Centrality Analysis Research Articles

Blood molecular subtypes to guide precision treatment strategies in systemic juvenile idiopathic arthritis

BackgroundSystemic juvenile idiopathic arthritis (sJIA) is the most severe subtype of JIA, with a combination of diverse clinical manifestations and a variable clinical course. A comprehensive understanding of molecular signatures at the systems level and the discovery of molecular subtypes are the initial steps toward personalized medicine in sJIA.MethodsA blood transcriptomic dataset was collected from patients with systemic JIA (sJIA) (n = 168), polyarticular JIA (n = 254), oligoarticular JIA (n = 96), enthesitis-related arthritis (n = 40), and healthy controls (n = 220). Gene expression profiles were filtered for differentially expressed genes and unsupervised clustering, gene set enrichment, and network-based centrality analyses. The molecular signatures of three novel sJIA subgroups (designated as C1, C2, and C3) were investigated, focusing on their distinct features and treatment responses.ResultsNeutrophil degranulation and the IL-1 signaling pathway were the shared key processes for the three subgroups. Proinflammatory signals, including TNF, IL-6, TLR, and G-CSF signaling pathways, were identified with variation across the subgroups. C1 was the most inflammatory subset with a high-risk profile for macrophage activation syndrome. The C2 subset had the most activated IL-1 and IL-18 signaling pathways. C2 and C3 have higher levels of interferon-stimulated signatures. In a canakinumab-treated dataset, treatment response was correlated with IL1B expression and NF-κB signaling pathway, and neutrophil activation-associated processes were effectively suppressed in a good responder group. GSK3B and p38 MAPK inhibitors showed a significant counteracting effect on the perturbed gene expression of sJIA.ConclusionsNeutrophil activation was the key feature in active sJIA. The three molecular subtype scheme enables the formulation of precision medicine strategies in sJIA.

Real-World Implementations of Network Centrality Algorithms across Various

The tutorial analyses how and in what ways the algorithms are used and can be employed in different fields and in the scientific context. This paper revisits the centrality measures; degree, betweenness, closeness and eigenvector centrality and how they are used to study complex networks from social networks, to biology, economy and telecommunications. From the couple of cases and examples, the article shows how these algorithms are utilized to find these vital nodes, improve network performance as well as decision making. Pros and Cons Each of the methods and results evidence the flexibility of centrality measures in enhancing the knowledge and analysis of complex systems. However, issues of scaling these algorithms to operational networks and environments are still explicit. The need for tailor-made centrality analysis methods of scalability and adaptiveness are also outlined for future research agendas in this review.

A Cross-Sectional Network Analysis of Intimate Partner Violence and Suicidal Ideation Among Arab Women.

The current study aimed to evaluate the relationship between intimate partner violence (IPV) and suicidal ideation among women using a network analysis approach. Secondary data including 440 women (mean age ± SD = 32.78 ± 8.09) who experienced IPV in the last 6 months were used. The Revised Composite Abuse Scale-Short Form, Columbia-Suicide Severity Rating Scale and Revised Scale of Economic Abuse were used to assess IPV and suicidal ideation. Network analysis, centrality measures and bridge centrality were applied to understand the interplay between IPV and suicide risk factors. The strongest association in the network analysis was found between 'Wish to be Dead' (S.1) and 'Non-Specific Active Suicidal Thoughts' (S.2) with a weight of 0.523, indicating a substantial correlation between general suicidal thoughts and particular desires to die. IPV symptoms, particularly economic exploitation and psychological abuse, showed strong links to suicidal ideation. 'Active Suicidal Ideation with Specific Plan and Intent' (S.5) was significantly related to 'Make you take out a loan or buy something on credit when you didn't want to' (EA.11) with a weight of 0.276. Additionally, centrality analysis showed that 'Wish to be Dead' (S.1) was the most central node in the network of suicidal ideation, with the highest centrality score (betweenness = 2.625). These findings suggest that IPV, mainly psychological abuse, plays a pivotal role in suicidal ideation among women. Mental health interventions should focus on addressing IPV-related psychological abuse. Suicide prevention efforts must integrate IPV screening into clinical practice.

