bluuebunny/crossref_metadata_2025_split huggingface dataset:
Toll Like Receptor 4 and IFNγ Signalling axis as a therapeutic target in Triple negative Breast Cancer
Authors: Jondhale Mayur , Salunke Rushigandha , Saldanha Elveera , Singh Amit , Bandre Bipin , Patel Vainav , Joshi Shalaka , Sahay Ayushi , Chandrani Pratik , Mukherjee Nupur 
Published: December 2024

Abstract

Triple Negative Breast Cancer (TNBC) is a hormone-receptor-negative (ER/PR/Her2)- subtype of breast cancer which has an immunogenic character and shows extensive molecular heterogeneity. TLRs (Toll like receptors) are a class of pattern recognition receptors regulating innate immune response. In the present study, we want to envisage the immunomodulatory role of TLR4 in TNBC. The TLR expression was assessed in TNBC tumors using qPCR/flowcytometry/multiplex immunofluorescence imaging. Also, the same was compared using TNBC-TCGA datasets. Also, the correlation of TLR expression with distinct cytokines was compared in TNBC tumors using ELISA and by analysis of TNBC-TCGA transcriptome datasets. A variable pattern of TLR expression (TLR 3/4/6/9) was observed in TNBC tumors with comparably higher expression in TIL (Tumor-infiltrating lymphocyte)-enriched TNBC tumors than TIL-low tumors. Interestingly, TLR4 expression was observed to be higher in tumor cells (CDH1+) compared to that in stromal cells (CDH1-) within TNBC tumors. The TLR4 expression was found to significantly correlate with IL10/IFNϒ cytokine expression in TNBC tumors. Analysis of TNBC-TCGA datasets predicted longer survival probability in TIL-enriched TNBC patients with high TLR 4 and IFNϒ expression. This study suggests that targeting the TLR4 and IFNϒ signaling could be a potential therapeutic target in TNBC.
Serum β-Hydroxybutyrate decline: a common pathological factor for metabolic dysfunction-associated steatotic liver disease and cognitive impairment
Authors: Nie Lijuan , Yang Xinyi , Wang Ying , Sun Jing , Sun Yaohuan , Zhang Yujuan , Zhou Xiqiao 
Published: December 2024

Abstract

Background: Recently, cognitive impairment has emerged as a consequential aspect of metabolic dysfunction-associated steatotic liver disease (MASLD), prompting speculation that diminished β-Hydroxybutyrate (BHB) production might be intricately involved in its pathogenesis, along with that of cognitive decline. However, the intricate interplay between the circulating BHB levels and these conditions remains unclear.
Methods: A BHB ELISA assay kit was used to quantify serum BHB concentrations in both healthy individuals and patients with MASLD or cognitive impairment. Pearson’s correlation analysis assessed associations between BHB levels and pertinent clinical parameters. The diagnostic utility of serum BHB levels in identifying MASLD and cognitive impairment was assessed using Receiver Operating Characteristic (ROC) curves.
Results: The findings revealed a stark contrast: patients with MASLD and cognitive impairment exhibited notably lower serum BHB levels than healthy individuals. Furthermore, BHB levels were inversely associated with BMI (r = -0.407, P < 0.001), AST (r = -0.233, P = 0.044), TC (r = -0.266, P= 0.021), LDL-C (r = -0.236, P = 0.046), and HbA1c (r = -0.416, P < 0.001), while demonstrating a positively relationship with cognitive performance, as indicated by Mini-Mental State Examination (r = 0.434, P < 0.001) and Montreal Cognitive Assessment scores (r = 0.433, P < 0.001). Notably, ROC curve analysis underscored potential diagnostic significance of serum BHB in both MASLD and cognitive impairment. Intriguingly, supplementary research illuminated a negative correlation between serum BHB levels and age among healthy individuals (r = -0.775, P < 0.001).
Conclusion: This work implies that a decrease on circulating BHB levels may lead to the occurrence of MASLD and cognitive impairment.
Design of enhanced deep belief network based on APSO
Authors: Wang Longyang , Xu Sheng , Pan Jiacheng , Lin Cong 
Published: December 2024

