Population pharmacokinetics of daptomycin in critically ill patients receiving extracorporeal membrane oxygenation.

BACKGROUND: Daptomycin is widely used in critically ill patients for Gram-positive bacterial infections. Extracorporeal membrane oxygenation (ECMO) is increasingly used in this population and can potentially alter the pharmacokinetic (PK) behaviour of antibiotics. However, the effect of ECMO has not been evaluated in daptomycin. Our study aims to explore the effect of ECMO on daptomycin in critically ill patients through population pharmacokinetic (PopPK) analysis and to determine optimal dosage regimens based on both efficacy and safety considerations. METHODS: A prospective, open-label PK study was carried out in critically ill patients with or without ECMO. The total concentration of daptomycin was determined by UPLC-MS/MS. NONMEM was used for PopPK analysis and Monte Carlo simulations. RESULTS: Two hundred and ninety-three plasma samples were collected from 36 critically ill patients, 24 of whom received ECMO support. A two-compartment model with first-order elimination can best describe the PK of daptomycin. Creatinine clearance (CLCR) significantly affects the clearance of daptomycin while ECMO has no significant effect on the PK parameters. Monte Carlo simulations showed that, when the MICs for bacteria are  ≥1 mg/L, the currently recommended dosage regimen is insufficient for critically ill patients with CLCR > 30 mL/min. Our simulations suggest 10 mg/kg for patients with CLCR between 30 and 90 mL/min, and 12 mg/kg for patients with CLCR higher than 90 mL/min. CONCLUSIONS: This is the first PopPK model of daptomycin in ECMO patients. Optimal dosage regimens considering efficacy, safety, and pathogens were provided for critical patients based on pharmacokinetic-pharmacodynamic analysis.

Transcriptomic and physiological analysis of the effect of octanoic acid on Meloidogyne incognita.

Root knot nematodes are the most devastating root pathogens, causing severe damage and serious economic losses to agriculture worldwide. Octanoic acid has been reported as one of the nematicides, and its mode of action is not fully understood. The main objective of this study was to elucidate the effect of octanoic acid on Meloidogyne incognita by transcriptomic analysis combined with physiological and biochemical assays. In the toxicity assays with octanoic acid, the threshold concentration with nematicidal activity and the maximum concentration to which nematodes could respond were 0.03 µL/mL and 0.08 µL/mL respectively. Microscopic observation combined with protein and carbohydrates assays confirmed that the structure of the second-stage juveniles (J2s) was severely disrupted after 72 h of immersion in octanoic acid. Transcriptome analysis has shown that octanoic acid can interfere with the nematode energy metabolism, lifespan and signaling. Although the effects are multifaceted, the findings strongly point to the cuticle, lysosomes, and extracellular regions and spaces as the primary targets for octanoic acid. In addition, nematodes can withstand the negative effects of low concentration of octanoic acid to some extent by up-regulating the defense enzyme system and heterologous metabolic pathways. These findings will help us to explore the nematicidal mechanism of octanoic acid and provide important target genes for the development of new nematicides in the future.

Identification of apoptosis-associated protein factors distinctly expressed in cigarette smoke condensate-exposed airway bronchial epithelial cells.

Smoking is associated with an increased risk of respiratory diseases, including lung cancer and asthma. However, the mechanisms or diagnostic markers for smoking-related diseases remain largely unknown. Here we investigated the role of cigarette smoke condensate (CSC) in the regulation of human bronchial epithelial cell (BEAS-2B) behavior. We found that exposure to CSC significantly inhibited BEAS-2B cell viability, impaired cell morphology, induced cell apoptosis, triggered oxidative damage, and promoted inflammatory response, which suggests a deleterious effect of CSC on bronchial epithelial cells. In addition, CSC markedly altered the expression of apoptosis-associated protein factors, including p21, soluble tumor necrosis factor receptor 1, and Fas ligand. In sum, our study identified a panel of novel protein factors that may mediate the actions of CSC on bronchial epithelial cells and have a predictive value for the development and progression of smoking-related diseases, thus providing insights into the development of potential diagnostic and therapeutic strategies against these diseases.

