bluuebunny/crossref_metadata_2025 huggingface dataset:
A Reliable Medium for Monitoring Atmospheric Deposition near Emission Sources by Using Snow from Agricultural Areas
Authors: Liu Jiayang , Sun Zaijin , Lei Wenkai , Xu Jingwen , Sun Qian , Shen Zhicheng , Lyu Yixuan , Shi Huading , Zhou Ying , Zhang Lan , Wu Zefeng , Pan Yuepeng
Published: December 2024

Atmospheric deposition is an important source of heavy metal in soil and the use of dust collection cylinders is a traditional monitoring method. This method has limitations in agricultural areas because polluted soil particles may become resuspended and eventually deposited into these cylinders, leading to overestimates in the amount of atmospheric deposition in soil. To address this concern, we propose that frequent snowfall can help suppress local soil dust resuspension and that fresh snow can serve as an efficient surrogate surface when collecting atmospheric deposition samples. To investigate the rationality of this method, 52 snow samples were collected from sites surrounding smelting plants in Anyang, an industrial region of North China. The results revealed that the concentration of cadmium in the melted snow ranged between 0.03 and 41.09 μg/L, with mean values three times higher around the industrial sites (5.31 μg/L) than background farmlands (1.54 μg/L). In addition, the cadmium concentration in the snow from sites surrounding the factories was higher in the north than in the south because of prevailing winds blowing from the southwest. Moreover, snow samples from sites with high concentrations of cadmium and sulfate can be categorized into different groups via the clustering method, conforming to the spatial distribution of particulate matter emissions and sulfur dioxide satellite column concentrations. Finally, a positive correlation was found between the cadmium content in the snow and the production capacity (R2 = 0.90, p < 0.05) and total permitted emissions (R2 = 0.69, p > 0.05) of the nearby factories. These findings demonstrate that snow is a reliable medium for documenting atmospheric dry deposition associated with specific industrial emissions.
Mapping Heatwave Socioeconomic Exposure in the Chinese Mainland for the Period of 2000–2019
Authors: Wu Wei , Liu Qingsheng , Li He , Huang Chong , Cheng Weiming
Published: December 2024

Mounting evidence suggests an increasing heatwave risk in the Chinese mainland, posing notable threats to public health and the socioeconomic landscape. In a comprehensive analysis, considering both climate and socioeconomic factors, including Gross Domestic Product (GDP) and population dynamics, we systematically evaluated the spatiotemporal distribution of heatwave socioeconomic exposure in the Chinese mainland from 2000 to 2019, utilizing a more comprehensive heatwave hazard index (HHI) that synthesizes heatwave intensity, frequency, and duration as climate factor for the first time. Results show that (1) Heatwave exposure is pronounced in eastern China, particularly in Southeast (SE), North China (NC), and Southwest (SW) regions. From 2000 to 2019, heatwave exposure showed an overall upward trend, with the most rapid escalation observed in the SE, NC, and SW regions. Population exposure manifests as a clustered expansion pattern, while GDP exposure demonstrates a more centralized distribution. (2) Climatic factors exert the most notable influence on population exposure, while GDP predominantly impacts economic exposure. The combination of climate and socioeconomic factors contributes less to exposure rates, except in the Northeast (NE) and Southwest (SW) regions where it impacts GDP exposure most. (3) High-risk hotspot cities include Shanghai, Beijing, Chongqing, Guangzhou, Wuhan, Zhengzhou, Hangzhou, Xi’an, Tianjin, and Nanjing. These findings underscore the urgent need for targeted interventions and mitigation strategies in these vulnerable areas.
Impact of Cyclonic Storm “Sitrang” over the Bay of Bengal on Heavy Rain and Snow in Eastern Tibet
Authors: Zhao Xiaotao , Danzeng Lunzhu , Chi Qu , Ma Xulin , Tan Yuting , Duodian Luozhu , Danzeng Ranzhen
Published: December 2024

Rainstorms and blizzards are common extreme weather events occurring in the eastern Tibet region. Their complex dynamic and thermodynamic mechanisms present challenges for regional meteorological research and forecasting. Based on station observation data and ERA5 atmospheric reanalysis datasets, a diagnostic analysis of the heavy rain and snow event in eastern Tibet from 24 to 27 October 2022 was conducted. The results indicate that (1) the influence of the cloud systems surrounding the Bay of Bengal storm “Sitrang” was a significant factor contributing to the occurrence of this heavy rain and snow weather. (2) Sustained stability of the southern branch trough and the western Pacific subtropical high favored the establishment and maintenance of the mid-level jet stream ahead of the storm. Storm “Sitrang” transported warm and moist air to eastern Tibet through the southwest mid-level jet stream, providing favorable moisture, dynamic, and thermal conditions for the heavy rain and snow. (3) Most importantly, symmetrical instability generated by the inclined motion of the storm’s warm and moist air emerged as the decisive mechanism driving the occurrence and development of the heavy rain and snow.
Zenith Tropospheric Delay Forecasting in the European Region Using the Informer–Long Short-Term Memory Networks Hybrid Prediction Model
Authors: Yuan Zhengdao , Lin Xu , Xu Yashi , Zhao Jie , Du Nage , Cai Xiaolong , Li Mengkui
Published: December 2024

