Intestinal absorption of compounds is significant in drug research and development. To evaluate this efficiently, a method combining mathematical modeling and molecular simulation was proposed, from the perspective of molecular structure. Based on the quantitative structure-property relationship study, the model between molecular structure and their apparent permeability coefficients was successfully constructed and verified, predicting intestinal absorption of drugs and interpreting decisive structural factors, such as AlogP98, Hydrogen bond donor and Ellipsoidal volume. The molecules with strong lipophilicity, less hydrogen bond donors and receptors, and small molecular volume are more easily absorbed. Then, the molecular dynamics simulation and molecular docking were utilized to study the mechanism of differences in intestinal absorption of drugs and investigate the role of molecular structure. Results indicated that molecules with strong lipophilicity and small volume interacted with the membrane at a lower energy and were easier to penetrate the membrane. Likewise, they had weaker interaction with P-glycoprotein and were easier to escape from it and harder to export from the body. More in, less out, is the main reason these molecules absorb well.

Investigations into the therapeutic potential of Astragalus Mongholicus (AM, huáng qí) and Largehead Atractylodes (LA, bái zhú) reveal significant efficacy in mitigating the onset and progression of knee osteoarthritis (KOA), albeit with an elusive mechanistic understanding. This study delineates the primary bioactive constituents and their molecular targets within the AM-LA synergy by harnessing the comprehensive Traditional Chinese Medicine (TCM) network databases, including TCMSP, TCMID, and ETCM. Furthermore, an analysis of 3 gene expression datasets, sourced from the gene expression omnibus database, facilitated the identification of differential genes associated with KOA. Integrating these findings with data from 5 predominant databases yielded a refined list of KOA-associated targets, which were subsequently aligned with the gene signatures corresponding to AM and LA treatment. Through this alignment, specific molecular targets pertinent to the AM-LA therapeutic axis were elucidated. The construction of a protein-protein interaction network, leveraging the shared genetic markers between KOA pathology and AM-LA intervention, enabled the identification of pivotal molecular targets via the topological analysis facilitated by CytoNCA plugins. Subsequent GO and KEGG enrichment analyses fostered the development of a holistic herbal-ingredient-target network and a core target-signal pathway network. Molecular docking techniques were employed to validate the interaction between 5 central molecular targets and their corresponding active compounds within the AM-LA complex. Our findings suggest that the AM-LA combination modulates key biological processes, including cellular activity, reactive oxygen species modification, metabolic regulation, and the activation of systemic immunity. By either augmenting or attenuating crucial signaling pathways, such as MAPK, calcium, and PI3K/AKT pathways, the AM-LA dyad orchestrates a comprehensive regulatory effect on immune-inflammatory responses, cellular proliferation, differentiation, apoptosis, and antioxidant defenses, offering a novel therapeutic avenue for KOA management. This study, underpinned by gene expression omnibus gene chip analyses and network pharmacology, advances our understanding of the molecular underpinnings governing the inhibitory effects of AM and LA on KOA progression, laying the groundwork for future explorations into the active components and mechanistic pathways of TCM in KOA treatment.

