Network Analysis Research Articles

Functional investigation and two-sample Mendelian randomization study of primary biliary cholangitis hub genes

BACKGROUND The identification of specific gene expression patterns is crucial for understanding the mechanisms underlying primary biliary cholangitis (PBC) and finding relevant biomarkers for diagnosis and therapeutic evaluation. AIM To determine PBC-associated hub genes and assess their clinical utility for disease prediction. METHODS PBC expression data were obtained from the Gene Expression Omnibus database. Overlapping genes from differential expression analysis and weighted gene co-expression network analysis (WGCNA) were identified as key genes for PBC. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed to explore the potential roles of key genes. Hub genes were identified in protein-protein interaction (PPI) networks using the Degree algorithm in Cytoscape software. The relationship between hub genes and immune cells was investigated. Finally, a Mendelian randomization study was conducted to determine the causal effects of hub genes on PBC. RESULTS We identified 71 overlapping key genes using differential expression analysis and WGCNA. These genes were primarily enriched in pathways related to cytokine-cytokine receptor interaction, and Th1, Th2, and Th17 cell differentiation. We utilized Cytoscape software and identified five hub genes (CD247 , IL10 , CCL5 , CCL3 , and STAT3 ) in PPI networks. These hub genes showed a strong correlation with immune cell infiltration in PBC. However, inverse variance weighting analysis did not indicate the causal effects of hub genes on PBC risk. CONCLUSION Hub genes can potentially serve as valuable biomarkers for PBC prediction and treatment, thereby offering significant clinical utility.

Exploring the Effect of Active Components in Oil Tree Peony Seed Meal on Swine Disease Resistance and its Potential Mechanisms Based on Network Pharmacology and Molecular Docking.

This study aims to explore the feasibility of using network pharmacology and molecular docking technology to predict the effects of active components from oil tree peony seed meal (PSM) on swine diseases. Ten active components of PSM were screened Screening through literature search and network pharmacology standards, including Betulinic acid, Quercetin, Kaempferol, Luteolin, Isorhamnetin, Hydroxygenkwanin, Hederagenin, Benzoyl Paeoniflorin, Albiflorin, Paeoniflorin. Ten types of swine diseases were selected, including African Swine Fever, Aftosa, Swine Vesicular Disease, Transmissible Gastroenteritis, Swine Streptococcal Infection, Blue Aural Disease, Swine Infectious Atrophic Rhinitis, Swine Influenza, Swine Erysipelas, Swine Epidemic Encephalitis. The results showed that the average number of cross genes between the potential target genes of PSM active components and each swine disease target gene accounted for 7.64 % of the total number of swine disease target genes. The GO enrichment analyses showed that putative targets exist in endosomes, lysosomes, cell membranes, nerves, growth factor activity, receptor tyrosine kinase binding, enzyme binding, growth factor binding, transcription coactivator binding, oxidoreductase activity, prostaglandin E receptor activity and insulin receptor substrate binding. The KEGG enrichment analysis results showed that these putative genes were involved in various cancer progression pathways, signaling pathways, and hormone regulatory pathways. A total of 8 core targets were obtained through protein-protein interaction networks analysis, including Protein Kinase CAMP-Activated Catalytic Subunit Alpha (PRKACA), Non-Receptor Tyrosine Kinase (SRC), Mitogen-Activated Protein Kinase 1 (MAPK1), E1A Binding Protein P300 (EP300), Hypoxia Inducible Factor 1 Subunit Alpha (HIF1A), Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Beta (PIK3CB), C-X-C chemokine receptor type 4 (CXCR4) and Estrogen Receptor 2 (ESR2). The HIF-1 signaling pathway was found to be associated with all 10 selected swine diseases. The PD-L1 expression, and PD-1 checkpoint pathway in cancer, and thyroid hormone signaling pathway were not only enriches the core target with a quantity of 7, but also associated with 9 Swine diseases. In addition, the molecular docking results indicate that the core ingredients have strong affinity with hub genes. The research suggests that the active components of PSM may intervene in swine diseases through multiple components, targets, and pathways.