Zenith tropospheric delay (ZTD) is a significant atmospheric error that impacts the Global Navigation Satellite System (GNSS). Developing a high-precision, long-term forecasting model for ZTD can provide valuable insights into the overall trends of predicted ZTD, which is essential for improving GNSS positioning and analyzing changes in regional climate and water vapor. To address the challenges of incomplete information extraction and gradient explosion in a single neural network when forecasting ZTD long-term, this study introduces an Informer–LSTM Hybrid Prediction Model. This model employs a parallel ensemble learning strategy that combines the strengths of both the Informer and LSTM networks to extract features from ZTD data. The Informer model is effective at capturing the periodicity and long-term trends within the ZTD data, while the LSTM model excels at understanding short-term dependencies and dynamic changes. By merging the features extracted by both models, the prediction capabilities of each can complement one another, allowing for a more comprehensive analysis of the characteristics present in ZTD data. In our research, we utilized ERA5-derived ZTD data from 11 International GNSS Service (IGS) stations in Europe to interpolate the missing portions of GNSS-derived ZTD. We then employed this interpolated data from 2016 to 2020, along with an Informer–LSTM Hybrid Prediction Model, to develop a long-term prediction model for ZTD with a prediction duration of one year. Our numerical results demonstrate that the proposed model outperforms several comparative models, including the LSTM–Informer based on a serial ensemble learning model, as well as the Informer, Transformer, LSTM, and GPT3 empirical ZTD models. The performance metrics indicate a root mean square error (RMSE) of 1.91 cm, a mean absolute error (MAE) of 1.45 cm, a mean absolute percentage error (MAPE) of 0.60, and a correlation coefficient (R) of 0.916. Spatial distribution analysis of the accuracy metrics showed that predictive accuracy was higher in high-latitude regions compared to low-latitude areas, with inland regions demonstrating better performance than those near the ocean. This study introduced a novel methodology for high-precision ZTD modeling, which is significant for improving accurate GNSS positioning and detecting water vapor content.
Intensified Drought Threatens Future Food Security in Major Food-Producing Countries
Authors: Liu Zihao , Lv Aifeng , Li Taohui
Published: December 2024

Drought is one of the most severe natural disasters globally, with its frequency and intensity escalating due to climate change, posing significant threats to agricultural production. This is particularly critical in major food-producing regions, where drought profoundly impacts crop yields. Such impacts can trigger food crises in affected regions and disrupt global food trade patterns, thereby posing substantial risks to global food security. Based on historical data, this study examines the yield response characteristics of key crops—maize, rice, soybean, spring wheat, and winter wheat—under drought conditions during their growth cycles, highlighting variations in drought sensitivity among major food-producing countries. The findings reveal that maize and soybean yield in China, the United States, and Brazil are among the most sensitive and severely affected by drought. Furthermore, using precipitation simulation data from CMIP6 climate models, the study evaluates drought trends and associated crop yield risks under different future emission scenarios. Results indicate that under high-emission scenarios, crops face heightened drought risks during their growth cycles, with China and the United States particularly vulnerable to yield reductions. Additionally, employing copula functions, the study analyzes the probability of simultaneous drought occurrences across multiple countries, shedding light on the evolving trends of multicountry drought events in major food-producing regions. These findings provide a scientific basis for assessing global food security risks and offer policy recommendations to address uncertainties in food supply under climate change.
Preliminary Evaluation of a Numerical System of Prediction for Surface Solar Irradiance and Cloudiness in a Site with a Subtropical Humid Climate
Authors: Cazes Boezio Gabriel
Published: December 2024

This study explores a prediction system for global horizontal irradiance and cloudiness in a humid subtropical terrestrial region. This system consists of regional simulations performed with the Weather Research and Forecasting model using the initial and boundary conditions from the Global Forecast System. The predictions show significant biases for the variable of interest, with notable variations within the daily and annual cycles. This study also finds significant biases in cloud incidence and clarity index predictions, with relevant diurnal and seasonal variations. During austral summer, multiplying the relative humidity of initial and boundary conditions by a fixed factor improves the forecasts of global horizontal irradiance and cloud incidence for the central hours of the day and the afternoon. During austral winter, an empirical correction of the clarity index obtained from the simulation’s outputs also shows the potential to improve the forecasts’ biases. This work proposes a hypothesis about the causes of the forecast biases.
Classification of the Crystal Structures of Orthosilicate Cathode Materials for Li-Ion Batteries by Artificial Neural Networks
Authors: Premasudha Mookala , Srinivasulu Reddy Bhumi Reddy , Cho Kwon-Koo , Hyo-Jun Ahn , Sung Jae-Kyung , Subba Reddy Nagireddy Gari
Published: December 2024

The crystal structures of orthosilicate cathode materials play a critical role in determining the physical and chemical properties of Li-ion batteries. Accurate predictions of these crystal structures are essential for estimating key properties of cathode materials in battery applications. In this study, we utilized crystal structure data from density functional theory (DFT) calculations, sourced from the Materials Project, to predict monoclinic and orthorhombic crystal systems in orthosilicate-based cathode-based materials with Li–Si–(Fe, Mn, Co)–O compositions. An artificial neural network (ANN) model with a 6-22-22-22-1 architecture was trained on 85% of the data and tested on the remaining 15%, achieving an impressive accuracy of 97.3%. The model demonstrated strong predictive capability, with only seven misclassifications from 267 datasets, highlighting its robustness and reliability in predicting the crystal structure of orthosilicate cathodes. To enhance interpretability and model reliability, we employed the Index of Relative Importance (IRI) to identify critical features influencing predictions. Additionally, a user-friendly graphical user interface was also developed to facilitate rapid predictions, enabling researchers to explore structural configurations efficiently and accelerating advancements in battery materials research.
Face-to-Face Interactions Estimated Using Mobile Phone Data to Support Contact Tracing Operations
Authors: Cumbane Silvino Pedro , Gidófalvi Gyözö , Cossa Osvaldo Fernando , Júnior Afonso Madivadua , Sousa Nuno , Branco Frederico
Published: December 2024

