Network Analysis Research Articles

Comparison of different surgical strategies for cervical dystonia: Evidence from Bayesian network analysis.

Several surgical techniques have been used to treat cervical dystonia (CD), however, to date, the optimal surgical technique for CD remains controversial. We therefore conducted the first network meta-analysis to compare different surgical strategies for CD to inform clinical practice. Electronic databases were searched for surgical strategies for treating CD. The primary outcome was improvement in total Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) score. Subgroup analysis was performed to compare short-term (< 1 year) and long-term (≥ 1 year) outcomes. Safety outcomes included surgery-related adverse events (AEs). A total of 55 trials with 2032 patients employing five surgical strategies were identified, including globus pallidus internus (GPi)/subthalamic nucleus (STN)-deep brain stimulation (DBS), selective peripheral denervation (SPD), microvascular decompression (MVD) and pallidotomy. All strategies led to significant improvement in total TWSTRS score (mean improvement range 18.65-28.22). GPi-DBS showed significantly greater enhancement than SPD for the whole dataset (mean difference [MD] 7.03, 95% credible interval [Crl] 1.53-12.56), while both GPi-DBS (MD 8.05, 95% Crl 2.35-13.80) and STN-DBS (MD 10.71, 95% Crl 2.22-19.20) exhibited more long-term improvement than SPD. Regarding safety outcomes, GPi/STN-DBS and MVD were associated with fewer surgery-related AEs than SPD (ln odds ratio range -1.68 to -1.41). We conclude that DBS should be the preferred surgical option for CD, and the STN is a promising alternative target choice due to its comparable efficacy with the GPi. However, more direct evidence is still required.

Cyclin-dependent kinase 4/6 inhibitors and cardiotoxic events in breast cancer: A pharmacovigilance study based on the FAERS database.

Recent trials have highlighted the cardiotoxicity of ribociclib, a CDK4/6 inhibitor, particularly its association with QT prolongation. However, studies on the link between CDK4/6 inhibitors and cardiotoxic events show inconsistent results, and the factors influencing these events and related drug interactions remain underexplored. To address these uncertainties, our study utilizes the FDA adverse event reporting system database (Q1 2015 to Q1 2024) to examine the cardiotoxic events of CDK4/6 inhibitors in breast cancer patients. We employed a comprehensive analytical framework, applying disproportionality methods including Bayesian confidence propagation neural network, proportional reporting ratio, and reporting odds ratio. Our findings highlight significant variability in cardiotoxic events among different CDK4/6 inhibitors, with ribociclib (IC: 0.26, 95%CI: 0.18-0.34) exhibiting pronounced cardiotoxicity. Notably, ribociclib was associated with serious cardiotoxic events such as torsade de pointes/QT prolongation (IC: 2.11, 95% CI: 1.90-2.29) and conduction defects (IC: 2.07, 95% CI: 1.87-2.23). For the first time, palbociclib has been identified with positive signals for cardiotoxic events at the preferred terms level, including pulmonary oedema, increased blood pressure, myocardial infarction, and cardiac flutter. Moreover, multivariable logistic regression and Bayesian network analyses reveal that age, geographic location, and the number of concomitant medications significantly influence cardiotoxic events. Our study also highlights significant drug interactions that increase the probability of specific cardiotoxic outcomes, notably with drugs like sertraline, lansoprazole, capecitabine, and torasemide. These findings highlight the need for personalized treatment plans to mitigate cardiotoxic events and improve patient safety.

