bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Spacing on Growth, Yield and Chemical Composition of Stevia Plants (Stevia rebaudiana Bert.)
Authors: Śniegowska Joanna , Biesiada Anita
Published: June 2024

The aim of this study was to determine the effect of plant spacing on the growth, yield and chemical composition of stevia grown in Poland. The variable factor was spacings of 50 cm × 30 cm, 45 cm × 25 cm and 30 cm × 30 cm. Seedlings were planted during the first ten days of June, and the plant material was harvested between the 10th and 20th of September. In July and September, seedlings were subjected to biometric measurements, and plant samples were taken for chemical analyses. Plants grown at 50 cm × 30 cm spacing were characterised by an increased content of reducing sugars, nitrates, phosphorous, potassium and magnesium, but the yield of the herb and leaves was smallest. The greatest yield of the herb and leaves was found for the 30 cm × 30 and 45 cm × 25 cm spacings with the lowest mass of single plants. Leaves from plants grown in the 30 cm × 30 cm spacing were also characterised as having the greatest concentration of dry mass and phenolic compounds and the lowest concentration of phosphorus and magnesium. Spacing did not significantly affect the biometric parameters of the plants in July, but in September, a difference was observed in the lateral width and total number of shoots. Spacing did not affect the plant pigment contents in the leaves of stevia.
On the Importance of Diversity When Training Deep Learning Segmentation Models with Error-Prone Pseudo-Labels
Authors: Yang Nana , Rongione Charles , Jacquemart Anne-Laure , Draye Xavier , Vleeschouwer Christophe De
Published: June 2024

The key to training deep learning (DL) segmentation models lies in the collection of annotated data. The annotation process is, however, generally expensive in human resources. Our paper leverages deep or traditional machine learning methods trained on a small set of manually labeled data to automatically generate pseudo-labels on large datasets, which are then used to train so-called data-reinforced deep learning models. The relevance of the approach is demonstrated in two applicative scenarios that are distinct both in terms of task and pseudo-label generation procedures, enlarging the scope of the outcomes of our study. Our experiments reveal that (i) data reinforcement helps, even with error-prone pseudo-labels, (ii) convolutional neural networks have the capability to regularize their training with respect to labeling errors, and (iii) there is an advantage to increasing diversity when generating the pseudo-labels, either by enriching the manual annotation through accurate annotation of singular samples, or by considering soft pseudo-labels per sample when prior information is available about their certainty.
Deep Learning Realizes Photoacoustic Imaging Artifact Removal
Authors: He Ruonan , Chen Yi , Jiang Yufei , Lei Yuyang , Yan Shengxian , Zhang Jing , Cao Hui
Published: June 2024

Photoacoustic imaging integrates the strengths of optics and ultrasound, offering high resolution, depth penetration, and multimodal imaging capabilities. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their “view” of the imaging target, which result in image reconstruction artifacts degrading image quality. To address this problem, YOLOv8-Pix2Pix is proposed as a hybrid artifact-removal algorithm, which is advantageous in comprehensively eliminating various types of artifacts and effectively restoring image details compared to existing algorithms. The proposed algorithm demonstrates superior performance in artifact removal and segmentation of photoacoustic images of brain tumors. For the purpose of further expanding its application fields and aligning with actual clinical needs, an experimental system for photoacoustic detection is designed in this paper to be verified. The experimental results show that the processed images are better than the pre-processed images in terms of reconstruction metrics PSNR and SSIM, and also the segmentation performance is significantly improved, which provides an effective solution for the further development of photoacoustic imaging technology.
Inference of Constant-Stress Model of Fréchet Distribution under a Maximum Ranked Set Sampling with Unequal Samples
Authors: Liu Jia , Wang Liang , Tripathi Yogesh Mani , Lio Yuhlong
Published: June 2024

This paper explores the inference for a constant-stress accelerated life test under a ranked set sampling scenario. When the lifetime of products follows the Fréchet distribution, and the failure times are collected under a maximum ranked set sampling with unequal samples, classical and Bayesian approaches are proposed, respectively. Maximum likelihood estimators along with the existence and uniqueness of model parameters are established, and the corresponding asymptotic confidence intervals are constructed based on asymptotic theory. Under squared error loss, Bayesian estimation and highest posterior density confidence intervals are provided, and an associated Monte-Carlo sampling algorithm is proposed for complex posterior computation. Finally, extensive simulation studies are conducted to demonstrate the performance of different methods, and a real-data example is also presented for applications.
Recent Advances in Proximity Point Theory Applied to Fractional Differential Equations
Authors: Mlaiki Nabil , Sagheer Dur-e-Shehwar , Noreen Sana , Batul Samina , Aloqaily Ahmad
Published: June 2024

This article introduces the concept of generalized (ﬀF,b,ϕ˘) contraction in the context of b-metric spaces by utilizing the idea of F contraction introduced by Dariusz Wardowski. The main findings of the research focus on the existence of best proximity points for multi-valued (ﬀF,b,ϕ˘) contractions in partially ordered b-metric spaces. The article provides examples to illustrate the main results and demonstrates the existence of solutions to a second-order differential equation and a fractional differential equation using the established theorems. Additionally, several corollaries are presented to show that the results generalize many existing fixed-point and best proximity point theorems.
Mechanobiology of Adipocytes
Authors: Blade Sean P. , Falkowski Dylan J. , Bachand Sarah N. , Pagano Steven J. , Chin LiKang
Published: June 2024

