bluuebunny/crossref_metadata_2025 huggingface dataset:
Mechanical Properties, Durability Performance, and Microstructure of CaO-Fly Ash Solidified Sludge from Northeast, China
Authors: Chen Chen , Zhang Kai , Ma Chunyu , Yin Zhigang , Wang Liang , Chen Yao , Lin Ziqi , Liu Yi
Published: September 2024

In order to investigate the influence of the CaO and fly ash (FA) dosage and proportion on the mechanical properties, durability, and microstructure of solidified sludge, freeze–thaw (F-T) cycles and dry–wet (D-W) cycles are conducted to study the change in appearance and the strength attenuation of CaO-FA solidified sludge. Low-field nuclear magnetic resonance (LF-NMR) is used to analyze the microstructure of the solidified sludge with various dosages and ratios of CaO-FA. The results demonstrate that the unconfined compressive strength (UCS) and direct shear strength of solidified sludge increase with the prolongation of the curing age. Furthermore, the mechanical properties of solidified sludge are improved as the ratio of CaO-FA increases. As the curing age increases, the distribution of transverse relaxation time (T2) becomes narrow, the spectral area decreases, and the amplitude of the LF-NMR signal shows a downward and leftward tendency. Additionally, with the increase in the number of F-T cycles and D-W cycles, the UCS of solidified sludge declines and the degree of pore deterioration increased gradually. This study offers a theoretical foundation and empirical data for the dredging and treatment of sludge in cold regions.
Study on the Influence of Plugging Position and Fit on the Motion Stability of Precision Cross Roller Bearing
Authors: Dong Pu , Niu Rongjun , Wang Yushuo , Lv Ruifang , Li Lanlan , Xie Wenchao
Published: September 2024

This study addresses the issue of unsatisfactory smoothness in the movement of integrated internal and external cross roller bearings post-assembly, which compromises the movement flexibility of the finished bearing and fails to meet index requirements. Focusing on a specific type of precision cross roller bearing, this paper establishes a finite element explicit dynamic simulation model that takes into account the plugging position and matching relationship. A transient dynamic simulation of the roller blockage process was conducted, yielding insights into the contact pressure and deformation experienced by the roller and plug during this blockage. The results indicate that when both the taper pin are positioned centrally, and the plug matching clearance, plug sag and protruding amount, and plug rotation offset degrees are all set to 0 μm, the contact pressure between the roller and raceway, as well as the roller deformation displacement, are minimized. The plugging position and fit were subsequently validated through testing, which also measured the impact of these parameters on the roundness of the raceway surface and the bearing’s friction torque. The test findings corroborate that when the taper and pin are centrally aligned, and the stopper clearance is 5 μm, with the plug sag, protrusion, and offset all at 0 μm, the roundness of the raceway surface and the bearing’s friction torque reach their lowest values, thereby optimizing the stability of bearing motion. By comparing the simulation and experimental results, it is concluded that during bearing assembly, it is crucial to maintain the taper pin in a central position, control the plug matching clearance to approximately 5 μm, and ensure the plug sag, protrusion, and rotation offset amount are both at 0 μm. This approach guarantees optimal contact conditions and motion stability during operation. The findings of this research offer valuable design guidance for the selection of appropriate plugging positions and fits in precision cross roller bearings.
Analysis of Friction and Wear Properties of Friction Ring Materials for Friction Rings under Mixed Lubrication
Authors: Li Tan , Ahmat Mutellip , Yin Runsheng
Published: September 2024

Aiming at the problem of mechanical seal failure due to serious wear and tear in operation, the numerical model of the thermal–solid coupling wear of the seal ring is established by taking the friction sub-material as the research object, and the hardness, wear coefficient, and friction coefficient of different soft-ring materials are obtained by a test to verify the accuracy of the numerical model of wear. Additionally, the temperature field and deformation field of the seal ring of different materials are calculated, and the effects of the material parameters, such as elasticity modulus and thermal conductivity, on the temperature, relative deformation, and axial deformation trend are reported. The wear relation of the mechanical seal was optimized, and the correction coefficients of several materials were calculated. The results show the following: the main wear of the seal ring is due to adhesive wear leading to particle shedding and extrusion, adhesive wear causes material transfer, which alters the composition of the worn surface.in turn leading to cratering, which also causes the wear of the seal ring; the friction performance is better when the soft-ring material is graphite (C); the temperature, as well as the deformation, is smaller when the soft-ring material is silicon carbide (SIC); the correction coefficients for the life of SIC are calculated to be 0.23, for C, to be 0.14, and for stainless steel (Ss), to be 0.31, and the corrected equations can more accurately predict the corresponding material. The corrected equation can more accurately predict the service life of the corresponding material.
Identifying Different Components of Oil and Gas Shale from Low-Field NMR Two-Dimensional Spectra Based on Deep Learning
Authors: Jia Zijian , Liang Can , Zeng Chunlin , Chen Rui
Published: September 2024

