To compare samples across different individuals and settings, the conventional practice has involved the normalization of raw AFB1-lysine adduct concentrations  to the total circulating HSA concentration . It is hypothesized that this practice corrects for technical error, between-person variance in HSA synthesis or AFB1 metabolism, and other factors. However, the validity of this hypothesis has been largely unexamined by empirical analysis. The objective of this work was to test the concept that HSA normalization of AFB1-lysine adduct concentrations effectively adjusts for biological and technical variance and improves AFB1 internal dose estimates. Using data from AFB1-lysine and HSA measurements in 763 subjects, in combination with regression and Monte Carlo simulation techniques, we found that HSA accounts for essentially none of the between-person variance in HSA-normalized (R2 = 0.04) or raw AFB1-lysine measurements (R2 = 0.0001), and that HSA normalization of AFB1-lysine levels with empirical HSA values does not reduce measurement error any better than does the use of simulated data . These findings were robust across diverse populations , AFB1 exposures (105 range), HSA assays (dye-binding and immunoassay), and disease states . HSA normalization results in arithmetic transformation with the addition of technical error from the measurement of HSA. Combined with the added analysis time, cost, and sample consumption, these results suggest that it may be prudent to abandon the practice of normalizing adducts to HSA concentration when measuring any HSA adducts—not only AFB1-lys adducts—when using LCMS in serum/plasma.The assessment of aflatoxin B Conventionally, these values are transformed by dividing the measured AFB1-lys concentration by the concentration of total circulating human serum albumin (HSA), resulting in the quantitation of the AFB1-lys adduct relative to the total HSA concentration . This practice is grounded in prior analytical approaches for AFB1-lys adduct quantitation, such as immunoassays [1-lys from serum/plasma digests prior to quantification by liquid chromatography and fluorescence detection [1-lys increases assay sensitivity. However, since quantitated AFB1-lys values are no longer directly representative of the original volume of the sample from which they were derived (due to the precipitation or immunoaffinity purification), the values had to be normalized for assay input with a denominator other than the input volume. Thus, after the measurement of the total HSA concentration using one of the several available analytical approaches, AFB1-lys adduct values were normalized for assay input through their expression relative to the total HSA in the sample. These analytical methods are no longer the state-of-the-art approach for the measurement of AFB1-lys adducts, but the practice of normalization to HSA measured in a separate aliquot of serum (or plasma) has remained as a convention, including in previous reports from our group [Aflatoxin Borldwide . Hepatictabolite  results exposure . Quantitl method  results noassays  or immunetection . In bothur group ,12,13,141-lys adduct levels  to total HSA concentration  is warranted, given the added financial, labor, and sample consumption costs. Two approaches to assess this question could be: (1) to compare the internal dose values yielded by normalized vs. raw AFB1-lys adduct concentrations to measured levels of aflatoxin intake, or (2) to compare the predictive or prognostic value of normalized vs. raw AFB1-lys adduct values with disease incidence or outcomes in which aflatoxin plays an accepted etiological role. In regard to option 1, while our group has reported AFB1-lys adduct concentrations in humans with known levels of AFB1 intake [1-lys analytical methods rather than the current LCMS approach [1-lys values within associations of AFB1 and hepatocellular carcinoma incidence [1-lys adduct levels, such that the total HSA measurement was used for normalization of the assay input. Additionally, while AFB1-lys measurement was conducted in samples from individuals, the association with hepatocellular carcinoma incidence was limited to ecological analysis, limiting the precision of the results.The purpose of this study was to assess whether the conventional practice of normalizing raw AFB1 intake ,15, thesncidence ; however1-lys values determined by LCMS. To do so, we have paired a dataset of AFB1-lys adduct and HSA concentrations from nearly 800 persons with analysis by Monte Carlo simulation and regression, to interrogate whether normalization of AFB1-lys adduct levels to total HSA performs better than chance—independent of HSA assay format, study population, longitudinal changes in aflatoxin exposure, or the presence of hepatobiliary disease. Consistency in inferences between simulated and empirical data would suggest that the standardization of AFB1-lys values to HSA is not necessary and that it only reflects random error that is not associated with biological processes or technical variance.In this report, we used a simulation approach to determine the efficacy of HSA normalization of AFB1-lys (pg/mL) and total HSA (mg/mL) measurements, within normalized AFB1-lys (pg/mg HSA) values. To do so, using data from our previously published cross-sectional study of AFB1 exposure in Guatemalan adults [1-lys normalized to HSA (pg AFB1-lys/mg HSA), was regressed against the components of that calculated value, i.e., raw AFB1-lys (pg/mL) or HSA (mg/mL). In addition to AFB1-lys and total HSA measurements . However, despite the significant linear relationship and in contrast with raw AFB1-lys, total HSA was a very poor predictor of normalized AFB1-lys concentrations  than was total HSA. These results, while expected, demonstrate that HSA levels account for very little of the variance in AFB1 internal dose , bringing into question whether the HSA normalization practice removes biological or technical variance, as proposed.Initial studies compared the relative contribution of variance components derived from AFBn adults , we consurements A–C, thispulation  were inc1-lys and HSA values . As seen in 1-lys concentrations , or whether it was non-informative . To do so, we compared empirical data from the Guatemala cross-sectional study to a simulated data set with the same distribution parameters as the empirical data, but populated with “participants” assigned randomly generated AFBtrations B, and nos  to the elimination of nearly all exposure by 2012 (7%). Thus, using these data , we attempted to replicate our findings from the Guatemalan study. As seen in 1-lys . Since the HSA-normalized AFB1-lys value is, by definition, the quotient of error-prone estimates of raw AFB1-lys and HSA concentrations, this is to be expected. However, while the presence of error is to be expected, the sources and structures of the error can be informative and are worth further investigation. In this study, we sought to test the hypothesis that the normalization of AFB1-lys values are derived from the errors accompanying analytical estimates of both HSA and raw AFB1-lys adduct levels. However, in the case of the regression analysis in 1-lys value—if the dependent variable were also the raw AFB1-lys concentration instead of being the HSA-normalized AFB1-lys value, the mean squared error would be 0 and the R2 would equal 1. Thus, the regression analysis presented in 1-lys adduct estimates. Although the true error is unobservable, it is approximated by the residuals of linear regression analysis: at a given level of the independent variable, the residual is the difference between an empirical observation of the dependent variable and the best-fit regression prediction of the dependent variable. Furthermore, although the residuals shown in 1-lys adduct concentration imprecisely reflects the AFB1 internal dose or AFB1-related disease risk, and that the adjustment made by HSA normalization minimizes error and moves the estimate closer to the unknown true value. Conversely, if the HSA concentration does not adequately account for or is entirely unassociated with error contributed by the confounding variable(s), then the regression residuals document a deleterious addition of error. This would result in a diminution in statistical power, a bias towards or away from the null hypothesis, or both. Clearly, distinguishing which case is more reflective of the truth is critical to the use of the AFB1-lys adduct biomarker going forward. The regression residuals are either informative , are a result of statistical noise, or are a combination of the two. Under an assumption that HSA normalization adds information by systematically accounting for variation in hepatic function or other confounding variables, the regression residuals would reflect the gains in precision from the normalization operation. In other words, HSA normalization adds value if it is true that the raw AFB1-lys to HSA is uninformative and adds error to estimates of exposure or disease risk. On the other hand, if the observed data deviate significantly from simulated distributions, then it can be assumed that biological mechanisms, or some other systematic source of error, account for this deviation and that HSA normalization is—to some degree—informative. One way to test this question is to compare relationships observed empirically in data from human samples with those that arise from randomly simulated values obtained from distributions with parameters equivalent to those in the empirical data. If the observed data are largely identical to those derived from simulated data, it can be deduced that the normalization of AFB1-lys adduct levels, since the residuals of this regression analysis isolate the variance derived from the HSA component of the HSA-normalized AFB1-lys value. However, we must first define the null hypothesis, where there exists no biological relationship between HSA concentrations and HSA-normalization regression residuals. In fact, this null hypothesis is presented by the simulated data sets, since simulated HSA and AFB1-lys values within each of the n = 20,000 “participants” per data set were randomly generated and paired to produce simulated HSA-normalized AFB1-lys values. As seen in Specifically, we focused on the relationship between HSA concentrations and the residuals from the linear regression of raw vs. HSA-normalized AFB1-lys to HSA is biologically valid and effective in reducing measurement error. If low HSA concentration reflects reduced hepatic metabolic capacity or a limited availability of protein substrate for the adduction of AFB1 to HSA, one could hypothesize that in the presence of low circulating HSA levels, raw AFB1-lys adduct concentration would underestimate the AFB1 internal dose. Thus, at constant levels of AFB1 exposure, AFB1-lys adduct concentrations would decrease in some proportion with HSA and normalization could be useful to correct for the underestimation. To support this hypothesis, it would be expected that, in contrast to simulated data, the empirically observed HSA-normalization regression residuals do not deviate greatly from zero at low concentrations of HSA, as this is the range where normalization is proposed to be most effective. Instead, we consistently observed the opposite—empirical residuals at HSA concentrations lower than the normal reference range (35–55 mg/mL) exhibit a monotonic increasing trend with decreasing HSA and demonstrate no inflection point where the hypothesized normalizing effect may occur. Additionally, aside from sample size, the residuals in the empirical data and the simulated data appear nearly identical—indicating that empirically determined HSA is no better than randomly simulated values for the purposes of reducing error in AFB1-lys adduct internal dose estimates. We now compare the null hypothesis above with the alternative hypothesis, formed on the assumption that the normalization of raw AFB1 exposures, we repeated our analyses with raw data from a previously published report in Qidong, People‘s Republic of China [1-lys adduct levels progressively decreased by approximately 80%; our original publication found that this was likely due to shifts in Chinese agro-economic policy [1 exposure contributed significantly to a now 30-year trend of decreasing liver cancer mortality in Qidong [1-lys adducts in this study were measured using the same method as that used in the Guatemalan study , this Qidong ecological study utilized a different HSA assay , featured a