Person-centered and qualitative approaches to network analysis in physics education research

Network analysis has become a well-recognized methodology in physics education research (PER), with study topics including student performance and persistence, faculty change, and the structure of conceptual networks. The social network analysis side of this work has focused on quantitative analysis of whole-network cases, such as the structure of networks in single classrooms. Egocentric or personal network approaches are largely unexplored, and qualitative methods are underdeveloped. In this paper, we outline theoretical and practical differences between two major network paradigms—whole-network and egocentric—and introduce theoretical frameworks and methodological considerations for egocentric studies. We also describe qualitative and mixed-methods approaches that are currently missing from the PER literature. We identify areas where these additional network methods may be of particular interest to physics education researchers and end by discussing example cases and implications for new PER studies. Published by the American Physical Society 2024

Testing Neuroprotective Strategies in Prolonged Field Care Model of Traumatic Brain Injury and Hemorrhagic Shock.

Prolonged Field Care (PFC) is a military adaptation of Tactical Combat Casualty Care providing extended pre-hospital management during delayed extrication. Effects of addition of Valproic Acid (VPA) to Fresh Frozen Plasma (FFP) in a PFC model of hemorrhagic shock and traumatic brain injury (TBI) are not known. We hypothesized that VPA is associated with decreased neurological impairment, and its protective changes are detected at the transcriptomic level. Swine underwent TBI and 40%-blood volume hemorrhage. After 2-hours of shock, they were randomized to: 1)normal saline (NS), 2)NS+250 ml FFP (NS+FFP), or 3)NS+FFP+150 mg/kgVPA(NS+FFP+VPA). At 72-hours, they were transfused packed red blood cells before being euthanized. Intraoperative variables and neurological outcomes were compared. Brain lesion size was measured, and gene expression profiles were analyzed using RNA-sequencing. Pathway and network analyses were performed on differentially expressed genes. Real-time PCR was performed to validate key genes. NS+FFP and NS+FFP+VPA required significantly less crystalloid resuscitation(974 ml-NS+FFP; 1461ml-NS+FFP+VPA vs. 4540 ml-NS, p<0.001), had smaller brain lesion size(2477mm3-NS+FFP; 3018.0mm3-NS+FFP+VPA vs. 4517.0mm3-NS, p<0.01), and less functional neurologic impairment compared to NS. Per pathway analysis of differentially expressed genes, VPA was associated with enrichment of numerous metabolic changes in injured brains, which were not observed with FFP. Network analysis showed enrichment of various gene networks. MT-ATP8 gene was downregulated in VPA-treated animals. The addition of FFP to the resuscitation protocol resulted in a significant reduction in crystalloid requirements. Both, the FFP and FFP+VPA groups showed improved neurological recovery compared to NS alone and had distinctive transcriptomic profiles in injured brains at 72-hours. MT-ATP8, involved in worsening ischemia following brain injury, was down-regulated in VPA-treated animals.

Revealing the Feedback Loop: Comparing Satellite and Sensor-derived Meteorological Parameters in Groundnut Cultivation

Background: Studying soil moisture’s impact on groundnut, particularly under rainfed conditions, is crucial for improving quality and yield. This research explores soil moisture dynamics using satellite and in-situ sensor data to enhance water management and crop predictions, focusing on the feedback mechanisms between soil moisture and weather variables. Methods: The study was conducted at the Oil Seed Research Station (TNAU) in Tindivanam, Tamil Nadu, using the groundnut variety TMV 14. Soil moisture and weather parameters were monitored at critical crop growth stages, using an in-situ soil moisture sensor (Cr 300) and compared with satellite data from SMAP, ERA5 and Sentinel 1A with environmental factors. Statistical analysis was carried out using SAS JMP Pro and network analysis via Cytoscape 3.8.2 software. Result: Pre-monsoon soil moisture SMAP, SMAP-RZSM and sensor (VWC at 10cm) were found to increase in post-monsoon. Strong positive correlations were attained between SMAP-SM and SMAP-RZSM (r=0.94), indicating a reinforcing loop. Whereas, the negative feedback was observed between SMAP-SM and deeper soil temperatures (-0.91). It underscores the dynamic interplay between soil moisture and atmospheric conditions, essential for developing efficient water management strategies and enhancing crop resilience to climate variability.