Longitudinal mitochondrial bioenergetic signatures of blood monocytes and lymphocytes improve during treatment of drug-susceptible pulmonary tuberculosis patients Monocyte/lymphocyte bioenergetic signatures post-TB treatment

The impact of human pulmonary tuberculosis (TB) on the bioenergetic metabolism of circulating immune cells remains elusive, as does the resolution of these effects with TB treatment. In this study, the rates of oxidative phosphorylation (OXPHOS) and glycolysis in circulating lymphocytes and monocytes of patients with drug-susceptible TB at diagnosis, 2 months, and 6 months during treatment, and 12 months after diagnosis were investigated using extracellular flux analysis. At diagnosis, the bioenergetic parameters of both blood lymphocytes and monocytes of TB patients were severely impaired in comparison to non-TB and non-HIV-infected controls. However, most bioenergetic parameters were not affected by HIV status or glycemic index. Treatment of TB patients restored the % spare respiratory capacity (%SRC) of the circulating lymphocytes to that observed in non-TB and non-HIV infected controls by 12 months. Treatment also improved the maximal respiration of circulating lymphocytes and the %SRC of circulating monocytes of the TB patients. Notably, the differential correlation of the clinical and bioenergetic parameters of the monocytes and lymphocytes from the controls and TB patients at baseline and month 12 was consistent with improved metabolic health and resolution of inflammation following successful TB treatment. Network analysis of the bioenergetic parameters of circulating immune cells with serum cytokine levels indicated a highly coordinated immune response at month 6. These findings underscore the importance of metabolic health in combating TB, supporting the need for further investigation of the bioenergetic immunometabolism associated with TB infection for novel therapeutic approaches aimed at bolstering cellular energetics to enhance immune responses and expedite recovery in TB patients.

Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes

Breast cancer is a heterogeneous disease comprising various subtypes with distinct molecular characteristics, clinical outcomes, and therapeutic responses. This heterogeneity evidences significant challenges for diagnosis, prognosis, and treatment. Traditional genomic co-expression network analyses often overlook individual-specific interactions critical for personalized medicine. In this study, we employed single-sample gene co-expression network analysis to investigate the structural and functional genomic alterations across breast cancer subtypes (Luminal A, Luminal B, Her2-enriched, and Basal-like) and compared them with normal breast tissue. We utilized RNA-Seq gene expression data to infer gene co-expression networks. The LIONESS algorithm allowed us to construct individual networks for each patient, capturing unique co-expression patterns. We focused on the top 10,000 gene interactions to ensure consistency and robustness in our analysis. Network metrics were calculated to characterize the topological properties of both aggregated and single-sample networks. Our findings reveal significant fragmentation in the co-expression networks of breast cancer subtypes, marked by a change from interchromosomal (TRANS) to intrachromosomal (CIS) interactions. This transition indicates disrupted long-range genomic communication, leading to localized genomic regulation and increased genomic instability. Single-sample analyses confirmed that these patterns are consistent at the individual level, highlighting the molecular heterogeneity of breast cancer. Despite these pronounced alterations, the proportion of CIS interactions did not significantly correlate with patient survival outcomes across subtypes, suggesting limited prognostic value. Furthermore, we identified high-degree genes and critical cytobands specific to each subtype, providing insights into subtype-specific regulatory networks and potential therapeutic targets. These genes play pivotal roles in oncogenic processes and may represent important keys for targeted interventions. The application of single-sample co-expression network analysis proves to be a powerful tool for uncovering individual-specific genomic interactions.

Proteomic identification of potential biomarkers for heat tolerance in Caracu beef cattle using high and low thermotolerant groups.