Total flavonoids from sea buckthorn ameliorates lipopolysaccharide/cigarette smoke-induced airway inflammation.

The total flavonoids from sea buckthorn (TFSB) exhibit a potent anti-inflammatory activity; however, the effect of TFSB on respiratory inflammatory disease is not fully known. The present study evaluated the potential of TFSB to prevent airway inflammation and the underlying mechanism. The results showed that TFSB remarkably inhibited lipopolysaccharide/cigarette smoke extract (LPS/CSE)-induced expression of IL-1ß, IL-6, CXCL1, and MUC5AC at both mRNA and protein levels in HBE16 bronchial epithelial cells. TFSB also decreased the production of PGE2 through inhibition the expression of COX2 in LPS/CSE-stimulated HBE16 cells. Furthermore, bronchoalveolar fluid and histological analyses revealed that LPS/cigarette smoke exposure-induced elevated cell numbers of neutrophils and macrophages in bronchoalveolar fluid, inflammatory cell infiltration, and airway remodeling were remarkably attenuated by TFSB in mice. Immunohistochemical results also confirmed that TFSB decreased the expression of IL-1ß, IL-6, COX2, CXCL1, and MUC5AC in LPS/CS-exposed mice. Mechanistically, TFSB blocked LPS/CSE-induced activation of ERK, Akt, and PKCα. Molecular docking further confirmed that the main components in TFSB including quercetin and isorhamnetin showed potent binding affinities to MAPK1 and PIK3CG, two upstream kinases of ERK and Akt, respectively. In summary, TFSB exerts a potent protective effect against LPS/CS-induced airway inflammation through inhibition of ERK, PI3K/Akt, and PKCα pathways, suggesting that TFSB may be a novel therapeutic agent for respiratory diseases.

Main active constituent identification in Guanxinjing capsule, a traditional Chinese medicine, for the treatment of coronary heart disease complicated with depression.

Guanxinjing capsules (GXJCs) are used in traditional Chinese medicine as a common therapy for coronary heart disease (CHD) complicated with depression. In this study, we aimed to identify the main active constituents in GXJCs and to investigate the mechanisms of GXJC action on CHD complicated with depression. The chemical constituent profile of the GXJC was identified by UHPLC-LTQ-Orbitrap assay, and oral bioavailability was evaluated to screen the GXJC drug-like chemical constituents. A total of 16 GXJC drug-like chemical constituents were identified. Then, putative targets of the GXJC drug-like chemical constituents were predicted using MedChem Studio, with 870 genes found to be the putative targets of these molecules. After that, a GXJC putative target-known CHD/depression therapeutic target network was constructed, and four topological features, including degree, betweenness, closeness and K-coreness, were calculated. According to the topological feature values of the GXJC putative targets, 14 main active constituents were identified because their corresponding putative targets had topological importance in the GXJC putative target-known CHD/depression therapeutic target network, which were defined as the candidate targets of GXJC against CHD complicated with depression. Functionally, these candidate targets were significantly involved in several CHD/depression-related pathways, including repairing pathological vascular changes, reducing platelet aggregation and inflammation, and affecting patient depression. This study identified a list of main active constituents of GXJC acting on CHD complicated with depression using an integrative pharmacology-based approach that combined active chemical constituent identification, drug target prediction and network analysis. This method may offer an efficient way to understand the pharmacological mechanisms of traditional Chinese medicine prescriptions.

[Effect of proinflammatory factors TNF-α,IL-1ß, IL-6 on neuropathic pain].

Cytokines can be divided into two types： proinflammatory cytokines and anti-inflammatory cytokines. Proinflammatory cytokines are a kind of small molecular peptides synthesized and excreted by immune and non-immune cells, which can regulate a variety of physiological functions and play an important role in the process of trauma, pain and infection. Proinflammatory cytokines include TNF, IL-1, IL-6 and IL-8. More and more evidences suggest that proinflammatory cytokines(PICs), such as interleukin-1ß(IL-1ß), interleukin-6(IL-6) and tumor necrosis factor-α(TNF-α), are induced in the spinal cord(SC) and dorsal root ganglion (DRG) under various injury conditions, and contribute to pain hypersensitivity. In recent years, with the deepening of studies on neuropathic pain mechanism and the increasing expansion of the neuroinflammation study field, the action mechanisms of cytokines and molecules in regulating cytokines in neuropathic pain are expected to provide new targets for the development of analgesic drugs. This review aims to provide an overview of inflammatory mechanisms for proinflammatory cytokines TNF-α, IL-1ß, IL-6, with a focus on neuropathic pain.

