bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Clinical Validation for Early Cardiovascular Disease Prediction through Simulation, AI, and Web Technology
Authors: Sufian Md Abu , Hamzi Wahiba , Zaman Sadia , Alsadder Lujain , Hamzi Boumediene , Varadarajan Jayasree , Azad Md Abul Kalam
Published: June 2024

Cardiovascular diseases (CVDs) remain a major global health challenge and a leading cause of mortality, highlighting the need for improved predictive models. We introduce an innovative agent-based dynamic simulation technique that enhances our AI models’ capacity to predict CVD progression. This method simulates individual patient responses to various cardiovascular risk factors, improving prediction accuracy and detail. Also, by incorporating an ensemble learning model and interface of web application in the context of CVD prediction, we developed an AI dashboard-based model to enhance the accuracy of disease prediction and provide a user-friendly app. The performance of traditional algorithms was notable, with Ensemble learning and XGBoost achieving accuracies of 91% and 95%, respectively. A significant aspect of our research was the integration of these models into a streamlit-based interface, enhancing user accessibility and experience. The streamlit application achieved a predictive accuracy of 97%, demonstrating the efficacy of combining advanced AI techniques with user-centered web applications in medical prediction scenarios. This 97% confidence level was evaluated by Brier score and calibration curve. The design of the streamlit application facilitates seamless interaction between complex ML models and end-users, including clinicians and patients, supporting its use in real-time clinical settings. While the study offers new insights into AI-driven CVD prediction, we acknowledge limitations such as the dataset size. In our research, we have successfully validated our predictive proposed methodology against an external clinical setting, demonstrating its robustness and accuracy in a real-world fixture. The validation process confirmed the model’s efficacy in the early detection of CVDs, reinforcing its potential for integration into clinical workflows to aid in proactive patient care and management. Future research directions include expanding the dataset, exploring additional algorithms, and conducting clinical trials to validate our findings. This research provides a valuable foundation for future studies, aiming to make significant strides against CVDs.
Enhancing Lung Cancer Survival Prediction: 3D CNN Analysis of CT Images Using Novel GTV1-SliceNum Feature and PEN-BCE Loss Function
Authors: Tas Muhammed Oguz , Yavuz Hasan Serhan
Published: June 2024

Lung cancer is a prevalent malignancy associated with a high mortality rate, with a 5-year relative survival rate of 23%. Traditional survival analysis methods, reliant on clinician judgment, may lack accuracy due to their subjective nature. Consequently, there is growing interest in leveraging AI-based systems for survival analysis using clinical data and medical imaging. The purpose of this study is to improve survival classification for lung cancer patients by utilizing a 3D-CNN architecture (ResNet-34) applied to CT images from the NSCLC-Radiomics dataset. Through comprehensive ablation studies, we evaluate the effectiveness of different features and methodologies in classification performance. Key contributions include the introduction of a novel feature (GTV1-SliceNum), the proposal of a novel loss function (PEN-BCE) accounting for false negatives and false positives, and the showcasing of their efficacy in classification. Experimental work demonstrates results surpassing those of the existing literature, achieving a classification accuracy of 0.7434 and an ROC-AUC of 0.7768. The conclusions of this research indicate that the AI-driven approach significantly improves survival prediction for lung cancer patients, highlighting its potential for enhancing personalized treatment strategies and prognostic modeling.
The Association between Echocardiographic Parameters of Heart Failure with Preserved Ejection Fraction and Fluid Status Biomarkers in Hemodialysis Patients
Authors: Lupa Mariusz , Pardała Agnieszka , Bednarek Anna , Mrochem-Kwarciak Jolanta , Deja Regina , Mizia-Stec Katarzyna , Kolonko Aureliusz
Published: June 2024

Overhydration and cardiac function abnormalities are common in hemodialysis patients. The association of N-terminal prohormone for brain natriuretic peptide (NT-proBNP) and other fluid status biomarkers with echocardiographic parameters of heart failure with preserved ejection fraction (HFpEF) is scarcely investigated in this population. A total of 100 separate measurements performed in 50 dialysis patients (29 male, aged 60 ± 17 years) in NYHA class II/II and preserved left ventricle ejection fraction were analyzed. Plasma levels of NT-proBNP, mid-regional prohormone for atrial natriuretic peptide (MR-proANP) and copeptin (CPP) were measured. The E/e’ ratio as an index of HFpEF and other echocardiographic parameters were calculated. An E/e’ ratio >9 was associated with higher median right ventricular systolic pressure (RVSP) and LVMI values. Left atrium volume index (LAVI) as well as NT-proBNP and MR-proANP, but not CPP levels were significantly higher in this group. In a stepwise multivariate analysis, only CPP and IL-6 levels were found to be independently associated with the E/e’ ratio in the study group, whereas NT-proBNP and MR-proANP were associated only with left heart structure parameters and LVEF. Of the analyzed biomarkers, only the CPP level was found to be independently associated with the E/e’ ratio in maintenance hemodialysis patients.
Presepsin in Critical Illness: Current Knowledge and Future Perspectives
Authors: Formenti Paolo , Gotti Miriam , Palmieri Francesca , Pastori Stefano , Roccaforte Vincenzo , Menozzi Alessandro , Galimberti Andrea , Umbrello Michele , Sabbatini Giovanni , Pezzi Angelo
Published: June 2024