The growing obesity epidemic necessitates increased research on adipocyte and adipose tissue function and disease mechanisms that progress obesity. Historically, adipocytes were viewed simply as storage for excess energy. However, recent studies have demonstrated that adipocytes play a critical role in whole-body homeostasis, are involved in cell communication, experience forces in vivo, and respond to mechanical stimuli. Changes to the adipocyte mechanical microenvironment can affect function and, in some cases, contribute to disease. The aim of this review is to summarize the current literature on the mechanobiology of adipocytes. We reviewed over 100 papers on how mechanical stress is sensed by the adipocyte, the effects on cell behavior, and the use of cell culture scaffolds, particularly those with tunable stiffness, to study adipocyte behavior, adipose cell and tissue mechanical properties, and computational models. From our review, we conclude that adipocytes are responsive to mechanical stimuli, cell function and adipogenesis can be dictated by the mechanical environment, the measurement of mechanical properties is highly dependent on testing methods, and current modeling practices use many different approaches to recapitulate the complex behavior of adipocytes and adipose tissue. This review is intended to aid future studies by summarizing the current literature on adipocyte mechanobiology.
Postprandial Plasma Glucose between 4 and 7.9 h May Be a Potential Diagnostic Marker for Diabetes
Authors: Wang Yutang , Fang Yan , Aberson Christopher L. , Charchar Fadi J. , Ceriello Antonio
Published: June 2024

Postprandial glucose levels between 4 and 7.9 h (PPG4–7.9h) correlate with mortality from various diseases, including hypertension, diabetes, cardiovascular disease, and cancer. This study aimed to assess if predicted PPG4–7.9h could diagnose diabetes. Two groups of participants were involved: Group 1 (4420 participants) had actual PPG4–7.9h, while Group 2 (8422 participants) lacked this measure but had all the diabetes diagnostic measures. Group 1 underwent multiple linear regression to predict PPG4–7.9h using 30 predictors, achieving accuracy within 11.1 mg/dL in 80% of the participants. Group 2 had PPG4–7.9h predicted using this model. A receiver operating characteristic curve analysis showed that predicted PPG4–7.9h could diagnose diabetes with an accuracy of 87.3% in Group 2, with a sensitivity of 75.1% and specificity of 84.1% at the optimal cutoff of 102.5 mg/dL. A simulation on 10,000 random samples from Group 2 revealed that 175 participants may be needed to investigate PPG4–7.9h as a diabetes diagnostic marker with a power of at least 80%. In conclusion, predicted PPG4–7.9h appears to be a promising diagnostic indicator for diabetes. Future studies seeking to ascertain its definitive diagnostic value might require a minimum sample size of 175 participants.
New Developments in Pharmacological Treatment of Obesity and Type 2 Diabetes—Beyond and within GLP-1 Receptor Agonists
Authors: Sztanek Ferenc , Tóth László Imre , Pető Attila , Hernyák Marcell , Diószegi Ágnes , Harangi Mariann
Published: June 2024

Guidelines for the management of obesity and type 2 diabetes (T2DM) emphasize the importance of lifestyle changes, including a reduced-calorie diet and increased physical activity. However, for many people, these changes can be difficult to maintain over the long term. Medication options are already available to treat obesity, which can help reduce appetite and/or reduce caloric intake. Incretin-based peptides exert their effect through G-protein-coupled receptors, the receptors for glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), and glucagon peptide hormones are important regulators of insulin secretion and energy metabolism. Understanding the role of intercellular signaling pathways and inflammatory processes is essential for the development of effective pharmacological agents in obesity. GLP-1 receptor agonists have been successfully used, but it is assumed that their effectiveness may be limited by desensitization and downregulation of the target receptor. A growing number of new agents acting on incretin hormones are becoming available for everyday clinical practice, including oral GLP-1 receptor agonists, the dual GLP-1/GIP receptor agonist tirzepatide, and other dual and triple GLP-1/GIP/glucagon receptor agonists, which may show further significant therapeutic potential. This narrative review summarizes the therapeutic effects of different incretin hormones and presents future prospects in the treatment of T2DM and obesity.
Enhancing Sensitivity in SARS-CoV-2 Rapid Antigen Testing through Integration of a Water-Soluble Polymer Wall
Authors: Wang Xiuzhen , Wang Yu , Jie Huiyang , Liu Sidi , Shen Chenguang , Liu Qian
Published: June 2024