Abstract

The deep belief network (DBN) has been widely applied in various fields. However, the traditional DBN has low prediction accuracy and it is difficult to determine the network structure when processing the continuous data. To solve these problems, an enhanced continuous deep belief network (EDBN) based on adaptive particle swarm optimization (APSO) is proposed in this paper. First, based on the gaussian noise transformation and deep learning theory, a EDBN is presented to improve the prediction accuracy of the deep belief network. Second, an APSO with adaptive mutation strategy is employed to optimize the network structure. Finally, the performance of EDBN-APSO is verified by comparing with other algorithms on Lorentz time series, concrete compressive strength test and the total phosphorus prediction. The results demonstrate the effectiveness of the proposed EDBN.
Development of a coumarin-based Schiff base fluorescent probe and its application in detection of Cu²⁺
Authors: Ma Qiurui , Yang Xinli , Zhao Yingying 
Published: December 2024

Abstract

A highly practical Schiff base fluorescent probe, (E)-3-amino-N'-((7-(diethylamino)-2-oxo-2H-chromen-3-yl)methylene)thiophene-2-carbohydrazide (M), with a facile synthetic route has been successfully developed. M has been utilized for the specific detection of Cu2+ in THF/H2O Tris buffer solution (v/v=9:1, 0.01 M, pH=7.4) via the fluorescence quenching mechanism. The detection of Cu2+ by M has been largely unaffected by interfering ions and has demonstrated a distinct dual-channel response in both colorimetry and fluorescence. The response time of M towards Cu2+ is remarkably fast, taking only 30 s. Additionally, M exhibits exceptional sensitivity with a limit of detection (LOD) as low as 1.76×10-7 M. The stoichiometric ratio between M and Cu2+ has been determined to be 1:1 through Job's Plot, while the binding constant has been calculated as 1.19×104 M-1 using the Benesi-Hildebrand equation. The structure of M has been elucidated by 1H NMR and ESI-MS analyses, thereby confirming the binding mode between M and Cu2+. Further validation has been achieved through DFT calculations. The test paper based on M has finally been prepared for the rapid and convenient detection of Cu2+. The M has also been utilized for the detection of Cu2+ in real samples, including lake water, onions, and coffee, demonstrating favorable recovery rates. Moreover, successful visual detection has been achieved in food samples such as bean sprouts and rice. The aforementioned examples have collectively illustrated the practical applicability of M in authentic samples.
Coupled SWAT and SWT-CNN-LSTM model to improve watershed streamflow simulation
Authors: Ren chengqing , Chang Jianxia , Wang Xuebin , Niu Chen , Wang Liyuan , Zhang Junhao 
Published: December 2024

Abstract

Accurately simulating hydrological conditions is a significant challenge for hydrologists globally, particularly in capturing patterns of high and low streamflow due to the ongoing impacts of climate change and human activities on water resource availability. To address this challenge, this study proposes an innovative integrated approach that combines the Soil and Water Assessment Tool (SWAT), Stationary Wavelet Transform (SWT), and interpretable machine learning models, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The hydrological and meteorological features generated by SWAT were preprocessed using SWT and then used as inputs for the CNN-LSTM model. During the testing period, the SWAT-SWT-CNN-LSTM model achieved R² and NSE values of 0.90 and 0.88, respectively, outperforming other machine learning models (e.g., Support Vector Machine, Random Forest, LSTM) as well as the calibrated SWAT model. Additionally, it effectively reduced the underestimation of high streamflow and the overestimation of low streamflow, with deviations in both categories maintained within 1.3%. From a machine learning perspective, solar radiation and percolation volume are identified as key factors influencing local streamflow, while precipitation shows the highest sensitivity to streamflow variation. Even with a reduced sliding window length, the model's R² and NSE during the training and testing periods remained above 0.88 and 0.80, respectively, demonstrating exceptional stability. Furthermore, the SWAT-CNN-LSTM comparative experiment showed that SWT effectively mitigates the overfitting issue in machine learning. This study highlights that the organic integration of SWAT, SWT, CNN, and LSTM, along with the application of interpretable methods such as Individual Conditional Expectation (ICE), Partial Dependence Plots (PDP), and Shapley Additive Explanations (SHAP), not only enhances model performance significantly but also increases the credibility of machine learning results, paving a valuable new pathway for long-term streamflow simulations in watersheds.
Protein Language Model Supervised Scalable Approach for Diverse and Designable Protein Motif-Scaffolding with GPDL
Authors: Chen Haifeng , Zhang Bo , Liu kexin , Zheng Zhuoqi , Zhu Junjie , Li Zhengxin , Liu YUnfeiyang , Mu Junxi , Wei Ting 
Published: December 2024

