bluuebunny/crossref_metadata_2025 huggingface dataset:
Percentile Values of Specific Physical Performances in Tunisian Basketball Players Aged 9 to 21: Considering Maturity Status
Authors: Aouichaoui Chirine , Tounsi Mohamed , Racil Ghazi , Padulo Johnny , Martone Domenico , Trabelsi Yassine
Published: November 2024

Background: Success in basketball is influenced by various factors, including biological maturation. Peak height velocity (PHV) is a key indicator of maturation, playing an important role in assessing the performance of basketball players. This study aimed to analyze the effects of maturational status, chronological age, and gender on anthropometric characteristics and physical performance parameters in Tunisian basketball players and to establish local percentile reference values for physical performance. Methods: A total of 470 basketball players (240 males and 230 females) were categorized into three maturity status groups: pre-PHV (n = 111), circa-PHV (n = 170), and post-PHV (n = 189). Anthropometric and physical fitness parameters were assessed to provide percentile reference values. Results: Our findings revealed significantly higher anthropometric and physical performance values in the post-PHV athletes compared to those in the pre- and circa-PHV groups. Additionally, male athletes outperformed females in most measured variables. Basketball-specific skills appeared to be more dependent on chronological age than on pubertal status. Conclusions: Percentile values were established for both boys and girls, offering valuable references for trainers to quantify and individualize training programs. These findings may assist practitioners in identifying potentially talented basketball players based on their maturation status.
Fault Diagnosis of Lithium-Ion Batteries Based on the Historical Trajectory of Remaining Discharge Capacity
Authors: Jiang Jiuchun , Qu Bingrui , Liu Shuaibang , Yan Huan , Zhang Zhen , Chang Chun
Published: November 2024

In recent years, the number of safety accidents in new-energy electric vehicles due to lithium-ion battery failures has been increasing, and the lithium-ion battery fault diagnosis technology is particularly important to ensure the safe operation of electric vehicles. This paper proposes a method for lithium-ion battery fault diagnosis based on the historical trajectory of lithium-ion battery remaining discharge capacity in medium and long time scales. The method first utilizes the sparrow search algorithm (SSA) to identify the parameters of the second-order equivalent circuit model of the lithium-ion battery, and then estimates the state of charge (SOC) of the lithium-ion battery using the extended Kalman filter (EKF). The remaining discharge capacity is estimated according to the SOC, and finally the feature vectors are used to diagnose the faults using box plots on the medium and long time scales. Experimental results verify that the root mean squared error (RSME) and mean absolute error (MAE) of the proposed SOC estimation method are 0.0049 and 0.0034, respectively. This method can accurately identify the faulty single cell in a battery pack with low-capacity single cells and promptly detect any abnormalities in the single cell when a micro-short circuit fault occurs.
Response of Corroded Steel Pipeline Without and with CFRP Reinforcement to Reverse Fault Movement
Authors: Han Junyan , Bi Yansong , Hou Benwei , Zhao Wenle , El Naggar Mohamed Hesham
Published: November 2024

Corroded steel pipelines are particularly vulnerable to failure due to ground movement, which highlights the need to improve their seismic resistance through reinforcement methods. This paper establishes a three-dimensional finite element model of a corroded steel pipeline subjected to a reverse fault, which considers the effects of the corrosion position and depth, winding thickness, and length of carbon fiber-reinforced polymer (CFRP), to investigate the stress, strain, elliptic deformation, and failure modes of the pipeline before and after CFRP reinforcement. Results indicate that the main failure mode of the intact and corroded pipeline crossing the reverse fault is local buckling. Corrosion intensifies the response of the cross-fault pipeline, accelerates its failure occurrence, and promotes transformation from a single failure mode to multiple failure modes. For CFRP reinforcement, an increase in CFRP winding thickness can effectively inhibit the growth of the pipeline’s compressive strain, thus reducing the buckling potential. Each additional CFRP layer can further enhance the overall buckling resistance but at a decreasing rate. Similarly, longer CFRP winding improves buckling resistance though the effectiveness per meter decreases. Therefore, it is recommended that the thickness and length of CFRP winding on the pipeline should be optimized to obtain the best reinforcement at a reasonable cost.
The Effects of Combined Aloe vera Gel and Blended Antioxidants on Cooked Chicken Meat Quality
Authors: Al-Hijazeen Marwan
Published: November 2024

The effects of the inclusion of Aloe vera gel mixed with blended antioxidants on cooked chicken meat quality have been evaluated. This study was designed in five different treatments, where Aloe gel at level 5% was mixed into all treatment meat batches, including (1) control (without antioxidant additives); (2) blend 1 [90 ppm oregano essential oil (OE), 150 ppm rosemary extract (RE), and 60 ppm of germander extract (GR)]; (3) blend 2 (150 ppm OE, 120 ppm RE, and 30 ppm GR); (4) 200 ppm nitrite of sodium (E-250); and (5) 14 ppm of butylatedhydroxyanisole (BHA). The cooked patties of meat were refrigerated for a duration of one week. For meat quality evaluation, proximate composition, oxidation indicator for lipid and protein, total volatiles, and sensorial traits were tested at different times. Across all parameters, the treatment additives showed a considerable (p < 0.05) antioxidant effect compared to the control samples. No considerable variations (p > 0.05) were observed among treatment samples in relation to their proximate composition. However, the second blend exhibited the strongest antioxidant effect for all the parameters tested. Blend 1’s antioxidant effect was very comparable to 200 ppm of sodium nitrite (E-250) additive. Among all the additives, BHA showed the least significant antioxidant effect. Overall, blend 2 was classified as the most preferred treatment by the panelists. The recommendation that could be summarized is that blend 2 was an excellent natural alternative where the Aloe vera gel enhances and prolongs its antioxidant activity using cooked chicken meat.
Generative AI in Improving Personalized Patient Care Plans: Opportunities and Barriers Towards Its Wider Adoption
Authors: Baig Mirza Mansoor , Hobson Chris , GholamHosseini Hamid , Ullah Ehsan , Afifi Shereen
Published: November 2024