Lateral flow immunoassays (LFIAs) are recognized for their practicality in homecare and point-of-care testing, owing to their simplicity, cost-efficiency, and rapid visual readouts. Despite these advantages, LFIAs typically fall short in sensitivity, particularly in detecting viruses such as SARS-CoV-2, thus limiting their broader application. In response to this challenge, we have innovated an approach to substantially enhance LFIA sensitivity. This involves the integration of a water-soluble dextran–methacrylate polymer wall with a 15% grafting degree positioned between the test and control lines on the LFIA strip. This novel modification significantly improved the sensitivity of the assay, achieving detection limits as low as 50 pg mL−1 and enhancing the sensitivity by 5–20-fold relative to existing LFIA kits available on the market. Furthermore, our developed LFIA kit (WSPW-LFIA) demonstrated exceptional specificity for SARS-CoV-2. Coupled with a straightforward fabrication process and robust stability, the WSPW-LFIA represents a promising advancement for real-time in vitro diagnosis across a spectrum of diseases.
Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct
Authors: Tahmasebinia Faham , Lin Lin , Wu Shuo , Kang Yifan , Sepesgozar Samad
Published: June 2024

The global demand for energy is significantly impacted by the consumption patterns within the building sector. As such, the importance of energy simulation and prediction is growing exponentially. This research leverages Building Information Modelling (BIM) methodologies, creating a synergy between traditional software methods and algorithm-driven approaches for comprehensive energy analysis. The study also proposes a method for monitoring select energy management factors, a step that could potentially pave the way for the integration of digital twins in energy management systems. The research is grounded in a case study of a newly constructed educational building in New South Wales, Australia. The digital physical model of the building was created using Autodesk Revit, a conventional software for BIM methodology. EnergyPlus, facilitated by OpenStudio, was employed for the traditional software-based energy analysis. The energy analysis output was then used to develop preliminary algorithm models using regression strategies in Python. In this regression analysis, the temperature and relative humidity of each energy unit were used as independent variables, with their energy consumption being the dependent variable. The sigmoid algorithm model, known for its accuracy and interpretability, was employed for advanced energy simulation. This was combined with sensor data for real-time energy prediction. A basic digital twin (DT) example was created to simulate the dynamic control of air conditioning and lighting, showcasing the adaptability and effectiveness of the system. The study also explores the potential of machine learning, specifically reinforcement learning, in optimizing energy management in response to environmental changes and usage conditions. Despite the current limitations, the study identifies potential future research directions. These include enhancing model accuracy and developing complex algorithms to boost energy efficiency and reduce costs.
Probabilistic Fatigue Crack Growth Prediction for Pipelines with Initial Flaws
Authors: Choi Youngjin , Lee Seung-Jung
Published: June 2024

This paper presents a probabilistic method to predict fatigue crack growth for surface flaws in pipelines using a particle filtering method based on Bayes theorem. The random response of the fatigue behavior is updated continuously as measured data are accumulated by the particle filtering method. Fatigue crack growth is then predicted through an iterative process in which particles with a high probability are reproduced more during the update process, and particles with a lower probability are removed through a resampling procedure. The effectiveness of the particle filtering method was confirmed by controlling the depth and length direction of the cracks in the pipeline and predicting crack growth in one- and two-dimensional cases. In addition, the fatigue crack growth and remaining service life with a 90% confidence interval were predicted based on the findings of previous studies, and the relationship between the fatigue crack growth rate and the crack size was explained through the Paris’ law, which represents fatigue crack growth. Finally, the applicability of the particle filtering method under different diameters, aspect ratios, and materials was investigated by considering the negative correlation between the Paris’ law parameters.
Progressive Collapse Behavior of a Precast Reinforced Concrete Frame System with Layered Beams
Authors: Kolchunov Vitaly I. , Fedorova Natalia V. , Savin Sergei Y. , Kaydas Pavel A.
Published: June 2024

A possible way to improve the structural safety and robustness of precast building structures is to develop effective precast frame systems with layered beams, which combine prefabricated parts with cast-in situ ordinary concrete, high-performance concrete, fiber concrete, or FRP. The paper provides a new type of precast reinforced concrete frame system with layered beams for rapidly erected multi-story buildings resistant to accidental actions. Using a combination of the variational method and two-level design schemes, a simplified analytical model has been developed for structural analysis of the precast reinforced concrete frame system, both for serviceable and ultimate limit states as well as for accidental actions. The proposed model allows for determining shear deformations and the formation and opening of longitudinal cracks in the intermediate contact zone between precast and monolithic parts of reinforced concrete structural elements of the frame, as well as the formation and opening of normal cracks because of the action of axial tensile force or bending moment in these elements. The design model was validated by comparing the calculated and experimental data obtained from testing scaled models of the precast reinforced concrete frame system with layered beams. The paper investigates and thoroughly analyzes the factors affecting the stiffness and bearing capacity of the intermediate contact zone, discusses the criteria for the formation of shear cracks along the contact zone of precast and monolithic concrete, and examines the change in the stiffness and dissipative properties of layered elements at different stages of their static–dynamic loading. The robustness of the experimental models of the structural system was not ensured under the specified load, section dimensions, and reinforcement scheme. Following an accidental action, longitudinal cracks were observed in the contact joint between the monolithic and prefabricated parts in the layered beams. This occurred almost simultaneously with the opening of normal cracks in adjacent sections. A comprehensive analysis of the results indicated a satisfactory degree of agreement between the proposed semi-analytical model and the test data.
The Decision-Making Trial and Evaluation Laboratory Approach to the Assessment and Hierarchy of Factors Shaping the Costs of Facade Systems
Authors: Górka-Stańczyk Monika , Leśniak Agnieszka
Published: June 2024