Have a Good Travel Time in Routing: Bibliometric Analysis, Literature Review, and Future Directions

ABSTRACTTravel time serves as a critical factor in routing problems, affecting routing decisions, congestion avoidance, and environmental sustainability. In this study, a thorough bibliometric and network analysis is presented based on 1752 documents collected from Web of Science published in the past three decades. CiteSpace is adopted to generate research clusters for topological analysis that graphically illustrate the publications' evolution over time and identify the current state and the emerging trends of routing problems with travel time focus. The collaboration network explores the cooperation patterns between countries and institutions. Attention toward the time‐dependency, uncertainty, dynamic characteristics of travel time, and the influence of travel speed on emissions, is uncovered through document co‐citation analysis. Furthermore, a comprehensive review of the literature has been conducted from an information quality and evolution perspective. Realistic scenarios such as fuel consumption and traffic conditions are eliciting more attention. Significant emphasis is increasingly placed on the time dependency, reliability, uncertainty, and dynamics of travel time. This study systematically reviews and analyzes relevant research on routing problems with travel time focus, identifying key emerging trends and knowledge gaps, and offering valuable insights for future research.

Integrating machine learning and bioinformatics approaches for identifying novel diagnostic gene biomarkers in colorectal cancer.

This study aimed to identify diagnostic gene biomarkers for colorectal cancer (CRC) by analyzing differentially expressed genes (DEGs) in tumor and adjacent normal samples across five colon cancer gene-expression profiles (GSE10950, GSE25070, GSE41328, GSE74602, GSE142279) from the Gene Expression Omnibus (GEO) database. Intersecting identified DEGs with the module with the highest correlation to gene expression patterns of tumor samples in the gene co-expression network analysis revealed 283 overlapped genes. Centrality measures were calculated for these genes in the reconstructed STRING protein-protein interaction network. Applying LASSO logistic regression, eleven genes were ultimately recognized as candidate diagnostic genes. Among these genes, the area under the receiver operating characteristic curve (AUROC) values for nine genes (CDC25B, CDK4, IQGAP3, MMP1, MMP7, SLC7A5, TEAD4, TRIB3, and UHRF1) surpassed the threshold of 0.92 in both the training and validation sets. We evaluated the diagnostic performance of these genes with four machine learning algorithms: random forest (RF), support vector machines (SVM), artificial neural network (ANN), and gradient boosting machine (GBM). In the testing dataset (GSE21815 and GSE106582), the AUROC scores were greater than 0.95 for all of the machine learning algorithms, indicating the high diagnostic performance of the nine genes. Besides, these nine genes are also significantly correlated to twelve immune cells, namely Mast cells activated, Macrophages M0, M1, and M2, Neutrophils, T cells CD4 memory activated, T cells follicular helper, T cells CD8, T cells CD4 memory resting, B cells memory, Plasma cells, and Mast cells resting (P < 0.05). These results strongly suggest that all of the nine genes have the potential to serve as reliable diagnostic biomarkers for CRC.

