bluuebunny/crossref_metadata_2025 huggingface dataset:
ResnetCPS for Power Equipment and Defect Detection
Authors: Yan Xingyu , Jia Lixin , Liao Xiao , Cui Wei , Xue Shuangsi , Yan Dapeng , Cao Hui
Published: November 2024

Routine visual inspection is fundamental to the preventive maintenance of power equipment. Convolutional neural networks (CNNs) substantially reduce the number of parameters and efficiently extract image features for classification tasks. In the actual production and operation process of substations, due to the limitation of safety distance, camera monitoring, inspection robots, etc., cannot be very close to the target. The operational environment of power equipment leads to scale variations in the main target and thus compromises the performance of conventional models. To address the challenges posed by scale fluctuations in power equipment image datasets, while adhering to the requirements for model efficiency and enhanced inter-channel communication, this paper proposed the ResNet Cross-Layer Parameter Sharing (ResNetCPS) framework. The core idea is that the network output should remain consistent for the same object at different scales. The proposed framework facilitates weight sharing across different layers within the convolutional network, establishing connections between pertinent channels across layers and leveraging the scale invariance inherent in image datasets. Additionally, for substation image processing mainly based on edge devices, smaller models must be used to reduce the expenditure of computing power. The Cross-Layer Parameter Sharing framework not only reduces the overall number of model parameters but also decreases training time. To further enhance the representation of critical features while suppressing less important or redundant ones, an Inserting and Adjacency Attention (IAA) module is designed. This mechanism improves the model’s overall performance by dynamically adjusting the importance of different channels. Experimental results demonstrate that the proposed method significantly enhances network efficiency, reduces the total parameter storage space, and improves training efficiency without sacrificing accuracy. Specifically, models incorporating the Cross-Layer Parameter Sharing module achieved a reduction in the number of parameters and model size by 10% to 30% compared to the baseline models.
Development of Grain Dryer Control Technology from the Perspective of Low Carbon and Intelligentization
Authors: Chang Kezhen , Li Jinquan , Jin Yi , Liu Chunshan
Published: November 2024

The grain-drying process plays a critical role in grain storage and quality assurance. In recent years, with the advancement of low-carbon and intelligent technologies, the control technology of grain dryers has significantly improved. This paper systematically reviews the development status of grain dryer control technology from the perspective of low-carbon and intelligentization, analyzing the technological differences in control systems between domestic and international approaches. Current research challenges include the insufficient integration of control technologies with the drying process, limited control variables, the inadequate application of intelligent control strategies, and unstable sensor accuracy. To enhance the performance of grain-drying systems, this paper suggests optimizing control mechanisms, adopting efficient and environmentally friendly energy sources, improving sensor performance, introducing advanced intelligent control algorithms, and strengthening system monitoring capabilities. Looking ahead, with the further integration of AI, IoT, and green energy, grain-drying control systems are expected to evolve towards greater intelligence, remote operation, and low carbonization, providing technical support for enhancing drying efficiency and environmental performance.
Characterization of Hydrocolloids Extracted from Fenugreek and Sweet Basil Seeds and Their Effect on Rheological Properties of Wheat Starch Paste
Authors: Bieniek Angelika , Kapelko-Żeberska Małgorzata , Gryszkin Artur , Zięba Tomasz , Nemś Agnieszka , Buksa Krzysztof
Published: November 2024

Starch-hydrocolloid systems are crucial texturizing agents, ingredients and additives in many food products. The aim of this work was to investigate the effect of extraction temperature on the composition and properties of hydrocolloids extracted from milled fenugreek and sweet basil seeds, as well as the effect of the obtained non-starch polysaccharide preparations on the rheological properties of wheat starch paste. Hydrocolloids were extracted from milled seeds of fenugreek and sweet basil at room temperature (PFRT and PSBRT, respectively) and at 70 °C (PF70 and PSB70, respectively), with the extraction yields ranging from 32 to 47%. Extracted hydrocolloids contained polysaccharides (mainly high molar mass galactomannan) and protein. Preparations extracted at 70 °C from fenugreek (PF70) and sweet basil (PSB70) seeds were characterized by a higher content of polyphenols (16.2% and 50.9%, respectively), as well as higher antioxidant properties compared to preparations extracted at room temperature (PFRT and PSBRT). The 6% share of the fenugreek and sweet basil seed preparations obtained by extraction at room temperature increased the consistency index and reduced the flow behavior index compared to the starch paste without the preparations. The share of fenugreek seed and sweet basil preparations increased (p < 0.05) the tan δ parameter of the starch paste compared to the starch paste without the preparations.
Food Waste as Feedstock for Anaerobic Mono-Digestion Process
Authors: Tomczak Wirginia , Daniluk Monika , Kujawska Anna
Published: November 2024