Understanding people’s face-to-face interactions is crucial for effective infectious disease management. Traditional contact tracing, often relying on interviews or smartphone applications, faces limitations such as incomplete recall, low adoption rates, and privacy concerns. This study proposes utilizing anonymized Call Detail Records (CDRs) as a substitute for in-person meetings. We assume that when two individuals engage in a phone call connected to the same cell tower, they are likely to meet shortly thereafter. Testing this assumption, we evaluated two hypotheses. The first hypothesis—that such co-located interactions occur in a workplace setting—achieved 83% agreement, which is considered a strong indication of reliability. The second hypothesis—that calls made during these co-location events are shorter than usual—achieved 86% agreement, suggesting an almost perfect reliability level. These results demonstrate that CDR-based co-location events can serve as a reliable substitute for in-person interactions and thus hold significant potential for enhancing contact tracing and supporting public health efforts.
Glioma Image-Level and Slide-Level Gene Predictor (GLISP) for Molecular Diagnosis and Predicting Genetic Events of Adult Diffuse Glioma
Authors: Le Minh-Khang , Kawai Masataka , Masui Kenta , Komori Takashi , Kawamata Takakazu , Muragaki Yoshihiro , Inoue Tomohiro , Tahara Ippei , Kasai Kazunari , Kondo Tetsuo
Published: December 2024

The latest World Health Organization (WHO) classification of central nervous system tumors (WHO2021/5th) has incorporated molecular information into the diagnosis of each brain tumor type including diffuse glioma. Therefore, an artificial intelligence (AI) framework for learning histological patterns and predicting important genetic events would be useful for future studies and applications. Using the concept of multiple-instance learning, we developed an AI framework named GLioma Image-level and Slide-level gene Predictor (GLISP) to predict nine genetic abnormalities in hematoxylin and eosin sections: IDH1/2, ATRX, TP53 mutations, TERT promoter mutations, CDKN2A/B homozygous deletion (CHD), EGFR amplification (EGFRamp), 7 gain/10 loss (7+/10−), 1p/19q co-deletion, and MGMT promoter methylation. GLISP consists of a pair of patch-level GLISP-P and patient-level GLISP-W models, each pair of which is for a genetic prediction task, providing flexibility in clinical utility. In this study, the Cancer Genome Atlas whole-slide images (WSIs) were used to train the model. A total of 108 WSIs from the Tokyo Women’s Medical University were used as the external dataset. In cross-validation, GLISP yielded patch-level/case-level predictions with top performances in IDH1/2 and 1p/19q co-deletion with average areas under the curve (AUCs) of receiver operating characteristics of 0.75/0.79 and 0.73/0.80, respectively. In external validation, the patch-level/case-level AUCs of IDH1/2 and 1p/19q co-deletion detection were 0.76/0.83 and 0.78/0.88, respectively. The accuracy in diagnosing IDH-mutant astrocytoma, oligodendroglioma, and IDH-wild-type glioblastoma was 0.66, surpassing the human pathologist average of 0.62 (0.54–0.67). In conclusion, GLISP is a two-stage AI framework for histology-based prediction of genetic events in adult gliomas, which is helpful in providing essential information for WHO 2021 molecular diagnoses.
Are Collagen Protons Visible with the Zero Echo Time (ZTE) Magnetic Resonance Imaging Sequence: A D2O Exchange and Freeze-Drying Study
Authors: Guo Tan , Moazamian Dina , Suprana Arya A. , Jerban Saeed , Chang Eric Y. , Ma Yajun , Carl Michael , Chen Min , Du Jiang
Published: December 2024

It is known that ultrashort echo time (UTE) magnetic resonance imaging (MRI) sequences can detect signals from water protons but not collagen protons in short T2 species such as cortical bone and tendons. However, whether collagen protons are visible with the zero echo time (ZTE) MRI sequence is still unclear. In this study, we investigated the potential of the ZTE MRI sequence on a clinical 3T scanner to directly image collagen protons via D2O exchange and freeze-drying experiments. ZTE and UTE MRI sequences were employed to image fully hydrated bovine cortical bone (n = 10) and human patellar tendon (n = 1) specimens. Then, each specimen was kept in a 30 mL syringe filled with D2O solution for two days. Fresh D2O was flushed every 2 h to reach a more complete D2O–H2O exchange. Later, the samples were lyophilized for over 40 h and then sealed in tubes. Finally, the samples were brought to room temperature and visualized using the identical 3D ZTE and UTE sequences. All hydrated bone and tendon specimens showed high signals with ZTE and UTE sequences. However, all specimens showed zero signal after the D2O exchange and freeze-drying procedures. Therefore, similar to UTE imaging, the signal source in ZTE imaging is water. The ZTE sequence cannot directly detect signals from collagen protons in bone and tendons.
Using Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules
Authors: Yu Yunguo , Gomez-Cabello Cesar A. , Makarova Svetlana , Parte Yogesh , Borna Sahar , Haider Syed Ali , Genovese Ariana , Prabha Srinivasagam , Forte Antonio J.
Published: December 2024

