bluuebunny/crossref_metadata_2025 huggingface dataset:
Intermittent Fasting Protects Against the Progression from Acute Kidney Injury to Chronic Kidney Disease
Authors: Jang Yoonjoo , Kim Young Suk , Kim Seo Rin , Lee Dong Won , Lee Soo Bong , Kim Il Young
Published: January 2025

Acute kidney injury (AKI) is a major but often underestimated risk factor for the development of chronic kidney disease (CKD). Exploring innovative approaches to prevent this progression is critical. Intermittent fasting (IF), recognized for its metabolic and anti-inflammatory benefits, may offer protective effects in this context. Using a unilateral ischemia-reperfusion injury (UIRI) model in male C57BL/6 mice, we evaluated the impact of IF on tubulointerstitial fibrosis and tubular epithelial–mesenchymal transition (EMT) over 8 weeks. Mice in the IF group followed a 5:2 regimen, fasting for 24 h twice weekly. Four groups were studied: control, IF, UIRI, and IF + UIRI. The UIRI group exhibited increased fibrosis and EMT, both of which were significantly attenuated in the IF + UIRI group. IF also reduced levels of TGF-β1, phosphorylated NF-κB p65, inflammatory cytokines, and F4/80-positive macrophages, along with markers of oxidative stress. These findings highlight IF’s ability to mitigate fibrosis and EMT through reductions in inflammation and oxidative stress during AKI-to-CKD progression. Our study suggests that IF may serve as a promising dietary strategy to prevent AKI from advancing into CKD.
Detection of Manipulations in Digital Images: A Review of Passive and Active Methods Utilizing Deep Learning
Authors: Duszejko Paweł , Walczyna Tomasz , Piotrowski Zbigniew
Published: January 2025

The modern society generates vast amounts of digital content, whose credibility plays a pivotal role in shaping public opinion and decision-making processes. The rapid development of social networks and generative technologies, such as deepfakes, significantly increases the risk of disinformation through image manipulation. This article aims to review methods for verifying images’ integrity, particularly through deep learning techniques, addressing both passive and active approaches. Their effectiveness in various scenarios has been analyzed, highlighting their advantages and limitations. This study reviews the scientific literature and research findings, focusing on techniques that detect image manipulations and localize areas of tampering, utilizing both statistical properties of images and embedded hidden watermarks. Passive methods, based on analyzing the image itself, are versatile and can be applied across a broad range of cases; however, their effectiveness depends on the complexity of the modifications and the characteristics of the image. Active methods, which involve embedding additional information into the image, offer precise detection and localization of changes but require complete control over creating and distributing visual materials. Both approaches have their applications depending on the context and available resources. In the future, a key challenge remains the development of methods resistant to advanced manipulations generated by diffusion models and further leveraging innovations in deep learning to protect the integrity of visual content.
Lightweight and Efficient CSI-Based Human Activity Recognition via Bayesian Optimization-Guided Architecture Search and Structured Pruning
Authors: Youm Sungkwan , Go Sunghyun
Published: January 2025

This paper presents an integrated approach to developing lightweight, high-performance deep learning models for human activity recognition (HAR) using WiFi Channel State Information (CSI). Motivated by the need for accuracy and efficiency in resource-constrained environments, we combine Bayesian Optimization-based Neural Architecture Search (NAS) with a structured pruning algorithm. NAS identifies optimal network configurations, while pruning systematically removes redundant parameters, preserving accuracy. This approach allows for robust activity recognition from diverse WiFi datasets under varying conditions. Experimental results across multiple benchmark datasets demonstrate that our method not only maintains but often improves accuracy after pruning, resulting in models that are both smaller and more accurate. This offers a scalable and adaptable solution for real-world deployments in IoT and mobile platforms, achieving an optimal balance of efficiency and accuracy in HAR using WiFi CSI.
Evaluating the Performance of Artificial Intelligence-Based Large Language Models in Orthodontics—A Systematic Review and Meta-Analysis
Authors: Albalawi Farraj , Khanagar Sanjeev B. , Iyer Kiran , Alhazmi Nora , Alayyash Afnan , Alhazmi Anwar S. , Awawdeh Mohammed , Singh Oinam Gokulchandra
Published: January 2025