Abstract

Proteins perform essential roles in numerous biological processes, largely driven by the three-dimensional structure of several key motif residues. Recently, a variety of energy-based and machine learning backbone generation methods have been developed to solve the motif-scaffolding task. However, it is still challenging to generate diverse and accurate scaffold structures around motifs for models either fine-tuned pre-trained multiple sequence alignment-based (MSA-based) structure prediction models or trained from scratch. Here, we introduced Generative Protein Design by Language model (GPDL) for effectively replacing traditional MSA-based pretraining. Using our scalable design strategy, GPDL successfully solved 22 out of 24 benchmark problems and outperformed other methods by generating 33.5% more unique designable clusters than RFdiffusion. This demonstrates that our approach can generate accurate and physically plausible structures across diverse protein design scenarios. GPDL also showed strong robustness in orphan proteins that have low sequence similarity with the training set. Our approach underscores the promise of protein language models in protein design and has the potential to accelerate the discovery of novel functional proteins for a wide range of biological and therapeutic applications.
Scalable Variational Learning for Noisy-OR Bayesian Networks with Normalizing Flows for Complex Cascading Disaster Systems
Authors: Li Xuechun , Xu Susu 
Published: December 2024

Abstract

Sudden-onset disasters such as earthquakes often induce multiple cascading hazards and impacts, causing human and economic losses. While recent advances in remote sensing technologies provide valuable data for rapid hazard assessment, current methods face two fundamental limitations. First, the inherent causal relationship and co-location of disaster-induced hazards and impacts makes the decoupling of individual hazard or impact particularly challenging. Second, and critically for disaster response, existing methods cannot adapt to new information during the critical response period - they provide only static initial estimates despite the continuous influx of ground truth data from field reconnaissance. This inability to update and refine assessments in real-time severely limits their practical utility for emergency response, where understanding of the disaster's impact typically evolves significantly over time. Herein, we present online-DisasterVINF, a framework that uniquely addresses both limitations. We leverage Noisy-OR Bayesian networks, which are particularly suited for modeling how multiple hazards independently contribute to the observed impacts, combined with normalizing flows to provide a more expressive alternative to simple log-linear relationships. The framework is also capable of improving its estimation by incorporating ground truth data as it becomes available through post-disaster reconnaissance. We develop a novel variational inference approach that jointly approximates posteriors by leveraging complex causal relationships and remote sensing techniques. We evaluate our framework on multiple seismic events from diverse countries around the globe. Our results demonstrate that online-DisasterVINF significantly enhances estimation accuracy compared to existing methods, while our analysis shows the online updating mechanism substantially improves model performance as ground truth becomes available, underlining its adaptability for real-time disaster response.
Examining the Effectiveness of an Educational and Assessment Cardiopulmonary Resuscitation Simulator Using Extended Reality and Artificial Intelligence Technology in Red Crescent Rescuers
Authors: Arjmand  , Kolivand Pir Hossein , Namdar Peyman , RamezanianLangeroodi MARYAM 
Published: December 2024