Cost estimation is the process of creating cost forecasts by quantitative determination and pricing of the necessary resources for a project’s implementation. This process is iterative, where estimates are regularly updated based on the available information. Studying the relationship between the costs of construction projects is crucial for establishing reliable practices of cost estimation and management. Variability in construction costs can significantly impact investors’ or clients’ decisions. Greater coherence and confirmed relationships between construction costs and factors influencing them can assist investors, developers, and contractors in making informed decisions and ensuring effective cost management. Therefore, the aim of this article is to identify the factors shaping the costs of facade systems of public utility buildings and to examine the mutual influences and dependencies that occur between these factors. The factors were selected based on an analysis of project documentation, and then their assessment was made through expert opinions. The DEMATEL method was used to investigate the mutual relationships and dependencies between the factors as one of the tools of multi-criteria analysis. Through the analysis, it can be pointed out that factors such as the height of the building, the type of facade analyzed, and the level of complexity of the facade have the greatest impact on the cost of facade systems. Moreover, the type of facade analyzed and the number of floors are the factors showing the greatest direct influence on the other factors of the cost of facade systems. The identification and prioritization of factors carried out by the authors provide a basis for future research, which are models that support the prediction of the cost of making facade systems.
Exploring Historical Perspectives in Building Hygrothermal Models: A Comprehensive Review
Authors: Jalili Habib , Ouahbi Tariq , Eid Joanna , Taibi Said , Hamrouni Ichrak
Published: June 2024

The necessity of understanding and simulating hydrological phenomena as well as their interactions and the effect of anthropogenic and climate conditions on the ecosystem have encouraged researchers for years to investigate the moisture transfer in soil. Considering the moisture transfer as an isothermal phenomenon might cause a wrong estimation due to the non-isothermal nature of the moisture movement in porous media. Hygrothermal (coupled heat and moisture transfer) models are quite diverse and are the engine of the various hygrothermal software tools used to analyze the heat and moisture in building envelopes, drying technologies, and many other applications. This paper is a literature survey conducted to provide an overview on the classical hygrothermal models to address the historical perspectives on these models. First, it investigated, from a historical point of view, the challenges behind the development of hygrothermal models as unsaturated flow theories, beginning with Buckingham theory. The non-isothermal nature of moisture was the starting point for researchers to deal with new challenges during mathematical modeling and experimental analysis. In general, the theory of coupled heat and moisture transfer first developed by J.R. Philip and De Vries and the authors in the mid-1950s inspired the novel hygrothermal models, including Sophocleous and Milly’s model, Rode’s model, Künzel’s model, and Grunewal’s model. In a parallel of hygrothermal model developments, the models of Whitaker and Luikov can also be classified as hygrothermal models; they were mostly applied in modeling the phenomenon of drying. The study highlights the application of hygrothermal models in building physics and gathered a summary of international efforts such as Annex 24, Annex 41, and the HAMSTAD project and advancements performed from the classical dew point or steady-state Glaser method. Moreover, this study emphasizes the advantages of the standard of EN 15026 and limitations of the Glaser method. To sum up, hygrothermal models are still under development based on various assumptions of moisture driving potentials and transfer coefficients.
Evaluation of a Simplified Direct SSI Method in the Dynamic Seismic Behavior of Traditional RC Minarets
Authors: Türkeli Erdem
Published: June 2024

Several reinforced concrete minarets in Turkey have suffered significant damage during earthquakes, resulting in fatalities and economic losses. These structures might be considered the most frequently built thin structures in Turkey. To improve seismic resistivity, it is necessary to figure out the exact nature of these tall structures. In this way, the existing ones can be strengthened. This study examined the most widely built (traditional) forms of reinforced concrete minarets under two earthquakes, the Mw 7.2 Van on 23 October 2011 and the Mw 7.4 İzmit on 17 August 1999, by considering three types of soils, i.e., stiff, medium and soft, with the viscous boundary method proposed from Burman et al. Moreover, diameter of the soil was selected as ten times the diameter of the foundation of the minarets. After conducting numerous analyses, it was concluded that the RC minarets’ structural behavior was altered by the softening of the earth, leading to a sharp increase in internal forces. Furthermore, it was discovered that the regions of stress accumulation indicated for the representative minarets matched the damage shown in recent earthquakes.
Morphology and Microstructural Optimization of Zeolite Crystals Utilizing Polymer Growth Modifiers for Enhanced Catalytic Application
Authors: Zhan Junling , Bi Chongyao , Du Xiaohui , Liu Tao , Jia Mingjun
Published: June 2024

Rationally controlling the morphology and microstructure of the zeolite crystals could play a significant role in optimizing their physicochemical properties and catalytic performances for application in various zeolite-based heterogeneous catalysis processes. Among different controlling strategies, the utilization of zeolite growth modifiers (ZGMs), which are molecules capable of altering the anisotropic rates of crystal growth, is becoming a promising approach to modulate the morphology and microstructural characteristics of zeolite crystals. In this mini-review, we attempt to provide an organized overview of the recent progress in the usage of several easily available polymer-based growth modifiers in the synthesis of some commonly used microporous zeolites and to reveal their roles in controlling the morphology and various physicochemical properties of zeolite crystals during hydrothermal synthesis processes. This review is expected to provide some guidance for deeply understanding the modulation mechanisms of polymer-based zeolite growth modifiers and for appropriately utilizing such a modulation strategy to achieve precise control of the morphology and microstructure of zeolite crystals that display optimal performance in the target catalytic reactions.
Impact of Inorganic Anions on the Photodegradation of Herbicide Residues in Water by UV/Persulfate-Based Advanced Oxidation
Authors: Pérez-Lucas Gabriel , Campillo Aitor , Navarro Simón
Published: June 2024