Heat stress has deleterious effects on physiological and performance traits in livestock. Within this context, using tropically adapted cattle breeds in pure herds or terminal crossbreeding schemes to explore heterosis is attractive for increasing animal production in warmer climate regions. This study aimed to identify biological processes, pathways, and potential biomarkers related to thermotolerance in Caracu, a tropically adapted beef cattle breed, by proteomic analysis of blood plasma. To achieve this goal, 61 bulls had their thermotolerance evaluated through a heat tolerance index. A subset of 14 extreme animals, including the seven most thermotolerant (HIGH group) and the seven least thermotolerant (LOW group), had their blood plasma samples used for proteomic analysis by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). The differentially regulated proteins detected between HIGH and LOW groups were used to perform functional enrichment analysis and a protein-protein interaction network analysis. A total of 217 proteins were detected only in the HIGH thermotolerant group and 51 only in the LOW thermotolerant group. In addition, 81 and 87 proteins had significantly higher and lower abundancies in the HIGH group, respectively. Regarding proteins with the highest absolute log-fold change values, we highlighted those encoded by DUSP5, IGFALS, ROCK2, RTN4, IRAG1, and NNT genes based on their functions. The functional enrichment analysis detected several biological processes, molecular functions, and pathways related to cellular responses to stress, immune system, complement system, and hemostasis in both HIGH and LOW groups, in addition to terms and pathways related to lipids and calcium only in the HIGH group. Protein-protein interaction (PPI) network revealed as important nodes many proteins with roles in response to stress, hemostasis, immune system, inflammation, and homeostasis. Additionally, proteins with high absolute log-fold change values and proteins detected as essential nodes by PPI analysis highlighted herein are potential biomarkers for thermotolerance, such as ADRA1A, APOA1, APOB, APOC3, C4BPA, CAT, CFB, CFH, CLU, CXADR, DNAJB1, DNAJC13, DUSP5, FGA, FGB, FGG, HBA, HBB, HP, HSPD1, IGFALS, IRAG1, KNG1, NNT, OSGIN1, PROC, PROS1, ROCK2, RTN4, RYR1, TGFB2, VLDLR, VTN, and VWF. Identifying potential biomarkers, molecular mechanisms and pathways that act in response to heat stress in tropically adapted beef cattle contributes to developing strategies to improve performance and welfare traits in livestock under tropical climates.

Integrative Analysis of Metabolome and Transcriptome Reveals Molecular Mechanisms Regulating Oil and Protein Content in Peanut (Arachis hypogaea L).

Peanut (Arachis hypogaea L.) is an important source of edible vegetable oils and plant proteins globally. However, the complex mechanisms regulating the oil and protein contents of peanut seeds remain unclear. Here, comparative broad-target metabolomics and quantitative lipidomics, together with transcriptome analysis, of peanut seeds at four developmental stages from the high-oil content variety "YH15" and high-protein content variety "KB008" were performed to search for oil and protein content control genes. A total of 984 differential metabolites, including 128 amino acids and derivatives and 310 differentially accumulated lipids, were identified between "YH15" and "KB008" in four seed developmental stages. The weighted gene coexpression network analysis and module-trait relationship analysis revealed that MEbrown, MEyellow, and MEturquoise modules were key contributors to the quality discrepancies observed between "YH15" and "KB008." Crucial genes potentially regulating the differences in oil and protein contents between "YH15" and "KB008" were identified within the aforementioned three modules, including genes involved in amino acid synthesis and degradation, nitrogen allocation, triglyceride synthesis and degradation, and fatty acid synthesis and degradation, as well as transcription factors. Overall, this study provides valuable insights into the molecular regulation of oil and protein contents in peanut seeds and may help cultivate specialized peanut varieties with enhanced nutritional and economic values.

Structural Characteristics of Expressway Carbon Emission Correlation Network and Its Influencing Factors: A Case Study in Guangdong Province

Understanding the spatial correlation of transportation carbon emissions and their influencing factors is significant in achieving an overall regional carbon emission reduction. This study analyzed the structure characteristics of the expressway carbon emission correlation network in Guangdong Province and examined its influencing factors with intercity expressway traffic flow data using social network analysis (SNA). The findings indicate that the correlation network of expressway carbon emissions in Guangdong Province exhibited a “core-edge” spatial pattern. The overall network demonstrated strong cohesion and stability, and a significant difference existed between the passenger vehicle and freight vehicle carbon emission networks. The positions and roles of different cities varied within the carbon emission network, with the Pearl River Delta (PRD) cities being in a dominant position in the carbon network. Cities such as Guangzhou, Foshan, and Dongguan play the role of “bridges” in the carbon network. The expansion of differences in GDP per capita, industrial structure, technological level, and transportation intensity facilitates the formation of a carbon emission network. At the same time, geographical distance between cities and policy factors inhibit them. This study provides references for developing regional collaborative carbon emission governance programs.

