bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring the Mechanism of Gentiana rigescens in the Treatment of Pulmonary Fibrosis (PF) Based on Network Pharmacology
Authors: Hu Ming-xing , Song Yan-Qiu , Gao Peng-Fei , Li Hai-Feng , Liu Wei-Hong 
Published: May 2023

Abstract
Background Pulmonary fibrosis (PF) is a severe chronic interstitial lung disease with high mortality, and there is currently a lack of definite and effective treatment methods. Studies have found that gentiopicroside, a secoiridoid glycoside compound derived from plants of the Gentiana genus, can significantly improve pulmonary inflammation and fibrosis lesions in mice with pulmonary fibrosis. However, the mechanism of its anti-fibrotic effect is unclear. Therefore, in this study, we used the virtual computer technology of network pharmacology to theoretically explore the mechanism of Gentiana rigescens 's anti-pulmonary fibrosis effect, providing theoretical reference and direction for subsequent experiments.Results A total of 10 active compounds and 920 drug-target proteins were identified from the TCMSP database. The compound-target-pathway-disease network showed that G. rigescens could potentially treat PF by regulating the MDM2, ERBB2 and VEGFA, signaling pathways through its key targets, including AKT1, TNF, and MAPK1. The protein‒protein interaction network revealed that these targets had strong interactions with each other, indicating a potential synergistic effect of G. rigescens in treating PF. The GO and KEGG enrichment analyses further supported the potential anti-pulmonary fibrosis mechanisms of Gentiana rigescens, including regulating the inflammatory response, ECM-receptor interaction, and TGF-beta signaling pathways.Conclusion Our study provides a systematic analysis of the potential anti-pulmonary fibrosis mechanisms of G. rigescens based on network pharmacology. These findings could contribute to the development of novel treatments for PF and provide a basis for further experimental studies.
Integrated Effecient Approach to Botnet Detection using Supervised Machine Learning
Authors: Gwarzo Zahraddeen , Anyaiwe Destiny , Olawoyin Richard , Tawalbeh Loai , Zohdy Mohamed , Allaf Zirak 
Published: May 2023

Abstract
Botnets are one of the most significant threats to cybersecurity. These are networks of compromised nodes of computers and other devices on the internet commonly used for a wide range of malicious activities across the globe. When deployed they can cause immense damage to computer systems by infecting them with malware in the likes of worms, trojans, viruses, rootkits, spyware, etc., stealing data, and launching large-scale attacks. As botnets are becoming increasingly sophisticated, traditional methods for detecting them are no longer effective. In this research paper, we propose a fully distributed collaborative botnet detection model using a binary classifier that decomposes the problem based on the expertise and experience of a distributed collaborative network, to efficiently detect a wide range of botnets. We measured the accuracy of collaborators’ responses ta o series of requests that we broadcast over a period of time accurately and efficiently detect botnets. We used 4 different classification algorithms to process the dataset. We evaluate our approach on a real-world dataset and each classifier outperforms existing methods for botnet detection and performed well with an average detection rate of 93.5%. Effective algorithms were also developed to improve the reputation and robustness of the detection system.
Health-Related Quality of Life Analyses in Nonfunctioning Pituitary Macroadenoma Patients Identifies At-Risk Populations
Authors: Wisdom Amy J. , Dyer M. Aiven , Horick Nora K. , Yeap Beow. Y. , Miller Karen K. , Swearingen Brooke , Loeffler Jay S. , Shih Helen A. 
Published: May 2023

