High Closeness Centrality Research Articles

Abstract 4140151: Network Analysis of Differentially Expressed Genes after Silencing Dynamin 2 in Pulmonary Arterial Hypertension

Rationale: Pulmonary arterial hypertension (PAH) is a rare cardiopulmonary disease that can cause right heart failure and even death. In PAH, excessive mitochondrial fission is responsible for the hyperproliferation of pulmonary artery smooth muscle cells (PASMC), which is incompletely mediated by the large GTPase dynamin-related protein 1 (Drp1). We hypothesize that another GTPase, Dynamin 2 (DNM2), elevated in PASMC of PAH patients, is responsible for the final stage of mitochondrial fission. Objectives: 1. Identify the differentially expressed gene (DEG) signature in PAH PASMC after silencing DNM2 using small interfering RNA (siRNA) by means of RNA sequencing (RNA-seq); 2. Identify the biological processes or pathways associated with these DEGs. Methods: Human PAH PASMCs (n=5 cell line) were transfected with control siRNA or siRNA targeting DNM2. 3’RNA-seq was used to create libraries which we sequenced on the Illumina Nextseq550. Bioinformatic analysis revealed DEGs which we placed into context using the STRING: protein query database and visualized using Cytoscape 3.10.2. Network functional enrichment analysis was performed using STRING. Results: Inhibiting DNM2 significantly reduced mitochondrial fission and slowed PAH PASMC proliferation. RNA-seq identified 156 DEGs (80 down-regulated, 56 up-regulated). The top 5 down-regulated DEGs were DNM2 , RGCC , TICAM1 , RASSF3 , MRPL39 and the top 5 up-regulated DEGs were ITIF1 , ARL6IP1 , POLR3G , CBX1 , CMPK2 . We tested the biologic relevance of RGCC which encodes the Regulator of Cell Cycle using siRNA and showed inhibition of cell proliferation in human PAH PASMC. Network analysis identified 152 edges amongst all DEGs. The largest subnetwork contains 77 nodes and 151 edges. PXDN , which encodes Peroxidasin, a heme-containing peroxidase that is secreted into the extracellular matrix, has the highest degree of connectivity (15 connections), the highest betweenness centrality (0.266), and the highest closeness centrality (0.441). Enrichment analysis found that the p53 signaling pathway and the cytokine signaling in the immune system are the top KEGG and Reactome pathways affected by siDNM2, respectively. Conclusions: DNM2 appears to be an important regulator of the increased mitochondrial fission in PAH. Silencing DNM2 not only inhibits fission but also slows cell proliferation, apparently by reducing the expression of RGCC. Silencing DNM2 in human PAH PASMC results in the disruption of 136 genes.

A Study on Internet News for Patient Safety Campaigns: Focusing on Text Network Analysis and Topic Modeling.

Background/Objectives: This study aimed to identify the main issues related to public patient safety campaigns reflected in Korean online news. This study utilized a text-mining method to identify keywords and topics related to patient safety campaigns. Methods: The data collection period was from 1 January 2022 to 31 December 2023, and 4110 news articles were extracted. Through data preprocessing, 2661 duplicated news and 1213 unrelated news were removed, and 236 news were selected. Using the NetMiner program, keyword co-occurrence frequency calculation, keyword centrality analysis, and topic modeling analysis were performed. Results: The results showed that the most frequently mentioned keywords with high degree centrality, betweenness centrality, and closeness centrality in online news were "hospital", "medical", "medicine", "project", and "treatment". The topics of online news related to the patient safety campaign were "patient-centered care for medical safety", "health promotion projects at a regional institution", "hand hygiene education to prevent infection", "healthcare quality improvement through the Mint Festival", and "safe use of medicines". Conclusions: This study analyzed patient safety campaign news topics using text network analysis and topic modeling. It was confirmed that patient safety campaigns are essential for fostering a patient safety culture, improving medical quality, and encouraging patient participation in hospitals. Therefore, to build a safe medical environment, it is necessary to establish an effective patient safety campaign for not only medical staff providing medical care, but also patients and their caregivers, and for this, cooperation and participation from various professional occupations are necessary.