Artificial Intelligence and Deep Learning in Stock Prediction: A Bibliometric Review

Artificial intelligence (AI) and deep learning (DL) are advancing in stock market prediction, attracting the attention of researchers in computer science and finance. This bibliometric review analyzes 525 articles published from 1991 to 2024 in Scopus-indexed journals, utilizing VOSviewer software to identify key research trends, influential contributors, and burgeoning themes. The bibliometric analysis encompasses a performance analysis of the most prominent scientific contributors and a network analysis of scientific mapping, which includes co-authorship, co-occurrence, citation, bibliographical coupling, and co-citation analyses enabled by the VOSviewer software. Among the 693 countries, significant hubs of knowledge production include China, the US, India, and the UK, highlighting the global relevance of the field. Various AI and DL technologies are increasingly employed in stock price predictions, with artificial neural networks (ANN) and other methods such as long short-term memory (LSTM), Random Forest, Sentiment Analysis, Support Vector Machine/Regression (SVM/SVR), among the 1399 keyword counts in publications. Influential studies such as LeBaron (1999) and Moghaddam (2016) have shaped foundational research in 8159 citations. This review offers original insights into the bibliometric landscape of AI and DL applications in finance by mapping global knowledge production and identifying critical AI methods advancing stock market prediction. It enables finance professionals to learn about technological developments and trends to enhance decision-making and gain market advantage.

The more self-control, the more anxious?- A network analysis study of the relationship between self-control and psychological anxiety among Chinese university students.

With the growing prevalence of anxiety symptoms among university students, self-control is an important potential influence. This study aims to understand the network structure of self-control and anxiety, and to identify the core symptoms within this network. It will provide a theoretical basis for the prevention and intervention of anxiety symptoms in university students. We used network analysis to study anxiety and self-control in 3,792 university students from six schools in Heilongjiang Province, China. We checked for linear connections in the networks using a restricted cubic spline. We conducted the analyses and made graphs using R software. (i) The total sample network showed that higher levels of self-control in university students were associated with lower anxiety levels and were validated by the restrictive cubic spline. (ii) There was the strongest negative correlation (edge weight = -0.42) between Without thinking (SC7) and Panic (A5), and the edge weight coefficients of this self-control component and anxiety symptoms were greater than those of other self-control components. (iii) Physical exertion (A6) and Scared (A7) were identified as the core symptoms of the overall network, with expected influence of 1.08 and 1.08 (Z-score). (iv) A significant difference was observed between the anxiety positive network structure and the total sample network structure, with the strongest positive correlation between Iron self-control (SC3) and Breathing difficulty (A2) (edge weight = 0.22), with the strongest negative correlation between Certain things (SC2) and Situations (A4) (edge weight = -0.35). (v) The self-control component Iron self-control (SC3) had only one positive edge in the rural network, and only two positive edges in not one child network. The present study offered a new perspective on the relationship between self-control and anxiety using network analysis for the first time. The control component Without thinking (SC7) was an important concept influencing the negative correlation of anxiety, and Physical exertion (A6) and Scared (A7) were core symptoms in the total network. Heterogeneity analyses showed a tendency for the more self-controlled to be more anxious in the anxiety positive sample. These results may be a potential target for preventing and intervening anxiety in university students.

Abstract 4137713: Epsin-Mediated PCSK9-LDLR Interaction: A New Therapeutic Target for Atherosclerosis