The removal of pesticides and other organic pollutants from water through advanced oxidation processes (AOPs) holds great promise. The main advantage of these technologies is that they remove, or at least reduce, pesticide levels by mineralization rather than transfer, as in conventional processes. This study first evaluated the effectiveness of UV/S2O8= compared to heterogeneous photocatalysis using UV/TiO2 processes on the degradation of two commonly used herbicides (terbuthylazine and isoproturon) in aqueous solutions using a laboratory photoreactor. In addition, the effect of the UV wavelength on the degradation efficiency of both herbicides was investigated. Although the degradation rate was greater under UV(254)/S2O8= nm than under UV(365)/S2O8= nm, complete degradation of the herbicides (0.2 mg L−1) was achieved within 30 min under UV-366 nm using a Na2S2O8 dose of 250 mg L−1 in the absence of inorganic anions. To assess the impact of the water matrix, the individual and combined effects of sulfate (SO4=), bicarbonate (HCO3−), and chloride (Cl−) were evaluated. These can react with hydroxyl (HO•) and sulfate (SO4•−) radicals generated during AOPs to form new radicals with a lower redox potential. The results showed negligible effects of SO4=, while the combination of HCO3− and Cl− seemed to be the key to the decrease in herbicide removal efficiency found when working with complex matrices. Finally, the main intermediates detected during the photodegradation process are identified, and the likely pathways involving dealkylation, dechlorination, and hydroxylation are proposed and discussed.
CCT and Cullin1 Regulate the TORC1 Pathway to Promote Dendritic Arborization in Health and Disease
Authors: Lottes Erin N. , Ciger Feyza , Bhattacharjee Shatabdi , Timmins Emily A. , Tete Benoit , Tran Tommy , Matta Jais , Patel Atit A. , Cox Daniel N.
Published: June 2024

The development of cell-type-specific dendritic arbors is integral to the proper functioning of neurons within their circuit networks. In this study, we examine the regulatory relationship between the cytosolic chaperonin CCT, key insulin pathway genes, and an E3 ubiquitin ligase (Cullin1) in dendritic development. CCT loss of function (LOF) results in dendritic hypotrophy in Drosophila Class IV (CIV) multi-dendritic larval sensory neurons, and CCT has recently been shown to fold components of the TOR (Target of Rapamycin) complex 1 (TORC1) in vitro. Through targeted genetic manipulations, we confirm that an LOF of CCT and the TORC1 pathway reduces dendritic complexity, while overexpression of key TORC1 pathway genes increases the dendritic complexity in CIV neurons. Furthermore, both CCT and TORC1 LOF significantly reduce microtubule (MT) stability. CCT has been previously implicated in regulating proteinopathic aggregation, thus, we examine CIV dendritic development in disease conditions as well. The expression of mutant Huntingtin leads to dendritic hypotrophy in a repeat-length-dependent manner, which can be rescued by Cullin1 LOF. Together, our data suggest that Cullin1 and CCT influence dendritic arborization through the regulation of TORC1 in both health and disease.
Parallel Analysis of Exosomes and Cytokines in Aqueous Humor Samples to Evaluate Biomarkers for Glaucoma
Authors: Shin Da Young , Han Jeong-Sun , Park Chan Kee , Lee Na Young , Jung Kyoung In
Published: June 2024

Recent emerging studies have demonstrated numerous critical roles of exosomes in cell-to-cell signaling. We investigated exosomes in the aqueous humor of glaucoma patients and controls and compared their characteristics with other biomarkers such as cytokines. Glaucoma patients exhibited higher exosome particle counts and smaller sizes compared to controls. Higher exosome density was correlated with more severe visual field loss. Conversely, concentrations of aqueous humor cytokines, particularly PD-L1, were primarily associated with intraocular pressure, and none of the cytokines showed a significant association with visual field damage. This may reflect the characteristics of exosomes, which are advantageous for crossing various biological barriers. Exosomes may contain more information about glaucoma functional damage occurring in the retina or optic nerve head. This highlights the potential importance of exosomes as signaling mediators distinct from other existing molecules.
CARD8: A Novel Inflammasome Sensor with Well-Known Anti-Inflammatory and Anti-Apoptotic Activity
Authors: Karakaya Tugay , Slaufova Marta , Di Filippo Michela , Hennig Paulina , Kündig Thomas , Beer Hans-Dietmar
Published: June 2024