different participant population, and exhibited a lower range of aflatoxin internal dose . Despite the differences with the Guatemalan study, we replicated our earlier findings—across four screenings spanning 14 years, HSA was consistently no better of a normalizer for raw AFB1-lys than randomly simulated values. To assure ourselves that our initial results in the Guatemalan study were not idiosyncratic to a specific population, a particular method of HSA measurement, or a particular range of AFBof China . As showc policy  and thatn Qidong . While A1 exposure may be concurrent with hepatobiliary disease, where decreases in hepatic metabolic capacity and HSA concentrations are etiologically linked. Specifically, we explored the use of HSA for normalization of raw AFB1-lys adduct levels in two case-control studies of gallbladder cancer. In contrast to the temporal stability of HSA concentrations in the longitudinal Qidong study, HSA concentrations were not equivalent between gallbladder cancer cases and controls—HSA levels in the cancer cases were significantly lower than in the controls (p < 0.0001), likely a result of hepatic injury secondary to bile duct obstruction or damage [1-lys internal dose likely also differ in hepatic function, normalization with HSA as a surrogate for liver injury may be warranted. Nonetheless, we found that regression residuals from gallbladder cancer cases and controls were indistinguishable from one another and were no different than residuals from simulated data. Thus, these results indicate that HSA normalization does not reduce the error in AFB1-lys internal dose estimates in the setting of hepatic disease secondary to gallbladder cancer, but likely adds technical variance, reducing estimate precision and potentially increasing the risk of a type 2 error in statistical inference. Finally, we wanted to test our approach in a context where AFBr damage ,19,20. I1-lys adduct levels, the various justifications for its continued use do not withstand scrutiny. Fundamentally, all rationales cited in support of HSA normalization argue that the practice corrects for between-sample variation in HSA concentration and thus the pool of HSA from which the stoichiometrically rare HSA molecules carrying an AFB1-lys adduct are sampled. This variation in true HSA concentration within a given sample originates from either random chance during sampling of a volume of serum or plasma or from biological factors , and is added to the technical variation inherent in the assay used to produce estimates of HSA concentrations (assay precision). Utility of the HSA normalization approach thus depends on both effective correction for random and/or biological variance sources and low technical variance within the assay. While the HSA normalization approach has been considered conventional practice for reporting AFB1-lys measurement, analysis is performed directly using serum/plasma samples rather than HSA precipitates or immunoaffinity purified fractions, as in prior methods [1-lys measurement error and does not necessitate correction with HSA normalization. With the current LCMS approach for AFB methods ,7. Thus,1-lys internal standard, which corrects for analyte recovery by solid-phase extraction (SPE) during processing [1-lys levels are not performed in the same aliquot of serum/plasma. Although measurement of HSA in a second aliquot clearly should produce values that are correlated with the concentration of HSA present in the aliquot used for LCMS analysis, this correlation between repeated measurements is subject to technical error within the HSA assay—error which can be substantial, depending on the approach used for HSA determination [1-lys analysis, the utility of such an approach is dependent upon the technical variance within the HSA assay being substantially smaller than the true between-person variance in HSA concentration. Furthermore, samples exhibiting significant hemolysis can lead to aberrant HSA determinations, adding additional technical error [1-lys adduct concentrations determined by LCMS in pooled serum quality control samples—measured at 3 separate levels in each of 35 separate analytical batches—was 10.8% [1-lys numerator.Additionally, the LCMS assay utilizes an isotopically labeled AFBocessing . HSA assmination . As a real error ,23. In aal error , the peras 10.8% . Thus, H1-lys adduct values: (1) AFB1-lys adduct formation likely occurs exclusively in the liver, as it is the site of both HSA synthesis and the metabolic activation of AFB1 by cytochrome P450 enzymes to its reactive carcinogenic metabolite (for which the AFB1-lys adduct is a surrogate biomarker) [Several rationales based on the following established physiological facts have been presented in support of HSA normalization of AFBomarker) . 1, HSA concentrations can be used as a surrogate biomarker of HSA synthesis and, ultimately, hepatic function. It is true that HSA synthesis and HSA levels are reduced in severe hepatic or perihepatic disease, such as cirrhosis and gallbladder cancer [First, it has been proposed that since hepatic injury or disease can broadly impair hepatic protein synthesis and metabolism, including the metabolism of AFBr cancer ,20,26,27r cancer ,29. Hower cancer ,31. Thusr cancer . In an a1-lys adducts, despite a constant level of AFB1 exposure and a constant rate of AFB1-lys adduct formation. It has been proposed that HSA concentration is similarly altered by changes in plasma volume and thus can be used to adjust for between-person variation in plasma osmolarity, comparable to how creatinine is used to normalize concentrations of urinary biomarkers [Plasma volume can be affected by hydration status , disease , and certain physiological states . A second rationale for HSA normalization suggests that alterations in plasma volume could increase or decrease the volumetric concentration of circulating AFBomarkers . Howeveromarkers , which domarkers . Thus, aomarkers ), it is omarkers ,34, enou1-lys adducts. Assuming this hypothesis is correct, given that circulating HSA concentration is generally reflective of hepatic HSA synthesis [1-lys adduct formation. However, circulating HSA is not a reliable indicator of nutritional status, except in severe cases of protein malnutrition, such as kwashiorkor [Finally, certain exogenous factors  can decrease circulating HSA concentrations. It has been proposed that decreased hepatic HSA production could reduce the availability of HSA as a substrate for the formation of AFBynthesis , the sershiorkor ,35,36. Ishiorkor . 1-lys adduct formation. While HSA synthesis is indeed reduced due to liver disease or injury, even in a setting of extremely high aflatoxin exposure , HSA is present in the liver at a molar ratio of roughly 100,000:1 relative to AFB1  and  and 1-lyer (~40% ). Thus, 1-lys measurements, (2) may add more technical variance than the existing between-person biological variance that it is meant to adjust for, and (3) cannot or is not needed to control for between-person biological variance in hepatic metabolism, plasma osmolarity, or HSA synthesis. Furthermore, while AFB1-lys internal dose values can be translated to estimates of dietary intake—as we have done in a recent study of mother-child dyads in Bangladesh and Malawi [1-lys determination, there is no compelling basis for the continued use of HSA normalization in AFB1-lys adduct dosimetry when using the current state-of-the-art LCMS AFB1-lys assay. Thus, HSA normalization: (1) does not or cannot address random technical variance present in AFBd Malawi —HSA norm1-lys adduct values appears to be the result of arithmetic transformation and the addition of technical error, rather than a reduction in biological or technical variance. In future work, this conclusion could be further tested using subject-level data on hepatocellular carcinoma incidence and AFB1-lys adduct levels, assessing whether raw or HSA-normalized AFB1-lys values better recapitulate the well-established causal relationship between this cancer and AFB1 exposure, or by comparing measured AFB1 intake with raw vs. normalized AFB1-lys values. However, given our current results and combined with the added cost, analysis time, sample consumption, and heterogeneity between HSA assays, it seems prudent to abandon the practice of normalizing HSA adducts to the total circulating HSA concentration when measuring any HSA adducts—not only AFB1-lys adducts—by isotope-dilution mass spectrometry in serum or plasma. As a result, we have initiated this shift within our own laboratory, recently showing that elevated raw AFB1-lys concentrations were significantly associated with the cervical detection of oncogenic strains of HPV in Kenyan women [1-lys adducts will allow for more efficient use of precious samples and research funds, while producing superior data.Overall, the effect of HSA “normalization” in HSA-corrected AFBan women  and, sepan women . Eschewi1-lys adduct data used for this secondary analysis have been previously peer-reviewed.All HSA and AFB1-lys adduct determination by LCMS [1-lys (≥0.01 pg/μL), valid determination of HCV and HBV infection status, and self-reported weekly ethanol consumption. A total of 400 persons without any missing data were available for the present analysis. Individuals were excluded from further analysis on the basis of excessive alcohol use , positive HBV or HCV status (n = 54), excessive alcohol use and positive HBV or HCV status (n = 4), or missing HBV or HCV status (n = 4). The final analytical sample was n = 327.This study was conducted between May and October of 2016 and was approved by the institutional review boards at the Johns Hopkins Bloomberg School of Public Health and the Institute of Nutrition of Central America and Panama (INCAP). Study details have been published previously ,40. All  by LCMS , HSA by  by LCMS . Partici1-lys by LCMS [1-lys (≥0.01 pg/μL). Due to the very low rate of detectable AFB1-lys in samples from 2012 (7%), subjects from the 2012 sub-sample were excluded from further analysis in the present study. A total of 306 persons from four community screenings  had AFB1-lys and HSA data available for analysis .This study was approved by the institutional review boards at the Johns Hopkins Bloomberg School of Public Health and the Qidong Liver Cancer Institute (QDLCI). All study participants provided informed consent. Samples for this study were collected as part of screening protocols for liver cancer early detection and chemoprevention studies in Qidong, People’s Republic of China, dating to the 1980s. From our biorepository, we randomly selected age-matched subsamples of 50 males and 50 females, residing in two neighboring townships , obtained from screening protocols conducted during the years of 1995, 1999, 2003, 2009, and 2012. Each subsample consisted of a unique set of community residents; subject-level repeated sampling across survey years was not available. Serum samples from the participants were analyzed for AFB by LCMS . Partici1-lys by LCMS [1-lys (≥0.01 pg/μL); in prior publications [1-lys levels ≥0.05 pg/mg HSA were included for analysis. Data were pooled across both study sites, resulting in a final analytical sample size of n = 130 persons with GBC  and n = 85 controls . The combined data set was analyzed as one study, referred to hereafter as the “GBC case-control” study.We have previously reported positive associations between aflatoxin exposure and gallbladder cancer (GBC) in two case-control studies, conducted in the country of Chile  and in S by LCMS  and HSA  by LCMS ,12. Partications ,12, only1-lys adduct refers to serum AFB1-lys—albumin adduct concentration—in the units of pg AFB1-lys adduct/mL serum. “HSA-normalized” or “normalized” AFB1-lys adduct levels refers to the raw AFB1-lys adduct level divided by the total HSA concentration (in mg/mL), resulting in the final units of pg AFB1-lys adduct/mg HSA. Within the GBC case-control study, the distributions of HSA, raw AFB1-lys adduct levels, and HSA-normalized AFB1-lys concentrations were compared in cases and controls using the non-parametric Wilcoxon signed-rank test using the PROC NPAR1WAY procedure. All analyses were conducted using SAS 9.4 . “Raw” AFB1-lys levels, and various demographic parameters to the total observed variance in AFB1-lys internal dose in the Guatemalan dataset. Multiple models were constructed, with either raw AFB1-lys or HSA-normalized AFB1-lys as the dependent variable and the independent variables consisting of