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.

UNASSIGNED: Renshen Yangrong decoction (RSYRD) has been shown therapeutic effects on secondary malaise and fatigue (SMF). However, to date, its bioactive ingredients and potential targets remain unclear.
UNASSIGNED: The purpose of this study is to assess the potential ingredients and targets of RSYRD on SMF through a comprehensive strategy integrating network pharmacology, Mendelian randomization as well as molecular docking verification.
UNASSIGNED: Search for potential active ingredients and corresponding protein targets of RSYRD on TCMSP and BATMAN-TCM for network pharmacology analysis. Mendelian randomization (MR) was performed to find therapeutic targets for SMF. The eQTLGen Consortium (sample sizes: 31,684) provided data on cis-expression quantitative trait loci (cis-eQTL, exposure). The summary data on SMF (outcome) from genome-wide association studies (GWAS) were gathered from the MRC-IEU Consortium (sample sizes: 463,010). We built a target interaction network between the probable active ingredient targets of RSYRD and the therapeutic targets of SMF. We next used drug prediction and molecular docking to confirm the therapeutic value of the therapeutic targets.
UNASSIGNED: In RSYRD, network pharmacology investigations revealed 193 possible active compounds and 234 associated protein targets. The genetically predicted amounts of 176 proteins were related to SMF risk in the MR analysis. Thirty-seven overlapping targets for RSYRD in treating SMF, among which six (NOS3, GAA, IMPA1, P4HTM, RB1, and SLC16A1) were prioritized with the most convincing evidence. Finally, the 14 active ingredients of RSYRD were identified as potential drug molecules. The strong affinity between active components and putative protein targets was established by molecular docking.
UNASSIGNED: This study revealed several active components and possible RSYRD protein targets for the therapy of SMF and provided novel insights into the feasibility of using Mendelian randomization for causal inference between Chinese medical formula and disease.

UNASSIGNED: Based on network pharmacology and experimental validation, this study aimed to screen the potential targets of Liuwei Dihuang decoction (LW) against mild cognitive impairment (MCI).
UNASSIGNED: Based on network pharmacology, this study preliminarily explored the targets and molecular mechanisms of LW in the treatment of MCI. The results showed that the mechanism of action of LW against MCI may be related to the cAMP pathway. Then, an aging cell and animal model was established to further verify its molecular mechanism.
UNASSIGNED: A total of 23 active ingredients were identified in LW. In addition, through network pharmacological analysis, we found 22 anti-MCI active ingredients in LW, of which alisol B had the most significant effect, and predicted the potential mechanism pathway by which LW may improve MCI through the cAMP signaling pathway. Further in vivo and in vitro experiments confirmed that LW can alleviate cognitive dysfunction in aging mice and reduce D-galactose-induced senescent cells, which may be through activation of the cAMP/PKA/CREB signaling pathway.
UNASSIGNED: This study found that the traditional Chinese medicine formula LW may play a role in improving MCI by regulating the cAMP/PKA/CREB signaling pathway, which provides a reference for further clinical research on the anti-MCI effect of LW and its molecular mechanism.

Antibiotic-associated diarrhea (AAD) is a common side effect of antibiotic therapy, characterized by intestinal inflammation which reduces the quality of life of patients. Xianglian Pill (XLP) has long been used to treat abdominal pain, diarrhea, bacillary dysentery and enteritis. Studies found that XLP has curative effect on AAD; however, the chemical constituents and mechanism of XLP have not been fully elucidated because of the lack of in vitro and in vivo studies. In this study, ultra-high performance liquid chromatography mass spectrometry method (UPLC-Q-Exactive-Orbitrap-HRMS) was used to examine the components of the XLP. Then, the binding between active compounds and the key targets was studied using network pharmacology and molecular docking. A comparative tissue distribution study was established for the simultaneous determination of the 10 active components in healthy and AAD mouse models. Forty-six components were characterized from XLP. According to the network pharmacology degree value, a prediction was made that encompassed 42 components and 14 core targets, which were intricately involved in crucial biological pathways, such as the AGE-RAGE signaling, cellular senescence, and MAPK signaling. Tissue distribution analysis showed that the 10 components were widely distributed in the heart, liver, spleen, lungs, kidneys, small intestine, and large intestine of mice, with varying concentrations in healthy and AAD mice. Molecular docking analysis also indicated that the active compounds in the tissue distribution could bind tightly to key targets of network pharmacological studies. This study provides a reference for further investigations of the relationships between the chemical components and pharmacological activities of XLP.