Honoring donors: medical students’ reflections on cadaveric dissection

BackgroundCadaveric dissection is an essential practice for medical students to understand human anatomy and internalize professional attitudes toward death, such as empathy and respect for the deceased. Previous studies have focused on emotional responses such as shock, anxiety, and fear at the start of dissection practice. However, limited research exists on students’ reflective thinking and perceptions before practice. This study aims is to understand premedical students’ perceptions and attitudes before dissection by analyzing their reflective writing.MethodsText network analysis of essays written in 2022 by 136 s-year premedical students before cadaveric dissection was performed. Keywords were preprocessed to extract significant themes. Centrality analysis and topic modeling were performed to analyze students’ perceptions and attitudes.ResultsCentrality analysis identified “cadaveric dissection,” “donation,” and “gratitude” as major keywords, indicating the students’ deep appreciation and respect for donors. Two main topics emerged from topic modeling. The first, “gratitude and respect for donors,” included keywords such as “gratitude” and “donation.” The second, “growth and responsibility of doctors through medical study and practice,” included keywords such as “study” and “medical school.”ConclusionThe participants had profound gratitude and respect for donors before cadaveric dissection, which increased their motivation to learn. Analyzing reflective writing is an effective approach to understanding students’ initial perceptions and attitudes toward cadaveric dissection; further, it can inform educational strategies that aid students in forming their professional identity.

P1303 A machine – learning approach of therapeutic response prediction for Ustekinumab in ulcerative colitis

Abstract Background Ustekinumab, a monoclonal antibody targeting the p40 subunit of interleukin (IL)-12 and IL-23, is a promising therapeutic approach in patients with ulcerative colitis (UC), but predicting treatment response remains a challenge. In the present study, we focused to identify prognostic response markers to ustekinumab in patients with active UC, utilizing mucosal transcriptome information and machine-learning approaches. Methods We used 36 drug naive UC patients initiating treatment with ustekinumab. Colonic mucosal biopsies were obtained before treatment initiation. The gene expression analysis was performed using a microarray panel of 84 inflammation related genes. A differential gene expression analysis (DGEA), correlation analysis, and network centrality analysis on co-expression networks were performed to identify potential biomarkers. Additionally, machine learning (ML) models were employed to predict treatment response based on gene expression data. Results The genes BCL6, CXCL5, and FASLG, were among the significantly upregulated, while IL23A and IL23R were downregulated in non-responders compared to responders. The co-expression analysis revealed distinct patterns between responders and non-responders. The ML algorithms demonstrated a high predictive power, emphasizing the significance of the IL23R, IL23A, and BCL6 genes. Conclusion Through a variety of computational approaches, our study indicates the predictive power of mucosal expression data in predicting patient response to ustekinumab treatment.

의미연결망 분석을 통한 웹소설 연구 동향 : 국내 학술 논문의 초록을 중심으로

This study examines research trends in Korean web novels by analyzing the abstracts of academic papers published in domestic journals and exploring key issues using semantic network analysis. The findings of this study are as follows: First, research on web novels has steadily increased since 2015. Second, such research has deepened through specialized discussions in certain journals and diversified approaches across various academic journals. Third, network analysis of centrality revealed that keywords such as ‘genre’, ‘platform’ and ‘reader’ were frequently addressed. Fourth, word cloud visualization indicated that core interests in web novel research include genre diversity, the interaction between content production and consumption, the importance of platform ecosystems, and reader-centered studies. Fifth, a concordance network analysis identified eight distinct clusters. Based on these findings, the study suggests: (1) the need to expand the diversity of web novel genres, (2) the need to expand the diversity of web novel subjects, (3) the importance of fostering reader-centered research, and (4) active exploration of strategies for utilizing web novels as primary content sources.

Inclusive universities for students with intellectual disabilities from a teacher's perspective.

The aim of this research is to analyse the elements necessary for the construction of inclusive universities in the European context from the perspective of university professors. Three focus groups are conducted in different countries: Spain, Italy and Portugal. The discourses have been analysed following Grounded Theory. The codes obtained were subjected to a centrality analysis in which rank, intermediation and proximity were analysed. The results show inclusion, teaching, experience and well-being as central elements of the discourses. Two large semantic blocks are obtained, one referring to educational factors and the other to social and emotional factors. Educational factors refer to how methodologies, assessment and support elements should be configured. In relation to social and emotional factors, issues related to inclusion, inclusive experiences, support and accompaniment and the role of families are presented. It concludes with the need to address both factors with a commitment from the entire university community.

Microbiome Structures and Beneficial Bacteria in Soybean Roots Under Field Conditions of Prolonged High Temperatures and Drought Stress.