HSA, raw AFB1-lys, and various demographic factors  that we have found to be associated with aflatoxin exposure in Guatemala [2 was used to assess the proportion of explained variation (vs. coefficient of determination) of the AFB1-lys internal dose.Linear regression models were used to assess the relative contributions of HSA, raw AFBuatemala . In all 1-lys adduct concentrations, modeled on the empirical data from each study. Specifically, within each study, the empirically observed mean and standard deviation values for HSA concentration were used to construct 20 normally or lognormally distributed simulated samples of 1000 randomly generated observations each, using the RDRAND function in SAS and a seed value of 0 [1-lys adduct concentrations using the empirically observed raw AFB1-lys adduct mean and standard deviation. Next, the randomly generated HSA and AFB1-lys values in each dataset were sequentially paired, without replacement, to create a third dataset of simulated study participants, and the HSA-normalized AFB1-lys adduct values were calculated for each simulated participant. Within each study , these simulated datasets were constructed using distribution parameters drawn from the entire sample, independent of the year of sample collection (China), disease status (GBC case-control), or study site (GBC case-control). Thus, for each of the three studies, we created a unique 20,000-observation simulated dataset with distribution parameters essentially identical to the empirical data, but where intra-person relationships between raw and HSA-normalized AFB1-lys adduct levels were the result of arithmetic transformation and random variation rather than biology. A Monte Carlo simulation approach  was usedlue of 0 ; these 21-lys adduct levels and HSA-normalized adduct levels. Residuals were obtained from linear regression models in which the raw AFB1-lys adduct level was the lone independent variable and the HSA-normalized AFB1-lys concentration was the dependent variable to be predicted. Thus, the regression residuals constitute the differences between the empirically observed HSA-normalized AFB1-lys adduct level (pg/mg HSA) at a given concentration of the raw AFB1-lys adduct (pg/mL serum or plasma) vs. the predicted HSA-normalized AFB1-lys adduct level at the same concentration of the raw AFB1-lys adduct. In other words, the residuals were visualized as the ordinate (y-axis) deviation of an individual observation from the best-fit linear regression line describing the relationship between raw and HSA-normalized adduct levels. To compare residuals across five orders of magnitude of raw AFB1-lys adduct levels, Studentized regression residuals were standardized as Z-scores (Linear regression was used to assess the relationship between raw AFB


Aloe vera has long been considered an anticancer herb in different parts of the world.  To explore the potential mechanism of aloe vera in the treatment of cancer using network pharmacology and molecule docking approaches.  The active ingredients and corresponding protein targets of aloe vera were identified from the TCMSP database. Targets related to cancer were obtained from GeneCards and OMIM databases. The anticancer targets of aloe vera were obtained by intersecting the drug targets with the disease targets, and the process was presented in the form of a Venn plot. These targets were uploaded to the String database for protein-protein interaction (PPI) analysis, and the result was visualized by Cytoscape software. Go and KEGG enrichment were used to analyze the biological process of the target proteins. Molecular docking was used to verify the relationship between the active ingredients of aloe vera and predicted targets.  By screening and analyzing, 8 active ingredients and 174 anticancer targets of aloe vera were obtained. The active ingredient-anticancer target network constructed by Cytoscape software indicated that quercetin, arachidonic acid, aloe-emodin, and beta-carotene, which have more than 4 gene targets, may play crucial roles. In the PPI network, AKT1, TP53, and VEGFA have the top 3 highest values. The anticancer targets of aloe vera were mainly involved in pathways in cancer, prostate cancer, bladder cancer, pancreatic cancer, and non-small-cell lung cancer and the TNF signaling pathway. The results of molecular docking suggested that the binding ability between TP53 and quercetin was the strongest.  This study revealed the active ingredients of aloe vera and the potential mechanism underlying its anticancer effect based on network pharmacology and provided ideas for further research. Aloe vera (L.) Burm.f (synonym A. barbadensis Mill.), a traditional herb belonging to the Liliaceae family, has been used around the world for a long time [ong time . It contong time . Aloe veong time . Meanwhiong time , 5, the ong time –8. In reong time . It has ong time –12. HoweNetwork pharmacology is a newly developed interdisciplinary subject of drug system research based on artificial intelligence and big data . It is wIn this report, network pharmacology was performed to reveal the active ingredients, therapeutic targets, and pharmacological mechanisms of aloe vera for treating cancer. The predictions of the relationship between active ingredients and key targets were verified by molecular docking. The flowchart of this investigation is presented in http://tcmspw.com/) is a systems pharmacology platform that consists of information about Chinese herbs, including ingredients, targets, associated diseases, and ADME properties [https://www.uniprot.org), and only the genes that have been “Reviewed” and belong to “Human” were left [http://www.GeneCards.org) and OMIM (https://omim.org/) databases.The TCMSP (operties . “Aloe” operties . The relere left . “Cancerhttps://bioinfogp.cnb.csic.es/tools/venny/) was used to draw a Venn diagram to compare the targets of aloe vera and cancer. The overlap of the diagram represents the anticancer targets of aloe vera. The network of active ingredients of aloe vera and the corresponding targets was constructed and analyzed by using the software Cytoscape3.6.0. The nodes in the network represented molecules or genes, which were distinguished by different shapes, and the line between one molecule and one gene meant they were related.Venny online tool (https://string-db.org) with “Homo sapiens” chosen in the Organism box [The PPI network was obtained by uploading the anticancer targets of aloe vera to the STRING platform (nism box . After snism box .https://david.ncifcrf.gov/) to obtain the database of GO functions and KEGG pathway. Sorted in descending order of p value, the top 10 data of GO analysis were visualized via GraphPad Prism 7 and the top 10 pathways were presented in the form of an enrichment dot bubble by uploading the data to the Bioinformatics platform (http://www.bioinformatics.com.cn/).GO and KEGG pathway enrichment analysis was used to reveal the biological function of the anticancer targets of aloe vera. The GO analysis includes molecular function, biological process, and cellular component. The anticancer targets of aloe vera were uploaded into the DAVID platform (https://pubchem.ncbi.nlm.nih.gov) and prepared using the Chem3D software. Then, the ligands were saved in the format of PDBQT after adding hydrogens, computing Gasteiger charges, detecting the root, and choosing rotatable bonds by using AutoDock Tools. Targets with the top three highest values in the PPI network were selected to be the protein receptors, and the 3D structures were extracted from the Protein Data Bank (PDB) database (http://www.rcsb.org/) [x = 15.2, center y = 24.4, center z = 16.3, size x = 15.5, size y = 18.7, size z = 19.8, and spacing = 0.375 Å; TP53 (PDB ID: 3ZME), target center x = 91.9, center y = 94.1, center z = -45.7, size x = 14.0, size y = 20.4, size z = 17.2, and spacing = 0.375 Å; and VEGFA (PDB ID: 6ZFL), target center x = 22.9, center y = 19.4, center z = 2.2, size x = 13.7, size y = 15.6, size z = 15.7, and spacing = 0.375 Å. After removing water in the PyMOL software and adding hydrogens, computing charges, and adding AssignAD4 type atoms using AutoDock Tools, the receptors were saved in the PDBQT format. Molecular docking was performed via Vina, and during the docking process, all residues were kept rigid. The results were shown in the form of Vina score ) [The network pharmacology revealed potential active ingredients of aloe vera and the key targets and pathways of aloe vera against cancer. The active compounds of aloe vera which have more than 4 gene targets were selected as the ligands and their 3D structures were searched in the PubChem database (sb.org/) . The Grial/mol)) .By using “Aloe” as the keyword in the TCMSP, 53 ingredients were retrieved. By setting the filter criteria that OB ≥ 30 and DL ≥ 0.18, 8 active ingredients were achieved . The 239A Venn diagram was acquired by uploading the targets of aloe vera and cancer, respectively, to the Venny platform . The resThe PPI network of potential targets was constructed by entering 179 anticancer targets into the STRING database and visualized by Cytoscape software. The PPI network involved 172 nodes and 2781 edges . With thTo further analyze the cancer targets of aloe vera, GO enrichment was performed. The top 10 representative enrichment terms, sorted by count in descending order, are displayed in p value <0.001 were obtained. The top 10 pathways were shown by an enrichment dot bubble in By uploading 174 anticancer targets to the DAVID platform, 75 pathways with AKT1, TP53, and VEGFA with the top 3 largest values in the PPI network were used as the receptors, and quercetin, arachidonic acid, aloe-emodin and beta-carotene, which have more than 4 gene targets, were chosen as the ligands. The ligands and receptors were prepared by using the PubChem database, PDB database, Chem3D software, PyMOL software, and AutoDock Tools. To verify the prediction, the four compounds were docked to three targets via AutoDock Vina. The result was presented in the form of affinity value . The comAloe vera, as one of the oldest medical plants, has long been used to reduce inflammation, treat constipation, treat skin diseases, and speed wound healing . At the In the TCMSP database, 8 active ingredients of aloe vera were screened out and then the 181 related targets were obtained by collecting and screening. The Venn diagram showed that 174 of the 181 targets associated with aloe vera were included in the cancer targets, suggesting that most of the relevant targets in aloe vera were involved in antitumor effects. The PPI network showed the interaction between various targets of aloe vera. Among these targets, AKT1 (120), TP53 (104), and VEGFA (101) with the top 3 highest degree values may be the main anticancer targets. Molecular docking was performed to further validate the possible interaction between the key targets and active molecules. AKT1 is a known oncogene which plays a direct role in cancer . The proThe results of KEGG pathway analysis revealed the anticancer pathways of aloe vera. Ten of the top 20 pathways were directly related to cancer, including prostate cancer, bladder cancer, pancreatic cancer, non-small-cell lung cancer, small-cell lung cancer, chronic myeloid leukemia, colorectal cancer, glioma, endometrial cancer, and melanoma, which were involved in many systems, such as the urinary system, digestive system, respiratory system, and blood system. The results indicated that the anticancer targets of aloe vera play important roles in treating many cancers and aloe vera may have great potential in the treatment of cancer. Some of the potential targets were also involved in the TNF, HIF-1, and p53 signaling pathway and apoptosis. The TNF signaling pathway plays a critical role in various processes, including cell proliferation, differentiation, apoptosis, regulating immune response, and inducing inflammation . There aIn this study, 8 active ingredients and 179 potential targets of aloe vera were extracted for constructing the PPI network and performing GO and KEGG analysis. The binding potential between the active compounds and potential targets was evaluated by molecular docking. This study systematically elucidated the relationship between aloe vera and cancer at the molecular level, which indicated that aloe vera has great potential as an anticancer agent for drug development. Moreover, further in vitro and in vivo experiments should be performed to confirm the results obtained in this study.