There is a growing recognition that food waste (FW) comprises a significant amount of unused energy. Indeed, FW shows great potential to produce methane (CH4)-rich biogas via an anaerobic digestion (AD) process. Nevertheless, to ensure high AD process performance, deepening the knowledge of FW characteristics is required. Furthermore, the biogas yield is strongly influenced by several operational parameters. Taking into account the above, in the current study, based on the data in the literature, the physicochemical parameters of FW generated throughout the world are presented and discussed. In addition, the performance profile of the single-stage anaerobic mono-digestion process with the use of FW as a feedstock was investigated. The performed analysis clearly demonstrated that FW is characterized by significant variations in several parameters, such as pH, the total solid (TS) and volatile solid (VS) contents, the volatile solids to total solids ratio (VS/TS), soluble chemical oxygen demand (sCOD), the concentrations of VFAs and ammonium nitrogen (NH4+-N), and the carbon-to-nitrogen ratio (C/N). Moreover, it was shown that the selected operational parameters, such as temperature, pH, the ratio of food waste to inoculum (I) (FW/I), and the organic loading rate (OLR), may have the most significant impact on the performance of the single-stage anaerobic mono-digestion process. In addition, it was found that most of the experimental investigations presented in the literature were conducted on a laboratory scale. Hence, in future research, more effort should be made to determine the biogas yield with the use of full-scale systems. To summarize, it should be clearly highlighted that the analysis presented in this study may have important implications for the management and application of FW as feedstock for an anaerobic mono-digestion process on an industrial scale.
The Technical–Tactical Patterns of Scoring Actions in Male Visually Impaired Judokas: A Weight Category Analysis
Authors: Gutiérrez-Santiago Alfonso , Fernández-Moscoso Anxo , Reguera-López-de-la-Osa Xoana , Silva-Pinto Antonio José , Argibay-González Juan Carlos , Prieto-Lage Iván
Published: November 2024

In Para-judo, the technical actions and tactical situations suitable for scoring in a combat have not been studied. The aim of this research was to determine the technical–tactical patterns of scoring actions in Para-judo, focusing on weight categories. An observational methodology was employed to conduct this study. The sample consisted of all male judokas with visual impairment (n = 172) in the senior category who participated in the World Championships held in Odivelas in 2018. A total of 232 combats and 313 scoring actions were analyzed. To obtain the results, we used different analytical techniques with SPSS: descriptive analysis, chi-square test, and T-pattern analysis. The significance level used was p < 0.05. Key findings show that the majority of scoring actions occurred in the first two minutes of combat, highlighting the importance of early initiative. Techniques such as ashi-waza, te-waza, and sutemi-waza demonstrated particular effectiveness across categories, with a focus on movements like sumi-otoshi, ouchi-gari, and ko-soto-gake. Moreover, the most common grip—lapel-sleeve—proved advantageous, aiding judokas in controlling the bout and achieving scores, especially when coupled with effective transition techniques. The study’s weight-specific analysis further revealed distinct patterns, emphasizing the importance of tailored training approaches. For instance, lighter judokas benefited from countering leg attacks with arm or sacrifice techniques to score waza-ari, while heavier judokas favored leg-to-leg counterattacks, often achieving ippon. The results suggest that judokas and coaches could benefit from a more systematic focus on grip stability, strategic positional work, and transition efficiency, particularly from standing to ground. This research contributes valuable insights into optimal techniques and strategies for visually impaired judokas, providing clear guidelines for training and competition.
Nonlinear Vibrations of Low Pressure Turbine Bladed Disks: Tests and Simulations
Authors: Usmanov Umidjon , Battiato Giuseppe , Firrone Christian Maria , Conte Marta , Rosso Emanuele , D’Ettole Antonio Giuseppe
Published: November 2024

One of the most effective methods to limit the mechanical vibrations of bladed disks is the use of friction damping at mechanical joint interfaces. Unfortunately, dedicated tests to assess the impact of mistuning and the effectiveness of friction dampers are uncommon. This paper presents an original design of an academic demonstrator to perform an experimental analysis of the dynamic response of a tip-free bladed disk with under-platform dampers (UPDs), including an identification of intrinsic and contact mistuning introduced by the UPDs. The 48-blade disk was tested in a vacuum spinning rig by using permanent magnets. Vibration measurements were performed with the Blade Tip-Timing system. Tests were simulated using the Policontact tool, which predicted the average experimental nonlinear response in the presence of UPD, confirming the tool’s ability to capture the general nonlinear dynamic behavior of the mistuned bladed disk. This study presents a novel approach combining experimental Blade Tip Timing (BTT) with numerical simulations using Policontact (ver. 3.0) software and a model update based on experimental evidence to validate nonlinear dynamic responses. It distinguishes between intrinsic and contact mistuning effects, providing new insights into their impact on bladed disk vibrations. Additionally, a comparison of aluminum and steel UPDs reveals that steel offers a 26% greater damping efficiency due to its higher density and preload, significantly improving vibration reduction.
Analysis of Fatigue Performance of Spot-Welded Steel T-Profiles Under Cyclic Torsional Loading
Authors: Demiral Murat , Kadioglu Ferhat
Published: November 2024