Background: In recent years, there has been remarkable growth in AI-based applications in healthcare, with a significant breakthrough marked by the launch of large language models (LLMs) such as ChatGPT and Google Bard. Patients and health professional students commonly utilize these models due to their accessibility. The increasing use of LLMs in healthcare necessitates an evaluation of their ability to generate accurate and reliable responses. Objective: This study assessed the performance of LLMs in answering orthodontic-related queries through a systematic review and meta-analysis. Methods: A comprehensive search of PubMed, Web of Science, Embase, Scopus, and Google Scholar was conducted up to 31 October 2024. The quality of the included studies was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST), and R Studio software (Version 4.4.0) was employed for meta-analysis and heterogeneity assessment. Results: Out of 278 retrieved articles, 10 studies were included. The most commonly used LLM was ChatGPT (10/10, 100% of papers), followed by Google’s Bard/Gemini (3/10, 30% of papers), and Microsoft’s Bing/Copilot AI (2/10, 20% of papers). Accuracy was primarily evaluated using Likert scales, while the DISCERN tool was frequently applied for reliability assessment. The meta-analysis indicated that the LLMs, such as ChatGPT-4 and other models, do not significantly differ in generating responses to queries related to the specialty of orthodontics. The forest plot revealed a Standard Mean Deviation of 0.01 [CI: 0.42–0.44]. No heterogeneity was observed between the experimental group (ChatGPT-3.5, Gemini, and Copilot) and the control group (ChatGPT-4). However, most studies exhibited a high PROBAST risk of bias due to the lack of standardized evaluation tools. Conclusions: ChatGPT-4 has been extensively used for a variety of tasks and has demonstrated advanced and encouraging outcomes compared to other LLMs, and thus can be regarded as a valuable tool for enhancing educational and learning experiences. While LLMs can generate comprehensive responses, their reliability is compromised by the absence of peer-reviewed references, necessitating expert oversight in healthcare applications.
Thermodynamics of Liquid Uranium from Atomistic and Ab Initio Modeling
Authors: Landa Alexander , Söderlind Per , Roehling John , McKeown Joseph T.
Published: January 2025

We present thermodynamic properties for liquid uranium obtained from classical molecular dynamics (MD) simulations and the first-principles theory. The coexisting phases method incorporated within MD modeling defines the melting temperature of uranium in good agreement with the experiment. The calculated melting enthalpy is in agreement with the experimental range. Classical MD simulations show that ionic contribution to the total specific heat of uranium does not depend on temperature. The density of states at the Fermi level, which is a crucial parameter in the determination of the electronic contribution to the total specific heat of liquid uranium, is calculated by ab initio all electron density functional theory (DFT) formalism applied to the atomic configurations generated by classical MD. The calculated specific heat of liquid uranium is compared with the previously calculated specific heat of solid γ-uranium at high temperatures. The liquid uranium cannot be supercooled below Tsc ≈ 800 K or approximately about 645 K below the calculated melting point, although, the self-diffusion coefficient approaches zero at TD ≈ 700 K. Uranium metal can be supercooled about 1.5 times more than it can be overheated. The features of the temperature hysteresis are discussed.
Evaluation of the Variability of Vibration Measurement Results in Rolling Bearing Quality Control
Authors: Zmarzły Paweł , Wrzochal Mateusz , Adamczak Stanisław
Published: January 2025

The final result of rolling bearing quality control is influenced by many factors that are not always taken into account. If non-conforming products leave the factory, then the reliability of equipment containing rolling bearings is significantly weakened. The paper presents an analysis of the reasons for the variability of vibration measurement results in rolling bearing quality control. The paper describes the organisation of rolling bearing acceptance inspection on the production line and its place in the rolling bearing production process. The study evaluates the influence of the measuring device and the location of the sensor on the result of the rolling bearing vibration monitoring process. The problem of repeatability and reproducibility of rolling bearing vibration measurement was also investigated. On the basis of the obtained results, conclusions were drawn regarding the improvement of the quality control process of rolling bearings in production plants.
Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
Authors: Zamani Sanaz , Nguyen Minh , Sinha Roopak
Published: January 2025

Mental health disorders constitute a significant global challenge, compounded by the limitations of traditional management approaches that rely heavily on subjective self-reports and infrequent professional evaluations. This study presents a groundbreaking IoT-based system that integrates big data analytics, fuzzy logic, and machine learning to revolutionise mental health monitoring. In contrast to existing solutions, the proposed system uniquely incorporates environmental factors, such as temperature and humidity in enclosed spaces—critical yet often overlooked contributors to emotional well-being. By leveraging IoT devices to collect and process large-scale ambient data, the system provides real-time classification and personalised visualisation tailored to individual sensitivity profiles. Preliminary results reveal high accuracy, scalability, and the potential to generate actionable insights, creating dynamic feedback loops for continuous improvement. This innovative approach bridges the gap between environmental conditions and mental healthcare, promoting a transformative shift from reactive to proactive care and laying the groundwork for predictive environmental health systems.
Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings
Authors: Öztürk Hacar Özge , Hacar Müslüm , Gülgen Fatih , Pappalardo Luca
Published: January 2025

In urban environments, eating and drinking out (EDO) is a widespread activity among residents and visitors, generating a wealth of digital footprints that reflect consumer experiences. These digital traces provide businesses with opportunities to enhance their services and guide entrepreneurs in selecting optimal locations for new establishments. This study investigates the relationship among urban spatial features, pedestrians and digital consumer interactions at EDO venues. It highlights the utility of integrating urban mobility and spatial data to model digital consumer behavior, offering potential urban planning and business strategies. By analyzing Melbourne’s city center, we evaluate how factors, such as pedestrian count by sensors on the streets, residential density, the centralities and geometric properties of streets, and place-specific characteristics, influence consumer reviews and ratings on Google Maps. The study employs a random forest machine learning model to predict review volumes and ratings, categorized into high and low classes. The results indicate that pedestrian counts and residential density are key predictors for both metrics, while centrality measures improve the prediction of visitor scores but negatively impact review volume predictions. The geometric features of streets play varying roles across different prediction tasks. The model achieved a 65% F1-score for review volume classifications and a 62% for visitor score. These findings not only provide actionable understanding for urban planners and business stakeholders but also contribute to a deeper understanding of how spatial dynamics affect digital consumer behavior, paving the way for more sustainable urban development and data-driven decision-making.
Two-Stage Efficient Parking Space Detection Method Based on Deep Learning and Computer Vision
Authors: Jiang Junzhe , Tang Rongnian , Kang Weian , Xu Zengcai , Qian Cheng
Published: January 2025

