Performed Network Analysis Research Articles

The trajectory of artificial intelligence for competency-based personalised learning: past, present and future

PurposeThe study aims to reflect on past research, uncover current trends and propose a future research agenda in the field of artificial intelligence (AI) for competency-based personalised learning.Design/methodology/approachThe study followed the SPAR-4-SLR protocol to retrieve 855 articles related to the field indexed in the Scopus database. Performance analysis, network analysis and science mapping were then performed using VOSviewer and the Biblioshiny app.FindingsThe analysis identified nine clusters of intellectual structure (healthcare, competencies, learning systems, digital transformation, AI literacy, computer-aided education, AI ethics, e-learning and active learning) and twelve themes (including motor, basic, emerging and niche).Originality/valueFollowing an extensive review of the literature, this would appear to be the first study to provide a panoramic view of AI for competency-based personalised learning based on the Scopus database. The core gaps in the current literature have been identified and the corresponding future agenda will be instrumental in shaping future research directions in the field.

Validation of network-based correlation analysis with [11C]glyburide whole-body PET

Network analysis is a form of correlation analysis that is particularly useful in performing whole-body studies with positron emission tomography (PET), as it provides a way to quantify the similarities and differences in the data from a range of organs and patients. Network analysis was performed on dynamic whole-body PET with [11C]glyburide in humans, with and without infusion of rifampicin [1]. Glyburide is a substrate of several Organic Anion-Transporting Polypeptides (OATPs), which are proteins that control the uptake of drugs into the liver, although OATP expression in other organs has been reported. Rifampicin is a strong OATP inhibitor that has been shown to decrease the body’s ability to eliminate glyburide [1]. Time-activity curves (TACs) in kBq/mL from several organs of interest were used to perform network analysis with the Pearson correlation coefficient employed as the linear correlation measure. In the liver, network analysis was able to differentiate the control and rifampicin-treated groups (Fig. 1A). In this network, each node represents an average TAC from the liver of one patient (Fig. 1C), and the edges between nodes represent significant correlations. The edges are colour-coded to the strength of the Pearson value, showing stronger, more dense correlations within the control group. Additionally, network analysis was performed on static PET data (Fig. 1B), where each node represents a patient curve comprised of the average activity in each organ at equilibrium (Fig. 1D). With the static data, network analysis was able to separate the control and treatment groups into two components. This is highly dependent on the liver though and removing it from the curves leads to reduced differentiation. Overall, network analysis is sensitive to the differing uptake of [11C]glyburide throughout the body under normal and rifampicin-treated conditions. It provides confirmation that the control and treatment groups differ largely through the uptake in the liver, which is the predominant site of OATP function. Please click on the 'PDF' for the full abstract!

Network analysis for the steady-state thermodynamic uncertainty relation.

We perform network analysis of a system described by the master equation to estimate the lower bound of the steady-state current noise, starting from the level 2.5 large deviation function and using the graph theory approach. When the transition rates are uniform, and the system is driven to a nonequilibrium steady state by unidirectional transitions, we derive a noise lower bound, which accounts for fluctuations of sojourn times at all states and is expressed using mesh currents. This bound is applied to the uncertainty in the signal-to-noise ratio of the fluctuating computation time of a schematic Brownian computation plus reset process described by a graph containing one cycle. Unlike the mixed and pseudo-entropy bounds that increase logarithmically with the length of the intended computation path, this bound depends on the number of extraneous predecessors and thus captures the logical irreversibility.

Quantifying hubness to predict surgical outcomes in epilepsy: Assessing resection-hub alignment in interictal intracranial EEG networks.