The accurate identification of infections is critical for effective treatment in intensive care units (ICUs), yet current diagnostic methods face limitations in sensitivity and specificity, alongside cost and accessibility issues. Consequently, there is a pressing need for a marker that is economically feasible, rapid, and reliable. Presepsin (PSP), also known as soluble CD14 subtype (sCD14-ST), has emerged as a promising biomarker for early sepsis diagnosis. PSP, derived from soluble CD14, reflects the activation of monocytes/macrophages in response to bacterial infections. It has shown potential as a marker of cellular immune response activation against pathogens, with plasma concentrations increasing during bacterial infections and decreasing post-antibiotic treatment. Unlike traditional markers such as procalcitonin (PCT) and C-reactive protein (CRP), PSP specifically indicates monocyte/macrophage activation. Limited studies in critical illness have explored PSP’s role in sepsis, and its diagnostic accuracy varies with threshold values, impacting sensitivity and specificity. Recent meta-analyses suggest PSP’s diagnostic potential for sepsis, yet its standalone effectiveness in ICU infection management remains uncertain. This review provides a comprehensive overview of PSP’s utility in ICU settings, including its diagnostic accuracy, prognostic value, therapeutic implications, challenges, and future directions.
Taste and Smell Alterations (TSAs) in Cancer Patients
Authors: Rosati Davide , Mastino Pierluigi , Romeo Martina , de Soccio Giulia , Pentangelo Daniele , Petrella Carla , Barbato Christian , Minni Antonio
Published: June 2024

Recently, smell and taste disorders have seen renewed interest, as these symptoms are frequent complications of SARS-CoV-2 infection, since approximately 60% of patients affected by COVID-19 have shown olfactory and gustatory alterations. Otolaryngology pays attention to taste and smell abnormalities (TSAs), especially when associated with oncology. TSAs are common symptoms in people affected by cancer, yet they are ignored and underestimated. The clinical outcome of TSAs in cancer evidences the importance of identifying them with chemotherapy or radiotherapy in general, and they are associated with many types of cancer. We recognize the findings of the literature on TSAs in cancer, evaluating how it is important to consider and identify these disorders concerning reduced food enjoyment or inappropriate nutrient intake, and modulating the nutritional status, quality of life, and impact of therapy. This review aims to critically evaluate and recognize the assessment and clinical perspectives of taste and smell disorders in a cancer population.
Neural Network and Extended State Observer-Based Model Predictive Control for Smooth Braking at Preset Points in Autonomous Vehicles
Authors: Chen Jianlin , Xu Yang , Zheng Zixuan
Published: June 2024

In this paper, we explore the problem of smooth braking at preset points in autonomous vehicles using model predictive control (MPC) with a receding horizon extended state observer (RHESO) and a neural network (NN). An NN-based modeling method is proposed to intuitively describe the relationship between vehicle speed and the vehicle controllers (brake and throttle), and establish a dynamic model of autonomous vehicles. A sufficient condition is put forward to guarantee the convergence of the proposed NN. Furthermore, a composite MPC strategy based on RHESO is designed, which optimizes a given cost function over the receding horizon while mitigating the effects of modeling inaccuracies and disturbances. Additionally, easily verifiable conditions are provided to ensure the autonomous driving vehicles’ uniform boundedness. Numerically illustrative examples are given to demonstrate the effectiveness of the proposed approach.
The Impact of Online Interactive Teaching on University Students’ Deep Learning—The Perspective of Self-Determination
Authors: Zhou Qingyi , Zhang Hongfeng , Li Fanbo
Published: June 2024

In the process of educational practice in the digital age, the higher education system has shifted to the online model, and the training of college students’ deep learning has become the core issue. While online teaching offers great possibilities for education, its inherent lack of interaction has always been a major limiting factor in its effectiveness. To address this challenge, interactive pedagogy is an interaction-based teaching model in which students learn knowledge more effectively through communication and interaction with teachers and classmates, which helps to improve students’ learning abilities. This study, rooted in the theoretical framework of self-determination theory and utilizing structural equation modeling for empirical analysis, seeks to investigate the influence of online interactive teaching on deep learning among university students. The research reveals that interactive teaching significantly and directly contributes to deep learning, while the fulfillment of university students’ three fundamental psychological needs indirectly fosters deep learning by fostering intrinsic motivation. This study uncovers novel dimensions of factors influencing deep learning and underscores the vital role of interactive teaching in fostering deep learning among university students. Moreover, this not only deepens the application of self-determination theory in education but also provides valuable theoretical support for online education practices.
5G Reconfigurable Intelligent Surface TDOA Localization Algorithm
Authors: Liu Changbao , Zhang Yuexia
Published: June 2024