One of the basic requirements of an automated parking system is to quickly and accurately detect parking spaces. With the development of deep convolutional neural networks, parking space detection systems are becoming increasingly mature. However, the existing detection systems are often too complicated, resulting in a long detection time. In order to solve this problem, this paper proposes a two-stage parking space detection system, which aims to combine the advantages of deep learning and traditional computer vision and treat parking space detection as a two-stage process. First, we take the vehicle’s circumnavigation image as input and extract the target box of the parking space key points from the global image through the YoloV11 network. Then, using the special convolution kernel proposed in this paper, the local image is convolved to obtain the direction information of the parking space. Finally, the correct parking space is deduced based on its characteristic parameters. The method in this paper achieved a detection accuracy of 98.24% on the public data set ps2.0 (Parking-slot 2.0), and the parking space inference time on a single image was only 12.3 ms. Compared with the existing methods, the two-stage parking space detection system proposed in this paper not only demonstrates higher robustness, but also significantly reduces the time required for parking space inference.
Effectiveness Analysis of Deep Learning Methods for Breast Cancer Diagnosis Based on Histopathology Images
Authors: Korkmaz Merve , Kaplan Kaplan
Published: January 2025

The early detection of breast cancer is crucial for both accelerating the treatment process and preventing the spread of cancer. The accuracy of diagnosis is also significantly influenced by the experience of pathologists. Many studies have been conducted on the correct diagnosis of breast cancer to help specialists and increase the accuracy of diagnosis. This study focuses on classifying breast cancer using deep learning models, including pre-trained VGG16, MobileNet, DenseNet201, and a custom-built Convolutional Neural Network (CNN), with the final dense layer optimized via the particle swarm optimization (PSO) algorithm. The Breast Histopathology Images Dataset was used to evaluate the performance of the model, forming two datasets: one with 157,572 images at 50 × 50 × 3 (Experimental Study 1) and another with 1116 images resized to 224 × 224 × 3 (Experimental Study 2). Both original (50 × 50 × 3) and rescaled (224 × 224 × 3) images were tested. The highest success rate was obtained using the custom-built CNN model with an accuracy rate of 93.80% for experimental study 1. The MobileNet model yielded an accuracy of 95.54% for experimental study 2. The experimental results demonstrate that the proposed model exhibits promising, and superior classification accuracy compared to state-of-the-art methods across varying image sizes and dataset volumes.
Development of a Simplified Human Body Model for Movement Simulations
Authors: Olinski Michał , Marciniak Przemysław
Published: January 2025

The main goal of this paper is to develop a simplified model of the human body motion system that enables its application for movement simulations and the analysis of the kinematic and dynamic parameters occurring during the performance of activities. The model is established on the basis of the modified Hanavan model and consists of rigid solids with simple geometry that are connected mostly with spherical joints. Based on anthropometric data from the literature, a complete set of equations parameterizing the dimensions and mass of each segment was formulated. The equations depend on only two body measurements (height and mass). The model is built in the Adams system as a 3D numerical dynamic model and tested using data gathered with an IMU sensors system. A volunteer lifting an object with a bent spine from the ground with both hands is used for this purpose. Three angles (from the IMUs) are applied to each model’s joint to best simulate human movement and to analyze the angular displacements, velocities, and torques. These results are consistent with theoretical expectations and assumptions, thus proving that reproducing human movements with the developed model is possible and that it also allows various parameters of the human body to be obtained.
Missed Detection of Entanglement in Two-Mode Squeezed States Based on the Inseparability Criterion
Authors: Cai Chunxiao , Diao Wenting , Feng Fupan , Yang Wenhai , Su Xinyu , Zhao Weigang , Ge Jinman , Ma Long , Huo Nan , Liu Yanhong , Duan Chongdi
Published: January 2025

The inseparability criterion provides a straightforward and efficient method for identifying and quantifying two-mode Gaussian quantum entanglement, making it a crucial tool in quantum optics experiments. However, it is crucial to recognize that the inseparability criterion serves only as a sufficient condition for entanglement assessment, thereby posing a risk of missed detection during evaluation. This paper investigates the use of the inseparability criterion in assessing two-mode squeezed states, with a particular focus on examining missed entanglement detection due to entanglement asymmetry. The results show that when decoherence symmetrically affects both modes, the inseparability criterion effectively detects entanglement. In contrast, when this symmetry is broken, the criterion may fail to identify entanglement, with the likelihood of missed detection increasing with increasing asymmetry. By comparing these results with the positive partial transpose criterion, which serves as a necessary and sufficient condition, the occurrence of missed detections by the inseparability criterion is confirmed. Our research not only provides valuable insights into the application of the inseparability criterion in quantum information tasks but also deepens the understanding of its operational principles and limitations.
Deep-Learning-Based Identification of Broad-Absorption Line Quasars
Authors: Pang Sen , Kong Hoiio , Li Zijun , Kao Weibo , Zhang Yanxia
Published: January 2025

