Integrated bioinformatics and network pharmacology to explore the therapeutic target and molecular mechanisms of Bailing capsule on polycystic ovary syndrome

Polycystic Ovary Syndrome (PCOS) is one of the most prevalent endocrine disorder in women of reproductive age characterized by hyperandrogenism (HA). Current treatment options for PCOS are either with adverse effects or ineffective. Saptasaram kashayam (SK), an ayurvedic formulation is often been a safe traditional alternative medicine to improve the PCOS symptoms as well as its pathological development. However, its principle phytoconstituents or underlying mechanisms have not been investigated. In order to achieve this, the current study systematically utilized computational tools, network pharmacology approaches and molecular docking studies. All identified phytoconstituents of SK were screened by QikProp ADME prediction and 47 were selected based on oral bioavailability and drug likeliness scores. Their 3D structures were submitted to three online target fishing webservers PharmMapper, ChemMapper and Swiss Target Prediction which produced 1084 biological targets for SK comprehensively. 350 known PCOS therapeutic targets were retreived as common targets from three different interrogative disease centric bioinformatic platforms DisGeNET, OMIM and GeneCards. Intersection of 1084 biological targets of SK and 350 PCOS therapeutic targets produced, 88 potential therapeutic targets of SK against PCOS. STRING PPI and Compound-Target-Pathway networks were constructed and analysed using Cytoscape software. GO & KEGG pathway enrichment analysis was performed using DAVID database. 15 PCOS therapeutic target proteins were short listed from network analysis report- PIK3CA, PDPK1, AKT1, PIK3R1, STAT3, MAPK1, MAPK3, EGFR, AR, ESR1, ESR2, SHGB, NOS3, F2 & CREBBP. Targets that were likely to be inhibited/modulated by SK for treatment of PCOS were docked against the screened phytoconstituents and their respective standard inhibitors using GLIDE-SP of Schrodinger suite, Maestro version- 13.0. Results showed that Quercetin, Catechin, Boeravinone J, Genistein, Protocatechuic Acid, Gentisic Acid, Xanthoarnol, Luteolin, Boeravinone F, Tyrosine, Kaempferol, Dalbergioidin, etc exhibited good binding affinities when compared to standard drugs and might be responsible for synergistic/additive protective effect of SK against PCOS. Meanwhile PI3K-Akt signaling pathway, Prolactin signaling pathway, AGE-RAG diabetic complications, HIF-1 signaling pathway and Estrogen signaling pathway were found to be involving the hub genes of interest and in this way, they might be intervened during treatment of PCOS by SK. Present study succeeded in identifying the drug like principle phytoconstituents, probable PCOS therapeutic targets and the underlying molecular mechanism of SK apart from providing reliable evidence for therapeutic potential of SK against PCOS. However further validation by in vitro and in vivo investigations is necessary.

To explore the pharmacological mechanism of Bushen Huatan (BSHT) recipe in the treatment of polycystic ovary syndrome (PCOS). The active ingredients in the component drugs of the recipe were screened through TCMSP, and their potential targets were predicted by PubChem and Swiss target prediction. Genecards and OMIM were used to screen the therapeutic targets in the treatment of PCOS. The drug targets and disease targets were corrected using Uniprot, and the intersection targets were obtained. The protein-protein interaction (PPI) network was constructed using STRING, and the intersection targets were analyzed with CytoNCA to screen the core targets. DAVID was used for GO enrichment analysis and KEGG pathway enrichment analysis, and the core components and core targets were verified using AutoDock. Animal experiment was performed to verify the results using a female C57BL/6J mouse model of PCOS, treated daily with 1 mg/kg BSHT recipe granule for 35 days, and the ovarian expressions of the core targets and pathways were detected using Western blotting. We identified a total of 125 potential active ingredients from the 14 component drugs in the recipe, 990 drug targets, 4759 PCOS targets and 434 intersection targets. The core active ingredients of the recipe included β -Sitosterol, kaempferol, and quercetin, whose core targets included PIK3CA, PIK3R1, APP, AKT1, and MAPK1. GO enrichment analysis highlighted such processes as drug reaction, negative regulation of apoptosis, and positive regulation of transcription from RNA polymerase Ⅱ promoter. The enriched KEGG pathways included primarily the cancer pathway and PI3K-Akt signaling pathway. Molecular docking showed that the core active ingredients had strong binding ability with the core targets. In the animal experiment, BSHT recipe was shown to improve the symptoms, down-regulate the expressions of PI3K and Akt proteins and up-regulate MAPK1 expression in the ovary of mice with PCOS. The therapeutic mechanism of BSHT recipe for PCOS involves multiple active ingredients, multiple therapeutic targets and multiple pathways.