In everyday life, 5G-based localization technology is commonly used, but non-line-of-sight (NLOS) environments can block the propagation of the localization signal, thus preventing localization. In order to solve this problem, this paper proposes a reconfigurable intelligent surface non-line-of-sight time difference of arrival (TDOA) localization (RNTL) algorithm. Firstly, a model of a reflective-surface-based intelligent localization (RBP) system is constructed, which utilizes multiple RISs deployed in the air to reflect signals. Secondly, in order to reduce the localization error, this paper establishes the optimization problem of minimizing the distance between each estimated coordinate and the actual coordinate and solves it via the piecewise linear chaotic map–gray wolf optimization algorithm (PWLCM-GWO). Finally, the simulation results show that the RNTL algorithm significantly outperforms the traditional gray wolf optimization and particle swarm optimization algorithms in different signal-to-noise ratios, and the localization errors are reduced by 46% and 53.5%, respectively.
Data-Driven Machine Fault Diagnosis of Multisensor Vibration Data Using Synchrosqueezed Transform and Time-Frequency Image Recognition with Convolutional Neural Network
Authors: Łuczak Dominik
Published: June 2024

Accurate vibration classification using inertial measurement unit (IMU) data is critical for various applications such as condition monitoring and fault diagnosis. This study proposes a novel convolutional neural network (CNN) based approach, the IMU6DoF-SST-CNN in six variants, for robust vibration classification. The method utilizes Fourier synchrosqueezed transform (FSST) and wavelet synchrosqueezed transform (WSST) for time-frequency analysis, effectively capturing the temporal and spectral characteristics of the vibration data. Additionally, was used the IMU6DoF-SST-CNN to explore three different fusion strategies for sensor data to combine information from the IMU’s multiple axes, allowing the CNN to learn from complementary information across various axes. The efficacy of the proposed method was validated using three datasets. The first dataset consisted of constant fan velocity data (three classes: idle, normal operation, and fault) at 200 Hz. The second dataset contained variable fan velocity data (also with three classes: normal operation, fault 1, and fault 2) at 2000 Hz. Finally, a third dataset of Case Western Reserve University (CWRU) comprised bearing fault data with thirteen classes, sampled at 12 kHz. The proposed method achieved a perfect validation accuracy for the investigated vibration classification task. While all variants of the method achieved high accuracy, a trade-off between training speed and image generation efficiency was observed. Furthermore, FSST demonstrated superior localization capabilities compared to traditional methods like continuous wavelet transform (CWT) and short-time Fourier transform (STFT), as confirmed by image representations and interpretability analysis. This improved localization allows the CNN to effectively capture transient features associated with faults, leading to more accurate vibration classification. Overall, this study presents a promising and efficient approach for vibration classification using IMU data with the proposed IMU6DoF-SST-CNN method. The best result was obtained for IMU6DoF-SST-CNN with FSST and sensor-type fusion.
Enhancing Steganography through Optimized Quantization Tables
Authors: Brūzgienė Rasa , Venčkauskas Algimantas , Grigaliūnas Šarūnas , Petraška Jonas
Published: June 2024

This paper addresses the scientific problem of enhancing the security and capacity of steganographic methods for protecting digital media. The primary aim is to develop an advanced steganographic technique that optimizes quantization tables to surpass the traditional F5 algorithm in terms of security, capacity, and robustness. The novelty of this research lies in the introduction of the F5A method, which utilizes optimized quantization tables to significantly increase the capacity for concealed information while ensuring high-quality image retention and resistance to unauthorized content recovery. The F5A method integrates cryptographic keys and features to detect and prevent copyright infringement in real time. Experimental evaluations demonstrate that the F5A method improves the mean square error and peak signal-to-noise ratio indices by 1.716 and 1.121 times, respectively, compared to the traditional F5 algorithm. Additionally, it increases the steganographic capacity by up to 1.693 times for smaller images and 1.539 times for larger images. These results underscore the effectiveness of the F5A method in enhancing digital media security and copyright protection.
Adaptive Multi-Surface Sliding Mode Control with Radial Basis Function Neural Networks and Reinforcement Learning for Multirotor Slung Load Systems
Authors: Peris Clevon , Norton Michael , Khoo Suiyang
Published: June 2024

While using multirotor UAVs for transport of suspended payloads, there is a need for stability along the desired path, in addition to avoidance of any excessive payload oscillations, and a good level of precision in maintaining the desired path of the vehicle. However, due to the nonlinear and underactuated nature of the system, in addition to the presence of mismatched uncertainties, the development of a control system for this application poses an interesting research problem. This paper proposes a control architecture for a multirotor slung load system by integrating a Multi-Surface Sliding Mode Control, aided by a Radial Basis Function Neural Network, with a Deep Q-Network Reinforcement Learning agent. The former will be used to ensure asymptotic tracking stability, while the latter will be used to suppress payload oscillations. First, we will present the dynamics of a multirotor slung load system, represented here as a quadrotor with a single pendulum load suspended from it. We will then propose a control method in which a multi-surface sliding mode controller, based on an adaptive RBF Neural Network for trajectory tracking of the quadrotor, works in tandem with a Deep Q-Network Reinforcement Learning agent whose reward function aims to suppress the oscillations of the single pendulum slung load. Simulation results demonstrate the effectiveness and potential of the proposed approach in achieving precise and reliable control of multirotor slung load systems.
Future Green Energy: A Global Analysis
Authors: Finecomess Sairoel Amertet , Gebresenbet Girma
Published: June 2024