LC-MS/MS determination and pharmacokinetic study of dehydrocorydaline in rat plasma after oral administration of dehydrocorydaline and Corydalis yanhusuo extract.

A rapid, sensitive and selective liquid chromatography/tandem mass spectrometry method (LC-MS/MS) was developed and validated for determination of dehydrocorydaline (DHC) in rat plasma using nitidine chloride as an internal standard. The analytes were solid-phase extracted and eluted on a C18 chromatography column using a mobile phase of acetonitrile and water (containing 0.8% formic acid and 10 mM ammonium acetate) (28:72, v/v). Detection was performed using positive ion electrospray ionization in multiple reaction monitoring modes. The assay was linear over the concentration range 0.625-250 ng/mL with a quantification limit of 0.625 ng/mL. The precision was <13.7%, the accuracy >93.1%, and extraction recovery ranged from 92.1% to 107%. The validated method was successfully applied to the pharmacokinetics and excretion study of DHC in rat plasma after oral administration of pure DHC and an effective fraction of Corydalis yanhusuo (EFY). The pharmacokinetic parameters showed that DHC from EFY was absorbed more rapidly and eliminated more slowly than pure DHC. The result suggests that the differences might be due to the presence of P-glycoprotein (P-gp) inhibitors and that other alkaloids co-existing in the EFY may compete with DHC for transportation by P-gp, metabolization by P450, and binding to plasma proteins.

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.

Machine Learning: A New Approach for Dose Individualization.

The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N = 23, 35.9%) and anti-infectives (N = 21, 32.8%), and there is currently only very limited attention for special populations, such as children (N = 22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.

Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future.

The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.

5-mRNA-based prognostic signature of survival in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common non-small-cell lung cancer, with a high incidence and a poor prognosis. AIM: To construct effective predictive models to evaluate the prognosis of LUAD patients. METHODS: In this study, we thoroughly mined LUAD genomic data from the Gene Expression Omnibus (GEO) (GSE43458, GSE32863, and GSE27262) and the Cancer Genome Atlas (TCGA) datasets, including 698 LUAD and 172 healthy (or adjacent normal) lung tissue samples. Univariate regression and LASSO regression analyses were used to screen differentially expressed genes (DEGs) related to patient prognosis, and multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model. Receiver operating characteristic curve and Kaplan-Meier survival analyses with clinically independent prognostic parameters were performed to verify the predictive power of the model and further establish a prognostic nomogram. RESULTS: A total of 380 DEGs were identified in LUAD tissues through GEO and TCGA datasets, and 5 DEGs (TCN1, CENPF, MAOB, CRTAC1 and PLEK2) were screened out by multivariate Cox regression analysis, indicating that the prognostic risk model could be used as an independent prognostic factor (Hazard ratio = 1.520, P < 0.001). Internal and external validation of the model confirmed that the prediction model had good sensitivity and specificity (Area under the curve = 0.754, 0.737). Combining genetic models and clinical prognostic factors, nomograms can also predict overall survival more effectively. CONCLUSION: A 5-mRNA-based model was constructed to predict the prognosis of lung adenocarcinoma, which may provide clinicians with reliable prognostic assessment tools and help clinical treatment decisions.

Optimizing ganciclovir and valganciclovir dosing regimens in pediatric patients with cytomegalovirus infection: a spotlight on therapeutic drug monitoring.