Inflammasomes comprise a group of protein complexes with fundamental roles in the induction of inflammation. Upon sensing stress factors, their assembly induces the activation and release of the pro-inflammatory cytokines interleukin (IL)-1β and -18 and a lytic type of cell death, termed pyroptosis. Recently, CARD8 has joined the group of inflammasome sensors. The carboxy-terminal part of CARD8, consisting of a function-to-find-domain (FIIND) and a caspase activation and recruitment domain (CARD), resembles that of NLR family pyrin domain containing 1 (NLRP1), which is recognized as the main inflammasome sensor in human keratinocytes. The interaction with dipeptidyl peptidases 8 and 9 (DPP8/9) represents an activation checkpoint for both sensors. CARD8 and NLRP1 are activated by viral protease activity targeting their amino-terminal region. However, CARD8 also has some unique features compared to the established inflammasome sensors. Activation of CARD8 occurs independently of the inflammasome adaptor protein apoptosis-associated speck-like protein containing a CARD (ASC), leading mainly to pyroptosis rather than the activation and secretion of pro-inflammatory cytokines. CARD8 was also shown to have anti-inflammatory and anti-apoptotic activity. It interacts with, and inhibits, several proteins involved in inflammation and cell death, such as the inflammasome sensor NLRP3, CARD-containing proteins caspase-1 and -9, nucleotide-binding oligomerization domain containing 2 (NOD2), or nuclear factor kappa B (NF-κB). Single nucleotide polymorphisms (SNPs) of CARD8, some of them occurring at high frequencies, are associated with various inflammatory diseases. The molecular mechanisms underlying the different pro- and anti-inflammatory activities of CARD8 are incompletely understood. Alternative splicing leads to the generation of multiple CARD8 protein isoforms. Although the functional properties of these isoforms are poorly characterized, there is evidence that suggests isoform-specific roles. The characterization of the functions of these isoforms, together with their cell- and disease-specific expression, might be the key to a better understanding of CARD8’s different roles in inflammation and inflammatory diseases.
Design, Fabrication and Testing of a Multifrequency Microstrip RFID Tag Antenna on Si
Authors: Korfiati Timothea , Vazouras Christos N. , Bolakis Christos , Stavrinidis Antonis , Stavrinidis Giorgos , Arapogianni Aggeliki
Published: June 2024

A configurable design of a microstrip square spiral RFID tag antenna, for a wide range of microwave frequencies in the S- and C-band, is presented. The design is parameterized in dimensions, and hence changing the design frequency (or frequencies) is easy, by changing only an initial value for the spiral geometry. A tag specimen was fabricated using a Cu electroplating technique according to the design for frequencies of interest in the areas of 2.4 and 5.8 GHz. The substrate material is 320 μm high-resistivity Si and the bridge dielectric is 15 μm polyimide PI2525. The steps of the microfabrication process involve metallic structure pattern transfer techniques with optical UV lithography procedures. The reflection coefficient and antenna gain of the specimen were measured inside an anechoic enclosure using a vector network analyzer (VNA) and a TEM horn test antenna over a frequency range of up to 6 GHz. Simulated and measured results, exhibiting reasonable agreement, are presented and discussed.
Application of Quantitative Ultrasonography and Artificial Intelligence for Assessing Severity of Fatty Liver: A Pilot Study
Authors: Kwon Hyuksool , Kim Myeong-Gee , Oh SeokHwan , Kim Youngmin , Jung Guil , Lee Hyeon-Jik , Kim Sang-Yun , Bae Hyeon-Min
Published: June 2024

Non-alcoholic fatty liver disease (NAFLD), prevalent among conditions like obesity and diabetes, is globally significant. Existing ultrasound diagnosis methods, despite their use, often lack accuracy and precision, necessitating innovative solutions like AI. This study aims to validate an AI-enhanced quantitative ultrasound (QUS) algorithm for NAFLD severity assessment and compare its performance with Magnetic Resonance Imaging Proton Density Fat Fraction (MRI-PDFF), a conventional diagnostic tool. A single-center cross-sectional pilot study was conducted. Liver fat content was estimated using an AI-enhanced quantitative ultrasound attenuation coefficient (QUS-AC) of Barreleye Inc. with an AI-based QUS algorithm and two conventional ultrasound techniques, FibroTouch Ultrasound Attenuation Parameter (UAP) and Canon Attenuation Imaging (ATI). The results were compared with MRI-PDFF values. The intraclass correlation coefficient (ICC) was also assessed. Significant correlation was found between the QUS-AC and the MRI-PDFF, reflected by an R value of 0.95. On other hand, ATI and UAP displayed lower correlations with MRI-PDFF, yielding R values of 0.73 and 0.51, respectively. In addition, ICC for QUS-AC was 0.983 for individual observations. On the other hand, the ICCs for ATI and UAP were 0.76 and 0.39, respectively. Our findings suggest that AC with AI-enhanced QUS could serve as a valuable tool for the non-invasive diagnosis of NAFLD.
Rare Superior and Middle Trunk Fusion Accompanied by Altered Division Rearrangement Results in a Unique Brachial Plexus Variant: A Case Report
Authors: Schlüter Andreas Marco , Redl Konstantin , Tschernig Thomas , Maxeiner Stephan , Krasteva-Christ Gabriela
Published: June 2024