The accurate classification of broad-absorption line (BAL) quasars and non-broad-absorption line (non-BAL) quasars is key in understanding active galactic nuclei (AGN) and the evolution of the universe. With the rapid accumulation of data from large-scale spectroscopic survey projects (e.g., LAMOST, SDSS, and DESI), traditional manual classification methods face limitations. In this study, we propose a new method based on deep learning techniques to achieve an accurate distinction between BAL quasars and non-BAL quasars. We use a convolutional neural network (CNN) as the core model, in combination with various dimensionality reduction techniques, including principal component analysis (PCA), t-distributed stochastic neighborhood embedding (t-SNE), and isometric mapping (ISOMAP). These dimensionality reduction methods help extract meaningful features from high-dimensional spectral data while reducing model complexity. We employ quasar spectra from the 16th data release (DR16) of the Sloan Digital Sky Survey (SDSS) and obtain classification labels from the DR16Q quasar catalogues to train and evaluate our model. Through extensive experiments and comparisons, the combination of PCA and CNN achieve a test accuracy of 99.11%, demonstrating the effectiveness of deep learning for classifying the spectral data. Additionally, we explore other dimensionality reduction methods and machine learning models, providing valuable insights for future research in this field.
A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
Authors: Battazza Igor Felipe Carboni , Rodrigues Cleyton Mário de Oliveira , Oliveira João Fausto L. de
Published: January 2025

In this study, we introduce a detailed framework for predicting market conditions and selecting stocks by integrating machine learning techniques with ontological financial analysis. The process starts with ontology-based stock selection, categorizing companies using fundamental financial indicators such as liquidity, profitability, debt ratios, and growth metrics. For instance, firms showcasing favorable debt-to-equity ratios along with robust revenue growth are identified as high-performing entities. This classification facilitates targeted analyses of market dynamics. To predict market states—categorizing them into bull, bear, or neutral phases—the framework utilizes a Non-Stationary Markov Chain (NMC), BERT, to assess sentiment in financial news articles and Long Short-Term Memory (LSTM) networks to identify temporal patterns. Key inputs like the Sentiment Index (SI) and Illiquidity Index (ILLIQ) play essential roles in dynamically influencing regime predictions within the NMC model; these inputs are supplemented by variables including GARCH volatility and VIX to enhance predictive precision further still. Empirical findings demonstrate that our approach achieves an impressive 97.20% accuracy rate for classifying market states, significantly surpassing traditional methods like Naive Bayes, Logistic Regression, KNN, Decision Tree, ANN, Random Forest, and XGBoost. The state-predicted strategy leverages this framework to dynamically adjust portfolio positions based on projected market conditions. It prioritizes growth-oriented assets during bull markets, defensive assets in bear markets, and maintains balanced portfolios in neutral states. Comparative testing showed that this approach achieved an average cumulative return of 13.67%, outperforming the Buy and Hold method’s return of 8.62%. Specifically, for the S&P 500 index, returns were recorded at 6.36% compared with just a 1.08% gain from Buy and Hold strategies alone. These results underscore the robustness of our framework and its potential advantages for improving decision-making within quantitative trading environments as well as asset selection processes.
Tectonic Evolution of the Upper Paleozoic Erathem in the Northeastern Part of the Sulige Gas Field in the Ordos Basin and Its Effect on Reservoir Control
Authors: Ning Xinghui , Wang Aiguo , Wang Yufei , Fu Bin , Li Yijun
Published: January 2025

Sandstone bodies are distributed across a large area in the northeastern part of the Sulige gas field in the Ordos Basin. However, the production characteristics of gas wells in different areas are significantly different, and the success rate of drilling effective reservoirs is low. Therefore, studies on the patterns of natural gas enrichment are urgently needed. In this study, from the perspective of tectonic evolution, the mudstone sonic transit time method was used to calculate the denudation thickness of the study area in the Late Cretaceous; the denudation thickness was between 820 m and 1200 m, and the paleo-tectonic map of the top of He 8, which was the main layer at that time, was restored and analyzed in comparison with the present structure at the top of He 8, revealing that tectonic evolution has a controlling effect on the migration, accumulation and dispersion of natural gas after formation. During the critical period of hydrocarbon accumulation at the end of the Early Cretaceous, the short-axis nose uplift zone remaining in the central and western regions, and the long-axis nose uplift zone remaining in the central and eastern regions were favorable areas for natural gas migration and accumulation. The up-dip direction has lithological traps, and the gas reservoirs have survived to the present day. The short-axis nose uplift zone and anticline at the western margin disappeared through tectonic adjustment; thus, the paleo-gas reservoirs that formed there were destroyed, and the natural gas was adjusted to new traps.
Analysis of Connectivity in Electromyography Signals to Examine Neural Correlations in the Activation of Lower Leg Muscles for Postural Stability: A Pilot Study
Authors: Alderink Gordon , McCrumb Diana , Zeitler David , Rhodes Samhita
Published: January 2025