BACKGROUND: Salidroside (SAL), the main component of Rhodiola rosea extract, is a flavonoid with biological activities, such as antioxidative stress, anti-inflammatory, and hypolipidemic. In this study, the potential therapeutic targets and mechanisms of SAL against oxidative stress in retinal ganglion cells (RGCs) were investigated on the basis of in-vitro experiments, network pharmacology, and molecular docking techniques.
METHODS: RGC oxidative stress models were constructed, and cell activity, reactive oxygen species (ROS), and apoptosis levels were examined for differences. The genes corresponding to rhodopsin, RGCs, and oxidative stress were screened from GeneCards, TCMSP database, and an analysis platform. The intersection of the three was taken, and a Venn diagram was drawn. Protein interactions, GO functional enrichment, and KEGG pathway enrichment data were analyzed by STRING database, Cytohubba plugin, and Metascape database. The key factors in the screening pathway were validated using qRT-PCR. Finally, molecular docking prediction was performed using MOE 2019 software, molecular dynamic simulations was performed using Gromacs 2018 software.
RESULTS: In the RGC oxidative stress model in vitro, the cell activity was enhanced, ROS was reduced, and apoptosis was decreased after SAL treatment. A total of 16 potential targets of oxidative stress in SAL RGCs were obtained, and the top 10 core targets were screened by network topology analysis. GO analysis showed that SAL retinal oxidative stress treatment mainly involved cellular response to stress, transcriptional regulatory complexes, and DNA-binding transcription factor binding. KEGG analysis showed that most genes were mainly enriched in multiple cancer pathways and signaling pathways in diabetic complications, nonalcoholic fatty liver, and lipid and atherosclerosis. Validation by PCR, molecular docking and molecular dynamic simulations revealed that SAL may attenuate oxidative stress and reduce apoptosis in RGCs by regulating SIRT1, NRF2, and NOS3.
CONCLUSIONS: This study initially revealed the antioxidant therapeutic effects and molecular mechanisms of SAL on RGCs, providing a theoretical basis for subsequent studies.

BACKGROUND: Patients with non-small cell lung cancer (NSCLC) are susceptible to coronavirus disease-2019 (COVID-19), but current treatments are limited. Icariside II (IS), a flavonoid compound derived from the plant epimedin, showed anti-cancer,anti-inflammation and immunoregulation effects. The present study aimed to evaluate the possible effect and underlying mechanisms of IS on NSCLC patients with COVID-19 (NSCLC/COVID-19).
METHODS: NSCLC/COVID-19 targets were defined as the common targets of NSCLC (collected from The Cancer Genome Atlas database) and COVID-19 targets (collected from disease database of Genecards, OMIM, and NCBI). The correlations of NSCLC/COVID-19 targets and survival rates in patients with NSCLC were analyzed using the survival R package. Prognostic analyses were performed using univariate and multivariate Cox proportional hazards regression models. Furthermore, the targets in IS treatment of NSCLC/COVID-19 were defined as the overlapping targets of IS (predicted from drug database of TMSCP, HERBs, SwissTarget Prediction) and NSCLC/COVID-19 targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of these treatment targets were performed aiming to understand the biological process, cellular component, molecular function and signaling pathway. The hub targets were analyzed by a protein-protein interaction network and the binding capacity with IS was characterized by molecular docking.
RESULTS: The hub targets for IS in the treatment of NSCLC/COVID-19 includes F2, SELE, MMP1, MMP2, AGTR1 and AGTR2, and the molecular docking results showed that the above target proteins had a good binding degree to IS. Network pharmacology showed that IS might affect the leucocytes migration, inflammation response and active oxygen species metabolic process, as well as regulate the interleukin-17, tumor necrosus factor and hypoxia-inducible factor-1 signaling pathway in NSCLC/COVID-19.
CONCLUSIONS: IS may enhance the therapeutic efficacy of current clinical anti-inflammatory and anti-cancer therapy to benefit patients with NSCLC combined with COVID-19.

