Search Results (58)

Cannabidiol (CBD), the principal non-psychoactive phytocannabinoid of Cannabis sativa, exhibits diverse pharmacological activities through interactions with multiple molecular targets. Thus, breast, colorectal, and lung cancers arise from distinct molecular mechanisms. This study investigated the potential multi-target pharmacological mechanisms of CBD using an integrated approach combining network pharmacology and molecular docking. CBD-associated targets from three prediction platforms were intersected with disease-associated genes for each cancer type, yielding 143 overlapping targets that formed a significantly enriched protein–protein interaction network. Maximal Clique Centrality (MCC) analysis identified 10 hub proteins, including SRC, SIRT1, PTGS2 (COX-2), PPARG, NFKB1, MMP2, IGF1R, ESR2, ESR1, and EGFR, which represent key regulators of hormone signaling, inflammation, cell proliferation, and tumor progression. Molecular docking against these targets, benchmarked using each protein’s authentic co-crystallized ligand, predicted predominantly moderate binding affinities for CBD. Compared with the corresponding reference ligands, CBD generally exhibited lower predicted binding affinity, although comparable or slightly stronger scores were observed for PTGS2, ESR2, and EGFR. Independent validation using AutoDock Vina demonstrated overall agreement with the MOE docking results, supporting the robustness of the predicted binding profiles. Collectively, these findings suggest that CBD may exert its biological activity through coordinated modulation of multiple cancer-related signaling pathways rather than a single molecular target. By integrating pooled cancer-associated network pharmacology with co-crystallized ligand benchmarking, this study provides a computational framework for prioritizing biologically relevant CBD targets for future experimental validation. These findings should be regarded as hypothesis-generating rather than evidence of clinical efficacy. Full article

To address dimensional reduction for continuous single-label and multi-label data, this paper proposes an improved variable precision rough set method based on maximal consistent blocks. We formulate dimensional reduction as an attribute reduction problem in continuous decision information systems, construct a distance-based tolerance relation, and design a maximal consistent block generation algorithm based on pivoted Bron–Kerbosch maximal clique mining for direct continuous data modeling. We establish a generalized variable precision rough set model, define $\beta$-approximation sets and distribution reduction objectives for single- and multi-label scenarios, analyze the stage-wise complexity of the procedure, separating polynomial stages from output-sensitive enumeration stages, and develop a discernibility matrix-based reduction algorithm. Experiments on fourteen public benchmark datasets against seven baselines under Equal-d (fixed feature number) and Nested-d (training-partition tuning) protocols show that the proposed method attains the lowest average rank under Equal-d, where the Friedman test indicates overall differences among methods and Holm-adjusted Wilcoxon comparisons confirm significant advantages over MCLS and the neighborhood rough-set dependency baseline; under Nested-d, the comparison with MCLS remains significant after Holm adjustment. Parameter sensitivity analysis, distance metric comparison, ablation study, and a resource audit further confirm the robustness and feasibility of the method. Full article

Bisphenol A (BPA) is a potential risk factor for pancreatic ductal adenocarcinoma (PDAC). This study integrated network toxicology, molecular docking, molecular dynamics (MD) simulations, and TCGA clinical data analysis to explore the potential molecular mechanisms linking BPA exposure to PDAC risk. BPA–PDAC intersection targets were identified through multi-database screening, followed by protein–protein interaction (PPI) network construction to screen core hub genes. A total of 10 core hub genes were identified via PPI analysis combined with the Maximal Clique Centrality (MCC) algorithm. Molecular docking demonstrated that ESR1 exhibited one of the strongest binding affinities for BPA (−8.2 kcal/mol), and MD simulations confirmed favorable thermodynamic stability of the BPA–ESR1 complex. TCGA analysis revealed stage-dependent expression patterns: early stages showed downregulation of TP53 and BCL2, whereas advanced stages showed upregulation of BCL2L1, HSP90AA1, and HSP90AB1, while ESR1, HIF1A, and PARP1 remained consistently low. These findings suggest that BPA may promote PDAC progression by disrupting ERα-mediated endocrine signaling and impairing DNA repair through PARP1 interference, providing candidate molecular targets and a hypothesis-generating foundation for pancreatic cancer risk assessment, warranting further experimental validation. Full article