Steel T-profiles with the spot-welding manufacturing process are extensively used in various sectors such as construction, automotive, renewable energy, etc., due to their versatility and reliability. These profiles are exposed to various loading modes during their service life, which include axial, bending, shear, torsional, or combinations thereof. This paper investigates the fatigue performance of a spot-welded T-profile assembly subjected to torsional cyclic loading. The extended finite element method (XFEM) analysis was performed to simulate the intricate behavior of spot welds under the loading, elucidating critical areas prone to fatigue initiation and propagation especially around the spot welds. The simulation results were compared with previously obtained experimental results. Both results are consistent. The effects of various parameters, including the spot-weld diameters, the amount of torque applied, thickness of the profile parts, and the presence of base part, on the fatigue performance of the assembly were studied critically.
Arterial Multi-Path Green Wave Control Model Concurrently Considering Motor Vehicles and Electric Bicycles
Authors: Jing Binbin , Yang Fan
Published: November 2024

Arterial green wave control can effectively reduce the delay time and number of stops of the coordinated traffic flows. However, existing arterial green wave control methods mostly focus on motor vehicles and provide them with green wave bands, neglecting the electric bicycles that are widespread on the roads. In fact, electric bicycles have become an important tool for short-to-medium trips among urban residents because they are convenient, low-cost, and eco-friendly. To tackle this, an arterial multi-path green wave control model that considers both motor vehicles(cars and buses) and electric bicycles is presented in this paper. The presented model is formulated as a mixed integer linear programming problem. The optimization objective of the model is to maximize the sum of the green wave bandwidths for all coordinated paths of each traffic mode on all road segments. The key constraints of the presented model can be addressed by analyzing the relationships among the green wave bandwidth, coordinated path, common cycle time, offset, phase sequence, etc., to utilize the time–space diagram. The results of the numerical example show that compared with the traditional model for through motor vehicles (cars and buses), the total green wave bandwidths of cars, buses, and electric bicycles generated by the presented model at the entire arterial level has been increased by 36.8%, 47.9%, and 19.3%, respectively.
Evaluation of Dynamics of a Freight Wagon Model with Viscous Damping
Authors: Melnik Rafał , Koziak Seweryn , Seńko Jarosław , Dižo Ján , Caban Jacek
Published: November 2024

The aim of this work was to perform a simulation analysis of the dynamics of a freight wagon with a variant vibration damping: dry friction and viscous damping. The following mathematical models of the damping characteristics are presented: the Maxwell model and the Kolsch model. The differences among the types of damping were first analyzed based on the dynamic responses of the 1 DOF model. Simulation studies were then carried out in a VI-Rail environment with the use of S-curved track models comprising short straight sections connecting the curves. The track models differed in the values of curve radii, cant, and length, which made it possible to run at different speeds. The multibody model of the vehicle represents a typical two-axle freight wagon. The dynamics of the wagon model were investigated for two states: empty and laden. Standard kinematic and dynamic values were compared in order to investigate if the nature of the damping has a significant impact on the dynamic properties of a freight wagon. The analysis of the simulation study showed that replacing dry friction damping with the viscous one can generally reduce forces acting on the wheel–rail contact, which, in turn, can be related to improving the running behavior of wagons while reducing the negative impact on the track.
Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach
Authors: Krolik Artur , Drelich Radosław , Pakuła Michał , Mikołajewski Dariusz , Rojek Izabela
Published: November 2024

This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). The aim of the study was to: (1) assess the presence of defects as well as their size, type, and orientation based on the amplitudes of Lamb ultrasonic waves measured in plates made of polyamide (PA6) and polyethylene (PE) due to their homogeneous internal structure, which mainly determined the selection of such model materials for testing; and (2) verify the possibilities of building automatic quality control and defect detection systems based on ML based on the results of the above-mentioned studies within the Industry 4.0/5.0 paradigm. Tests were conducted on plates with generated synthetic defects resembling defects found in real materials such as delamination and cracking at the edge of the plate and a crack (discontinuity) in the center of the plate. Defect sizes ranged from 1 mm to 15 mm. Probes at 30 kHz were used to excite Lamb waves in the slab material. This method is sensitive to the slightest changes in material integrity. A significant decrease in signal amplitude was observed, even for defects of a few millimeters in length. In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method’s potential for building cyber-physical systems and digital twins. By training ML models on ultrasonic data, algorithms can distinguish subtle differences between signals reflected from normal and defective areas of the material. Defect types such as voids, cracks, or weak bonds often produce distinct acoustic signatures, which ML models can learn to recognize with high accuracy. Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. This combination of automation and precision significantly enhances the reliability and efficiency of quality control in industrial manufacturing settings. The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. This approach demonstrates the novelty and contribution of artificial intelligence (AI) methods and tools, significantly extending and automating existing applications of traditional methods. The susceptibility to augmentation by AI/ML may represent an important new property of traditional methods crucial to assessing their suitability for future Industry 4.0/5.0 applications.
Permeability Enhancement Induced by Fracture Shear Dilation During Close-Range Coal Seam Mining
Authors: Li Chao , Liu Yingke , Wei Mingyao , Zhang Jinmiao
Published: November 2024