The main problem confronting the world is human-caused climate change, which is intrinsically linked to the need for energy both now and in the future. Renewable (green) energy has been proposed as a future solution, and many renewable energy technologies have been developed for different purposes. However, progress toward net zero carbon emissions by 2050 and the role of renewable energy in 2050 are not well known. This paper reviews different renewable energy technologies developed by different researchers and their potential and challenges to date, and it derives lessons for world and especially African policymakers. According to recent research results, the mean global capabilities for solar, wind, biogas, geothermal, hydrogen, and ocean power are 325 W, 900 W, 300 W, 434 W, 150 W, and 2.75 MWh, respectively, and their capacities for generating electricity are 1.5 KWh, 1182.5 KWh, 1.7 KWh, 1.5 KWh, 1.55 KWh, and 3.6 MWh, respectively. Securing global energy leads to strong hope for meeting the Sustainable Development Goals (SDGs), such as those for hunger, health, education, gender equality, climate change, and sustainable development. Therefore, renewable energy can be a considerable contributor to future fuels.
Optimized Fabrication Process and PD Characteristics of MVDC Multilayer Insulation Cable Systems for Next Generation Wide-Body All-Electric Aircraft
Authors: Rahman Md Asifur , Saha Anoy , Ghassemi Mona
Published: June 2024

For wide-body all-electric aircraft (AEA), a high-power-delivery, low-system-mass electric power system (EPS) necessitates advanced cable technologies. Increasing voltage levels enhances power density yet poses challenges in aircraft cable design, including managing arc-related risks, partial discharges (PDs), and thermal management. Developing multilayer multifunctional electrical insulation (MMEI) systems for aircraft applications is a feasible option to tackle these challenges and reduce the size and mass of cable systems. This approach involves selecting layers of different materials to address specific challenges. Our prior research concentrated on the modeling and simulation-based design of MMEI systems for MVDC power cables. Experimental tests are essential for determining the behavior of PDs under varying pressure conditions. Also, the dielectric strength and time to failure of the designs need to be assessed. In this work, the fabrication process of a down-selected MMEI flat configuration is discussed and analyzed. This paper analyzes the fabrication process of power cables employing MMEI configurations and evaluates the PD characteristics of down-selected ARC-SC-T-MMEI cable samples. This study presents a detailed analysis of the characteristics of PD under atmospheric and low-pressure conditions, which will provide essential insights into the design of MVDC cables for future AEA applications.
Simulation of Power Generation System with Co-Combustion of Coal and Torrefied Biomass by Flue Gas
Authors: Song Chunshuo , Guo Ning , Ren Fengying , Ren Xiaohan
Published: June 2024

At present, there is a global rise in electricity consumption, leading to an accelerated depletion of natural resources due to the reliance on fossil fuels to fulfill this energy demand. Consequently, there exists a worldwide emphasis on enhancing the proportion of renewable energy sources in electricity generation. Biomass, as a renewable energy source, presents a viable alternative to certain fossil energy sources for combustion in electricity generation. This study focuses on a 660 MW coal-fired power plant as the subject of investigation, employing Aspen Plus simulation software (V11) to replicate the operational dynamics of the plant. A model of the direct mixed combustion biomass system within the coal-fired boiler is constructed, and its accuracy is validated against operational data obtained from the power plant. Moreover, a model elucidating the direct co-combustion of biomass in a coal-fired boiler, augmented by flue gas recirculation, was developed through the integration of biomass pre-treatment and flue gas recirculation technologies. This study explores the impacts of varying biomass blending ratios and flue gas recirculation on parameters, including flue gas volume, power generation efficiency, boiler performance, water vapor content, and emissions of pollutants. These findings indicate an inverse relationship between the mixing ratio and various performance metrics as follows: power generation, boiler efficiency, as well as NOX and SO2 content, with larger mixing ratios resulting in diminished values. Furthermore, the incorporation of flue gas recirculation was observed to mitigate furnace temperatures and suppress NOX emissions.
The Degradation Prediction of Proton Exchange Membrane Fuel Cell Performance Based on a Transformer Model
Authors: Meng Xuan , Mei Jian , Tang Xingwang , Jiang Jinhai , Sun Chuanyu , Song Kai
Published: June 2024

Proton exchange membrane fuel cells have attracted widespread attention due to their cleanliness and high energy density, but the performance degradation during operation greatly limits their commercialization. Therefore, the reliable degradation prediction of fuel cell performance is of great significance. The recovery phenomenon of the reversible voltage loss that occurs during the operation of fuel cells has posed great difficulties for model training and prediction. Moreover, the models may easily and erroneously learn the combined trends in the recovery of reversible voltage loss and performance degradation. To address this issue, this paper employs the Transformer model to predict the performance degradation of fuel cells. By utilizing the unique self-attention structure and masking mechanism of the Transformer model, the signal for the recovery of the reversible voltage loss is adopted as the input for the model to avoid interference from information before voltage recovery on subsequent predictions. Experimental results show that the model has the highest prediction accuracy at various prediction starting points. Meanwhile, it can predict the accelerated performance degradation of fuel cells, which has positive implications for health management.
Unraveling the Role of Bacteria in Nitrogen Cycling: Insights from Leaf Litter Decomposition in the Knyszyn Forest
Authors: Khomutovska Nataliia , Jasser Iwona , Isidorov Valery A.
Published: June 2024