Current clinical care relies heavily on complex, rule-based systems for tasks like diagnosis and treatment. However, these systems can be cumbersome and require constant updates. This study explores the potential of the large language model (LLM), LLaMA 2, to address these limitations. We tested LLaMA 2′s performance in interpreting complex clinical process models, such as Mayo Clinic Care Pathway Models (CPMs), and providing accurate clinical recommendations. LLM was trained on encoded pathways versions using DOT language, embedding them with SentenceTransformer, and then presented with hypothetical patient cases. We compared the token-level accuracy between LLM output and the ground truth by measuring both node and edge accuracy. LLaMA 2 accurately retrieved the diagnosis, suggested further evaluation, and delivered appropriate management steps, all based on the pathways. The average node accuracy across the different pathways was 0.91 (SD ± 0.045), while the average edge accuracy was 0.92 (SD ± 0.122). This study highlights the potential of LLMs for healthcare information retrieval, especially when relevant data are provided. Future research should focus on improving these models’ interpretability and their integration into existing clinical workflows.
Exploring the Effect of Sampling Frequency on Real-World Mobility, Sedentary Behaviour, Physical Activity and Sleep Outcomes Measured with Wearable Devices in Rheumatoid Arthritis: Feasibility, Usability and Practical Considerations
Authors: Sarvestan Javad , Baker Kenneth F. , Del Din Silvia
Published: December 2024

Modern treat-to-target management of rheumatoid arthritis (RA) involves titration of drug therapy to achieve remission, requiring close monitoring of disease activity through frequent clinical assessments. Accelerometry offers a novel method for continuous remote monitoring of RA activity by capturing fluctuations in mobility, sedentary behaviours, physical activity and sleep patterns over prolonged periods without the expense, inconvenience and environmental impact of extra hospital visits. We aimed to (a) assess the feasibility, usability and acceptability of wearable devices in patients with active RA; (b) investigate the multivariate relationships within the dataset; and (c) explore the robustness of accelerometry outcomes to downsampling to facilitate future prolonged monitoring. Eleven people with active RA newly starting an arthritis drug completed clinical assessments at 4-week intervals for 12 weeks. Participants wore an Axivity AX6 wrist device (sampling frequency 100 Hz) for 7 days after each clinical assessment. Measures of macro gait (volume, pattern and variability), micro gait (pace, rhythm, variability, asymmetry and postural control of walking), sedentary behaviour (standing, sitting and lying) and physical activity (moderate to vigorous physical activity [MVPA], sustained inactive bouts [SIBs]) and sleep outcomes (sleep duration, wake up after sleep onset, number of awakenings) were recorded. Feasibility, usability and acceptability of wearable devices were assessed using Rabinovich’s questionnaire, principal component (PC) analysis was used to investigate the multivariate relationships within the dataset, and Bland–Altman plots (bias and Limits of Agreement) and Intraclass Correlation Coefficient (ICC) were used to test the robustness of outcomes sampled at 100 Hz versus downsampled at 50 Hz and 25 Hz. Wearable devices obtained high feasibility, usability and acceptability scores among participants. Macro gait outcomes and MVPA (first PC) and micro gait outcomes and number of SIBs (second PC) exhibited the strongest loadings, with these first two PCs accounting for 40% of the variance of the dataset. Furthermore, these device metrics were robust to downsampling, showing good to excellent agreements (ICC ≥ 0.75). We identified two main domains of mobility, physical activity and sleep outcomes of people with RA: micro gait outcomes plus MVPA and micro gait outcomes plus number of SIBs. Combined with the high usability and acceptability of wearable devices and the robustness of outcomes to downsampling, our real-world data supports the feasibility of accelerometry for prolonged remote monitoring of RA disease activity.
Application of Nano-Titanium Dioxide in Food Antibacterial Packaging Materials
Authors: Li Jiarui , Zhang Dequan , Hou Chengli
Published: December 2024

Food waste and food safety issues caused by food spoilage have been brought into focus. The inhibition of food spoilage bacteria growth is the key to maintaining food quality and extending the shelf life of food. Photodynamic inactivation (PDI) is an efficient antibacterial strategy which provides a new idea for the antibacterial preservation of food. Nano-titanium dioxide (nano-TiO2) with PDI characteristics has attracted the interest of many researchers with its elevated efficiency, broad-spectrum antibacterial resistance, low cost, safety, and non-toxicity. Nano-TiO2 photodynamic antibacterial properties have been studied extensively and has a great application value in the field of food packaging. The antibacterial properties of nano-TiO2 are linked to its photocatalytic activity and are influenced by factors such as reactive oxygen species production, bacterial types, etc. Polymer-based nano-TiO2 packaging has been prepared using various methods and applied in various foods successfully. In this review, the latest research on photocatalytic and antibacterial mechanisms and factors of nano-TiO2 is discussed, and its applications in food antibacterial packaging are also explored comprehensively. Challenges and future perspectives for nano-TiO2-based food packaging applications have been proposed. This review aims to provide a whole comprehensive understanding of novel antibacterial packaging systems based on nano-TiO2.
Plant-Scale Circular Economy Using Biological Reuse of Electrolyte Residues in the Amino Acid Industry
Authors: Kim Jun-Woo
Published: December 2024