Introduction: Atherosclerosis, a leading cause of cardiovascular diseases, causes about 17.9 million deaths annually. It is driven by high levels of LDL cholesterol (LDL-C). The liver's LDL receptor (LDLR) removes LDL-C from the bloodstream, but its degradation by PCSK9 exacerbates the condition. Epsin, an endocytic adaptor protein crucial for clathrin-mediated endocytosis, plays a significant role in atherosclerosis, particularly lipid metabolism. Hypothesis: We propose that hepatic epsins significantly contribute to atherosclerosis by facilitating the PCSK9-induced degradation of LDLR, thus impeding LDL-C clearance. Objectives: This study aims to investigate hepatic epsins' role in PCSK9-mediated LDLR degradation and its effects on LDL-C levels and atherosclerosis development. Using bioinformatics, we will analyze enriched pathways and networks to study epsins modulation, which will be validated through biochemical assays and in vivo experiments. Methods: Liver-specific double knockout (DKO) mice lacking epsin1 and epsin2 were created on an ApoE -/- background. Atherosclerosis was induced using a Western diet (WD), and blood cholesterol and triglyceride levels were monitored. We employed high-resolution single-cell RNA sequencing (scRNA-seq) to analyze cellular transitions, intercellular interactions, and Gene Ontology pathway enrichment within hepatocyte-derived data. Results: RNA velocity reveals the transition from lipogenic Alb hi Hepatocytes to glucogenic Hnf4a hi Hepatocytes in Liver-DKO mice on an ApoE -/- background on a WD. Liver-DKO mice on an ApoE -/- background on a WD exhibited significantly reduced atherogenesis and lower blood cholesterol and triglyceride levels compared with wild-type on an ApoE -/- background on a WD. Gene Ontology enrichment analysis showed elevated pathways for LDL particle clearance and enhanced LDLR-cholesterol communication scores in Liver-DKO mice, suggesting improved LDL-C clearance. Nanoparticle-encapsulated siRNAs targeting liver epsins effectively inhibited atherosclerosis. Conclusion: Epsin depletion in the liver upregulated LDLR protein levels, and PCSK9-triggered LDLR degradation was abolished, preventing atheroma progression. Targeting liver epsins presents a novel therapeutic strategy for atherosclerosis, supported by network analysis and predictive modeling for effective interventions.

Abstract 4117767: Interplay Between ST2 Gene Downregulation and Inflammatory Processes in Hypertrophic Cardiomyopathy

Introduction: Hypertrophic Cardiomyopathy (HCM) is an inherited heart disease and the pathogenesis of HCM involves genetic mutations, hemodynamic stress, and metabolic factors, with myocardial fibrosis playing a crucial role in severe clinical events. IL-33/ST2 signaling pathway known for its roles in immune response and tissue repair, participates in cardiac protection and anti-cardiac fibrosis in heart failure. The role of ST2 in HCM remains unclear, and IL-33/ST2 pathway and broader inflammatory responses may be critical in HCM. Hypothesis: ST2 gene downregulation and dysregulation of inflammatory pathways represent a novel mechanism in HCM. Methods: We re-analyzed the raw whole transcriptome sequencing data using a comprehensive bioinformatics pipeline from 9 GEO datasets. After removing low-quality samples, myocardial tissue samples from 109 HCM patients and 210 non-HCM controls were included. HTSeq-count was used to obtain the read count of each gene. Differential expression analyses were conducted using DESeq2. Spearman correlation analysis was performed for ST2 with all other protein-coding genes. Gene set enrichment analysis (GSEA) was performed to explore the biological significance of ST2 related genes. CIBERSORTx was used to estimate the immune cell composition of heart tissues. Additionally, network analyses and statistical correlations were conducted. Results: Our analysis identified significant downregulation of the ST2 gene in HCM patients compared to controls (log2FC = -5.0, p = 2.7E-147). In total, 5,180 upregulated and 741 downregulated genes were detected. Additionally, 208 genes were significantly correlated with the expression of ST2. GSEA based on the correlation coefficients revealed a significant increase in immune response, inflammation, and IFN-γ pathway activity, along with a decrease in the expression of genes related to sarcomeric function, cardiac morphogenesis, and metabolism in HCM. Notably, the significant inverse correlation between ST2 expression and regulatory T cells (r = -0.34) and a positive correlation with neutrophils (r = 0.39) suggest that ST2 may modulate immune cell populations in HCM. Pathway analysis indicated ST2's central role in networks involving inflammatory and fibrotic responses. Conclusion: The interplay between ST2 and inflammatory pathways may drive myocardial remodeling and fibrosis in HCM. This study paves a new way for investigating pathogenic mechanisms and therapeutic strategies for HCM.

