Background: Salinity is one of the main influencing factors in the culture environment and is extremely important for the survival, growth, development and reproduction of aquatic animals.Methods: In this study, a comparative transcriptome analysis , 18 psu (MC group) and 3 psu (LC group)) was performed by high-throughput sequencing of economically cultured Penaeus monodon. P. monodon gill tissues from each treatment were collected for RNA-seq analysis to identify potential genes and pathways in response to low salinity stress.Results: A total of 64,475 unigenes were annotated in this study. There were 1,140 upregulated genes and 1,531 downregulated genes observed in the LC vs. HC group and 1,000 upregulated genes and 1,062 downregulated genes observed in the MC vs. HC group. In the LC vs. HC group, 583 DEGs significantly mapped to 37 signaling pathways, such as the NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, and PI3K-Akt signaling pathway; in the MC vs. HC group, 444 DEGs significantly mapped to 28 signaling pathways, such as the MAPK signaling pathway, Hippo signaling pathway and calcium signaling pathway. These pathways were significantly associated mainly with signal transduction, immunity and metabolism.Conclusions: These results suggest that low salinity stress may affect regulatory mechanisms such as metabolism, immunity, and signal transduction in addition to osmolarity in P. monodon. The greater the difference in salinity, the more significant the difference in genes. This study provides some guidance for understanding the low-salt domestication culture of P. monodon. The stress response is a complex physiological mechanism activated by stimuli that an organism perceives as potentially threatening . It invoPenaeus monodon  is one of the most commercially valuable species of aquaculture shrimp . P. monodon has a wide range of salinity adaptations and can grow in a wide range of salinities from one psu ∼57 psu . In recent years, its culture has advanced significantly from coastal marine waters to inland low-salinity waters due to mariculture disease outbreaks and near-coastal pollution  of the South China Sea Fisheries Research Institute of the Chinese Academy of Fisheries Science (NO: SCSFRI96-253). Our experiments were implemented in accordance with national and institutional guidelines for the protection and use of CAFS laboratory animals.P. monodon were obtained from the Shenzhen base of the South China Sea Fisheries Research Institute of the Chinese Academy of Fisheries Science. The experimental P. monodon were 7.8 ± 0.8 cm in length and 7.3 ± 1.0 g in body mass. The experiments were carried out in concrete ponds at a salinity of approximately 30 psu, 27–28°C and pH 7.6–7.8 for 1 week prior to the start of the experiment. During this period, the ponds were aerated and fed commercial bait four times a day. One-third of the water volume was replaced daily using multistage filtered and disinfected seawater.Healthy The experiment was divided into three groups: a pure seawater group with a salinity of 30 psu , a group with a salinity of 18 psu , and a group with a salinity of 3 psu . The different salinities required for the experiments were adjusted by mixing bay water disinfected by multistage sand filtration with tap water in proportions, and the salinity was measured using a high-precision salinometer . The formal experiments were carried out in special buckets for shrimp culture with a volume of 500 L and 400 L of culture water. Four parallels were set up in each group, using one bucket per parallel. Each bucket contained 60 juvenile shrimp. The salinities used were all 18 psu at the beginning and then adjusted by 3 psu each day until each group reached the desired salinity of 30 psu, 18 psu and 3 psu, respectively, before the official start of the culture trials at the three salinities. For 45 days of culture, water quality was tested daily, and shrimp growth and feeding were observed. Feeding was performed 3 times a day, weighing the same bait and feeding regularly. Water was changed completely every 3 days to maintain the water quality. At the end of the culture, gill tissue samples were taken from each of the three groups for transcriptome sequencing. Every three shrimp from the same group were mixed into one tube. All tissues were immediately frozen in liquid nitrogen and stored at −80°C until RNA extraction.P. monodon gill tissue according to the instructions of the mirVana™ miRNA ISOlation Kit . The product (concentration ≥50 ng mL−1) was examined by 1.5% agarose gel electrophoresis for completeness, which showed clear 28S and 18S bands with no obvious dragging. RNA purity was measured by a NanoDrop 2000  with A260/280 ≈ 2.0. A total of 4 μg of total RNA was used to construct RNA-seq libraries according to the instructions of the TruSeq Stranded mRNA LTSample Prep Kit   . The RNATranscript sequences were obtained using the paired-end splicing method with Trinity trinityrnaseq_r20131110 software , and the−5 were taken to obtain the protein with the highest sequence similarity to the given Unigene. The KEGG annotation information of UniGene was obtained by using KAAS (http://www.genome.jp/kaas-bin/kaas_main), and based on the annotation results of SWISSPROT, the GO term was mapped to obtain the GO protein function annotation information of the UniGene. The annotation summary table information was obtained by merging Unigene with the best one from each database.Unigene sequences were aligned by blastx to non-redundant (NR), Swiss-Prot protein (Swiss-Prot), Clusters of orthologous groups for eukaryotic complete genomes (KOG), Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) libraries for comparison. The annotations with e < 1ehttp://bioconductor.org/packages/release/bioc/html/DESeq.html). The NB  was used to test the significance of differences in the number of reads, and baseMean values were used to estimate the amount of gene expression.The expression levels of all DEGs were quantified based on the negative binomial distribution test in DESeq software  were longer than 1,000 bp, and the 300–500 bp and 500–1,000 bp unigenes were 21,796 (33.81%) and 18,898 (29.31%), respectively.To investigate the dynamic mRNA expression levels of quencing . Due to −60 were present in 55.02% of the 20,990 unigenes (32.56%) annotated to the NR database. When mapped to other species, 10,780 unigenes (27.75%) showed a preference for sequence similarity to Hyalella azteca species. Sequences from 472 unigenes (2.25%) showed similarities to P. monodon species. Due to the shortage of decapoda genomic resources, up to 51.36% and 26.65% of unigenes could not be annotated in the NR and Swiss-Prot databases, respectively, and may represent the genetic specificity of P. monodon.A total of 64,475 unigenes were annotated in five commonly used databases, including NR, Swiss-Prot, KOG, KEGG and GO. E-values less than 1eThe 64,475 unigenes  were examined using MISA and Primmer 3 software, and their SSR loci were analyzed. We identified 51,544 SSR loci , of which 12,634 sequences contained more than one SSR. Among all SSRs, 5,387 SSRs were present in compound formation. Among them, the most represented repeat type was mononucleotide (46.04%), and the least represented repeat types were pentanucleotide and hexanucleotide (0.17%) .P. monodon adaptation to low salinity stress were screened. The expression levels were calculated and compared between the three groups , and the DEGs of the MC and LC groups compared to the HC group were determined . The DEG results showed that compared to the HC group, 1,000 upregulated genes and 1,062 downregulated genes were observed in the MC group, and 1,140 upregulated genes and 1,531 downregulated genes were observed in the LC group compared to the HC group  in the three clusters of biological processes, molecular functions and cellular components. Functional annotation analysis of DEGs was used to further compare and explore the transcriptome regulatory patterns under different salt concentration stresses. KEGG pathway analysis can provide detailed information on the pathways in which DEGs are involved. In this study, 1,223 DEGs were annotated into 36 pathways. These pathways were divided into six main groups at Level 1: Metabolism, genetic information processing, environmental information processing, cellular processes, biological systems, and human diseases . There wP. monodon, gill tissues were screened for metabolism-related DEGs after 45 days of stress with different salinities. Twelve metabolic pathways were identified, involving processes in amino acid metabolism, glucose metabolism and lipid metabolism. Five significantly enriched metabolic pathways were detected in the LC vs. HC group, including pentose and glucuronate interconversions, ascorbate and aldarate metabolism, cutin, suberin and wax biosynthesis, and sulfur metabolism. Three significantly enriched metabolic pathways were detected in the MC vs. HC group, including cutin, suberin and wax biosynthesis, and butanoate. Some representative differentially expressed genes are shown in To investigate the effects of low salinity on the metabolic regulation of P. monodon, the study analyzed immune system pathways. A total of 17 immune system regulatory signaling pathways were detected, including the NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, and Toll and IMD signaling pathways. The study detected more differentially expressed genes in the NOD-like receptor signaling pathway and Toll and IMD signaling pathways. In addition, antigen processing and presentation were significantly different only in the LC vs. HC group, and cell differentiation was only different in the MC vs. HC group. Some representative differentially expressed genes are shown in To investigate the effect of chronic low salinity on immune regulation in P. monodon under chronic low salinity stress were analyzed during this study. A total of 31 signal transduction pathways were identified, including the PI3K-Akt signaling pathway, MAPK signaling pathway, AMPK signaling pathway, Hippo signaling pathway, calcium signaling pathway, sphingolipid signaling pathway, cGMP-PKG signaling pathway, HIF-1 signaling pathway, cAMP signaling pathway and Ras signaling pathway. Among them, the MAPK signaling pathway was significantly different in both the LC vs. HC and MC vs. HC groups, while the Hippo signaling pathway was only significantly different in the MC vs. HC groups. Some representative differentially expressed genes are shown in Understanding signal transduction pathways is an important guide for resolving the adaptation mechanisms of organisms to environmental changes. Therefore, the signal transduction pathways of P. monodon can tolerate a wide range of salinities, studies on its osmotic adaptation mechanisms under chronic low salinity stress are still lacking. The mechanisms of adaptation of P. monodon to long-term varying salinity were assessed by full-length transcriptome amplification in our study. This study identified 2,062 DEGs from the HC vs. MC group, including 1,000 upregulated and 1,062 downregulated genes, and identified 2,671 DEGs from the HC vs. LC group, including 1,140 upregulated and 1,531 downregulated genes. This difference in gene regulation suggests that although P. monodon can cope with this stress, 18 psu and 3 psu may cause subtle stress. The GO analysis results are consistent with previous studies on aquatic animals that classified DEGs into three categories of biological processes, molecular functions and cellular components, such as P. vannamei  was carried out, and the high-quality data obtained are the best guarantee of experimental reliability. These changes were verified by this experiment to be related to metabolic, immune system and signal transduction properties. Thus, P. monodon may adapt to changes in environmental salinity by regulating mechanisms such as metabolism, immunity and signal transduction. These data will provide basic data for future selection and breeding for resistance in the P. monodon industry, which will contribute to the sustainable and healthy development of shrimp farming.In this study, RNA-seq analysis of