Background: Polycystic ovary syndrome (PCOS) is the most common metabolic and endocrinopathies disorder in women of reproductive age and non-alcoholic fatty liver (NAFLD) is one of the most common liver diseases worldwide. Previous research has indicated potential associations between PCOS and NAFLD, but the underlying pathophysiology is still not clear. The present study aims to identify the differentially expressed genes (DEGs) between PCOS and NAFLD through the bioinformatics method, and explore the associated molecular mechanisms. Methods: The microarray datasets GSE34526 and GSE63067 were downloaded from Gene Expression Omnibus (GEO) database and analyzed to obtain the DEGs between PCOS and NAFLD with the GEO2R online tool. Next, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for the DEGs were performed. Then, the protein-protein interaction (PPI) network was constructed and the hub genes were identified using the STRING database and Cytoscape software. Finally, NetworkAnalyst was used to construct the network between the targeted microRNAs (miRNAs) and the hub genes. Results: A total of 52 genes were identified as DEGs in the above two datasets. GO and KEGG enrichment analysis indicated that DEGs are mostly enriched in immunity and inflammation related pathways. In addition, nine hub genes, including TREM1, S100A9, FPR1, NCF2, FCER1G, CCR1, S100A12, MMP9, and IL1RN were selected from the PPI network by using the cytoHubba and MCODE plug-in. Then, four miRNAs, including miR-20a-5p, miR-129-2-3p, miR-124-3p, and miR-101-3p, were predicted as possibly the key miRNAs through the miRNA-gene network construction. Conclusion: In summary, we firstly constructed a miRNA-gene regulatory network depicting interactions between the predicted miRNA and the hub genes in NAFLD and PCOS, which provides novel insights into the identification of potential biomarkers and valuable therapeutic leads for PCOS and NAFLD.

Oral proprietary Chinese medicine for lupus nephritis: A bayesian network meta-analysis

BackgroundPolycystic ovary syndrome (PCOS) is a common metabolic problem in women of reproductive age that can lead to infertility and other metabolic disorders. Recent evidence indicates that inflammation might be one of the contributing factors in PCOS progression. However, there is a lack of information on the regulation of inflammatory genes in PCOS. Therefore, the aim of the study is to investigate the role of inflammation-associated genes and pathways in relation to PCOS.MethodThe bulk RNA-seq data of granulosa cells of human ovaries of PCOS-affected and healthy women were analyzed to evaluate the inflammatory regulation in PCOS. After quality trimming, the raw RNA-seq data were aligned to the human genome, and gene expression was quantified using featureCounts with Ensembl annotation. Further, downstream analyses of the resulting count matrix were performed in R Studio, where differentially expressed genes (DEG) were identified and CO-DEG analysis was performed.ResultsThe study identifies the various differentially expressed inflammatory genes in the case of PCOS such as SPI1, HSPB1, MNDA, and ITGA. These DEG are closely associated with the activation of inflammatory responses, i.e., activation of lymphocytes and leukocytes, leukocyte migration and mononuclear cell proliferation, stimulating binding of various cytokines, immunoglobulins, and chemokines. PCOS group also exhibited an increased expression of androgen-mediated genes (SPI1 and ETS transcription factors) and genes associated with hyperlipidemia and insulin resistance (TNFRSF1B). Further, KEGG pathway enrichment analysis revealed significant upregulation of various pathways (autophagy, endocytosis) in the PCOS group. In addition, network analysis (cnetplot) of the top 10 KEGG GSEA pathways also highlights the key pathways in the PCOS group such as SNARE complex assembly pathway, SNAP-25, nucleophagy, and regulation of mast cell activation.ConclusionTherefore, the study highlights that inflammation is a major effector in PCOS, which also fuels obesity, an independent effector that further worsens the PCOS condition. In addition, the genes related to hyperandrogenism, hyperlipidemia, and insulin resistance were also overexpressed in PCOS, exacerbating the condition.Supplementary InformationThe online version contains supplementary material available at 10.1186/s44342-025-00051-6.