Abstract
Purpose
The quality of life (QoL) impact of multidisciplinary treatment for patients with nonfunctioning pituitary macroadenomas (NFPMA) is unclear. We sought to investigate associations between patient factors, clinical data, and patient-reported QoL in patients with NFPMA.
Methods
Patients with treated NFPMA and > 1 year of follow up after transsphenoidal surgery (TSS) and with no evidence of progressive disease were evaluated utilizing the following patient-reported outcome measures: RAND-36-Item Health Survey, Multidimensional Fatigue Inventory, Cognitive Failures Questionnaire.
Results
229 eligible patients completed QoL questionnaires a median of 7.7 years after initial transsphenoidal surgery (TSS). 25% of participants received radiation therapy (RT) a median of 2.0 years (0.1–22.5) after initial TSS. Patients who received RT were younger (median age 46 v 58, p < 0.0001), had larger tumors (28 mm v 22 mm, p < 0.0001), were more likely to have visual symptoms (65% v 34%, p = 0.0002), and were more likely to have hypopituitarism (93% v 62%, p < 0.0001). Patients with hypopituitarism reported worse energy and fatigue and cognitive function (p < 0.03). Patients who received RT reported significantly worse general health, physical health, physical fatigue and cognitive functioning (p < 0.05). The largest QoL differences were in patients who experienced a financial stressor, independent of treatment type.
Conclusion
Hypopituitarism, radiation therapy after TSS, and financial stressors are associated with more impaired QoL in patients with NFPMA. Awareness of these factors can better guide use and timing of radiation therapy in addition to identifying patients who can benefit from multidisciplinary surveillance.
A machine learning-based model for predicting lymph node metastasis risk in vulvar cancer patients
Authors: Liao Huiming , Liu Tingyan , Xia Jianhong 
Published: May 2023

Abstract
Background As the accuracy of predictive models for vulval cancer patients is limited, this study aims to construct and compare the risk of lymph node metastasis of vulval cancer based on machine learning (ML) algorithms using the Surveillance, Epidemiology, and End Results public database of the National Cancer Institute .Methods Data from the SEER database were extracted for registrations between 2010 and 2015 and randomly divided into a training set and a validation set (7:3). Six machine learning (ML) technologies were used to develop predictive models for distant metastasis, including multi-layer perception models (MLP), support vector machines (SVM), naïve Bayes (NBC), decision trees (DT), random forests (RF), and k-nearest neighbors (KNN). Evaluation and comparison of different predictive models were performed using receiver operating characteristic (ROC) curves (AUC-ROC) and decision curve analysis (DCA).Results A total of 6,813 patients were involved and randomly divided into a training set (N = 4,768) and a validation set (N = 2,045). Based on the Boruta algorithm, 11 important factors were identified. In the training set, the RandomForest model performed best (AUC = 0.820), significantly better than the other five models. In the validation set, the RandomForest model also demonstrated better predictive ability than the other models (AUC = 0.799), according to DCA results. Feature importance analysis showed that the recursive feature elimination (RFE) algorithm was used to select key variables in the RandomForest model, and finally five important factors were determined, among which the T stage of the tumor was the most important variable.Conclusion The RandomForest model was proven to be an effective algorithm with better predictive ability. This model is intended to support future decisions regarding the risk of lymph node metastasis in vulval cancer
Computational Surgical Planning Simulation of Intracranial Aneurysms with AneuPlan TM - An exploratory study
Authors: Wang Ming , Zou Rong , Feng Lei , Leng Xiaochang , Wang Ning , Gao Yuhai , Chu Jifa , Wu Jiong , Fiehler Jens , Siddiqui Adnan H , Xiang Jianping , Wan Shu 
Published: May 2023

Abstract
Background It is still a challenge to choose the most appropriate endovascular treatment strategy for intracranial aneurysms. In this study, we aimed to investigate the application of AneuPlan™, based on computational fluid dynamics analysis, in the surgical planning of intracranial aneurysms.Methods: 9 patients with intracranial aneurysm were enrolled, whose surgical planning was made according to the Surgical Planning Program analysis, before endovascular treatment. Clinical characteristics, procedural details, stimulated hemodynamics analysis after surgery, site effect and 6-month follow-up were recorded. A descriptive analysis was performed.Results: Among the 9 patients, 5 patients received flow diverter implantation, 3 patients received stent-assisted coiling and 1 patient received simple coiling. The velocity of blood flow into the aneurysm and wall shear stress of the aneurysm were obviously reduced. No hemorrhagic or ischemic complication occurred in all patients. No recurrence of the aneurysm at 6-month follow-up.Conclusions: The good clinical prognosis obtained according to the Surgical Planning Program shows its potential for the optimization of the endovascular treatment for intracranial aneurysms.
Wagner’s Theory and Good Governance: A Comparative Analysis of Nigeria’s Second and Fourth Republic Regimes
Authors: UZOMBA Peter Chika 
Published: May 2023