Cytokine and growth factor correlation networks associated with morbidities in extremely preterm infants.

Cytokines and growth factors (GF) have been implicated in the development of retinopathy of prematurity (ROP) and bronchopulmonary dysplasia (BPD). We hypothesize that even small coordinated changes in inflammatory proteins or GFs may reveal changes in underlying regulating mechanisms that do not induce obvious changes in concentration of individual proteins. We therefore applied correlation network analysis of serum factors to determine early characteristics of these conditions. Concentrations of 17 cytokines and five GFs were measured and analysed in blood samples from cord blood, on day one and during the following month in 72 extremely preterm infants. Spearman's correlation networks distinguishing BPD and severe ROP patients from non-affected were created. Most cytokine concentrations correlated positively with each other and negatively with GFs. Very few individual cytokines differed between patients with and without ROP or BPD. However, networks of differently correlated serum factors were characteristic of the diseases and changed with time. In ROP networks, EPO, G-CSF and IL-8 (cord blood), BDNF and VEGF-A (first month) were prominent. In BPD networks, IL-1β, IGF-1 and IL-17 (day one) were noted. Network analysis identifies protein signatures related to ROP or BPD in extremely preterm infants. The identified interactions between serum factors are not evident from the analysis of their individual levels, but may reveal underlying pathophysiological mechanisms in the development of these diseases.

Abstract 4121198: Structural Network Alterations After Cardiopulmonary Bypass In a Juvenile Porcine Model

Background: Cardiopulmonary bypass (CPB) can trigger inflammatory and oxidative stresses in the developing brain. Clinical studies showed altered brain network structure in children after infant cardiac surgery. However, the impact of CPB on structural connectivity remains poorly understood. This study aims to determine the effects of CPB on structural connectivity and underlying cellular events using a juvenile porcine model. Methods: CPB (150 min total) was performed on 2-week-old Yorkshire piglets. High-resolution diffusion tensor imaging (DTI) and neurite orientation dispersion and density (NODDI) imaging were performed 4 weeks post-CPB in a total of 12 animals (Control; n=7, CPB; n=5). Structural connectivity assay together with histological analysis was conducted to assess the brain network organization. Results: Our network analysis (Image 1) revealed a CPB-induced reduction of structural connections in particular nodes, such as in the anterior prefrontal cortex (APC), primary somatosensory cortex, substantia nigra (SN), and globus pallidus (GP). In contrast, there was an increase in the premotor cortex (PreMC), primary motor cortex (PriMC), dorsal posterior cingulate cortex (DPCC), and somatosensory cortex (SAC). Three-dimensional images were generated based on these altered nodes, and tractography was generated to indicate changed fiber tracking (Image 2). We found reduced network connections between the basal ganglia and frontal cortex, and increased connections between the motor and posterior region. DTI and NODDI indicated that microstructures of the superior corona radiata (SCR), internal capsule (PLIC), and anterior prefrontal (AP-WM), motor (PreM-/PriM-WM), and somatosensory white matter (SA-WM) were significantly altered after CPB (Image 3). While Iba1+ microglia was activated across all brain regions after CPB (P&lt;.05), CPB-induced oligodendrocyte immatureness was identified in the WMs involved in APC-SN and APC-GP pathways (P&lt;.05). Notably, CPB increased the number of mature oligodendrocytes in motor and SA-WMs (P&lt;.05) where structural connections were increased after CPB. In addition, we found SN dopaminergic neuron injury after CPB (P&lt;.05). Conclusion: CPB altered structural network organization in the developing brain. Spatial differences in oligodendrocyte responses and susceptibility of specific subpopulations of neurons may contribute to the unique connectivity alterations observed after CPB.