Polycystic ovary syndrome (PCOS) affects women of reproductive age globally with an incidence rate of 5% - 26%. Growing evidence reports important roles for microRNAs (miRNAs) in the pathophysiology of granulosa cells (GCs) in PCOS. The objectives of this study were to identify the top differentially expressed miRNAs (DE-miRNAs) and their corresponding targets in hub gene-miRNA networks, as well as identify novel DE-miRNAs by analyzing three distinct microarray datasets. Additionally, functional enrichment analysis was performed using bioinformatics approaches. Finally, interactions between the 5 top-ranked hub genes and drugs were investigated. Using bioinformatics approaches, three GC profiles from the gene expression omnibus (GEO), namely gene expression omnibus series (GSE)-34526, GSE114419, and GSE137684, were analyzed. Targets of the top DE-miRNAs were predicted using the multiMiR R package, and only miRNAs with validated results were retrieved. Genes that were common between the "DE-miRNA prediction results" and the "existing tissue DE-mRNAs" were designated as differentially expressed genes (DEGs). Gene ontology (GO) and pathway enrichment analyses were implemented for DEGs. In order to identify hub genes and hub DE-miRNAs, the protein-protein interaction (PPI) network and miRNA-mRNA interaction network were constructed using Cytoscape software. The drug-gene interaction database (DGIdb) database was utilized to identify interactions between the top-ranked hub genes and drugs. Out of the top 20 DE-miRNAs that were retrieved from the GSE114419 and GSE34526 microarray datasets, only 13 of them had "validated results" through the multiMiR prediction method. Among the 13 DE-miRNAs investigated, only 5, namely hsa-miR-8085, hsa-miR-548w, hsa-miR-612, hsa-miR-1470, and hsa-miR-644a, demonstrated interactions with the 10 hub genes in the hub gene-miRNA networks in our study. Except for hsa-miR-612, the other 4 DE-miRNAs, including hsa-miR-8085, hsa-miR-548w, hsa-miR-1470, and hsa-miR-644a, are novel and had not been reported in PCOS pathogenesis before. Also, GO and pathway enrichment analyses identified "pathogenic E. coli infection" in the Kyoto encyclopedia of genes and genomes (KEGG) and "regulation of Rac1 activity" in FunRich as the top pathways. The drug-hub gene interaction network identified ACTB, JUN, PTEN, KRAS, and MAPK1 as potential targets to treat PCOS with therapeutic drugs. The findings from this study might assist researchers in uncovering new biomarkers and potential therapeutic drug targets in PCOS treatment.

The purpose of this study was to screen differentially expressed genes in PCOS using gene chip data and investigate the biological functions of these DEGs in PCOS. Additionally, the study aimed to analyze the potential clinical significance of these genes using clinical data. In this study, we first screened the DEGs related to PCOS by using the gene chip data (GSE5090) from GEO database. Target gene prediction software was used to predict the target genes for these DEGs, and their functional enrichment was analysed. Subsequently, the STRING online tool and Cytoscape software were utilized to identify key genes by constructing protein-protein interaction networks (PPI). In the analysis of the GSE5090 dataset, seventeen differentially expressed genes (DEGs) were identified. Functional enrichment analysis revealed that these DEGs are predominantly associated with biological functions related to polycystic ovary syndrome (PCOS). Moreover, the tissue-specific expression analysis highlighted immune system markers, with a notable difference observed in 18 of these markers, accounting for 20.5% of the total. By constructing PPI networks and key gene regulatory networks, a total of three genes (RPL13, LEP, and ANXA1) were identified as key genes. In addition, the column-line graphical model performed well in predicting the risk of PCOS. Using ROC curves, the model proved to be effective in diagnosis. This study represents the first application of a bioinformatics approach to identify and confirm high expression levels of RPL13, LEP, and ANXA1 in patients with Polycystic Ovary Syndrome (PCOS). These key genes-RPL13, LEP, and ANXA1-may present viable targets for therapeutic interventions in PCOS, underscoring their potential clinical importance.