Abstract
Traditionally, the essence of a republican state is to uphold civic virtue, serve the interests of the entire citizens and guarantee social welfare through the precepts of good governance. This by implication suggests that, unarguably, good governance gives effects to the law of increasing state activities for the purposes of ensuring short term benefit (economic growth) and long term benefit (economic development). However, the Nigerian state is very far from reaping any of the benefits; especially, the latter one. This therefore raises concerns on the inherent efficacy of Wagner’s theory in offering opportunities that guarantee and improve on the general welfare of the Nigerian society. Consequently, this study sets out to compare the exactitude of Wagner’s theory and good governance in the Nigeria’s second republic (1979–1983) and the first five years of fourth republic (1999–2003). In specific terms, it seeks to empirically establish how increase in government capital and recurrent has exacted impacts on and related with the effective and efficient quality of good governance, proxied by eight development indicators, GDP per capita, literacy rate (secondary school enrolment), national poverty, life expectancy at birth, and exchange rate. For analytical purposes, descriptive statistical tools, correction, coefficient of variation and econometric method of ordinary least squares are employed. Comparatively, Wagner’s theory did not have exact impact on good governance indices as measured in this study. It is recommended that government should settle in programmes and policies used in the second republic.
JEL Classification: D63, E24, H11, H51, H52, I11, I21, I31
Topographic Distribution of Inflammation Factors in a Healing Aneurysm
Authors: Grüter Basil E. , Canzanella Gwendoline , Hägler Joshua , Rey Jeannine , Wanderer Stefan , von Gunten Michael , Galvan José A. , Grobholz Rainer , Widmer Hans-Rudolf , Remonda Luca , Andereggen Lukas , Marbacher Serge 
Published: May 2023

Abstract
Background Healing of intracranial aneurysms following endovascular treatment relies on the organization of early thrombus into mature scar tissue and neointima formation. Activation and deactivation of the inflammation cascade plays an important role in this process. In addition to timely evolution, its topographic distribution is hypothesized to be crucial for successful aneurysm healing.Methods Decellularized saccular sidewall aneurysms were created in Lewis rats and coiled. At follow-up, (after 3 days (n = 16); 7 days (n = 19); 21 days (n = 8)) aneurysms were harvested and assessed for healing status. In-situ hybridization was performed for soluble inflammatory markers (IL6, MMP2, MMP9, TNF-α, FGF23, VEGF), and immunohistochemical analysis to visualize inflammatory cells (CD45, CD3, CD20, CD31, CD163, HLA-DR). These markers were specifically documented for five regions of interest: aneurysm neck, dome, neointima, thrombus, and adjacent vessel wall.Results Coiled aneurysms showed enhanced patterns of thrombus organization and neointima formation, whereas those without treatment demonstrated heterogeneous patterns of thrombosis, thrombus recanalization, and aneurysm growth (p = 0.02). In coiled aneurysms, inflammation markers tended to accumulate inside the thrombus and in the neointima (p < 0.001). Endothelial cells accumulated directly in the neointima (p < 0.0001), and their presence was associated with complete aneurysm healing.Conclusion The presence of proinflammatory cells plays a crucial role in aneurysm remodeling after coiling. Whereas thrombus organization is hallmarked by a pronounced intra-thrombotic inflammatory reaction, neointima maturation is characterized by direct invasion of endothelial cells. Knowledge concerning topographic distribution of regenerative inflammatory processes may pave the way for future treatment modalities which enhance aneurysm healing after endovascular therapy.
Stabilized Platform Attitude Control Based on Deep Reinforcement Learning Using Disturbance Observer-Based
Authors: Huo Aiqing , Jiang Xue , Zhang Shuhan 
Published: May 2023

Abstract
In order to address the difficulties of attitude control for stabilized platform in rotary steerable drilling, including instability, difficult to control, and severe friction, we proposed a Disturbance Observer-Based Deep Deterministic Policy Gradient (DDPG_DOB) control algorithm. The stabilized platform in rotary steering drilling was taken as a research object. On the basis of building a stabilized platform controlled object model and a LuGre friction model, DDPG algorithm is used to design a deep reinforcement learning controller. After the overall framework of the stabilized platform control system was given, appropriate state vectors were selected, a reward function satisfying the system requirement was designed, an Actor-Critic network structure was constructed and the network parameters was updated. Moreover considering the non-linear friction disturbance that causes steady-state errors, oscillations, and hysteresis phenomena in the stabilized platform control system, a DDPG algorithm based on the disturbance observer was proposed to eliminate the effects of friction disturbance so that to enhance robustness and anti-interference ability of the stabilized platform control system. Experimental results show that the DDPG_DOB control method had good set-point control performance and tracking effect. The tracking error of the tool face angle can be maintained within ± 8.7% and the DDPG_DOB control method can effectively suppress friction interference and improve the nonlinear hysteresis phenomenon when the system is affected by friction interference,enhancing the robustness of the system.
GPR68 inhibited nucleus or ER to exosome non-protein based circadian exercise based on Thalamus big data
Authors: Wang Lin , hong tao , Cui Wenbin , Jiang Minghu 
Published: May 2023