The general zeroth-order Randić index $R_{\alpha }^0\left(G\right)={\sum }_{v\in V}d{\left(v\right)}^{\alpha }$ ($\alpha \ne 0,1$) unifies several classical indices, including the first Zagreb ($\alpha =2$), forgotten ($\alpha =3$), and inverse degree ($\alpha =-1$) indices. We characterize the connected graphs that attain extremal $R_{\alpha }^0$ values for most parameter regimes. Nested stars maximize for $\alpha \ge 2$, quasi-stars for $\alpha <0$, and quasi-regular graphs for $0<\alpha <1$. For $1<\alpha <2$, we show that the maximizer is a threshold graph, but the exact structure depends on n, m, and $\alpha$; a counterexample demonstrates that the previously claimed optimality of the quasi-star in this regime is not always valid, and a complete characterization remains open. Minimizers follow the opposite pattern. Our majorization-based framework, combined with the sign of the third derivative of $x^{\alpha }$, yields explicit closed-form formulas for the extremal values. We also extend the analysis to disconnected graphs under $\delta \left(G\right)\ge 1$, identifying star-forests and clique-forests as the extremal maximizers (for $\alpha \ge 2$ and $\alpha <0$, respectively), while the regimes $0<\alpha <1$ and $1<\alpha <2$ are addressed with quasi-regular graphs and left as open problems, respectively. Full article

Background: Colorectal cancer (CRC) remains a heterogeneous disease, and improved biomarkers are needed to support prognostic assessment. This study aimed to characterize hub genes in CRC and evaluate whether a gene signature provides biologically meaningful and prognostic information in clinical–genomic models. Methods: We integrated three GEO microarray datasets (GSE110223, GSE110224, and GSE23878) to identify common differentially expressed genes using adjusted $p<0.05$ and $\mid {\mathrm{l}\mathrm{o}\mathrm{g}}_{2}FC\mid >1$. Hub genes and protein expression were identified through protein–protein interaction network analysis using maximal clique centrality and Human Protein Atlas, respectively. Prognostic relevance was evaluated in TCGA-COAD/READ using Kaplan–Meier analysis, multivariable Cox regression, Cox-derived prognostic indices, time-dependent ROC analysis, and regression-based machine learning for internal robustness. Principal component analysis (PCA) was used to derive a standardized PC1-based score from the 10-hub gene signature. Results: A ten-gene mitotic hub signature (TPX2, UBE2C, AURKA, NEK2, PRC1, CCNB1, CDK1, CEP55, FOXM1, and RRM2) was consistently upregulated across the three datasets and enriched for cell-cycle and mitotic pathways. Protein-level and survival analyses supported the biological relevance of several hub genes. In TCGA-COAD/READ, the signature showed limited standalone prognostic value and did not retain independent significance after adjustment for clinical variables, although it contributed modestly in integrated clinical–genomic models. PCA showed a one-dimensional signature, with PC1 capturing the dominant shared expression pattern. Gradient Boosting Regressor (R² = 0.8035, MSE = 0.0473) supported the internal robustness of the DEG-based expression pattern. Conclusions: The ten-gene mitotic hub signature represents a coherent CRC-related proliferative program with limited value as an isolated prognostic marker, but it may still be useful as part of integrated risk models that require external validation. Full article