INTRODUCTION: Infants and immunocompromised children with cytomegalovirus (CMV) infection have significant morbidity and mortality. Ganciclovir (GCV) and its oral prodrug valganciclovir (VGCV) are the major antiviral options of choice for the prophylaxis and treatment of CMV infection. However, with the currently recommended dosing regimens used in pediatric patients, large intra- and inter-individual variability of pharmacokinetic (PK) parameters and exposure are observed. AREAS COVERED: This review describes the PK and pharmacodynamic (PD) characteristics of GCV and VGCV in pediatrics. Moreover, the role of therapeutic drug monitoring (TDM) and current clinical practice for GCV and VGCV dosing regimens optimization in pediatrics are discussed. EXPERT OPINION: GCV/VGCV TDM has shown the potential value to improve the benefit/risk ratio in pediatrics when using the therapeutic ranges derived from adults. However, well-designed studies are required to evaluate the relationship of TDM with clinical outcomes. Furthermore, studies to explore the children-specific dose-response-effect relationships will be helpful to facilitate the TDM practice. In the clinical setting, optimal sampling methods such as limited sampling strategies for pediatrics can be used in TDM and intracellular ganciclovir triphosphate may be used as an alternative TDM marker.

[Pathogenesis of glucocorticoid-induced osteoporosis based on label-free mass proteomics].

OBJECTIVE: To explore pathogenesis of glucocortocoid-induced osteoporosis(GIOP) based on label-free mass proteomics. METHODS: Twevle female Sprague-Dawley(SD) rats were randomly divided into two groups, named as sham group and GIOP group. After one-week adaptive feeding, the rats of GIOP group were administered with dexamethasone via intramuscular injection according to 2.5 mg/kg weighting, while the rats of sham group were administered with the same amount of saline, twice a week. The tibias of each group were collected after 8-week modeling and made pathological sections to confirm the success of modeling. Three samples of each group were picked up to perform label-free mass proteomics. After quality control, differentially expressed proteins were identified according to qualitative and quantitative analyses. Then gene ontology(GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, cluster analysis as well as protein-protein interaction analysis were performed using bioinformatics analysis. RESULTS: Compared with sham group, the structure of bone trabecular in GIOP group showed abnormal arrangement, uneven distribution and obvious fragmentation, which could demonstrate successful modeling. A total of 47 differentially expressed proteins (DEPs) were identified including 20 up-regulated and 27 down-regulated proteins. The expression of protein nucleophosmin 1(NPM1), adipocyte plasma membrane associated protein (APMAP), cytochromec oxidase subunit 6A1 (COX6A1) and tartrate-resistant acid phosphatase (ACP5) showed a significant difference between two groups. KEGG results showed DEPs were enriched on metabolism-related pathways, immune-related pathways and AMP-activated kinase (AMPK) signaling pathway. CONCLUSION: Protein NPM1, APMAP, COX6A1 and ACP5 showed a close relationship with pathogenesis of GIOP, which could serve as potential biomarkers of GIOP. AMPK signaling pathway played an important role in the occurrence and development of GIOP, which could be regarded as potential signaling pathway to treatment GIOP.

Evaluation Scale or Output Format: The Attentional Mechanism Underpinning Time Preference Reversal.

Time preference reversals refers to systematic inconsistencies between preferences and valuations in intertemporal choice. When faced with a pair of intertemporal options, people preferred the smaller-sooner option but assign a higher price to the larger-later one. Different hypotheses postulate that the differences in evaluation scale or output format between the choice and the bid tasks cause the preference reversal. However, these hypotheses have not been distinguished. In the present study, we conducted a hybrid task, which shares the same evaluation scale with the bid task and shares the same output format with the choice task. By comparing these three tasks, we can figure out the key reason for time preference reversal. The eye-tracking measures reflecting attention allocation, cognitive effort and information search pattern were examined. Results showed that participants' time preference and eye-tracking measures in the hybrid task were similar to those in the choice task, but different from those in the bid task. Our findings suggest that the output format is the core reason for time preference reversal and may deepen our understanding of the mechanisms that underlie time preference reversal.

Cognitive process underlying ultimatum game: An eye-tracking study from a dual-system perspective.