In quiet standing, the central nervous system implements a pre-programmed ankle strategy of postural control to maintain upright balance and stability. This strategy comprises a synchronized common neural drive delivered to synergistically grouped muscles. This study evaluated connectivity between EMG signals of the unilateral and bilateral homologous muscle pairs of the lower legs during various standing balance conditions using magnitude-squared coherence (MSC). The leg muscles examined included the right and left tibialis anterior (TA), medial gastrocnemius (MG), and soleus (S). MSC is a frequency domain measure that quantifies the linear phase relation between two signals and was analyzed in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–100 Hz) neural frequency bands for feet together and feet tandem, with eyes open and eyes closed conditions. Results showed that connectivity in the beta and lower and upper gamma bands (30–100 Hz) was influenced by standing balance conditions and was indicative of a neural drive originating from the motor cortex. Instability was evaluated by comparing less stable standing conditions with a baseline—eyes open feet together stance. Changes in connectivity in the beta and gamma bands were found to be most significant in the muscle pairs of the back leg during a tandem stance regardless of dominant foot placement. MSC identified the MG:S muscle pair as significant for the right and left leg. The results of this study provided insight into the neural mechanism of postural control.
Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling
Authors: Mo Yapeng , Chen Lijiang , Zhang Lingfeng , Zhao Qi
Published: January 2025

Due to the labor-intensive manual annotations for nuclei segmentation, point-supervised segmentation based on nuclei coordinate supervision has gained recognition in recent years. Despite great progress, two challenges hinder the performance of weakly supervised nuclei segmentation methods: (1) The stable and effective segmentation of adjacent cell nuclei remains an unresolved challenge. (2) Existing approaches rely solely on initial pseudo-labels generated from point annotations for training, and inaccurate labels may lead the model to assimilate a considerable amount of noise information, thereby diminishing performance. To address these issues, we propose a method based on center-point prediction and pseudo-label updating for precise nuclei segmentation. First, we devise a Gaussian kernel mechanism that employs multi-scale Gaussian masks for multi-branch center-point prediction. The generated center points are utilized by the segmentation module to facilitate the effective separation of adjacent nuclei. Next, we introduce a point-guided attention mechanism that concentrates the segmentation module’s attention around authentic point labels, reducing the noise impact caused by pseudo-labels. Finally, a label updating mechanism based on the exponential moving average (EMA) and k-means clustering is introduced to enhance the quality of pseudo-labels. The experimental results on three public datasets demonstrate that our approach has achieved state-of-the-art performance across multiple metrics. This method can significantly reduce annotation costs and reliance on clinical experts, facilitating large-scale dataset training and promoting the adoption of automated analysis in clinical applications.
Melatonin Promotes Yield Increase in Wheat by Regulating Its Antioxidant System and Growth Under Drought Stress
Authors: Li Xue , Liu Jia , Zhang Cuiping , Liu Ze , Guo Xiang , Li Shaoxiang , Li Hongsheng , Liu Kun , Li Kunzhi , Ding Mingliang
Published: January 2025

Drought stress constitutes a major challenge to wheat production. Melatonin plays a vital role in plants’ resistance to drought stress. Nevertheless, the influence of melatonin seed coating on the drought resistance ability of wheat remains unclear. Hence, in this study, wheat (Yunmai 112) was chosen as the experimental material. The research results indicated that 100 µM exogenous melatonin treatment enhanced the germination rate of wheat seeds by 11% compared to the CK group. Melatonin seed coating (100 or 200 µM) significantly inhibited the accumulation of ROS in wheat seedlings under drought stress conditions and facilitated the growth of wheat seedlings. Then, 100 µM melatonin seed coating elevated the activity of antioxidant enzymes (CAT, Cu/Zn-SOD, POD, and T-GSH) in wheat seedlings and strengthened the resistance of wheat to drought stress. In contrast to the control, 100 or 200 µM melatonin seed coating significantly raised the contents of soluble protein and chlorophyll in wheat seedlings. Further studies demonstrated that 100 µM melatonin seed coating promoted the increase in the thousand-grain weight and yield of wheat under drought stress. Taking together, melatonin seed coating is an effective approach for enhancing the stress resistance and the yield of wheat under drought stress.
Targeting SMOX Preserves Optic Nerve Myelin, Axonal Integrity, and Visual Function in Multiple Sclerosis
Authors: Henry-Ojo Harry O. , Liu Fang , Narayanan S. Priya
Published: January 2025

