bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transcutaneous Auricular Vagus Stimulation Attenuates LPS-Induced Depression-like Behavior by Regulating Central α7nAChR/JAK2 Signaling
Authors: wang junying , Wang Yu , Chen Yu , Zhang Jinling , Zhang Yue , Li Shaoyuan , Zhu Haohan , Song Xingke , Hou Liwei , Lei Wang , Wang Yifei , Zhang Zixuan , Rong Peijing 
Published: October 2023

Abstract

Depression is a serious disabling disease worldwide. Accumulating evidence supports that there is a close relationship between depression and inflammation, then inhibition of neuroinflammation may be another mechanism for the treatment of depression. Transcutaneous Auricular Vagus Stimulation(taVNS), as a non-invasive transcutaneous electrical stimulation, could effectively treat depression, but its mechanism is unclear. In this study, rats with depression-like behavior were induced by intraperitoneal injection of lipopolysaccharide (LPS). The rats were randomly divided to Control group, LPS group, taVNS + LPS group and the same as the α7 nicotinic acetylcholine chloride receptor(α7nAChR) (-/-) gene knockout rats. The expressions of tumor necrosis factor-alpha (TNF-ɑ) and phosphorylated-Janus kinase2(p-JAK2),phosphorylated-signal transducer and activator of transcription3(p-STAT3) in the hypothalamus, amygdala and hippocampus were detected by Western Blot. We observed that LPS significantly decreased the sucrose preference, the time of into the open arms in the elevated plus maze, and the number of corssing and reaing in the open field test. TaVNS treatment improves these depression-like behaviors, but taVNS is not effective in α7nAChR (-/-) gene knockout rats. The expression of TNF-ɑ signifciantly increased, and the expression of p-Jak2 and p-STAT3 markedly decreased in the hypothalamus and amygdala induced by LPS. TaVNS could significantly reverse the above-mentioned phenomena but had rare improvement effect for α7nAChR(-/-) rats. We conclude that the antidepressant effect of taVNS for LPS-induced depressive rats is related to α7nAchR/JAK2 signal pathway in the hypothalamus and amygdala.
Embayed Beach Configuration Explained by Wave Sheltering
Authors: Silva Ana Nobre , Taborda Rui , Andrade César 
Published: October 2023

Abstract
Embayed beaches, characterized by their distinctive planform curvature, are a common feature along coastlines worldwide. This study introduces a novel approach to describe bay shape that extends our understanding about the processes that control embayed beach development. The novel approach was thoroughly tested against one-line models and applied to real-world cases covering a wide range of spatial scales, wave climate conditions and geomorphological settings. Here we show that the equilibrium curvature of embayed beaches can be correctly described and predicted by considering headland-provided offshore wave sheltering alone, without explicitly accounting for wave shoaling, refraction, diffraction, or longshore transport. This holds true as long as the offshore wave climate is accurately characterized, including complete information regarding wave direction and spread. For narrow-banded dominant swell wave regimes, the inclusion of background wind sea components has been identified as crucial for predicting curvature in the more sheltered embayed domain. The presented model significantly contributes to the understanding of how waves shape embayed beaches.
Initial Insights into Deep Learning Analysis for Detecting Brain Oxygenation Changes from MRI
Authors: Andrei Alexandra-Georgiana , Brătan Costin Andrei , Tocilă-Mătășel Claudia , Mureșanu Bogdan , Ionescu Bogdan , Tebeanu Ana Voichița , Dascălu Monica , Bobeș Gabriela , Popescu Ionuț , Neagu Adriana , Franți Eduard , Iana Gheorghe , Iorgulescu Gabriela 
Published: October 2023