According to the dual-system theories, the decisions in an ultimatum game (UG) are governed by the automatic System 1 and the controlled System 2. The former drives the preference for fairness, whereas the latter drives the self-interest motive. However, the association between the contributions of the two systems in UG and the cognitive process needs more direct evidence. In the present study, we used the process dissociation procedure to estimate the contributions of the two systems and recorded participants eye movements to examine the cognitive processes underlying UG decisions. Results showed that the estimated contributions of the two systems are uncorrelated and that they demonstrate a dissociated pattern of associations with third variables, such as reaction time (RT) and mean fixation duration (MFD). Furthermore, the relative time advantage (RTA) and the transitions between the two payoffs can predict the final UG decisions. Our findings provide evidence for the independent contributions of preference for fairness (System 1) and self-interest maximizing (System 2) inclinations to UG and shed light on the underlying processes.

Gastric Mucosa Pathology in Rats with Precancerous Lesions of Gastric Cancer with Spleen Deficiency and Blood Stasis.

Objective: This research aimed at better understanding the histopathological development of precancerous lesions of gastric cancer (PLGC) and organelle ultrastructure changes. Methods: Sprague-Dawley rats were randomly assigned to the model and control groups. Model rats drank N-methyl-N'-nitro-N-nitrosoguanidine solution, while control rats drank pure water ad libitum. At 1, 3, 5, 6, and 8 months after the start of feeding, eight rats were randomly chosen from each group, and gastric mucosa tissues were removed for histopathological analysis. H&E staining was applied to analyze the pathological histological structure of the rat gastric mucosa via a light microscope, and the ultrastructural changes were observed via a transmission electron microscope. Results: Gastric mucosal pathologies of model rats such as mucosal atrophy, intestinal metaplasia, inflammatory lesions, and even intraepithelial neoplasia deteriorated over time. The endoplasmic reticulum gap widened, the mitochondrial endothelial cristae were disrupted, the nuclear membrane thickened, and chromatin condensed with heterotypic alterations in the main and parietal cells. Additionally, endothelial cell enlargement and thickening of the microvascular intima were seen. Conclusion: Our research showed that the PLGC progression of rats is correlated with the pathological alteration axis of "normal gastric mucosa-gastric mucosa inflammatory changes-intestinal metaplasia with mild dysplasia-moderate to severe dysplasia." Ultrastructure analysis of model rats is compatible with the structural changes in the gastric mucosa with spleen deficiency and blood stasis. The pathological evolutionary axis and ultrastructural analysis are helpful for evaluating potential novel herbal therapies for PLGC.

Using omics approaches to dissect the therapeutic effects of Chinese herbal medicines on gastrointestinal cancers.

Chinese herbal medicines offer a rich source of anti-cancer drugs. Differences between the pharmacology of Chinese herbal medicines and modern synthetic chemicals hinder the development of drugs derived from herbal products. To address this challenge, novel omics approaches including transcriptomics, proteomics, genomics, metabolomics, and microbiomics have been applied to dissect the pharmacological benefits of Chinese herbal medicines in cancer treatments. Numerous Chinese herbal medicines have shown potential anti-tumor effects on different gastrointestinal (GI) cancers while eliminating the side effects associated with conventional cancer therapies. The present study aimed to provide an overview of recent research focusing on Chinese herbal medicines in GI cancer treatment, based on omics approaches. This review also illustrates the potential utility of omics approaches in herbal-derived drug discovery. Omics approaches can precisely and efficiently reveal the key molecular targets and intracellular interaction networks of Chinese herbal medicines in GI cancer treatment. This study summarizes the application of different omics-based approaches in investigating the effects and mechanisms of Chinese herbal medicines in GI cancers. Future research directions are also proposed for this area of study.

Structural characterization and identification of biflavones in Selaginella tamariscina by liquid chromatography-diode-array detection/electrospray ionization tandem mass spectrometry.