Objective: To investigate the molecular mechanisms of chronic rhinosinusitis (CRS), to identify key cell subgroups and genes, to construct effective diagnostic models, and to screen for potential therapeutic drugs. Methods: Key cell subgroups in CRS were identified through single-cell transcriptomic sequencing data. Essential genes associated with CRS were selected and diagnostic models were constructed by hdWGCNA (high dimensional weighted gene co-expression network analysis) and various machine learning algorithms. Causal inference analysis was performed using Mendelian randomization and colocalization analysis. Potential therapeutic drugs were identified using molecular docking technology, and the results of bioinformatics analysis were validated by immunofluorescence staining. Graphpad Prism, R, Python, and Adobe Illustrator software were used for data and image processing. Results: An increased proportion of basal and suprabasal cells was observed in CRS, especially in eosinophilic CRS with nasal polyps (ECRSwNP), with P=0.001. hdWGCNA revealed that the \"yellow module\" was closely related to basal and suprabasal cells in CRS. Univariate logistic regression and LASSO algorithm selected 13 key genes (CTSC, LAMB3, CYP2S1, TRPV4, ARHGAP21, PTHLH, CDH26, MRPS6, TENM4, FAM110C, NCKAP5, SAMD3, and PTCHD4). Based on these 13 genes, an effective CRS diagnostic model was developed using various machine learning algorithms (AUC=0.958). Mendelian randomization analysis indicated a causal relationship between CTSC and CRS (inverse variance weighted: OR=1.06, P=0.006), and colocalization analysis confirmed shared genetic variants between CTSC and CRS (PPH4/PPH3>2). Molecular docking results showed that acetaminophen binded well with CTSC (binding energy:-5.638 kcal/mol). Immunofluorescence staining experiments indicated an increase in CTSC+cells in CRS. Conclusion: This study integrates various bioinformatics methods to identify key cell types and genes in CRS, constructs an effective diagnostic model, underscores the critical role of the CTSC gene in CRS pathogenesis, and provides new targets for the treatment of CRS.
目的： 旨在深入探索慢性鼻窦炎（CRS）的分子机制，识别关键细胞亚群和基因，构建有效的诊断模型，并筛选潜在的治疗药物。 方法： 通过单细胞转录组测序数据鉴定CRS中的关键细胞亚群。通过高维加权基因共表达网络分析（high dimensional weighted gene co-expression network analysis，hdWGCNA）和多种机器学习算法的联合应用，筛选CRS的关键基因并构建CRS的诊断模型。通过孟德尔随机化和共定位分析进行因果推断分析。使用分子对接技术进行靶点药物的鉴定，并通过免疫荧光染色对生信分析的结果进行验证。采用Graphpad Prism、R、python和Adobe Illustrator软件进行数据及图像处理。 结果： CRS尤其是嗜酸性慢性鼻窦炎伴鼻息肉（ECRSwNP）中基底细胞和基上皮细胞占比增加（P=0.001）。hdWGCNA显示“黄色模块”与CRS中基底细胞和基上皮细胞密切相关。采用单因素逻辑回归和最小绝对值收敛和选择算法（least absolute shrinkage and selection operator，LASSO）筛选出13个关键基因（CTSC、LAMB3、CYP2S1、TRPV4、ARHGAP21、PTHLH、CDH26、MRPS6、TENM4、FAM110C、NCKAP5、SAMD3和PTCHD4）。基于这13个基因，使用多种机器学习算法构建出有效的CRS诊断模型（AUC=0.958）。孟德尔随机化分析显示组织蛋白酶C（cathepsin C，CTSC）与CRS具有因果关系（逆方差加权：OR=1.06，P=0.006），共定位分析证实CTSC与CRS具有共享的遗传变异（PPH4/PPH3>2）。分子对接结果显示，对乙酰氨基酚与CTSC具有较好的结合能力（结合能：-5.638 kcal/mol）。免疫荧光染色实验表明CRS中CTSC+细胞增多。 结论： 本研究综合运用多种生物信息学方法，识别了CRS中的关键细胞类型和基因，构建了有效的诊断模型，强调了CTSC在CRS中的关键作用，为CRS的治疗提供了新的靶点。.