Drought stress has a significant impact on agricultural productivity, affecting key crops such as soybeans, the second most widely cultivated crop in the United States. Endophytic and rhizospheric microbial diversity analyses were conducted with soybean plants cultivated during the 2023 growing season amid extreme weather conditions of prolonged high temperatures and drought in Louisiana. Specifically, surviving and non-surviving soybean plants were collected from two plots of a Louisiana soybean field severely damaged by extreme heat and drought conditions in 2023. Although no significant difference was observed between surviving and non-surviving plants in microbial diversity of the rhizosphere, obvious differences were found in the structure of the endophytic microbial community in root tissues between the two plant conditions. In particular, the bacterial genera belonging to Proteobacteria, Pseudomonas and Pantoea, were predominant in the surviving root tissues, while the bacterial genus Streptomyces was conspicuously dominant in the non-surviving (dead) root tissues. Co-occurrence patterns and network centrality analyses enabled us to discern the intricate characteristics of operational taxonomic units (OTUs) within endophytic and rhizospheric networks. Additionally, we isolated and identified bacterial strains that enhanced soybean tolerance to drought stresses, which were sourced from soybean plants under a drought field condition. The 16S rDNA sequence analysis revealed that the beneficial bacterial strains belong to the genera Acinetobacter, Pseudomonas, Enterobacter, and Stenotrophomonas. Specific bacterial strains, particularly those identified as Acinetobacter pittii and Pseudomonas sp., significantly enhanced plant growth metrics and reduced drought stress indices in soybean plants through seed treatment. Overall, this study advances our understanding of the soybean-associated microbiome structure under drought stress, paving the way for future research to develop innovative strategies and biological tools for enhancing soybean resilience to drought.

Xuebijing and somatostatin against acute pancreatitis: A systematic review and network pharmacology.

Acute pancreatitis (AP) is a common pancreatic disease. Xuebijing injection (XBJ) combined with somatostatin in the treatment of AP is frequently used in clinical practice. There is, however, a lack of high-quality evidence-based evidence and network pharmacology to regard the therapeutic efficacy and pharmacological mechanisms. The purpose of this study is to investigate the potential therapeutic targets and pharmacological mechanism of XBJ in AP using integrating evidence-based medicine and network pharmacology. PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Weipu, and Wanfang databases were searched. Randomized controlled trials of XBJ combined with somatostatin (experimental group) versus somatostatin alone (control group) in the treatment of AP were collected. After extracting data from the literature that meets the inclusion criteria, and using the Cochrane Scale to evaluate the quality of the literature, we used Rev Man 5.3.0 statistical software to perform meta-analysis of the effective rate, the disappearance time of abdominal pain and bloating, the recovery of gastrointestinal motility, serum-related indicators, inflammatory factors, ventilator evacuation time, and hospitalization time. A network pharmacology is used to analyze the potential active ingredients and related crucial targets of the XBJ in the treatment of AP, and we explored key regulatory pathways and potential biomarkers related to XBJ for AP with integrated bioinformatics analysis. It was significant that the total effective rate in the study group was higher than that in the control group (P < .05). The time of recovery of gastrointestinal motility, serum-related indicators, inflammatory factors, ventilator withdrawal time, and hospitalization time were significantly lower than that of the control group. The differences were statistically significant (P < .05). Signal transducer and activator of transcription 3, tumor protein P53, interleukin 6, tumor necrosis factor, Jun Proto-Oncogene, SRC Proto-Oncogene, Heat Shock Protein 90 Alpha Family Class A Member 1, Vascular Endothelial Growth Factor A, Epidermal Growth Factor Receptor, and Mitogen-Activated Protein Kinase 1 were identified as the key hub of the protein-protein interaction network according to an analysis of network centrality. According to the Kyoto Encyclopedia of Genes and Genomes pathway analysis, the main pathways are involved in PI3K-Akt signaling pathway, HIF-1 signaling pathway, and tumor necrosis factor signaling pathway. The effectiveness of combination therapy of XBJ and somatostatin on AP is likely to be better than somatostatin. In addition, XBJ and somatostatin synergistically treated AP through a multi-pathway network.

Adopting Network Analysis to Understand Grief and Loneliness Among Bereaved Older Adults.