The advance of the working face in coal seams alters the local stress field and may give rise to fractures in the vicinity of the excavation. In this study, a constitutive model in which damage is defined as a function of volumetric strain was established and utilized in a numerical model to prognosticate the fracture development around the excavation. The predicted fractures that emerged in the overlying rock mass were found to exhibit hybrid characteristics. A permeability model was also constructed, taking into account both tension- and shear-induced fracture development. The permeability increase of the upper adjacent coal seam is most notable within 40 m from the goaf boundary. As the working face progresses, the permeability of the upper adjacent coal seam is further enhanced while that of the lower adjacent coal seam remains unaffected. The permeability at the goaf boundary is high and reaches its maximum at the rear of the working face, indicating that for the permeability change, the effect of shear-induced dilation plays a more crucial role than that of pressure-dependent compaction. This study can be used to guide the design of coal seam methane drainage for the mining of closely spaced coal seams.
Numerical Study of Cone Penetration Tests in Lunar Regolith for Strength Index
Authors: Zhao Xueliang , Liu Zixiong , Li Yu , Wang Hao , Xu Zhaodong
Published: November 2024

The cohesive properties of lunar regolith, combined with a low-gravity environment, result in it having a distinct mechanical behavior from sandy soil on Earth. Consequently, empirical formulas derived from cone penetration tests (CPTs) for calculating the shear strength parameters of Earth’s sand cannot be directly applied to lunar regolith. This study utilized the three-dimensional discrete element method (DEM) to numerically simulate triaxial shear tests and cone penetration tests in a lunar environment. The particle contact model for lunar regolith in the discrete element method (DEM) simulation incorporated the hysteresis effect of van der Waals forces, thereby simulating the cohesive properties of lunar regolith in a lunar environment. We proposed a relationship for calculating the shear strength index of lunar regolith based on normalized cone tip resistance using the results from triaxial and CPT simulations and referencing empirical formulas derived from ground-based CPT data. The results of this study provide a valuable reference for future lunar CPTs.
An Efficient One-Dimensional Texture Representation Approach for Lung Disease Diagnosis
Authors: Alabdulwahab Abrar , Park Hyun-Cheol , Jeong Heon , Lee Sang-Woong
Published: November 2024

The remarkable increase in published medical imaging datasets for chest X-rays has significantly improved the performance of deep learning techniques to classify lung diseases efficiently. However, large datasets require special arrangements to make them suitable, accessible, and practically usable in remote clinics and emergency rooms. Additionally, it increases the computational time and image-processing complexity. This study investigates the efficiency of converting the 2D chest X-ray into one-dimensional texture representation data using descriptive statistics and local binary patterns, enabling the use of feed-forward neural networks to efficiently classify lung diseases within a short time and with cost effectiveness. This method bridges diagnostic gaps in healthcare services and improves patient outcomes in remote hospitals and emergency rooms. It also could reinforce the crucial role of technology in advancing healthcare. Utilizing the Guangzhou and PA datasets, our one-dimensional texture representation achieved 99% accuracy with a training time of 10.85 s and 0.19 s for testing. In the PA dataset, it achieved 96% accuracy with a training time of 38.14 s and a testing time of 0.17 s, outperforming EfficientNet, EfficientNet-V2-Small, and MobileNet-V3-Small. Therefore, this study suggests that the dimensional texture representation is fast and effective for lung disease classification.
Efficient Robot Manipulation via Reinforcement Learning with Dynamic Movement Primitives-Based Policy
Authors: Li Shangde , Huang Wenjun , Miao Chenyang , Xu Kun , Chen Yidong , Sun Tianfu , Cui Yunduan
Published: November 2024

Reinforcement learning (RL) that autonomously explores optimal control policies has become a crucial direction for developing intelligent robots while Dynamic Movement Primitives (DMPs) serve as a powerful tool for efficiently expressing robot trajectories. This article explores an efficient integration of RL and DMP to enhance the learning efficiency and control performance of reinforcement learning in robot manipulation tasks by focusing on the forms of control actions and their smoothness. A novel approach, DDPG-DMP, is proposed to address the efficiency and feasibility issues in the current RL approaches that employ DMP to generate control actions. The proposed method naturally integrates a DMP-based policy into the actor–critic framework of the traditional RL approach Deep Deterministic Policy Gradient (DDPG) and derives the corresponding update formulas to learn the networks that properly decide the parameters of DMPs. A novel inverse controller is further introduced to adaptively learn the translation from observed states into various robot control signals through DMPs, eliminating the requirement for human prior knowledge. Evaluated on five robot arm control benchmark tasks, DDPG-DMP demonstrates significant advantages in control performance, learning efficiency, and smoothness of robot actions compared to related baselines, highlighting its potential in complex robot control applications.
Marginalized Textile Producers in New Kingdom Egypt
Authors: Galczynski Jordan
Published: November 2024

Textiles were ubiquitous in the elite Egyptian cultural sphere—from clothing, furniture coverings, and wall decorations to grave goods and temple offerings. The Egyptian world was draped in cloth, yet the producers were often marginalized members of society—immigrants, war captives, and women, who produced for a select few to whom they did not often belong. This paper aims to use textiles as a medium to investigate how the New Kingdom textile industry maintained social inequalities and power differentials. This paper highlights the efforts of marginalized labor in the production of textiles in New Kingdom Egypt, utilizing an intersectional approach to understand the interactions of the producers, varying levels of management, and elite consumers. Textiles were a high-value resource and one of the only goods that increased in value with labor, unlike other crafted goods, like metals, for example. I posit that this was inherently linked to the marginalization of the labor involved. The fabrics woven were a display of the Egyptian hegemony and reaffirmed the social order between the elites and the rest of society.
Replacement of Sulfur Dioxide in White, Rosé, and Red Wines by a Blend of Tannins Extracted from Multiple Plant Materials
Authors: Karampatea Aikaterini , Skendi Adriana , Irakli Maria , Bouloumpasi Elisavet
Published: November 2024