The detection and quantitative analysis of shale components are of great significance for comprehensively understanding the properties of shale, assessing its resource potential and promoting efficient development and utilization of resources. The low-field NMR T1-T2 two-dimensional spectrum can detect shale components non-destructively and effectively. Unfortunately, due to its complexity, the two-dimensional spectral results of low-field NMR are mainly analyzed using manual qualitative analysis, and accurate results of the composition cannot be obtained. Since the information contained in its two-dimensional map is determined by the morphological texture and the position in the map, commonly used image analysis networks cannot adapt. In order to solve these problems, this paper improves a novel Faster Region-based Convolutional Neural Network (Faster-RCNN). Compared with previous models, the improved Faster-RCNN has better image classification and visual key point estimation capabilities. The results show that compared with traditional methods, the deep learning method using this model can directly obtain key information such as kerogen and movable oil and gas content in rocks. The information provided in this study can help complement and improve the development of analytical methods for low-field 2D NMR spectra.
Exploring Downscaling in High-Dimensional Lorenz Models Using the Transformer Decoder
Authors: Shen Bo-Wen
Published: September 2024

This paper investigates the feasibility of downscaling within high-dimensional Lorenz models through the use of machine learning (ML) techniques. This study integrates atmospheric sciences, nonlinear dynamics, and machine learning, focusing on using large-scale atmospheric data to predict small-scale phenomena through ML-based empirical models. The high-dimensional generalized Lorenz model (GLM) was utilized to generate chaotic data across multiple scales, which was subsequently used to train three types of machine learning models: a linear regression model, a feedforward neural network (FFNN)-based model, and a transformer-based model. The linear regression model uses large-scale variables to predict small-scale variables, serving as a foundational approach. The FFNN and transformer-based models add complexity, incorporating multiple hidden layers and self-attention mechanisms, respectively, to enhance prediction accuracy. All three models demonstrated robust performance, with correlation coefficients between the predicted and actual small-scale variables exceeding 0.9. Notably, the transformer-based model, which yielded better results than the others, exhibited strong performance in both control and parallel runs, where sensitive dependence on initial conditions (SDIC) occurs during the validation period. This study highlights several key findings and areas for future research: (1) a set of large-scale variables, analogous to multivariate analysis, which retain memory of their connections to smaller scales, can be effectively leveraged by trained empirical models to estimate irregular, chaotic small-scale variables; (2) modern machine learning techniques, such as FFNN and transformer models, are effective in capturing these downscaling processes; and (3) future research could explore both downscaling and upscaling processes within a triple-scale system (e.g., large-scale tropical waves, medium-scale hurricanes, and small-scale convection processes) to enhance the prediction of multiscale weather and climate systems.
Generalized Net Model of the Processes in a Petroleum Refinery—Part I: Theoretical Study
Authors: Stratiev Dicho , Shishkova Ivelina , Angelova Nora , Stratiev Danail D. , Atanassov Krassimir
Published: September 2024

Oil refining is a branch of industry that delivers energy to move our vehicles. The transportation of people and goods by airplanes, ships, trains, trucks, buses, and cars is unthinkable for modern mankind without the use of refined petroleum automotive fuels. Thus, the optimal functioning of this industrial branch is vital to contemporary human society. The modeling of processes that take place during refined oil products’ manufacturing, which are parallel in their essence, by generalized nets enables their activity optimization and better management. The generalized nets, which are in principle extensions of Petri nets, are applied in this research as a toolkit to model all processes from crude oil selection and delivery to a high complex refinery (Nelson index of 10.6) to the production of a great diversity of fuels, propylene, and polypropylene. The proposed article is a continuation and extension of the articles, published in Mathematics Journal in 2021 and 2023. It is the first (theoretical part) of our comprehensive study of modeling petroleum products’ production processes in a refinery, and the second part will discuss the results of the software implementation of the model.
Study on Compensation Method of Encoder Pulse Errors for Permanent Magnet Synchronous Motor Control
Authors: Park Beom-Do , Kim Seon-Jung , Moon Ju-Hyeong , Kang Dong-Woo , Go Sung-Chul , Su Khac-Huan
Published: September 2024

In vector-controlled Permanent Magnet Synchronous Motors (PMSMs), measuring the motor flux angle, particularly the rotor position, is essential. If a pulse error occurs during motor control using an encoder, it becomes impossible to accurately estimate the rotor position, leading to incorrect position angle information being used in the coordinate transformation. This mismatch causes discrepancies between the commanded three-phase stator current and the actual current applied to the motor. As a result, the motor and inverter output and efficiency decrease, and the control performance deteriorates. Therefore, research is necessary to compensate for such errors. This paper proposes an algorithm that detects pulse errors occurring in incremental encoders and automatically switches to the Hall sensor control mode to compensate for the encoder pulse errors. The proposed algorithm, based on Hall sensors, has the advantage of not significantly affecting control delays, which could be problematic in high-speed operating ranges; thus, effective control is maintained even in such situations.
C-KAN: A New Approach for Integrating Convolutional Layers with Kolmogorov–Arnold Networks for Time-Series Forecasting
Authors: Livieris Ioannis E.
Published: September 2024