A major contributor to older disability is osteoarthritis. Radix Angelicae Biseratae  and Dipsaci Radix  herb pair (DXHP) is widely used to treat osteoarthritis, but the underlying molecular mechanisms still have not been revealed. This research aimed to illustrate the therapeutic mechanism of DXHP against osteoarthritis through the techniques of network pharmacology and molecular docking.  Gene targets for osteoarthritis and active ingredients for DXHP were screened based on the pharmacology public database and the gene-disease target database. The software program Cytoscape was used to visualize the active chemical target-disease gene network. The STRING biological information website was used to investigate protein interactions. On the Metascape bioinformatics website, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out. The molecular docking of the important chemicals and primary targets identified by the aforementioned screening was performed using Autodock software. β-sitosterol have excellent binding activity with main targets.  Twenty-six active substances from the DXHP that had strong connections to 138 osteoarthritis-related targets were screened out. According to network analysis, TNF, GAPDH, IL-6, AKT-1, IL-1B, and VEGFA are prospective therapeutic targets, while osthole, cauloside A, ammidin, angelicone, beta-sitosterol, and asperosaponin VI may be significant active components. 1705 biological processes (BP), 155 molecular functions (MF), and 89 cellular components (CC) were identified by GO analysis. KEGG analysis indicated that IL-17, NF-kappa B, HIF-1, MAPK, and AGE-RAGE signaling pathways are potentially involved. Molecular docking showed that cauloside A, osthole, and  This study comprehensively illuminated the active ingredients, potential targets, primary pharmacological effects, and relevant mechanisms of the DXHP in the treatment of OA. These findings provide fresh thoughts into the therapeutic mechanisms of the main active ingredients of DXHP and provide a reference for further exploration and clinical applications of DXHP. Destruction of cartilage, remodeling of the subchondral bone, the development of osteophytes, and synovial inflammation are all symptoms of osteoarthritis (OA), a degenerative joint disease –3. OA isRadix Angelicae Biseratae (known as Duhuo (DH), the dried rhizome of Angelica pubescens) and Dipsaci Radix (known as Xuduan (XD), the dried rhizome of Dipsacus asper Wall) are a classic herbal drug combination that has been widely used in TCM. Clinical statistical studies have shown that DXHP is one of the most frequently used Chinese medicine pairs in the treatment of OA [Traditional Chinese medicine (TCM), as a supplementary therapy, has accumulated rich theoretical knowledge that can potentially help advance clinical prevention and treatment strategies for OA . The earnt of OA . In modent of OA , while tnt of OA . In addint of OA . Hence, In recent years, network pharmacology, which combines multidisciplinary knowledge and methods, has the characteristics of being systematic and holistic. Molecular docking can calculate the binding energies between ligands and receptors and predict reasonable binding mode and has been used to explore the molecular process of drug active ingredients acting on the human body. Therefore, the aim of this research is to clarify the molecular underpinnings behind the effects of DXHP on OA using network pharmacology and molecular docking technologies. The specific technical route adopted in this study is shown in https://tcmsp-e.com/) [https://www.tcmip.cn/ETCM/index.html) [https://www.symmap.org/) [To obtain more comprehensive compositional data, the active ingredients of DH and XD were retrieved from three herbal pharmacological databases, including the TCMSP data platform (-e.com/) , the ETCex.html) , and theap.org/) . Accordiap.org/) , oral biap.org/) . Finallyhttps://pubchem.ncbi.nlm.nih.gov/) for SMILES codes of their potential ingredients, which were entered into Swiss Target Prediction (https://www.swisstargetprediction.ch/) for further prediction of active ingredient targets, setting the screening parameter criteria to the probability value >0.1. To normalize gene nomenclature and species and avoid over-annotation of homologous proteins, these target genes were transformed into matching gene symbols that corresponded to the “Homo sapiens” species using the UniProt information system (https://www.uniprot.org/) [The active ingredients obtained after the aforementioned screening were queried by PubChem (database ot.org/) .https://www.ncbi.nlm.nih.gov/geo/) [P < 0.05 and |log FC| >1.5. The differentially expressed genes for OA were obtained. Then, we search the OA-related genes in the following databases: GeneCards (https://www.genecards.org) [https://db.idrblab.net/ttd) [https://www.omim.org) [Homo sapiens.” The above-obtained genes were merged, and the duplicated genes were deleted to obtain the defined disease targets of osteoarthritis. Subsequently, the OA-related targets were matched with the active compound targets of DXHP to obtain the target of DXHP in the treatment of OA. The Venn diagram was drawn using the Venny2.1 plotter (https://bioinfogp.cnb.csic.es/tools/venny/index.html).We retrieved OA-related genes from the GEO data platform (ov/geo/)  databaserds.org) , the TTDnet/ttd) , and themim.org) . For thehttps://www.string-db.org/), a protein-protein interaction (PPI) network was crafted for the common targets of compounds and diseases [Based on the STRING Bioinformatics system  and BioGRID data are used in the Physical Interaction module. A subset of the proteins in the resultant network physically interacts with at least one other item on the list. The MCODE methodology was used to find components of highly linked networks in subnetworks with 3 to 500 proteins.https://metascape.org/) is a powerful broad-coverage and fast-updating gene function annotation analysis tool that can analyze a large number of gene or protein functions. In order to effectively research the biological ontology of DXHP in the regulation of OA and to clarify the biological process of each core target protein and its function in signaling pathway transduction, GO functional annotation and KEGG pathway enrichment analysis were performed based on the Metascape biological system. The common targets of DXHP and OA obtained in the above screening were imported into the target gene list, with confined to just human species and correction of all target genes to their recognized gene symbols. Multiple comparisons were performed using Benjamini–Hochberg's FDR correction to avoid false positives. Bonferroni-correctedP values <0.01 for GO and KEGG terms were considered significant. The minimal count for the KEGG analysis was 3, and the enrichment factor was more than 1.5. Finally, the top 20 items were picked, and the annotated chart was formed on the Bioinformatics (https://www.bioinformatics.com/) platform for visualization.The Metascape database (https://www.rcsb.org/) and their PDBID files were downloaded. For these 3D structures, the species source was limited to “Homo sapiens” and contains the crystal structure of the complete pocket. MGLtool 1.5.7 was used to process the protein by adding hydrogen, calculating the charge, merging the nonpolar hydrogen, and saved as a PDBQT format file as a docking ligand. Gridbox coordinates and docking box sizes were set, and molecular docking was performed using Autodock Vina1.1.2. The lowest binding energy score conformation was selected, with lower binding energy scores indicating better docking activity and strength. The molecular docking structures were demonstrated using Pymol 2.3.In network pharmacology, molecular docking is a critical tool for verifying compound-target interactions. It works by combining proteins of known targets obtained from network pharmacology with small compounds of active ingredients in natural drugs and then evaluating the strength and activity of the binding . The molWe obtained 106 active components of DH and 63 active components of XD. After screening by OB and DL standards, 20 active ingredients with good ADME properties were obtained for the subsequent study. In addition to this, DH and XD were found to contain some pharmacologically active ingredients, which were excluded because their OB and DL values were less than the screening criteria. Thus, according to previous literature reports, we included a total of six active ingredients such as osthole, columbianadin, umbelliferone, asperosaponin VI, ursolic acid, and loganin , 40. A tIn the GEO database, we identified the dataset that met the criteria as GSE169077. The chip contained six osteoarthritis cartilage tissue samples as the experimental group and five normal knee cartilage tissue samples as the control group. The results of differential expression gene analysis of osteoarthritis showed that 792 genes were found to be differentially expressed. The heatmap and Limma package in the R language were used to draw the dataset volcano map of differential genes . The GenHomo sapiens,” the PPI network was constructed , the top 10 terms of BP, CC, and MF were rated. The terms are presented in GO functional annotation analysis showed that the enrichment results included 1705 biological processes (BPs), 155 molecular functions (MFs), and 89 cellular components (CCs). Most GO annotations of BP were contained to regulation of inflammatory response, response to hormone, inflammatory response, positive regulation of locomotion, cellular response to lipid, cellular response to organic cyclic compound, positive regulation of cell death, protein phosphorylation, positive regulation of MAPK cascade, and cell activation. MF annotation was mainly involved in protein kinase activity, endopeptidase activity, transcription factor binding, nuclear receptor activity, cytokine receptor binding, protease binding, protein homodimerization activity, transmembrane receptor protein kinase activity, oxidoreductase activity, and phosphatase binding. The majority of CC annotations were included to the extracellular matrix, membrane raft, receptor complex, cell body, organelle outer membrane, ficolin-1-rich granule lumen, endolysosome, protein kinase complex, side of the membrane, and endoplasmic reticulum lumen. Based on their Q-value, the top 20 critical signaling pathways are presented in KEGG pathway analysis enriched 179 signaling pathways after FDR correction. The top 20 pathways included pathways in cancer, the AGE-RAGE signaling pathway in diabetic complications, the IL-17 signaling pathway, the NF-kappa B signaling pathway, lipid and atherosclerosis, the TNF signaling pathway, the MAPK signaling pathway, hepatitis B, the toll-like receptor signaling pathway, prostate cancer, the HIF-1 signaling pathway, the PI3K-Akt signaling pathway, microRNAs in cancer, apoptosis, fluid shear stress and atherosclerosis, chagas disease, the VEGF signaling pathway, human cytomegalovirus infection, the FoxO signaling pathway, and osteoclast differentiation. Based on their P values were used as the functional description of the corresponding components  and inhibit the degradation of cartilage [According to this investigation, DXHP's primary therapeutic ingredients for OA include substances such as osthole, asperosaponin VI, angelicone, beta-sitosterol, ammidin, and cauloside A, among which scholars . Many inscholars . In addi tissues . Osthole tissues . Recent d NFATc1 . β-sitoscolation . Chen etartilage .