For a long time, sulfur dioxide (SO2) has been the most universally used additive in winemaking. With its wide range of effects, ease of use, and low cost, sulfur dioxide has not yet been completely replaced by any process or substance. Since the expected trend for the near future is to keep reducing the concentration of sulfites, many investigations focus on alternative chemical, biological, or physical processes. This study aims to evaluate the chemical, antioxidant, and sensory impact of a plant-based product used as sulfur dioxide replacement (SDR) in white, rosé, and red wines produced as a result of the application of different vinification protocols. The physicochemical and sensory evaluation of the different wines produced showed that this plant-based product could be a good candidate, but appropriate winemaking treatments and optimization are needed to limit wine defects.
Classical Batch Distillation of Anaerobic Digestate to Isolate Ammonium Bicarbonate: Membrane Not Necessary!
Authors: Moure Abelenda Alejandro , Baltrusaitis Jonas
Published: November 2024

The excessive mineralization of organic molecules during anaerobic fermentation increases the availability of nitrogen and carbon. For this reason, the development of downstream processing technologies is required to better manage ammonia and carbon dioxide emissions during the storage and land application of the resulting soil organic amendment. The present work investigated classical distillation as a technology for valorizing ammoniacal nitrogen (NH4+-N) in anaerobic digestate. The results implied that the direct isolation of ammonium bicarbonate (NH4HCO3) was possible when applying the reactive distillation to the food waste digestate (FWD) with a high content of NH4+-N, while the addition of antifoam to the agrowaste digestate (AWD) was necessary to be able to produce an aqueous solution of NH4HCO3 as the distillate. The reason was that the extraction of NH4HCO3 from the AWD required a higher temperature (>95 °C) and duration (i.e., steady state in batch operation) than the recovery of the inorganic fertilizer from the FWD. The titration method, when applied to the depleted digestate, offered the quickest way of monitoring the reactive distillation because the buffer capacity of the distillate was much higher. The isolation of NH4HCO3 from the FWD was attained in a transient mode at a temperature below 90 °C (i.e., while heating up to reach the desired distillation temperature or cooling down once the batch distillation was finished). For the operating conditions to be regarded as techno-economically feasible, they should be attained in the anaerobic digestion plant by integrating the heat harvested from the engines, which convert the biogas into electricity.
Artificial Intelligence Diagnosing of Oral Lichen Planus: A Comparative Study
Authors: Yu Sensen , Sun Wansu , Mi Dawei , Jin Siyu , Wu Xing , Xin Baojian , Zhang Hengguo , Wang Yuanyin , Sun Xiaoyu , He Xin
Published: November 2024

Early diagnosis of oral lichen planus (OLP) is challenging, which traditionally is dependent on clinical experience and subjective interpretation. Artificial intelligence (AI) technology has been widely applied in objective and rapid diagnoses. In this study, we aim to investigate the potential of AI diagnosis in OLP and evaluate its effectiveness in improving diagnostic accuracy and accelerating clinical decision making. A total of 128 confirmed OLP patients were included, and lesion images from various anatomical sites were collected. The diagnosis was performed using AI platforms, including ChatGPT-4O, ChatGPT (Diagram-Date extension), and Claude Opus, for AI directly identification and AI pre-training identification. After OLP feature training, the diagnostic accuracy of the AI platforms significantly improved, with the overall recognition rates of ChatGPT-4O, ChatGPT (Diagram-Date extension), and Claude Opus increasing from 59%, 68%, and 15% to 77%, 80%, and 50%, respectively. Additionally, the pre-training recognition rates for buccal mucosa reached 94%, 93%, and 56%, respectively. However, the AI platforms performed less effectively when recognizing lesions in less common sites and complex cases; for instance, the pre-training recognition rates for the gums were only 60%, 60%, and 20%, demonstrating significant limitations. The study highlights the strengths and limitations of different AI technologies and provides a reference for future AI applications in oral medicine.
Assessing the Impact of Bedaquiline, Clofazimine, and Linezolid on Mycobacterial Genome Integrity
Authors: Molnár Dániel , Surányi Éva Viola , Gálik Nikoletta , Tóth Judit , Hirmondó Rita
Published: November 2024

