L, pH 8.0, dose 1.0 g/L and temperature 25 °C, with 90.2 µg/g removal capacity. The results obeyed Langmuir, Temkin and D-Rs isotherms with a first-order kinetic and liquid-film-diffusion kinetic model. The positive entropy value was 0.47 kJ/mol K, while the negative value of enthalpy was −0.11 kJ/mol. The negative values of free energy were −53.18, −74.17 and −76.14 kJ/mol at 20, 25 and 30 °C. These values confirmed exothermic and spontaneous sorption of triclosan. The combined effects of 3D parameters were also discussed. The supramolecular model was developed by simulation and chemical studies and suggested electrovalent bonding between triclosan and N-methyl butyl imidazolium bromide ionic liquid. Finally, this method is assumed as valuable for the elimination of triclosan in water.
LC-MS Metabolite Profiling and the Hypoglycemic Activity of Morus alba L. Extracts
Authors: Zhou Qing Yi-Jun , Liao Xin , Kuang Hao-Ming , Li Jia-Yu , Zhang Shui-Han
Published: August 2022

Morus alba L. is used in traditional Chinese medicine for its anti-diabetic activity; however, the part of the hypoglycemic activity and related active metabolites are still not fully clarified. In this study, the metabolites in the M. alba roots, leaves, twigs, and fruits extracts (70% ethanol extracts) were systematically identified, and their hypoglycemic activity was evaluated by the high-fat diet/streptozotocin-induced 2 diabetes mellitus (T2D) mouse model. A total of 60 high-level compounds, including 16 polyphenols, 43 flavonoids, and one quinic acid, were identified by high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS) combined with the fragmentation pathways of standards and the self-established database. Among them, 23 metabolites were reported for the first time from this plant. In contrast to the extracts of M. alba leaves and fruits, the extracts of roots and twigs displayed significant hypoglycemic activity The glycemia was significantly reduced from 32.08 ± 1.27 to 20.88 ± 1.82 mmol/L and from 33.32 ± 1.98 to 24.74 ± 1.02 mmol/L, respectively, after 4 weeks of treatment with roots and twigs extracts. Compound 46 (morusin), which is a high-level component identified from the extracts of M. alba roots, also displayed significant activity in decreasing the blood glucose level of T2D mice reduced from 31.45 ± 1.23 to 23.45 ± 2.13 mmol/L. In addition, the extracts of roots and twigs displayed significant activity in reducing postprandial glycemia. This work marks the first comparison of the metabolites and hypoglycemic activity of M. alba roots, leaves, twigs, and fruits extracts, and provides a foundation for further development of M. alba extracts as anti-diabetic drugs.
Quantification of Gut Microbiota Dysbiosis-Related Organic Acids in Human Urine Using LC-MS/MS
Authors: Lee Yu-Tsung , Huang Sui-Qing , Lin Ching-Hao , Pao Li-Heng , Chiu Chun-Hui
Published: August 2022

Urine organic acid contains water-soluble metabolites and/or metabolites—derived from sugars, amino acids, lipids, vitamins, and drugs—which can reveal a human’s physiological condition. These urine organic acids—hippuric acid, benzoic acid, phenylacetic acid, phenylpropionic acid, 4-hydroxybenzoic acid, 4-hydroxyphenyl acetic acid, 3-hydroxyphenylpropionic acid, 3,4-dihydroxyphenyl propionic acid, and 3-indoleacetic acid—were the eligible candidates for the dysbiosis of gut microbiota. The aim of this proposal was to develop and to validate a liquid chromatography–tandem mass spectrometry (LC-MS/MS) bioanalysis method for the nine organic acids in human urine. Stable-labeled isotope standard (creatinine-d3) and acetonitrile were added to the urine sample. The supernatant was diluted with deionized water and injected into LC-MS/MS. This method was validated with high selectivity for the urine sample, a low limit of quantification (10–40 ng/mL), good linearity (r > 0.995), high accuracy (85.8–109.7%), and high precision (1.4–13.3%). This method simultaneously analyzed creatinine in urine, which calibrates metabolic rate between different individuals. Validation has been completed for this method; as such, it could possibly be applied to the study of gut microbiota clinically.