Ulva prolifera green tides in the Yellow Sea, China, which have been occurring since 2007, are a serious environmental problem attracting worldwide attention. Despite extensive research, the outbreak mechanisms have not been fully understood. Comprehensive analysis of anthropogenic and natural biotic and abiotic factors reveals that human activities, regional physicochemical conditions and algal physiological characteristics as well as ocean warming and biological interactions  are closely related to the occurrence of green tides. Dynamics of these factors and their interactions could explain why green tides suddenly occurred in 2007 and decreased abruptly in 2017. Moreover, the consequence of green tides is serious. The decay of macroalgal biomass could result in hypoxia and acidification, possibly induce red tide and even have a long-lasting impact on coastal carbon cycles and the ecosystem. Accordingly, corresponding countermeasures have been proposed in our study for future reference in ecosystem management strategies and sustainable development policy.The  Ulva prolifera (U. Prolifera) have successively occurred for 12 years (2007–18) in the Yellow Sea off the coasts of Jiangsu and Shandong Province, China [U. prolifera fronds floated into the coasts of Qingdao, posing a great challenge to the local government owing to the upcoming Olympic Sailing Regatta to be held in Qingdao. The direct economic losses caused by that macroalgal bloom were as high as 1.3 billion RMB [2, U. prolifera bloom in the Yellow Sea is the largest green-tide event in the world so far , which could almost meet the nutrient demands of U. prolifera for its rapid growth [U. prolifera that germinate on the mariculture rafts in seawater or sediments after the Porphyra harvest grow into new fronds as the temperature increases, thus serving as the original species of green tides occurring in the following spring [Research and monitoring over 10 years have shown that the outbreak mechanisms of su coast  and genellow Sea . It alsorolifera ,15. Mored growth . In addig spring ,18.U. prolifera is considered to be the dominant green-tide-forming seaweed in the Yellow Sea [Ulva genus, such as U. intestinalis, U. linza, U. compressa and U. clathrate, have also been observed in Porphyra mariculture areas along the Jiangsu coast [U. prolifera can outcompete others owing to its higher nutrient-uptake rate, faster growth and stronger reproduction capacity [U. prolifera among the raft-attached macroalgae usually increases with Porphyra growth. When the attached macroalgae are discarded into seawater after the Porphyra harvest, the proportion of U. prolifera in floating algae can increase from 63 to 91% within 24 hours under optimal environmental conditions [U. prolifera, appropriate physicochemical conditions , southeastward monsoons, etc., can all contribute greatly to the occurrence of green tides [As indicated by morphology and genomics, llow Sea . Other ssu coast . Howevercapacity . The pronditions . Moreoveen tides .U. prolifera green tides decreased abruptly in the Yellow Sea, which aroused further curiosity and attention. Why did U. prolifera green tides first occur in 2007 and decrease remarkably in 2017? What have not been identified so far that are closely related to the green-tide outbreak? We are gradually unravelling the reasons behind this phenomenon.As a matter of fact, the outbreak mechanisms of green tides in the Yellow Sea are far more complicated than had been thought before. In the past decade, various attempts have been made to prevent the annual occurrence of large-scale green tides but were unsuccessful. However, in 2017, Massive macroalgal blooms can have a negative impact on the coastal environment; for example, they can seriously upset the balance of coastal ecosystems, affect air–sea exchanges and even cause the death of cultured organisms, such as sea cucumber and shellfish, in the Yellow Sea . During U. prolifera green tides as well as the impact on the environment of the Yellow Sea. Based on the recognition of the outbreak mechanisms of green tides and their following consequences, we proposed corresponding countermeasures to prevent the frequent occurrence of green tides in the future.This review is aimed at a systematic analysis of multiple factors in facilitating the outbreaks of U. prolifera green tide is a trans-regional disaster in the Yellow Sea, China. These macroalgal blooms usually originate in the coastal areas of the southern Yellow Sea during mid-April to early May, and start as small patches of free-floating green algae. Subsequently, the free-floating algal patches move northward and northeastward, driven by seasonal monsoons and ocean currents. During the migration period, U. prolifera grows fast and accumulates gradually. In late May, large floating U. prolifera aggregates into long large floating mats. Nearly all the floating macroalgae are observed in linear bands ranging from hundreds of metres to tens of kilometres in the open sea area. In mid-June, floating U. prolifera accumulates with a huge biomass (∼1.5 × 106 tons) and covers a large area in the coastal sea of the Shandong Peninsula. Thereafter, great social and economic losses emerge due to the large algal biomass piling up on beaches and coastal waters. At the end of June, U. prolifera continues to grow, causing the biomass to reach as high as ∼3.5 × 106 tons. In mid-July, U. prolifera reaches its maximum biomass of ∼1.2 × 107 tons. After that, U. prolifera green tides begin to decline. In early August, the floating U. prolifera can only be sporadically observed in the coastal areas [The al areas ,25.U. prolifera green tides have developed from small-scale floating patches into massive macroalgal blooms, which is closely related to various factors including seawater temperature, salinity, light intensity, wind direction and ocean currents in the Yellow Sea.Every spring from 2007, U. prolifera [Ulva prolifera generally grows well when temperature ranges between 10 and 20°C; its biomass can increase by as much as 23% per day when the temperature exceeds 15°C, while it cannot grow when the temperature is below 10°C [U. prolifera [U. prolifera, which may contribute to the rapid growth of the fronds. The algal fragment of U. prolifera grows by 28% per day at 20°C and only by 3% per day at 10°C in laboratory incubations [U. prolifera is 20–30°C [2 and CO2 are produced separately in photosynthesis and respiration and fill the fronds of U. prolifera, causing them to float in the surface water [Ulva prolifera can have normal photosynthesis only when the temperature exceeds 10°C; the water temperature during May to June in the Yellow Sea ranges from 15 to 25°C, which is quite suitable for the floating and accumulation of U. prolifera. As mentioned in the introduction, Porphyra mariculture rafts are good substrata for the early propagation of U. prolifera. Considering the obvious impact of temperature on the growth of U. prolifera, earlier harvest of Porphyra in the southern Jiangsu Province, especially before the temperature reaches over 10°C in April, may reduce the biomass of U. prolifera fall off the rafts and thus lower the outbreak scale of green tides [Seawater temperature has been identified as a key factor in influencing the growth of rolifera ,15. Ulvalow 10°C ,27. The  20–30°C  and the  20–30°C ,22,30. Ten tides .U. prolifera cannot be ignored either. Suitable salinity for the growth of U. prolifera has been reported to range between 8 and 32 [U. prolifera to short-term salinity fluctuations indicate that U. prolifera is quite sensitive to salinity changes. The photosynthetic rate is significantly higher with high salinity (26–32), optimum at 32 and low salinity (<8) has adverse effects on the photosynthesis. In coastal waters of the Yellow Sea, the salinity usually varies between 28 and 31 during the spring and summer periods (from April to June), which aligns within the suitable salinity ranges for the growth of U. prolifera [U. prolifera; for example, lower salinity (i.e. 20) promotes its sporangium formation and increases spore germination as well [U. prolifera was compared under different temperatures (i.e. 15 or 22°C) and salinities (i.e. 10 or 25), the results of which indicate that, with low salinity (i.e. 10), the growth of U. prolifera decreases compared with high salinity (i.e. 25) [U. prolifera can endure drastic variability in salinity and modest salinity variations, to some extent, can benefit its growth and blooming.The impact of salinity on rolifera . Besides as well . Furtheri.e. 25) . All of U. prolifera, including the growth, photosynthesis, reproduction, nutrient uptake, utilization, etc. [U. prolifera, suitable light intensity to drive its growth has been reported to vary from 18 to 175 μM photons m−2 s−1 [2 g FW−1 h−1 at the optimal light level of 120 μM photons m−2 s−1 [U. prolifera by regulating the release or germination of spores and gametes. The optimal light level for the release of spores and gametes has been reported to be 240 μM photons m−2 s−1 while, for their germination, it is 120 μM photons m−2 s−1 [−2 s−1 [Ulva prolifera was observed to have optimal ammonium absorption at a light level of 80 μM photons m−2 s−1; as the light level decreases, the nitrogen balance within the fronds is disrupted, leading to non-absorption and even excretion of ammonium [−2 s−1 during May to June, which can thus fully satisfy the growth of U. prolifera [Apart from temperature and salinity, light intensity also affects the physiology of on, etc. ,35. Lighon, etc. , owing ton, etc. . For the m−2 s−1 . Of cour m−2 s−1 ,35. Unde m−2 s−1 . For ase [−2 s−1 . Furtherammonium . Light irolifera .U. prolifera fronds [U. prolifera. Remote sensing data show that the distribution of U. prolifera in surface waters appears as a linear band and is consistent with the main path of monsoons [U. prolifera fronds that are scraped off the Porphyra mariculture rafts or the fragments mixed up from the bottom by upwelling are scattered throughout the surface water [U. prolifera reaches the open sea, the dynamics of the algal accumulation are driven by a complex ocean dynamic process, including the southeast monsoons, Ekman drift, Langmuir circulation, Subei and Lunan coastal currents [U. prolifera fronds reaches the southwestern East China Sea in early May along with severe local eutrophication, leading to the occurrence of green tides in this new area [U. prolifera could reach the coastline of the Shandong Peninsula [U. prolifera did not pile up at the coastline as usual, instead most was floating in the ocean, so the U. prolifera green tides in the Yellow Sea in 2009 had the largest distribution area during the past decade.The southeastward monsoons prevail in the Yellow Sea from April to July, driving surface ocean currents northwards and thus the northward migration of a fronds ,38,39. Wmonsoons . In the ce water . These fcurrents . In addicurrents . The mudnew area ,43. Moreeninsula . As U. p2 concentration is also increasing. In October 2016, the World Meteorological Organization (WMO) in Geneva claimed that the global average CO2 concentration reached 400 ppm for the first time in 2015 [2 reached 406 ppm according to the latest WMO dataset. In fact, both the rising temperature and atmospheric CO2 facilitate the growth and photosynthetic carbon fixation of phytoplankton. Thus, we inferred that rising atmospheric CO2 and continuous global warming primarily caused by anthropogenic activities are likely to be among the important factors that facilitate the early occurrence and continuous expansion of green tides. Ocean acidification has a typical negative impact on the increasing of atmospheric CO2 and global warming. Laboratory studies have indicated that ocean acidification and warming could accelerate the onset and magnitude of gamete settlement and increase the reproduction of Ulva rigida—a causative green-tide species closely related to U. prolifera [Ulva would dominate coastal regions as populations of non-blooming species would decrease faster and maintain lower growth rates, even under mild carbon enrichment [Global warming has become one of the most important issues worldwide and is mainly attributed to increasing population density and intensive anthropogenic activities . Coastal in 2015 . In 2017rolifera . Based orichment . Thus, crichment .Green