α has been demonstrated in many diseases, including osteoarthritis, autoimmune diseases, ankylosing spondylitis, insulin resistance, psoriasis, nephropathy, and cancer [α is associated with many cytokines, and it has been found that TNF-α implicated in angiogenesis by synergistic induction of VEGF production with IL-1β and IL-6 [α could regulate the harmony between Treg cells and TH17 and TH1 in the joint synovium through FOXP3 dephosphorylation [β gene in OA organisms was positively correlated with the pain index [β was detected in the synovial membrane and fluid of OA organisms and was positively correlated with OA disease [The observations of the coincide target PPI analysis demonstrated that the core targets of DXHP in the healing of OA encompassed multiple targets such as TNF, GAPDH, IL6, AKT1, IL1B, VEGFA, CASP3, STAT3, MMP9, and PTGS2. TNF-d cancer . TNF-α iand IL-6 . Nie et rylation . When cerylation . The resrylation  knockdowrylation . Shahinein index . Corresp disease . Apoptot disease . TP53 in disease . In addi disease . The expκb pathway is closely related to cartilage destruction in OA and targeted therapy of OA [κb in joint synovial fluid and peripheral blood of OA patients is increased, and the degree of up-regulation of the NF-κb pathway is positively correlated with the degree of cartilage erosion and destruction [κBα dissociates from NF-κB, and NF-κB is activated and enters the nucleus, promoting the synthesis and secretion of TNF-α and IL-6 [α decreases [α can up-regulate the expression of MMP13, degrade col2A1 and ACAN, promote chondrocyte degradation, and promote the development of OA [κB and MAPK in a variety of cells including osteocytes [The KEGG enrichment analysis showed that the main enriched inflammation-related signaling pathways included AGE-RAGE, IL-17, NF-kappa B, MAPK, TLR, HIF-1, PI3K-Akt, and the VEGF signaling pathway. The other signaling pathways enriched included pathways in cancer, lipid and atherosclerosis, hepatitis B, prostate cancer, microRNAs in cancer, apoptosis, fluid shear stress and atherosclerosis, chagas disease, human cytomegalovirus infection, FoxO signaling pathway, and osteoclast differentiation. The MCODE analysis revealed that the regulatory response to inflammation was the most significant biological process in treating OA by DXHP. The NF-py of OA . Previoutruction . After pand IL-6 , 64 and and IL-6 , 66. In and IL-6 . Bai et and IL-6  found thand IL-6 . In OA, ecreases , and thent of OA . The advnt of OA . AGE is nt of OA . RAGE isteocytes , it can teocytes .β-sitosterol, as its overlapping active ingredient, is associated with anti-inflammatory and antioxidant effects, immune regulation, and bone metabolic balance [β, AKT1, VEGFA, MMP13, and STAT3 are vital links in inflammatory signalling pathways such as AGE-RAGE, IL-17, MAPK, and NF-kappa B and are extensively involved in the biological processes of regulating the inflammatory response in OA. This finding may help us to better understand the compatibility mechanism of DXHP from a molecular perspective.As an herb pair, DH and XD have many overlapping targets and active ingredients, and the compatibility mechanism of DXHP in treating OA may be related to this.  balance . Furtherβ-sitosterol have excellent binding activity with multiple key targets, such as TNF, GAPDH, IL-6, AKT1, IL-1β, and VEGFA. This also explains the therapeutic mechanism of DXHP from another perspective. These results offer insight for the subsequent application of network pharmacology methods to improve the efficiency of natural medicine ingredient development, which could facilitate the development of new high-efficiency, low-toxicity, multi-target OA drugs capable of improving symptoms and delaying disease progression.Molecular docking tests were carried to further confirm the molecular mechanism of DXHP in the treatment of OA. The results revealed that osthole, angelicone, cauloside A, and Compared to recently published network pharmacology studies of similar diseases , we haveDespite the study's advantages, several limitations should be mentioned. Firstly, the sources of component and target data are scattered and vary widely between databases, resulting in insufficiently accurate data sources . SecondlIn this study, we found that DXHP is a valuable TCM herb pair for treating patients with OA through multiple components, targets, and pathways. Its pharmacological mechanism could be through the AGE-RAGE, IL-17, NF-kappa B, MAPK, TLR, HIF-1, PI3K-Akt, and VEGF signaling pathways to alleviate OA. Hopefully, our research may provide a scientific basis for the prodrug discovery of its natural ingredients and the identification of therapeutic targets in the future.


Huanglian Jiedu decoction (HLJDD) is a famous traditional Chinese medicine prescription, which is widely used in the treatment of Alzheimer’s disease (AD). However, the interaction between bioactive substances in HLJDD and AD-related targets has not been well elucidated.A network pharmacology-based approach combined with molecular docking was performed to determine the bioactives, key targets, and potential pharmacological mechanism of HLJDD against AD, through the regulation of microbial flora.Bioactives and potential targets of HLJDD, as well as AD-related targets, were retrieved from Traditional Chinese Medicine Systems Pharmacology Analysis Database (TCMSP). Key bioactive components, potential targets, and signaling pathways were obtained through bioinformatics analysis, including protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Subsequently, molecular docking was performed to predict the binding of active compounds with core targets.102 bioactive ingredients of HLJDD and 76 HLJDD-AD-related targets were screened. Bioinformatics analysis revealed that kaempferol, wogonin, beta-sitosterol, baicalein, acacetin, isocorypalmine, (S)-canadine, (R)-canadine may be potential candidate agents. AKT1, TNF, TP53, VEGFA, FOS, PTGS2, MMP9 and CASP3 could become potential therapeutic targets. 15 important signaling pathways including the cancer pathway, VEGF signaling pathway, and NF-κB signaling pathway might play an important role in HLJDD against AD. Moreover, molecular docking analysis suggested that kaempferol, wogonin, beta-sitosterol, baicalein, acacetin, isocorypalmine, (S)-canadine, and (R)-canadine combined well with AKT1, TNF, TP53, VEGFA, FOS, PTGS2, MMP9, CASP3, respectively.Our results comprehensively illustrated the bioactives, potential targets, and possible molecular mechanisms of HLJDD against AD. HLJDD may regulate the microbiota flora homeostasis to treat AD through multiple targets and multiple pathways. It also provided a promising strategy for the use of traditional Chinese medicine in treating human diseases. HLJDD is widely used in China and Japan to treat cerebral ischemia and to exert neuroprotective effects , also known as senile dementia, is a degenerative disease of the central nervous system characterized by progressive cognitive impairment and memory loss . The his effects . Sun et  effects . HoweverNetwork pharmacology and molecular docking analysis was performed in this study to explore the possible mechanisms of HLJDD in the treatment of AD through constructing the network of the “herb-component-target-pathway-disease”. It will provide a new theoretical support for the clinical treatment of AD and a reference for screening bioactive components with potential medicinal value. The workflow of the whole process of our study was shown in 22.1Rhizoma coptidis, Radix scutellariae, Cortex phellodendri and Fructus gardeniae in HLJDD were searched by application analysis platform and database system pharmacology of Chinese medicine  (The active components of msp.php)  and the msp.php) .2.2https://www.uniprot.org/) (https://www.genecards.org/) (https://omim.org/search/advanced/) (https://db.idrblab.net/ttd/)   as standds.org/) , OMIM (hvanced/) , TTD (htet/ttd/)  and othe2.3https://bioinfogp.cnb.csic.es/tools/venny/index.html) to draw Venn diagram and obtain the intersection genes of drug and component targets. The intersection genes were imported to STRING gene database (https://string-db.org/cgi/input)   to consti/input) .2.4https://david.ncifcrf.gov) was used for Gene Ontology (GO) functional annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and P-value was set to screen the top 10 important pathways.For the screened core targets, the DAVID platform website (2.5Cytoscape 3.9.1 software was used to construct the composition-target network and the herb-component-target-pathway-disease (H-C-T-P-D) interaction network. Node represents active components, drugs, diseases and targets, while edge represents the relationship between different nodes. Through the Network Analyzer function of Cytoscape software, combined with the main active ingredients, core targets and concentrated main signaling pathways, the possible core active ingredients of HLJDD in the treatment of AD were speculated. It can also show the functional relationship of traditional Chinese medicine, active ingredients, targets and pathways in the treatment of AD.2.6https://www.rcsb.org/). They were imported into PyMOL and AutoDockTools and Autodock vina (The 3D structures of the main active ingredients in HLJDD were downloaded from the TCMSP database while the 3D structure of the core target protein (top 8 of degree