Multiple sclerosis (MS) is a highly disabling chronic neurological condition affecting young adults. Inflammation, demyelination, and axonal damage are key pathological features of MS and its animal model, experimental autoimmune encephalomyelitis (EAE). Our previous work demonstrated that inhibiting spermine oxidase (SMOX) with MDL72527, a selective irreversible pharmacological inhibitor, significantly reduced clinical symptoms, retinal ganglion cell (RGC) loss, and optic nerve inflammation in EAE mice. The present study explored the broader therapeutic potential of SMOX inhibition, focusing on myelin preservation, axonal integrity, and visual function in the EAE model. Electron microscopy of optic nerve cross-sections showed significant preservation of myelin thickness and axonal integrity due to SMOX inhibition. The quantitative assessment showed that g-ratio and axon count metrics were significantly improved in MDL72527-treated EAE mice compared to their vehicle-treated counterparts. Immunofluorescence studies confirmed these findings, showing increased preservation of myelin and axonal proteins in MDL72527-treated EAE mice compared to the vehicle-treated group. Functional assessment studies (Electroretinography) demonstrated significant improvement in RGC function and axonal conduction in EAE mice treated with MDL72527. Furthermore, SMOX inhibition downregulated the expression of galectin3 (Gal3), a mediator of neuroinflammation, indicating Gal3’s role in SMOX-mediated neuroprotection. This study provides compelling evidence for the potential of SMOX inhibition as a therapeutic strategy in multiple sclerosis and other demyelinating disorders.
Combination of Brentuximab Vedotin and Pembrolizumab as Salvage Treatment Before Autologous Stem Cell Transplantation and Maintenance in Patients with Relapsed/Refractory Hodgkin Lymphoma
Authors: Massa Hanne , Massaro Fulvio , Maerevoet Marie
Published: January 2025

Background: In relapsed or refractory classical Hodgkin lymphoma, achieving complete remission on 18FDG PET-CT before autologous stem cell transplantation improves progression-free survival. However, the optimal salvage therapy to achieve this remains undefined. Brentuximab vedotin combined with PD1 inhibitors has shown promise, though limited data exist on the combination of brentuximab vedotin and pembrolizumab. Methods: We retrospectively collected data from 24 adult patients with confirmed relapsed or refractory classical Hodgkin lymphoma, who started salvage treatment with brentuximab vedotin and pembrolizumab with the intention of consolidation with high-dose chemotherapy, followed by autologous stem cell transplantation and brentuximab vedotin maintenance. Results: After two cycles of brentuximab vedotin and pembrolizumab, 95.2% achieved an overall response and 81.0% achieved complete metabolic response. 20 patients (83.3%) were in complete response at the end of maintenance, of whom one relapsed at 28 months after the end of treatment. Grade 3 and 4 toxicities during salvage treatment consisted mainly of hematological toxicity, one thyrotoxicosis, one hemophagocytic lymphohistiocytosis, and one arthralgia. Non-hematological grade 3–4 toxicities following transplantation were an inflammatory pneumonitis and one cryptococcal meningitis. One death occurred during prolonged post-transplant aplasia. During maintenance, dose reductions for toxicity were necessary in 16 patients, mainly due to peripheral neuropathy. Conclusions: For heavily pretreated relapsed or refractory classical Hodgkin lymphoma patients, our data suggest that salvage therapy with brentuximab vedotin and pembrolizumab before autologous stem cell transplantation followed by brentuximab vedotin maintenance is a highly active strategy, with acceptable toxicities. Further studies with larger cohorts are necessary to confirm these data.
Targeting Inflammation with Galectin-3 and PIIINP Modulation Among ST-Segment Elevation Acute Coronary Syndrome Patients Underwent Delayed Percutaneous Coronary Intervention
Authors: Handari Saskia Dyah , Rohman Mohammad Saifur , Sargowo Djanggan , Aulanni’am  , Qosimah Dahliatul , Lestari Bayu , Nugraha Ricardo Adrian
Published: January 2025

Background/Objectives: ST-segment elevation acute coronary syndrome (STE-ACS) represents a significant global health challenge, with cardiac remodeling and fibrosis critically affecting recovery after percutaneous coronary intervention (PCI). Colchicine, known for its anti-inflammatory effects, may regulate key fibrotic markers such as Procollagen III N-terminal Propeptide (PIIINP) and Galectin-3. This study assesses colchicine’s effect on these biomarkers in STE-ACS patients undergoing delayed PCI. Methods: In this multicenter, randomized, double-blind trial, we examined colchicine’s impact on Galectin-3 and PIIINP in 164 STE-ACS patients undergoing early or delayed PCI. Patients received colchicine shortly after hospital admission. Biomarker changes were evaluated at 24 h and five days post-treatment using two-way ANOVA. Results: Clinical trials in the early PCI group revealed that Galectin-3 levels decreased significantly on day one (p < 0.01) and further on day five (p < 0.0001), indicating Primary PCI has benefits to inhibition of fibrosis beyond colchicine add-on treatment. But, in the delayed PCI group, Galectin-3 levels significantly increased on day one (p < 0.01), but the decrease observed by day five was not statistically significant. It is related that the benefits of colchicine treatment may exceed PCI implantation in preventing cardiac remodeling. In the delayed PCI group, PIIINP levels showed a significant reduction on day five (p < 0.0001). Conclusions: This Colchicine demonstrates novel efficacy in delayed PCI, with a significant increase in Galectin-3 and a sharp reduction in PIIINP, indicating its ability to control fibrosis. This positions colchicine as a breakthrough therapy for improving outcomes in STE-ACS patients with delayed intervention.
Heatstroke-Induced Inflammatory Response and Therapeutic Biomarkers
Authors: Baindara Piyush , Jana Aritra , Dinata Roy , Mandal Santi M.
Published: January 2025