The main aim of this study is to investigate the opportunities, challenges, and barriers in implementing generative artificial intelligence (Gen AI) in personalized patient care plans (PPCPs). This systematic review paper provides a comprehensive analysis of the current state, potential applications, and opportunities of Gen AI in patient care settings. This review aims to serve as a key resource for various stakeholders such as researchers, medical professionals, and data governance. We adopted the PRISMA review methodology and screened a total of 247 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2021 and 2024 (inclusive). The selection criteria were based on the inclusion of studies that report on the opportunities and challenges in improving PPCPs using Gen AI. We found that a holistic approach is required involving strategy, communications, integrations, and collaboration between AI developers, healthcare professionals, regulatory bodies, and patients. Developing frameworks that prioritize ethical considerations, patient privacy, and model transparency is crucial for the responsible deployment of Gen AI in healthcare. Balancing these opportunities and challenges requires collaboration between wider stakeholders to create a robust framework that maximizes the benefits of Gen AI in healthcare while addressing the key challenges and barriers such as explainability of the models, validation, regulation, and privacy integration with the existing clinical workflows.
Practical Methodology for a Three-Dimensional-Printed Hybrid Desalination System
Authors: De la Cruz-Barragán Ziomara , Sandoval-Sánchez Elier , Hernández-Hernández Jonathan Israel , Miranda-Hernández Margarita , Mendoza Edgar
Published: November 2024

In response to the growing demand for potable water, this study presents a practical methodology for designing and fabricating a hybrid desalination system that integrates reverse electrodialysis and electrodialysis using 3D-printing technology. The hybrid system combines the energy generation potential of RED with the salt removal capabilities of ED, reducing energy consumption. Customized reactors were designed to enhance flow distribution and ion exchange, with computational fluid dynamics simulations validating the hydrodynamic performance. The reactors were fabricated using 3D printing, allowing rapid, cost-effective production, with functional reactors constructed in under 24 h. The system achieved a 15% reduction in salt concentration within one hour, with a specific energy consumption of 0.1388 Wh/m3 and a water recovery rate of 50%. These results demonstrate the functionality of the RED-ED hybrid system for achieving energy savings and performing water desalination. This methodology provides a scalable and replicable solution for water treatment applications, especially in regions with abundant salinity gradients and limited freshwater resources, while offering a multidisciplinary approach that integrates physicochemical and engineering principles for effective device development.
Diagnostic Model for Transformer Core Loosening Faults Based on the Gram Angle Field and Multi-Head Attention Mechanism
Authors: Chen Junyu , Duan Nana , Zhou Xikun , Wang Ziyu
Published: November 2024

Aiming to address the problems of difficulty in selecting characteristic quantities and the reliance on manual experience in the diagnosis of transformer core loosening faults, a diagnosis method for transformer core looseness based on the Gram angle field (GAF), residual network (ResNet), and multi-head attention mechanism (MA) is proposed. This method automatically learns effective fault features directly from GAF images without the need for manual feature extraction. Firstly, the vibration signal is denoised using ensemble empirical mode decomposition (EEMD), and the one-dimensional temporal signal is converted into a two-dimensional image using Gram angle field to generate an image dataset. Subsequently, the image set is input into ResNet to train the model, and the output of ResNet is weighted and summed using a multi-head attention module to obtain the deep feature representation of the image signal. Finally, the classification probabilities of different iron-core loosening states of the transformer are output through fully connected layers and Softmax layers. The experimental results show that the diagnostic model proposed in this paper has an accuracy of 99.52% in identifying loose iron cores in transformers, and can effectively identify loose iron cores in different positions. It is suitable for the identification and diagnosis of loose iron cores in transformers. Compared with traditional methods, this method has better fault classification performance and noise resistance.
Predictive Analytics for Sucker Rod Pump Failures in Kazakhstani Oil Wells Using Machine Learning
Authors: Utemissova Laura , Merembayev Timur , Bekbau Bakbergen , Omirbekov Sagyn
Published: November 2024