Study question What is the underlying mechanism contributing to the transgenerational defects of oocytes and embryos of polycystic ovary syndrome (PCOS)? Summary answer The transgenerational inheritance of abnormal chromatin accessibility in the PCOS oocytes is responsible for the defect of oocytes and embryos in transgeneration. What is known already Studies have provided evidence of compromised oocyte quality and related adverse impacts. Observed PCOS animal-model oocyte defects include meiotic abnormalities, mitochondrial dysfunction, and increased oxidative stress. Elevated androgen levels are associated with reduced fertilization rates and lower cleavage rates during in vitro fertilization (IVF) treatment. PCOS could be transgenerationally transmitted, and unique altered gene expression has been observed in metaphase II (MII) oocytes from all offspring in PCOS mice. Study design, size, duration We profiled the transcriptome and chromatin accessibility of oocytes and preimplantation embryos in F0-F2 offspring of PCOS mice, to explored whether the inheritance of chromatin accessibility defects in PCOS oocytes was responsible for the abnormalities of oocytes and embryos in PCOS offspring. Besides, oocytes from 13 healthy women and 8 women with PCOS, 24 blood samples from 24 women with/without PCOS were analyzed to validate the molecular mechanisms of PCOS inheritance. Duration: 2019.5∼2022.12. Participants/materials, setting, methods PCOS mice models were established by means of DHEA-exposure. Smart-seq2 and DNase-seq were utilized to investigate the transcriptome and chromatin accessibility of oocytes and preimplantation embryos in F0-F2 offspring of PCOS mice. Bioinformatic analysis was applied to explore the differential gene expression and chromatin accessibility of oocytes and embryos in PCOS mice and offspring comparing to controls. Common gene signatures in the tissues of PCOS women and PCOS daughters are analyzed compared to control samples. Main results and the role of chance In PCOS mice, we observed transcriptional alteration in oocytes and 2-cell embryos compared to normal mice. There are 4229 and 1197 differentially expressed genes (DEGs) in PCOS oocytes and 2-cell embryos. The DEGs includes both maternal factors and zygotic genome activation (ZGA) genes. Interestingly, in PCOS 2-cell embryos, we observed hundreds of pre-activated genes which are silence in normal 2-cell embryo but expressed in PCOS 2-cell embryos. Notably, many DEGs in the oocytes of PCOS mice were also differentially expressed in the oocytes of F2 PCOS mice compared to control mice, supporting the transgenerational inheritance of transcriptional alteration in PCOS oocytes. Mechanismly, the changes of chromatin accessibility were associated with the DEGs in PCOS oocytes and embryos. In particular, chromatin accessibility of the pre-activated gene promoters is elevated in PCOS oocytes compared to that in normal oocytes. Furthermore, many of the abnormal chromatin accessibility signals detected in F0 PCOS oocytes were also observed in F2 PCOS oocytes. Finally, our data showed that some DEGs in the oocytes of PCOS mice were also differentially expressed in human PCOS oocytes compared to the control oocytes. Common genes identified in PCOS offspring may be used as potential predictors of PCOS phenotype. Limitations, reasons for caution Which factors contribute to the abnormalities of chromatin accessibility in PCOS oocytes are still unknown. Wider implications of the findings The candidate genes may be used as potential predictors of PCOS phenotype and provide the clues for PCOS offspring . Trial registration number not applicable