macroalgae blooms are occurring more frequently and the magnitudes of the blooms are getting larger worldwide. In recent decades, the Baltic Sea, Knysna Estuary, South Africa, Biscayne Bay, Narragansett Bay, South Korea and Japan coasts all have suffered from massive macroalgal blooms ,49–55; BP < 0.05) [U. prolifera green tide for the first time in 2012, indicating a close relationship between U. prolifera green tides and severe local eutrophication [With the increase in population and rapid economic development, nutrient loadings have been influenced by anthropogenic perturbations in the coastal waters of the Yellow Sea ,58. At p < 0.05) . The She < 0.05) . Moreove < 0.05) . Discharhication .U. prolifera [U. prolifera can reach 56% day−1 with sufficient N and P supplies [U. prolifera. Based on a mesocosm experiment, U. prolifera fronds can grow vigorously by consuming DIN quickly, even though the P concentration is fairly low in seawater [4–P concentration showed a decreasing trend during the study period  are higher than those of other Ulva species (e.g. Vmax-NO3-N = 109 and Vmax-NH4-N = 250 μM g−1 DM h−1 for U. linza) in Jiangsu coastal waters. Accordingly, the growth rate of U. prolifera is higher than that of U. linza (U. proliferaS = 13% d−1 vs U. linzaS = 10% d−1) [U. prolifera, as most of the nutrients in seawater are absorbed and utilized by U. prolifera [U. prolifera can be as high as 110 μM g−1 DM h−1 [U. prolifera outcompete microalgae by reducing N to levels below which microcells cannot grow [nditions . The nit10% d−1) . Moreoverolifera . The max1 DM h−1 , which mnot grow . Hence, U. prolifera [U. prolifera is most prone to taking up inorganic nutrients and subsequently large amounts of DIN and dissolved inorganic phosphorus (DIP) are utilized in the development of green tides [U. prolifera to organic nutrients satisfies their nutrient demand and subsequent blooms. Temporal variations of dissolved nutrients were investigated in the southern Yellow Sea during the green-tide-blooming period, and it was found that the DIN decreased rapidly and varied from 21 to 8.0 μM during late April and early May (i.e. early blooms). The DIN decreased to below 5 μM in June and July; simultaneously, the concentrations of dissolved organic nitrogen (DON) began decreasing very fast and varied from 28 to 16.5 μM. Even in late July and August (i.e. late blooms), the DON always maintained ∼15 μM in seawater and the DON provided sufficient nutritional conditions for the growth of U. prolifera [U. prolifera [In addition to inorganic nutrients, some low-molecular-weight organic nutrients like urea, glycine and adenosine triphosphate can be absorbed by rolifera . The upten tides . Hence crolifera . Similarrolifera , the conrolifera ,15, espe3 photosynthesis. Ulva prolifera fronds also have the key enzymes for C4 photosynthesis, so it may perform both C3 and C4 photosynthesis [Ulva prolifera also has the lhc SR gene and non-photochemical quenching mechanism, both of which give U. prolifera a high photosynthetic carbon-fixation capacity and environmental adaptability to changing temperatures and light levels [U. prolifera has high photosynthetic plasticity, which may ensure its ability to obtain sufficient energy to increase its biomass and to adapt to a long-distance migration [U. prolifera can be periodically transferred from the surface to the bottom of the water column due to vertical mixing when floating from the southern to northern Yellow Sea. When exposed to the surface water, U. prolifera fronds can dissipate excess energy via photosynthetic quantum control (energy quenching and energy redistribution between PSII/PSI) to avoid light-caused damages to the photosynthetic system; when mixed into deeper waters, the fronds can promote light utilization by increasing their cellular chlorophyll a (Chl a), chlorophyll b (Chl b) and Chl b/Chl a ratio [U. prolifera contributes to its free-floating proliferation and prevalence among floating green algae species in the Yellow Sea [Most marine algae primarily perform Cynthesis ,74. Ulvat levels ,76. Moreigration . For exa a ratio . The higllow Sea .U. prolifera is very complicated. Ulva prolifera has multiple reproduction modes, including sexual, asexual and vegetative propagation [U. prolifera can grow by (i) increasing algal tubular diameter, (ii) forming new branches, (iii) releasing spores and (iv) expanding fronds [Ulva prolifera can release 1.2 × 107 spores or 2.3 × 107 gametes per cm2 frond, and 92–97% of them can bind to new micropropagules to germinate [U. prolifera to maintain colonization and excessive growth, resulting in the formation of massive green tides.The life history of pagation , among wg fronds . Ulva prerminate . These uUlva prolifera, being no exception, is closely associated with microbial organisms. Ulva prolifera contains endophytes within and around its fronds. All these microbial organisms may play a vital role in regulating the formation and recession of macroalgal blooms. For example, the morphogenesis of U. linza, which is similar to that of U. prolifera, depends on the regulation of the key microbial group Cytophaga-Flexibacter-Bacteroides that ensures the normal growth of its fronds [U. prolifera are commonly surrounded by abundant heterotrophic diazotrophs during the blooms [U. prolifera during the mid or late development stages of green tides [U. prolifera through N fixation.Algae are closely associated with bacteria in natural seawater. It is extremely difficult to obtain one sterile alga without using antibiotics or a streaking-based algal separation method with multiple plates. The relationship between algae and bacteria is sophisticated, involving symbiosis, competition, lethality, gene transfer, etc. s fronds . Moreovee blooms . Our preen tides . Given tU. prolifera was observed to be related to or to reciprocally have a significant effect on certain microbial communities, and the abundance of microorganisms increased significantly by the end of U. prolifera green tides [U. prolifera after the green-tide demise, do influence the environment of the Yellow Sea.Microorganisms have been reported to grow vigorously in seawater at the end of red tides . These men tides ,83. So fU. prolifera green tides, efficient photosynthesis of U. prolifera quickly assimilates inorganic carbon, which supports its growth, thus leading to an increase of pH in seawater. The field investigation carried out in 2014 showed that the pH increased up to 8.6 at the centre of a free-floating Ulva patch (about 100 000 m2) along the Dafeng coast (Jiangsu Province). Massive carbon fixation and abrupt pH increase caused by large green tides may have a significant impact on the phytoplankton population and the community structure and ecosystem functions at the regional scale. At the end of green tides (mid-July to early August), massive U. prolifera fronds that fail to be salvaged will sink down to the bottom or suspend at certain depths where they die and are decomposed gradually by microorganisms [U. prolifera fronds consumes a large amount of oxygen, resulting in low-oxygen or even hypoxic conditions in the coastal waters. Hypoxia can not only cause massive deaths of cultured organisms  and induce considerable economic losses, but also affect benthic community structure, biomass and inhabitant density [U. prolifera fronds can release abundant C, N and P, lowering the water quality and affecting the reproduction and respiratory metabolism of heterotrophic microorganisms [2 will dissolve into seawater, decreasing the pH of seawater and accelerating coastal acidification, which are well known to have a profound impact on marine ecosystems [2 partial pressure (pCO2) increased markedly while the pH decreased substantially during the late-bloom period (22–25 July 2008) of green tides, indicating that coastal waters become a carbon source after a green tide [U. prolifera releases substantial dissolved organic matter into the surrounding waters, promoting the growth of microbial organisms, which in turn accelerates the decomposition of dead U. prolifera. Accompanied with this process is the release of more CO2 driven by microbial respiration, resulting in worse coastal acidification. On the other hand, the released dissolved organic carbon (DOC) during U. prolifera decomposition is partially transformed into refractory DOC (RDOC) via microbial metabolism according to the microbial carbon pump theory [Green tides have profound and long-lasting effects on the environment. In the early and middle development stages of rganisms . The dec density ,86. Simurganisms . Accordiosystems ,88. As seen tide . Owing tp theory . The RDOU. prolifera fronds floated into the coastal waters of Qingdao, with the largest coverage area in the Golden Beach waters , followed by Tuandao , Maidao  and Shilaoren  near-shore waters. We monitored the water physiochemical parameters in the above four waters and found the lowest seawater pH and dissolved oxygen (DO) appeared in the Golden Beach near-shore waters, along with the highest chemical oxygen demand (COD), DOC and bacterial abundance. The DON concentration in this area was similar to that in the Maidao near-shore waters , in the Golden Beach, the pH and DO decreased by 7 and 68%, respectively, whereas the COD during the post-bloom period was 22 times higher than that of the pre-bloom period  may also account greatly for the green-tide shrinkage in Qingdao, as it can decrease the amount of U. prolifera released into the water. Moreover, during this period, ‘golden tides’ of Sargassum blooms, another macroalgae, occurred in the Yellow Sea, covering an area of over 47 713 km2 under uncommon currents and wind conditions [Sargassum thallus has well-developed gas bladders and differentiated parts resembling leaves and stems, giving it a competitive advantage for space and nutrients compared to the Ulva thallus, thus inhibiting the growth of U. prolifera. Moreover, the floating Sargassum golden tide occurred in the Porphyra aquaculture area, resulting in the decrease of attached U. prolifera seaweed and further the shrinkage of the U. prolifera green tide during the year.In 2017, the  reasons , since sriginate . Adjustmnditions . The SarU. prolifera is an edible macroalgae, as its protein content reaches as high as 35%. In recent years, U. prolifera has been considered good material for foods and the economic benefits thus incentivize local freshmen to widely collect the attached or floating Ulva seaweed in the Yellow Sea. Such Ulva collection would finally decrease the number of floating blooms in the northern Yellow Sea.Furthermore, U. prolifera green-tide scale in 2017; the El Niño-caused decrease in precipitation and increase in temperature may respectively lower the levels of nutrients that are derived from the land  or from the stratified bottom layer [U. prolifera fronds northward [In addition, the abnormal weather conditions caused by the El Niño phenomenon were likely related to the reduction of the om layer . Furtherorthward .2 in the southern Yellow Sea , algal physiological advantages  and human activities , etc. In addition, it has been gradually realized that climate changes, aquaculture modes and biological interactions  also significantly affect the occurrences of U. prolifera green tides in the Yellow Sea. Moreover, the consequences of green tides are serious. In the short to medium term, green tides can degrade water quality, increase the outbreak risk of red tides and cause coastal hypoxia and acidification, while, in the long term, they may have a long-lasting impact on coastal carbon cycles. The identification of the outbreak mechanisms of green tides and the following consequences could provide implications for the corresponding countermeasures to be adopted to avoid future occurrences of green tides in the Yellow Sea, and provide reference for the future in ecosystem management and sustainable development.The annual outbreak of