in PPI network) treated by HLJDD was downloaded from the Worldwide Protein Data Bank (PDB) database ”, 54 “Cellular Components (CC)”, and 88 “Molecular Functions (MF) “. The first 15 “Biological Processes” items, 10 “Cellular Components” items, and 10 “Molecular Functions” items were selected based on the analysis Figure 4P value and the number of genes, 15 signal pathways with high probability were screened out for visual analysis as shown in https://www.bioinformatics.com.cn). Moreover, 137 signal pathways were enriched by KEGG pathway analysis of the core targets using the DAVID platform. According to the 3.6The data of potential active ingredients and potential targets of HLJDD in the treatment of AD were imported into Cytoscape 3.9.1 software to obtain a diagram of the traditional Chinese medicine composition-target network Figure 6The “herb-component-target-pathway-disease” network was constructed using the 66 active ingredients, 76 intersection targets, and 15 KEGG signal pathways of HLJDD in the treatment of AD Figure 73.7https://www.bioinformatics.com.cn), The binding energies of the above eight compounds with AKT1, TNF, TP53, VEGFA, FOS, CASP3, MMP9, and PTGS2 were less than -5.0 kcal · mol-1, showing good binding ability. The binding of AKT1 to beta-sitosterol, TNF to beta-sitosterol, TP53 to kaempferol, VEGFA to baicalein, FOS to kaempferol, CASP3 to kaempferol, MMP9 to kaempferol, and PTGS2 to baicalein were shown in According to 4HLJDD, which is the representative medicine of heat-clearing and detoxification of traditional Chinese medicine, has been reported to show significant anti-inflammatory and neuroprotective effects in the neurodegenerative diseases. In our study, bioinformatics analysis revealed that kaempferol, wogonin, beta-sitosterol, baicalein, acacetin, isocorypalmine, (S)-canadine, (R)-canadine may be potential candidate agents of HLJDD; AKT1, TNF, TP53, VEGFA, FOS, PTGS2, MMP9 and CASP3 could become potential therapeutic targets in the treatment of AD.2+ signaling pathways. Recent studies have shown that VEGF, NF-κB, and Ca2+ are all involved in the process of inflammation, which is closely associated to AD  triggered immune responses in the body and increased the production of Aβ, thus promoted the occurrence of AD . Zeng etce of AD . In the ce of AD . Specifiathology .Therefore, we hypothesize that HLJDD played a therapeutic role in AD by regulating the homeostasis of microbial flora and inhibiting the neuroinflammatory response Figure 1The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.RZ, JG, and PJ contributed to the conception of the study. RZ, SS, QZ, and PJ contributed significantly to analysis and manuscript preparation. SY and TG helped perform the analysis with constructive discussions. All authors contributed to the article and approved the submitted version.


Chronic kidney disease (CKD) is a progressive disease that poses a huge economic burden to society. Liuwei Dihuanng pill is an effective treatment for chronic kidney disease, but its treatment mechanism is unclear. The rapid development of network pharmacology has provided new strategies for studying Chinese medicine.The traditional Chinese medicine systems pharmacology database and analysis platform was used to obtain the bioactive components and targets of Liuwei Dihuanng pill. The sources for the CKD-related targets were then obtained from the Genecards, OMIM, TTD, and DisGeNET databases. R was used to identify the intersecting genes for Liuwei Dihuang pill and CKD-related targets. Analysis of protein-protein interactions (PPI) was performed using STRING, and PPI networks and drug-component-target networks were constructed using Cytoscape software. Kyoto encyclopedia of genes and genomes pathway and gene ontology enrichment analyses were performed using R. Finally, molecular docking was performed to determine the binding activity between bioactive components and the targets.After screening and data de-duplication of 74 active components, 209 drug targets, and 14,794 disease targets, a total of 204 drug-disease targets were acquired. Subsequently, a drug-component-target network and PPI network were established. The primary components of Liuwei Dihuang pill included quercetin, stigmasterol, kaempferol, beta-sitosterol, tetrahydroalstonine, kadsurenone, hederagenin, hancinone C, diosgenin, and sitosterol. In addition, JUN, AKT1, TP53, RELA, MAPK1, FOS, TNF, IL6, ESR1, and RXRA were identified as the main targets. Gene ontology function enrichment analysis revealed that these targets were involved in reactive oxygen species metabolic processes, responses to metal ions and to chemical stimuli, G protein-coupled amine receptor activity, and nuclear factor receptor activity. Kyoto encyclopedia of genes and genomes enrichment analysis showed that these targets were involved in the AGE-RAGE signaling pathway, IL-17 signaling pathway, TNF signaling pathway, and so on. Molecular docking results indicated good binding activity between the core targets and core components.The potential mechanism of Liuwei Dihuanng pill in the treatment of CKD was preliminarily discussed in this study, providing a theoretical basis and evidence for further experimental research. CKD represents a growing global public health issue, imposing a significant economic burden on society, as current statistics estimate a prevalence rate of approximately 11% to 13% for this condition.,3 The progressive decline in the glomerular filtration rate (GFR) causes structural, functional, and molecular alterations in the renal unit, which is a distinctive feature of CKD. The decline in GFR triggers compensatory hypertrophy of the glomerulus, which can eventually lead to glomerulosclerosis and tubular necrosis. This process ultimately results in renal failure when the GFR falls below 15 mL/min·1.73 m2 or when the urine protein exceeds 300 mg/g.,5 Hypertension, diabetes, atherosclerosis, and autoimmune diseases are traditional risk factors for the development of CKD. Poor lifestyle habits, such as smoking, alcohol consumption, and a high purine diet, are also considered to contribute to the development of these conditions. nontraditional risk factors include pregnancy, kidney stones, nephrotoxic drugs, infectious diseases, and acute kidney injury (AKI). The prevention of CKD is typically approached at 3 distinct levels. The first level involves establishing a healthy lifestyle, while the second level focuses on monitoring risk factors that may increase the likelihood of developing CKD. The third level centers around controlling blood pressure and proteinuria as a means of preventing or delaying the progression of CKD. The available treatments for CKD are currently quite limited, primarily focusing on strategies such as controlling protein intake, reducing blood sugar and blood lipids, inhibiting the renin-angiotensin-aldosterone system, and addressing poor lifestyle habits to slow the progression of the disease. Among these modalities, inhibition of the renin-angiotensin-aldosterone system is generally considered the most important intervention. The pathogenesis of CKD is complex, and existing studies suggest the involvement of the Wntβ-catenin signaling pathway, Transforming Growth Factor-β (TGFβ)-samds signaling pathway, Hippo/YAP signaling pathway, tumor necrosis factor (TNFα), HF-1, and P53.–11Chronic kidney disease (CKD) is characterized by impaired kidney function or structural damage persisting for more than 3 months, resulting in a decline in overall health. The condition is typically diagnosed based on a glomerular filtration rate below 60 mL/min-1.73 mP < .05) and reduce the ratio of urinary albumin to creatinine (P < .05) in patients with CKD. Furthermore, another study found that patients with CKD who used LWDH as adjuvant therapy exhibited a lower incidence of ischemic stroke (P < .05). Liu et al used LWDH to treat osteoporotic rats with diabetic nephropathy, demonstrating that LWDH significantly reduced the pathological damage of renal tissues, such as glomerular hypertrophy, tubular necrosis, and basement membrane thickening, and decreased serum TNF, IL-6, IL-8, and IL-1 levels in the model group (P < .05). All the above studies showed that LWDH is effective in treating CKD. However, the components of LWDH are complex and have many action targets. Therefore, further research on the bioactive components of LWDH in CKD treatment should be carried out to investigate the mechanism of action of LWDH in CKD. The concept of network pharmacology was first introduced by Andrew L Hopkins. Disease development is not mediated by a single target or signaling pathway, but involves multiple targets; similarly, a drug achieves its therapeutic effect through multiple active ingredients acting on multiple targets. Chinese medicines often involve multiple active components, signaling pathways, and active targets for treating diseases. The rapid development of network pharmacology provides new strategies for studying Chinese medicines, which can help elucidate the pharmacological mechanisms of the active components of Chinese medicines at the molecular biology level.Liuwei Dihuanng pill is a prescription created by Yi Qian, a famous Chinese medical practitioner from the Northern Song Dynasty. Clinical studies have shown that Liuwei Dihuang pill (LWDH) can effectively increase GFR  ≥ 30% (indicates the rate and degree of drug entering the human blood circulation) and drug-likeness (DL) ≥ 0.18 (indicates the similarity of a compound to a known drug) were set as screening conditions to identify the bioactive compounds of each herbal medicine in LWDH, with their corresponding targets of action.As an open-source database, Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform (TCMSP) allows for the analysis of active ingredients, pharmacokinetics, pharmacometabolomic, and genomics of Chinese medicines.