Loneliness is common among older adults and may adversely influence their wellbeing. However, its relations with bereavement-related grief are underexamined. This study utilized network analysis to examine the symptom structures of grief reactions and loneliness and the relations among the symptoms by using secondary survey data from 300 Chinese bereaved older adults in Hong Kong. Centrality analysis revealed that bitterness, disbelief over the death, and upsetting memories were found to be the most central grief symptoms were the most central grief symptoms while not having enough people to feel close to or trust completely was most central for loneliness. The strongest bridge strengths between indicators of loneliness and grief were symptoms of emptiness, feeling lonely, and perceived rejection from others. These findings highlight potential symptoms for working with bereaved older adults. In addition, the results underscore the need for older adult-specific research and tailored bereavement and grief interventions.

Order Degree and Centrality Analysis of Advanced Fighter Environmental Control System and Thermal Management System

As fighter aircraft become more advanced, traditional air cycle systems have fallen short of meeting the complex environmental control requirements. Advanced fighters such as the F-22 Raptor have responded to these challenges by adopting integrated thermal management systems. These systems feature complex thermodynamic processes and intricate information transmission pathways among various components, creating a sophisticated network structure. To unravel the complexity of such systems, this paper employs a blend of thermodynamics and information theory. We conduct multi-level analysis using structure entropy method and centrality algorithms to explore the information transmission characteristics within these thermodynamic systems. At the system level, we find that variation in environmental parameters have only a 1% impact on order degree, whereas the influence from the system's own structure is more pronounced. At the component level, within the whole system, the varies components in AFT PAO and fuel loops emerge as critical hubs for information transmission. Among these components, the PAO/Fuel stands out as the most important, with closeness and betweenness centrality exceeding that of all other components by at least 11.5% and 29.1%. This study offers a theoretical foundation for the optimization of thermodynamic system structure and layout, viewed through the lens of information theory.

빅데이터분석을통해살펴본인공지능(AI)에 대한 인식변화

The purpose of this study is to establish basic data on the application of AI in counseling and education field. To this end, we collected and analyzed big data from 2014 to 2023, focusing on the keyword ‘artificial intelligence(AI).’ Using the TEXTOM, we collected data from portal site Naver and Google. Afterwards, the final refined data was subjected to keyword frequency analysis, degree centrality analysis, and CONCOR analysis using TEXTOM and UCINET. The research results are as follows. First, the frequency of AI increased rapidly over time. Second, similar words appeared in the keyword frequency analysis, but there were differences by year, and new words appeared. Third, the degree centrality analysis showed the AI technology and application areas that people consider important by year. Fourth, the CONCOR analysis confirmed that AI formed different clusters by year. Finally, the limitations of this study and future research tasks were discussed.

Identification of Research Trends Related to Marriage Values Using Keyword Network Analysis

The purpose of this study was to analyze research trends related to marriage values using keyword network analysis. To this end, 157 dissertations and academic journal papers related to marriage values published in Korea from 2001 to April 2024, along with 247 keywords were selected for analysis. Keyword network analysis was applied to the selected keywords using Microsoft Excel 365 and NetMiner 4.0. The results were as follows. First, research related to marriage values was low for the 10 years starting in 2001; however, the number of research materials gradually increased in the 2010s, and then increased significantly from the late 2010s. The 30 keywords that appeared most frequently were related to various personal psychological and socio-cultural variables that influence marriage values, and the keyword that appeared most frequently was 'family values'. Second, an analysis of keyword network centrality revealed that 'marriage intent' appeared in the connection centrality, 'sex role values' appeared in the proximity centrality, and 'generation' appeared in the mediation centrality. In terms of the overall centrality, 'child value' also exerted a relatively high influence. By identifying the various variables that affect marriage values, this study provides a wealth of information useful for the development of educational programs aimed at forming marriage values among adolescents, college students, unmarried men and women, prospective couples, prospective parents, and parents.

Pemilihan Pusat Pertumbuhan Baru pada Daerah Mitra Ibu Kota Nusantara: Studi Kasus Kecamatan Penajam

The relocation of Indonesia’s Capital City of Nusantara (IKN) represents the government’s strategy to reduce regional disparities in the country. Penajam Subdistrict, which directly borders IKN area and serves as one of its partner areas, has significant potential as an economic driver, particularly for food security. Its geographical location gives it agricultural advantages, especially in the plantation subsector, which supports the national agricultural activities as directed by Presidential Regulation No. 63 of 2022. This potential may encourage the establishment of new growth centers that drive the economic sector through their leading plantation commodities. The development of these centers may also generate optimal polarization and downward trickling effects that benefit IKN partner areas. This study aims to identify new growth centers at a micro level in Penajam Subdistrict to support spatial utilization that aligns with IKN needs and sustainable regional development. Employing a quantitative method, this research utilizes centrality and accessibility index analysis as well as Location Quotient (LQ) techniques. The results of the study show that eight villages in Penajam have the potential to become new growth centers and serve as partner areas to IKN.