Unveiling the multi-dimensional frailty network among older cancer survivors in China: A network analysis study

IntroductionFrailty has a significant impact on the overall quality of life of older cancer survivors, but the relationships among frailty symptoms are not well understood. This study aims to explore the specific associations among multidimensional symptoms of frailty among older cancer survivors by employing network analysis to provide supportive evidence for targeted interventions in the future. Materials and MethodsData were obtained by cluster sampling from three large Grade-A tertiary hospitals in Shandong Province, China, and collected through face-to-face interviews by trained investigators. We included patients who were diagnosed with a solid malignant tumor at the age of 60 years or older. Frailty indicators were measured by the Groningen Frailty Indicator (GFI) and analyzed primarily through network analysis, including network estimation, centrality, and stability analysis. The relative importance of a node in a network was tested by centrality analyses, and Spearman correlations were applied to estimate the relationships between symptom pairs (symptom score) and symptom clusters (standardized symptom score) in the symptom network. In terms of centrality, the indexes of strength, closeness, and betweenness were adopted to measure the importance of nodes. ResultsFive hundred and eight older cancer survivors were included, with an average age of 68.4 years (standard deviation [SD] = 5.4), and a higher proportion were male (n = 307[60.4%]). The prevalence of frailty among older cancer survivors was 58.9% (n = 299), with a mean GFI score of 4.46 (SD = 2.87). The strongest edge was between “dressing and undressing” and “going to the toilet” (r = 0.58). The nodes with the higher strength centrality were “going to the toilet” (rS=1.09), “walking around outside” (rS=0.97), and “part of social network” (rS=0.96); and the nodes with the higher closeness centrality were “mark physical fitness” (rC=0.005), “calm and relaxed” (rC=0.005), and “nervous or downhearted” (rC=0.005). DiscussionThis study demonstrated that older cancer survivors in China have a high prevalence of frailty, with self-care and social participation-related symptoms playing a key role in the multidimensional network of frailty symptoms. Psychological symptoms can rapidly influence other symptoms within this network. Therefore, prioritizing psychological symptoms in the assessment of older adults with cancer is essential for effective frailty management.

빅데이터를 활용한 유보통합에 대한 사회적 인식 분석

The purpose of this study is to find out the social discourse of ECEC integration using big data. Big data related to ECEC integration from 2013 to 2023 were collected using Textorm. The collected raw data were refined, and among them 50 keywords were selected as a result of frequency analysis. The results of the study are as follows. First, the social discourse of ECEC integration showed high frequency in the order of ‘teacher’, ‘child care center’, ’kindergarten’, ‘child care’, and ‘qualification’. As a result of cluster analysis, social discourse was formed centering on three core discourses such as teacher qualification acquisition, policy direction, and consultation subject. Second, major keywords such as ‘child care center’, and ‘kindergarten’ have a higher degree of centrality, while word such as ‘policy’, and ‘Korea National Open University’ have a higher closeness centrality. Third, the ego network analysis was conducted on the main keywords with higher centrality and frequency, such as ‘teacher’ and ‘qualification’. In addition, the ego network analysis was performed and visualized the main keywords and visualized. Through this study, the social discourse was investigated and implications for approach of ECEC integration were discussed.

Analysis of the Driving Mechanism of Urban Carbon Emission Correlation Network in Shandong Province Based on TERGM

Analyzing the driving factors and mechanisms of urban carbon emission correlation networks can provide effective carbon reduction decision-making support for Shandong Province and other regions with similar industrial characteristics. Based on industrial carbon emission data from various cities in Shandong Province from 2013 to 2021, the spatial correlation network of carbon emission was established by using a modified gravity model. The characteristics of the network were explored by using the Social Network Analysis (SNA) method, and significant factors affecting the network were identified through Quadratic Assignment Procedure (QAP) correlation analysis and motif analysis. The driving mechanism of the carbon emission correlation network was analyzed by using Temporal Exponential Random Graph Models (TERGMs). The results show that: (1) The spatial correlation network of urban carbon emission in Shandong Province exhibits multi-threaded complex network correlations with a relatively stable structure, overcoming geographical distance limitations. (2) Qingdao, Jinan, and Rizhao have high degree centrality, betweenness centrality, and closeness centrality in the network, with Qingdao and Jinan being relatively central. (3) Shandong Province can be spatially clustered into four regions, each with distinct roles, displaying a certain “neighboring clustering” phenomenon. (4) Endogenous network structures such as Mutual, Ctriple, and Gwesp significantly impact the formation and evolution of the network, while Twopath does not show the expected impact; FDI can promote the generation of carbon emission reception relationships in the spatial correlation network; IR can promote the generation of carbon emission spillover relationships in the spatial correlation network; GS, differences in GDP, differences in EI, and similarities of IR can promote the generation of organic correlations within the network; on the temporal level, the spatial correlation network of urban carbon emission in Shandong Province has shown significant stability during the study period.