“Chronic kidney disease” was used as the search term and CKD-related protein targets were collected from the databases shown in Table https://www.r-project.org) to obtain the intersection of LWDH and CKD targets. Subsequently, the intersecting protein targets were uploaded to the STRING database (https://string-db.org) to construct a PPI network. The species was set to “human,” the confidence level was set to greater than 0.9 (the highest confidence level), and the other setting options were kept at their default values. Next, the PPI results were exported in TSV format and a visualization network was constructed in Cytoscape. According to the parameters of the network, the critical targets of LWDH in the treatment of CKD were predicted.Venn diagrams were plotted using R  enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis on the potential targets. https://www.rcsb.org), and the 3D structure of the drug’s active ingredient was obtained from the TCMSP. PyMOL was used to remove water molecules the target protein and basic ligands before importing it into AutoDock Tools to perform hydrogenation, charge calculation, and non-polar hydrogen combination. Molecular docking was performed using AutoDock Vina, and the results were visualized using PyMOL.The core targets obtained in section 2.3 and the bioactive components obtained in 2.4 were selected for molecular docking to evaluate the binding activity. The 3D structure of the target proteins was acquired from the PDB database . Then, bubble plots were prepared for additional analysis using the first 10 results for each item that was preserved Fig. . These tMolecular docking was performed and the core components of LWDH were visualized with the core drug-disease targets. The top 10 active components were screened as ligands and the top 10 central LWDH-CKD targets as binding sites for molecular docking. The binding energy is an important indicator for evaluating whether the active ingredient and the target have good binding activity. The lower the binding energy, the stronger the binding activity. Using R, a heat map of the molecular docking binding energy was plotted Fig. . The resNetwork pharmacology is a novel drug design approach that aims to identify disease-target-drug associations systematically and comprehensively. This method provides a holistic understanding of complex drug synergies in the human body by observing the effects and mechanisms of drug-disease interactions at the network level. Network pharmacology has allowed the discovery of active ingredients and mechanisms of action in Chinese medicine in treating various ailments, which has transformed the study of Chinese medicine from an empirical to evidence-based practice.In this study, open-source databases were utilized to gather information on CKD and identify the active components of LWDH for CKD treatment. The interactions between LWDH and CKD protein targets were predicted and molecular docking analysis was performed. The PPI results suggest that LWDH can act on CKD through multiple targets and pathways, with the top 10 targets being JUN, AKT1, TP53, RELA, MAPK1, FOS, TNF, IL6, ESR1, and RXRA. KEGG enrichment analysis revealed that LWDH exerts its therapeutic effects on CKD by regulating the AGE-RAGE signaling pathway, IL-17 signaling pathway, TNF signaling pathway, HIF-1 signaling pathway, and multiple viral infections. Based on the analysis of central targets and KEGG enrichment results, we can attribute the mechanism of LWDH intervention in CKD to the following factors. Renal fibrosis is the primary pathological process leading to the progression of AKI to CKD. Recent research indicates that mice with AKT1 gene knockdown exhibit lower levels of renal fibrosis after unilateral ischemia-reperfusion injury (IRI) compared to wild-type mice. This finding highlights the crucial role of AKT1 in the development of renal fibrosis and suggests that AKT1 may be a significant factor in the progression of this disease. In contrast, a mouse model of renal IRI has shown that an upsurge in mitochondrial AKT1 is essential for safeguarding renal structure and function. Interestingly, the inhibition of AKT1 expression can lead to an increase in oxidative stress injury, suggesting that AKT1 plays a critical role in mitigating the damaging effects of oxidative stress in this context. Thus, AKT1 may exert contradictory effects in renal disease. The JUN and FOS proteins are encoded by proto-oncogene c-Jun and proto-oncogene c-Fos, respectively. JUN proteins can form homodimers or heterodimers in combination with FOS proteins, which are involved in forming Activator Protein 1 (AP-1). AP-1 participates in the progression of multiple pathological processes, including fibrosis and inflammation. Activation of AP-1 may lead to glomerulosclerosis, interstitial fibrosis, and inflammation, which are essential causes of kidney injury. As one of the 3 MAPK signaling pathways, c-Jun amino-terminal kinase (JNK) can transmit extracellular signals to regulate cell proliferation, differentiation, and apoptosis. The JNK signaling pathway mediates the progression of CKD by participating in inflammation, fibrosis, oxidative stress, and nephrotoxicity. Studies have shown that G2/M blockade in renal tubular epithelial cells activates the JNK signaling pathway and promotes fibroblast factor secretion; blocking JNK signaling pathway activation inhibits TGF-β1-mediated renal fibrosis.,25 The role of the TGF-β signaling pathway in the pathogenesis of CKD has been researched. According to Zhou, the knockdown of JUN and FOS genes in normal kidney RPTEC-TERT1 OAT3 cells or using AP-1 inhibitors significantly reduced TGF-β expression (P < .05). These findings indicate that AP-1 can mediate the activation of TGF-β signaling to promote the occurrence of renal fibrosis. As a member of the NF-κB protein superfamily, P65 (RELA) is mainly involved in inflammation, apoptosis, and tumor suppression, while its role in developing chronic kidney disease is gradually increasing. Tang identified P65 as a potential target for reducing inflammation and fibrosis in unilateral ureteral obstruction (UUO) mice, and inhibition of P65 expression could slow down the progression of AKI to CKD. In a gentamicin-induced kidney injury rat model, significantly increased NF-κB-P65 protein expression was observed by immunoblotting (P < .05), and HE staining showed dilated renal tubules and massive epithelial cell degeneration and necrosis. The tumor suppressor P53, a key transcription factor, is poorly expressed in normal cells, and activation of P53 stimulated by factors such as oxidative stress can lead to cell cycle arrest, apoptosis, DNA repair, and autophagy.,30 Activation of P53 plays a crucial role in IRI models and cisplatin-induced acute kidney injury models, while P53 knockdown or inhibition can effectively protect kidney tissue from injury. As the main component of LWDH in treating CKD, quercetin exerts its anti-fibrosis and anti-inflammatory effects through multiple pathways. In the glucose-induced CKD cell model, quercetin can lead to reduced TNF-α levels and inhibit the activation of the TGFβ/smads pathway, suggesting its therapeutic potential in CKD. In an animal adenine-induced chronic renal failure model, quercetin reduced the expression of NLRP3, caspase1, p-PI3k, and p-Akt in kidney tissues, suggesting that quercetin can inhibit cellular apoptosis and achieve a protective effect. Hederagenin also exerted the same anti-fibrotic effect on fibroblast NRK-49 cells. Hyoxytopoly induction factor-1 (HIF-1) is a specific transcription factor that plays an important role in oxidation stimulation damage. The formation of AGES, reactive oxygen species (ROS), and inflammatory factors can activate HIF-1. In the IRI model and UUO model, the activation of HIF-1 prevents renal fibrosis, while its inhibition increases the degree of renal fibrosis.Renal fibrosis is now acknowledged as a critical factor contributing to the irreversible progression of CKD, and multiple core targets are involved in this process. Among the members of the AKT superfamily, AKT1 has been extensively researched. Multiple causes of inflammation have been reported in CKD, including reduced GFR, increased production or reduced clearance of pro-inflammatory factors, oxidative stress, uremia, infection, dialysis, etc. IL-6 is a soluble protein produced by T cells that activates B cells to differentiate into antibody-producing cells and is closely associated with inflammation. IL-6 is a risk factor for many complications of CKD, such as anemia, pruritus, and coronary syndrome.–42 In a recent study, IL-6 was found to mediate increased expression of FGF23, leading to increased morbidity and mortality in AKI and CKD. TNF-α is mainly secreted by macrophages, NK cells, T cells, and B cells, and mainly mediates the development of inflammation and cell death. TNF-α inhibitors reduced cisplatin-induced renal structural and functional damage. At the same time, TNF-α-deficient mice were resistant to cisplatin nephrotoxicity. TNF-α was also involved in ciprofloxacin-induced nephrotoxicity, suggesting the essential role of TNF-α in the pathogenesis of renal injury.,46 As an anti-inflammatory factor, IL-10 can protect the kidney by suppressing excessive inflammatory responses, modulating immunosuppression, delaying fibrosis, and promoting repair. IL-10 knockout mice showed lipid accumulation, elevated cholesterol, glomerulosclerosis, tubular and interstitial fibrosis, immune cell infiltration, and elevated serum inflammatory factor expression levels in the kidney and serum after 12 weeks of high-fat diet feeding, suggesting that IL-10 is involved in obesity-associated renal disease. IL-10 also delays the progression of UUO-induced renal fibrosis by inhibiting endoplasmic reticulum stress. IL-1a and IL-1b belong to the IL-1 superfamily. IL-1a can readily release pro-inflammatory alarmins during cell necrosis, causing local tissue inflammation, and IL-1b promotes systemic inflammation primarily by initiating acute phase response proteins (such as C-reactive protein) in the liver, activating endothelial cells, triggering fever, mobilizing neutrophils (leukocytosis), and activating all types of leukocytes. A clinical study shows that IL-1b inhibitors reduce the incidence of major adverse cardiovascular events in patients with high-risk atherosclerosis in CKD.Secondly, most of these essential targets are related to inflammation. Inflammation is a major mechanism for the immune system to adapt and defend against external stimuli or harm. In CKD, inflammation is a critical factor that can impair kidney repair and lead to the development of renal fibrosis. Studies have shown that high levels of serum TNFα, IL-6, and other inflammatory factors in CKD patients are closely linked to the loss of renal function over time. The antioxidant system includes antioxidant enzymes, glutathione peroxidase, and catalase, which are responsible for scavenging ROS. Furthermore, oxidative stress injury is associated with the development of many diseases, including CKD. Studies have shown that AKI and CKD lead to increased levels of oxidative stress and that advanced oxidative protein products and fructosamine can be used as biomarkers for CKD. Reducing ROS levels can mediate the inhibition of the TGF-β signaling pathway and thereby reduce the extent of renal fibrosis in UUO mice. Clinical studies have found that as CKD progresses, patients’ serum malondialdehyde and oxidized glutathione levels increase, whereas glutathione levels decrease, suggesting increasing levels of oxidative stress and significantly lower antioxidant levels in the body. Advanced-glycation end products (AGEs) are heterogeneous molecules obtained by post-translational non-enzymatic modification of macromolecules, including proteins and nucleic acids by other types of sugars such as glucose or fructose. In various age-related diseases, binding AGEs to RAGEs can promote oxidative stress and thus enhance inflammatory responses. An RCT revealed that patients’ AGEs/sRAGE levels gradually increased as CKD progressed, suggesting that AGEs/sRAGE may be a