BACKGROUND: Porcine epidemic diarrhea virus (PEDV) mainly causes acute and severe porcine epidemic diarrhea (PED), and is highly fatal in neonatal piglets. No reliable therapeutics against the infection exist, which poses a major global health issue for piglets. Luteolin is a flavonoid with anti-viral activity toward several viruses.
RESULTS: We evaluated anti-viral effects of luteolin in PEDV-infected Vero and IPEC-J2 cells, and identified IC50 values of 23.87 µM and 68.5 µM, respectively. And found PEDV internalization, replication and release were significantly reduced upon luteolin treatment. As luteolin could bind to human ACE2 and SARS-CoV-2 main protease (Mpro) to contribute viral entry, we first identified that luteolin shares the same core binding site on pACE2 with PEDV-S by molecular docking and exhibited positive pACE2 binding with an affinity constant of 71.6 µM at dose-dependent increases by surface plasmon resonance (SPR) assay. However, pACE2 was incapable of binding to PEDV-S1. Therefore, luteolin inhibited PEDV internalization independent of PEDV-S binding to pACE2. Moreover, luteolin was firmly embedded in the groove of active pocket of Mpro in a three-dimensional docking model, and fluorescence resonance energy transfer (FRET) assays confirmed that luteolin inhibited PEDV Mpro activity. In addition, we also observed PEDV-induced pro-inflammatory cytokine inhibition and Nrf2-induced HO-1 expression. Finally, a drug resistant mutant was isolated after 10 cell culture passages concomitant with increasing luteolin concentrations, with reduced PEDV susceptibility to luteolin identified at passage 10.
CONCLUSIONS: Our results push forward that anti-PEDV mechanisms and resistant-PEDV properties for luteolin, which may be used to combat PED.

The elucidation of the interaction mechanism between phospholipids and milk proteins within emulsions is pivotal for comprehending the properties of infant formula fat globules. In this study, multispectral methods and molecular docking were employed to explore the relationship between phosphatidylcholine (PC) and whey protein isolate (WPI). Observations indicate that the binding constant, alongside thermodynamic parameters, diminishes as temperature ascends, hinting at a predominantly static quenching mechanism. Predominantly, van der Waals forces and hydrogen bonds constitute the core interactions between WPI and PC. This assertion is further substantiated by Fourier transform infrared spectroscopy, which verifies PC\'s influence on WPI\'s secondary structure. A detailed assessment of thermodynamic parameters coupled with molecular docking reveals that PC predominantly adheres to specific sites within α-lactalbumin, β-lactoglobulin, and bovine serum albumin, propelled by a synergy of hydrophobic interactions, hydrogen bonding, and van der Waals forces, with binding energies noted at -5.59, -6.71, and -7.85 kcal/mol, respectively. An increment in PC concentration is observed to amplify the emulsification properties of WPI whilst concurrently diminishing the zeta potential. This study establishes a theoretical foundation for applying the PC-WPI interaction mechanism in food.

BACKGROUND: Programmed cell death (PCD) has recently been implicated in modulating the removal of neutrophils recruited in acute myocardial infarction (AMI). Nonetheless, the clinical significance and biological mechanism of neutrophil-related PCD remain unexplored.
METHODS: We employed an integrative machine learning-based computational framework to generate a predictive neutrophil-derived PCD signature (NPCDS) within five independent microarray cohorts from the peripheral blood of AMI patients. Non-negative matrix factorization was leveraged to develop an NPCDS-based AMI subtype. To elucidate the biological mechanism underlying NPCDS, we implemented single-cell transcriptomics on Cd45+ cells isolated from the murine heart of experimental AMI. We finally conducted a Mendelian randomization (MR) study and molecular docking to investigate the therapeutic value of NPCDS on AMI.
RESULTS: We reported the robust and superior performance of NPCDS in AMI prediction, which contributed to an optimal combination of random forest and stepwise regression fitted on nine neutrophil-related PCD genes (MDM2, PTK2B, MYH9, IVNS1ABP, MAPK14, GNS, MYD88, TLR2, CFLAR). Two divergent NPCDS-based subtypes of AMI were revealed, in which subtype 1 was characterized as inflammation-activated with more vibrant neutrophil activities, whereas subtype 2 demonstrated the opposite. Mechanically, we unveiled the expression dynamics of NPCDS to regulate neutrophil transformation from a pro-inflammatory phase to an anti-inflammatory phase in AMI. We uncovered a significant causal association between genetic predisposition towards MDM2 expression and the risk of AMI. We also found that lidoflazine, isotetrandrine, and cepharanthine could stably target MDM2.
CONCLUSIONS: Altogether, NPCDS offers significant implications for prediction, stratification, and therapeutic management for AMI.