Perceptions and responses to diseases among patients with inflammatory bowel disease: text-mining analysis of posts on a Japanese patient community website

Introduction: Patients with inflammatory bowel disease (IBD) are increasingly using online platforms to communicate with other patients and healthcare professionals seeking disease-related information and support. Free-text posts on these platforms could provide insights into patients’ everyday lives, which could help to improve patient care. In this proof-of-concept study, we applied text-mining to extract patient needs from free-text posts on a community forum in Japan, and holistically visualized the patients’ perceptions and their connections, and explored the patient characteristic-dependent trends in the use of words. Methods: Free-text posts written between May 11, 2020 and May 31, 2022 on the community forum were retrieved and subjected to text-mining analysis. Trends in the use of words were extracted from the posts for correspondence and co-occurrence network analyses using KH Coder open-source text-mining software. Results: Seventy-four posts were analyzed. Using text mining methods, we successfully extracted and visualized a variety of patient concerns and their connections. The correspondence and co-occurrence analyses revealed patient segment-dependent trends in the use of words. For example, patients with a disease duration of ≤5 yrs were more likely to use words related to emotions or their desire to change or quit their job, such as ‘anxiety’ and ‘resignation.’ Patients with a disease duration of &gt;10 yrs were more likely to use words showing that they are finding ways to live with or accept their disease, and are getting used to the lifestyle but some patients continued to experience worsening disease. Conclusions: We found that free texts posted on an IBD community forum can be a useful source of information to capture the wide variety of thoughts of patients. Text mining procedures can help to visualize the relative importance of the topics identified from free text posts. Our findings of this proof-of-concept study will be useful for generating new hypotheses to better understand and address the needs of patients with IBD.

Advantages of Cell Proliferation and Immune Regulation in CD146+NESTIN+ HUMSCs: Insights from Single-Cell RNA Sequencing.

The heterogeneity of stem cells is a significant factor inhibiting their clinical application, as different cell subpopulations may exhibit substantial differences in biological functions. We performed single-cell sequencing on HUMSCs from three donors of different gestational ages (22+5 weeks, 28 weeks, 39 weeks). We also compared the data with single-cell sequencing data from BMSCs from two public databases. The content of CD146+Nestin+ MSCs in preterm HUMSCs (22+5W: 30.2%, 28W: 25.8%) was higher than that in full-term HUMSCs (39W: 0.5%) and BMSCs (BMSC1: 0, BMSC2: 0.9%). Cell cycle analysis indicated a higher proportion of cells in the proliferative G2M phase in CD146+Nestin+ MSCs (40.8%) compared to CD146+Nestin- MSCs (20%) and CD146-Nestin- MSCs (12.5%). Degree of differentiation assessment suggested that CD146+Nestin+ MSCs exhibited lower differentiation than other cell subpopulations. Differential gene analysis revealed that CD146+Nestin+ MSCs overexpressed immune regulation-related factors. GO and KEGG enrichment analysis of modules identified by WGCNA suggested enrichment in pathways related to cellular immune regulation, antimicrobial activity, and proliferation. Immune-related gene analysis indicated that CD146+Nestin+ MSCs exhibited expression of multiple immune-related genes associated with "Antimicrobials," "Cytokines," and "Cytokine Receptors." Gene regulatory network analysis revealed high expression of immune-related regulators RELB, GAPB1, and EHF in CD146+Nestin+ MSCs.Our study provides a single-cell atlas of preterm HUMSCs, demonstrating the expression of CD146+Nestin+ MSCs across different tissues and confirming their advantages in cellular proliferation, antimicrobial activity, immune regulation, and low differentiation at the RNA level. This contributes valuable insights for the clinical application of HUMSCs.

Screening differentially expressed proteins to distinguish thymoma (B1 and B3) from thymic cysts based on tandem mass tag (TMT) technology.