risk factor for CKD. In addition, AGEs levels were negatively correlated with the physical condition and vitality of dialysis patients, and the accumulation of AGEs in the gastrocnemius muscle in the 5/6 nephrectomy mouse model was closely associated with morphological abnormalities and altered mitochondrial function. In RAGE knockout mice, the time to CKD-induced arterial thrombosis was significantly reduced, suggesting that AGEs-RGEs are involved in this process and that blocking RAGE may be a mechanism to protect against CKD-induced cardiovascular disease.Thirdly, in our study, GO enrichment analysis showed that LWDH intervened in CKD mainly through their involvement in oxidative stress. The imbalance of the body’s oxidative and antioxidant systems induces oxidative stress. The oxidative system mainly consists of ROS, which includes superoxide, hydroxyl radicals, and hydrogen peroxide. Superoxide can be catalyzed by superoxide dismutase into hydrogen peroxide, and hydrogen peroxide is dismutated into hydroxyl radicals through redox reactions, which is the most active ROS. Apoptosis is observed in AKI and CKD, which may be the main mechanism responsible for acute and chronic tubular cell and foot cell loss. Increased caspase-3 activity, Bax expression, and Bax/Bcl ratio were observed in renal dysfunctional rats. Cadmium-induced apoptosis in chicken kidney cells by promoting the increase of Bax, caspase-9, and caspase-3 levels at a dose of 2.15 g/kg. These studies all indicate that LWDH may treat CKD by inhibiting apoptosis, which was confirmed by our protein interaction analysis results.In addition to the inflammation, fibrosis, and oxidation stress discussed above, many factors can participate in the occurrence and treatment of CKD, such as apoptosis. Apoptosis is an important cell death mechanism in the body. Eukaryotic cell are mainly based on external apoptosis pathways, internal mitochondrial pathways, Granzyme B -mediated cell apoptosis pathways, and internal quality network stress channels. Granzyme B, Cysteine proteases (Caspases) and BCL-2 protein molecules are involved in the apoptosis mechanism. Cell pigmentation C is released from mitochondria to the cytoplasm through the BAX/BAK hole to activate cysteine proteases that start the apoptotic process, such as Caspase-9, and then activate Caspase-3 to exert the apoptosis effect.The current uncertainty surrounding therapeutic targets for CKD highlights the urgent need for new strategies. Our study has identified several potential targets for the treatment of CKD, providing a fresh perspective for the development of new drugs. Despite the impressive effectiveness of LWDH, limited research has been conducted on its mechanism of action. Using network pharmacology and molecular docking studies, this report attempts to elucidate the mechanism of action of LWDH on CKD. Nevertheless, the limitations of this study should be acknowledged. Firstly, due to the real-time updates of the database, we were unable to obtain the latest information on biological activity ingredients and targets, which may be crucial in studying LWDH for CKD. Secondly, while the molecular docking results validated the predictions, further animal or cell experiments are necessary to explore the predicted active ingredients. Despite considerable research in this area, experimental verification is essential to supplement the evidence of LWDH for CKD and is also necessary to further determine the dose-effect relationship of LWDH in the treatment of CKD. Lastly, while our results predict target proteins and signal channels, there may be other pathways through which LWDH can treat CKD. Our future research will explore these pathways and their relationships. Our ultimate goal is to contribute to the development of more effective and targeted therapies for this debilitating disease.The multiple active components present in LWDH make it an effective treatment for CKD. LWDH effectively addresses the complex pathophysiology of CKD by acting on multiple targets, participating in multiple biological processes, and activating various signaling pathways. The central components of LWDH, including quercetin, stigmasterol, kaempferol, beta-sitosterol, tetrahydroalstonine, kadsurenone, hederagenin, hancinone C, diosgenin, and sitosterol, play a vital role in regulating the response to chemical stimuli, reactive oxygen species metabolism, and key signaling pathways such as AGE-RAGE, IL-17, and TNF pathways for CKD. These components interact with central targets such as AKT1, JUN, and IL6 to produce therapeutic effects. Through network pharmacology analysis, this study identified the potential mechanisms of LWDH in treating CKD. The results indicate the multi-component-multi-target-multi-pathway pharmacological effects of LWDH on CKD, highlighting its significant potential for the treatment of chronic kidney disease and warranting further investigation.Conceptualization: Xi Xie, Hongjun Lou, Xi Gao.Data curation: Xi Xie, Hongjun Lou, Ye Shi, Guang Gan, Hanqing Deng.Formal analysis: Hanqing Deng.Methodology: Xi Xie, Ye Shi, Guang Gan, Hanqing Deng, Mingfang Meng.Supervision: Xi Gao.Visualization: Xi Xie, Hongjun Lou, Xinwei Ma.Writing – original draft: Xi Xie, Hongjun Lou, Ye Shi.Writing – review & editing: Hongjun Lou, Xinwei Ma, Mingfang Meng.


Pulsatilla decoction (PD), a traditional Chinese medicine formula, can treat inflammatory bowel disease and cancers. In this study, we explored the bioactive components, potential targets, and molecular mechanisms of PD in the treatment of GC.Gastric cancer (GC) is a prevalent malignancy worldwide. in vitro experiments.We conducted a thorough search of online databases to gather gene data, active components, and potential target genes associated with the development of GC. Subsequently, we conducted bioinformatics analysis utilizing protein–protein interaction (PPI), network construction, and Kyoto Encyclopedia of Genes and Genomes (KEGG) to identify potential anticancer components and therapeutic targets of PD. Finally, the efficacy of PD in treating GC was further validated through Network pharmacological analysis identified 346 compounds and 180 potential target genes associated with the impact of PD on GC. The inhibitory effect of PD on GC may be mediated through modulation of key targets such as PI3K, AKT, NF-κB, FOS, NFKBIA, and others. KEGG analysis showed that PD mainly exerted its effect on GC through the PI3K–AKT, IL-17, and TNF signaling pathways. Cell viability and cell cycle experiments showed that PD could significantly inhibit proliferation and kill GC cells. Moreover, PD primarily induces apoptosis in GC cells. Western blotting analysis confirmed that the PI3K–AKT, IL-17, and TNF signaling pathways are the main mechanisms by which PD exerts its cytotoxic effects on GC cells.We have validated the molecular mechanism and potential therapeutic targets of PD in treating GC through network pharmacological analysis, thereby demonstrating its anticancer efficacy against GC. The majgetables . Chronicorldwide . At presorldwide . The maiorldwide . Furtherorldwide . For exaorldwide . In addiorldwide . HoweverPulsatilla decoction (PD), consisting of Radix Pulsatillae, Cortex Phellodendri, Rhizoma Coptidis, and Cortex Fraxini, is a TCM used to treat diseases such as inflammatory bowel disease and colon cancer  is widely used in the prevention and treatment of various malignant tumors . In addin cancer , 15. PD n cancer . Currentn cancer . HoweverAt present, no comprehensive studies on the use of Pulsatilla decoction (PD) for treating GC are available, warranting further exploration of its therapeutic effects and mechanisms. Network pharmacology, a hot topic in pharmacological research, is a new drug research method based on the multidirectional analysis of systems pharmacology and biology . The netvia controlled trials. This study aims to lay a foundation for further elucidating the mechanism of action of PD for treating GC and to provide a basis for its extensive clinical applications. In the present study, the active components and targets in PD were studied for the first time. Using network pharmacology, we identified the potential therapeutic targets of PD and verified the results 22.12.1.1https://old.tcmsp-e.com/tcmsp.php) and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine , were used to retrieve the ingredients in the four herbs of PD.Two public databases, namely, Traditional Chinese Medicines for Systems Pharmacology Database and Analysis Platform , Therapeutic Target Database , and PharmGkb (https://www.pharmgkb.org/) databases. Thereafter, the intersection genes of PD and GC were obtained, which were the target genes for PD treating GC.TCMSP was used to identify the ingredient targets in the four herbs of PD; these targets were imported into the UniProt database to convert the protein target names to their official gene symbols. The GC-associated targets were obtained from the GeneCards (2.22.2.1https://cn.string-db.org/); the species was chosen as Homo sapiens. The minimum required interaction score was >0.990, and disconnected nodes were hidden in the network. Then, the TSV file, containing the results of the PPI analysis, was downloaded from STRING and imported into Cytoscape 3.7.1, which is widely used to construct and visualize networks. Degree, a key topological parameter, was used to characterize the most significant nodes in the network and was calculated using the CytoNCA plugin in the Cytoscape software. The more significant the node, the higher the quantitative values of the topological parameters.Protein–protein interaction (PPI) analysis was conducted by submitting the intersection genes to the online Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database  and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using tools available in the R package, such as clusterProfiler, ggplot2, and enrichplot. A 2.2.3Three networks were constructed and visualized using Cytoscape 3.7.1. The networks were as follows: (1) bioactive compound–disease–target network (C–D–T) of PD treatment for GC; (2) PPI network of PD treatment for GC; and (3) bioactive compound–pathway–target (C–P–T) network.2.2.4H. sapiens and with higher degree values in the PPI and C–D–T networks were selected for molecular docking. The Molecular Operating Environment (MOE) (v2015.10) software was used for molecular docking to validate compound–target interaction. Molecular docking was performed as follows. First, the three-dimensional (3D) structure of the candidate proteins was obtained from Protein Data Bank (PDB) (http://www.rcsb.org/), with the species limited to Homo sapiens, and the structure of the compound was downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/). Second, the 3D structure of the candidate protein was imported into MOE to construct the mating pockets after removing water molecules, preparing the protein structure, and minimizing energy. Lastly, mating pocket was performed molecular docking with the candidate