In the process of developing mature deposits, a number of geological and technological complications arise. In order to increase the smooth operation of downhole pumping equipment in oil and gas wells, companies use various methods and techniques. This article presents a novel methodology for predicting downhole pumping equipment failures. A detailed analysis was conducted on historical data regarding downhole pumping equipment failures, which were then incorporated into algorithms to calculate the operation of downhole equipment. As a result, it was discovered that in order to predict failures of downhole equipment, it is crucial to consider the historical data of the field and perform an assessment of the well’s potential. In the process of building a failure prediction model, the authors encountered the quality and completeness of historical data from the pilot field. They concluded that the data classes needed to be more balanced. The authors applied machine learning approaches to an imbalanced dataset. The significance of our approach lies in its ability to forecast equipment failures, thereby ensuring the smooth operation of wells operated by sucker rod pumps.
Real-Time Milk Quality Control Using Multi-Spectral Sensing and Edge Computing: Advancing On-Site Detection of Milk Components with XGBoost
Authors: Durgun Mahmut
Published: November 2024

This study explores the use of edge computing technologies to enhance the quality control processes in the dairy industry. Traditional milk quality control methods can be time-consuming and sometimes inadequate, whereas this new approach offers real-time data processing and rapid decision-making capabilities. The objective of the study is to assess the effectiveness of evaluating various spectral characteristics of milk in predicting critical parameters such as protein and fat content. In this research, a multi-channel sensor capable of collecting spectral data at various wavelengths was utilized. The collected data were processed using advanced machine learning models, where XGBoost and other regression models were assessed for their accuracy in predicting protein and fat content. The findings demonstrate the suitability of this technology for quality control in the dairy industry. The results reveal that edge computing-based systems can determine milk quality more quickly and accurately. This technology holds significant potential for overcoming the challenges faced in milk quality control, particularly in developing countries. This study provides valuable insights into how the use of edge computing can enhance operational efficiency and ensure product quality in the dairy industry. This research represents an important step towards developing more effective quality control mechanisms in the dairy industry and aims to establish a robust foundation for future studies. Recommendations focus on the adaptation of this technology to other food safety applications and its diversification for widespread industrial use.
An Integrated Model for Dam Break Flood Including Reservoir Area, Breach Evolution, and Downstream Flood Propagation
Authors: Liu Huiwen , Wang Zhongxiang , Zhang Dawei , Xiang Liyun
Published: November 2024

The reasonable and efficient prediction of dam failure events is of great significance to the emergency rescue operations and the reduction in dam failure losses. This work presents a model that is based on the physical mechanism. It is coupled with a multi-architecture (multi-CPU and GPU) open-source two-dimensional flood model, which is based on high-precision terrain and land use data. The aim is to enhance the accuracy of dam break flood process simulations. The model uses DEM data as a computational grid and updates it at each time step to reflect breach evolution. Simultaneously, the breach evolution model incorporates an analysis of stress on sediment particles, establishing the initial erosion state and lateral expansion model while accounting for seepage. The determination of the overflow of the breach is resolved through the application of a two-dimensional hydrodynamic model. This approach achieves a robust connection between the upstream reservoir, the dam structure, and the downstream inundation area. The coupled model is utilized to calculate the failure of earth-rock dams and landslide dams, and a sensitivity analysis is conducted. Taum Sauk Dam and Tangjiashan landslide dam were selected to represent earth dam break and barrier lake break, respectively, which are the main types of dam breaks. The obtained results demonstrate strong concurrence with the measured data, the relative errors of the four important parameters of the application case, the peak discharge of the breach, the top width of the final breach, the depth of the breach and the arrival time of the maximum peak discharge are all within ±10%. Although the relative error of the completion time of the final breach is greater than 10%, it is about 30% less than the relative error of the physical model.
Research on Key Parameters of Wellbore Stability for Horizontal Drilling in Offshore Hydrate Reservoirs
Authors: Shan Zhengfeng , Wang Xiansi , Li Zeqin , Gong Zhenggang , Ma Nan , Zhang Jianbo , Wang Zhiyuan , Sun Baojiang
Published: November 2024

The South China Sea has abundant reserves of natural gas hydrates, and if developed effectively, it can greatly alleviate the pressure on the energy supply in China. But the hydrate reservoirs in the sea area are loose, shallow, porous, and have poor mechanical properties. During the drilling process, the invasion of drilling fluid into this kind of reservoir is likely to induce mass decomposition of gas hydrate and, in turn, a significant reduction in mechanical strength around the wellbore as well as instability of the wellbore. In this study, in light of the engineering background of exploratory wells at the South China Sea, a temperature and pressure field model in a gas hydrate reservoir at sea during open circuit drilling was established, and then, based on this model, a comprehensive model for the stability analysis of the well drilled in the hydrate reservoir at sea was constructed, both of them with errors of less than 10%. With these two models, the effects of different drilling parameters on wellbore stability were investigated. The gas and liquid produced by the decomposition of hydrates in the formation will increase the pore pressure in the formation, thereby reducing the effective stress in the formation. The closer the formation is to the wellbore, the more thorough the decomposition of hydrates in the formation and the greater the effective plastic strain. Keeping all other conditions constant, the increase in drilling fluid invasion pressure and temperature, as well as reservoir permeability, will lead to a decrease in the mechanical strength of the formation around the wellbore and an expansion of the wellbore yield zone. The results can provide a theoretical reference for the stability analysis at sea.
Low-Power 8T SRAM Compute-in-Memory Macro for Edge AI Processors
Authors: Shin Hye-Ju , Jo Sung-Hun
Published: November 2024