Abstract
G protein-coupled receptor (GPR68) is widely distributed in the human body and participates in various physiological and pathological processes. The main GPR68-inhibited and -activated molecular network was constructed in Thalamus from GDS2678 of NCBI by integrating SAM, SPSS Pearson and GRNInfer mutual positive verification based on richness. The main GPR68 inhibited molecular subnetwork composed of upstream ALG8, or MAF, feedback SMA4, and downstream GSTM3_1 with TYMS. GPR68 activated molecular subnetwork contained upstream RAD50, feedback SPAG9, and downstream RFK. The relationship of SMA4 inhibition to SPAG9 -> MAF -> RAD50 -> ALG8 was found in Thalamus for negative main molecular verification. The relationship of ALG8 activation to SMA4 -> GSTM3_1 -> TYMS -> MAF in Thalamus for positive main molecular verification. Our results show GPR68 inhibited SMA4-nucleoplasm RNA splicing coupling ALG8, or MAF-nucleus sequence specific DNA binding, or ER amino acid glycosylation to GSTM3_1 with TYMS-extracellular exosome small molecule-based circadian exercise, and GPR68 activated SPAG9-integral component of membrane kinesin binding coupling RAD50-membrane positive regulation of kinase activity to RFK-mitochondrion apoptotic process for mutual negative knowledge verification. We put forwards GPR68 inhibited nucleus or ER to exosome non-protein based circadian exercise based on Thalamus big data. Our model is positive verified by the other similar and common inhibited and/or activated knowledge in Thalamus. The role and mechanism of our GPR68 inhibited nucleus or ER to exosome non-protein based circadian exercise contributes to better detection, evaluation, intervention, tracking, and other health management of brain diseases.
Approximate Analytical Solutions for Nonlinear Reaction-Diffusion Equations at Conducting Polymer Modified Ultramicroelectordes via Taylor’s Series Method
Authors: Yokeswari G. , Jayasimman I. Paulraj , Lynons M. E. G. , Abukhaled Marwan , Rajendran L 
Published: May 2023

Abstract
The primary goal of this article is to present novel analytical solutions for the coupled nonlinear equation found in polymer-modified conducting ultramicroelectrodes. Taylor's series method is utilized to obtain approximate analytical solutions for the reaction-diffusion equations, allowing for the determination of the substrate and mediator concentrations as well as the current response in relation to the substrate concentration at the electrode's surface. The impact of different factors on concentration and current is also explored. Our analytical findings exhibit a satisfactory degree of correspondence when compared to both numerical results obtained via MATLAB and prior analytical outcomes.
Mathematics Subject Classification 34B60. 65L10. 82D60
The efficacy of hybrid neuroprostheses in the rehabilitation of upper limb impairment after stroke, a systematic review with a meta-analysis
Authors: Höhler Chiara , Trigili Emilio , Astarita Davide , Hermsdörfer Joachim , Jahn Klaus , Krewer Carmen 
Published: May 2023