The therapeutic approach to thymic cysts remains a subject of controversy. Predicted biomarkers for identifying thymic cysts and thymoma (THYM) are crucial. In this research, patients diagnosed with thymic cysts (MTC, n = 6) and thymoma (B1, n = 6; B3, n = 6) were enrolled. Proteins of superior quality were subjected to TMT labeling and UPLC-MS, and differentially expressed proteins (DEPs) were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interactive network analyses were applied to the DEPs. Some key differentially expressed genes(DEGs) were corroborated through GEPIA 32. The pan-cancer expression levels of key DEGs remarkably linked with prognosis were determined utilizing The University of ALabama at Birmingham CANcer data analysis Portal (UALCAN). Eventually, 49 DEPs were identified in the B1 vs. MTC comparison (17 upregulated and 32 downregulated), 27 in the B3 vs. MTC comparison (8 upregulated and 19 downregulated), and 38 in the B3 vs. B1 comparison (9 upregulated and 29 downregulated). IL13RA1 (down), galectin-3 binding protein (LGALS3BP)(up), PRCSH (down), C3 (down), MXRA5 (down), TNN (down), CFHR1 (down), SUN3 (down) were jointly altered in both B1 vs. NZ and B3 vs. NZ. GEPIA validated that LGALS3BP was significantly upregulated in thymoma patients. In conclusion, LGALS3BP might be an essential biomarker to identify thymoma from the thymic cyst.

Integrated analysis of muscle lncRNA and mRNA of Chinese indigenous breed Ningxiang pig in four developmental stages

Meat and its derivatives serve as crucial sources of protein, vitamins, minerals, and other essential nutrients for humans. Pork stands as China’s primary animal-derived food product consumed widely across diverse dietary structures; evaluating intramuscular fat content becomes pivotal in assessing its quality standards. Nonetheless, the intricate molecular mechanisms governing intramuscular fat deposition remain elusive. Our study utilized sequencing technology to scrutinize longitudinal development stages within Ningxiang pig’s longest dorsal muscles aiming to unravel these underlying mechanisms. In three distinct comparisons (30d vs. 90d, 90d vs. 150d and 150d vs. 210d) there were 578, 1,000 and 3,238 differentially expressed mRNA, along with 16, 158 and 85 lncRNAs were identified. STEM analysis unveiled six enriched model profiles for lncRNAs while seven such profiles emerged for mRNAs; notably, multiple shared model profiles existed between both RNA types. Enriched analysis highlighted numerous genes from mRNA profile8 and lncRNA profile7 significantly associated with pathways linked to fat deposition. Weight Gene Co-Expression Network Analysis (WGCNA) revealed that differential expression modules (DMEs) &amp;amp; differential expression lncRNAs primarily clustered within cyan, dark slate blue and pale turquoise modules. Furthermore, target genes PKD2 (MSTRG21592.MTRSG8859 and MTRSG18175), COL5A1 (MTRSG9969 and MTRSG180) and SOX13 (MTRSG21592 and MTRSG9088) as core components all intricately tied into processes related to fat deposition. This study lays the groundwork for deeper exploration into the molecular mechanisms underlying LDM fat deposition traits, and it also presents candidate genes for future molecular marker-assisted breeding.

High expression of CNOT6L contributes to the negative development of type 2 diabetes.