In recent years, heatstroke has become one of the most dangerous illnesses associated with hyperthermia. Hyperthermia is described as an increased body temperature, where there is more heat accrual than dissipation, which happens during environmental heat stress conditions or exhaustive exercise and subsequently leads to heatstroke. Heatstroke is characterized as a dysfunction of the central nervous system (CNS), associated with neuroinflammation, including utmost hyperthermia, which eventually leads to multiorgan failure. Heatstroke-related fatalities have rapidly increased in the recent past; however, there is still a gap in the understanding of heatstroke and associated outcomes during heatstroke. Especially of note, early diagnosis of heatstroke-related complications is one of the important aspects that need to be addressed. This article reviewed current knowledge about heatstroke and associated inflammatory responses, including neuroinflammation and other clinical complications. Using molecular dynamics simulation analysis of triose phosphate isomerase (a housekeeping enzyme) at different temperatures, we demonstrated how protein structures, and thus their functions, can be varied with temperature increases. Additionally, we discussed therapeutically relevant biomarkers of heatstroke which might be helpful in the early detection of heatstroke possibilities and candidate drug targets to control or minimize heatstroke events.
A Time-Resolved Fluorescent Microsphere Immunochromatographic Assay for Determination of Vitamin B12 in Infant Formula Milk Powder
Authors: Lu Qianqian , Feng Yongwei , Zhou Qi , Yang Ting , Kuang Hua , Xu Chuanlai , Guo Lingling
Published: January 2025

Vitamin B12 (VB12) is an important nutrient, and its quality control in food is crucial. In this study, based on the principle of specific recognition of target analyte by monoclonal antibodies (mAbs), a time-resolved fluorescent microsphere immunochromatographic assay (TRFM-ICA) was developed to detect the content of VB12 in infant formula milk powder. First, the performance of the anti-VB12 mAb was evaluated, revealing a half-maximal inhibitory concentration of 0.370 ng/mL, an affinity constant of 2.604 × 109 L/mol and no cross-reactivity with other vitamins. Then, a highly sensitive TRFM-ICA was developed, with a visual limit of detection of 10 μg/kg and a cut-off value of 100 μg/kg for qualitative detection and a detection range of 4.125–82.397 μg/kg for quantitative detection. In addition, the test results of real samples were consistent with the results of quantification using microbiological methods, with a coefficient of variation of less than 10%, showing good accuracy and stability, and confirming that the TRFM-ICA is suitable for the analysis of VB12 in real infant formula milk powder samples. In this study, based on the principle of specific recognition of VB12 by monoclonal antibodies (mAbs) against VB12, a time-resolved fluorescence microsphere immunochromatographic assay (TRFM-ICA) was developed to detect the content of VB12 in infant formula by converting biological signals into optical signals.
Differential Impact of Risk Factors for Cognitive Decline in Heterosexual and Sexual Minority Older Adults in England
Authors: Manca Riccardo , Flatt Jason D. , Venneri Annalena
Published: January 2025

Background/Objectives: Sexual minority older adults (SMOAs) report greater subjective cognitive decline (SCD) than heterosexual older adults (HOAs). This study aimed to compare the impact of multiple psycho-social risk factors on objective and subjective cognitive decline in HOAs and SMOAs. Methods: Two samples of self-identified HOAs and SMOAs were selected from the English Longitudinal Study of Ageing. Reliable change indices for episodic and semantic memory were created to assess cognitive decline. SCD was self-reported for memory and general cognition. Depressive symptoms, loneliness, marital status and socio-economic status were investigated as risk factors. Results: No between-group differences were found in cognitive decline. Higher depression was associated with greater SCD risk and worse semantic memory decline. The latter effect was stronger in SMOAs. The findings were largely replicated in the sensitivity analysis. Conclusions: Poor mental health may represent the strongest driver of cognitive decline in SMOAs and to a greater extent than in HOAs.
Fostering Elementary School Students’ Self-Regulation Skills in Reading Comprehension: Effects on Text Comprehension, Strategy Use, and Self-Efficacy
Authors: Dermitzaki Irini
Published: January 2025

It has been maintained that active strategic reading is beneficial for students’ reading comprehension (RC) and has cognitive, motivational, and behavioral benefits for students. The aim of the present study was to implement and evaluate the effectiveness of a self-regulation skills training program with a focus on RC for elementary school students. One hundred and nine students (60 girls) from 5th and 6th grade were randomly assigned to an experimental (EG) and a control group (CG). The EG (n = 54) took an intervention program aiming at fostering students’ cognitive and metacognitive strategies to control and regulate the RC process. Before and after the intervention, students’ performance in RC, reported use of strategies for RC, and self-efficacy regarding RC were assessed. The analyses of the data showed that, after the intervention, the EG’s reported strategy use and RC performance were significantly improved in comparison to the CG. Students’ self-efficacy regarding RC did not improve significantly in either group, indicating that in order to increase self-efficacy for RC longer-term interventions might be needed. A follow-up three months after the intervention attested to the maintenance of the core elements of the training program. The findings of the study are discussed within the socio-cognitive approach of self-regulated learning.
The Effects of Value Conflicts on Stress in Chinese College Students: A Moderated Mediation Model
Authors: Ren Xiaoxiao , Xu Hao , Yue Tong , Wang Tong
Published: January 2025