Time-series forecasting represents of one of the most challenging and widely studied research areas in both academic and industrial communities. Despite the recent advancements in deep learning, the prediction of future time-series values remains a considerable endeavor due to the complexity and dynamic nature of time-series data. In this work, a new prediction model is proposed, named C-KAN, for multi-step forecasting, which is based on integrating convolutional layers with Kolmogorov–Arnold network architecture. The proposed model’s advantages are (i) the utilization of convolutional layers for learning the behavior and internal representation of time-series input data; (ii) activation at the edges of the Kolmogorov–Arnold network for potentially altering training dynamics; and (iii) modular non-linearity for allowing the differentiated treatment of features and potentially more precise control over inputs’ influence on outputs. Furthermore, the proposed model is trained using the DILATE loss function, which ensures that it is able to effectively deal with the dynamics and high volatility of non-stationary time-series data. The numerical experiments and statistical analysis were conducted on five challenging non-stationary time-series datasets, and provide strong evidence that C-KAN constitutes an efficient and accurate model, well suited for time-series forecasting tasks.
A Sample Average Approximation Approach for Stochastic Optimization of Flight Test Planning with Sorties Uncertainty
Authors: Ju Lunhao , Jiang Jiang , Wu Luofu , Sun Jianbin
Published: September 2024

In the context of flight test planning, numerous uncertainties exist, encompassing aircraft status, number of flights, and weather conditions, among others. These uncertainties ultimately manifest significantly in the actual number of flight sorties executed, rendering high significance to engineering problems related to the execution of flight test missions. However, there is a dearth of research in this specific aspect. To address this gap, this paper proposes an opportunity-constrained integer programming model tailored to the unique characteristics of the problem. To handle the uncertainties, Sample Average Approximation (SAA) is employed to perform oversampling of the uncertain parameters, followed by the Adaptive Large Neighborhood Search (ALNS) algorithm to solve for the optimal solution and objective function value. Results from numerical experiments conducted at varying scales and validated with diverse sampling distributions demonstrate the effectiveness and robustness of the proposed methodology. By decoding the generated execution sequences, comprehensive mission planning schemes can be derived. This approach yields sequences that exhibit commendable feasibility and robustness for the flight test planning problem with sorties uncertainty (FTPPSU), offering valuable support for the efficient execution of future flight test missions.
A Robust Human–Machine Framework for Project Portfolio Selection
Authors: Chen Hang , Zhang Nannan , Dou Yajie , Dai Yulong
Published: September 2024

Based on the project portfolio selection and scheduling problem (PPSS), the development of a systematic and scientific project scheduling plan necessitates comprehensive consideration of individual preferences and multiple realistic constraints, rendering it an NP-hard problem. Simultaneously, accurately and swiftly evaluating the value of projects as a complex entity poses a challenging issue that requires urgent attention. This paper introduces a novel qualitative evaluation-based project value assessment process that significantly reduces the cost and complexity of project value assessment, upon which a preference-based deep reinforcement learning method is presented for computing and solving project subsets and time scheduling plans. This paper first determines the key parameter values of the algorithm through specific examples. Then, using the method of controlling variables, it explores the sensitivity of the algorithm to changes in problem size and dimensionality. Finally, the proposed algorithm is compared with two classical algorithms and two heuristic algorithms across different instances. The experimental results demonstrate that the proposed algorithm exhibits higher effectiveness and accuracy.
Terpenoids from the Soft Coral Sinularia densa Collected in the South China Sea
Authors: Wang Cili , Zhang Jiarui , Li Kai , Yang Junjie , Li Lei , Wang Sen , Hou Hu , Li Pinglin
Published: September 2024

The chemical investigation of the South China Sea soft coral Sinularia densa has resulted in the isolation of seven new terpenoids, including two new meroterpenoids, namely sinudenoids F–G (1–2), and five new cembranes, namely sinudenoids H–L (3–7). Their structures and absolute configurations were elucidated based on extensive analyses of spectroscopic data, single-crystal X-ray diffraction, comparison with the literature data, and quantum chemical calculations. Among them, sinudenoid F (1) and sinudenoid G (2) are rare meroterpenoids featuring a methyl benzoate core. Sinudenoid H (3) possesses a rare carbon skeleton of 8, 19-bisnorfuranocembrenolide, which is the second reported compound with this skeleton. In a bioassay, sinudenoid H (3) exhibited better anti-inflammatory activity compared to the positive control indomethacin at 20 µM in CuSO4-treated transgenic fluorescent zebrafish. Moreover, sinudenoid J (5) and sinudenoid L (7) exhibited moderate anti-thrombotic activity in arachidonic acid (AA)-induced thrombotic zebrafish at 20 µM.
Vulvovaginal Atrophy Following Treatment for Oncogynecologic Pathologies: Etiology, Epidemiology, Diagnosis, and Treatment Options
Authors: Narutytė Ramunė , Žukienė Guoda , Bartkevičienė Daiva
Published: September 2024