Spatial structure and influencing factors of an emerging wine tourism network: a case study of the Ningxia wine region

PurposeNetworks linking tourist attractions or organizations are a major focus of tourism research. Despite extensive research on tourism networks, academic research on the spatial structure and formation of wine tourism networks is limited. This study aims to investigate the spatial structure and factors influencing the development of a network among Ningxia wineries, an emerging wine tourism destination in China.Design/methodology/approachThis study uses social network analysis to uncover “what” the spatial structure of wine tourism networks looks like. Sixteen in-depth interviews were conducted among key stakeholders to explain the “why” of such structural characteristics.FindingsThe results show that in an emerging wine tourism destination, popular tourist attractions enjoy high centrality and hold key positions in the wine tourism network. Small wineries exhibit high closeness centrality, and only one winery serves as a network broker. According to the stakeholders, the importance of network actors will increase as their economic and political importance increase, while small wineries that lack differentiation in the network may perish.Practical implicationsLocal governments can implement the suggested measures for improving network connections, and wineries are advised to find suitable positions to improve the experiences of tourists.Originality/valueThis study pioneers the identification of the distinct structure and factors influencing the network of an emerging wine tourism destination, thus enriching the understanding of the interplay and roles of different actors.

Innovation and Networks in the Bioeconomy: A Case Study from the German Coffee Value Chain

The transition to a circular bioeconomy requires innovation across many sectors, but social dynamics within a sector’s network may affect innovation potential. We investigate how network dynamics relate to the perceptions and adoption of bioeconomy innovation using a case study from the food processing sector. Our case study of the German coffee value chain represents a technologically advanced sector with a strong sustainability focus and potential for residue valorization, which is an important dimension of a sustainable circular bioeconomy. We identify three distinct views (pioneers, traditional and limited users) related to residue valorization, map linkages between actors using social network analysis, and highlight barriers to innovation. We collected data through an online survey and semi-structured interviews with key actors in the coffee roasting sector. Within the social network analysis, we find that public waste managers are closely linked to the most influential actors, state actors such as the customs and tax offices can quickly interact with others in the network and promote the spread of information (highest closeness centrality) and specific roasters play an important role as intermediaries for efficient communication (highest betweenness centrality). Finally, we identify four main barriers including the structure of the coffee network, inconsistencies in federal waste regulations, economies of scale, and visions of sustainability. To support a sustainable bioeconomy, we recommend that policy makers revise the primary regulatory frameworks for waste (e.g., German Recycling Act) to clarify how to classify food residues, their disposal structures and broaden their use streams.

Diffusion to peers in firm-hosted user innovation communities: Contributions by professional versus amateur users

Users can develop innovations that improve or complement a firm's product. To benefit from these, firms may host online user innovation communities (UICs) with two purposes: 1. Incorporate user innovations into the firm's products, and 2. Facilitate the direct diffusion of user innovations to peer users to increase the product's general value. The second of these objectives (antecedents of peer diffusion) is under-investigated. Peer diffusion comes with additional challenges: when the hosting firm's innovation experts (e.g., R&D workers) do not pick up the role of continued development, adoption by peers is frustrated—as many user innovators lack the expertise to improve their initial prototypes so that peers can easily adopt. We address this gap by exploring if contributions by professional external users of the hosting firm's product have better peer diffusion rates compared to those of amateur users. We argue that professionals' expertise in design and marketing enables them to improve their initial prototypes, which peers can adopt more easily. Next, taking an interactionist perspective, we hypothesize that the relationship between professional user status and peer diffusion is amplified by users' commercial motivation and their central position in the UIC's network. We analyze multiple-source data of 614 innovations contributed by 122 users of a firm-hosted UIC in 3D printing. We find that contributions by professionals indeed diffuse better, but only at high commercial motivation or favorable network positions (high closeness centrality). To firm-hosted UICs, professional users are an important asset advancing the free peer diffusion of user innovations without firm interventions and merit attention when designing UICs.