compound.Molecular docking is a common technology for network pharmacology owing to its characteristic of precisely predicting the conformation of small-molecule ligands within the target binding site and assessing the binding affinity. The candidate target proteins and compounds present in 2.32.3.1Radix Pulsatillae (20 g), Cortex Phellodendri (16 g), Rhizoma Coptidis (8 g), and Cortex Fraxini (16 g) were purchased from the Pharmacy of Traditional Chinese Medicine of the Second Xiangya Hospital, Central South University. The PD water extract was prepared by immersing the abovementioned herbs in eight volumes of distilled water for 30 min, followed by decoction for 1.5 h, centrifugation at 10,000 g for 30 min, and collection of the supernatants. The extraction step was repeated two times. The obtained supernatants were mixed to evaporate the solvent and vacuum dried to a powder. Then, the powder was dissolved in dimethyl sulfoxide (200 mg/ml). The solution was filtered through a 0.22-µm filter to remove insoluble matter. Finally, the solution was divided and stored at −20° until further use.2.3.22.The AGS and SGC7901 cell lines were purchased from the Cell Bank of the Chinese Academy of Sciences . All cells were maintained in RPMI 1640  supplemented with 10% fetal bovine serum (Gibco). The cells were cultured in a humidified incubator at 37°C with 5% CO2.3.34 cells/well. After culturing the cells at 37°C for 24 h, they were treated with different concentrations of PD . Cell viability was measured using the Cell Counting Kit-8 (CCK-8) reagent  after 24, 48, and 72 h. All data were normalized to those of the control without cells and expressed as mean ± SD.AGS and SGC7901 cells were inoculated into 96-well plates at a density of 1 × 102.3.43 cells/well. After culturing the cells at 37°C for 24 h, they were treated with different concentrations of PD  for 72 h. Then, the cells were fixed with 4% paraformaldehyde for 30 min, stained with crystal violet solution for 2 h, washed with PBS, and manually photographed.AGS and SGC7901 cells were inoculated into six-well plates at a density of 5 × 102.3.55 cells/well. After culturing the cells at 37°C for 24 h, they were treated with different concentrations of PD  for 48 h. The cells were digested with pancreatic enzymes and collected. Then, the samples were fixed with 70% ethanol at 4° overnight and stained with propyl iodide  in the dark for 45 min. Cell cycle analysis was performed using the Canto II flow cytometer .AGS and SGC7901 cells were inoculated into six-well plates at a density of 3 × 102.3.65 cells/well. After culturing the cells at 37°C for 24 h, they were treated with different concentrations of PD  for 48 h. Then, the cells were digested with EDTA-free pancreatic enzymes and collected. Subsequently, 5 µl of annexin V–fluorescein isothiocyanate (FITC) and 5 µl of the PI staining solution (Genview) were added and the samples were incubated for 15 min at 15-30°C in the dark. Apoptosis was detected using a flow cytometer (BD Bioscience). The data were analyzed using FlowJo 7.6 software .AGS and SGC7901 cells were inoculated into six-well plates at a density of 3 × 102.3.75 cells/well. After culturing the cells at 37°C for 24 h, they were treated with different concentrations of PD  for 48 h. The cells were collected using cell scrapers, and total proteins were extracted from the cell lysate. The denatured proteins were added to the electrophoresis chamber for an appropriate amount of time and then transferred onto a polyvinylidene difluoride membrane. The membrane was blocked with 5% bovine serum albumin for 2 h at room temperature. Then, the membrane was incubated with PI3K , AKT , NF-κB , FOS , caspase 3 , NFKBIA , ERK1/2 , MMP3 , and GAPDH  overnight at 4°C. Thereafter, the membrane was incubated with ABclonal antibody  for 1 h. The fluorescence intensity of the bands was detected using an enhanced chemiluminescence detection kit (Genview). The bands were quantitatively analyzed using ImageJ software (Version 11).AGS and SGC7901 cells were inoculated into six-well plates at a density of 3 × 102.4This study was approved by the Medical Ethics Committee of Guangdong Second Provincial General Hospital. All experiments were conducted following the study protocol.2.5t-test. Differences between groups were considered statistically significant at p < 0.05.Statistical analysis was performed using the GraphPad Prism 6 software . The data are expressed as the mean ± standard deviation and were analyzed using Student’s 33.1Rhizoma Coptidis, 27 were from Cortex Fraxini, 177 were from Cortex Phellodendri, and 71 were from Radix Pulsatillae. All PD compounds and their corresponding parameters, such as absorption, distribution, metabolism, and excretion, were obtained from the TCMSP database. The criteria to screen the compounds for subsequent analysis were as follows: oral bioavailability of ≥30% and DL value of ≥0.18 , isorhamnetin , and beta-sitosterol ; these compounds may play crucial roles in treating GC. The blue rectangle represents 130 genes; the larger the size, the higher the degree.The C–D–T network, comprising 196 nodes and 429 edges, was constructed to verify the relationship between PD compounds and PD and GC intersection genes, indicating a PD anti-GC effect 3.3JUN , transcription factor activity, direct ligand-regulated sequence-specific DNA binding (GO:0098531), steroid hormone receptor activity (GO:0003707), protein-coupled amine receptor activity (GO:0008227), and adrenergic receptor activity (GO:0004935), were mainly enriched in membrane raft (GO:0045121), membrane microdomain (GO:0098857), membrane region (GO:0098589), RNA polymerase II transcription factor complex (GO:0090575), and caveola (GO:0005901) and involved in response to lipopolysaccharide (GO:0032496), response to molecule of bacterial origin (GO:0002237), reactive oxygen species metabolic process (GO:0072593), response to steroid hormone (GO:0048545), and response to xenobiotic stimulus (GO:0009410) against GC.GO and KEGG pathway enrichment analyses were conducted to determine the functions and pathways of the 162 intersection genes using the R package. Biological process (BP), cell composition (CC), and molecular function (MF) were analyzed. In total, 2,563 GO terms were enriched from the 162 intersection genes between GC and PD. Among them, 2,302 were BP, 82 were CC, and 179 were MF terms , chemical carcinogenesis-receptor activation , and the PI3K–Akt signaling pathway . Moreover, among the top three pathways mentioned above, the PI3K–AKT , ESR1 (PDB code: 1A52), and AKT1 (PDB code: 3O96) were selected for docking experiments with quercetin and isorhamnetin owing to their higher degree values. The docking results of quercetin with JUN, ESR1, and AKT1 are demonstrated in The docking results of isorhamnetin with JUN, ESR1, and AKT1 are demonstrated in via different bonds. The matching degree between the ingredients and proteins was assessed using binding energy  for 48 h. Annexin V–FITC and PI staining was performed to detect the degree of GC cell apoptosis. As shown in 3.9via Western blotting: the PI3K–AKT, TNF, and IL-17 pathways. As shown in via the PI3K–AKT, TNF, and IL-17 pathways, thereby achieving an antitumor effect.To further explore the molecular mechanism of PD on GC, combined with the above network pharmacological analysis, we analyzed the key pathways involved in PD treating GC 4Helicobacter pylori infection, age, high salt intake, and diets low in fruit and vegetables , SMMC-7721 (human liver carcinoma), HCT-116 , and U251 (human glioma) cells  cells . Interesoperties –29. Incroperties –32. Wardoperties . Further pathway .via the ERK–c-Jun signaling pathway , an inducible transcription factor and mediator of oncogenic transformation, exerts various biological effects on apoptosis, proliferation, invasion, and migration –38. Lee  pathway . Jia et  pathway . ESR1, m pathway –43. AKT1 pathway –46. Incrpoptosis , 47–49.  pathway . Moreove pathway .in vitro experiments were performed in AGS and SGC7901 cells to further validate the biological functions of the three signaling pathways mentioned above when PD acts on GC. Previous studies have reported that the PI3K–AKT, IL-17, and TNF signaling pathways exert anticancer properties by inhibiting proliferation, arresting the cell cycle, and promoting apoptosis. The cellular experiments in the present study also verified that PD exerts similar anti-GC effects on AGS and SGC7901 cells. This finding is consistent with the prediction results of network pharmacology.We performed molecular docking of the three target proteins, namely, JUN, ESR1, and AKT1, with higher degree values, and two compounds, namely, quercetin and isorhamnetin, to further validate the prediction results of network pharmacology. Docking experiments revealed that quercetin and isorhamnetin could stably bind with JUN, ESR1, and AKT1. Interestingly, quercetin and JUN exhibited the lowest binding energy, consistent with the results of PPI and C–D–T network analyses. Moreover, based on the results of KEGG pathway analysis, the PI3K–AKT, IL-17, and TNF signaling pathways were identified as the important cancer-related pathways of PD anti-GC. Therefore, via clinical trials.The present study has several limitations that should be highlighted. First, these online public databases are continuously updated; therefore, other updated bioactive components and target genes may have not been included in this study. Moreover, the content of the 34 PD compounds remains unclear; therefore, quantitative analysis should be performed and compounds with anti-GC effects should be identified in future studies. Furthermore, the absorption pathways, structural domains, and metabolism form of PD against GC should be elaborated. Finally, the anti-GC effects of PD should be further validated in other GC cells and animal models and 5in vitro were performed to validate the prediction results of network pharmacology. Our work demonstrated that PD inhibited GC cell proliferation and induced apoptosis via the PI3K–AKT signaling pathway, IL-17 signaling pathway, and TNF signaling pathway, which provide valuable evidence of the role of PD in cancer development. Additionally, the present study provided a comprehensive and promising approach to explore bioactive compounds, hub genes, and the potential mechanisms of TCM in the treatment of diseases.In summary, this is the first report of PD in the treatment of GC based on network pharmacology, and experiments The original contributions presented in the study are included in the article/This study was approved by the Medical Ethics Committee of Guangdong Second Provincial General Hospital. All experiments were conducted following the study protocol.All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by FK, MQ, SY and XC. The first draft of the manuscript was written by SH and all authors commented on previous versions of the manuscript. All authors contributed to the article and approved the submitted version.