Type 2 diabetes (T2D) is a chronic metabolic disorder characterized by reduced responsiveness of body cells to insulin, leading to elevated blood sugar levels. CNOT6L is involved in glucose metabolism, insulin secretion regulation, pancreatic beta-cell proliferation, and apoptosis. These functions may be closely related to the pathogenesis of T2D. However, the exact molecular mechanisms linking CNOT6L to T2D remain unclear. Therefore, this study aims to elucidate the role of CNOT6L in T2D. The T2D datasets GSE163980 and GSE26168 profiles were downloaded from the Gene Expression Omnibusdatabase generated by GPL20115 and GPL6883.The R package limma was used to screen differentially expressed genes (DEGs). A weighted gene co-expression network analysis was performed. Construction and analysis of the protein-protein interaction (PPI) network, functional enrichment analysis, gene set enrichment analysis, and comparative toxicogenomics database (CTD) analysis were performed. Target Scan was used to screen miRNAs that regulate central DEGs. The results were verified by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR), western blotting (WB), and blood glucose measurements in mice. A total of 1951 DEGs were identified. GO and KEGG enrichment analysis revealed that differentially expressed genes were mainly enriched in the insulin signaling pathway, ECM-receptor interaction, and PPAR signaling pathway. Metascape analysis indicated enrichment primarily in the cAMP signaling pathway and enzyme-linked receptor protein signaling pathway. WGCNA analysis yielded 50 intersecting genes. PPI network construction and algorithm identification identified two core genes (CNOT6L and GRIN2B), among which CNOT6L gene was associated with multiple miRNAs. CTD analysis revealed associations of core genes with type 2 diabetes, diabetic complications, dyslipidemia, hyperglycemia, and inflammation. WB and RT-qPCR results showed that in different pathways, CNOT6L protein and mRNA levels were upregulated in type 2 diabetes. CNOT6L is highly expressed in type 2 diabetes mellitus, and can cause diabetes complications, inflammation and other physiological processes by regulating miRNA, PPAR and other related signaling pathways, with poor prognosis. CNOT6L can be used as a potential therapeutic target for type 2 diabetes.

Transcriptomic Analysis Reveals Dynamics of Gene Expression in Liver Tissue of Spotted Sea Bass Under Acute Thermal Stress.

The spotted sea bass (Lateolabrax maculatus), a eurythermal species, exhibits strong adaptability to temperature variations and presents an ideal model for studying heat stress-responsive mechanisms in fish. This study examined the liver transcriptome of spotted sea bass over a 24-h period following exposure to elevated temperatures, rising from 25 to 32°C. The results revealed significant alterations in gene expression in response to this thermal stress. Specifically, we identified 1702, 1199, 3128, and 2636 differentially expressed genes at 3, 6, 12, and 24h post-stress, respectively. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify specific gene modules responsive to heat stress, containing hub genes such as aco2, eci2, h6pd, suclg1, fgg, fga, fgb, f2, and apoba, which play central roles in the heat stress response. Enrichment analyses via KEGG and GSEA indicated that upregulated differentially expressed genes (DEGs) are predominantly involved in protein processing in the endoplasmic reticulum, while downregulated genes are primarily associated with the AGE-RAGE signaling pathways. Additionally, 272 genes exhibited differential alternative splicing, primarily through exon skipping, underscoring the complexity of transcriptomic adaptations. These findings provide deeper insights into the molecular responses to thermal stress and are crucial for advancing the breeding of heat-resistant strains of spotted sea bass.

Strengthening Rural Governance for Rural Development Through Collaborative Strategy: the Application of Soft System Methodology and Textual Network Analysis

Strengthening Rural Governance for Rural Development Through Collaborative Strategy: the Application of Soft System Methodology and Textual Network Analysis

Historical analogies, traumatic past and responses to the war in Ukraine

Abstract This article examines the use of historical analogies by political leaders during foreign policy crises. Specifically, we focus on the German, Polish and Czech leaderships' reactions to Russia's invasion of Ukraine and investigate the historical analogies which leaders invoked to justify their responses to Russia's aggression. The existing scholarship recognizes the importance of historical analogies in foreign policy decision-making, but the literature focuses predominantly on several high-profile analogies (i.e. the Munich Agreement or the Vietnam War) and lacks comparative perspective. We employ a dual-method approach that combines qualitative coding with semantic network analysis, and conduct a comparative analysis of the full spectrum of analogies used by Polish, Czech and German policy-makers. We find that politicians did not focus on a single overarching analogy, but employed a wide array of historical references. We also find that even though the countries' policies largely aligned and that their leaders often invoked the same events, the meaning of analogies and the signals they sent differed substantially across cases. Analysing the full spectrum of analogies demonstrates that justifications for supporting Ukraine were shaped more by these states' own historical traumas than by their sympathy for Ukraine, support for international law, or desire to uphold the rules-based international order.