Polycystic ovary syndrome (PCOS), a common endocrine condition affecting multiple systems, is tied to atherosclerosis (AS) progression among reproductive-aged women. The present study aimed to explore the underlying associations and uncover potential biological indicators for PCOS complicated with AS.Gene expression datasets for PCOS and AS were obtained from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) from PCOS tissues (granulosa cells, adipose tissue, skeletal muscle) and arterial wall of AS were analyzed via weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Immune infiltration and chemokine/receptor-immunocyte networks were constructed to explore immune cell recruitment. Key findings were validated in PCOS and AS murine models. The gradient boosting machine (GBM) and the extreme gradient boosting (XGBoost) algorithms were employed to identify potential biomarkers, further verified by the AS murine model, nomograms, and PCOS murine model.We identified 238, 60, and 76 secretory protein-encoding DEGs in PCOS tissues (granulosa cells, adipose tissue, and skeletal muscle) and 604 key AS-related DEGs. The enrichment analysis suggested associations between immune inflammation, dysregulated lipid metabolism, insulin signaling, and PCOS-related AS. Then, immunoinfiltration analysis revealed elevated naive B cell, follicular T helper cell, and neutrophil proportions in AS samples. In addition, six chemokines (CCL5, CCL20, CCL23, CCL28, CXCL1, and CXCL6) were involved in four immunocyte recruitments (B cells, neutrophils, NK cells, and CD4+ T cells) in AS, with CXCL1 and CXCL6 upregulated in the peripheral blood of PCOS mice. And CXCR2, the shared receptor for CXCL1/6, showed an increase in aortic tissues of both AS and PCOS mice. Machine learning identified five signature genes (LILRA5, CSF2RA, S100A8, CD6, and CCL24; AUC 0.856-0.983), two of which (CSF2RA and LILRA5) were verified in the AS murine model and the nomogram incorporating these genes showed strong predictive accuracy (AUC = 0.966). Finally, further validation in the PCOS murine model confirmed significantly elevated CSF2RA and reduced LILRA5 expression, suggesting a close association between PCOS and AS pathogenesis.This study identified potential associations between PCOS and AS, and screened the potential biological biomarkers for predicting PCOS-related AS, offering a foothold for future exploration of the diagnosis and risk stratification for PCOS-related AS.

PCOS is a common endocrine disorder in women of reproductive age, with granulosa cells playing a key role in its development. This study aims to identify shared differentially expressed genes (DEG) in granulosa cells and blood from PCOS patients, offering potential biomarkers and therapeutic targets. Transcriptomic data were obtained from the Gene Expression Omnibus (GEO) database: GSE95728 (granulosa cells; 7 PCOS, 7 controls) and two blood-based datasets, GSE85932 and GSE54248 (12 PCOS, 12 controls). In silico tools were employed to identify DEG, hub genes, and protein-protein interactions and to explore predicted drug targets. DEG analysis revealed that Vanin-2 (VNN2) and Interleukin-1 receptor type 2 (IL1R2) were consistently dysregulated in PCOS patients. Dysferlin (DYSF), Carbonic Anhydrase 4 (CA4), and Solute Carrier Family 2 Member 14 (SLC2A14) were differentially expressed between the blood datasets, while Aquaporin 9 (AQP9), C-X-C Motif Chemokine Receptor 1 (CXCR1), and Annexin A3 (ANXA3) were dysregulated in GSE95728 (granulosa cells) and GSE54248 (blood). Functional enrichment highlighted immune and metabolic pathways. Protein-protein interaction analysis identified AQP9 as a hub gene. Drug prediction analysis suggested that IL1R2 could be targeted by anakinra, while VNN2 was predicted to interact with pantothenic acid. The dysregulation of VNN2 and IL1R2 across tissue types suggests their involvement in immune-inflammatory processes in PCOS. The expression of AQP9 and other metabolism-related genes indicates possible immune-metabolic crosstalk, aligning with known PCOS pathophysiology. VNN2 and IL1R2 show potential as biomarkers or therapeutic targets in PCOS. Further validation is needed to confirm their clinical significance and roles.

BackgroundOne of the most common hormonal disorders in women of reproductive age is polycystic ovary syndrome (PCOS). In recent years, it has been found that insulin resistance is a common metabolic abnormality in women with PCOS and leads to an elevated risk of type 2 diabetes mellitus. To explore the differentially expressed genes (DEGs) that regulate these kinds of metabolic risks in PCOS women, we chose the gene expression profile of GSE8157 from the gene expression omnibus (GEO) database.ResultsUsing the GEO2R tool, we identified a total of 339 DEGs between the case and the control sample groups. Gene ontology and Kyoto encyclopedia of gene and genome pathway enrichment analysis were subsequently conducted. High connectivity, betweenness centrality, bottleneck centrality, closeness centrality, and radiality measures were used to rank the ten hub genes. Furthermore, the overlap of these genes resulted in the development of two key genes, AR and STK11. The AMPK and adipocytokine signaling pathways are the two main pathways that these DEGs are involved.ConclusionsThe backbone genes, hub genes and pathways identified would assist us in further exploring the molecular basis of developing risk of type 2 diabetes mellitus in PCOS women and thus provide diagnostic or therapeutic clues.