The amino acid industry generates significant amounts of electrolyte residues, such as ammonium sulfate, acetic acid, and phosphoric acid, which cause challenges to sustainability. This short article investigates the feasibility of implementing a plant-scale circular economy through the recycling and biological reuse of these electrolyte residues. Scenario analyses of L-lysine (LYS) HCl, L-methionine (MET), and L-cysteine (CYS) HCl production highlight the environmental and economic benefits of the plant-scale circular economy. Despite these advances, challenges remain, including improving reuse rates for residues and reducing sodium ion content in the salt. This analysis emphasizes the need for integrated process design to enhance the circular economy, not only in amino acid production but also in other fermentation-based industries.
Spatial Cognitive EEG Feature Extraction and Classification Based on MSSECNN and PCMI
Authors: Wan Xianglong , Sun Yue , Yao Yiduo , Wan Hasan Wan Zuha , Wen Dong
Published: December 2024

With the aging population rising, the decline in spatial cognitive ability has become a critical issue affecting the quality of life among the elderly. Electroencephalogram (EEG) signal analysis presents substantial potential in spatial cognitive assessments. However, conventional methods struggle to effectively classify spatial cognitive states, particularly in tasks requiring multi-class discrimination of pre- and post-training cognitive states. This study proposes a novel approach for EEG signal classification, utilizing Permutation Conditional Mutual Information (PCMI) for feature extraction and a Multi-Scale Squeezed Excitation Convolutional Neural Network (MSSECNN) model for classification. Specifically, the MSSECNN classifies spatial cognitive states into two classes—before and after cognitive training—based on EEG features. First, the PCMI extracts nonlinear spatial features, generating spatial feature matrices across different channels. SENet then adaptively weights these features, highlighting key channels. Finally, the MSCNN model captures local and global features using convolution kernels of varying sizes, enhancing classification accuracy and robustness. This study systematically validates the model using cognitive training data from a brain-controlled car and manually operated UAV tasks, with cognitive state assessments performed through spatial cognition games combined with EEG signals. The experimental findings demonstrate that the proposed model significantly outperforms traditional methods, offering superior classification accuracy, robustness, and feature extraction capabilities. The MSSECNN model’s advantages in spatial cognitive state classification provide valuable technical support for early identification and intervention in cognitive decline.
Molecular and Cellular Mechanisms of Immunosenescence: Modulation Through Interventions and Lifestyle Changes
Authors: Pangrazzi Luca , Meryk Andreas
Published: December 2024

Immunosenescence, the age-related decline in immune function, is a complex biological process with profound implications for health and longevity. This phenomenon, characterized by alterations in both innate and adaptive immunity, increases susceptibility to infections, reduces vaccine efficacy, and contributes to the development of age-related diseases. At the cellular level, immunosenescence manifests as decreased production of naive T and B cells, accumulation of memory and senescent cells, thymic involution, and dysregulated cytokine production. Recent advances in molecular biology have shed light on the underlying mechanisms of immunosenescence, including telomere attrition, epigenetic alterations, mitochondrial dysfunction, and changes in key signaling pathways such as NF-κB and mTOR. These molecular changes lead to functional impairments in various immune cell types, altering their proliferative capacity, differentiation, and effector functions. Emerging research suggests that lifestyle factors may modulate the rate and extent of immunosenescence at both cellular and molecular levels. Physical activity, nutrition, stress management, and sleep patterns have been shown to influence immune cell function, inflammatory markers, and oxidative stress in older adults. This review provides a comprehensive analysis of the molecular and cellular mechanisms underlying immunosenescence and explores how lifestyle interventions may impact these processes. We will examine the current understanding of immunosenescence at the genomic, epigenomic, and proteomic levels, and discuss how various lifestyle factors can potentially mitigate or partially reverse aspects of immune aging. By integrating recent findings from immunology, gerontology, and molecular biology, we aim to elucidate the intricate interplay between lifestyle and immune aging at the molecular level, potentially informing future strategies for maintaining immune competence in aging populations.
Comparative Genomic Hybridization (CGH) in New World Monkeys (Primates) Reveals the Distribution of Repetitive Sequences in Cebinae and Callitrichinae
Authors: Milioto Vanessa , Arizza Vincenzo , Vizzini Aiti , Perelman Polina L. , Roelke-Parker Melody E. , Dumas Francesca
Published: December 2024

The intraspecies and interspecies Comparative Genomic Hybridization (CGH) between the closely related Cebidae species, capuchin monkeys (Cebus capucinus, Sapajus apella), and the tamarins (Saguinus mystax, Leontocebus fuscicollis) was performed to analyze their genomes. In particular, this approach determines balanced and unbalanced repetitive DNA sequence distribution and reveals dynamics during evolution. Capuchin monkeys are considered the most ancestral group with conserved syntenies compared to the hypothetical ancestral New World monkeys’ karyotype. Also, more derived karyotypes of phylogenetically distant species from the Saguinus and Leontocebus genera are analyzed here. The distribution of repetitive sequences has been traditionally studied through classical staining methods of cytogenetics. It has been hypothesized that repeats are species-specific and their conservation across closely related species are also common; their role in the genome has been extensively studied even though its role in speciation is not well studied and understood. The CGH shows bright signals with balanced and imbalanced DNA involving different genome regions: such as predominantly repetitive DNA at centromeric positions, and interstitial distribution with extended blocks. Cross-species CGH demonstrated the origin of some heterochromatic regions and identified apomorphic heterochromatin expansion events. The uncovered distribution of repetitive sequences is analyzed from an evolutionary perspective to elucidate the genomic dynamics of the repetitive sequences at the level of chromosomal organization.
Effects of Different River Crab Eriocheir sinensis Polyculture Practices on Bacterial, Fungal and Protist Communities in Pond Water
Authors: Bao Yun , Li Bing , Jia Rui , Zhou Linjun , Hou Yiran , Zhu Jian
Published: December 2024