BACKGROUND: Dysregulation of lipid metabolism is a key factor influencing the progression of diabetic nephropathy (DN). Morroniside (MOR) is a major active compound isolated from the traditional Chinese herb Cornus officinalis, our previous research found that it can improve the lipid deposition of renal tubular epithelial cells. The purpose of this study is to explore whether MOR can improve podocyte lipid deposition and its mechanism of reducing DN.
METHODS: Initially, we used network pharmacology and bioinformatics techniques to predict the relationship between renal lipid metabolism of MOR and DN. Subsequently, the binding activity of MOR with lipid-related proteins was studied by molecular docking to determine how MOR acts through these proteins. After determining the target of MOR, animal experiments and cell tests were carried out to verify it.
RESULTS: Using network pharmacology, bioinformatics, and molecular docking, target proteins for MOR treatment of DN were predicted and screened, including PGC-1α, LXRs, ABCA1, PPARY, CD36, and nephrin. It is particularly noted that MOR effectively binds to PGC-1α, while LXRs, ABCA1, PPARY and CD36 are downstream molecules of PGC-1α. Silencing the PGC-1α gene significantly reduced the therapeutic effects of MOR. Conversely, in groups without PGC-1α knockdown, MOR was able to increase the expression levels of PGC-1α and influence the expression of downstream proteins. Furthermore, through in vivo and in vitro experiments, utilizing techniques such as lipid droplet staining, PAS, MASSON staining, immunofluorescence, and Western blot, we found that MOR effectively elevated the expression levels of the podocyte protein nephrin and lipid metabolism-regulating proteins PGC-1α, PPARY, and ABCA1, while significantly inhibiting the expression of the lipid accumulation promoter CD36.
CONCLUSIONS: MOR can regulate the cholesterol efflux in podocytes via the PGC-1α/LXRs/ABCA1 signaling pathway, and control cholesterol intake via the PGC-1α/PPARY/CD36 signaling pathway, thereby ameliorating lipid deposition in DN.

Bailing capsule (BLC), a drug that is clinically administered to modulate the autoimmune system, exhibits promising therapeutic potential in the treatment of thyroiditis. This study elucidates the chemical profile of BLC and its potential therapeutic mechanism in thyroiditis, leveraging network pharmacology and molecular docking techniques. Utilizing ultra-high-performance liquid chromatography coupled with linear trap-Orbitrap mass spectrometry (UHPLC-LTQ-Orbitrap MS), 58 compounds were identified, the majority of which were nucleosides and amino acids. Utilizing the ultra-high-performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (UHPLC QqQ MS/MS) strategy, 16 representative active components from six batches of BLCs were simultaneously determined. Network pharmacology analysis further revealed that the active components included 5\'-adenylate, guanosine, adenosine, cordycepin, inosine, 5\'-guanylic acid, and l-lysine. Targets with higher connectivity included AKT1, MAPK3, RAC1, and PIK3CA. The signaling pathways primarily focused on thyroid hormone regulation and the Ras, PI3K/AKT, and MAPK pathways, all of which were intricately linked to inflammatory immunity and hormonal regulation. Molecular docking analysis corroborated the findings from network pharmacology, revealing that adenosine, guanosine, and cordycepin exhibited strong affinity toward AKT1, MAPK3, PIK3CA, and RAC1. Overall, this study successfully elucidated the material basis and preliminary mechanism underlying BLC\'s intervention in thyroiditis, thus laying a solid basis for further exploration of its in-depth mechanisms.

BACKGROUND: Dengue fever can progress to dengue hemorrhagic fever (DHF), a more serious and occasionally fatal form of the disease. Indicators of serious disease arise about the time the fever begins to reduce (typically 3 to 7 days following symptom onset). There are currently no effective antivirals available. Drug repurposing is an emerging drug discovery process for rapidly developing effective DHF therapies. Through network pharmacology modeling, several US Food and Drug Administration (FDA)-approved medications have already been researched for various viral outbreaks.
OBJECTIVE: We aimed to identify potentially repurposable drugs for DHF among existing FDA-approved drugs for viral attacks, symptoms of viral fevers, and DHF.
METHODS: Using target identification databases (GeneCards and DrugBank), we identified human-DHF virus interacting genes and drug targets against these genes. We determined hub genes and potential drugs with a network-based analysis. We performed functional enrichment and network analyses to identify pathways, protein-protein interactions, tissues where the gene expression was high, and disease-gene associations.
RESULTS: Analyzing virus-host interactions and therapeutic targets in the human genome network revealed 45 repurposable medicines. Hub network analysis of host-virus-drug associations suggested that aspirin, captopril, and rilonacept might efficiently treat DHF. Gene enrichment analysis supported these findings. According to a Mayo Clinic report, using aspirin in the treatment of dengue fever may increase the risk of bleeding complications, but several studies from around the world suggest that thrombosis is associated with DHF. The human interactome contains the genes prostaglandin-endoperoxide synthase 2 (PTGS2), angiotensin converting enzyme (ACE), and coagulation factor II, thrombin (F2), which have been documented to have a role in the pathogenesis of disease progression in DHF, and our analysis of most of the drugs targeting these genes showed that the hub gene module (human-virus-drug) was highly enriched in tissues associated with the immune system (P=7.29 × 10-24) and human umbilical vein endothelial cells (P=1.83 × 10-20); this group of tissues acts as an anticoagulant barrier between the vessel walls and blood. Kegg analysis showed an association with genes linked to cancer (P=1.13 × 10-14) and the advanced glycation end products-receptor for advanced glycation end products signaling pathway in diabetic complications (P=3.52 × 10-14), which indicates that DHF patients with diabetes and cancer are at risk of higher pathogenicity. Thus, gene-targeting medications may play a significant part in limiting or worsening the condition of DHF patients.
CONCLUSIONS: Aspirin is not usually prescribed for dengue fever because of bleeding complications, but it has been reported that using aspirin in lower doses is beneficial in the management of diseases with thrombosis. Drug repurposing is an emerging field in which clinical validation and dosage identification are required before the drug is prescribed. Further retrospective and collaborative international trials are essential for understanding the pathogenesis of this condition.