The mining of China's policies against COVID-19 based on a policy target and tool co-evolution framework

Mining all COVID-19 policies issued by China can provide valuable lessons for both China and other countries in future pandemic control efforts. In this paper, we introduce a novel framework for mining the co-evolution of policy targets and policy tools. We employ bibliometric methods, text mining, and network analysis to explore the entire evolution of China's COVID-19 policies. Examining 1154 central government policies, (a) we extract policy targets from each policy, uncovering their evolution across different stages of the pandemic; (b) propose to identify the policy tool used in each policy unit by integrating an automatic identification model and active learning. We also reveal the categorical structure of these tools; (c) characterize the co-evolution pattern between policy targets and policy tools, shedding light on their dynamic relationship. Our findings indicate that policy targets have shifted across various stages, revealing unique characteristics in China's COVID-19 prevention and control efforts. Notably, there is a self-paradox between prevention measures and economic development. We identify the inadequateness in the distribution and utilization of policy tools. Ensuring the alignment of policy targets with appropriate tools is crucial. This synchronization and co-evolution between policy targets and tools are essential for enhancing the functional approach to policy implementation. This paper is the first systematic mining and review about the COVID-19 policies issued by the Chinese government, and our policy target and tool co-evolution mining framework provides a new quantitative framework for policy mining, especially an improved large language model and active learning theory are integrated to identify the policy tools automatically.

Mouse Corneal Immune Cell Heterogeneity Revealed by Single-Cell RNA Sequencing.

This study aimed to define the heterogeneity, spatial localization, and functional roles of immune cells in the mouse cornea using single-cell RNA sequencing (scRNA-seq) and immunofluorescent staining. Enriched mouse corneal immune cells (C57BL/6 strain, age 16-20 weeks) underwent single-cell RNA sequencing library preparation, sequencing, and analysis with Seurat, Monocle 3, and CellChat packages in R. Pathway analysis used Qiagen Ingenuity Pathway Analysis software. Immunostaining confirmed cell distribution. We identified 14 distinct immune cell clusters (56% myeloid and 44% lymphoid). Myeloid populations included resident macrophages, conventional dendritic cells (cDC2s), Langerhans cells, neutrophils, monocytes, and mast cells. Additionally, lymphocyte subsets (B, CD8, CD4, γδT, natural killer, natural killer T, and group 2 innate lymphoid cells) were found. We also found three new subtypes of resident macrophages in the cornea. Trajectory analysis suggested a differentiation pathway from monocytes to conventional dendritic cells, resident macrophages, and LCs. Intercellular communication network analysis using cord diagram identified amyloid precursor protein, chemokine (C-C motif) ligands (2, 3, 4, 6, 7, 9, and 12), Cxcl2, Mif, Tnf, Tgfb1, Igf1, and Il10 as prominent ligands involved in these interactions. Sexually dimorphic gene expression patterns were observed, with male myeloid cells expressing genes linked to immune regulation (Egr1, Foxp1, Mrc1, and Il1rn) and females showing higher expression of antigen presentation genes (Clic1, Psmb8, and Psmb9). Finally, immunostaining confirmed the spatial distribution of these cell populations within the cornea. This study unveils a diverse immune landscape in the mouse cornea, with evidence for cell differentiation and sex-based differences. Immunostaining validates the spatial distribution of these populations, furthering our knowledge of corneal immune function.

Modelling the bilingual lexicon as a multiplex phonological network.