Delving into Macrolide Binding Affinities and Associated Structural Modulations in Erythromycin Esterase C: Insights into the Venus Flytrap Mechanism.

Since their inception in antibacterial therapy, macrolide-based antibiotics have significantly shaped the evolutionary pathways of pathogenic bacteria, driving them to develop diverse antimicrobial resistance (AMR) mechanisms. Among these, macrolide esterase, commonly referred to as erythromycin esterase, emerged as a critical defense mechanism, enabling bacteria to detoxify macrolides by hydrolyzing the macrolactone ring within the bacterial cell. In this study, we delve into the intricate interactions and conformational dynamics of erythromycin esterase C (EreC), a key member of the Ere enzyme family. We have focused on three FDA-approved and widely prescribed macrolides─erythromycin, clarithromycin, and azithromycin─by employing classical molecular dynamics, absolute binding free energy calculations, and 2D well-tempered metadynamics simulations to explore their interactions with EreC. To estimate the absolute binding free energies, we have used the recently developed and robust "Streamlined Alchemical Free Energy Perturbation (SAFEP)" protocol. The results from our molecular dynamics simulations and advanced analyses portrayed the crucial role of hydrophobic interactions within the macrolide binding cleft of EreC, along with the significant influence of the minor lobe in facilitating overall structural fluctuation. In silico alanine scanning identified top three hydrophobic residues, i.e., PHE248, MET333, and PHE344, responsible for macrolide binding inside that cleft. According to the free energy calculations, azithromycin and clarithromycin showed greater binding affinities toward EreC than the parent macrolide erythromycin. Moreover, 2D metadynamics simulations along with graph theory-based eigenvector centrality analyses revealed a metastable "semiopen" state during the hypothesized "active loop closure" of the EreC protein triggered by subtle conformational changes of an important histidine residue, HIS289, upon macrolide capture, drawing a fascinating parallel to the renowned "Venus flytrap" mechanism.

Plasma proteomics in epilepsy: Network-based identification of proteins associated with seizures

PurposeIdentification of potential biomarkers of seizures. MethodsIn this exploratory study, we quantified plasma protein intensities in 15 patients with recent seizures compared to 15 patients with long-standing seizure freedom. Using TMT-based proteomics we found fifty-one differentially expressed proteins. ResultsNetwork analyses including co-expression networks and protein-protein interaction networks, using the STRING database, followed by network centrality and modularity analyses revealed 22 protein modules, with one module showing a significant association with seizures. The protein-protein interaction network centered around this module identified a subnetwork of 125 proteins, grouped into four clusters. Notably, one cluster (mainly enriching inflammatory pathways and Gene Ontology terms) demonstrated the highest enrichment of known epilepsy-related genes. ConclusionOverall, our network-based approach identified a protein module linked with seizures. The module contained known markers of epilepsy and inflammation. The results also demonstrate the potential of network analysis in discovering new biomarkers for improved epilepsy management.

Exploring factors influencing the quality of life in diabetes: a network analysis-based study.

The purpose of this study was to explore the key pathways leading to low quality of life in type 2 diabetes patients by means of network analysis, so as to provide the possibility of effective interventions. The study involved 1,011 adult type 2 diabetes patients from a tertiary hospital in Harbin. Data was collected through questionnaires, and network analysis was performed using R software to assess the centrality and predictability of each node. "Depression" and "Submission" (weight = 0.26), "Depression" and "Physiological field" (weight = -0.16), exhibit the strongest associations. Overall, "Depression" has the highest weight in the association with diabetes symptom, regarding betweenness, "Depression" and "Submission" exhibit the highest scores, Furthermore, the analysis of closeness centrality reveals that "Depression" and "Submission" share the highest level of proximity, it suggests that they have the shortest distances to other network factors in our research network. Depression and Submission are likely to be key factors affecting the quality of life of people with diabetes. Providing psychological support and scientific coping strategies for diabetes patients may be an effective way to help them live a better life.