Intracranial EEG can identify epilepsy-related networks in patients with focal epilepsy; however, the association between network organization and post-surgical seizure outcomes remains unclear. Hubness serves as a critical metric to assess network organization by identifying brain regions that are highly influential to other regions. In this study, we tested the hypothesis that favorable post-operative seizure outcomes are associated with the surgical removal of interictal network hubs, measured by the novel metric "Resection-Hub Alignment Degree (RHAD)." We analyzed Phase II interictal intracranial EEG from 69 patients with epilepsy who were seizure-free (n = 45) and non-seizure-free (n = 24) 1 year post-operatively. Connectivity matrices were constructed from intracranial EEG recordings using imaginary coherence in various frequency bands, and centrality metrics were applied to identify network hubs. The RHAD metric quantified the congruence between hubs and resected/ablated areas. We used a logistic regression model, incorporating other clinical factors, and evaluated the association of this alignment regarding post-surgical seizure outcomes. There was a significant difference in RHAD in fast gamma (80-200 Hz) interictal network between patients with favorable and unfavorable surgical outcomes (p = .025). This finding remained similar across network definitions (i.e., channel-based or region-based network) and centrality measurements (Eigenvector, Closeness, and PageRank). The alignment between surgically removed areas and other commonly used clinical quantitative measures (seizure-onset zone, irritative zone, high-frequency oscillations zone) did not reveal significant differences in post-operative outcomes. This finding suggests that the hubness measurement may offer better predictive performance and finer-grained network analysis. In addition, the RHAD metric showed explanatory validity both alone (area under the curve [AUC] = .66) and in combination with surgical therapy type (resection vs ablation, AUC = .71). Our findings underscore the role of network hub surgical removal, measured through the RHAD metric of interictal intracranial EEG high gamma networks, in enhancing our understanding of seizure outcomes in epilepsy surgery.

Prevalence and repercussions of stress and mental health issues on primary and middle school students: a bibliometric analysis.

This study analyzes the presence and reverberations of stress, anxiety, and other mental health issues on primary and middle school students using bibliometric analysis. The aim of this study is to map the research landscape by statistically analyzing existing literature and identifying key themes, trends, and research hotspots in the domain of stress in students. This study also presents analysis related to top contributing countries, journals, authors, citations, and collaboration networks. A total of 1,335 publications from 1962 to 10 September 2023 were included in this study using the Web of Science, PubMed, and Scopus databases. The steps involved in the bibliometric study included data collection, cleaning, and various analyses such as performance analysis, citation analysis, and network analysis. Biblioshiny by RStudio and Microsoft Excel were used for bibliometric analysis to determine the collaboration between countries and authors and to explore keyword analysis and thematic evolution. The findings show that China and USA have contributed the highest number of publications. Frontiers in Psychology with 50 publications turns out to be the most prominent journal. The study presents the thematic evolution and the trend topics in this research domain. Some of the trend topics are stress, test anxiety, bullying, depression, cyberbullying, virtual reality, mathematics anxiety, childhood maltreatment and self-compassion, primary school, and middle school. The paper also highlights the prominent authors and their collaboration network. The study has highlighted the various reasons for stress and its potential repercussions on students. This information can be used to help parents, teachers, and the school administration to spot the most susceptible group of students who need immediate intervention to address various mental health issues. We see a gradual progress in the research areas being covered under this domain. More relevant areas of concern related to stress are being explored with time. With the technological advancement and the vast unmonitored internet usage (especially for primary and middle school students), the stress caused by cyberbullying and peer victimization has also become an important topic of research in later years.

The proteomic landscape and temporal dynamics of mammalian gastruloid development.

Gastrulation is the highly coordinated process by which the early embryo breaks symmetry, establishes germ layers and a body plan, and sets the stage for organogenesis. As early mammalian development is challenging to study in vivo, stem cell-derived models have emerged as powerful surrogates, e.g. human and mouse gastruloids. However, although single cell RNA-seq (scRNA-seq) and high-resolution imaging have been extensively applied to characterize such in vitro embryo models, a paucity of measurements of protein dynamics and regulation leaves a major gap in our understanding. Here, we sought to address this by applying quantitative proteomics to human and mouse gastruloids at four key stages of their differentiation (naïve ESCs, primed ESCs, early gastruloids, late gastruloids). To the resulting data, we perform network analysis to map the dynamics of expression of macromolecular protein complexes and biochemical pathways, including identifying cooperative proteins that associate with them. With matched RNA-seq and phosphosite data from these same stages, we investigate pathway-, stage- and species-specific aspects of translational and post-translational regulation, e.g. finding peri-gastrulation stages of human and mice to be discordant with respect to the mitochondrial transcriptome vs. proteome, and nominating novel kinase-substrate relationships based on phosphosite dynamics. Finally, we leverage correlated dynamics to identify conserved protein networks centered around congenital disease genes. Altogether, our data (https://gastruloid.brotmanbaty.org/) and analyses showcase the potential of intersecting in vitro embryo models and proteomics to advance our understanding of early mammalian development in ways not possible through transcriptomics alone.

Prevalence and Phylogenetic Network Analysis of Nosema apis and Nosema ceranae Isolates from Honeybee Colonies in Türkiye.