Abstract
Background.
After stroke, paresis of the upper limb (UL) is the most frequent impairment. Hybrid neuroprostheses, i.e., the combination of robots and electrical stimulation, emerge as an option to treat these impairments.
Methods.
To give an overview on existing devices, their features and how they are linked to clinical metrics, four different databases were systematically searched for studies on hybrid neuroprostheses for the UL rehabilitation after stroke. The evidence on the efficacy of hybrid therapies was synthesized.
Results.
Seventy-three studies were identified, introducing 32 hybrid systems. Among the most recent devices (n = 20), most actively reinforce movement (3 passively) and are typical exoskeletons (3 end effectors). If classified according to the ICF, systems for proximal support are rather expected to affect body structures and functions, while the activity and participation level is targeted when applying FES distally plus the robotic component proximally. The meta-analysis reveals a significant positive effect on UL functions (p < .001), evident in a 7.8 point Mdiff between groups in the Fugl-Meyer assessment. This positive effect remains at 3-months follow-up (Mdiff=8.4, p < .001).
Conclusions.
Hybrid neuroprostheses have a positive effect on UL recovery after stroke, with effects persisting at least three months after the intervention. Non-significant studies were those with the shortest intervention periods and the oldest patients. Providentially, improvements in UL functions are not only present in the subacute phase after stroke, but also in long-term chronic stages. In addition to further technical development, more RCTs are needed to make assumptions about determinants of a successful therapy.
Real-time power inspection method based on multi-attention and strong semantic feature extractor
Authors: Zhao Qian , Ji Tangyu , Liang Shuang , Yu Wentao , Yan Chao 
Published: May 2023

Abstract
This paper proposed a multi-objective power inspection method based on multiple attention mechanisms and strong semantic feature extraction.Based on the Deeplabv3+ network model,the CBAM attention mechanism is introduced into the MobileNetV2 network to enchance the contextual information interaction capability; the ASPPAttention fast feature fusion structure is proposed to achievefast extraction of multi-dimensional valid information by designing deep separa-ble convolutions with different perceptual fields, and to enhance the pixel-levelfeature encoding capability with the CA attention mechanism; the lightweightfeature encoding capability is proposed. The proposed lightweight inverted con-volutional decoder structure improves the feature extraction capability of themodel by designing an inverted bottleneck convolutional structure in two quadru-ple downsampling layers with a low number of parameters, and introduces the CAattention mechanism to avoid the heterogeneous gap; in the training process, theconvergence speed of the model is accelerated by using migration learning, andDice Loss is introduced to avoid the effect of the number of samples on the gener-alization of the model. The experimental results show that the MIoU of the powersegmentation inspection method based on attention and information decouplingcan reach 48.5%, the accuracy rate can reach 97.5%, and the detection speed ofthe model can reach 40.8 FPS, which is a better balance of speed and accuracycompared with the network models of HRNet, PSPNet and DeeplabV3+.
Fabrication of g-C 3 N 4 @porphyrin nanorods hybrid material via CTAB surfactant-assisted self-assembly for photocatalytic degradation of Cr(VI) and methylene blue
Authors: Ho Quang Dinh , Le Tam The , Nguyen Giang T. , Nguyen Du Hoa , Nguyen Hao Hoang , Le Hiep Thu Thi , Chu Lam Thanh T. , Tran Chinh , Nguyen Phuong T. Hoai , Um Myoung-Jin , Nguyen T. Tung , Nguyen D. Duc , La Duong D. 
Published: May 2023

Abstract
Photocatalysts are an attractive solution for pollutant degradation under sunlight irradiation. One approach that has been proposed to enhance their activity is to combine two semiconductors, which can broaden the photon energy harvesting regions and improve charge separation. Herein, a facile approach to fabricating a g-C3N4@porphyrin nanorods hybrid material is presented using CTAB surfactant-assisted self-assembly of monomeric porphyrin molecules and g-C3N4 nanomaterials. The hybrid material was characterized using various technical methods, revealing a uniform distribution of porphyrin nanorods on the surface of g-C3N4. The photocatalytic performance of the hybrid material was evaluated by investigating its behavior for the photo -oxidation and -degradation of Cr6+ ions and methylene blue organic dye under simulated sunlight irradiation. High photocatalytic performance towards these two pollutants was exhibited by the hybrid material with a removal percentage of nearly 100% after 100 min of reaction time under the simulated sunlight spectrum. Furthermore, a potential photocatalytic mechanism of the C3N4@porphyrin nanorods photocatalyst was proposed, which involved the efficient separation and transfer of photo-induced electrons and holes on the surface of the hybrid material. An effective and facile method for developing high-performance photocatalysts is offered by this work, and advances in our understanding of their photocatalytic mechanisms have been made. The findings have important implications for wastewater treatment and solar energy conversion. The use of this hybrid material may contribute to addressing environmental challenges and assist in building sustainable energy systems.