Abstract

Introduction:
The advancement of technology today has made it essential to train Red Crescent rescuers using innovative learning tools, such as Extended Reality (XR) and Artificial Intelligence (AI). This study aimed to examine the effectiveness of an educational and assessment CPR (Cardiopulmonary Resuscitation) simulator using modern technologies among Red Crescent rescuers.
Methodology:
This interventional cross-sectional study was conducted between 2023 and 2024 in Iran. The study population consisted of rescuers in Tehran. The simple random sampling method was used. Initially, a pre-test was administered to two groups of 15 participants: one received traditional training and the other received modern XR-based training. Following the training, both groups underwent tests in four stages, with each stage one week apart. The performance data during the resuscitation process were collected using various sensors, image processing cameras, virtual reality (VR) glasses, smart mannequins, robotic components, a central microcontroller processor, trackers, and other smart simulator equipment. The data were evaluated quantitatively and qualitatively in accordance with the standards of the international OSCE (Objective Structured Clinical Examination).
Findings:
The results showed that groups trained using simulators demonstrated a significant improvement in post-test scores compared to pre-test scores. In groups that had no prior CPR training, the simulator-based training increased the average scores by 9.54, which was significantly higher than the 3.55 increase observed in the traditional training method. The group trained with the simulator showed an average score increase of 10.64, which was much higher than the 3.89 increase in the traditional training method.
Conclusion
The use of XR simulators and AI led to a significant improvement in the practical skills of rescuers in cardiopulmonary resuscitation. It is recommended that this technology be widely implemented in educational programs. For those who have already undergone traditional training, complementary courses using simulators could further enhance their skills and knowledge.
Squidiff: Predicting cellular development and responses to perturbations using a diffusion model
Authors: Leong Kam , He Siyu , Zhu Yuefei , Tavakol Daniel , Ye Haotian , Lao Yeh-Hsing , Zhu Zixian , Xu Cong , Chauhan Shradha , Garty Guy , Tomer Raju , Vunjak-Novakovic Gordana , Zou James , Azizi Elham 
Published: December 2024

Abstract

Single-cell sequencing has revolutionized our understanding of cellular heterogeneity and responses to environmental stimuli. However, mapping transcriptomic changes across diverse cell types in response to various stimuli and elucidating underlying disease mechanisms remains challenging. Studies involv- ing physical stimuli, such as radiotherapy, or chemical stimuli, like drug testing, demand labor-intensive experimentation, often hindering the rapid advancement of mechanistic insight and drug discovery. To address this, we present Squidiff, a diffusion model-based generative framework designed to predict transcriptomic changes across diverse cell types in response to a wide range of environmental changes. We demonstrate Squidiff’s robustness across various scenarios, including cell differentiation, gene perturbation, and drug response prediction. Through continuous denoising and semantic feature integration, Squidiff effectively learns transient cell states and predicts high-resolution transcriptomic landscapes over time and conditions. Furthermore, we applied Squidiff to model the development of blood vessel organoids and cellular responses to neutron irradiation and growth factors. Our results demonstrate that Squidiff enables in silico screening of cell molecular landscapes, facilitating rapid hypothesis generation and providing valuable insights for precision medicine.
Assessment of Bacterial Contamination, Physical, and Chemical Characteristics of Table Eggs in Khartoum North
Authors: Hemeda Sahar , Gadallh Mogahid , Ibrahim Bashir , A.Ali Elrasheed 
Published: December 2024

Abstract

Background
Table eggs are an essential dietary component due to their high nutritional value; however, they are prone to bacterial contamination, particularly on eggshells, posing significant health risks. In Sudan, contamination risks are heightened by inadequate hygiene practices, poor storage conditions, and limited regulatory oversight. These factors compromise the safety and quality of eggs, which are widely distributed in local markets. This study aimed to evaluate bacterial contamination, assess physical characteristics, examine chemical properties (pH), and investigate associations among these factors in table eggs from Khartoum North.
Results
A total of 138 eggs were sampled, with bacterial analysis revealing significant contamination on eggshells. Staphylococcus spp., was the most prevalent contaminant (89.1%), followed by Corynebacterium spp., (23.9%), E. coli (19.5%), and Salmonella spp., (15.2%). No bacterial presence was detected in the albumen or yolk. Physical quality assessments classified 32.6% of eggs as grade AA, 34.8% as grade A, and 32.6% as grade B. Pearson correlation analysis identified weak associations between shell strength and Haugh unit (r = 0.133) and between egg weight and Haugh unit (r = -0.175). One-way ANOVA showed no significant differences in shell strength or shell thickness across grades. Chemical analysis indicated stable pH values across grades, with yolk pH ranging from 6.363 to 6.555 and albumen pH from 8.463 to 8.604.
Conclusions
Table eggs in Khartoum North exhibit significant bacterial contamination on eggshells, particularly Staphylococcus spp., while internal contents remain sterile. Physical characteristics and pH values showed minimal variation across grades. These findings highlight the need for improved hygiene practices and storage conditions to enhance the safety and quality of table eggs in Sudan.
Distinct solvation patterns of OH– versus H3O+ charge defects at
electrified gold/water interfaces govern their properties
Authors: Park Chanbum , Ghosh Soumya , Forbert Harald , Marx Dominik 
Published: December 2024