Nosemosis is a disease that infects both Western honeybees (Apis mellifera L.) and Asian honeybees (Apis cerana) and causes colony losses and low productivity worldwide. In order to control nosemosis, it is important to determine the distribution and prevalence of this disease agent in a particular region. For this purpose, a national study was conducted to assess the prevalence of Nosema ceranae and N. apis throughout Türkiye, to perform network analyses of the parasites, and to determine the presence of nosemosis. In this study which aimed to assess the prevalence of N. apis and N. ceranae in different colony types and regions where beekeeping is intensive in Türkiye, specimens were collected from hives with no clinical signs. A total of 1194 Western honeybee colonies in 400 apiaries from 40 provinces of Türkiye were examined by microscopic and molecular techniques. Nosemosis was found in all of 40 provinces. The mean prevalence ratio was 64.3 ± 3.0, with 95% CI in apiaries and 40.5 ± 2.9, 95% CI in hives. Nosema ceranae DNA was detected in all of positive hives, while N. ceranae and N. apis co-infection was detected in only four colonies. This study showed that nosemosis has spread to all provinces, and it is common in every region of Türkiye. All of the N. ceranae or N. apis samples examined were 100% identical within themselves. Network analysis showed that they were within largest haplotype reported worldwide.

Spatial database applications for network analysis: Case study of bicycle accessibility of forested areas in the Poznań Metropolitan Area, Poland

Abstract The main aim of this paper is to introduce a solution for network analysis based on pgRouting to resolve the bicycle accessibility of forested green spaces. The proposed application uses open-source software tools such as PostgreSQL with PostGIS extension. The solution includes a complete description of how to perform network analysis using a spatial database with SQL and pgRouting. The implemented functionalities consist of solutions for finding the equidistance or isochrone area for any selected point location. The method is tested on case study data drawn from a total of 9,500 km of roads suitable for cyclists in the Poznań Metropolitan Area, located in western Poland. The results of the analysis were isochrones determining the bicycle accessibility of forested areas. The accessibility analysis was performed considering an urbanised residential area. As a result of the analysis, locations with the best and limited access to forested green areas were identified. Moreover, the described methodology is ready to be used to solve various accessibility problems.

Mapping the Research on Periocular Basal Cell Carcinoma in the Past 20 Years: A Bibliometric Network Analysis.

This study aims to analyze the literature on periocular basal cell carcinoma, identify research trends, and offer insights into future research areas in this field to assist clinicians and researchers. 903 publications on periocular basal cell carcinoma were collected from the Web of Science Core Collection database. We assessed the contributions from various countries, institutions, journals, and authors, and performed network analysis using Excel, VOSviewer, and R Studio to represent the prominent areas of research visually. The country with the highest number of publications and citations in this study was the United States of America, with 250 publications, 5917 citations, and the highest H-index of 44. Ophthalmic Plastic and Reconstructive Surgery is the leading journal. The UTMD Anderson Cancer Center had the highest number of publications, accounting for 43, or 4.76% of the total. Selva D from the University of Adelaide, Australia, is the top author with 26 publications, and 751 citations. Targeted therapy for PBCC-related pathways has been a hot topic in recent years. This study using bibliometrics seeks to explore the patterns and focal points of research and analyzes publication patterns, key research areas, influential authors, and prominent journals in periocular basal cell carcinoma during the last 2 decades.

A Systematic Review of Application of Machine Learning in Curriculum Design Among Higher Education

Machine learning has become an increasingly popular area of research in the field of education, with potential applications in various aspects of higher education curriculum design. This study aims to review the current applications of AI in the curriculum design of higher education. We conducted an initial search for articles on the application of machine learning in curriculum design in higher education. This involved searching three core educational databases, including the Educational Research Resources Information Centre (ERIC), the British Education Index (BEI), and Education Research Complete, to identify relevant literature. Subsequently, this study performed network analysis on the included literature to gain a deeper understanding of the common themes and topics within the field. The results showed a growing trend in publishing research on the application of machine learning within the educational domain. Our review pinpointed merely 11 publications specifically targeting the application of machine learning in higher education course design, with only three being peer-reviewed articles. Through the word cloud visualization, we discerned the most prominent keywords to be AI, foreign countries, pedagogy, online courses, e-learning, and course design. Collectively, these keywords underscore the significance of AI in molding the educational landscape, as well as the expanding tendency to incorporate AI technologies into online and technology-enhanced learning experiences. Although there is a significant amount of research on the application of machine learning in education, the literature on its specific use in higher education course design still needs to be expanded. Our review identified only a small number of studies that directly focused on this topic, and among them. The network analysis generated from the included literature highlights important themes related to student learning and performance and the use of models and algorithms. However, there is still a need for further research in this area to fully understand the potential of machine learning in higher education course design. This study would contribute literature in this specific field. The review can update teacher’s awareness of using machine learning in teaching practice. Additionally, it implies more and more researchers conduct related research in this area. Future studies should consider the limitations of the existing literature and explore new approaches to incorporate machine learning into curriculum design to improve student learning outcomes.