Tuberculosis (TB) presents significant medical challenges, largely due to the genetic diversity of Mycobacterium tuberculosis, which enhances the resilience and resistance of the pathogen to first-line treatments. In response to the global rise of drug-resistant TB, second-line antitubercular drugs like bedaquiline (BDQ), linezolid (LZD), and clofazimine (CFZ) have become critical treatment options. Understanding the molecular changes these drugs induce is essential for optimizing TB therapy. To contribute to this effort, we investigated their impact on genome maintenance and stability using Mycobacterium smegmatis as a model organism. Using mutation accumulation assays and whole-genome sequencing, we found that the second-line antibiotics did not significantly increase mutation rates, unlike the positive control UV treatment. However, upon BDQ treatment, we detected mutations in transporter proteins and transcription factors without any increase in the minimal inhibitory concentration. Additionally, BDQ and CFZ were found to alter DNA repair pathways and reduce cellular dNTP levels, particularly CFZ, which depleted dGTP, impacting DNA synthesis. CFZ also upregulated DNA repair enzymes, enhancing error-free repairs. Despite minimal mutagenic effects, both drugs displayed distinct impacts on cellular mechanisms, suggesting additional modes of action.
Chaga Mushroom Triterpenoids Inhibit Dihydrofolate Reductase and Act Synergistically with Conventional Therapies in Breast Cancer
Authors: Wang Junbiao , Beghelli Daniela , Amici Augusto , Sut Stefania , Dall’Acqua Stefano , Lupidi Giulio , Dal Ben Diego , Bistoni Onelia , Tomassoni Daniele , Belletti Barbara , Musa Sanaa , Mahajna Jamal , Pucciarelli Stefania , Marchini Cristina
Published: November 2024

Inonotus obliquus (Chaga) is a medicinal mushroom with several pharmacological properties that is used as a tea in traditional Chinese medicine. In this study, Chaga water extract was digested in vitro to mimic the natural processing and absorption of its biocomponents when it is consumed as functional beverage, and its anticancer activities were evaluated in breast cancer (BC) cell lines, representing HER2-positive and triple-negative subtypes. After chemical characterization by liquid chromatography/mass spectrometry (HR-QTOF) analysis, the effect of Chaga biocomponents on cell viability and cell cycle progression was assessed by MTT assay, FACS analysis, and Western blot. Dihydrofolate reductase (DHFR) activity was measured by an enzymatic assay. Four highly bioactive triterpenoids (inotodiol, trametenolic acid, 3-hydroxy-lanosta-8,24-dien-21-al, and betulin) were identified as the main components, able to decrease BC cell viability and block the cell cycle in G0/G1 by inducing the downregulation of cyclin D1, CDK4, cyclin E, and phosphorylated retinoblastoma protein. DHFR was identified as their crucial target. Moreover, bioactive Chaga components exerted a synergistic action with cisplatin and with trastuzumab in SK-BR-3 cells by inhibiting both HER2 and HER1 activation and displayed an immunomodulatory effect. Thus, Inonotus obliquus represents a source of triterpenoids that are effective against aggressive BC subtypes and display properties of targeted drugs.
Investigating the Imperative Role of microRNAs Expression in Human Embryo Implantation: A Narrative Review Based on Recent Evidence
Authors: Potiris Anastasios , Stavros Sofoklis , Zouganeli Ioanna , Machairiotis Nikolaos , Drakaki Eirini , Zikopoulos Athanasios , Anagnostaki Ismini , Zachariou Athanasios , Gerede Angeliki , Domali Ekaterini , Drakakis Peter
Published: November 2024

Background/Objectives: Embryo implantation is a highly complex process that requires the precise regulation of numerous molecules to be orchestrated successfully. Micro RNAs (miRNAs) are small non-coding RNAs that regulate gene expression and play a crucial role in the regulation of embryo implantation. This article aims to summarize the key findings of the literature regarding the role of miRNAs in human embryo implantation, emphasizing their involvement in critical stages such as decidualization, endometrial receptivity and trophoblast adhesion. Methods: This review includes primary research articles from the past decade. The studies utilize a range of experimental methodologies, including gene expression analysis and in vitro studies. Results: MicroRNAs, like miR-320a, miR-149, and miR30d secreted by preimplantation embryos and blastocysts significantly influence endometrial receptivity by promoting essential cellular processes, such as cell migration and trophoblast cell attachment, while others—miR17-5p, miR-193-3p, miR-372, and miR-542-3p—secreted from the endometrium regulate the decidualization phase. During the apposition and adhesion phases, miRNAs play a complex role by promoting, for example, miR-23b-3p, and inhibiting—as do miR-29c and miR-519d-3p—important biological pathways of these stages. During invasion, miR-26a-5p and miR-125-5p modulate important genes. Conclusions: This review underscores the critical impact of miRNAs in the regulation of embryo implantation and early pregnancy. The ability of miRNAs to modulate gene expression at various stages of reproduction presents promising therapeutic avenues for improving assisted reproductive technologies outcomes and addressing infertility. Further research into miRNA-based diagnostic tools and therapeutic strategies is essential to enhance our understanding of their role in reproductive health and to exploit their potential for clinical applications.
Pancreatic Morphology, Immunology, and the Pathogenesis of Acute Pancreatitis
Authors: Mihoc Tudorel , Latcu Silviu Constantin , Secasan Cosmin-Ciprian , Dema Vlad , Cumpanas Alin Adrian , Selaru Mircea , Pirvu Catalin Alexandru , Valceanu Andrei Paul , Zara Flavia , Dumitru Cristina-Stefania , Novacescu Dorin , Pantea Stelian
Published: November 2024