Background: Mitochondrial bioenergetic dysregulation disrupts immune−metabolic homeostasis and promotes pro−inflammatory microenvironments in osteoarthritis (OA) synovitis. However, the mechanistic contributions of mitochondrial energy metabolism to synovitis pathogenesis in OA remain poorly defined. Methods: We analyzed mitochondrial energy metabolism−related genes (MEMRGs) in OA synovitis by integrating transcriptomic data from OA synovial tissues (GSE55235, GSE55457). LASSO regression and maximal clique centrality (MCC) algorithms were applied to identify hub genes, and single−cell RNA sequencing (GSE152805) was used to examine cell−type−specific expression patterns. Functional validation was performed in IRS2−knockdown THP−1 macrophages. Results: We identified 22 mitochondrial energy metabolism−related differentially expressed genes (MEMR−DEGs), which were enriched in the AMPK signaling, glucagon signaling, and insulin signaling pathways. Four hub genes (FOXO3, FASN, PTGS2, IRS2) were identified, and their expression was negatively correlated with synovial macrophage infiltration. Single−cell RNA sequencing revealed that IRS2 was specifically upregulated in a synovial macrophage cluster. Functional studies in IRS2−knockdown THP−1 macrophages demonstrated that IRS2 deficiency impaired IL−4−induced M2 macrophage polarization and reduced mitochondrial membrane potential and ATP synthesis, which was mediated by the suppression of the AKT/FOXO1 signaling. Conclusions: IRS2 potentially influences mitochondrial energy metabolism, as evidenced by the maintenance of mitochondrial membrane potential and ATP synthesis, via the AKT/FOXO1 signaling pathways to maintain synovial macrophage M2 polarization homeostasis. These findings provide novel molecular targets for addressing immune−metabolic pathways in OA therapy. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a multifactorial disorder driven by tightly interconnected metabolic, inflammatory, and lipid dysregulation pathways. Glycyrrhetinic acid, a pentacyclic triterpenoid derived from Glycyrrhiza species, has demonstrated anti-inflammatory and hepatoprotective activities in previous experimental studies. Objectives: This study aimed to systematically investigate the potential molecular targets and signaling pathways of glycyrrhetinic acid in MASLD using an integrated network pharmacology and molecular docking strategy. Methods: Predicted protein targets of glycyrrhetinic acid and MASLD-associated genes were collected from public databases. A protein–protein interaction (PPI) network was constructed, and hub genes were identified using the maximal clique centrality algorithm in Cytoscape. Functional annotation was performed through Gene Ontology and KEGG pathway enrichment analyses. Molecular docking simulations were subsequently conducted to assess the binding affinity of glycyrrhetinic acid with biologically prioritized targets derived from the network analysis. Results: Intersection analysis identified 26 shared targets between glycyrrhetinic acid and MASLD. PPI network analysis highlighted IL6, TNFα, AKT1, and PPARγ as central hub genes. Functional enrichment indicated that these targets were mainly involved in NF-κB, TNFα, and PI3K–Akt signaling pathways. Molecular docking results revealed favorable predicted binding affinities, with glycyrrhetinic acid exhibiting the strongest binding toward PPARγ among the evaluated targets. Conclusions: This integrative in silico analysis suggests that glycyrrhetinic acid may interact with multiple MASLD-related targets involved in inflammatory and metabolic regulation. These findings provide a computational framework for target prioritization and support further experimental investigations to elucidate the pharmacological relevance of glycyrrhetinic acid in MASLD. Full article

A k-partite graph is one whose vertices can be partitioned into k disjoint partite sets, with edges allowed between but not within these sets. In such a graph, a maximal k-partite clique is a subgraph with at least one vertex from each partite set and every allowable edge such that the subgraph cannot be enlarged by the incorporation of additional vertices. A maximum k-partite clique is of course a maximal k-partite clique of the greatest size. The results reported here describe a novel and practical modification of the best previously published algorithm for the enumeration of these special subgraphs. The relative performance of this new method relies on implicit edge addition and search tree pruning and is evaluated on graphs constructed from both pseudorandom and real-world data. Full article