UNASSIGNED: Abnormal renal lipid metabolism causes renal lipid deposition, which leads to the development of renal fibrosis in diabetic kidney disease (DKD). The aim of this study was to investigate the effect and mechanism of chlorogenic acid (CA) on reducing renal lipid accumulation and improving DKD renal fibrosis.
UNASSIGNED: This study evaluated the effects of CA on renal fibrosis, lipid deposition and lipid metabolism by constructing in vitro and in vivo models of DKD, and detected the improvement of Notch1 and Stat3 signaling pathways. Molecular docking was used to predict the binding between CA and the extracellular domain NRR1 of Notch1 protein.
UNASSIGNED: In vitro studies have shown that CA decreased the expression of Fibronectin, α-smooth muscle actin (α-SMA), p-smad3/smad3, alleviated lipid deposition, promoted the expression of carnitine palmitoyl transferase 1 A (CPT1A), and inhibited the expression of cholesterol regulatory element binding protein 1c (SREBP1c). The expression of Notch1, Cleaved Notch1, Hes1, and p-stat3/stat3 were inhibited. These results suggested that CA might reduce intercellular lipid deposition in human kidney cells (HK2) by inhibiting Notch1 and stat3 signaling pathways, thereby improving fibrosis. Further, in vivo studies demonstrated that CA improved renal fibrosis and renal lipid deposition in DKD mice by inhibiting Notch1 and stat3 signaling pathways. Finally, molecular docking experiments showed that the binding energy of CA and NRR1 was -6.6 kcal/mol, which preliminarily predicted the possible action of CA on Notch1 extracellular domain NRR1.
UNASSIGNED: CA reduces renal lipid accumulation and improves DKD renal fibrosis by inhibiting Notch1 and stat3 signaling pathways.

UNASSIGNED: Monocytes play a critical role in tumor initiation and progression, with their impact on prostate adenocarcinoma (PRAD) not yet fully understood. This study aimed to identify key monocyte-related genes and elucidate their mechanisms in PRAD.
UNASSIGNED: Utilizing the TCGA-PRAD dataset, immune cell infiltration levels were assessed using CIBERSORT, and their correlation with patient prognosis was analyzed. The WGCNA method pinpointed 14 crucial monocyte-related genes. A diagnostic model focused on monocytes was developed using a combination of machine learning algorithms, while a prognostic model was created using the LASSO algorithm, both of which were validated. Random forest and gradient boosting machine singled out CCNA2 as the most significant gene related to prognosis in monocytes, with its function further investigated through gene enrichment analysis. Mendelian randomization analysis of the association of HLA-DR high-expressing monocytes with PRAD. Molecular docking was employed to assess the binding affinity of CCNA2 with targeted drugs for PRAD, and experimental validation confirmed the expression and prognostic value of CCNA2 in PRAD.
UNASSIGNED: Based on the identification of 14 monocyte-related genes by WGCNA, we developed a diagnostic model for PRAD using a combination of multiple machine learning algorithms. Additionally, we constructed a prognostic model using the LASSO algorithm, both of which demonstrated excellent predictive capabilities. Analysis with random forest and gradient boosting machine algorithms further supported the potential prognostic value of CCNA2 in PRAD. Gene enrichment analysis revealed the association of CCNA2 with the regulation of cell cycle and cellular senescence in PRAD. Mendelian randomization analysis confirmed that monocytes expressing high levels of HLA-DR may promote PRAD. Molecular docking results suggested a strong affinity of CCNA2 for drugs targeting PRAD. Furthermore, immunohistochemistry experiments validated the upregulation of CCNA2 expression in PRAD and its correlation with patient prognosis.
UNASSIGNED: Our findings offer new insights into monocyte heterogeneity and its role in PRAD. Furthermore, CCNA2 holds potential as a novel targeted drug for PRAD.