Navigating Sustainability A Bibliometric Analysis Of Carbon Management Accounting

In the era of increasing concerns over climate change and sustainable development, Carbon Management Accounting (CMA) research gains paramount attention. Previous studies focused on specific aspects of carbon accounting, leaving a gap in comprehensive exploration of CMA's holistic approach to sustainability challenges. This study aims to provide a comprehensive overview of the current state of CMA research and its implications for sustainability efforts. Bibliometric and network analyses, utilizing Harzing's Publish or Perish (PoP) and VOSviewer (VOS) software, 200 Scopus-indexed documents from 2013 to 2022 were examined. Performance analysis, citation analysis, journal analysis, and network analysis were employed to examine the field from research practice and system perspectives. From a practice-perspective, the analysis reveals CMA research as an "advancement," depicting progressive, impactful, and dynamism within the field. The system-perspective displays a “transformative” role characterized by prominence, synergy, and integration. Popular themes among researchers and countries include keywords like "carbon," "carbon accounting," "environmental policy," and "climate change." A significant gap between sustainability development and the carbon accounting model in CMA is identified, emphasizing the need for further integrated research. This study is pivotal for practitioners, policymakers, and researchers, aiding sustainability efforts and addressing global climate change.

Curcumin for gastric cancer: Mechanism prediction via network pharmacology, docking, and in vitro experiments

BACKGROUND Curcumin originates from the natural herb turmeric, and its antitumor effects have been known about for a long time. However, the mechanism by which curcumin affects gastric cancer (GC) has not been elucidated. AIM To elucidate the potential mechanisms of curcumin in the treatment of GC. METHODS Network pharmacological approaches were used to perform network analysis of Curcumin. We first analyzed Lipinski’s Rule of Five for the use of Curcumin. Curcumin latent targets were predicted using the PharmMapper, SwissTargetPrediction and DrugBank network databases. GC disease targets were mined through the GeneCard, OMIM, DrugBank and TTD network databases. Then, GO enrichment, KEGG enrichment, protein-protein interaction (PPI), and overall survival analyses were performed. The results were further verified through molecular docking, differential expression analysis and cell experiments. RESULTS We identified a total of 48 curcumin-related genes with 31 overlapping GC-related targets. The intersection targets between curcumin and GC have been enriched in 81 GO biological processes and 22 significant pathways. Following PPI analysis, 6 hub targets were identified, namely, estrogen receptor 1 (ESR1 ), epidermal growth factor receptor (EGFR ), cytochrome P450 family 3 subfamily A member 4 (CYP3A4 ), mitogen-activated protein kinase 14 (MAPK14 ), cytochrome P450 family 1 subfamily A member 2 (CYP1A2 ), and cytochrome p450 family 2 subfamily B member 6 (CYP2B6 ). These factors are correlated with decreased survival rates among patients diagnosed with GC. Molecular docking analysis further substantiated the strong binding interactions between Curcumin and the hub target genes. The experimental findings demonstrated that curcumin not only effectively inhibits the growth of BGC-823 cells but also suppresses their proliferation. mRNA levels of hub targets CYP3A4 , MAPK14 , CYP1A2 , and CYP2B6 in BGC-823 cells were significantly increased in each dose group. CONCLUSION Curcumin can play an anti-GC role through a variety of targets, pathways and biological processes.