Radiation Dermatitis (RD) is the most common side effect of radiotherapy, severely affecting the implementation of the antitumor treatment plan. The traditional Chinese medicine(TCM) compound Danxiong Granules (TDX105) is an external formulation used clinically for over a decade with notable efficacy, though its pharmacological mechanisms remain unclear, This study delves into the intricate mechanism by which TDX105 confers protection against RD by modulating the inflammation-related pathway, employing a combination of network pharmacology and experimental validation. The active components and potential targets of TDX105 were gathered from databases such as TCMSP, PubChem, PharmMapper and Swiss Target Prediction, disease-related target genes were collected by retrieving the Genecards and DisGeNET databases; STRING database was used for protein-protein interaction analysis and mapping; Cytoscape software was applied for core target analysis and network diagram construction; GO functional and KEGG pathway enrichment analyses were performed using the Metascape database.Subsequent validation was accomplished through molecular docking and in vitro cell experiments. In the current study, network pharmacology analysis identified 73 active compounds and 973 potential target genes associated with TDX105, along with 1507 disease-related genes, revealing 289 common genes. The top ten active compounds included quercetin, kaempferol, luteolin, sitosterol, perlolyrine, phellochin, baicalein, cavidine, palmatine and fumarine. PPI network analysis identified core target genes including PIK3R1, SRC, and TP53, with molecular docking demonstrating favorable binding interactions between the active components and these hub targets. Enrichment analysis indicated that TDX105 may exert its effects by modulating inflammatory responses, with KEGG pathway analysis uncovering associations with EGFR, MAPK, PI3K-Akt, and NF-κB signaling pathways. In vitro experiments demonstrated that TDX105 significantly reduced levels of inflammation markers NO, IL-6, and TNF-α compared to the control group (P < 0.05), displayed dose-dependent inhibition of NF-κB activity, and markedly inhibited mRNA expression of iNOS, NLRP3, IL-6, TNF-α, and IL-1β (P < 0.05). Western blot analyses confirmed significant downregulation of NLRP3, COX-2, p-NF-κB, and p-ERK protein levels in the treatment group (P < 0.05). Overall, our findings suggest that TDX105 may exert anti-inflammatory effects through the inhibition of NF-κB and MAPK signaling pathways, providing a potential therapeutic approach for radiation dermatitis.

Cognitive function in women with polycystic ovary syndrome (PCOS): impact of reproductive and metabolic factors

Abdominal fat distribution and insulin resistance in Indian women with polycystic ovarian syndrome

To study the material basis and mechanism of volatile oil from Alpinia oxyphylla in treating Alzheimer's disease(AD) based on GC-MS and network pharmacology. Ingredients of volatile oil from A.oxyphylla were analyzed by GC-MS. Targets of those ingredients were obtained through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). Relevant targets of AD were obtained through such databases as DrugBank, STITCH, OMIM. Intersection targets of ingredients and diseases were obtained by Online Venny map, and PPI network was established by STRING to screen out core targets. Gene ontology(GO) functional enrichment analysis and KEGG pathway enrichment analysis were performed by DAVID. The &quot;ingredients-target-pathway&quot; network was constructed by software Cytoscape 3.8.1 to screen out potential active ingredients of volatile oil from A.oxyphylla in the treatment of AD. The results showed that a total of 6 active ingredients were screened from the volatile oil of A.oxyphylla by GC-MS, 17 targets corresponding to 6 active ingredients were found in TCMSP database, and 3 448 AD targets were found in DrugBank database. &quot;Ingredients-target-pathway&quot; network and PPI network showed there were 4 potential active ingredients in the treatment of AD and 4 core targets. GO analysis and KEGG analysis showed 34(P&lt;0.05) and 5(P&lt;0.05) pathways, respectively, including nerve ligand receptor interaction, calcium signaling pathway, cholinergic synapse and 5-hydroxytryptaminergic synapse. This suggested that volatile oil from A.oxyphylla could synergistically treat AD by regulating calcium balance, cholinergic balance and phosphorylation. This study provided reference and guidance for further study of volatile oil from A.oxyphylla in the treatment of AD.