Background: Patients with type 2 diabetes mellitus (T2DM) undergoing cardiac surgery represent a high-risk population characterized by substantial cardiometabolic stress and increased susceptibility to postoperative heart failure, renal dysfunction, and unplanned rehospitalization. Although sodium-glucose cotransporter 2 (SGLT2) inhibitors provide established cardiorenal protection in ambulatory populations, their perioperative impact in cardiac surgery cohorts remains insufficiently defined. Methods: In a single-center retrospective cohort of 620 T2DM patients, inverse probability of treatment weighting and time-dependent Cox regression were applied to account for perioperative treatment interruption and delayed postoperative reinitiation when evaluating the association between chronic SGLT2 inhibitor therapy and 12-month rehospitalization risk. To provide biological context for the observed clinical associations, target-driven systems pharmacology, molecular docking against SGLT2, NHE1, AMPK, and NLRP3, and protein–protein interaction (PPI) network analysis were performed. Hub proteins were identified using Maximal Clique Centrality, followed by functional enrichment (GO/KEGG) analysis. Results: Chronic SGLT2 inhibitor therapy was associated with reduced first rehospitalization (HR 0.64; 95% CI 0.48–0.85; p = 0.002) and a lower cumulative rehospitalization burden (IRR 0.61; 95% CI 0.46–0.82; p = 0.001), primarily driven by heart failure-related and metabolic phenotypes. Molecular docking analyses identified favorable binding with SGLT2 and additional cardiometabolic and inflammatory targets, including NHE1, AMPK, NLRP3, IKKβ, IL-6Rα, and PPAR isoforms, suggesting modulation of myocardial ion homeostasis, metabolic resilience, and inflammatory signaling. PPI analysis identified eight hub proteins (AKT1, MTOR, STAT3, EGFR, PIK3CA, SRC, MAPK1, and MAPK3) significantly enriched in PI3K/AKT, MAPK/ERK, and ErbB signaling pathways. Conclusions: Chronic SGLT2 inhibitor therapy was independently associated with reduced postoperative rehospitalization and cumulative event burden in T2DM patients undergoing cardiac surgery. Integrated in silico analyses offer mechanistic hypotheses consistent with the observed clinical associations. These findings suggest that structured perioperative SGLT2 inhibitor management may contribute to improved postoperative outcomes, while prospective validation in future studies would strengthen these findings. However, given the retrospective observational design, these findings should be interpreted as associative rather than causal. Full article

This paper establishes a graph-theoretic framework for idealization semigroups arising from Bruck–Reilly extensions. Building on a recent study by Wazzan and Ozalan, we introduce five graph families—${\Gamma }_E$, ${\Gamma }_0$, ${\Gamma }_{\mathrm{Cay}}$, ${\Gamma }_{\mathcal{K}}$, and $\Gamma \left(G_k\right)$—each encoding a distinct algebraic facet of $S{B}_i(\bowtie )B$. We prove explicit correspondences linking combinatorial invariants to algebraic structure: diameter captures generating efficiency and semilattice height; girth signals short relations; chromatic number bounds idempotent cardinalities and $\mathcal{D}$-class counts; clique number measures maximal commuting subsets; and Laplacian spectra encode ideal size and Schützenberger groups. Our central result demonstrates that Green’s relations are combinatorially recoverable from graph pairs. For commutative $S{B}_i(\bowtie )B$, $({\Gamma }_E,{\Gamma }_{\mathcal{K}})$ uniquely determines $\mathcal{J}$-order, $\mathcal{D}$-classes, and $\mathcal{H}$-classes via neighborhood inclusions, bipartite components, and automorphism orbits, yielding the first algorithmic reconstruction of ideal-theoretic structure from graph data. The framework is implemented in SageMath as a reproducible open-source toolkit validated on concrete examples. This work synthesizes algebraic graph theory, semigroup theory, and computational mathematics into a unified algebraic-combinatorial dictionary, providing both new analytical tools and a methodological template for studying algebraic constructions via graph invariants. Full article

Accurate and comprehensive characterization of high-steep slopes is crucial for real-time risk prediction, disaster assessment, and damage evolution monitoring. The study focused on a high-steep rocky slope along the Yanjiang Expressway in Sichuan Province, China. A novel digital reconstruction method was introduced, which integrates terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry through a Transformer-based method combining GeoTransformer with the Maximal Cliques (MAC) algorithm. The results indicated that TLS excels in capturing fine-scale features, whereas UAV demonstrates superior performance in large-scale terrain reconstruction. However, multi-sensor data exhibit heterogeneity in terms of partial overlap, large outliers, and density differences. To address these challenges, the GeoTransformer-MAC framework extracts geometrically invariant features from cross-source point cloud (CSPC) to establish initial correspondences, followed by rigorous screening of high-quality locally consistent correspondences to optimize transformation parameters. This method achieves accurate digital reconstruction of the high-steep rock slope. Global and local error analyses verify the model’s superiority in both overall slope characterization and fine-scale feature representation. Compared with the TLS-only model and the conventional method, the Transformer-based method improves the slope model integrity by 85.58%, increases the data density by 9.71%, and improves the accuracy by nearly threefold. This study provides a novel approach for the digital modeling of complex terrains, which serves the refined identification and modeling of geohazards for high-steep slopes in complex mountainous regions. Full article