Understanding dockless bike-sharing spatiotemporal travel patterns: Evidence from ten cities in China

Dockless bike-sharing (DBS) has gained popularity in many cities in recent years. However, little attention has been given to the commonalities and differences in DBS travel patterns from multiple cities. This study aims to fill this gap by comparing DBS travel patterns in ten Chinese cities of varying scales. It collected data from these cities and analysed them from two perspectives: service usage characteristics and complex network properties. The results reveal a coexistence of similarities and differences in DBS travel patterns across different cities. The ten major cities show significant similarities in temporal distribution, with a similarity coefficient exceeding 0.7. Southern cities have a higher percentage of night riding compared to their northern counterparts. Moreover, over 85% of the travel distances are within 2 km. Concerning spatial distribution, all ten cities demonstrate an imbalanced demand distribution during peak hours, particularly in downtown areas, exhibiting evident tidal patterns. Network analysis outcomes demonstrate that the DBS networks in all ten cities possess small-world and scale-free properties. Furthermore, nodes with high degree centrality and closeness centrality are predominantly concentrated in downtown areas, displaying a diminishing trend from the city centre to the outskirts and showcasing strong global spatial autocorrelation. The betweenness centrality exhibits a property of random distribution. Nodes with high PageRank values are mainly concentrated in the city centre and around metro stations. The findings offer valuable insights for transportation planners and managers seeking to enhance their understanding of DBS systems.

Bacterial community dynamics and co-occurrence network patterns during different stages of biochar-driven composting

Bacterial communities were dynamically tracked at four stages of biochar-driven sheep manure pile composting, and the co-occurrence networks with keystone taxa were established. The succession of bacterial community obvious varied during the composting process, Proteobacteria predominant in initial stage (39%) then shifted into Firmicutes in thermophilic (41%) and mesophilic (27%) stages, finally the maturation stage dominant by Bacteroidota (26%). Visualizations of bacterial co-occurrence networks demonstrate more cooperative mutualism and complex interactions in the thermophilic and mesophilic phases. Noticeably, the 7.5 and 10% biochar amended composts shown highest connections (736 and 663 total links) and positive cooperation (97.37 and 97.13% positive link) as well as higher closeness centrality and betweenness centrality of keystone taxa. Overall, appropriate biochar addition alters bacterial community succession and strengthens connection between keystone taxa and other bacteria, with 7.5 and 10% biochar amended composts has intense mutualistic symbiosis among bacterial communities.

Identification and evaluation of collaborative relationships of internal project stakeholders: a social network analysis approach

PurposeThe present research is trying to construct the network of relationships between different department of an organization during the design and development of car engine. Analyzing the structure of the network, finding the patterns of collaborations, and determining the important departments are the main purposes of the present research.Design/methodology/approachImproving relationships during a project life is an effective way to enhance employee performance in project-oriented organizations. This paper examines the collaborative relationships between internal project stakeholders through social network analysis (SNA) in a project for the design and development of car engine. In the first step of the research, the network of internal stakeholders was studied based on collaboration in the common activities performed by the resources. Then, the network of correspondences between internal stakeholders was studied. Finally, the two networks were integrated into a single network.FindingsIn the integrated network, the “fuel and combustion department” had the largest degree centrality (i.e. highest collaboration with others). The “integration department” was found to have the highest closeness centrality (i.e. more rapid access to other nodes). Furthermore, the “procurement department” had the highest betweenness centrality (i.e. the most strategic department). Our results revealed the potential capabilities of SNA method for the project management in the vehicle industry.Originality/valueThe obtained results of the present research show us the value of applying SNA methods and concepts to analyze the inter-organizational network of the Project Stakeholders relationship.