Microorganisms are vital in leaf litter decomposition and contribute significantly to global nutrient cycling. However, there is a need for improved understanding of the taxonomic and functional diversity of litter-associated bacteria. The Knyszyn Forest comprises a unique ecosystem providing diverse microhabitats for microorganisms in central Europe, similar to the southwestern taiga in many respects. This study presents the results of high-throughput sequencing performed for Betula pendula, B. pubescens, and Carpinus betulus litter-associated microbial communities from northern Poland. Microbial assemblage composition and structure at different stages of litter decomposition revealed the domination of phyllosphere-associated taxa of Sphingomonas and Pseudomonas in bacterial communities in the early stages. Meanwhile, at the later stages of decomposition, the representation of soil-associated bacterial communities, such as Pedobacter, was higher. This study identifies key bacteria (Pedobacter, Mucilaginibacter, and Luteibacter) as pivotal in nutrient cycling through cellulose and hemicellulose decomposition, dominating later decomposition phases. Taxonomic analysis based on functional markers associated with nitrogen metabolism highlights the pivotal role of specific Pseudomonadota (Proteobacteria) taxa in driving nitrogen cycling dynamics during litter decomposition. Most of these taxa were unclassified at the genus level, particularly in the later stages of litter decomposition, and are crucial in mediating nitrogen transformation processes, underscoring their significance in ecosystem nutrient cycling.
A Scientometric Analysis and Visualization of Forest Soil Contamination Research from Global Perspectives
Authors: Wu Qingyu , Cao Yifei , Yu Ting , Yang Jinglin , Fan Siyu , Feng Chonglin , Liu Zhiming , Huang Chao
Published: June 2024

Forest soil contamination is a significant problem with risks to ecosystems and human health. It reduces soil quality, hampers plant growth, and disrupts ecosystems. To understand current research and identify future directions, this study analyzed 2659 documents on forest soil contamination published on the Web of Science from 1970 to 2023. Using bibliometrics, this study systematically analyzed the knowledge structure, research hotspots, and development trends in forest soil pollution. China, the United States, and Poland were the top contributors, with 11.28%, 8.42%, and 7.15% of publications, respectively. Despite fewer publications, the Netherlands and Sweden had significant research influence. The Chinese Academy of Sciences had the most publications. The primary research topics included heavy metals, ecosystems, deposition, air pollution, and organic matter. Keyword cluster and burst analysis highlighted the importance of heavy metals, microbial communities, atmospheric deposition, and organic matter. Notably, microplastics emerged as a notable gap in the existing research by highly cited papers analysis, indicating they can be a future research focus. Overall, this paper provides a comprehensive analysis of forest soil contamination, offering insights into current research themes and emerging trends.
Growth, Metabolic, Hepatic and Redox Parameters, Digestive Enzymes and Innate Immunity in Mugil liza Fed a Citral-Supplemented Diet
Authors: Mori Natacha C. , Michelotti Bruna T. , Magnotti Caio C. F. , Bressan Caroline A. , Bianchin Letícia B. , Sutili Fernando J. , Almeida Ana Paula G. , Kreutz Luiz C. , Pavanato Maria A. , Cerqueira Vinicius R. , da Costa Silvio T. , Baldisserotto Bernardo , Heinzmann Berta M.
Published: June 2024

Mugil liza juveniles (6.69 ± 0.06 g) were subjected to dietary citral (0-control, 0.5, 1.0 and 2.0 mL per kg feed) for 45 days, and its possible effects on zootechnical and metabolic parameters, digestive enzymes, innate immunity, oxidative status and liver damage were evaluated. At the end of the experiment, fish fed 2.0 mL citral per kg feed showed a greater weight gain and protein retention efficiency, as well as enhanced activities of pepsin (stomach) and amylase (intestine) compared with control fish. Citral supplementation decreased liver lipoperoxidation and increased the activities of glutathione peroxidase, glutathione-S-transferase and superoxide dismutase in the gills, liver and brain. The highest level of citral inclusion augmented non-protein thiol content in the brain and gills. Myeloperoxidase activity was lower in fish offered 1.0 and 2.0 mL citral per kg feed. Dietary citral did not influence the plasma levels of aspartate aminotransferase and alanine aminotransferase or the hepatic histology. As it improved growth, the activity of digestive enzymes and general health, dietary citral may be recommended for M. liza at 2.0 mL per kg feed.
Exploring the Internal Defense System of Cerastoderma glaucum (Bruguière, 1789) Exposed to Pristine Microplastics: The Sentinel Role of Haemocytes as Biomarkers
Authors: Alesci Alessio , Di Paola Davide , Marino Sebastian , De Gaetano Federica , Albano Marco , Morgante Silvana , Rigano Gabriele , Giuffrè Letterio , Kotanska Magdalena , Spanò Nunziacarla , Lauriano Eugenia Rita
Published: June 2024