Acute pancreatitis is a complex inflammatory disorder with significant morbidity and mortality. This review aims to integrate the current knowledge of pancreatic morphology and immunology with the pathogenesis of acute pancreatitis, providing a comprehensive understanding of this critical condition. We conducted an extensive literature review, synthesizing data from recent studies and authoritative sources on pancreatic anatomy, histology, immunology, and the pathophysiology of acute pancreatitis. We also incorporated epidemiological data, clinical features, diagnostic criteria, and prognostic factors. The pancreas exhibits a complex morphology with intricate interactions between its exocrine and endocrine components. Its unique immunological landscape plays a crucial role in maintaining homeostasis and orchestrating responses to pathological conditions. In acute pancreatitis, the disruption of intracellular calcium signaling leads to premature enzyme activation, triggering a cascade of events including mitochondrial dysfunction, ATP depletion, and the release of proinflammatory mediators. This process can escalate from localized inflammation to systemic complications. The interplay between pancreatic morphology, immune responses, and pathophysiological mechanisms contributes to the varied clinical presentations and outcomes observed in acute pancreatitis. Understanding the intricate relationships between pancreatic morphology, immunology, and the pathogenesis of acute pancreatitis is crucial for developing more effective diagnostic and therapeutic strategies. This integrated approach provides new insights into the complex nature of acute pancreatitis and may guide future research directions in pancreatic disorders.
Chamomile Tincture and Lidocaine Hydrochloride Gel Ameliorates Periodontitis: A Preclinical Study
Authors: Sun Jiahui , Wang Huiyi , Xiao Junhong , Yang Qiudong , Liu Heyu , Yang Zhengkun , Liu Yuqi , Huang Xin , Yang Liu , Ma Li , Cao Zhengguo
Published: November 2024

Background/Objectives: Periodontitis is a common oral disease marked by gingival inflammation and alveolar bone loss. This study evaluated the efficacy of chamomile tincture and lidocaine hydrochloride (CLH) gel in mitigating periodontal inflammation and bone loss and uncovered the molecular mechanisms involved, both in vitro and in vivo. Methods: A periodontitis model was induced in Sprague Dawley rats by ligating the mandibular first molars. Sixty rats were divided into four groups: control (C), periodontitis (PD), periodontitis + CLH gel once daily (G1), and periodontitis + CLH gel thrice daily (G3). Clinical, micro-computed tomography (micro-CT), biological, and histological evaluations were performed, focusing on osteoclastogenesis, osteogenesis, and inflammatory cytokine production. The effect of CLH gel on inflammatory responses in RAW264.7 cells was also assessed through co-culture assays under Porphyromonas gingivalis (P. gingivalis) infection, with RNA-sequencing, qPCR, and Western blot analyses to explore underlying mechanisms. Results: CLH gel significantly reduced gingival and systemic inflammation and mitigated bone loss by enhancing the bone volume to tissue volume ratio and trabecular thickness via the RANKL/OPG axis in rats. The G3 group showed marked reductions in osteoclasts and increases in osterix-positive cells compared to other groups. In vitro, CLH gel reduced the inflammatory phenotype of macrophages in the periodontitis microenvironment by modulating Type II interferon (IFN-γ) networks. Conclusions: CLH gel reduced inflammation and bone loss in rat periodontitis, promoting osteogenesis and inhibiting osteoclastogenesis. It also suppressed macrophage inflammation via Type II interferon networks under P. gingivalis stimulation. These findings suggest that CLH gel has potential as an adjunctive therapy for periodontitis.
Digestive Amyloidosis Trends: Clinical, Pathological, and Imaging Characteristics
Authors: Bucurica Sandica , Nancoff Andreea-Simona , Moraru Miruna Valeria , Bucurica Ana , Socol Calin , Balaban Daniel-Vasile , Mititelu Mihaela Raluca , Maniu Ionela , Ionita-Radu Florentina , Jinga Mariana
Published: November 2024

Amyloidosis is a group of diseases characterized by the extracellular deposition of abnormally folded, insoluble proteins that lead to organ dysfunction. While it commonly affects the cardiovascular system, gastrointestinal (GI) tract involvement is undetermined. Recent research has focused on understanding the pathophysiology, diagnostic challenges, and therapeutic approaches to GI amyloidosis, particularly in systemic amyloid light-chain (AL) and amyloid A (AA) forms. GI manifestations can include motility disorders, bleeding, and, in severe cases, bowel obstruction. This review highlights the importance of the early recognition of digestive symptoms and associated imagistic findings in GI amyloidosis by analyzing the research that included clinical, pathological, and endoscopic approaches to amyloidosis. A systematic search of the PubMed and Scopus databases identified 19 relevant studies. Our findings showed that amyloid deposits commonly affect the entire GI tract, with AL amyloidosis being the most predominant form. Endoscopic evaluations and biopsy remain key diagnostic tools, with Congo Red staining and mass spectrometry being used to confirm amyloid type. Although progress has been made in diagnosis, the absence of targeted therapies and the indistinct nature of GI symptoms continue to be challenging.
Types of Injuries and the Severity of Shoulder Dysfunction Associated with Diabetes Mellitus in Patients with Functional Impairment: A Case–Control Study
Authors: Fuentes-Murguia Mercedes , Carrazco-Peña Karla B. , Delgado-Enciso Osiris G. , Castellanos-Gomez Joel , Hernandez-Fuentes Gustavo A. , Rojas-Larios Fabian , Sanchez-Ramirez Carmen A. , Martinez-Fierro Margarita L. , Rodriguez-Sanchez Iram P. , Guzmán-Esquivel José , Garza-Veloz Idalia , Del-Río-Valdivia José E. , Plata-Florenzano Jorge E. , Delgado-Enciso Iván
Published: November 2024