KG-CFSA: a comprehensive approach for analyzing multi-source heterogeneous social network knowledge graph

Analyzing opinions, extracting and modeling information, and performing network analysis in online information studies are challenging tasks with multi-source social network data. This complexity arises from the difficulty in harnessing data across various platforms and the absence of a unified data modeling approach. Although social network analysis has used a multiplex approach to study complex networks, no previous work has integrated data from multiple social networks, knowledge graph fusion, and contextual focal structure analysis (CFSA) for an online study. This study has developed a multi-source graph model and applied a Cartesian merge to model relations across multiple documents, entities, and topics. We improved the information modeled with third-party data sources such as WikiData and DiffBot. This approach has created a multiplex network instance for CFSA detection, incorporating topic-topic, entity-entity, and document-document models. We applied this method to a dataset from the Indo-Pacific region and identified 40,000 unique focal sets of influential topics, entities, and documents. The top sets discussed economics, elections, and policies such as the Indo-Pacific Economic Framework, Ekonomi baru, #NKRIHargaMati, #IndonesiaJaya, and the Xinjiang Supply Chain. Our model tracks information spread across multiple social media platforms and enhances the visibility of vital information using various relationships. The results underscore the effectiveness of KG-CFSA in contextualizing large-scale information from multiple sources.

Intensive care unit carbon footprint: A bibliometric and document content analysis.

Sustainable low-carbon health systems are among the United Nations Climate Change Conference (COP27) goals. The literature states that 4.9% of greenhouse gas emissions originate from the health sector. This study aimed to quantify and visualize trends, citations, key terms and countries of publications about the carbon footprint of intensive care and review their contents. A bibliometric analysis was conducted on 14 articles selected by searching the SCOPUS database using the keywords 'Environmental sustainability', 'Environmental footprint', 'Carbon footprint' and 'Intensive care unit'. MS Excel was used to create graphs, and VOSviewer was used to perform network analysis. Content analysis methods were used to describe the details in each document. Most articles on intensive care carbon footprint and environmental sustainability were published in 2023 (n = 7, 47%), with the first publication in 2014. The article 'Environmental sustainability in anaesthesia and critical care', from the British Journal of Anaesthesia, was the most cited, with 166 citations. The carbon footprinting studies were conducted in the United Kingdom, Australia, United States and the Netherlands. Minimizing electricity usage and waste generation (reducing, reusing and recycling more) can reduce the carbon footprint of intensive care. The number of studies on the subject was limited, and although none were specifically about nurses, sustainability and environmental impact are relevant topics for all intensive care staff. Nurses and other health care professionals can improve environmental sustainability by better understanding how to reduce the carbon footprint of intensive care units.

Research Trends of Flipped Classroom Model in Mathematics Education: A Bibliometric Mapping Analysis

The COVID-19 pandemic has expedited the integration of technology into instructional practices through the transition to emergency distance education. The flipped classroom has emerged as a prominent approach among the notable models in this process. The flipped classroom leverages technological opportunities to enhance productivity by shifting the teacher's role from in-person explanations to facilitating individualized learning outside the classroom. CiteSpace, VOSviewer, and R Bibliometrix are three knowledge graph software tools used in this work to carefully look over, evaluate, and diversify the selected data, drawing on previous research on the subject. By querying the Web of Science database, 226 articles on the flipped classroom model in mathematics education were accessed. Performance analysis, science mapping, and network analysis were performed on the obtained articles. The earliest publications on this subject date back to 2014, and the number of publications has grown steadily. Lo is the author who ranks first in productivity due to his most publications on flipped classrooms in mathematics education. The countries that show the most interest in flipped classroom in mathematics education and have publications on this subject are the USA, China, and Spain. In addition, international co-authorship results of 21.7% of the publications were reached. Also, future research implications based on the findings were discussed.

Development and Validation of an Immune Prognostic Index Related to Infiltration of CD4+ and CD8+ T Cells in Colorectal Cancer.

Colorectal cancer (CRC) is a highly prevalent cancer worldwide, but treatment outcomes can vary significantly among patients with similar clinical or historical stages. This study aimed to investigate the differences in immune cell abundance associated with malignant progression in CRC patients. We utilized data from patients with CRC obtained from The Cancer Genome Atlas as our training set. To assess immune cell infiltration levels, an immune cell risk score (ICRS) was calculated. Furthermore, we performed network analysis to identify effective T cell-related genes (ETRGs) and subsequently constructed an effective T cell prognostic index (ETPI). The performance of the ETPI was evaluated through external validation using four Gene Expression Omnibus datasets. Additionally, a nomogram analysis and drug sensitivity analysis were conducted to explore the clinical utility of the ETRGs. We also examined the expression of ETRGs in clinical samples. Based on the ICRS, we identified activated CD4+ and CD8+ T cells as protective factors in terms of prognosis. Six ETRGs were identified to develop the ETPI, which exhibited remarkable prognostic performance. In the external validation of immunotherapy, the low ETPI group demonstrated a significantly lower recurrence rate. To optimize therapeutic strategies, we developed a nomogram. Notably, patients with different ETPI values exhibited varying responses to tumor pathway inhibitors. Finally, we observed higher protein expression of certain ETRGs in normal tissues compared to tumors. Our findings suggest that the ETPI may contribute to the precise selection of patients based on tumor microenvironment and key genomic landscape interactions, thereby optimizing drug benefits and informing clinical strategies in future.