The ubiquitous presence of microplastics (MPs) in the environment is a major concern for the conservation of biodiversity. Numerous studies show the detrimental effects of MPs on marine species, especially because of their small size and their capacity to absorb organic contaminants from their surroundings. The bivalve mollusk Cerastoderma glaucum (Bruguière, 1789), because of its wide geographic distribution and immobile feeding habits, can be used as a sentinel and bioindicator species. By examining the presence, localization, and co-localization of Toll-like receptor 2 (TLR2) and inducible nitric oxide synthetase (iNOS), this study aims to evaluate the response of the internal defense system of C. glaucum to pristine MPs through the employment of confocal microscopy and bioinformatics techniques. The results show haemocytes immunoreactive to the antibodies tested; in particular, a higher number of TLR2-positive haemocytes can be observed in the group exposed to pristine MPs. These findings suggest that haemocytes can play a key biomarker role as sentinels to environmental pollutants. Furthermore, bioinformatics analyses on the antibodies tested confirmed an evolutionary conservation of these molecules. These data highlight the critical role of phagocytosis in identifying ecosystem damage and are helpful in developing biosensors with less negative effects on the environments in which they are applied.
DDEYOLOv9: Network for Detecting and Counting Abnormal Fish Behaviors in Complex Water Environments
Authors: Li Yinjia , Hu Zeyuan , Zhang Yixi , Liu Jihang , Tu Wan , Yu Hong
Published: June 2024

Accurately detecting and counting abnormal fish behaviors in aquaculture is essential. Timely detection allows farmers to take swift action to protect fish health and prevent economic losses. This paper proposes an enhanced high-precision detection algorithm based on YOLOv9, named DDEYOLOv9, to facilitate the detection and counting of abnormal fish behavior in industrial aquaculture environments. To address the lack of publicly available datasets on abnormal behavior in fish, we created the “Abnormal Behavior Dataset of Takifugu rubripes”, which includes five categories of fish behaviors. The detection algorithm was further enhanced in several key aspects. Firstly, the DRNELAN4 feature extraction module was introduced to replace the original RepNCSPELAN4 module. This change improves the model’s detection accuracy for high-density and occluded fish in complex water environments while reducing the computational cost. Secondly, the proposed DCNv4-Dyhead detection head enhances the model’s multi-scale feature learning capability, effectively recognizes various abnormal fish behaviors, and improves the computational speed. Lastly, to address the issue of sample imbalance in the abnormal fish behavior dataset, we propose EMA-SlideLoss, which enhances the model’s focus on hard samples, thereby improving the model’s robustness. The experimental results demonstrate that the DDEYOLOv9 model achieves high Precision, Recall, and mean Average Precision (mAP) on the “Abnormal Behavior Dataset of Takifugu rubripes”, with values of 91.7%, 90.4%, and 94.1%, respectively. Compared to the YOLOv9 model, these metrics are improved by 5.4%, 5.5%, and 5.4%, respectively. The model also achieves a running speed of 119 frames per second (FPS), which is 45 FPS faster than YOLOv9. Experimental results show that the DDEYOLOv9 algorithm can accurately and efficiently identify and quantify abnormal fish behaviors in specific complex environments.
Evaluation of Cookies Enriched with Osmodehydrated Wild Garlic from Nutritional and Sensory Aspects
Authors: Filipović Vladimir , Nićetin Milica , Filipović Jelena , Stupar Alena , Kojić Jovana , Lončarević Ivana , Šobot Kosana , Laličić-Petronijević Jovanka
Published: June 2024

In the present study, the nutritional and sensory properties of spelt cookies without wild garlic, cookies with fresh wild garlic, cookies with osmodehydrated wild garlic in sugar beet molasses, and cookies with osmodehydrated wild garlic in an aqueous solution of sucrose and salt were evaluated and compared. The tested cookie samples were characterized in terms of total antioxidative activity, the total content of phenols, flavonoids, and thiosulfates, the presence of dominant phenols, the content of betaine and dietary fiber, antioxidant activity after in vitro digestion, and sensory attributes for appearance, taste, smell, and texture. The results proved that the addition of wild garlic leaves osmodehydrated in molasses provided the cookies with the best nutritional and bioactive properties: 1.75 times higher total phenols content, 2.4 times higher total flavonoids content, 1.52 times higher total thiosulfates content, and 1.56 times higher betaine content, and a total quality increase of 54% compared to the control cookies. The cookies enriched with osmodehydrated wild garlic in molasses were rated as pleasant and acceptable, but also more complex compared to other cookies. The production of this nutritionally and sensory-improved cookie would contribute to expanding the assortment of flour confectionery products, especially for consumers who care about health and nutrition.
Effect of Air Classification and Enzymatic and Microbial Bioprocessing on Defatted Durum Wheat Germ: Characterization and Use as Bread Ingredient
Authors: Longo Angela , Amendolagine Gianfranco , Miani Marcello Greco , Rizzello Carlo Giuseppe , Verni Michela
Published: June 2024

Its high dietary fiber and protein contents and nutritional quality make defatted wheat germ (DWG) a valuable cereal by-product, yet its negative impact on food structure limits its use as a food ingredient. In this research, DWG underwent air classification, which identified two fractions with high fiber (HF) and low fiber/high protein (LF) contents, and a bioprocessing protocol, involving treatment with xylanase and fermentation with selected lactic acid bacterial strains. The degree of proteolysis was evaluated through electrophoretic and chromatographic techniques, revealing differences among fractions and bioprocessing options. Fermentation led to a significant increase in free amino acids (up to 6 g/kg), further enhanced by the combination with xylanase. When HF was used as an ingredient in bread making, the fiber content of the resulting bread exceeded 3.6 g/100 g, thus reaching the threshold required to make a “source of fiber” claim according to Regulation EC No.1924/2006. Meanwhile, all breads could be labeled a “source of protein” since up to 13% of the energy was provided by proteins. Overall, bioprocessed ingredients lowered the glycemic index (84 vs. 89) and increased protein digestibility (80 vs. 63%) compared to control breads. Technological and sensory analysis showed that the enzymatic treatment combined with fermentation also conferred a darker and more pleasant color to the bread crust, as well as better crumb porosity and elasticity.
Prospective Analysis of Time-Fractional Emden–Fowler Model Using Elzaki Transform Homotopy Perturbation Method
Authors: Nadeem Muhammad , Iambor Loredana Florentina
Published: June 2024