The traditional Von Neumann architecture creates bottlenecks due to data movement. The compute-in-memory (CIM) architecture performs computations within memory bit-cell arrays, enhancing computational performance. Edge devices utilizing artificial intelligence (AI) address real-time problems and have established themselves as groundbreaking technology. The 8T structure proposed in this paper has strengths over other existing structures in that it better withstands environmental changes within the SRAM and consumes lower power during memory operation. This structure minimizes reliance on complex ADCs, instead utilizing a simplified voltage differential approach for multiply-and-accumulate (MAC) operations, which enhances both power efficiency and stability. Based on these strengths, it can achieve higher battery efficiency in AI edge devices and improve system performance. The proposed integrated circuit was simulated in a 90 nm CMOS process and operated on a 1 V supply voltage.
Indoor Air Quality in a Museum Storage Room: Conservation Issues Induced in Plastic Objects
Authors: Catrambone Maria , Cappellina Marianna , Olivini Francesca , Possenti Elena , Saccani Ilaria , Sansonetti Antonio
Published: November 2024

This study focuses on assessing the indoor air quality in a storage room (SR) belonging to Museo Nazionale Scienza e Tecnologia Leonardo da Vinci in Milan (MUST), covering pollutants originating from outdoor sources and emissions from historical plastic objects made from cellulose acetate (CA), cellulose nitrate (CN), and urea–formaldehyde (UF) stored in metal cabinets. The concentrations of SO2 (sulphur dioxide), NO2 (nitrogen dioxide), NOx (nitrogen oxides), HONO (nitrous acid), HNO3 (nitric acid), O3 (ozone), NH3 (ammonia), CH3COOH (acetic acid), and HCOOH (formic acid) were determined. The concentrations of SO2, O3, and NOx measured inside the metal cabinets were consistently lower compared to the other sampling sites. This result was expected due to their reactivity and the lack of internal sources. The SR and metal cabinets showed similar concentrations of NO and NO2, except for CA, where a high NO concentration was detected. The interaction between the CA surfaces and NO2 altered the distribution of NO and NO2, leading to a significant increase in NO. The presence of HNO3 potentially led to the formation of ammonium nitrate, as confirmed by ER-FTIR measurements. High levels of HONO and HNO3 in CN and NH3 in the UF indicate object deterioration, while elevated concentrations of CH3COOH in CA and HCOOH in the SR suggest specific degradation pathways for cellulose acetate and other organic materials, respectively. These results could direct conservators towards the most appropriate practical actions.
Investigation of the Measurement Uncertainties in the Measurement of BTEX in the Volatile Organic Compound Group
Authors: Sıdal Hayri Cihan , Akdemir Andaç
Published: November 2024

In this study, repeatability, intermediate precision, and recovery were considered within the Type A uncertainty budget, while measurement uncertainties due to the sampling system used (instrument), VOC mixture standard, internal standard, micropipette, temperature effect, methanol, and carbon disulfide were considered Type B uncertainties. As a result of the studies on the uncertainty components of the BTEX parameters belonging to the group of volatile organic compounds (VOCs), the highest uncertainty component for benzene was intermediate certainty at 24%. The highest uncertainty component for toluene was sampling at 23%. The highest uncertainty component for ethyl benzene was sampling at 25%. The highest uncertainty component for m,p-xylene and o-xylene was sampling at 25%. As a result, intermediate precision, sampling, and calibration uncertainties were identified as the most significant uncertainty components.
Second Hankel Determinant Bound Application to Certain Family of Bi-Univalent Functions
Authors: Mamon Mohamed A. , Halouani Borhen , Elshazly Ibrahim S. , Murugusundaramoorthy Gangadharan , El-Qadeem Alaa H.
Published: November 2024

A novel family of bi-univalent holomorphic functions is introduced by the use of the Lindelöf principle. The upper bound of the second Hankel determinant, H2,2(χ), is evaluated. Furthermore, specific results are obtained as special cases of the main conclusion. These cases coincide with certain recently obtained results and improve or enhance specific ones.
A Unified Approach to Aitchison’s, Dually Affine, and Transport Geometries of the Probability Simplex
Authors: Pistone Giovanni , Shoaib Muhammad
Published: November 2024

A critical processing step for AI algorithms is mapping the raw data to a landscape where the similarity of two data points is conveniently defined. Frequently, when the data points are compositions of probability functions, the similarity is reduced to affine geometric concepts; the basic notion is that of the straight line connecting two data points, defined as a zero-acceleration line segment. This paper provides an axiomatic presentation of the probability simplex’s most commonly used affine geometries. One result is a coherent presentation of gradient flow in Aichinson’s compositional data, Amari’s information geometry, the Kantorivich distance, and the Lagrangian optimization of the probability simplex.
Revealing Relationship Between In Situ Impedance and Lithium Plating Onset Based on Lithium–Graphite Half-Cells
Authors: Jiang Zhao , Wang Jiangwei , Li Haiwei , Luo Bingyin , Wu Wentao , Wang Xueyuan , Dai Haifeng , Jiang Jianjun , Wei Xuezhe
Published: November 2024