Biflavonoids, a special class of flavonoids, are widely distributed in gymnosperm plants and have various biological activities. They are also major bioactive ingredients in Selaginella tamariscina. In this work, we report the use of high-performance liquid chromatography with a diode-array detector (HPLC-DAD) and electrospray ionization multi-stage tandem mass spectrometry (ESI-MS(n)) to study the fragmentation behavior of three main types of biflavonoids using seven biflavonoid reference compounds and analyze the biflavonoids in Selaginella tamariscina. The most useful fragmentations in terms of structural identification are those involving the C-ring cleavage of biflavonoids. For amentoflavone-type biflavonoids (containing flavonoid parts I and II), fragmentation on the flavonoid part II at positions 1/3 and 0/4 are the primary pathways, whereas the chances are greater for C-ring cleavage fragmentation occurring on flavonoid part I at positions 1/3 and 1/4 for robustaflavone-type biflavonoids. However, the predominant diagnostic ions of the specific C-O-C-connected hinokiflavone-type biflavonoids are a series of ions resulting from the rupture of the connective C-O bond. Based on the fragmentation patterns of these reference compounds, 17 biflavonoids were identified in an extract of Selaginella tamariscina, three of which have not been previously reported as constituents of this plant. This study provides a powerful approach for the online structural elucidation and identification of different types of biflavonoids and positional isomers from Selaginella tamariscina and other biflavonoids distributed in related plants and prescriptions.

[Identification of foliumn pyrrosiae from different habitats and species by HPLC fingerprint].

OBJECTIVE: To establish HPLC fingerprint of Folium Pyrrosiae for identification. METHODS: The HPLC method was developed with Diamonsil C18 column (250 mm x 4.6 mm, 5 microm),and a mixture liquid of acetonitrile-0.8% acetic acid solution as mobile phase in a gradient elution. HPLC fingerprints of44 samples were analyzed by similarity, cluster and principal component analysis. RESULTS: The HPLC fingerprint common pattern of Pyrrosia petiolosa, Pyrrosia lingua and common pattern of Pyrrosia sheareri were set up separately. Samples from different species were classified based on the result of cluster and principal component analysis. Fingerprints of Pyrrosia sheareri and Pyrrosia lingua have high degree of similarity, but were different from Pyrrosia petiolosa, while Pyrrosia calvata and Pyrrosia assimlis were classified as adulterants with their dissimilar fingerprints. CONCLUSION: The method is stable and reliable with a good reproducibility and provides a reference standard for identifying Folium Pyrrosiae from different habitats and species.

Study on the Mechanism of Ginseng in the Treatment of Lung Adenocarcinoma Based on Network Pharmacology.

BACKGROUND: Ginseng, a traditional Chinese medicine, was used to prevent and treat many diseases such as diabetes, inflammation, and cancer. In recent years, there are some reports about the treatment of lung adenocarcinoma with ginseng monomer compounds, but there is no systematic study on the related core targets and mechanism of ginseng in the treatment of lung adenocarcinoma up to now. Therefore, this study systematically and comprehensively studied the molecular mechanism of ginseng in the treatment of lung adenocarcinoma based on network pharmacology and further proved the potential targets by A549 cell experiments for the first time. METHODS: The targets of disease and drug were obtained from Gene database. Subsequently, the compound-target network was constructed, and the core potential targets were screened out by plug-in into Cytoscape. Furthermore, the core targets and mechanism of ginseng in the treatment of lung adenocarcinoma were verified by MTT test, cell scratch test, immunohistochemistry, and qRT-PCR. RESULTS: 1791 disease targets and 144 drug targets were obtained by searching the Gene database. Meanwhile, 15 core targets were screened out: JUN, MAPK8, PTGS2, CASP3, VEGFA, MMP9, AKT1, TNF, FN1, FOS, MMP782, IL-1ß, IL-2, ICAM1, and HMOX1. The results of cell experiments indicate that ginseng could treat lung adenocarcinoma by cell proliferation, migration, and apoptosis. In addition, according to the results of the 15 core targets by qRT-PCR, JUN, IL-1ß, IL-2, ICAM1, HMOX1, MMP9, and MMP2 are upregulated core targets, while PTGS2 and TNF are downregulated core targets. CONCLUSION: This study systematically and comprehensively studied 15 core targets by network pharmacology for the first time. Subsequently, it is verified that 9 core targets for ginseng treatment of lung adenocarcinoma, namely, JUN, IL-1ß, IL-2, ICAM1, HMOX1, MMP9, MMP2, PTGS2, and TNF, are closely related to the proliferation, migration, and apoptosis of lung adenocarcinoma cells. This study has reference value for the clinical application of ginseng in the treatment of lung adenocarcinoma.