Microorganisms, including bacteria, fungi, and protists, are key drivers in aquatic ecosystems, maintaining ecological balance and normal material circulation, playing vital roles in ecosystem functions and biogeochemical processes. To evaluate the environmental impact of different river crab polyculture practices, we set up two different river crab (Eriocheir sinensis) polyculture practices: one where river crabs were cultured with mandarin fish (Siniperca chuatsi), silver carp (Hypophthalmichthys molitrix), and freshwater fish stone moroko (Pseudorasbora parva), and another where river crabs were cultured just with mandarin fish and silver carp. These two polyculture practices were referred to as PC and MC, respectively. We analyzed the water bacterial, fungal, and protist communities in the PC and MC groups using 16S, ITS, and 18S ribosomal RNA high-throughput sequencing. We found that the PC group obviously increased the diversity of microbial communities and altered their composition. The bacterial community held the narrowest habitat niche and exhibited the weakest environmental adaption compared to fungal and protist communities. The PC group altered the co-occurrence networks of bacteria, fungi, and protist, leading to more complex and stable communities of fungi and protist. Furthermore, the PC group shifted the assembly mechanism of the bacterial community from being predominantly deterministic to predominantly stochastic processes, with relatively minor impacts on the fungal and protist communities. Environmental factors, especially dissolved oxygen (DO), were significantly associated with the communities of bacteria, fungi, and protists, with DO being the major contributor to changes in the microbial communities. Our results suggest that the polyculture of river crab with mandarin fish, silver carp, and stone moroko was an effective and viable attempt, and it was superior in terms of microbial community diversity and stability.
Panduratin A Inhibits TNF Alpha-Stimulated Endothelial Cell Activation Through Suppressing the NF-κB Pathway
Authors: Kiatsoonthon Kriangkrai , Phimthong Nitchakarn , Potikanond Saranyapin , Wikan Nitwara , Nimlamool Wutigri
Published: December 2024

Upon exposure to inflammatory stimuli including TNF-α, endothelial cells are activated leading to the adhesion of monocytes to their surface. These events are involved in the pathophysiology of atherosclerosis. Since TNF-α activates the NF-κB pathway, which contributes to atherosclerosis, targeting this signaling pathway may help prevent the risk of developing the disease. The current study elucidated the inhibitory effect of panduratin A (PA) on TNF-α-induced endothelial activation and monocyte adhesion. We discovered that PA reduced the level of pro-inflammatory cytokine IL-6 and chemokine MCP-1 in the media collected from endothelial cells stimulated with TNF-α. In addition, PA inhibited the expression of ICAM-1 and VCAM-1 on the surface of TNF-α-induced endothelial cells resulting in a decrease in the number of monocytes attached to endothelial cell surface. Mechanistically, PA prevented IκB degradation and specifically suppressed NF-κB phosphorylation and nuclear translocation in endothelial cells. However, PA had no inhibitory effect on the phosphorylation of AKT, ERK1/2, p38, and JNK. Taken together, PA blocked the production of cytokine and chemokine, adhesion molecules, and monocyte adhesion in response to TNF-α stimulation, in part, through NF-κB inhibition. Our study suggests that PA may possibly be effective in blocking the pathophysiology of atherosclerosis.
Optimization of Carotenoids and Other Antioxidant Compounds Extraction from Carrot Peels Using Response Surface Methodology
Authors: Mantiniotou Martha , Athanasiadis Vassilis , Kalompatsios Dimitrios , Lalas Stavros I.
Published: December 2024

Carrots, scientifically known as Daucus carota L., are among the most popular and widely consumed vegetables. They are used for cooking and juice production, both industrially and in households, resulting in large amounts of waste each year, mainly from the peel. The peels are rich in antioxidant compounds that can be used either as cosmetics or as food and feed additives. Therefore, in this work, the extraction of these compounds was optimized using green techniques (pulsed electric field and/or ultrasonication) and solvents. Response surface methodology was applied to achieve the optimization. Under optimum conditions, the total polyphenol yield was 8.26 mg gallic acid equivalents per g dry weight (dw) and the total carotenoid content was 137.44 μg β-carotene equivalents per g dw. The optimum extract reportedly showed an antioxidant capacity of 76.57 μmol ascorbic acid equivalents (AAE) per g dw by FRAP assay and 63.48 μmol AAE per g dw by DPPH assay, while the total ascorbic acid content was 2.55 mg per g dw. Furthermore, chromatographic quantification of individual bioactive compounds through a diode array detector was performed, wherein catechin yielded the highest proportion (18.6%) of the total 6.88 mg/g dw. This study addressed inquiries regarding the valorization of bioactive compounds from carrot peels, as well as several strategies for recovering their diverse bioactive components using green procedures and solvents.
Diagnostic and Therapeutic Advances of RNAs in Precision Medicine of Gastrointestinal Tumors
Authors: Liu Runhan , Zhou Jiaxin , Chen Xiaochen , Zhang Jie , Chen Qunzhi , Liu Xiaoming , Yao Kunhou
Published: December 2024