Vulvovaginal atrophy, characterized by the thinning of vaginal mucosa typically resulting from reduced estrogen levels, is frequently exacerbated by oncogynecologic treatments such as chemotherapy, hormonal therapy, radiotherapy, or surgery. This condition significantly impacts the quality of life for cancer survivors, leading to persistent discomfort, heightened infection risk, and negative effects on sexual function and self-esteem. Despite being a relatively common complication, vulvovaginal atrophy is not always discussed before the start of treatment. Treatments typically mirror those used for natural menopause; however, efficacy and safety data specific to this population are limited due to the exclusion of these patients from clinical trials. A major safety concern is the risk of hormone-sensitive cancer recurrence associated with estrogen therapy, which drives a preference for non-hormonal alternatives. Newer treatments, such as laser therapy, radiofrequency, and vaginal injections, show promise with minimal side effects and hormone-independent mechanisms, though efficacy data varies, highlighting the need for further research. This narrative review explores the epidemiology, risk factors, diagnosis, and management of vulvovaginal atrophy after the treatment for oncogynecologic disorders.
Changes in Central Sensitivity to Thyroid Hormones vs. Urine Iodine during Pregnancy
Authors: Ilias Ioannis , Milionis Charalampos , Alexiou Maria , Michou Ekaterini , Karavasili Chrysi , Venaki Evangelia , Markou Kostas , Mamali Irini , Koukkou Eftychia
Published: September 2024

Introduction/Aim: Central sensitivity to thyroid hormones refers to the responsiveness of the hypothalamic–pituitary–thyroid (HPT) axis to changes in circulating free thyroxine (fT4). Although dose–response relationships between thyroid hormones per se and urinary iodine (UI) levels have been observed, central sensitivity to thyroid hormones in relation to UI remains unexplored. The aim of the present study was to evaluate central sensitivity to thyroid hormones (by means of the Thyroid Feedback Quantile-based Index [TFQI], which is a calculated measure, based on TSH and fT4, that estimates central sensitivity to thyroid hormones) in pregnancy and to assess whether it differs according to gestational age and/or iodine intake. Materials and Methods: One thousand, one hundred and two blood and urine samples were collected from pregnant women (with a mean age ± SD of 30.4 ± 4.6 years) during singleton pregnancies; women with known/diagnosed thyroid disease were excluded. Specifically, TSH and fT4, anti-thyroid peroxidase antibodies and UI were measured in each trimester and at two months postpartum, while the TFQI was calculated for all the study samples. After the elimination of outliers, statistical analysis was conducted with analysis of variance (ANOVA) for the variables versus time period, while Pearson’s correlation was used to assess the TFQI versus UI. Results: The mean TFQI index ranged from −0.060 (second trimester) to −0.053 (two months postpartum), while the corresponding UI was 137 and 165 μg/L, respectively. The TFQI-UI correlation was marginally negative (Pearson r: −0.323, p: 0.04) and significantly positive (r: +0.368, p: 0.050) for UI values over 250 μg/L, in the first and the second trimesters of pregnancy, respectively. Discussion: The TFQI is a new index reflecting central sensitivity to thyroid hormones. A lower TFQI indicates higher sensitivity to thyroid hormones. In our sample, the TFQI was mainly positively related to iodine intake in the second trimester of pregnancy (following the critical period of organogenesis). Thus, the observed changes in the TFQI may reflect the different ways of the central action of thyroid hormones, according to the phase of pregnancy. These results have the potential to enhance our comprehension of the changes in the HPT axis’ function via variations in central sensitivity to thyroid hormones and its interplay with nutritional iodine status during pregnancy.
Advancements and Future Prospects in Ocean Wave Energy Harvesting Technology Based on Micro-Energy Technology
Authors: Yang Weihong , Peng Jiaxin , Chen Qiulin , Zhao Sicheng , Zhuo Ran , Luo Yan , Gao Lingxiao
Published: September 2024

Marine wave energy exhibits significant potential as a renewable resource due to its substantial energy storage capacity and high energy density. However, conventional wave power generation technologies often suffer from drawbacks such as high maintenance costs, cumbersome structures, and suboptimal conversion efficiencies, thereby limiting their potential. The wave power generation technologies based on micro-energy technology have emerged as promising new approaches in recent years, owing to their inherent advantages of cost-effectiveness, simplistic structure, and ease of manufacturing. This paper provides a comprehensive overview of the current research status in wave energy harvesting through micro-energy technologies, including detailed descriptions of piezoelectric nanogenerators, electromagnetic generators, triboelectric nanogenerators, dielectric elastomer generators, hydrovoltaic generators, and hybrid nanogenerators. Finally, we provide a comprehensive overview of the prevailing issues and challenges associated with these technologies, while also offering insights into the future development trajectory of wave energy harvesting technology.
Novel Adaptive Hidden Markov Model Utilizing Expectation–Maximization Algorithm for Advanced Pipeline Leak Detection
Authors: Zadehbagheri Omid , Salehizadeh Mohammad Reza , Naghavi Seyed Vahid , Moattari Mazda , Moshiri Behzad
Published: September 2024

In the oil industry, the leakage of pipelines containing hydrocarbon fluids causes significant environmental and economic damage. Recently, there has been a growing trend in employing data mining techniques for detecting leaks. Among these methods is the Hidden Markov Model, which, despite good results with stationary data, becomes inefficient when a leak causes a drop in the pressure or flow, reducing its accuracy. This paper presents an adaptive Hidden Markov method. Previous methods had low accuracy due to insufficient information for accurate leak detection. They often classified the size and location of leaks broadly. In contrast, the proposed model extracts hidden features to accurately identify the location and size of leaks, even in noisy conditions. Simulating a leak in a section of an oil pipeline in the Iranian Oil Export Corridor demonstrates the proposed method’s superiority over common methods like K-NN, SVM, Naive Bayes, and logistic regression.
The Influence of Ag Addition and Different SiO2 Precursors on the Structure of Silica Thin Films Synthesized by the Sol–Gel Method
Authors: Adamczyk Anna , Brylewski Tomasz , Szymczak Patryk
Published: September 2024