Exploring the Link between Street Layout Centrality and Walkability for Sustainable Tourism in Historical Urban Areas

Walkability is considered a vital component of the urban configuration; urban spaces should promote pedestrian walking, which is healthier and increases social sustainability by connecting people in urban spaces. This article aims to find the link between the street layout centrality values and the people’s walkability for sustainable tourism in historic areas. Moreover, it attempts to explore the linkage between the urban layout and visiting historical spaces in the urban layout. The approach to the research has two phases; the first is to find people density (the tourist density) in the historical areas, and the second is to measure the centrality values of the urban layout utilizing the spatial design network analysis tool (sDNA). The research found that the street network considerably impacts the final tourist distribution, mainly because of the betweenness centrality; consequently, spaces with low betweenness centrality values are less reachable by the tourists in the historical area, although it has a high closeness centrality. The research concluded that considering the street network is necessary concerning the tourists’ walkability since it affects their density in the urban layout.

Network modeling and stability improvement of the water-energy-fertilizer-food nexus flows based on global agricultural trade

Water, energy, fertilizer and food are vital for sustainable development, yet their nexuses within various agricultural trade patterns remain ambiguous. Therefore, we analyzed the nexus of these four elements in the global trade of both total and basic agricultural products, focusing on exporting countries from 2000 to 2019. Our approach involved coupling models, complex network analysis and mathematical programming. The results showed that: (1) In the total agricultural trade nexus flows, the export-import flow pairs with the highest average nexus of continents changed from Europe-Europe to Oceania-Asia. Netherlands-Germany maintained the export-import flow pairs with the highest nexus of countries. In the basic agricultural trade nexus flows, Oceania-Asia maintained the highest average nexus export-import flow pairs of continents. Denmark-Germany maintained the export-import flow pairs with the highest nexus of countries. (2) The basic agricultural trade nexus flows network exhibited the higher density and average clustering coefficient. Argentina, Brazil and Canada played pivotal roles in maintaining high closeness centrality. Congo, Dominican Republic and Bosnia and Herzegovina displayed considerable potential on the indirectness of the network with high eigenvector centrality. (3) To improve the stability of the nexus flows network, taking the total agricultural trade nexus of the US, Netherlands and China as examples, the main largest beneficiary countries were Vanuatu, Eswatini and Saint Lucia. Taking the basic agricultural trade nexus of Canada, Brazil and China as examples, the main largest beneficiary countries were Kiribati and Niue. This study utilizes coupling and network models to analyze agricultural trade nexus flows, offering a deeper understanding of the complex nexus relationships within various agricultural trade patterns. Furthermore, the findings serve as a reference point for the formulation of strategies promoting sustainable development.

Research Topic Trends on Turnover Intention among Korean Registered Nurses: An Analysis Using Topic Modeling.

This study aimed to explore research topic trends on turnover intention among Korean hospital nurses by analyzing the keywords and topics of related articles. Methods: This text-mining study collected, processed, and analyzed text data from 390 nursing articles published between 1 January 2010 and 30 June 2021 that were collected via search engines. The collected unstructured text data were preprocessed, and the NetMiner program was used to perform keyword analysis and topic modeling. Results: The word with the highest degree centrality was "job satisfaction", the word with the highest betweenness centrality was "job satisfaction", and the word with the highest closeness centrality and frequency was "job stress". The top 10 keywords in both the frequency analysis and the 3 centrality analyses included "job stress", "burnout", "organizational commitment", "emotional labor", "job", and "job embeddedness". The 676 preprocessed key words were categorized into five topics: "job", "burnout", "workplace bullying", "job stress", and "emotional labor". Since many individual-level factors have already been thoroughly investigated, future research should concentrate on enabling successful organizational interventions that extend beyond the microsystem.

The Korean Movie Minari: A Big Data Analysis

The ultimate goal of this paper is to analyze 42 articles of Google regarding the Korean movie Minari. As a research tool for our goal, we used the software package NetMiner. A major point to note is that one word has the highest frequency (1,247 tokens) and the highest proportion (57.8%). More specifically, it occurs 1,247 times in 42 articles of Google. A further point to note is that topic 11 is the most occurred one in 40 articles, followed by topic 3, topic 4, and topic 10, in that order. When it comes to the use of keywords in 42 articles, the word film is the most widely used one, followed by the word Minari, the word family, the name Jacob, and the name Monica, in that order. Talking about degree centrality, the word family has the highest degree centrality, followed by the name Monica, the word Minari, and the word film, in that order. It is worthwhile noting, on the other hand, that the word family has the highest closeness centrality since the distance among the word family and the other words is the closest. Finally, this paper argues that the name Monica has the highest Eigenvector centrality since it has the most neighbors. Thus, the name Monica counts as the most influential and important.