Lithium plating may occur during charging, especially at high rates or overcharging conditions for lithium-ion batteries (LIBs), which would cause battery capacity degradation and even trigger thermal runaway. Thus, it is essential to detect lithium plating onset during the charging processes. Electrochemical impedance can reveal the dynamic electrode properties of the battery, which is promising for use in battery management systems for the online detection of lithium plating onset. In this article, the impedance at 1 Hz is measured during the over-discharge and fast discharge processes using lithium–graphite half-cells. For half-cells, the variation in graphite electrode potential vs. Li/Li+ during discharging is directly recorded. An equivalent circuit model is proposed and adopted to estimate the real lithium plating reaction overpotential, which is deemed the thermodynamic indicator of lithium plating and is used as validation for the detection of lithium plating onset. Through the auxiliary validation of the estimation of lithium plating overpotential and the shape of incremental capacity curves, the relationship between impedance changes at specific frequency and the lithium plating onset is revealed. The results lay a good foundation for proposing the online diagnostic method of lithium plating onset based on the in situ impedance.
Using Reinforcement Learning in a Dynamic Team Orienteering Problem with Electric Batteries
Authors: Ammouriova Majsa , Guerrero Antoni , Tsertsvadze Veronika , Schumacher Christin , Juan Angel A.
Published: November 2024

This paper addresses the team orienteering problem (TOP) with vehicles equipped with electric batteries under dynamic travel conditions influenced by weather and traffic, which impact travel times between nodes and hence might have a critical effect on the battery capacity to cover the planned route. The study incorporates a novel approach for solving the dynamic TOP, comparing two solution methodologies: a merging heuristic and a reinforcement learning (RL) algorithm. The heuristic combines routes using calculated savings and a biased-randomized strategy, while the RL model leverages a transformer-based encoder–decoder architecture to sequentially construct solutions. We perform computational experiments on 50 problem instances, each subjected to 200 dynamic conditions, for a total of 10,000 problems solved. The results demonstrate that while the deterministic heuristic provides an upper bound for rewards, the RL model consistently yields robust solutions with lower variability under dynamic conditions. However, the dynamic heuristic, with a 20 s time limit for solving each instance, outperformed the RL model by 3.35% on average. The study highlights the trade-offs between solution quality, computational resources, and time when dealing with dynamic environments in the TOP.
Flexible Carbon Fiber/SnO2@rGO Electrode with Long Cyclability for Lithium-Ion Batteries
Authors: Zhang Wenjie , Liu Yongqi , Qin Zhouyang , Yu Lingxiao , Lian Jiabiao , Tao Zhanliang , Huang Zheng-Hong
Published: November 2024

Flexible electrodes are highly desirable for next-generation wearable lithium-ion batteries. To achieve high-capacity flexible electrode materials, SnO2 with high theoretical capacity has been introduced into electrodes and shows promising capacity. However, the electrodes are still confronted with major challenges in terms of inferior rate capability and cycling stability, which are caused by large volume changes of SnO2 during the lithiation/delithiation process. Here, we adopt an adsorption assembly strategy to fabricate a flexible carbon fiber/SnO2@rGO electrode that effectively stabilizes the volume changes of SnO2 and enhances the charge transport kinetics in electrodes. The sandwich-like structure endows the electrode’s high flexibility and succeeds in improving both rate capability and cycling stability. The flexible carbon fiber/SnO2@rGO electrode delivers a high capacity of 453 mAh g−1 at 50 mA g−1 and outstanding capacity retention of 88% after 1000 cycles at 2 A g−1.
The Need for Standards in Evaluating the Quality of Electronic Health Records and Dental Records: A Narrative Review
Authors: Gurupur Varadraj P. , Vu Giang , Mayya Veena , King Christian
Published: November 2024

Over the past two decades, there has been an enormous growth in the utilization of electronic health records (EHRs). However, the adoption and use of EHRs vary widely across countries, healthcare systems, and individual facilities. This variance poses several challenges for seamless communication between systems, leading to unintended consequences. In this article, we outline the primary factors and issues arising from the absence of standards in EHRs and dental record implementation, underscoring the need for global standards in this area. We delve into various scenarios and concepts that emphasize the necessity of global standards for healthcare systems. Additionally, we explore the adverse outcomes stemming from the absence of standards, as well as the missed opportunities within the healthcare ecosystem. Our discussions provide key insights on the impacts of the lack of standardization.
Correlation Between Modified Functional Reach Test and Medio-Lateral Center of Pressure in Paraplegic Individuals With Motor-Complete Spinal Cord Injury
Authors: Gatica-Rojas Valeska , López-Monardez Juan Claudio , Cartes-Velásquez Ricardo
Published: November 2024

The Modified Functional Reach Test (mFRT) was developed to assess sitting balance in individuals with spinal cord injury (SCI). No studies have explored which mFRT reach directions correlate with the center of pressure (CoP) variables in patients with motor-complete SCI (mcSCI). Addressing this gap is important for improving the clinical usefulness of the mFRT. Thus, this study aims to determine the correlation between seated balance parameters based on CoP and the mFRT in individuals with MCSCI. A total of 10 individuals with mcSCI (9 males and 1 female; range 20–42 years; 4 high paraplegia and 6 low paraplegia). Individuals were tested using a force platform during the sitting postural balance test (SPBT) and the mFRT with/without force plate with three measures of reach: forward (FR), right (RR), and left (LR). The sway parameters investigated were the area CoP sway (CoPSway), the average velocity of CoP displacements along the anterior–posterior (VAP) and medial-lateral (VML) directions, and standard deviation in both directions (SDAP and SDML). The Pearson correlation test was used to analyze the data. Significant correlations were found between the mediolateral reaches of the mFRT and corresponding CoP variables. The rightward and leftward reaches of the mFRT both showed strong correlations with CoP variables during the leftward reach. Additionally, the leftward mFRT exhibited moderate correlations with CoP variables in the same, rightward, and forward directions. The mFRT medio-lateral direction correlates with CoP at medio-lateral directions in paraplegic individuals with mcSCI.
Detection of Disease Features on Retinal OCT Scans Using RETFound
Authors: Du Katherine , Nair Atharv Ramesh , Shah Stavan , Gadari Adarsh , Vupparaboina Sharat Chandra , Bollepalli Sandeep Chandra , Sutharahan Shan , Sahel José-Alain , Jana Soumya , Chhablani Jay , Vupparaboina Kiran Kumar
Published: November 2024