A fundamental problem in designing safe machine learning systems is identifying when samples presented to a deployed model differ from those observed at training time. Detecting so-called out-of-distribution (OoD) samples is crucial in safety-critical applications such as robotically guided retinal microsurgery, where distances between the instrument and the retina are derived from sequences of 1D images that are acquired by an instrument-integrated optical coherence tomography (iiOCT) probe.This work investigates the feasibility of using an OoD detector to identify when images from the iiOCT probe are inappropriate for subsequent machine learning-based distance estimation. We show how a simple OoD detector based on the Mahalanobis distance can successfully reject corrupted samples coming from real-world ex vivo porcine eyes.Our results demonstrate that the proposed approach can successfully detect OoD samples and help maintain the performance of the downstream task within reasonable levels. MahaAD outperformed a supervised approach trained on the same kind of corruptions and achieved the best performance in detecting OoD cases from a collection of iiOCT samples with real-world corruptions.The results indicate that detecting corrupted iiOCT data through OoD detection is feasible and does not need prior knowledge of possible corruptions. Consequently, MahaAD could aid in ensuring patient safety during robotically guided microsurgery by preventing deployed prediction models from estimating distances that put the patient at risk. Ensuring safe machine learning models is one of the key challenges for real-world medical systems. While the need for reliable models is highly important for image-based diagnostics with human-in-the-loop users, it is mission-critical when combined with medical robotic systems that tightly couple image-based sensing for augmented visualizations or automation.out-of-distribution (OoD) detection [In this context, one of the fundamental problems in designing safe machine learning is identifying when samples presented to a deployed model differ from those observed at training time. This problem, commonly known as etection , aims toetection , and in etection –8.OoD detection for robotically assisted surgery is particularly relevant as erratic machine learning predictions can have extremely serious consequences for the patient. For example, a misprediction in the distance estimation between an instrument and its targeted tissue could lead to important inadvertent trauma. Surprisingly, the topic of OoD detection for robotically assisted surgery has received little attention to date, despite its necessity and advantages. More broadly, the potential benefits of OoD detection in this context remain largely unexplored. This work aims to close this gap by analyzing the implications of integrating an OoD detector in a relevant robot-assisted surgery use case.optical coherence tomography (OCT) imaging probe which yields 1D OCT measures over time, also known as M-scans. When using such a probe to help guide the robot to an intra-retinal injection site, automatic estimation between the instrument and the retinal surface is key. Yet, for any robot-tissue interacting system, a critical necessity is to ensure that the inferred distances derived from the imaging probe are safe for the robotic system to use.Specifically, we consider the setting of retinal microsurgery, where a machine learning model is needed to infer the distance between a robotically manipulated instrument and the retina of the eye see Fig. . As withintraoperative instrument-integrated OCT (iiOCT) probe are inappropriate for subsequent machine learning-based distance estimation . The employed architecture has three blocks of 32 layers and was trained with the public implementation of [ed model  used as ation of .Uncertainty OoD samples tend to produce estimations with lower maximum softmax probabilities, . We takRaw-MahaAD Similar to MahaAD but, instead of the feature vectors SNR A simple measure of scan quality directly used as OoD score. We measure the signal-to-noise ratio (SNR) as No-rejection Reference baseline that considers all samples as inliers .To measure the impact of OoD samples on the retinal model r that was implemented as a one-dimensional U-Net-like architecture [r, the location of the ILM was manually annotated for a random collection of 14’700 M-scans from the original pig recordings.In all cases, we used a retinal model itecture  with four only receives the samples considered safe for estimation by the OoD detector. For this purpose, we employed a test set of 2’000 M-scans with annotated ILM locations. To account for the lack of real OoD samples, OoD samples were synthetically generated by perturbing a fraction p of elements from the test data with eight types of corruptions:Noise Additive Gaussian noise with Smoothing Gaussian filtering with Contrast Contrast increase/decrease by a factor uniformly sampled from Intensity Equally probable positive/negative shift of the intensity uniformly sampled from the set Stripes Randomly replacing one or two A-scans in a sequence with a constant intensity sampled from Rectangle Randomly placing a rectangle with size  between the estimated and the real distances for a progressively increasing ratio of corruptions p, which ranged from 0  to 0.9. To quantify the impact of each OoD detection approach, M-scans detected as OoD were discarded from MAE computation. For proper comparison, an M-scan was considered OoD if it was among the top-p highest OoD-scoring M-scans. Hence, a perfect OoD detector will discard all the corrupted M-scans, keeping the MAE low.We measured the performance of MahaAD outperformed all baselines, with its MAE staying almost constant for all the perturbation ratios . Figure MahaAD is outperforming Supervised in terms of OoD detection on the unseen corruptions. Specifically, the difference is notable for zoom and rectangle, which seem to be the most difficult perturbations to detect. This result indicates that MahaAD is a better OoD detector when the type of expected perturbations is unknown and for which we cannot train.Additionally, we compared MahaAD outperformed the other baselines, demonstrating its ability to generalize beyond simulated perturbations in a more realistic scenario. Furthermore, Supervised performs significantly worse than MahaAD on real data, suggesting that the results of Fig. Supervised is not suitable as an OoD detector in a realistic scenario. In contrast, SNR’s performance improved on real data, likely due to a selection bias facilitating discrimination of OoD samples through low-order statistics. Surprisingly, Glow and Raw-MahaAD seem to produce OoD scores that better describe in-distribution samples than OoD samples.Figure  MahaAD approach. The examples confirm that MahaAD typically classifies obvious in-distribution or OoD samples correctly but can misclassify borderline samples. For instance, some false negatives may be considered in-distribution based on their retinal-like structure, while false positives often exhibit a hyperreflective retinal pigment epithelium layer, which might lead to their OoD classification.Figure In this work, we showed how corrupted data from an iiOCT probe in the context of retinal microsurgery can be rejected from further evaluation by using unsupervised OoD detection. The simple MahaAD approach was able to maintain good performance of distance estimation by reliably detecting and rejecting simulated corruptions, and showed promising results on OoD cases from an ex vivo porcine trial.i.e., possible corruptions) are known and why unsupervised OoD detection might be suitable for improved generalization. In conclusion, we showed that detecting corrupted iiOCT data through unsupervised OoD detection is feasible and that MahaAD could potentially be used to improve safety in retinal microsurgery.The experiments revealed that the benefits of MahaAD observed for a variety of scenarios on 2D images  translatHowever, one limitation of this work is that the temporal component of the iiOCT is largely ignored as individual samples were considered for the distance estimation without any knowledge of the past. In the future, we plan to take this temporal information into account by combining the MahaAD OoD detection with dedicated techniques such as Bayesian filters to further improve performances.