Phonological word form networks of the mental lexicon are of psycholinguistic relevance, offering insights into the efficiency of lexical access. While much research has concentrated on first languages, there is growing evidence suggesting that phonological networks of second languages are equally significant for lexical processes. Bilingual language processing is proposed to involve the integration of first and second languages, with lexical activation spreading between similar word forms in both languages. Multiplex networks provide a framework to combine different phonological networks, allowing for the analysis of the integrated lexical system's behaviour during lexical processing. In the context of the present study, which focusses on German learners of English as a second language, a multiplex network analysis was constructed to model the interactive complexity of the bilingual mental lexicon. The study tested cross-linguistic effects in a word recognition task using English stimuli. Results revealed that during lexical processing in their second language English, German speakers also activate phonological neighbours from German. In addition, the bilinguals are attuned to the interconnectedness (i.e., clustering) of the German and English neighbours with one another in the phonological neighbourhood of the English target words. These findings can contribute to the ongoing debate on the degree of integration in the bilingual mental lexicon and shed light on the role that phonological networks can play in modelling bilingual lexical processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Integrated transcriptomic analysis reveals dysregulated immune infiltration and pro-inflammatory cytokines in the secretory endometrium of recurrent implantation failure patients

Abstract Recurrent implantation failure (RIF) is a leading impediment to assisted reproductive technology, yet the underlying pathogenesis of RIF remains elusive. Recent studies have sought to uncover novel biomarkers and etiological factors of RIF by profiling transcriptomes of endometrial samples. Nonetheless, the inherent heterogeneity among published studies and a scarcity of experimental validations hinder the identification of robust markers of RIF. Hence, we integrated six publicly accessible datasets with 209 samples, including microarray profiles of endometrial samples in the secretory phase. After removing batch effects, we identified 175 differentially expressed genes. Gene set enrichment analysis identified dysregulation of immunological pathways in RIF. We also observed altered immune infiltration and pro-inflammatory cytokines in RIF. Protein–protein interaction network analysis identified ten hub genes, representing two co-expression modules significantly related to RIF. Knockdown of ENTPD3, one of the hub genes, promoted the epithelial-mesenchymal transition (EMT) process and resulted in elevated levels of pro-inflammatory cytokines. Collectively, our study reveals abnormal gene expressions involving the regulation of EMT and immune status in RIF, providing valuable insights into its pathogenesis.

Identification of three subtypes of ovarian cancer and construction of prognostic models based on immune-related genes.

Immunotherapy has revolutionized the treatment of ovarian cancer (OC), but different immune microenvironments often constrain the efficacy of immunotherapeutic interventions. Therefore, there is an imperative to delineate novel immune subtypes for development of efficacious immunotherapeutic strategies. The immune subtypes of OC were identified by consensus cluster analysis. The differences in clinical features, genetic mutations, mRNA stemness (mRNAsi) and immune microenvironments were analyzed among subtypes. Subsequently, prognostic risk models were constructed based on differentially expressed genes (DEGs) of the immune subtypes using weighted correlation network analysis. OC patients were classified into three immune subtypes with distinct survival rates and clinical features. Different subtypes exhibited varying tumor mutation burdens, homologous recombination deficiencies, and mRNAsi levels. Significant differences were observed among immune subtypes in terms of immune checkpoint expression and immunogenic cell death. Prognostic risk models were validated as independent prognostic factors demonstrated great predictive performance for survival of OC patients. In this study, three distinct immune subtypes were identified based on gene sets related to vaccine response, with the C2 subtype exhibiting significantly worse prognosis. While no statistically significant differences in tumor mutation burden (TMB) were observed across the three subtypes, the homologous recombination deficiency (HRD) score and mRNA stemness index (mRNAsi) were notably elevated in the C2 group compared to the others. Immune infiltration analysis indicated that the C2 subtype may have an increased presence of regulatory T (Treg) cells, potentially contributing to a more favorable response to combination therapies involving PARP inhibitors and immunotherapy. These findings offer a precision medicine approach for tailoring immunotherapy in ovarian cancer patients. Moreover, the C3 subtype demonstrated significantly lower expression levels of immune checkpoint genes, a pattern validated by independent datasets, and associated with a better prognosis. Further investigation revealed that the immune-related gene FCRL5 correlates with ovarian cancer prognosis, with in vitro experiments showing that it influences the proliferation and migration of the ovarian cancer cell line SKOV3.