Eye diseases such as age-related macular degeneration (AMD) are major causes of irreversible vision loss. Early and accurate detection of these diseases is essential for effective management. Optical coherence tomography (OCT) imaging provides clinicians with in vivo, cross-sectional views of the retina, enabling the identification of key pathological features. However, manual interpretation of OCT scans is labor-intensive and prone to variability, often leading to diagnostic inconsistencies. To address this, we leveraged the RETFound model, a foundation model pretrained on 1.6 million unlabeled retinal OCT images, to automate the classification of key disease signatures on OCT. We finetuned RETFound and compared its performance with the widely used ResNet-50 model, using single-task and multitask modes. The dataset included 1770 labeled B-scans with various disease features, including subretinal fluid (SRF), intraretinal fluid (IRF), drusen, and pigment epithelial detachment (PED). The performance was evaluated using accuracy and AUC-ROC values, which ranged across models from 0.75 to 0.77 and 0.75 to 0.80, respectively. RETFound models display comparable specificity and sensitivity to ResNet-50 models overall, making it also a promising tool for retinal disease diagnosis. These findings suggest that RETFound may offer improved diagnostic accuracy and interpretability for specific tasks, potentially aiding clinicians in more efficient and reliable OCT image analysis.
Weighted Coherence Analysis as a Window into the Neurophysiological Effects of Traumatic Brain Injury
Authors: Méndez-Balbuena Ignacio , Betancourt-Navarrete Brenda Lesly , Hermosillo-Abundis Ana Cristina , Flores Amira , Rebolledo-Herrera Lucio Fidel , Lemuz-López Rafael , Huidobro Nayeli , Meza-Andrade Roberto , Pelayo-González Héctor Juan , Bonilla-Sánchez María del Rosario , López-Cortes Vicente Arturo , García-Flores Marco Antonio
Published: November 2024

Traumatic brain injury (TBI), resulting from external forces, is a leading cause of disability and death, often leading to cognitive deficits that affect attention, concentration, speech and language, learning and memory, reasoning, planning, and problem-solving. Given the diverse mechanisms underlying TBI symptoms, it is essential to characterize its neurophysiological and neuropsychological effects. To address this, we employed weighted coherence (WC) analysis in patients performing the Halstead–Reitan categorization task, alongside a control group of eight healthy individuals. Our findings indicate a significant decrease in WC within the theta and delta bands in the temporal regions during cognitive tasks in the TBI group compared to controls. Additionally, we observed a significant increase in WC in the beta and gamma bands in the parietal region during both rest and cognitive tasks in the TBI group, relative to the control group. Furthermore, there was a strong correlation between WC and task performance scores in the temporal regions.
Human Skin Fibroblasts as an In Vitro Model Illustrating Changes in Collagen Levels and Skin Cell Migration Under the Influence of Selected Plant Hormones
Authors: Jabłońska-Trypuć Agata , Pankiewicz Walentyn , Wołejko Elżbieta , Sokołowska Gabriela , Estévez Jorge , Sogorb Miguel A. , Wydro Urszula
Published: November 2024

Human skin fibroblasts are an excellent in vitro model for tracking the processes occurring in human skin and studying the potential impact of various biologically active substances on these processes. Two plant hormones, which are included in the cytokinins group—kinetin (K) and N-6-benzyladenine (BA)—have a positive effect on human skin. Therefore, an attempt was made to examine the effect they have on key skin functions, cell proliferation, and migration, as well as collagen synthesis in them. The effect of phytohormones was studied at selected concentrations for kinetin—10 μM and 1 μM—and for N-6-benzyladenine—1 μM and 0.1 μM. A wound-healing assay was used in order to analyze cell migration and proliferation. The content of total protein and collagen in cells and culture medium was determined. The obtained results confirm that the studied compounds induce cell migration and proliferation, as well as collagen biosynthesis. The positive effect of kinetin and N-6-benzyladenine on fibroblast metabolism that we have demonstrated allows us to indicate them as compounds with potentially therapeutic properties. Therefore, we conclude that they should be subjected to further molecular and in vivo studies focusing on pathologies connected with skin diseases and aging.
VDMNet: A Deep Learning Framework with Vessel Dynamic Convolution and Multi-Scale Fusion for Retinal Vessel Segmentation
Authors: Xu Guiwen , Hu Tao , Zhang Qinghua
Published: November 2024