Does the Achilles heel of guarantee networks drive financial distress?

To explore characteristics of guarantee networks that drive financial distress, we use a dataset comprising 20,467 firm-year observations from Chinese companies listed on the Shanghai and the Shenzhen Stock Exchanges to construct networks from 85,229 guarantee relationships. We show that guarantee networks have a negative effect on company financial distress, revealed by cash holdings and long-term liabilities. Larger networks, those with an Achilles heel, and companies with high closeness centrality exacerbate this effect, and companies in a stronger financial position suffer greater negative impacts. Guarantee networks may also exert their negative impact by acting as a channel for shareholder tunneling.

Do age and language impairment affect speed of recognition for words with high and low closeness centrality within the phonological network?

Purpose Speed and accuracy of lexical access change with healthy ageing and neurodegeneration. While a word's immediate phonological neighbourhood density (i.e. words differing by a single phoneme) influences access, connectivity to all words in the phonological network (i.e. closeness centrality) may influence processing. This study aimed to investigate the effect of closeness centrality on speed and accuracy of lexical processing pre- and post- a single word-training session in healthy younger and older adults, and adults with logopenic primary progressive aphasia (lvPPA), which affects phonological processing. Method Participants included 29 young and 17 older healthy controls, and 10 adults with lvPPA. Participants received one session of word-training on words with high or low closeness centrality, using a picture-word verification task. Changes in lexical decision reaction times (RT) and accuracy were measured. Result Baseline RT was unaffected by age and accuracy was at ceiling for controls. Post-training, only young adults' RT were significantly faster. Adults with lvPPA were slower and less accurate than controls at baseline, with no training effect. Closeness centrality did not influence performance. Conclusion Absence of training effect for older adults suggests higher threshold to induce priming, possibly associated with insufficient dosage or fatigue. Implications for word-finding interventions with older adults are discussed.

Analisis Kolaborasi Hubungan Karyawan Menggunakan Metode Social Network Analysis (Studi Kasus Dinas Kearsipan dan Perpustakaan Kabupaten Boyolali)

<p>Good local government performance, especially related to the realization of good corporate governance, can be created if there is good communication between agencies, employees and compact collaboration to synergize policies that facilitate public services. To optimize government performance, one of which is the Boyolali District Archives and Library Service through collaborative relationships between employees so as to create intense two-way communication. This study aims to identify social networks related to collaboration between employees using the Social Network Analysis method and a total of 40 people as respondents, but only 32 people filled out the questionnaire data completely. The indicators used in viewing the research results are density values, degree centrality values, closeness centrality values, betweenness centrality values, and clique values. The results of the analysis obtained a network density level of 14.8% (considered weak because it is less than 50 percent). Employee number 21 has the highest degree centrality value and betweenness centrality value, then employee number 9 has the highest closeness centrality. This research concludes that there is a good collaborative relationship between the four departments in the Archives and Library Office of Boyolali Regency, namely the Operations department, the Service department, the Archives department, and the Librarian department. </p>

The impact factors of social media users' forwarding behavior of COVID-19 vaccine topic: Based on empirical analysis of Chinese Weibo users.