Abstract

Understanding the solvation structures and charge migration mechanisms of OH– and H3O+ species at metal interfaces as a function of bias potential is imperative for developing more efficient electrochemical devices. In this paper, we report the detailed solvation structures of OH– and H3O+ at gold electrodes relevant to alkaline and acidic aqueous conditions using ab initio molecular dynamics simulations at finite bias potentials. Our findings reveal that the adsorption propensities of OH– and H3O+ are strongly influenced by the oscillating net atomic charge of water normal to the electrified interface. This leads to preferentially adsorbed OH– in the first water layer extremely close to the gold surface even at zero bias, while H3O+ is expelled toward the second layer even under negative bias potentials. Due to the combined effects of the net atomic charge of water in the different solvation layers and local polarization effects of the gold surface, OH– exhibits peculiar hypercoordinated solvation structures in its resting and active state for charge migration very different from those known from OH– in bulk water. We find that gold atoms strongly interact with both OH– solvation complexes and effectively replace hydrogen-bond-donating water molecules, while H3O+ is not much affected compared to bulk. The active state of OH– is particularly distorted due to being anchored at two neighboring gold atoms that are far apart such that the free energy barrier for charge transfer gets strongly increased, thus lowering the corresponding rate.
Productivity and hours worked: A circular theoretical approach
Authors: Cette Gilbert , Drapala Simon , Lopez Jimmy 
Published: December 2024

Abstract

We propose a theoretical model of the relationship between productivity and working hours, assuming a circular relationship between these two variables. It appears that the direction and magnitude of the impact of productivity on working hours depend on the returns to scale of hours worked and on the elasticity of substitution between leisure and labor income. These findings significantly contribute to our understanding of work and leisure trade-offs and explain the contrasted results in the empirical literature.
Evaluation of Renal Toxicity Induced by Coartem® and Protective Effects of Salvia Rosmarinus L. Nanoemulsion in an Acute Kidney Injury Model
Authors: Paes Alberto Souza , Koga Rosemary de Carvalho Rocha , Almeida Hellen Karine Santos , de Azevedo Maria Eduarda Garcia , Dantas Henrick Vinícius Prado , Lopes Gisele Rocha , Teixeira Thiago Afonso Carvalho , Carvalho José Carlos Tavares 
Published: December 2024

Abstract

Acute kidney injury (AKI) is a severe condition associated with high morbidity and mortality, often caused by ischemia-reperfusion (IR) injury. This study explored the potential of Salvia rosmarinus L. nanoemulsion (RoN) in mitigating renal damage in an ischemia-reperfusion-induced rat model, focusing on its combination with Coartem®. Adult male Wistar rats were divided into Negative Control, Water + Ischemia Control, Coartem®+Ischemia, S. rosmarinus L. nanoemulsion + Ischemia, and Coartem®+S.Rosmarinus L. nanoemulsion + Ischemia. The treatments were administered orally for 30 days, except for the Coartem® group, which was treated for seven days. Renal ischemia was induced for 20–25 minutes, followed by reperfusion. Subsequently, hematological, biochemical, and histopathological analyses were conducted. The S. rosmarinus L. nanoemulsion effectively preserved blood homeostasis in the treated groups, maintaining red blood cells, hemoglobin, and hematocrit levels. Additionally, it reduced protein, urea, potassium, and creatinine levels, indicating renal protection. Furthermore, decreased levels of NGAL in the nanoemulsion-treated groups suggested a protective effect against AKI. Histopathological analyses revealed reduced organ alterations in the nanoemulsion-treated groups. These findings indicate that the nanoemulsion may help preserve renal function and reduce the biochemical and histopathological changes associated with AKI, suggesting that RoN could be a promising therapeutic option for preventing IR-induced renal damage, even when combined with Coartem®.