Gastrointestinal tumors present a significant challenge for precision medicine due to their complexity, necessitating the development of more specific diagnostic tools and therapeutic agents. Recent advances have positioned coding and non-coding RNAs as emerging biomarkers for these malignancies, detectable by liquid biopsies, and as innovative therapeutic agents. Many RNA-based therapeutics, such as small interfering RNA (siRNA) and antisense oligonucleotides (ASO), have entered clinical trials or are available on the market. This review provides a narrative examination of the diagnostic and therapeutic potential of RNA in gastrointestinal cancers, with an emphasis on its application in precision medicine. This review discusses the current challenges, such as drug resistance and tumor metastasis, and highlights how RNA molecules can be leveraged for targeted detection and treatment. Additionally, this review categorizes specific diagnostic biomarkers and RNA therapeutic targets based on tissue type, offering a comprehensive analysis of their role in advancing precision medicine for gastrointestinal tumors.
Recent Advances and Perspectives on the Use of Mineralocorticoid Receptor Antagonists for the Treatment of Hypertension and Chronic Kidney Disease: A Review
Authors: Miyasako Kisho , Maeoka Yujiro , Masaki Takao
Published: December 2024

Chronic kidney disease (CKD) is a major public health concern around the world. It is a significant risk factor for cardiovascular disease (CVD), and, as it progresses, the risk of cardiovascular events increases. Furthermore, end-stage kidney disease severely affects life expectancy and quality of life. Type 2 diabetes and hypertension are not only primary causes of CKD but also independent risk factors for CVD, which underscores the importance of effective treatment strategies for these conditions. The current therapies, including angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, and sodium–glucose co-transporter 2 inhibitors, are administered to control hypertension, slow the progression of CKD, and reduce cardiovascular risk. However, their efficacy remains suboptimal in certain instances. Mineralocorticoid receptor (MR), a nuclear receptor found in various tissues, such as the kidney and heart, plays a pivotal role in the progression of CKD. Overactivation of MR triggers inflammation and fibrosis, which exacerbates kidney damage and accelerates disease progression. MR antagonists (MRAs) have substantial beneficial effects in patients with cardiac and renal conditions; however, their use has been constrained because of adverse effects, such as hyperkalemia and kidney dysfunction. Recently, novel non-steroidal MRAs are more efficacious and have superior safety profiles to steroidal MRAs, making them promising potential components of future treatment strategies. Here, we discuss recent findings and the roles of MRAs in the management of hypertension and CKD, with a focus on the evidence obtained from fundamental research and major clinical trials.
Direct-Acting Antivirals in Hepatitis C Treatment for Renal Impairment: Liver Safety Concerns and Effectiveness in Peritoneal Dialysis
Authors: Hung Hsuan-Yu , Hung Wei-Liang , Gu Ye , Chen Chung-Yu
Published: December 2024

Background/Objectives: Glecaprevir/pibrentasvir (G/P) and elbasvir/grazoprevir (EBR/GZR) are effective treatments for chronic hepatitis C (CHC), especially in patients with chronic kidney disease (CKD). However, both regimens carry a risk of drug-induced liver injury (DILI). This study investigates the association between renal failure and DILI, using real-world data, and assesses the effectiveness of these treatments in peritoneal dialysis patients. Methods: A retrospective cohort study was conducted using data from the Ditmanson Research Database, including patients with CHC treated with G/P or EBR/GZR from 1 August 2017 to 31 December 2020. Patients were categorized into CKD and normal kidney function (NKF) groups. Two sensitivity analyses were performed to assess DILI risk. The study was approved by the DMF-CYCH Institutional Review Board (CYCH IRB No.: 2021010). Results: In 837 patients, DILI risk, expressed as incidence rate ratios (IRR), was 0.64 (95% CI 0.25–1.63) in the NKF group and 1.29 (95% CI 0.12–14.23) in the CKD group. Sensitivity analyses showed consistent results. A case–time–control analysis suggested liver instability despite treatment, with comorbid liver tumors (aOR 18.89; 95% CI 5.4–66.12) and hypertension (aOR 4.25; 95% CI 1.49–12.15) linked to higher DILI risk. All peritoneal dialysis patients (n = 10) achieved a 100% SVR12 rate. Conclusions: This real-world study supports the effectiveness of G/P and EBR/GZR in peritoneal dialysis patients. Comorbidities that impair liver function are key predictors of abnormal liver parameters, highlighting the need for careful monitoring during CHC treatment.
DRG2 as a Biomarker to Enhance the Predictive Efficacy of PD-L1 Immunohistochemistry Assays
Authors: Mani Muralidharan , Choi Seong Hee , Kwon Hyuk Nam , Park Jeong Woo
Published: December 2024