Adipose-derived mesenchymal stromal/stem cells (AT-MSCs) can be typically isolated from adipose tissue using a minimally invasive procedure. However, since AT-MSCs are usually obtained from subcutaneous tissue, there is a risk of contamination with fibroblasts (FBs), which can reduce the differentiation potential of AT-MSCs. To avoid this contamination, it is crucial to identify specific markers to effectively distinguish AT-MSCs from FBs. Analysis of microarray data obtained from three studies (GSE9451, GSE66084, GSE94667, and GSE38947) revealed 123 genes expressed at levels more than 1.5-fold higher in AT-MSCs compared to FBs. Using STRING, a protein-protein interaction (PPI) network consisting of 80 nodes and 197 edges was identified within the 123 genes. Further investigation using Molecular Complex Detection in Cytoscape identified a module of 12 genes: COL3A1, FBN1, COL4A1, COL5A2, POSTN, CTGF, SPARC, HSPG2, FSTL1, LAMA2, LAMC1, COL16A1. Gene Ontology analysis revealed that these genes were enriched in extracellular region (GO: 0005576). Additionally, these 12 genes corresponded to the top 12 of the 15 hub genes calculated using the Maximal Clique Centrality algorithm. The results of this study suggest that these 12 genes may serve as markers for distinguishing AT-MSCs from FBs, offering potential applications in regenerative medicine. Full article

This article presents ant algorithms for single- and multi-criteria industrial optimization problems. A common factor in these algorithms is the determination of the set with the maximum number of cliques, which represent the solution to multidimensional assignment problems in d-partite graphs. In the case of weighted incomplete graphs, the goal is to determine the set with the maximum number of cliques and the maximum sum of the weights of their edges. In the case of unweighted incomplete graphs, the goal is to determine the set with the maximum number of maximum cliques. In the case of complete weighted graphs, the goal is to determine all maximum cliques with the minimal sum of their edge weights. These optimization problems are solved using the various ant algorithms proposed in this paper. The proposed algorithms differ not only in terms of the objective function, but also in terms of desirability functions, as previously established, and they achieved a smaller sum of weights for cliques in the case of weighted complete graphs than previous ant algorithms presented in the literature. The same applies to unweighted incomplete graphs. The presented algorithms resulted in a greater number of maximal cliques than previous ant algorithms presented in the literature. This study is the first to propose the presented ant algorithms in the case of weighted incomplete graphs. Full article

Federated Learning (FL) systems are increasingly vulnerable to data poisoning attacks, in which malicious clients attempt to manipulate their training data in order to compromise the corresponding machine learning model. Existing detection techniques rely mostly on identifying clients who provide weight updates that significantly diverge from the average across multiple training rounds. In this work, we propose a Clique-Based Detection Framework (CBDF) that focuses on similarity patterns between client updates instead of their deviation. Specifically, we make use of the Euclidean distance to measure similarity between the weight update vectors of different clients over training iterations. Clients that provide consistently similar weight updates and exceed a predefined threshold are flagged as potential adversaries. Therefore, this method detects the coordination patterns of the attackers and uses them to strengthen FL systems against sophisticated, coordinated data poisoning attacks. We validate the effectiveness of this approach through extensive experimental evaluation. Moreover, we provide suggestions regarding fine-tuning hyperparameters to maximize the performance of the detection method. This approach represents a novel advancement in protecting FL models from malicious interference. Full article

Three-dimensional reconstruction is an essential skill for robots to achieve complex operation tasks, including moving and grasping. Applying deep learning models to obtain stereoscopic scene information, accompanied by algorithms such as target detection and semantic segmentation to obtain finer labels of things, is the dominant paradigm for robots. However, large-scale point cloud registration and pixel-level labeling are usually time-consuming. Here, a novel two-branch network architecture based on PointNet features is designed. Its feature-sharing mechanism enables point cloud registration and semantic extraction to be carried out simultaneously, which is convenient for fast reconstruction of indoor environments. Moreover, it uses graph space instead of Euclidean space to map point cloud features to obtain better relationship matching. Through extensive experimentation, our method demonstrates a significant reduction in processing time, taking approximately one-tenth of the time required by the original method without a decline in accuracy. This efficiency enhancement enables the successful execution of downstream tasks such as positioning and navigation. Full article