The present study presents a combination of two famous analytical techniques for the analytical solutions of linear and nonlinear time-fractional Emden–Fowler models. We combine the Elzaki transform (ET) and the homotopy perturbation method (HPM) for the development of the Elzaki transform homotopy perturbation method (ET-HPM). In this paper, we demonstrate that the Elzaki transform (ET) simplifies fractional differential problems by transforming them into algebraic formulas within the transform space. On the other hand, the HPM has the ability to discretize the nonlinear terms in fractional problems. The fractional orders are considered in the Caputo sense. The main purpose of this strategy is to use an alternative approach that has never been employed in the time-fractional Emden–Fowler model. This strategy does not require any variable or hypothesis constraints that ruin the physical nature of the actual problem. The derived series yields a convergent series using the Taylor series formula. The analytical data and visual illustrations for several kinds of fractional orders validate the effectiveness of the suggested scheme. The significant results demonstrate that our recommended strategy is quick and simple to use on fractional problems.
Differentiation between Normal Cognition and Subjective Cognitive Decline in Older Adults Using Discrepancy Scores Derived from Neuropsychological Tests
Authors: López-Higes Ramón , Rubio-Valdehita Susana , Fernandes Sara M. , Rodrigues Pedro F. S.
Published: June 2024

Several studies have reported subtle differences in cognition between individuals with subjective cognitive decline (SCD) compared to those with normal cognition. This study aimed to (i) identify these differences using discrepancy scores (e.g., categorial–phonemic verbal fluency performance) derived from neuropsychological tests in three cognitive domains (memory: Wechsler’s Word List and Digits; executive functions: Stroop and verbal fluency; and language: BNT and ECCO_Senior) and (ii) determine which discrepancy scores are significant for classification. Seventy-five older adults were included: 32 who were labeled SCD+ (age 71.50 ± 5.29), meeting Jessen et al.’s criteria, and 43 in the normal cognition group (SCD−; age 69.81 ± 4.62). Both groups completed a protocol including screening and the specified neuropsychological tests. No differences were found between the groups in their age, education, episodic memory, global cognitive state, or mood. Significant differences between the groups were observed regarding the discrepancy scores derived from BNT (naming) and ECCO_Senior (sentence comprehension). These scores accurately classified participants (71.6%), with ECCO_Senior having a primary role. ROC curves indicated a poor-to-fair model quality or diagnostic accuracy (AUC_BNT = 0.690; AUC_ECCO = 0.722). In conclusion, discrepancy scores in the language domain are important for distinguishing between individuals with SCD and normal cognition, complementing previous findings in this domain. However, given their relatively poor diagnostic accuracy, they should be used with caution as part of a more detailed neuro-psychological assessment.
Sustainability of an Educational Program on Oral Care/Hygiene Provision by Healthcare Providers to Older Residents in Long-Term Care Institutions: A Follow-Up Study
Authors: Wong Florence M. F. , Leung Wai Keung
Published: June 2024

Background: The importance of oral health in older adults, especially those in long-term care institutions (LTCIs), has been widely recognized. This study aimed to evaluate the sustainability of an oral health educational program (OHEP) for healthcare providers by measuring changes in their knowledge, attitudes, and practice (KAP) towards oral care provision 3 and 6 months after completing the OHEP. Methods: A pragmatic direct care nursing education trial with a control group was conducted to evaluate the sustainability of an OHEP by examining changes in KAP 3 and 6 months after the OHEP. The OHEP comprised both knowledge and skills related to oral care, whereas the control group received standard support in accordance with usual oral care practice. Results: The study included 20 healthcare providers in the intervention group and 20 in the control group. At 6 months post-OHEP, a significant difference in knowledge was observed between the two groups, with the intervention group maintaining a positive effect (mean 13.90). Conversely, the control group showed a significant decline in knowledge (from mean 14.25 to 12.10). Both groups showed an improvement in attitudes regarding oral health, with the intervention group exhibiting better results 3 months post-OHEP. Intervention group participants rated oral care as a higher priority. Conclusions: An OHEP program for LTCI direct care staff provides enhanced knowledge and attitudes toward oral health care. Regular training in direct care and additional support may be needed to sustain optimal effects on oral care practice.
What Is a Disease for Doctors? A Realist Thematic Qualitative Analysis of the Interpretation of Clinical Vignettes
Authors: Consorti Fabrizio , Melcarne Rossella , Pisanelli Domenico , Scorziello Chiara , Giacomelli Laura
Published: June 2024