In this work, the structure of silica thin films synthesized with three different SiO2 precursors and obtained by the sol–gel method and dip coating technique was studied. Additionally, the influence of Ag addition on the obtained silica sols and then gel structure was investigated. Silica coatings show antireflective properties and high thermal resistance, as well as hydrophobic or hydrophilic properties. Three different silica precursors, TEOS (tetraethylorthosilicate), DDS (dimethyldietoxysilane) and AerosilTM, were selected for the synthesis. DDS added to silica sol act as a pore size modifier, while Ag atoms are known for their antibacterial activity. Coatings were deposited on two different substrates: steel and titanium, dried and annealed at 500 °C in air (steel substrate) and in argon (titanium substrate). For all synthesized films, IR (infrared) spectroscopic studies were performed together with GID and XRD (Grazing Incidence Diffraction, X-ray Diffraction) measurements. The topography and morphology of the surface were traced by SEM and AFM microscopic methods, providing information on the samples’ roughness, particle sizes and thickness of the particular layers. The wetting angle values were also measured. GID and XRD measurements pointed to the distinct contribution of an amorphous phase in the samples, allowing us to recognize the crystalline phases and calculate the silver crystallite sizes. The FTIR spectra gave information on the first coordination sphere of the studied samples.
Catalytic Reactivity Assessment of AgM and CuM (M = Cr, Fe) Catalysts for Dry Reforming of Methane Process with CO2
Authors: Barr Amel , Benrabaa Rafik , Henni Hayat , Meddour-Boukhobza Laaldja , Roussel Pascal , Löfberg Axel
Published: September 2024

CuM and AgM (M = Cr, Fe) catalysts were synthesized, characterized, and evaluated in methane reforming with CO2 with and without pretreatment under a H2 atmosphere. Their textural and structural characteristics were evaluated using various physicochemical methods, including XRD, B.E.T., SEM-EDS, XPS, and H2-TPR. It was shown that the nature of the species has a significant effect on these structural, textural, and reactivity properties. AgCr catalysts, presenting several oxidation states (Ag0, Ag+1, Cr3+, and Cr6+ in Ag, AgCrO2, and AgCr2O4), showed the most interesting catalytic performance in their composition. The intermediate Cr2O3 phase, formed during the catalytic reaction, played an important role as a catalytic precursor in the in situ production of highly dispersed nanoparticles, being less prone to coke formation in spite of the severe reaction conditions. In contrast, the AgFe catalyst showed low activity and a low selectivity for DRM in the explored temperature range, due to a significant contribution of the reverse water–gas shift reaction, which accounted for the low H2/CO ratios.
Solar Hydrogen Production and Storage in Solid Form: Prospects for Materials and Methods
Authors: Adaikalam Kathalingam , Vikraman Dhanasekaran , Karuppasamy K. , Kim Hyun-Seok
Published: September 2024

Climatic changes are reaching alarming levels globally, seriously impacting the environment. To address this environmental crisis and achieve carbon neutrality, transitioning to hydrogen energy is crucial. Hydrogen is a clean energy source that produces no carbon emissions, making it essential in the technological era for meeting energy needs while reducing environmental pollution. Abundant in nature as water and hydrocarbons, hydrogen must be converted into a usable form for practical applications. Various techniques are employed to generate hydrogen from water, with solar hydrogen production—using solar light to split water—standing out as a cost-effective and environmentally friendly approach. However, the widespread adoption of hydrogen energy is challenged by transportation and storage issues, as it requires compressed and liquefied gas storage tanks. Solid hydrogen storage offers a promising solution, providing an effective and low-cost method for storing and releasing hydrogen. Solar hydrogen generation by water splitting is more efficient than other methods, as it uses self-generated power. Similarly, solid storage of hydrogen is also attractive in many ways, including efficiency and cost-effectiveness. This can be achieved through chemical adsorption in materials such as hydrides and other forms. These methods seem to be costly initially, but once the materials and methods are established, they will become more attractive considering rising fuel prices, depletion of fossil fuel resources, and advancements in science and technology. Solid oxide fuel cells (SOFCs) are highly efficient for converting hydrogen into electrical energy, producing clean electricity with no emissions. If proper materials and methods are established for solar hydrogen generation and solid hydrogen storage under ambient conditions, solar light used for hydrogen generation and utilization via solid oxide fuel cells (SOFCs) will be an efficient, safe, and cost-effective technique. With the ongoing development in materials for solar hydrogen generation and solid storage techniques, this method is expected to soon become more feasible and cost-effective. This review comprehensively consolidates research on solar hydrogen generation and solid hydrogen storage, focusing on global standards such as 6.5 wt% gravimetric capacity at temperatures between −40 and 60 °C. It summarizes various materials used for efficient hydrogen generation through water splitting and solid storage, and discusses current challenges in hydrogen generation and storage. This includes material selection, and the structural and chemical modifications needed for optimal performance and potential applications.
Enhancement of Triple-Negative Breast Cancer-Specific Induction of Cell Death by Silver Nanoparticles by Combined Treatment with Proteotoxic Stress Response Inhibitors
Authors: Snyder Christina M. , Mateo Beatriz , Patel Khushbu , Fahrenholtz Cale D. , Rohde Monica M. , Carpenter Richard , Singh Ravi N.
Published: September 2024