During routine dissections of cadavers as part of the medical curriculum, we identified a rare unilateral variation in the brachial plexus on the right side of a female body donor. This variation consisted of four unusual changes to the regular pattering of nerve bundles and the dorsal scapular artery permeating the complex neural network. The variation included contributions of root C4 to the plexus by a root C4/C5 anastomosis, a rare fusion of the superior and middle trunks to a ‘superomiddle’ trunk, a preliminary, proximal branching of the suprascapular nerve off the C5 root. We further observed an accessory ‘medial anterior division’ branching off the fused upper and middle trunks merging with the anterior division of the inferior trunk forming the medial cord. The latter event potentially introduced nerve fibers from C5 to C7, which are absent in common patterns. We aim to relate these observations to previous categorizations and quantifications of brachial plexus patterns. We believe that the combination of different variations in this case resulted in a unique pattern. Since this observation was made in the dissection class, we further aim to raise awareness among medical students and anatomical instructors for the likelihood of variations to textbook patterns. This will hopefully foster an appreciation of uniqueness and individuality in the interaction with future patients demonstrating that proper preparation prior to surgical interventions is always a necessary prerequisite.
Comparison between the Friedewald, Martin and Sampson Equations and LDL-C Quantification by Ultracentrifugation in a Mexican Population
Authors: Fuentevilla-Álvarez Giovanny , Soto María Elena , Valdivia José Antonio García , Torres-Paz Yazmín Estela , Sámano Reyna , Perez-Torres Israel , Gamboa-Ávila Ricardo , Huesca-Gómez Claudia
Published: June 2024

Low-density lipoprotein cholesterol (LDL-C), which makes up about 70% of the cholesterol in the blood, is critical in the formation of arteriosclerotic plaques, increasing the risk of heart disease. LDL-C levels are estimated using Friedewald, Martin and Sampson equations, though they have limitations with high triglycerides. Our aim is to compare the effectiveness of these equations versus the ultracentrifugation technique in individuals with and without dyslipidemia and identify precision. There were 113 participants, 59 healthy controls and 54 dyslipidemic patients. Samples were collected after fasting. LDL-C was estimated using the Friedewald, Martin and Sampson equations. The purified LDL-C, ultracentrifugated and dialysized control group without dyslipidemia vs. patients with coronary artery disease (CAD) showed differences in age, HDL-C, triglycerides and glucose non-HDL-C (p = 0.001 in all). There were correlations in CGWD between ultracentrifugation and Sampson R-squared (R2) = 0.791. In the dyslipidemia control group, ultracentrifugation and Friedewald R2 = 0.911. In patients with CAD, correlation between ultracentrifugation and Sampson R2 = 0.892; Bland–Altman confirmed agreement in controls without dyslipidemia. The Martin and Sampson equations are interchangeable with ultracentrifugation. Conclusion: The role of LDL analysis using precise techniques is necessary to obtain better control of disease outcomes after the use of precise therapies and suggests verifying its importance through clinical trials.
Assessing Lung Fibrosis with ML-Assisted Minimally Invasive OCT Imaging
Authors: Steinberg Rebecca , Meehan Jack , Tavrow Doran , Maguluri Gopi , Grimble John , Primrose Michael , Iftimia Nicusor
Published: June 2024

This paper presents a combined optical coherence tomography (OCT) imaging/machine learning (ML) technique for real-time analysis of lung tissue morphology to determine the presence and level of invasiveness of idiopathic lung fibrosis (ILF). This is an important clinical problem as misdiagnosis is common, resulting in patient exposure to costly and invasive procedures and substantial use of healthcare resources. Therefore, biopsy is needed to confirm or rule out radiological findings. Videoscopic-assisted thoracoscopic wedge biopsy (VATS) under general anesthesia is typically necessary to obtain enough tissue to make an accurate diagnosis. This kind of biopsy involves the placement of several tubes through the chest wall, one of which is used to cut off a piece of lung to send for evaluation. The removed tissue is examined histopathologically by microscopy to confirm the presence and the pattern of fibrosis. However, VATS pulmonary biopsy can have multiple side effects, including inflammation, tissue morbidity, and severe bleeding, which further degrade the quality of life for the patient. Furthermore, the results are not immediately available, requiring tissue processing and analysis. Here, we report an initial attempt of using ML-assisted polarization sensitive OCT (PS-OCT) imaging for lung fibrosis assessment. This approach has been preliminarily tested on a rat model of lung fibrosis. Our preliminary results show that ML-assisted PS-OCT imaging can detect the presence of ILF with an average of 77% accuracy and 89% specificity.
Prediction Value of Initial Serum Levels of SERPINA3 in Intracranial Pressure and Long-Term Neurological Outcomes in Traumatic Brain Injury
Authors: Tan Haoyuan , Wang Jiamian , Li Fengshi , Peng Yidong , Lan Jin , Zhang Yuanda , Zhao Dongxu , Bao Yinghui
Published: June 2024