The aim of this study is to evaluate the anti-HPV potential of a Moringa olifera Lam seed, Nigella sativa L. seed, and Musa Acuminata peel herbal mixture in the form of polymer film-forming systems. A clinical trial conducted in outpatient clinics showed that the most significant outcome was wart size and quantity. Compared to the placebo group, the intervention group\'s size and number of warts were considerably better according to the results. Chemical profiling assisted by LC-HRMS led to the dereplication of 49 metabolites. Furthermore, network pharmacology was established for the mixture of three plants; each plant was studied separately to find out the annotated target genes, and then, we combined all annotated genes of all plants and filtered the genes to specify the genes related to human papilloma virus. In a backward step, the 24 configured genes related to HPV were used to specify only 30 compounds involved in HPV infection based on target genes. CA2 and EGFR were the top identified genes with 16 and 12 edges followed by PTGS2, CA9, and MMP9 genes with 11 edges each. A molecular docking study for the top active identified compounds of each species was conducted in the top target HPV genes, CA2 and EGFR, to investigate the mode of interaction between these compounds and the targets\' active sites.

Osteoporosis is a global health challenge characterized by bone loss and microstructure deterioration, which urgently requires the development of safer and more effective treatments due to the significant adverse effects and limitations of existing drugs for long-term treatment. Traditional Chinese medicine, like Epimedium, offers fewer side effects and has been used to treat osteoporosis, yet its active compounds and pharmacological mechanisms remain unclear. In this study, 65 potential active compounds, 258 potential target proteins, and 488 pathways of Epimedium were identified through network pharmacology analysis. Further network analysis and review of the literature identified six potential active compounds and HIF-1α for subsequent experimental validation. In vitro experiments confirmed that 2″-O-RhamnosylIcariside II is the most effective compound among the six potential active compounds. It can promote osteoblast differentiation, bind with HIF-1α, and inhibit both HIF-1α gene and protein expression, as well as enhance COL1A1 protein expression under hypoxic conditions. In vivo experiments demonstrated its ability to improve bone microstructures and reduce bone loss by decreasing bone marrow adipose tissue, enhancing bone formation, and suppressing HIF-1α protein expression. This study is the first to describe the therapeutic effects of 2-O-RhamnosylIcariside II on osteoporosis, which was done, specifically, through a mechanism that targets and inhibits HIF-1α. This study provides a scientific basis for the clinical application of Epimedium and offers a new candidate drug for the treatment of osteoporosis. Additionally, it provides new evidence supporting HIF-1α as a therapeutic target for osteoporosis.

Resistance of microorganisms to antibiotics represents a formidable global challenge, manifesting in intricate public health ramifications including escalated mortality rates and augmented healthcare costs. The current efforts to manage antimicrobial resistance (AMR) are limited mainly to the standard therapeutic approaches. The aim of this study is to present and analyze the role of artificial intelligence (AI) in the search for new phyto-compounds and novel interactions with antimicrobial effects. The ambition of the current research study is to support researchers by providing summarized information and ideas for future research in the battle with AMR. Inevitably, the AI role in healthcare is growing exponentially. The reviewed AI models reveal new data on essential oils (EOs) as potential therapeutic agents. In terms of antibacterial activity, EOs show activity against MDR bacteria, reduce resistance by sensitizing bacteria to the action of antibiotics, and improve therapeutic efficiency when combined with antibiotics. AI models can also serve for the detailed study of other therapeutic applications of EOs such as respiratory diseases, immune diseases, neurodegenerative diseases, and oncological diseases. The last 5 years have seen an increasing application of AI in the search for potential plant sources to control AMR. For the time being, the application of machine-learning (ML) models is greater in the studies of EOs. Future attention of research teams may also be directed toward a more efficient search for plant antimicrobial peptides (PAMPs). Of course, investments in this direction are a necessary preface, but the excitement of new possibilities should not override the role of human intelligence in directing research processes. In this report, tradition meets innovation to address the \"silent pandemic\" of AMR.