IntroductionSocial media, an essential source of public access to information regarding the COVID-19 vaccines, has a significant effect on the transmission of information regarding the COVID-19 vaccines and helps the public gain correct insights into the effectiveness and safety of the COVID-19 vaccines. The forwarding behavior of social media users on posts concerned with COVID-19 vaccine topics can rapidly disseminate vaccine information in a short period, which has a significant effect on transmission and helps the public access relevant information. However, the factors of social media users' forwarding posts are still uncertain thus far. In this paper, we investigated the factors of the forwarding COVID-19 vaccines Weibo posts on Chinese social media and verified the correlation between social network characteristics, Weibo textual sentiment characteristics, and post forwarding.MethodsThis paper used data mining, machine learning, sentiment analysis, social network analysis, and regression analysis. Using “新冠疫苗 (COVID-19 vaccine)” as the keyword, we used data mining to crawl 121,834 Weibo posts on Sina Weibo from 1 January 2021 to 31 May 2021. Weibo posts not closely correlated with the topic of the COVID-19 vaccines were filtered out using machine learning. In the end, 3,158 posts were used for data analysis. The proportions of positive sentiment and negative sentiment in the textual of Weibo posts were calculated through sentiment analysis. On that basis, the sentiment characteristics of Weibo posts were determined. The social network characteristics of information transmission on the COVID-19 vaccine topic were determined through social network analysis. The correlation between social network characteristics, sentiment characteristics of the text, and the forwarding volume of posts was verified through regression analysis.ResultsThe results suggest that there was a significant positive correlation between the degree of posting users in the social network structure and the amount of forwarding. The relationship between the closeness centrality and the forwarding volume was significantly positive. The betweenness centrality was significantly positively correlated with the forwarding volume. There was no significant relationship between the number of posts containing more positive sentiments and the forwarding volume of posts. There was a significant positive correlation between the number of Weibo posts containing more negative sentiments and the forwarding volume.ConclusionAccording to the characteristics of users, COVID-19 vaccine posts from opinion leaders, “gatekeepers,” and users with high-closeness centrality are more likely to be reposted. Users with these characteristics should be valued for their important role in disseminating information about COVID-19 vaccines. In addition, the sentiment contained in the Weibo post is an important factor influencing the public to forward vaccine posts. Special attention should be paid to the negative sentimental tendency contained in this post on Weibo to mitigate the negative impact of the information epidemic and improve the transmission effect of COVID-19 vaccine information.

System level modeling and analysis of TNF-α mediated sphingolipid signaling pathway in neurological disorders for the prediction of therapeutic targets.

Sphingomyelin (SM) belongs to a class of lipids termed sphingolipids. The disruption in the sphingomyelin signaling pathway is associated with various neurodegenerative disorders. TNF-α, a potent pro-inflammatory cytokine generated in response to various neurological disorders like Alzheimer’s disease (AD), Parkinson’s disease (PD), and Multiple Sclerosis (MS), is an eminent regulator of the sphingomyelin metabolic pathway. The immune-triggered regulation of the sphingomyelin metabolic pathway via TNF-α constitutes the sphingomyelin signaling pathway. In this pathway, sphingomyelin and its downstream sphingolipids activate various signaling cascades like PI3K/AKT and MAPK/ERK pathways, thus, controlling diverse processes coupled with neuronal viability, survival, and death. The holistic analysis of the immune-triggered sphingomyelin signaling pathway is imperative to make necessary predictions about its pivotal components and for the formulation of disease-related therapeutics. The current work offers a comprehensive in silico systems analysis of TNF-α mediated sphingomyelin and downstream signaling cascades via a model-based quantitative approach. We incorporated the intensity values of genes from the microarray data of control individuals from the AD study in the input entities of the pathway model. Computational modeling and simulation of the inflammatory pathway enabled the comprehensive study of the system dynamics. Network and sensitivity analysis of the model unveiled essential interaction parameters and entities during neuroinflammation. Scanning of the key entities and parameters allowed us to determine their ultimate impact on neuronal apoptosis and survival. Moreover, the efficacy and potency of the FDA-approved drugs, namely Etanercept, Nivocasan, and Scyphostatin allowed us to study the model’s response towards inhibition of the respective proteins/enzymes. The network analysis revealed the pivotal model entities with high betweenness and closeness centrality values including recruit FADD, TNFR_TRADD, act CASP2, actCASP8, actCASP3 and 9, cytochrome C, and RIP_RAIDD which profoundly impacted the neuronal apoptosis. Whereas some of the entities with high betweenness and closeness centrality values like Gi-coupled receptor, actS1PR, Sphingosine, S1P, actAKT, and actERK produced a high influence on neuronal survival. However, the current study inferred the dual role of ceramide, both on neuronal survival and apoptosis. Moreover, the drug Nivocasan effectively reduces neuronal apoptosis via its inhibitory mechanism on the caspases.