Traumatic brain injury (TBI) is a severe neurological condition characterized by inflammation in the central nervous system. SERPINA3 has garnered attention as a potential biomarker for assessing this inflammation. Our study aimed to explore the predictive value of postoperative serum SERPINA3 levels in identifying the risk of cerebral edema and its prognostic implications in TBI. This study is a prospective observational study, including 37 patients with TBI who finally met our criteria. The Glasgow Outcome Scale (GOS), Levels of Cognitive Functioning (LCF), Disability Rating Scale (DRS), and Early Rehabilitation Barthel Index (ERBI) scores at six months after trauma were defined as the main study endpoint. We further calculated the ventricle-to-intracranial-volume ratio (VBR) at 6 months from CT scans. The study included patients with Glasgow Coma Scale (GCS) scores ranging from 3 to 8, who were subsequently categorized into two groups: the critical TBI group (GCS 3–5 points) and the severe TBI group (GCS 6–8 points). Within the critical TBI group, SERPINA3 levels were notably lower. However, among patients with elevated SERPINA3 levels, both the peak intracranial pressure (ICP) and average mannitol consumption were significantly reduced compared with those of patients with lower SERPINA3 levels. In terms of the 6-month outcomes measured via the GOS, LCF, DRS, and ERBI, lower levels of SERPINA3 were indicative of poorer prognosis. Furthermore, we found a negative correlation between serum SERPINA3 levels and the VBR. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) demonstrated the predictive performance of SERPINA3. In conclusion, incorporating the novel biomarker SERPINA3 alongside traditional assessment tools offers neurosurgeons an effective and easily accessible means, which is readily accessible early on, to predict the risk of intracranial pressure elevation and long-term prognosis in TBI patients.
Predictive Factors of Immunotherapy in Gastric Cancer: A 2024 Update
Authors: Bintintan Vasile , Burz Claudia , Pintea Irena , Muntean Adriana , Deleanu Diana , Lupan Iulia , Samasca Gabriel
Published: June 2024

Many studies on gastric cancer treatment have identified predictors of immunotherapy benefits. This article provides an update on the major developments in research related to predictive factors of immunotherapy for gastric cancer. We used the search term “predictive factors, immunotherapy, gastric cancer” to find the most current publications in the PubMed database related to predictive factors of immunotherapy in gastric cancer. Programmed cell death, genetic, and immunological factors are the main study topics of immunotherapy’s predictive factors in gastric cancer. Other preventive factors for immunotherapy in gastric cancer were also found, including clinical factors, tumor microenvironment factors, imaging factors, and extracellular factors. Since there is currently no effective treatment for gastric cancer, we strongly propose that these studies be prioritized.
Utilizing Aerobic Capacity Data for EDSS Score Estimation in Multiple Sclerosis: A Machine Learning Approach
Authors: Tuncer Seda Arslan , Danacı Cagla , Bilek Furkan , Demir Caner Feyzi , Tuncer Taner
Published: June 2024

The Expanded Disability Status Scale (EDSS) is the most popular method to assess disease progression and treatment effectiveness in patients with multiple sclerosis (PwMS). One of the main problems with the EDSS method is that different results can be determined by different physicians for the same patient. In this case, it is necessary to produce autonomous solutions that will increase the reliability of the EDSS, which has a decision-making role. This study proposes a machine learning approach to predict EDSS scores using aerobic capacity data from PwMS. The primary goal is to reduce potential complications resulting from incorrect scoring procedures. Cardiovascular and aerobic capacity parameters of individuals, including aerobic capacity, ventilation, respiratory frequency, heart rate, average oxygen density, load, and energy expenditure, were evaluated. These parameters were given as input to CatBoost, gradient boosting (GBM), extreme gradient boosting (XGBoost), and decision tree (DT) machine learning methods. The most significant EDSS results were determined with the XGBoost algorithm. Mean absolute error, root mean square error, mean square error, mean absolute percent error, and R square values were obtained as 0.26, 0.4, 0.26, 16, and 0.68, respectively. The XGBoost based machine learning technique was shown to be effective in predicting EDSS based on aerobic capacity and cardiovascular data in PwMS.
Hyperspectral Imaging Detects Clitoral Vascular Issues in Gender-Affirming Surgery
Authors: Schulz Torsten , Köhler Hannes , Kohler Lukas Herbert , Langer Stefan , Nuwayhid Rima
Published: June 2024

The aim of this study was to assess the efficacy of hyperspectral imaging (HSI) as an intraoperative perfusion imaging modality during gender affirmation surgery (GAS). The hypothesis posited that HSI could quantify perfusion to the clitoral complex, thereby enabling the prediction of either uneventful wound healing or the occurrence of necrosis. In this non-randomised prospective clinical study, we enrolled 30 patients who underwent GAS in the form of vaginoplasty with the preparation of a clitoral complex from 2020 to 2024 and compared patients’ characteristics as well as HSI data regarding clitoris necrosis. Individuals demonstrating uneventful wound healing pertaining to the clitoral complex were designated as Group A. Patients with complete necrosis of the neo-clitoris were assigned to Group B. Patient characteristics were collected and subsequently a comparative analysis carried out. No significant difference in patient characteristics was observed between the two groups. Necrosis occurred when both StO2 and NIR PI parameters fell below 40%. For the simultaneous occurrence of StO2 and NIR PI of 40% or less, a sensitivity of 92% and specificity of 72% was calculated. Intraoperatively, the onset of necrosis in the clitoral complex can be reliably predicted with the assistance of HSI.