Metabolomics was used to detect the secondary metabolites in SLBZP; the target protein was acquired by target fishing according to the compound's structure. The SymMap database was used to search herbal medicines for the target protein. The target gene of IBS gave rise to the common gene protein which is the potential target of SLBZP in IBS therapy. The interactions between target proteins were analyzed in a STRING database, the protein relationship network was analyzed using Cytoscape software, and the Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the core target gene group was carried out in a DAVID database in order to construct the “compound-traditional Chinese medicine/molecule-target-pathway” network. Molecular docking was used to verify the core protein and its related small molecular compounds. β1 (CTNNB1), among others. Results from these targets indicated that the most enriched pathway was the tumor necrosis factor (TNF) signaling pathway (p < 0.001) and that the most abundant pathway was signal transduction. In the network nodes of the TNF signaling pathway, the Chinese medicines with the highest aggregation were Lablab semen album and Glycyrrhizae radix et rhizoma (degree = 11). The small molecules with the highest aggregation were oxypeucedanin and 3,5,6,7,8,3′,4′-heptamethoxyflavone (degree = 4). Molecular docking results confirmed that daidzein 7-O-glucoside (daidzin) had the highest degree of binding to TNF proteins in the TNF signaling pathway.  There were 129 types of secondary metabolites in SLBZP. 80 target proteins of these metabolites were potential core targets for IBS treatment including acetylcholinesterase (AChE), arachidonate-5-lipoxygenase (ALOX5), B-cell lymphoma-2 (BCL2), recombinant cyclin D1 (CCND1), and catenin- This study shows that SLBZP can treat IBS by influencing multiple targets and pathways, of which the TNF signaling pathway may be the most significant. This typifies the pharmacological characteristics of traditional Chinese medicine, i.e., multiple targets, numerous pathways, and specific therapeutic effects on diseases. SLBZP can therefore be used as a candidate drug for clinical IBS by intervening in human signal transduction. Irritable bowel syndrome (IBS) is one of the most frequently arising pathologies of the gastrointestinal tract presenting in the clinic. Its main manifestations are pain and discomfort within the abdomen, together with changes in bowel habits. With an incidence rate range of 7% in Southeast Asia to 21% in South America . AlthougShenling Baizhu powder (SLBZP) is derived from the “Prescriptions of Taiping Heji Bureau” 1148 A.D.) in the Song Dynasty of China. Its constituents include white lentils, Atractylodes macrocephala, Poria cocos, licorice, Platycodon grandiflorum, lotus seed, ginseng, Amomum villosum, yam, and coix seed. The main clinical effects of the drug are splenic and gastric fortification and replenishment and restoration of lung qi. In China, it is principally used to alleviate clinical symptoms such as splenic and gastric inadequacy, anorexia, loose motions, shortness of breath, cough, and limb fatigue. To date, several pharmacological studies have been published relating to SLBZP, as shown in 8 A.D. inIn 2007, Hopkins, from the United Kingdom, proposed the concept of network pharmacology which takes the biological databases as the processing object . From thA network pharmacology method was deployed in the current research in order to construct the “compound-traditional Chinese medicine/molecule-target-pathway” network of SLBZP for the treatment of IBS and to explore its potential pharmacological mechanism. The flow chart of the entire research design is shown in 7); glycyroside (relative content 4.81 × 107); apigenin 6,8-C-diglucoside (relative content 4.20 × 107); acacetin-7-O-glucuronide (relative content 4.00 × 107); and trigonelline (relative content 3.88 × 107). Fifty-eight types of flavonoids were noted, of which the following 21 were most abundant ; the most abundant pathway was signal transduction.As shown in Figures 7Figures According to the above results, molecular docking of the TNF protein was carried out; the results are shown in There are many causes of IBS, but the etiology is still not clear. Studies have shown that IBS may occur following only slight changes and disturbances, such as the improvement of modern social health conditions or personal hygiene habits or short-term infection or stress . Owing thttp://Drugs.com/ database suggests that in nonclinical studies on newborn mice, a single oral dose of linaclotide can cause dehydration and death, as well as abdominal pain, stomach ache, severe diarrhea, and other common side effects. Eluxadoline increased the risk of pancreatitis. In the United States, adverse event logs relating to eluxadoline, recorded between January and September 2016, revealed that 98 patients with IBS-D had pancreatitis due to taking eluxadoline, resulting in 2 deaths [In 2018, the American Society of Gastroenterology published a consensus opinion on IBS therapy , recomme2 deaths . It is tChinese Pharmacopoeia 2015 Edition, it is documented that this substance can be used as therapy for IBS-related clinical symptomatology, such as loss of appetite, loose stools, and abdominal discomfort, as shown in There are ten kinds of Chinese herbal medicine in SLBZP, among which lentil is the sovereign medicine. Studies have suggested that lentils within this compound are a potential core Chinese workhorse. The traditional Chinese medicine, white lentil, is the dried mature seed of the legume lentil Dolichos lablab L., which was first recorded in the “famous doctors' records” in the late Han Dynasty over 1500 years ago. In the Among the disease pathways suggested by the KEGG database pathway enrichment results, in addition to tuberculosis, pertussis, and other pathology pathways that are not directly related to IBS, there are predominantly two kinds of IBS-related diseases, i.e., inflammatory bowel disease (IBD) and colorectal cancer. IBS and IBD are two completely different pathologies. At present, with respect to the relationship between the two, studies have pointed out that about one-third of patients with IBD have persistent intestinal sensation and movement and intestinal flora abnormalities, which may cause symptoms similar to IBS . In termα, is a principal factor in vasodilation and edema and in the epithelial adhesion of leukocytes through adhesion molecule expression. It also governs hemostasis, promotes oxidative stress in inflammatory areas, and indirectly gives rise to pyrexia [TNF was selected for molecular docking because as an important cytokine, it is able to trigger a variety of intracellular signal pathways and is the initiating factor of the TNF signal pathway. Activated TNF will activate this pathway and induce downstream leukocyte recruitment and inflammatory factor release. It is involved in inducing cellular necrosis and survival, inflammation, and immunity. One of the most significant proinflammatory cytokines, TNF- pyrexia . At pres pyrexia . This ch pyrexia . Althoug pyrexia . Thus, iTo summarize, a network, founded on network pharmacology technology, was constructed in order to predict the interaction between substances in SLBZP and target genes. The data demonstrated that SLBZP may be a significant factor in IBS therapy by acting on the TNF signaling pathway. Based on the analysis of systems biology, it was speculated that SLBZP may not only be a player in multiple mechanisms that combine synergistically to treat IBS but also contribute to factors that diminish the IBS incidence. In addition, SLBZP may also be a potential treatment for IBD and colorectal cancer. However, the results obtained by systems biology analysis still need to be verified by pharmacological methods with the aid of omics technology.Three sachets, each containing 6 g of SLBZP , were used for the study . The substance was comminuted for 90 seconds in a mixer mill  with zirconia beads at 30 Hz. Centrifugation was then performed at 10,000 g for a period of 10 minutes. The extract was absorbed . Filtration was then performed prior to analysis by liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS).μm, 2.1 mm∗100 mm); solvent system: water (0.04% acetic acid) and acetonitrile (0.04% acetic acid); and gradient program: 100 : 0 V/V at 0 min, 5 : 95 V/V at 11.0 min, 5 : 95 V/V at 12.0 min, 95 : 5 V/V at 12.1 min, and 95 : 5 V/V at 15.0 min. A flow rate of 0.40 mL/min was utilized; the temperature was maintained at 40°C, and 2 μL injection volume was used. The effluent was alternately connected to an electrospray ionization-triple quadrupole-linear ion trap-mass spectrometry (ESI-QTRAP-MS/MS).An LC-ESI-MS system (high-performance liquid chromatography (HPLC) and Shim-pack ultraperformance liquid chromatography (UFLC), Shimadzu CBM30A system; mass spectrometry (MS), Applied Biosystems 6500 triple quadrupole-linear ion trap (QTRAP)) was used to analyze the resulting extracts. The following analytical conditions were employed: HPLC column: Waters ACQUITY UPLC HSS T3 C18  experiments; collision gas (nitrogen) was set to 5 psi. The declustering potential and collision energy of a single MRM conversion were further optimized. In accordance with the metabolites eluted during this period, a specific set of MRM transitions was monitored for each timeframe.The linear ion trap (LIT) and triple quadrupole (QQQ) scans were acquired on a QQQ-LIT (QTRAP) mass spectrometer and an API 6500 QTRAP LC/MS/MS system. The system incorporated an electrospray ionization (ESI) turbo ion spray interface, which operated under positive ion mode, and was controlled by Analyst 1.6.3 software (AB Sciex). The following working parameters of the ESI source were deployed: ion source and turbo spray; source temperature 500°C; ion spray voltage 5500 V; and ion source gas I and gas II and curtain gas at 55, 60, and 25.0 psi, respectively; collision gas was high. The instrument was tuned and mass calibrated with 10 Based on the Malware Database (MetWare) and public databases of metabolite information, respectively, the primary and secondary MS spectrum data were analyzed qualitatively. Iterative isotope signals containing potassium, sodium, and ammonium ions, together with repeating signals of fragment ions of other high-molecular weight compounds, were removed in the process. The structure of metabolites was analyzed with reference to MassBank, KNAPSAcK, the Human Metabolome Database (HMDB) , the MetSearches were performed in PubChem and ChemSpider databases to determine the molecular structure of small compounds obtained by metabolomics. The database SwissTargetPrediction was utilized for target fishing in order to obtain related targets for secondary metabolites. The SymMap database was utilized to search for all known target proteins of traditional Chinese medicines within SLBZP ; these wThe potential targets of the proven prescriptions for the treatment of diarrhea were entered into the STRING database, the organization selected was “Homo sapiens,” and the function “search” was clicked in order to analyze and obtain the PPI. The PPI network was imported into Cytoscape (v3.7.2) software, and the MCODE plugin was used to cluster the PPI network in order to obtain the core target group for the treatment of diarrhea.The core target group was entered into the DAVID database in order to perform enrichment analysis and to obtain the potential KEGG pathway. In the acquired pathways and according to the relationship between a target and the corresponding Chinese medicine, Cytoscape (v3.7.2) software was utilized in order to build a “Chinese medicine-molecule-target-pathway” network of nonspecific disease pathways so as to visualize the network relationship.Molecular docking was used to verify the marker proteins in the most enriched pathway. The 3-dimensional structure of the human target protein was downloaded from the Protein Data Bank (PDB) with the PDB ID of “1TNF” and stored in PDB format. Dehydration and hydrotreatment of the protein were carried out using AutoDockTools (v4.2) software and saved in PDBQT format. Following energy minimization, the 3-dimensional structure of the small molecular compound relating to SLBZP was saved in PDBQT format. The structure of the small molecule and the corresponding target protein was imported into PyRx (v0.9.8) software, and the AutoDock Vina algorithm was employed for molecular docking. According to the docking scoring results, the combination potential between the two was evaluated. The docking results were exported as PDB files; PDB format files of ligand-receptor binding were generated by PyMOL (v2.4.1) software, and 2-dimensional structure diagrams of ligand-receptor binding were generated by LigPlot (v4.5.3) software.