Retinal vessel segmentation is crucial for diagnosing and monitoring ophthalmic and systemic diseases. Optical Coherence Tomography Angiography (OCTA) enables detailed imaging of the retinal microvasculature, but existing methods for OCTA segmentation face significant limitations, such as susceptibility to noise, difficulty in handling class imbalance, and challenges in accurately segmenting complex vascular morphologies. In this study, we propose VDMNet, a novel segmentation network designed to overcome these challenges by integrating several advanced components. Firstly, we introduce the Fast Multi-Head Self-Attention (FastMHSA) module to effectively capture both global and local features, enhancing the network’s robustness against complex backgrounds and pathological interference. Secondly, the Vessel Dynamic Convolution (VDConv) module is designed to dynamically adapt to curved and crossing vessels, thereby improving the segmentation of complex morphologies. Furthermore, we employ the Multi-Scale Fusion (MSF) mechanism to aggregate features across multiple scales, enhancing the detection of fine vessels while maintaining vascular continuity. Finally, we propose Weighted Asymmetric Focal Tversky Loss (WAFT Loss) to address class imbalance issues, focusing on the accurate segmentation of small and difficult-to-detect vessels. The proposed framework was evaluated on the publicly available ROSE-1 and OCTA-3M datasets. Experimental results demonstrated that our model effectively preserved the edge information of tiny vessels and achieved state-of-the-art performance in retinal vessel segmentation across several evaluation metrics. These improvements highlight VDMNet’s superior ability to capture both fine vascular details and overall vessel connectivity, making it a robust solution for retinal vessel segmentation.
An Integrated Framework to Identify Prognostic Biomarkers and Novel Therapeutic Targets in Hepatocellular Carcinoma-Based Disabilities
Authors: Rahman Md. Okibur , Das Asim , Naeem Nazratun , Jabeen-E-Tahnim  , Hossain Md. Ali , Alam Md. Nur , Azad AKM , Alyami Salem A. , Alotaibi Naif , Al-Moisheer A. S. , Moni Mohammod Ali
Published: November 2024

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors globally, significantly affecting liver functions, thus necessitating the identification of biomarkers and effective therapeutics to improve HCC-based disabilities. This study aimed to identify prognostic biomarkers, signaling cascades, and candidate drugs for the treatment of HCC through integrated bioinformatics approaches such as functional enrichment analysis, survival analysis, molecular docking, and simulation. Differential expression and functional enrichment analyses revealed 176 common differentially expressed genes from two microarray datasets, GSE29721 and GSE49515, significantly involved in HCC development and progression. Topological analyses revealed 12 hub genes exhibiting elevated expression in patients with higher tumor stages and grades. Survival analyses indicated that 11 hub genes (CCNB1, AURKA, RACGAP1, CEP55, SMC4, RRM2, PRC1, CKAP2, SMC2, UHRF1, and FANCI) and three transcription factors (E2F1, CREB1, and NFYA) are strongly linked to poor patient survival. Finally, molecular docking and simulation identified seven candidate drugs with stable complexes to their target proteins: tozasertib (−9.8 kcal/mol), tamatinib (−9.6 kcal/mol), ilorasertib (−9.5 kcal/mol), hesperidin (−9.5 kcal/mol), PF−562271 (−9.3 kcal/mol), coumestrol (−8.4 kcal/mol), and clofarabine (−7.7 kcal/mol). These findings suggest that the identified hub genes and TFs could serve as valuable prognostic biomarkers and therapeutic targets for HCC-based disabilities.
XModNN: Explainable Modular Neural Network to Identify Clinical Parameters and Disease Biomarkers in Transcriptomic Datasets
Authors: Oldenburg Jan , Wagner Jonas , Troschke-Meurer Sascha , Plietz Jessica , Kaderali Lars , Völzke Henry , Nauck Matthias , Homuth Georg , Völker Uwe , Simm Stefan
Published: November 2024

The Explainable Modular Neural Network (XModNN) enables the identification of biomarkers, facilitating the classification of diseases and clinical parameters in transcriptomic datasets. The modules within XModNN represent specific pathways or genes of a functional hierarchy. The incorporation of biological insights into the architectural design reduced the number of parameters. This is further reinforced by the weighted multi-loss progressive training, which enables successful classification with a reduced number of replicates. The combination of this workflow with layer-wise relevance propagation ensures a robust post hoc explanation of the individual module contribution. Two use cases were employed to predict sex and neuroblastoma cell states, demonstrating that XModNN, in contrast to standard statistical approaches, results in a reduced number of candidate biomarkers. Moreover, the architecture enables the training on a limited number of examples, attaining the same performance and robustness as support vector machine and random forests. The integrated pathway relevance analysis improves a standard gene set overrepresentation analysis, which relies solely on gene assignment. Two crucial genes and three pathways were identified for sex classification, while 26 genes and six pathways are highly important to discriminate adrenergic–mesenchymal cell states in neuroblastoma cancer.
Validation of a Precise Impactor in a Rodent Cervical Spinal Cord Injury Hemi-Contusion Model
Authors: Castillo Jose , Le Michael , Pivetti Christopher , Jackson Jordan , Kulubya Edwin , Paxton Zachary , Reed Camille , Soufi Khadija , Ghaffari-Rafi Arash , Martin Allan , Price Richard , Kim Kee , Farmer Diana , Wang Aijun , Russo Rachel
Published: November 2024