Abstract
The act of screaming, often viewed as a primal expression of intense emotion, serves a cathartic purpose that can help individuals release negative emotions during times of nervousness. Psychologists have recognized the therapeutic benefits of emotional release through screaming and have incorporated it into various therapeutic approaches. Understanding the intricate workings of the human brain during the act of screaming can be achieved through innovative medical tools, with Magnetic Resonance Imaging (MRI) standing as a paramount choice. Even without dedicated fMRI analysis, extracting texture data from MRI images can contribute significantly to our understanding of the brain's functional processes. In this study, we involved a multidisciplinary team and the purpose was to investigate, for the fist time, if there are any brain oxygenation level changes that appear in the MRI scans while the subject is screaming and if these can be detected using deep learning models.MRI scans were carried out as part of evaluation of brain oxygenation levels during screaming for 4 subjects. Two types of recordings were acquired from each one of them: pre-screaming MRI and screaming MRI. The interpretation of the MRI images was carried out with deep-learning algorithms trained to automatically detect the differences in the images. We used an AlexNet classifier and the results show that brain oxygenation changes appear while a person is screaming and can be automatically detectable using MRI analysis. The classifier used attained an accuracy of 89.92\%, underscoring discernible distinctions between MRI scans captured during both normal, non-screaming conditions and instances of screaming. The article summarizes significant open questions, lessons learned, and these preliminary results are very valuable, opening up numerous study directions for future research.
A Novel Approach for Secure Communication Networks: Dual Key Extraction-based NOMA Technique
Authors: V Kaveri , Deepa Thangavel 
Published: October 2023

Abstract
With the exponential growth of wireless communication technologies, ensuring the confidentiality of transmitted data has become paramount. This paper introduces a cutting-edge encryption scheme utilizing Non-Orthogonal Multiple Access (NOMA) for secure key generation. It proposes a method that utilizes an identical secret key derived from channel randomness and distance to prevent eavesdropping in NOMA communication, thus enhancing the system's resistance against potential attacks. NOMA is employed to simultaneously serve multiple users at the same time and frequency resources by applying different power levels to each user. In the proposed encryption scheme, NOMA principles combine channel randomness and user-base station distance in a non-orthogonal manner. This approach allows the generation of identical encryption keys shared by multiple users, ensuring an efficient and robust security mechanism benefits increased spectral efficiency and an improved system. The non-orthogonal of the generated encryption key enhances the security level, as it becomes harder for eavesdroppers to intercept and decipher the key. Integrating NOMA in the key generation process introduces an additional layer of unpredictability, further bolstering security. Extensive simulations and analyses were conducted to assess the proposed NOMA-based dual key extraction performance. A qualitative comparison between sources and a new proposed direction shows the Secrecy rate, key secrecy capacity, and secrecy outage probability demonstrate its effectiveness in providing a high level of security while incurring minimal overhead on the communication system.
Investigation of drag reduction by slurry-like drag-reducing agent in microtube flow using response surface methodology (RSM)
Authors: Cheng Zhensong , Zhang Xin , Dai Xiaodong , Song Xinwang , Li Lei , Liu FeiFan , Zhang Guoxin , Lu Yuan , Wang Xudong 
Published: October 2023

Abstract

In this study, we investigated the drag reduction property of a premixed slurry drag reducer in a millimeter-scale pipe. The aim of this study is to establish the correlation between Fanning friction factor and drag reducer concentration (C) and volume flow (Q). First, the experimental plan was designed by using the response surface method (RSM), and then the experimental data were processed to establish the quadratic correlation between the response variable and the description variable. After that, ANOVA analysis of variance was used to verify the accuracy of the experimental data and the correlation. Finally, the prediction model is extended to a larger concentration and volume flow range, and it is found that the accuracy between the predicted value of friction coefficient and the experimental value is ±30%, thus verifying that the correlation is suitable for the small-scale fully turbulent region.
Identification of oxidative stress-related diagnostic markers from gestational diabetes mellitus and immune infiltration by machine learning
Authors: Gan Bei , Wu Xiuyan , Zhang Xia , Chen Lihong , Jianhua Li 
Published: October 2023