Metal nanoparticles have been tested for therapeutic and imaging applications in pre-clinical models of cancer, but fears of toxicity have limited their translation. An emerging concept in nanomedicine is to exploit the inherent drug-like properties of unmodified nanomaterials for cancer therapy. To be useful clinically, there must be a window between the toxicity of the nanomaterial to cancer and toxicity to normal cells. This necessitates identification of specific vulnerabilities in cancers that can be targeted using nanomaterials without inducing off-target toxicity. Previous studies point to proteotoxic stress as a driver of silver nanoparticle (AgNPs) toxicity. Two key cell stress responses involved in mitigating proteotoxicity are the heat shock response (HSR) and the integrated stress response (ISR). Here, we examine the role that these stress responses play in AgNP-induced cytotoxicity in triple-negative breast cancer (TNBC) and immortalized mammary epithelial cells. Furthermore, we investigate HSR and ISR inhibitors as potential drug partners to increase the anti-cancer efficacy of AgNPs without increasing off-target toxicity. We showed that AgNPs did not strongly induce the HSR at a transcriptional level, but instead decreased expression of heat shock proteins (HSPs) at the protein level, possibly due to degradation in AgNP-treated TNBC cells. We further showed that the HSR inhibitor, KRIBB11, synergized with AgNPs in TNBC cells, but also increased off-target toxicity in immortalized mammary epithelial cells. In contrast, we found that salubrinal, a drug that can sustain pro-death ISR signaling, enhanced AgNP-induced cell death in TNBC cells without increasing toxicity in immortalized mammary epithelial cells. Subsequent co-culture studies demonstrated that AgNPs in combination with salubrinal selectively eliminated TNBCs without affecting immortalized mammary epithelial cells grown in the same well. Our findings provide additional support for proteotoxic stress as a mechanism by which AgNPs selectively kill TNBCs and will help guide future efforts to identify drug partners that would be beneficial for use with AgNPs for cancer therapy.
Effects of GSH on Alcohol Metabolism and Hangover Improvement in Humans: A Randomized Double-Blind Placebo-Controlled Crossover Clinical Trial
Authors: Song Gunju , Han Hyein , Park Seyoung , Sa Soonok , Chung Wookyung , Lee Boo Yong
Published: September 2024

Background: The definition of alcohol hangovers refers to a combination of mental and physical side effects that occur after drinking. One of the ways that hangovers can be ameliorated is by promoting the rapid and effective elimination of acetaldehyde to alleviate the discomfort it causes. This study aimed to investigate the effects of GSH (yeast extract containing 50 mg of glutathione) on the hangover-relieving effect. Methods: A randomized double-blind placebo-controlled crossover clinical trial was conducted with 40 participants who reported experiencing hangover symptoms. Participants consumed alcohol at a rate of 0.78 g per kg body weight with 40% whiskey, adjusted according to their weight. Alcohol and acetaldehyde concentrations in serum were analyzed at 0, 0.25, 1, 2, 4, 6, and 15 h after alcohol consumption. Results: In the GSH group, the serum alcohol concentration decreased, although this change was not statistically significant. The serum acetaldehyde concentration was significantly lower in the GSH group in comparison to the placebo group (at 0.25, 1, 4, and 6 h (p < 0.01) and at 0.5, 2, and 15 h (p < 0.001) after alcohol consumption). However, there was no significant difference between the two groups on questionnaires such as the Acute Hangover Scale and the Alcohol Hangover Severity Scale. Conclusions: Overall, we consider the discovery that GSH lowered acetaldehyde concentration, a crucial factor in alcohol metabolism, to be more considerable. Therefore, GSH administration effectively reduces acetaldehyde levels in serum. This result suggests that this effect may contribute to the relief of hangover symptoms.
Rosehip Extract Decreases Reactive Oxygen Species Production and Lipid Accumulation in Hypertrophic 3T3-L1 Adipocytes with the Modulation of Inflammatory State
Authors: Kowalska Katarzyna , Olejnik Anna
Published: September 2024

Background: Rosa canina L. (rosehip) is used worldwide in traditional medicine as a plant with medicinal properties. However, its anti-obesity effects are not fully explained on a transcriptional level. Methods: In the present work, the 3T3-L preadipocytes were utilized to explore the impact of R. canina fruit extract (RCE) on the cellular and molecular pathways involved in adipocyte hypertrophy. Results: Obtained results showed the ability of RCE to reduce lipid overloads in hypertrophic adipocytes associated with the down-regulation of mRNA expressions of adipogenic transcription factors such as PPARγ, C/EBPα, and SREBP-1c as well as genes involved in lipid biosyntheses such as FAS, LPL, and aP2. Moreover, obesity-associated oxidative stress (antioxidant enzyme activities and ROS generation) and inflammation were ameliorated in RCE-treated hypertrophic adipocytes. The mRNA and protein levels of adipokines such as leptin, resistin, and adiponectin were restored to more favorable levels. Conclusions: Rosa canina fruit might be a valuable source of phytochemicals in preventing obesity and obesity-related metabolic complications.
Functioning in an Illness and Quality of Life versus the Prevalence of Depression and Anxiety Disorders in Patients with High Cardiovascular Risk
Authors: Michalski Piotr , Kosobucka-Ozdoba Agata , Pietrzykowski Łukasz , Kasprzak Michał , Grzelakowska Klaudyna , Rzepka-Cholasińska Alicja , Kubica Aldona
Published: September 2024