Background/Objectives: Patients with diabetes have been reported to experience a higher prevalence of shoulder disorders compared to those without diabetes or with other medical conditions. However, the specific types of shoulder injuries and the extent of functional impairment associated with diabetes mellitus remain unclear. This study aimed to assess the association between diabetes and specific shoulder injuries, as well as the degree of functional impairment in affected patients. Methods: A case–control study was conducted involving 136 patients with shoulder functional impairment (UCLA Shoulder Scale ≤ 27). The study included 38 patients with diabetes and 98 non-diabetic controls. Shoulder injuries were diagnosed using ultrasonography, focusing on the supraspinatus tendon, long head of the biceps tendon, subscapularis tendon, and the presence of adhesive capsulitis or rotator cuff tears. Results: Diabetic patients had significantly higher rates of poor shoulder function compared to non-diabetic controls (89.47% vs. 63.26%, adjusted OR [adOR] 5.22, 95% CI 1.57–17.32, p = 0.007). While both groups had high rates of supraspinatus and long head of the biceps tendon injuries (~80%), no significant differences were found between them (p > 0.300). However, diabetic patients were more than three times as likely to have subscapularis tendon injuries (adOR 3.15, 95% CI 1.26–7.90, p = 0.014) and massive rotator cuff tears (adOR 3.76, 95% CI 1.16–12.15, p = 0.027). Additionally, diabetes was associated with a fourfold increased risk of adhesive capsulitis (adOR 4.16, 95% CI 1.20–14.47, p = 0.025). Conclusions: Diabetes mellitus is linked to greater functional and structural deterioration of the shoulder, highlighting the importance of considering diabetes as a risk factor for specific shoulder injuries. Early diagnosis and treatment may improve outcomes for diabetic patients with shoulder disorders.
Intensive Treatment of Chronic Pain and PTSD: The PATRIOT Program
Authors: Otis John D. , Comer Jonathan S. , Keane Terence M. , Checko (Scioli) Erica , Pincus Donna B.
Published: November 2024

Military combat can result in the need for comprehensive care related to both physical and psychological trauma, most commonly chronic pain and post-traumatic stress disorder (PTSD). These conditions tend to co-occur and result in high levels of distress and interference in everyday life. Thus, it is imperative to develop effective, time-efficient treatments for these conditions before they become chronic and resistant to change. We developed and pilot-tested the Pain and Trauma Intensive Outpatient Treatment (PATRIOT) Program, a brief, intensive (3 weeks, six sessions) integrated chronic pain and PTSD treatment. An overview and session-by-session outline of the PATRIOT Program is provided, followed by results from the first pilot evaluation of the PATRIOT Program’s feasibility, acceptability, and preliminary efficacy in a sample of eight participating Veterans with comorbid chronic pain and PTSD. There were no treatment dropouts. At post-treatment, there were significant reductions in PTSD symptoms based on the Clinician-Administered Assessment of PTSD (CAPS). Pain and catastrophic thinking also decreased from pre- to post-treatment. With continued investigations and support, the PATRIOT Program may offer a brief, cost-effective, and more easily accessible treatment option for individuals who could benefit from learning skills to manage pain and PTSD more effectively.
Metacognition About Collaborative Learning: Students’ Beliefs Are Inconsistent with Their Learning Preferences
Authors: Wei Yunfeng , Soderstrom Nicholas C. , Meade Michelle L. , Scott Brandon G.
Published: November 2024

Collaboration plays an important role in educational contexts. However, little is known about students’ metacognitive beliefs about collaboration. The present study used an online survey to investigate students’ beliefs toward group study/recall, their studying preferences, strategies they use when studying individually and in groups, and important characteristics of their group members. Results indicate that, although students generally perceive collaboration as beneficial, they prefer individual study, indicating that their beliefs are inconsistent with their learning preferences. Students report social learning as the primary reason for collaborative benefits but prefer to study alone to minimize distraction and increase personal accountability. Further, they use different strategies when studying individually or in a group. When studying individually, students most frequently report re-reading their notes. However, when studying in groups, students most frequently use strategies emphasizing collaboration and interaction, such as quizzing each other. Also, students prefer to work with group members who are focused, motivated, and hard working. Students’ beliefs, preferences, and favored characteristics of group members are related to their frequency of using study groups. Students’ metacognitive beliefs about collaboration have implications for theories of self-regulated learning and better use of collaboration in educational contexts.