Abstract
Background
During pregnancy, the instability of glucose tolerance led to gestational diabetes mellitus (GDM) in the expectant mother, resulting in significant harm to both the pregnant woman and the infant. Oxidative stress-related genes (OSRGs) play a crucial role in diabetes. However, it is still not fully understood whether OSRGs also affect GDM. Therefore, this study sought to find hub genes from OSRGs can influence the diagnosis and treatment of GDM.
Methods
GSE203346 and GSE70493 from Gene Expression Omnibus (GEO) database were used as training and verification sets. OSRGs were downloaded from GeneCard database. The differentially expressed genes (DEGs) were identified with the limma package for GO/KEGG/GSEA analysis in the clusterProfiler package. These OSRGs were further analyzed using LASSO and SVM-RFE to identify diagnostic biomarkers for GDM. A diagnostic model was established using the Glm function and verified with ROC curve. The relationship between hub genes and immune infiltration was analyzed.
Results
The study identified 937 DEGs from GSE203346 dataset and 10 differentially expressed oxidative stress-related genes (DEOSRGs) were screened. The LASSO and SVM-RFE results showed that seven genes (DNMT3B, SLC6A2, DNMT1, CACNA1C, PENK, S100B and H2BC3) were identified as OSRGs diagnostic biomarkers for GDM. The AUC value of GDM diagnosis model was 0.9920 in training set and 0.8524 in verification set. Three hub genes (SLC6A2, PENK and S100B) were identified between DEOSRGs and LASSO and SVM-RFE results. ROC analysis showed that AUC values of SLC6A2, PENK and S100B were 0.7024, 0.7119 and 0.6964, respectively. The results of immune infiltration suggest that these SLC6A2, PENK and S100B might have regulatory role in the immune response of GDM. Finally, 19 drugs targeting S100B and SLC6A2 were retrieved from the DrugBank database, some of which may be useful for the treatment of GDM.
Conclusions
By combining DEOSRGs with two machine learning algorithms, three hub genes (SLC6A2, PENK and S100B) were explored. Three hub genes are strongly associated with several infiltrating immune cells, immunoinhibitors, immunostimulators and chemokines. These hub genes could potentially contribute to a better understanding of the development and drug treatment of GDM.
An 18-Gene Algorithm urine test for predicting prostate cancer metastasis and castration- resistance
Authors: Guo Jinan , Gu Di , Johnson Heather , Zeng Qingsong , Zhang Xuhui , Xia Taolin , Feng Xiaoyan , Zhang Heqiu , Simoulis Athanasios , Wu Alan HB , Li Fei , Tan Wanlong , Johnson Allan , Dizeyi Nishtman , Abrahamsson Per-Anders , Xiao Kefeng , Zou Chang , Chen Lingwu , Persson Jenny L. 
Published: October 2023

Abstract
Background
There is an urgent need to accurately predict the risk of distant metastasis and metastatic castration-resistant prostate cancer (mCRPC) for treatment decision-making and reducing mortality. An artificial intelliegnce machine learning screening method in combination with liquid biopsy urine test was used to develop a novel gene expression-based algorithm for predicting prostate cancer distant metastasis and mCRPC in newly diagnosed patients.
Methods
Random forest machine learning algorithm screening was conducted to develop and validate a gene expression-based algorithm to predict the risk of metastasis and mCRPC using liquid biopsy urine samples from the patients with distant metastasis and mCRPC collected from multi-center retrospective (n = 505) and prospective (n = 243) studies with a median follow-up period of 8 and 6 years respectively. The prognostic performance of the algorithm test was assessed using univariate/multivariate Cox regression analyses, Kaplan-Meier disease-free survival plot, and univariate/multivariate discriminant analyses.
Results
A novel 18-Gene Algorithm urine test was developed and validated. The algorithm showed high accuracy to predict distant metastasis with an area under the curve (AUC) of 0.96 (95% CI 0.87–1.05) and 0.98 (95% CI 0.96–1.02) in the retrospective and prospective cohort respectively. In the prospective cohort, a hazard ratio (HR) to predict metastasis-free survival was 93.8 (95% CI 29.3-300.6) (p < 0.0001). In a prospective mCRPC cohort (n = 205), the algorithm predicted mCRPC-free survival with a HR of 154.4 (95% CI 36.8-647.5) (p < 0.0001) and predicted mCRPC with AUC of 0.98 (95% CI 0.95–1.01). In contrast, currently using clinicopathological parameters, such as Gleason grade and pre-operative PSA, had much lower prognostic power.
Conclusions
The novel 18-Gene Algorithm is the first highly accurate and non-invasive liquid biopsy urine test to predict distant metastasis and mCRPC in newly diagnosed prostate cancer patients with the potential to improve prostate cancer treatment decision-making and reduce mortality.
Big Dermatological Data Service for Precise and Immediate Diagnosis by Utilizing Pre-trained Learning Models
Authors: Elbes Moahammad , AlZu'bi Shadi , Kanan Tarek , Mughaid Ala , Abushanab Samia 
Published: October 2023