텍스트마이닝을 활용한 청소년의 에너지드링크에 대한 인식 분석

Objectives The purpose of this study is to explore the adolescents' perception of energy-drinks using big data. Methods In order to analyze big data, ‘adolescents’ and ‘energy-drinks’ related documents that had been generated from March 31, 2023 to March 30, 2024 were collected from major portal sites (“Naver”, “Daum” and “Google”) using Textom. A total of 15,959 documents were collected. Non-relevant and inaccurate words were removed for text mining. Among them, 50 words with a high frequency of appearance were extracted, and 50 keywords were derived around words with high meaning through refining. Results After that, frequency analysis, TF-IDF (Term Frequency - Inverse Document Frequency) analysis, centrality analysis, and CONCOR (Convergence of iteration Correlation) analysis were performed using NetDraw and the UCINET program, and clustering results between major keywords were investigated. As a result, Group 1 was routine caffeine intakes, Group 2 was purchasing facilitating factors, Group 3 was purchasing determining factors, and Group 4 was concerns of nutrient deficiency. Conclusions Based on these results, adolescents’ perceptions and issues of energy-drinks were identified. Furthermore, this study will provide theoretical implications for future research as well as practical implications for preparing policy regulatory measures and developing educational programs.

언어네트워크 분석을 통한 예비영유아교사의 수업시연 내용 연구: 멘토링 중심 수업시연대회에 따른 변화

Objectives In this study, we conducted a language network analysis of the content of the changing lesson demonstrations as pre-service early childhood teacher experienced mentoring-centered lesson demonstrations. Methods For this reason, after 264 chapters of the 1st Selection Tournament Warrior Materials and mentoring were conducted for 34 participants in the Class Trial Competition, 362 chapters of the 2nd Final Selection Tournament Warrior materials were analyzed. Data were compared by keyword frequency analysis, network structure analysis, centrality analysis, and CONCOR analysis using KrKwic and UCINET 6 programs. Results First of all, In the first round of competitions, Guys was mentioned most often after We, and in the second round of competitions, Teacher was mentioned after We. Second, the relationships between keywords in the utterances in the second round competition were more concentrated than those in the first round competition. In terms of degree centrality, betweenness centrality, and eigenvector centrality, Guys was ranked in the top 3 in the first selection competition, and Teacher was ranked in the top 2 in the second selection competition. Third, according to the CONCOR analysis, two main clusters appeared in the first round of competitions is teacher-centered interaction and explanation about the content of the activity. Two main clusters appeared in the second round of competitions is child-centered interaction and a method cluster was formed for young children to participate in activities. Conclusions The results of this study suggest that it is necessary to develop a curriculum in collaboration between universities and early childhood educational institution in order to improve the professional abilities of pre-service early childhood teachers.

Transcriptome and interactome-based analyses to unravel crucial proteins and pathways involved in Acinetobacter baumannii pathogenesis.

The present study employed an integrated transcriptome and interactome-based analyses to identify key proteins and pathways associated with Acinetobacter baumannii infection towards the development of novel therapeutics against this pathogen. Transcriptome analysis of A.baumannii strains (ATCC 17978 and AbH12O-A2) identified 253 and 619 differentially expressed genes (DEGs), respectively. These genes were involved in essential molecular functions, including DNA binding, metal ion binding, and oxidoreductase activity. The centrality and module analyses of these identified DEGs had shortlisted 27 and 41 hub proteins, which were central to the ATCC 17978 and AbH12O-A2 networks, and essential for bacterial survival. Significantly, three proteins (SecA, glutathione synthase, and aromatic-amino-acid transaminase) from the ATCC 17978 strain and seven proteins (ATP synthase subunit alpha, translation initiation factor IF-2, SecY, elongation factors G, Tu, and Ts, and tRNA guanine-N1-methyltransferase) from the AbH12O-A2 strain showed interactions with human proteins, identified through host-pathogen interaction (HPI) analysis of hub proteins (referred as hub-HPI proteins). These proteins were observed to participate in vital pathways, including glutathione metabolism, secondary metabolite biosynthesis and quorum sensing. Targeting these hub-HPI proteins through novel therapeutic strategies holds the potential to disrupt the critical bacterial pathways, thereby controlling A. baumannii infections. Furthermore, their localization analysis indicated that nine proteins were cytoplasmic and one was membrane protein. Among them, six were druggable and four were novel proteins. Overall, this comprehensive study provides valuable insights into the crucial proteins and pathways involved during A. baumannii infection, and offers potential therapeutic targets for designing novel antimicrobial agents to tackle the pathogen.