India’s solar energy research: performance and social network analysis

India’s solar energy research: performance and social network analysis

Mapping the field of sensory marketing: a comprehensive bibliometric analysis

Purpose The objective of this review article is to meticulously examine and critically evaluate the present body of literature on sensory marketing. The study aims to explore this domain’s latest trends and prospects using diverse metrics such as annual publication trends, authorship patterns, most cited publications, sources analysis, co-citation patterns, keyword analysis and thematic analysis. Design/methodology/approach In the study, bibliometric analysis is used to assess the different advancements in the field. To perform performance analysis, science mapping and network analysis, VOSviewer, Bibloshiny and MS Excel are used. Additionally, the Scopus database is used to extract literature related to the subject area. Findings The most cited author of the domain is Biswas D, while the most prolific source of the domain is Plos One, additionally “An integrative review of sensory marketing: Engaging the senses to affect perception, judgment, and behavior” is stated as the most cited publication in the domain. Moreover, experiential marketing is reported as the most important keyword of the domain. Additionally, other findings reported are stated in the main body of the research study. Practical implications Practical implications of the study extended to marketing practitioners, future researchers and various stakeholders interested in the subject area. Originality/value What stands this research paper apart from other studies is the comprehensive approach to the evaluation of literature using VOSviewer, Bibloshiny and MS Excel and in-depth evaluation of the domain using performance analysis, science mapping, network analysis and thematic analysis.

The interdisciplinary nature of AI and human resource management: A bibliometric analysis

BACKGROUND: This study investigates the evolving relationship between artificial intelligence [AI] and human resource management [HRM] over the past two decades through a comprehensive analysis of 392 articles published between 2012 and 2023. OBJECTIVE: The study aims to provide a systematic understanding of this relationship by employing a rigorous three-stage methodology involving RStudio, VOSviewer, and Excel. METHODS: The methodology encompasses overall performance assessment, citation analysis, and network and content analysis to identify evolving research priorities, influential authors and publications, and underlying connections within the academic literature. RESULTS: The systematic approach reveals clusters of future research trends, including leveraging AI and big data for HRM advancements, enhancing employee engagement through machine learning, and investigating neural networks and procedural justice in HRM. IMPLICATIONS: This research agenda offers a roadmap for academics and HR professionals, identifying key areas for future exploration to ensure continued understanding of the dynamic interplay between AI and HRM.

Integrative analysis of blood transcriptome profiles in small-cell lung cancer patients for identification of novel chemotherapy resistance-related biomarkers.

Chemoresistance constitutes a prevalent factor that significantly impacts thesurvival of patients undergoing treatment for smal-cell lung cancer (SCLC). Chemotherapy resistance in SCLC patients is generally classified as primary or acquired resistance, each governedby distinct mechanisms that remain inadequately researched. In this study, we performed transcriptome screening of peripheral blood plasma obtainedfrom 17 patients before and after receiving combined etoposide and platinum treatment. We firs testimated pseudo-single-cell analysis using xCell and ESTIMATE and identified differentially expressed genes (DEGs), then performed network analysis to discover key hub genes involved in chemotherapy resistance. Our analysis showed a significant increase in class-switched memory B cell scores acrossboth chemotherapy resistance patterns, indicating their potential crucial role in mediatingresistance. Moreover, network analysis identifed PRICKLE3, TNFSFI0, ACSLl and EP300 as potential contributors to primary resistance, with SNWl, SENP2 and SMNDCl emerging assignificant factors in acquired resistance, providing valuable insights into chemotherapy resistancein SCLC. These findings offer valuable insights for understanding chemotherapy resistance and related gene signatures in SCLC, which could help further biological validation studies.