Mining text for causality: a new perspective on food safety crisis management

The aim of the present study was to quantitatively analyze the importance of each risk factor in a food safety event, so as to fully elucidate the correlation between different risk factors and provide a reference for food safety governance. Text mining and complex network analysis methods were utilized to explore the causal mechanism of food safety incidents. By performing text mining on food safety event news reports, 15 major risk factors were identified based on high-frequency words. A causal network for food safety accidents was then constructed using strong association rules among these factors. Through network centrality analysis, the five core factors of food safety incidents and their associated sets were clarified. Based on text mining of 6,282 cases of food safety incidents reported by online media, 168 keywords related to food risk factors were extracted and further categorized into 15 types of food safety risk factors. Network analysis results revealed that microbial infection emerged as the most critical risk factor, with its associated sets including biotoxins and parasites, counterfeiting or fraud, processing process issues, and non-compliance with quality indicators.

LC-MS/MS-based metabolic profiling: unraveling the impact of varying degrees of curing on metabolite transformations in tobacco

The curing process regulates metabolite transformations of leaves and significantly influences the formation of tobacco quality. This study investigated the major physicochemical compositions and metabolic profiles under normal curing (NC), excessive curing (EC), and insufficient curing (IC) treatments. The results indicated that the contents of nicotine, nitrogen, potassium, and chlorine remained stable among treatments, while the sugar content in EC was significantly lower than in IC. LC-MS/MS identified 845 metabolites, with flavonoids as the most abundant class. Comparative analyses identified a series of differentially expressed metabolites (DEMs) among fresh leaf, NC, EC, and IC leaves at the end of 42°C, 54°C, and 68°C, respectively. At the end of 68°C, 256 up-regulated and 241 down-regulated common DEMs across treatments were isolated in comparison to fresh leaf, underscoring the consistency of metabolic changes during curing. Notably, nonivamide varied markedly across treatments, suggesting its potential as a key curing indicator. NC_68°C displayed 11 up-regulated and 17 down-regulated unique DEMs, differing from EC_68°C and IC_68°C, suggesting their potential availability in evaluating tobacco leaf quality. KEGG pathway analysis revealed temporal shifts in metabolic pathways, particularly those involved in secondary metabolite biosynthesis (such as flavonoids, flavones, flavonols) and amino acid metabolism, during the transition from yellowing to color-fixing. Correlation analysis isolated the top 25 DEMs correlated with curing degree and stage, which might play pivotal roles in the curing process and could serve as potential biomarkers for assessing curing degree and stage. Specifically, D-(+)-cellobiose displayed the strongest negative correlation with curing degree, while 5,7-dihydroxychromone exhibited the highest positive correlation coefficient. Furthermore, curcurbitacin IIa showed the highest positive correlation with curing stage, followed by hesperetin and 8-shogaol. Additionally, random forest analysis emphasized morellic acid as a core molecular metabolite across curing degrees, suggesting its potential as a biomarker. Debiased sparse partial correlation (DSPC) network analysis further pinpointed hispidulin as a key metabolite, underscoring its significance in elucidating flavonoid metabolism during the curing process. Collectively, this study enhances the understanding of metabolite transformations underlying tobacco curing processes and provides a valuable reference for optimizing curing strategies to achieve desired outcomes.

Top 50 Most Cited Articles in Transplant Dermatology: A Bibliometric Analysis.

Solid organ transplant recipients are at high risk for developing skin malignancies due to prolonged immunosuppression. The field of transplant dermatology (TD) has experienced a surge in research and clinical advancements, yet there is no quantitative evaluation estimating the impact of TD literature. Identify and characterize the most frequently cited TD articles. Institute For Scientific Information Web of Science was used to identify the 50 most cited research articles in TD. Results were reviewed by 3 independent authors. A network analysis was performed to assess collaboration patterns among coauthors. Top articles held a combined total of 12,114 citations. The top-cited article was "Cancer incidence before and after kidney transplantation," by Vajdic and colleagues in the Journal of the American Medical Association (2006) with 872 citations. A total of 22 countries and 221 institutions were represented. This bibliometric analysis offers a detailed overview of the most cited manuscripts in TD and illustrates the discoveries steering TD research and practice.