Background: Cervical spinal cord injuries (SCIs) are the most common type of human SCI. Although various animal SCI contusion models have been developed to mirror human pathology, few have described cervical-level injuries. This study aims to validate and establish optimal impact parameters to produce consistent incomplete cervical SCIs for testing novel therapies. Methods: Using a precise impactor, 3 cervical spinal cord hemi-contusions of varying severities were induced by modifying penetration depths and controlling dwell times. Penetration depths of 2.11 mm (n = 4), 2.24 mm (n = 4), and 2.36 mm (n = 3) were used with a dwell time of 0.05 s to create mild, moderate, and severe injuries. Behavioral assessments in weeks 1, 2, 5, and 8 included grooming test, forelimb asymmetry test, and the Irvine, Beatties, and Bresnahan forelimb scale (IBB). After 8 weeks, rats were euthanized, and spinal cord histology was performed. Results: Within each group, animals exhibited consistent motor deficits and functional recovery. Mean IBB scores varied significantly between each group at week 8 (p < 0.0001). Ipsilateral forelimb usage significantly improved throughout the study period in the mild (2.11 mm) and moderate (2.24 mm) groups, while the severely (2.36 mm) injured group continued to exhibit 100% asymmetrical forelimb usage. Conclusions: This study demonstrates that a precise impactor can create reproducible models of incomplete cervical SCIs. A penetration depth of 2.24 mm resulted in moderate injury with significant motor deficits that slowly improved over time, permitting future therapeutic studies in functional recovery.
Comparative Ten-Year Outcomes in Chronic and Acute Coronary Syndrome Patients Undergoing Invasive Diagnostics—Insights from the KORONEF Registry
Authors: Kern Adam , Stompór Tomasz , Bojko Krystian , Sienkiewicz Ewa , Pawlak Sebastian , Pawlak Krystyna , Pawlak Dariusz , Poskrobko Grzegorz , Andrasz Ewa , Gromadziński Leszek , Jalali Rakesh , Onichimowski Dariusz , Piwko Grażyna , Zalewski Artur , Bil Jacek
Published: November 2024

Background: This study aimed to characterize acute coronary syndrome (ACS) patients undergoing invasive diagnostics and to evaluate prognostic factors for all-cause mortality over a 10-year follow-up period. Methods: The KORONEF study was a prospective, observational, single-center study that enrolled 492 patients, of whom 467 had confirmed coronary artery disease (CAD). Baseline demographic, clinical, laboratory, and procedural data were analyzed, focusing on the differences between ACS and chronic coronary syndrome (CCS) patients. Results: Males made up the majority of both the CCS and ACS groups (62.2% vs. 63.6%, p = 0.773), with no statistically significant difference in patient age between the CCS and ACS subpopulations (64.9 ± 9.5 vs. 63.7 ± 10.3, p = 0.106). The 10-year all-cause mortality was 29.8%, with no statistically significant difference between ACS and CCS patients. However, statistically significantly more patients with CCS underwent CABG in the follow-up than ACS patients (9.9% vs. 4.6%, p = 0.042). In a multivariable analysis, in the ACS subgroup, statistically significant predictors of all-cause mortality at 10 years included being between 75 and 90 years old (HR 4.99), not having had a previous stroke (HR 0.27), the absence of cardiac arrest (HR 0.20), and a left ventricular ejection fraction > 60% (HR 0.23). Conclusions: The long-term outcomes of the ACS patients highlight age and left ventricular function as significant prognostic factors, underscoring the importance of these parameters in risk stratification.
Impact of Endocrine Disrupting Pesticide Use on Obesity: A Systematic Review
Authors: Pérez-Bermejo Marcelino , Barrezueta-Aguilar Cristian , Pérez-Murillo Javier , Ventura Ignacio , Legidos-García María Ester , Tomás-Aguirre Francisco , Tejeda-Adell Manuel , Martínez-Peris Miriam , Marí-Beltrán Belén , Murillo-Llorente María Teresa
Published: November 2024

Background/Objectives: Endocrine disruptors are substances capable of altering the functions of the endocrine system. There is evidence that some pesticides can be endocrine disruptors and, among some of their effects, we find alterations in pubertal development and in the function of the thyroid gland, which could be related to a greater tendency of obesity. The aim was to evaluate the evidence from clinical and preclinical studies on the association between pesticides used in agriculture and found in plant-based foods with overweight/obesity. Methods: This is a systematic review of articles on the impact of the use of endocrine disrupting pesticides on obesity, conducted according to the PRISMA-2020 guidelines. Results: There was evidence that some pesticides, such as chlorpyrifos, pyrethroids, and neonicotinoids, may promote obesity and other anthropometric changes by altering lipid and glucose metabolism, modifying genes, or altering hormone levels such as leptin. Other studies suggest that perinatal exposure to chlorpyrifos or pesticides such as vinclozolin may alter lipid metabolism and promote weight gain in adulthood, whereas other pesticides such as boscalib, captan, thiacloprid, and ziram were not associated with changes in weight. Exposure to pesticides such as vinclozolin may be associated with a higher prevalence of overweight/obesity in later generations. Conclusions: The few studies that do not show these associations have methodological limitations in data collection with confounding variables. Further studies are needed to provide more and higher quality evidence to determine the true effect of these substances on obesity.