Identification of EML4 as a key hub gene for endometriosis and its molecular mechanism and potential drug prediction based on the GEO database

Objective:Key genes were screened to analyze molecular mechanisms and their drug targets of endometriosis by applying a bioinformatics approach.Methods:Gene expression profiles of endometriosis and healthy controls were obtained from the Gene Expression Omnibus database.Significant differentially expressed genes were screened using the limma package.Correlation pathways were screened by Spearman correlation analysis on the echinoderm microtubule-associated protein-like 4(EML4)and enrichment in endometriosis pathways and estimated by the GSVA package.Immune characteristics were assessed by the“ESTIMATE”R package.Potential regulatory pathways were determined by enrichment analysis.The SWISS-MODE website was used in homology modeling with EML4 and EML4 protein activity was predicted.VarElect was employed in molecular docking for screening potential compound inhibitors targeting endometriosis.Results:Ten endometriosis and 10 normal samples were included.EML4 was significantly upregulated in endometriosis(p<0.05).Thirty significantly correlated pathways involving 18 positive and 12 negative correlations,including GLYCOSAMINOGLYCAN_BIOSYNTHESIS_HEPARAN_SULFATE and GLYCOSPHINGOLIPID_BIOSYNTHESIS_GANGLIO_SERIES were screened between EML4 and endometriosis.Immunocorrelation analysis showed a significant difference in immune-related pathways in endometriosis and normal samples(p<0.05).In endometriosis,EML4 was associated with T-cell CD4 resting memory,activated mast cells,plasma cells,activated NK cells,M2 macrophages,and follicular helper T cells(p<0.05).Molecular docking identified five potential inhibitors of EML4,and compound DB05104(asimadoline)bound well to EML4 protein to exert its physiological effects.Conclusion:Differential gene expression and immune correlation analyses revealed that EML4 may affect endometriosis through multiple targets and pathways,the mechanism of which involved immune cell activation and infiltration.Molecular docking and dynamics simulation verified DB05104 as a potential inhibitor of EML4 and a powerful target for endometriosis treatment.

An improved high-fidelity adaptive model for integrated inlet-engine-nozzle based on mechanismdata fusion

Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system.Traditional component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model,failing to meet the accuracy requirements of supersonic conditions.This paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental calibration.Additionally,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is proposed.The study reveals the inlet model’s efficacy in reflecting installed performance,flow matching,and mitigating pressure distortion,while the nozzle model accurately predicts flow coefficients and thrust coefficients,and identifies various operational states.The model’s output closely aligns with typical experimental parameters.By combining offline optimization and online adaptive correction,the mechanismdata fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight cycle.Notably,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location correction.This approach significantly curbs performance deviations in supersonic conditions.For example,at Ma=2.0,the system error impressively drops from 34.17%to merely 6.54%,while errors for other flight conditions consistently stay below the 2.95%threshold.These findings underscore the clear superiority of the proposed method.