Limited research has explored the connection between stress and value conflicts, particularly the influence of self-construal and self-concept clarity. This study surveyed a sample of 752 Chinese college students using the Depression–Anxiety-Stress Scale, Self-Construal Scale, Self-Concept Clarity Scale, and Portrait Values Questionnaire. The findings demonstrated that stress levels among participants were significantly associated with conflicts between self-transcendence and self-enhancement values, but no significant relationship was observed with openness vs. conservation conflicts. Mediation analysis revealed that self-concept clarity partially mediated this relationship. Additionally, moderated mediation analysis showed that the association between value conflict and self-concept clarity was stronger in students with high levels of independent self-construal. These results offer a deeper understanding of how value conflicts contribute to stress, highlighting potential pathways for targeted mental health interventions. Future studies should address the limitations of the current research and explore these relationships in more diverse contexts.
The Role of Osteoprotegerin in Breast Cancer: Genetic Variations, Tumorigenic Pathways, and Therapeutic Potential
Authors: Radhi Janan Husain , El-Hagrasy Ahmed Mohsen Abbas , Almosawi Sayed Husain , Alhashel Abdullatif , Butler Alexandra E.
Published: January 2025

Introduction: Osteoprotegerin (OPG), encoded by the TNFRSF11B gene, is linked to the development of breast cancer via several pathways, including interactions with the receptor activator of nuclear factor-κB (RANK) ligands, apoptosis-inducing proteins like TRAIL, and genetic variations such as single nucleotide polymorphisms (SNPs), directly altering gene expression. This review aims to investigate the role of OPG expression in breast cancer. Methods: A comprehensive literature search was conducted using PubMed Medline, Google Scholar, and ScienceDirect. Only full-text English publications from inception to September 2024 were included. Results: Studies have demonstrated that certain SNPs in the OPG gene, specifically rs3102735 and rs2073618, are linked to a higher risk of breast cancer development. Additionally, OPG’s function as a TRAIL decoy receptor may inhibit the death of cancer cells. Furthermore, OPG in the serum and its interactions with BRCA mutations are being investigated for their potential influence on breast cancer progression. Studies have found that OPG promotes tumorigenesis by enhancing cell proliferation, angiogenesis, and aneuploidy in normal mammary epithelial cells. Moreover, OPG mediates the tumor-promoting effects of interleukin-1 beta and may serve as a biomarker for breast cancer risk, particularly in BRCA1 mutation carriers, through its role in dysregulated RANK signaling. Lastly, the use of recombinant OPG in mouse models has been found to exert anti-tumor effects. Conclusions: In this review, the role of OPG in breast cancer is examined. OPG has a multifaceted role in breast cancer tumorigenesis and exerts its effects through genetic variations (SNPs), interactions with TNF-related apoptosis-inducing ligand (TRAIL), and the modulation of the pro-tumorigenic microenvironment effects of angiogenesis, cell survival, and metastasis. Additionally, OPG’s dual role as a tumor suppressor and promoter serves as a possible therapeutic target to enhance apoptosis, limit bone metastasis, and modulate the tumor microenvironment. Whilst much is now known, further studies are necessary to fully delineate the role of OPG.
Correlation Between Intravascular Platelet Aggregation in Tumors and Hypoxia-Inducible Factor 1 Alpha Expression in Epithelial Ovarian Cancer: Implications for Prognosis and Staging
Authors: Ryu Jung Min , Jeong Yoon Young , Lee Sun-Jae , Choi Youn Seok
Published: January 2025

Background/Objectives: This study aimed to evaluate the association between intravascular platelet aggregation in tumors and ovarian carcinoma prognosis and investigate underlying mechanisms. Methods: A retrospective analysis was conducted on 144 patients with ovarian carcinoma. Immunohistochemical staining for CD42b, hypoxia-inducible factor-1α (HIF-1α), platelet-derived growth factor (PDGF), and vascular endothelial growth factor (VEGF) was performed using tissue microarrays to assess intravascular platelet aggregation. Staining grades ranged from 1 to 3 for CD42b (platelet aggregation or microthrombus) and 0 to 3 for HIF-1α, PDGF, and VEGF. Results: Among the patients, 25 (17.4%) had grade 1 (no platelet aggregation), 85 (59.0%) had grade 2 (platelet aggregation), and 34 (23.6%) had grade 3 (microthrombus). The grade of intravascular platelet aggregation in ovarian carcinoma was statistically significantly associated with a poor prognosis (p = 0.037). In addition, in patients with more advanced stages of ovarian cancer, higher levels of intravascular platelet aggregation were observed. Additional analysis revealed a correlation between platelet aggregation and HIF-1α expression (correlation coefficient = 0.226, p= 0.006), while PDGF and VEGF showed no significant correlations. Conclusions: Intravascular platelet aggregation in tumors is associated with advanced stages and poor prognosis in ovarian carcinoma. The results of our study suggest a potential association with hypoxia induced by intravascular platelet aggregation in ovarian carcinoma.