Given the long-standing debate about the nature of the concept of disease, the objective of this study was to understand how doctors categorize a condition as a disease or not, and what the kind of information they use is. A survey with a set of eighteen clinical vignettes was designed, and nineteen physicians and senior students purposefully selected were asked to interpret those situations as diseases or not and to produce an anonymous short written piece of text providing the motivation of their choice. Realist thematic analysis was used to analyse the answers, and four themes emerged: the temporal dimension of a disease, reification of disease, disease as an existential condition, and disease as a motivation to action. The respondents’ interpretations were very heterogeneous, supporting the idea that physicians do not share a common prototypical concept of disease. The results suggested that the interpretation of a condition as a disease or not is the final outcome of a process, in which information from objective, subjective, and socially mediated elements is taken into consideration. According to a critical realist and systemic approach, we hypothesize that the context of doctor–patient relationship could influence the interpretation of the same condition as being a disease or not.
The Need to Look at Transgender and Gender Diverse People’s Health: A Preliminary Descriptive Report on Pain, Sexual Distress, and Health Profile of Five Transmasculine People and One Non-Binary Person with Endometriosis
Authors: Carvalho Sérgio A. , Lapa Teresa , Pascoal Patrícia M.
Published: June 2024

The sexual health of transgender and gender diverse (TGD) people with endometriosis has been overlooked, and important emotional experiences, such as sexual distress and its correlates, have been ignored. This has prevented a more comprehensive look at the health experiences of TGD individuals. This descriptive online survey study preliminarily explored the experiences of pain symptoms, sexual distress, and mental health of N = 6 TGD individuals diagnosed with endometriosis. Descriptive results showed a mean delay of 10 years from the onset of symptoms to the diagnosis. Endometriosis-related pain was a common symptom, although with low to moderate intensity. Results also showed higher mean levels of pain impact, powerlessness and lack of control, somatization, depression, anxiety, and sexual distress, and lower mean levels of emotional well-being, social support, and worse self-image compared to reports on cisgender women with endometriosis in the literature. These results suggested that sexual and mental health in the context of TGD people with endometriosis has specificities and may be associated with factors that need to be accounted for to provide comprehensive and socially just healthcare, such as the recognition of the impact of endometriosis treatment on symptoms of gender dysphoria. To achieve sexual health equity for TGD people, continuous and updated professional training and inclusive research with multiple informants are necessary.
Attitudes, Self-Confidence, and Knowledge of Primary Care Professionals towards School Bullying
Authors: Celdrán-Navarro María del Carmen , Jiménez-Ruiz Ismael , Leal-Costa César , Moore James R. , López-Barranco Pedro José
Published: June 2024

Attitudes, practices, and knowledge about bullying were evaluated in a sample of 274 primary care professionals, including general practitioners, pediatricians, community, pediatric and school nurses, and residents of these specialties. This study was based on a mixed method with a parallel convergent design without dominance between phases, data were collected concurrently, and conversion of the results from both phases was carried out during data interpretation. The quantitative phase had a cross-sectional observational design, using The Healthcare Provider’s Practices, Attitudes, Self-confidence, and Knowledge Regarding Bullying Questionnaire as an instrument. Descriptive and bivariate analyses were performed, which showed a positive correlation between higher self-confidence and knowledge scores and a greater predisposition to detect cases. However, although the dimensions of attitudes and knowledge yielded generally high data, low self-confidence was evident in addressing this problem. In addition, a lack of clear guidelines in the workplace was expressed, highlighting the need to create and provide specific resources to intervene in bullying in said context, which could develop an improvement in self-confidence, leading to greater well-being for the educational community regarding bullying.
Exercise Effects on Autonomic Nervous System Activity in Type 2 Diabetes Mellitus Patients over Time: A Meta-Regression Study
Authors: Chiang Jui-Kun , Chiang Po-Chen , Kao Hsueh-Hsin , You Weir-Chiang , Kao Yee-Hsin
Published: June 2024

Background: Diabetic autonomic neuropathy is a common complication of type 2 diabetes mellitus (T2DM), especially in patients with long-term, poorly controlled diabetes. This study investigates the effects of exercise on autonomic nervous system activity in T2DM patients over time. Methods: A literature review using MEDLINE, Embase, Cochrane Library, Scopus, and PubMed identified studies assessed via heart rate variability. Papers were categorized into three groups: immediate effects (within 60 min), short-term effects (2–3 months), and long-term effects (over 4 months). Results: Nine articles with 161 T2DM patients were included in the meta-analysis. RMSSD changes after exercise were −4.3 (p = 0.227), 8.14 (p < 0.001), and 4.17 (p = 0.002) for the immediate, short-term, and long-term groups, respectively. LF/HF ratio changes were 0.21 (p = 0.264), −3.04 (p = 0.102), and −0.05 (p = 0.006) for the respective groups. Meta-regression indicated age, male gender, and exercise duration were associated with increased RMSSD, with coefficients of 2.36 (p = 0.001), 13.76 (p = 0.008), and 1.50 (p = 0.007), respectively. Age positively correlated with the LF/HF ratio, with a coefficient of 0.049 (p = 0.048). Conclusions: Regular exercise (≥3 times per week) for over 2 months increases parasympathetic activity in T2DM patients, while sympathetic activity decreases significantly after 4 months. Further study is needed to validate these findings.