PD-L1 immunohistochemistry (IHC) assays are used as a companion diagnostic for immunotherapy with immune checkpoint inhibitors (ICIs). However, despite the association between PD-L1 expression and clinical benefit from ICIs, the PD-L1 IHC assay is not sufficiently accurate in predicting response to ICIs; some patients with high PD-L1 expression do not respond to ICIs. Recently, researchers provided insights into why some patients with high PD-L1 expression fail to respond to ICIs. They discovered that DRG2 is a critical regulator of PD-L1 endosomal trafficking in cancer cells, which is essential for the proper localization of PD-L1 on the cell surface. Although DRG2-depleted cells express high levels of PD-L1 and are PD-L1 IHC-positive, the PD-L1 sequestered in early endosomes does not respond to ICIs. Therefore, a companion diagnostic combining DRG2 expression with a PD-L1 IHC assay may improve the therapeutic response to PD-1/PD-L1 ICIs.
Maternal Macronutrient Intake and Associated Risk for Gestational Diabetes Mellitus: Results from the BORN2020 Study
Authors: Tranidou Antigoni , Tsakiridis Ioannis , Magriplis Emmanuela , Apostolopoulou Aikaterini , Chroni Violeta , Tsekitsidi Eirini , Kalaitzopoulou Ioustini , Pazaras Nikolaos , Chourdakis Michail , Dagklis Themistoklis
Published: December 2024

Background/Objectives: Limited evidence links maternal macronutrient intake to gestational diabetes mellitus (GDM) risk. Therefore, we evaluated these intakes both before and during pregnancy, comparing macronutrient data against the European Food and Safety Authorities’ (EFSA) Dietary Reference Values (DRVs). Methods: Data were prospectively collected from the Greek BORN2020 epidemiologic pregnant cohort, which included 797 pregnant women, of whom 14.7% were diagnosed with GDM. A multinomial logistic regression model assessed the association between macronutrient intake and GDM, adjusting for maternal, lifestyle, and pregnancy-related factors. Results: Women with GDM had higher maternal age (34.15 ± 4.48 vs. 32.1 ± 4.89 years), higher pre-pregnancy BMI (median 23.7 vs. 22.7 kg/m2), and were more likely to smoke during mid-gestation (17.95% vs. 8.82%). Pre-pregnancy energy intake exceeding EFSA recommendations was associated with increased GDM risk (aOR = 1.99, 95%CI: 1.37–2.86). During mid-gestation, higher dietary fiber intake (aOR = 1.05, 95%CI: 1.00–1.10), higher protein intake (aOR = 1.02, 95% CI: 1.00–1.04), and higher protein percentage of energy intake (aOR = 1.08, 95%CI: 1.01–1.17) were all significantly associated with increased GDM risk. Changes from pre-pregnancy to pregnancy showed significant increases in dietary fiber intake (aOR = 1.07, 95%CI: 1.04–1.10), protein (aOR = 1.00, 95%CI: 1.00–1.01), fat (aOR = 1.00, 95%CI: 1.00–1.01), vegetable protein (aOR = 1.01, 95%CI: 1.00–1.03), animal protein (aOR = 1.00, 95%CI: 1.00–1.01), and monounsaturated fatty acid (MUFA) intake (aOR = 1.01, 95%CI: 1.00–1.02), all of which were associated with increased GDM risk. Conclusions: Energy intake above upper levels set by EFSA, as well as increased protein, MUFA, and fiber intake, although beneficial in balanced intakes, may negatively affect gestation by increasing GDM likelihood when consumed beyond requirements.
The Usefulness of Soluble ST2 Concentration in Heart Failure with Reduced Ejection Fraction to Predict Severe Impairment in Exercise Capacity Assessed in Cardiopulmonary Exercise Testing
Authors: Dudek Magdalena , Kałużna-Oleksy Marta , Sawczak Filip , Kukfisz Agata , Soloch Aleksandra , Migaj Jacek , Lesiak Maciej , Straburzyńska-Migaj Ewa
Published: December 2024

Background/Objectives: Heart failure (HF) constitutes a complex clinical syndrome that is highly prevalent worldwide, comprises a serious prognosis, and results in a reduced quality of life. Exercise capacity is one of the most significant parameters involved in the prognosis in HF patients. Our objective was to evaluate the relationship between the selected cardiopulmonary exercise testing (CPET) parameters and the concentration of novel biomarker sST2 in a group of patients with heart failure with reduced ejection fraction (HFrEF). Methods: A group of 135 patients with HFrEF was enrolled in this prospective cohort study. Patients were in the stable phase of the disease in the prior 4 weeks and received optimal medical treatment. Clinical and biochemical parameters were investigated. All patients performed maximal CPET. Results: The mean (SD) concentration of sST2 was 45.5 ± 39.2 ng/mL. Based on the CPET results, the cut-off value (52.377 ng/mL) was established, optimal for the discrimination of relative peakVO2 < 12 mL/kg/min. Patients were divided into two groups according to sST2 cut-off values determined with an ROC curve (AUC 0.692, 95% CI: 0.567–0.816). The mean relative peakVO2 in patients with higher sST2 was 14.5 ± 4.6 mL/kg/min, while in the second group, it was 17.6 ± 5.2 (p = 0.002). In the sST2 ≥ 52.377 ng/mL group, 55.6% of patients achieved VO2 < 50%. Subjects with lower sST2 values obtained higher values of PETCO2 (p < 0.001) and higher values of pulse O2 (p = 0.01). VE/VCO2slope (p = 0.002) was higher in patients with increased sST2 concentration. Conclusions: The concentration of sST2 protein is substantially associated with the clinical severity of heart failure with reduced left ventricular ejection fraction assessed by functional capacity through CPET.