This study used a combination of network pharmacology and experimental confirmation to clarify the mechanism of the compound kidney-invigorating granule (CKG) in treating osteoporosis (OP).  The main bioactive compounds and corresponding targets of CKG were collected and screened via the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Yet another Traditional Chinese Medicine (YaTCM), and UniProt databases. Disease targets of OP were summarized in GeneCards and the Comparative Toxicogenomics Database (CTD). Targets of CKG for OP were obtained by Venn diagram. The protein-protein interaction (PPI) network was constructed by the STRING database and then screened for hub genes through Cytoscape 3.7.2 software. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were analyzed and visualized by R software. Then, CB-Dock was used for molecular docking verification. Finally, we confirmed the antiosteoporosis effect of CKG through animal and cell experiments.  A total of 250 putative targets were obtained from 65 bioactive compounds in CKG. Among them, 140 targets were related to OP. Topological analysis of the PPI network yielded 23 hub genes. Enrichment analysis showed the targets of CKG in treating OP might concentrate on the MAPK signaling pathway, the TNF signaling pathway, the PI3K-Akt signaling pathway, etc. The results of molecular docking showed the bioactive components in CKG had good binding ability with the key targets. The experimental results showed that CKG-medicated serum had a promoting effect on proliferating hBMSCs, increasing the expression of AKT, PI3K, ERK1, and IkB in cells and decreasing the expression of IKK in cells. in vitro and in vivo experimental studies and further expands the clinical applications of CKG. CKG has a complex of multicomponent, multitarget, and multipathway. This study lays the theoretical foundation for further  Osteoporosis (OP) is a systemic metabolic bone disease characterized by low bone mass, low bone density, and bone microstructure destruction, which leads to increased bone fragility and susceptibility to bone fracture. OP is considered a “silent disease” since it progresses without symptoms until a fracture occurs . In the Compared with Western medicine, traditional Chinese medicine (TCM) has unique advantages in treating OP, such as low cost, multilevel, and prominent curative effect. Therefore, TCM is gradually attracting considerable attention from the international medical community and is accepted by non-Chinese as an additional and alternative medical treatment for OP due to these characteristics. Clinically, OP is regarded as “bone atrophy” and “lack of bone marrow” in TCM. According to TCM syndrome differentiation, OP is divided into kidney Yang deficiency syndrome, blood stasis syndrome and kidney deficiency syndrome, blood stasis and Qi stagnation syndrome, liver and kidney Yin deficiency syndrome, spleen and kidney Yang deficiency, and asthenia of the spleen and stomach syndrome.Compound kidney-invigorating granule (CKG) is a TCM formula commonly used at the First Affiliated Hospital of Anhui University of Chinese Medicine. CKG is an effective cure for the OP of blood stasis and kidney deficiency syndrome. It is composed of six kinds of traditional Chinese drugs, namely, Radix Astragali , Radix Paeoniae Alba , Radix Cyathulae , Herba Epimedii , Caulis Polygoni Multiflori , and Concha Ostreae . Preliminary clinical studies have shown that CKG can significantly improve the clinical symptoms, bone mineral density (BMD), and visual analog scale (VAS) in postmenopausal nonelderly patients with OP. There were no adverse reactions that occurred during the study, and there were no significant abnormalities in the safety observation index. Therefore, CKG is safe and effective in treating postmenopausal nonelderly osteoporosis . HoweverTCM has become a significant source that provides potential lead compounds for drug development, especially those used to treat complex diseases, owing to its multicomponent and multitarget characteristics . Guided The present study uses various platforms, databases, and tools to predict the bioactive compounds, potential targets, and pathways of CKG, aiming to explore the pharmacological network of CKG on OP and verify it through related experiments. https://tcmspw.com/tcmsp.php) [https://cadd.pharmacy.nankai.edu.cn/yatcm/home) [The bioactive ingredients of CKG were identified by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (msp.php) . It compcm/home) . Oral hecm/home) . As a qucm/home) . This sthttps://www.uniprot.org/) [The protein targets of the screened bioactive ingredients of CKG were obtained from the TCMSP database. The obtained protein targets were converted into gene targets in the Universal Protein Resource (UniProt) database (ot.org/) , a comprhttps://www.cytoscape.org/) for data visualization and construction of the “D-C-T” network [The obtained bioactive ingredients, gene targets, and herbs were entered into Cytoscape 3.7.2 software ( network . The nethttps://www.genecards.org/) and the comparative toxicogenomics (CTD) database (https://ctdbase.org/). The term “Osteoporosis” was used as the keyword for the search [OP-related human gene targets were obtained from two databases: GeneCards  was used to realize the visualization of the drug-disease target and to draw Venn diagrams [The results of gene targets of CKG were matched with the disease-associated genes of OP. The jvenn  [The common gene targets of CKG and OP were entered into the STRING database (db.org/) . The seldb.org/) . These thttps://www.r-project.org/). Firstly, the ENSMBL gene ID of each core gene was obtained from RStudio, AnnotationHub, and org. Hs.eg.db, and then the GO function enrichment  and KEGG enrichment were enriched by RStudio, DOSE, and Cluster Profiler. In the programming language, pvalueCutoff = 0.05 and qvalueCutoff = 0.05 were set. The results of the analysis selected the top 10 items of BP, MF, and CC in GO enrichment and the top 20 filtered pathways most closely related to OP, then displayed the results of the GO enrichment analysis as a bar graph and the results of the KEGG enrichment analysis as a bubble graph.Various bioinformatics analyses and visualization of the results can be achieved through the software R project (https://pubchem.ncbi.nlm.nih.gov/), converted into a 3D structure with ChemBio3D Ultra 14.0, and saved as a mol2 format file [https://www.rcsb.org/) was used to retrieve and download the files in “PDB” format of the core target proteins [https://clab.labshare.cn/cb-dock/php/index.php) for molecular docking. Finally, the docking model with the lowest binding energy was selected and visualized.Since there is no uniform standard for target screening of bioactive molecules, the 5 proteins with the highest degree of PPI network were selected for molecular docking with the 10 molecules with the highest degree of the “C-T” network. The bioactive compounds were downloaded as 2D structure files using the PubChem database  Medicinal Materials. All Chinese medicines of CKG were purchased from the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine. The prescription medicinal materials of each dose of CKG are HE (20 g), RC (15 g), RA (18 g), CO (10 g), RPA (20 g), and CPM (25 g), 30 doses in total.(2) Antibodies and Reagents. The following antibodies and reagents were used for this study: high-glucose Dulbecco minimum essential medium (HG-DMEM) and fetal bovine serum (FBS) ; 0.25% trypsin-EDTA solution, penicillin-streptomycin, and phosphate-buffered saline (PBS) ; protein extraction kit ; AKT, PI3K, ERK1, IkB, IKK antibody, and GAPDH antibody ; CD29 antibody, CD34 antibody, and CD44 antibody ; and cell counting kit-8 (CCK-8) .(3) Instrument. The following instruments were used for this study: CO2 incubator , Multiskan spectrum , flow cytometer , high-performance centrifuge , western blot system , ice machine , inverted microscope , cell counter , clean bench , cell culture plates, and cell culture flasks .μg/mL streptomycin, then cultured in a 37°C incubator with a 5% CO2 atmosphere. The medium was changed every 2 days until the cells became confluent, about 80%–90%. The cells were then passaged. The surface marker phenotypes  were characterized by using flow cytometry. After the cells were confirmed to be hBMSCs, cells from passages 3 to 5 were used in subsequent experiments.This investigation was approved by the Ethics Committee of the Anhui University of Chinese Medicine. Written informed consent was obtained from all participants before the study. Bone marrow was obtained from OP patients who underwent surgery in the orthopedics department of the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine and volunteered to participate in this study. Separating and culturing hBMSCs  via the method of whole bone marrow adherent. Bone marrow samples applied in cell culture plates were cultured in HG-DMEM containing 10% FBS, 100 U/mL penicillin, and 100 n = 12) and nonmedicated serum (NS) (n = 12) groups. MS group animals were administered intragastrically with CKG concentrated liquor (20 mL/kg) once a day for 7 days. NS group animals were administered intragastrically saline on the same schedule. 12 h before the final treatment, fasting but not watering. 