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.

The Relationships Between Short Video Usage and Subjective Well-Being: Mediation Models and Network Analysis

On short video platforms, individuals can not only passively browse videos but also actively engage in behaviors such as liking and commenting on videos. However, the mechanisms by how active and passive usage of short videos affect subjective well-being remains unclear. Thus, this study aims to explore these mechanisms through path analysis and network analysis. We employed the Short Video Usage Questionnaire, Subjective Well-Being Scale, and Interaction Anxiety Scale to survey 1086 participants. The results indicated (1) the active usage positively predicted life satisfaction and positive affect, while passive usage failed to predict any of them. (2) Social anxiety mediated the relationship between short video usage and subjective well-being. Active usage enhanced life satisfaction and positive affect by alleviating social anxiety, while reducing negative affect by decreasing social anxiety. At the same time, passive usage reduced life satisfaction and positive affect by exacerbating social anxiety while increasing negative affect by intensifying social anxiety. (3) Network analysis revealed that “live interaction” behavior was a core activity for alleviating social anxiety and enhancing subjective well-being. The findings highlight the significant role of short video usage in subjective well-being and provide empirical evidence to guide users in the rational and positive usage of short video platforms.

Exploring participants’ characteristics and self-assessed readiness to conduct clinical trials in general practice – baseline analysis of the RaPHaeL practice-based research network

Background Primary care is integral to healthcare systems extending beyond traditional illness management to include preventive care, chronic disease management, and health promotion. Practice-based research networks (PBRNs) have emerged as essential infrastructures for conducting clinical research in primary care. This study explores the establishment of the ‘Research-Practices Halle-Leipzig’ (RaPHaeL) PBRN in Germany, evaluating the characteristics and research readiness of participating practices. Methods A cross-sectional survey (MORNING II) was conducted among all general practitioners (GPs) joining the RaPHaeL PBRN, assessing socio-demographic characteristics, practice infrastructure, and research readiness. After a descriptive analysis, we compared data with a previous study (MORNING) to examine potential differences between PBRN participants and non-participants. We developed a research readiness score (RRS) to quantify practices’ ability to perform clinical research subtasks. Results The response rate was 97.1% and our participants were often male, involved in undergraduate education, generally interested in research or had previous research experiences. However, they differed widely in age and size, staff structure, and patient demographics of their practices. Initially, around two-thirds of the practices were not sufficiently prepared to conduct clinical trials (self-assessed feasibility of relevant subtasks). If further research and documentation tasks are required, patient recruitment estimations are lower than for patient identification and information. Conclusion This study highlights the variability in research readiness among GP practices and shows the need for targeted training. By systematically assessing and enhancing research capabilities of participating GPs, PBRNs can facilitate high-quality clinical research in primary care to improve patient outcomes and healthcare delivery.

FunCoup 6: advancing functional association networks across species with directed links and improved user experience.

FunCoup 6 (https://funcoup.org) represents a significant advancement in global functional association networks, aiming to provide researchers with a comprehensive view of the functional coupling interactome. This update introduces novel methodologies and integrated tools for improved network inference and analysis. Major new developments in FunCoup 6 include vastly expanding the coverage of gene regulatory links, a new framework for bin-free Bayesian training and a new website. FunCoup 6 integrates a new tool for disease and drug target module identification using the TOPAS algorithm. To expand the utility of the resource for biomedical research, it incorporates pathway enrichment analysis using the ANUBIX and EASE algorithms. The unique comparative interactomics analysis in FunCoup provides insights of network conservation, now allowing users to align orthologs only or query each species network independently. Bin-free training was applied to 23 primary species, and in addition, networks were generated for all remaining 618 species in InParanoiDB 9. Accompanying these advancements, FunCoup 6 features a new redesigned website, together with updated API functionalities, and represents a pivotal step forward in functional genomics research, offering unique capabilities for exploring the complex landscape of protein interactions.