Background: A chronic disease occurring in a person’s life is a stressor, disrupting every aspect of their life. Objectives: This study aims to assess the relationship between functioning in chronic illness and quality of life with the prevalence of symptoms of depression and anxiety in patients with high cardiovascular risk. Material and methods: This study included 200 patients (aged 18–80 years) under the care of a primary care physician, diagnosed with hypertension and/or hypercholesterolemia, and/or diabetes between 6 and 24 months before the enrollment. The presented analysis assessed the symptoms of anxiety and depression using the Hospital Anxiety and Depression Scale (HADS); and the quality of life of patients with cardiovascular disease using the Heart Quality of Life (HeartQoL) questionnaire and functioning in chronic illness using the Functioning in Chronic Illness Scale (FCIS). Results: The HADS scores amounted to 4.34 ± 3.414 points for the HADS-Anxiety subscale and 3.20 ± 2.979 points for the HADS-Depression subscale. The score indicative of functioning in chronic illness assessed with the FCIS was 98.32 ± 13.89 points. The independent predictors of HADS-anxiety were HeartQoL Emotional and FCIS Global, while HeartQoL Global and FCIS Global were the independent predictors for HADS-depression. Better functioning in chronic illness (FCIS Global) was associated with less frequent symptoms of anxiety and depression based on the HADS: HADS-Anxiety (R Spearmann = −0.3969; p < 0.0001) and HADS-Depression (R Spearmann = −0.5884; p < 0.0001). Higher HeartQoL scores, both globally, as well as in emotional and physical dimensions, were associated with a lower severity of anxiety and depression assessed with the HADS: HADS-Anxiety (R Spearmann = −0.2909; p = 0.0001) and HADS-Depression (R Spearmann = −0.2583; p = 0.0002). Conclusions: The quality of life and functioning in chronic illness are connected with symptoms of depression and anxiety. When assessing the severity of the depression symptoms in relation to the individual aspects of functioning in chronic illness, the areas requiring supportive-educational intervention can be identified. The assessment of both functioning in a chronic disease and the severity of the depression symptoms should be included in a standard nursing diagnosis and further supportive and educational intervention.
Polycyclic Tetramate Macrolactams and Their Potential as Anticancer Agents
Authors: Montavon Alexandria , Marchán-Rivadeneira M. Raquel , Han Yong
Published: September 2024

Natural products have been a reliable source of drug compounds in medical research. Technological advances have led to the discovery and characterization of many compounds that were previously difficult to isolate. However, when searching for anticancer drugs, finding natural compounds that can bind to specific targets is a daunting task. Polycyclic tetramate macrolactams (PoTeMs), specifically, have been a source of antibiotics for a long time, though they possess certain cytotoxic properties that make them attractive candidates for anticancer drug discovery. This review covers the structural diversity and widespread availability of PoTeM compounds and the past research that demonstrates their effects on human cancer cell lines. Additionally, this review documents the known receptors and molecular mechanisms of these compounds in mammalian cells.
Habitat Suitability of Ixodes ricinus Ticks Carrying Pathogens in North-East Italy
Authors: Huitink Maartje , de Rooij Myrna , Montarsi Fabrizio , Salvati Maria Vittoria , Obber Federica , Da Rold Graziana , Sgubin Sofia , Mazzotta Elisa , di Martino Guido , Mazzucato Matteo , Salata Cristiano , Vonesch Nicoletta , Tomao Paola , Mughini-Gras Lapo
Published: September 2024

Ixodes ricinus ticks are ubiquitous in Europe, including in North-East Italy. These ticks are important vectors of several zoonotic pathogens of public health relevance. In this study, the habitat suitability range of I. ricinus ticks infected with zoonotic pathogens was predicted in North-East Italy, and relevant spatial predictors were identified. In 2015–2021, ticks were collected at 26 sampling sites in the study area. The collected ticks were screened for the presence of pathogens using PCR assays. For Borrelia, Rickettsia and Anaplasma/Ehrlichia species, data allowed for ecological niche modelling using Maxent. Environmental determinants potentially related to tick habitat suitability were used as model inputs. Predicted suitable habitat distributions revealed hotspots of the probability of pathogen presence in I. ricinus ticks mainly in the central and upper parts of the study area. Key environmental predictors were temperature, rainfall and altitude, and vegetation index for specific pathogens (Rickettsia and Anaplasma/Ehrlichia species). Increased risk of exposure to tick-borne pathogens upon tick bites in the predicted hotspot areas can, therefore, be expected. This provides useful information for public health risk managers in this and other similar regions.
Ischemic Optic Neuropathy: A Review of Current and Potential Future Pharmacotherapies
Authors: Badla Omar , Badla Beshr Abdulaziz , Almobayed Amr , Mendoza Carlos , Kishor Krishna , Bhattacharya Sanjoy K.
Published: September 2024