1 h after the final treatment, rats were weighed and intraperitoneally anesthetized using 1% pentobarbital sodium (60 mg/kg). Blood was collected from the abdominal aorta. The collected blood was allowed to stand at room temperature for one hour, then centrifuged at 3000 r/min for 15 min, and the upper serum was taken from the ultraclean workbench. Serum samples from all individual animals of each group were pooled. Then, it was filtered through a 0.22 μm filter membrane, heat-inactivated at 56°C for 30 min, and stored at −80°C until used.The experiment was approved by the Animal Ethics Committee of the Anhui University of Chinese Medicine. Twenty-four 4-week-old SPF Sprague–Dawley rats weighing 200–250 g, half male and half female, were purchased from the Experimental Animal Center of Anhui Medical University  2017-001). Animals were placed in plastic cages at 22 ± 1°C with a 12 h light-dark cycle. Food and water were available ad libitum. They adapted for a week in this environment. The decoction method of the CKG prescription is as follows: after 60 minutes of soaking in sufficient water, the original drugs with the water were decocted in a Chinese herbal medicine decoction machine, then brought to a boil over high heat, and then simmered for 1 hour. The process was repeated three times. The decocting solution was pooled together, added into an electrothermal constant temperature water tank, and concentrated to 2.9 g of the original drugs per mL. The samples were sealed and stored in a refrigerator at 4°C. Animals were randomly divided into medicated serum (MS)  were added to the culture medium for 24 h. The culture medium was then removed and 100 μL of CKK-8 solution was added to the well and incubated for 24 h, 36 h, and 48 h. Subsequently, the CKK-8 solution was removed, and 100 μL of DMSO was added to dissolve the formazan crystals. The optical density (OD) value was read at 450 nm by using a microplate reader. Cell proliferation rate = (ODMS − ODNS)/ODNS.Cells were collected in the logarithmic growth phase and a single cell suspension was prepared. Cells were seeded into 96-well microplates at 1 × 10hBMSCs were plated into 6-well plates. After 12 h, they were treated with MS . The treating time is decided by the results of 2.9.3. Total proteins were extracted from the protein extraction kit, separated using SDS-PAGE, and then transferred to polyvinylidene difluoride (PVDF) membranes, which were blocked with 5% nonfat dry milk in TBST buffer. The primary antibodies were added at proper dilution and incubated at 4°C overnight, and the secondary antibody was added and incubated for 60 min, PVDF membranes were washed with precooled PBS three times. Finally, the signals for the immunoreactive proteins were visualized by enhanced chemiluminescence reagents and then analyzed with Quantity One software. The experiment was repeated 3 times independently.The screening criteria were OB ≥ 30% and DL ≥ 0.18. We got 76 bioactive ingredients via the TCMSP database, including 23 of RA, 16 of RPA, 5 of RC, 26 of HE, 3 of CPM, and 3 of CO. 65 bioactive ingredients were obtained after the removal of duplicate components. Results are detailed in By searching targets of the bioactive ingredients in the TCMSP database and performing protein-gene named transformations in the UniProt database, we got 1641 targets in this study. After removing duplicate data, 250 targets were obtained. After removing the bioactive ingredients without targets, they successfully constructed the “D-C-T” network by inputting the files containing drugs, bioactive ingredients with targets, and gene targets into the Cytoscape software. The squares represent drugs, the rounds represent bioactive ingredients, and the diamonds represent various targets. There are 310 nodes and 1705 edges in the “D-C-T” network. By searching and integrating them in the GeneCards and CTD databases, we got 1246 OP gene targets. By taking the intersection of targets of OP with targets of CKG, we got 140 intersecting genes. We imported the obtained intersecting genes into the STRING database to acquire the interaction relationships between them and saved the data as a file in TSV format. GO enrichment analysis got 1723 entries, of which 1621 were BP entries, 20 were CC entries, and 82 were MF entries. BP analysis showed the targets principally relate to regulating DNA-binding transcription factor activity, response to lipopolysaccharide, response to molecules of bacterial origin, response to mechanical stimulus, cellular response to biotic stimulus, cellular response to lipopolysaccharide, epithelial cell proliferation, cellular response to molecules of bacterial origin, response to reactive oxygen species, and muscle cell proliferation. Analysis of the CC category showed the targets mostly within nuclear chromatin, the RNA polymerase II transcription factor complex, the transcription factor complex, the nuclear transcription factor complex, the cyclin-dependent protein kinase holoenzyme complex, membrane raft, membrane microdomain, membrane region, the serine/threonine-protein kinase complex, the transferase complex, and the transferring phosphorus-containing groups. The MF of these proteins includes ubiquitin-like protein ligase binding, activating transcription factor binding, ubiquitin protein ligase binding, phosphatase binding, protein phosphatase binding, RNA polymerase II transcription factor binding, cytokine receptor binding, core promoter binding, DNA-binding transcription activator activity, RNA polymerase II-specific, and repressing transcription factor binding. The results of the GO analysis are as shown in Five core targets JUN, TNF, TP53, Akt1, and MAPK1 were selected based on the degree values, and then molecularly docked with the core bioactive ingredients kaempferol, quercetin, 22,23-dihydrostigmasterol, luteolin, anhydroicaritin, 8-(3-methylbut-2-enyl)-2-phenyl-chromone, isorhamnetin, formononetin, calycosin, and 7-O-methylisomucronulatol obtained by swiping the degree values. It is generally accepted that for a protein-ligand complex, the lower the binding energy, the higher the binding affinity. We set binding energy ≤ −5.0 kJ/mol as the standard. All primary cell cultures were used at early passages (3–5 passage), as shown in Figures P < 0.05).To detect the effect of CKG-medicated serum on hBMSCs' growth, we incubated cells in basic medium with different concentrations of NS and MS  for 24, 36, and 48 h. According to the results of the CCK-8 assay , the CKGα up-regulates several molecules such as the P2X7 receptor through the PI3K-Akt signaling pathway, the intracellular calcium concentration and secondary messengers, the entry of transcription factors into the nucleus, and the differentiation of bone marrow mesenchymal stem cells into osteoclasts [Comprehensively analyzing the screening results of the core targets and the KEGG enrichment analysis results, we can infer that CKG exerts its antiosteoporosis influence through multiple pathways. The top 3 pathways most closely related to OP in KEGG enrichment analysis are the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the TNF signaling pathway. The top 5 core targets of CKG for treating OP are JUN, TNF, TP53, AKT1, and MAPK1. AKT plays an important role in the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the TNF signaling pathway. AKT can negatively regulate B-Raf through phosphorylation of the B-Raf amino-terminal regulatory domain, which in turn affects the Ras/Raf pathway . The inheoclasts . TherefoWestern blot results are shown in OP is a metabolic disease caused by various factors, such as age, endocrine, viral infection, and various cytokines –33. GiveIn summary, this study analyzed the application of CKG in OP treatment by network pharmacology. We got 69 chemical ingredients, 250 targets of CKG, 1246 disease targets of OP, and 140 intersecting targets of CKG and OP. Topological analysis of the PPI network yielded 23 hub genes. GO enrichment analysis got 1723 entries, of which 1621 were BP entries, 20 were CC entries, and 82 were MF entries. The KEGG enrichment analysis yielded 180 entries. According to enrichment analysis and molecular docking technique, we inferred that the compounds of CKG -2-phenyl-chromone, isorhamnetin, formononetin, calycosin, and 7-O-methylisomucronulatol) could exert antiosteoporosis effects by targeting multiple proteins  and signaling pathways .α directly improves RANKL expression in osteoblasts and promotes osteoclast formation [in vivo and in vitro experiments indicated that TP53 gene expression and serum P53 levels were upregulated in OP patients and OP mouse models; high p53 levels were associated with reduced bone mass, which could be partially reversed by knocking down p53 [β-induced inflammatory damage of chondrocytes by increasing MAPK1 [+ T cells and Th17 cells [The three bioactive compounds in CKG with the highest degree values were 22,23-dihydrostigmasterol, kaempferol, and quercetin, which were obtained from collections of multidatabase information and stringent screening conditions. According to relevant studies, 22,23-dihydrostigmasterol (beta-sitosterol) can play an important role in antiosteoporosis treatment by affecting osteoblasts' activity . Kaempfeormation . Relateddown p53 . Restraidown p53 . MiR-186ng MAPK1 . Enrichm17 cells . Inhibit17 cells . The PI317 cells .The results of molecular docking showed the core bioactive ingredients in CKG had good binding activity to the core targets for treating OP. Comprehensively analyzing the results of PPI, KEGG enrichment analysis, and molecular docking, we speculated that CKG contributes to OP treatment by engaging in multiple pathways. Analyzing the results of KEGG, we concluded that the three most important pathways are the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the TNF signaling pathway. Through comprehensive analysis of the three pathways, we noticed that these pathways are all regulated by AKT, which can negatively regulate Raf, thereby affecting the MAPK signal pathway. AKT regulates the TNF pathway by phosphorylating IKK and is directly involved in the formation of the PI3K-Akt signal pathway. AKT is not only the core target screened by PPI but also the bridging protein between the top 3 pathways in the KEGG enrichment analysis. And related research studies have verified that regulating the AKT protein can affect these three pathways. Therefore, we speculate that CKG exerts an antiosteoporotic effect by affecting the expression of AKT. Then, we conducted related experiments. We used the CCK-8 method to observe the changes in cell proliferation rates after treating them with CKG-containing serum at different concentrations. Western blot was used to observe the changes of MAPK, PI3K-Akt, and TNF pathway-associated protein expression in cells treated with different concentrations of CKG-containing serum. The experimental results showed that CKG-containing serum can promote the proliferation of hBMSCs and that different concentrations of CKG-containing serum can increase the expression of AKT protein in hBMSCs. Therefore, we conclude that the antiosteoporosis effect of CKG may be achieved by regulating AKT and then engaging in the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the TNF signaling pathway.This study also has some limits, such as the accuracy and timeliness of database data, algorithms, and software characteristics of various analysis platforms. However, based on the completed research, the platforms and algorithms used in this study can meet the research needs. Besides, this study verified the mechanism of CKG in the OP treatment only through molecular docking and animal, cell, and protein experiments. But the results of this study can already indicate that CKG is involved in the main pathway of antiosteoporosis. Thus, we need to improve related experiments later to support the theory.In this study, the method of combining network pharmacology and experimental confirmation was used to investigate the complexity of multicomponent, multitarget, and multipathway of CKG, which provides essential information for further understanding of the drug-target interaction, a basis for extracting practical drug ingredients from CKG for OP treatment, and a reference for future demonstration of the scientific truth of TCM.