Abstract
Artificial intelligence (AI) approaches have been shown to be effective in classifying skin diseases and outperforming dermatologists in diagnosis. Using big data as a dermatological diagnosis service can present several challenges. One challenge is the need to accurately label and classify large amounts of data, such as images of infected skin. This can be time-consuming and resource-intensive. It is important to implement proper safeguards to protect sensitive medical information. Despite these challenges, the use of big data in dermatology can lead to several outcomes. It can improve the accuracy of diagnoses, leading to better patient outcomes. It can also help to identify patterns and trends in skin conditions, allowing for earlier detection and prevention. In addition, big data can be used to identify risk factors for certain conditions, enabling targeted preventative measures.
Convolutional neural networks (CNNs) have been widely used for skin lesion classification, and recent advances in machine learning algorithms have led to a decrease in misclassification rates compared to manual categorization by dermatologists. This article utilizes the use of big data for accurate dermatological diagnosis services, it introduces the use of various CNNs for classifying different types of skin cancer. While deep learning and pretrained transfer learning techniques have advantages over traditional methods, they also have limitations and the potential for incorrect identification under certain circumstances. This work discusses these vulnerabilities and employs pretrained models to classify 11 different skin diseases, including monkey pox. The performance of the system is evaluated using accuracy, loss, precision, recall, and F1 score, and the results show that the system is able to diagnose the 11 skin illnesses with an accuracy rate of 97% using transfer learning in Keras and computer vision models. The best model was found to be Inception_ResNetV2 with 50 epochs and the Adam optimizer.
Micromechanical Analysis of Diffuse Instability in 2D Granular Materials
Authors: Shi Jingshan , Guo Peijun 
Published: October 2023

Abstract
By taking advantage of the directional statistics and DEM simulated tests, diffuse instability in the 2D granulate assembly of circular particles was thoroughly investigated with particular attention paid to the microstructural origin for triggering this collapse. Hill’s stability postulate and criterion were employed during the micromechanical analysis. A Stress rate Force Fabric (SrFF) function and a Strain rate Displacement Fabric (SrDF) function are firstly derived, based on which a micro-structural second-order work is pursued for 2D granular systems. It is recognized that rate of contact density( mv), evolution of microstructure anisotropy (Ν2D ) characterizing both contact and contact forces networks, and variation of directional average normal contact force (f n0 ) are the three microstructural variables of the second-order work and therefore characterize the onset of diffuse instability. Numerical simulation results further indicate that or the rate of fabric anisotropy in the strong sub-network (a cs ) dominates the capability of shear resistance and can be used to predict the onset of diffuse instability with the assistance of the loading direction. However, f n0 is related to the rate of fabric anisotropy for the weak sub-network(a wc ), whose degradation is found to be microstructural origin for diffuse instability. Moreover, the initiation of static liquefication occurs when f n0 ≈0 and the collapse of the weak sub-network is always accompanied by a drastic decrease in contact density or coordination number which is frequently observed during diffuse instability.
Effect of intervention on screen time in preschoolers: A systematic review and meta-analysis
Authors: Xi Xiaobin , Jiang Jieying , Ding Yuming , Wu Yan , Zhou Xiaojiao , Liu Wanshuai , Huang Xin , Ye Sunyue 
Published: October 2023