The treatment of arteritic anterior ischemic optic neuropathy (AAION), non-arteritic ischemic optic neuropathy (NAAION), and posterior ischemic optic neuropathy (PION) is a topic of ongoing research with mixed evidence on some pharmacotherapies and a need for more consensus. This manuscript provides an overview of these conditions’ current, potential future, and attempted pharmacotherapies. AAION’s current treatment regimen consists of high-dose steroids, with methotrexate, tocilizumab, and abatacept, being the most viable steroid-sparing therapy candidates. As for NAAION, the treatments being tried are vast, with mixed evidence supporting each modality. Similarly, despite the various treatment options explored, there still needs to be a universally effective therapy for PION. More research is needed to formulate an agreed-upon treatment regimen for these conditions.
Experimental and Modelling Study of Controlled Release from Dextran-Based Cryogels
Authors: Lauriola Carolina , Di Muzio Laura , Paolicelli Patrizia , Casadei Maria Antonietta , Sergi Claudia , Tirillò Jacopo , Carriero Vito Cosimo , Adrover Alessandra
Published: September 2024

In this work, five different dextran-based cryogels for controlled drug release are investigated. Vitamin B12 was used as a model drug for in vitro release tests. Two different drug-loading procedures were adopted, leading to very different drug release curves. Indeed, a fast Fickian release was observed when freeze-dried samples of DEX40PEG360MA and DEX40PEG500MA were infused with the drug after cryogel formation. On the contrary, a slowed highly non-Fickian behavior arises when the drug is loaded before the low-temperature crosslinking step, leading to the cryogel formation. The non-Fickian drug release, observed for all the five different dextran-based cryogels investigated, is actually due to the cryoconcentration phenomenon, modeled with a two-step release process. The proposed transport model accurately predicts experimental release curves characterized by a long lag time, confirming that dextran-based cryogels are suitable for controlled release.
Developing Robust Human Liver Microsomal Stability Prediction Models: Leveraging Inter-Species Correlation with Rat Data
Authors: Shah Pranav , Siramshetty Vishal B. , Mathé Ewy , Xu Xin
Published: September 2024

Objectives: Pharmacokinetic issues were the leading cause of drug attrition, accounting for approximately 40% of all cases before the turn of the century. To this end, several high-throughput in vitro assays like microsomal stability have been developed to evaluate the pharmacokinetic profiles of compounds in the early stages of drug discovery. At NCATS, a single-point rat liver microsomal (RLM) stability assay is used as a Tier I assay, while human liver microsomal (HLM) stability is employed as a Tier II assay. We experimentally screened and collected data on over 30,000 compounds for RLM stability and over 7000 compounds for HLM stability. Although HLM stability screening provides valuable insights, the increasing number of hits generated, along with the time- and resource-intensive nature of the assay, highlights the need for alternative strategies. One promising approach is leveraging in silico models trained on these experimental datasets. Methods: We describe the development of an HLM stability prediction model using our in-house HLM stability dataset. Results: Employing both classical machine learning methods and advanced techniques, such as neural networks, we achieved model accuracies exceeding 80%. Moreover, we validated our model using external test sets and found that our models are comparable to some of the best models in literature. Additionally, the strong correlation observed between our RLM and HLM data was further reinforced by the fact that our HLM model performance improved when using RLM stability predictions as an input descriptor. Conclusions: The best model along with a subset of our dataset (PubChem AID: 1963597) has been made publicly accessible on the ADME@NCATS website for the benefit of the greater drug discovery community. To the best of our knowledge, it is the largest open-source model of its kind and the first to leverage cross-species data.
Role of In Vitro Tests in the Characterisation of Locally Applied, Locally Acting Drugs in the Throat: Application to Flurbiprofen
Authors: Perlik Vit , Ali Hafsa , Cardot Jean M. , Kulasekaran Anuradha
Published: September 2024

Background/Objectives: For locally applied, locally acting generic drug products, comparison to an originator product based on systemic exposure is usually not feasible due to low plasma concentrations and inadequate reflection of local exposure at the site of action. Where a validated PD model exists, a comparative clinical study can be performed in healthy subjects; where no surrogate endpoint is available, patients with the relevant indication need to be enrolled, with all the associated factors which could result in lack of sensitivity. Even though the need for alternative in vitro approaches has been acknowledged by both industry and regulatory bodies, the complexity of in vivo drug delivery processes makes the development of guidance documents particularly difficult. Our objective was to present in vitro approaches less classically used and to address in vivo relevance of the selected tests. Methods: This article analyses current regulatory approaches in Europe and the U.S., and highlights the key advantages of in vitro tests in terms of their sensitivity, reliability, reproducibility and in vivo relevance using locally applied flurbiprofen in various formulations. Results: The in vitro esophageal retention (IVOR) model demonstrates that the first 6–10 min after application of different flurbiprofen formulations is important for their comparison and also offers the best correlation with in vivo data using the partial area under the concentration-time curves (pAUCs). Rheological evaluations further demonstrated that the mucoadhesive properties of the gel spray formulation are based on interaction with mucin. Conclusions: Designing a relevant in vitro test requires adequate evaluation of the complexity of the drug substance, drug product, dosing conditions and delivery processes.