Abstract
Background
This study aimed to evaluate the effectiveness of interventions on screen time (ST) in preschool children and identify potential moderators of intervention effects.
Methods
A systematic review and meta-analysis were conducted to assess interventions on ST in preschool children. Databases searched included CNKI, Wanfang, SinoMed, Web of Science, Embase, PubMed, and Cochrane library, until December 31, 2022. The quality of the included studies was evaluated using the risk of bias tool, and the effect size was evaluated by standardized mean difference (SMD) between intervention and control group.
Results
A total of 26 studies with 7944 participants were included in the meta-analysis. The overall SMD (95%CI) of interventions on ST in preschoolers was − 0.61 (-0.92,-0.31). Subgroup analysis showed that the intervention effect was moderated by gender of child (X2 = 3.74, P = 0.05) and treatment received by the control group (X2 = 8.54, P = 0.003).
Conclusion
While these interventions have shown promise, there is still much to learn about their efficacy and applicability across diverse populations and intervention characteristics. This study is registered on the PROSPERO database (CRD42023402518).
Development and application of Chinese medical ontology for diabetes mellitus
Authors: Hu Jie , Huang Zixian , Ge Xuewen , Shen Yulin , Xu Yihan , Zhang Zirui , Zhou Guangyin , Wang Junjie , Lu Shan , Yu Yun , Wan Cheng , Zhang Xin , Huang Ruochen , Liu Yun , Cheng Gong 
Published: October 2023

Abstract
Objective
To develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies.
Materials and Methods
We used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering.
Results
The current CDMO consists of 3,752 classes, 182 fine-grained object properties with hierarchical relationships, 108 annotation properties, and over 12,000 mappings to other well-known medical ontologies in English. Based on the CDMO and clinical practice guidelines, we developed 200 rules for diabetes diagnosis, treatment, diet, and medication recommendations using the Semantic Web Rule Language. By injecting ontology knowledge, CDMO enhances the performance of the T5 model on a real-world Chinese medical question answering dataset related to diabetes.
Conclusion
CDMO has fine-grained semantic relationships and extensive annotation information, providing a foundation for medical artificial intelligence applications in Chinese contexts, including the construction of medical knowledge graphs, clinical decision support systems, and automated medical question answering. Furthermore, the development process incorporated natural language processing and cross-lingual ontology mapping to improve the quality of the ontology and improved development efficiency. This workflow offers a methodological reference for the efficient development of other high-quality Chinese as well as non-English medical ontologies.
The relationship between visceral adiposity index and acute pancreatitis
Authors: Wang Tingting , Cao Fei , He Jiajun , Zhu Lei , Yang Xiaoxi , Ma Shuli , Zhu Quping , Li Yuanhao , Yang Chuting , Liu Jun , Chen Weiwei , Chen Chaowu 
Published: October 2023

Abstract

Introduction: Visceral adipose tissue has been indicated closely connected with the severity of acute pancreatitis (AP). Visceral adiposity index (VAI) is a mathematical model that consists of waist circumference, body mass index, triglyceride and high-density lipoprotein cholesterol, which has been demonstrated to be a better indicator of visceral fat than other traditional indices.
Methods: A retrospective analysis was conducted on a cohort of 1174 patients diagnosed with AP. These patients were categorized into two groups based on their VAI values: the normal VAI (NVAI) group and the elevated VAI (EVAI) group.
Results: The EVAI group were much younger, mainly male and had higher incidence of severe acute pancreatitis (SAP) compared with the NVAI group (p < 0.001). The EVAI group developed higher incidences of persistent respiratory failure, acute peripancreatic fluid collection (APFC) and acute necrotic collection (ANC). The VAI level and the percentage of EVAI showed an increasing trend with the severity of AP (p < 0.001). EVAI was the most independent risk factor for persistent respiratory failure (OR = 6.405, 95% CI 2.317-17.705), APFC (OR = 2.093, 95% CI 1.255-3.578) and ANC (OR = 4.910, 95% CI 1.736-13.887).
Conclusions: EVAI was strongly related to the severity of AP. It was the most independent risk factor of persistent respiratory failure, APFC and ANC.