Familial hypercholesterolemia (FH) is an autosomal-dominant genetic disorder with a high risk of premature arteriosclerotic cardiovascular disease (ASCVD). There are many alternative risk assessment tools, for example, DLCN, although their sensitivity and specificity vary among specific populations. We aimed to assess the risk discovery performance of a hybrid model consisting of existing FH risk assessment tools and machine learning (ML) methods, based on the Chinese patients with ASCVD.In total, 5,597 primary patients with ASCVD were assessed for FH risk using 11 tools. The three best performing tools were hybridized through a voting strategy. ML models were set according to hybrid results to create a hybrid FH risk assessment tool (HFHRAT). PDP and ICE were adopted to interpret black box features.After hybridizing the mDLCN, Taiwan criteria, and DLCN, the HFHRAT was taken as a stacking ensemble method . The interpretation of HFHRAT suggests that patients aged <75 years with LDL-c >4 mmol/L were more likely to be at risk of developing FH.The HFHRAT has provided a median of the three tools, which could reduce the false-negative rate associated with existing tools and prevent the development of atherosclerosis. The hybrid tool could satisfy the need for a risk assessment tool for specific populations. Familial hypercholesterolemia (FH)  is an auAt present, genetic testing is the gold standard for FH . HoweverThe FH diagnosis rate is <0.1% in China . The pheAt present, prediction models based on datasets with a great number of variables and large sample sizes for FH risk assessment have been established in both the United States and Europe. For example, the FAMCAT in the United Kingdom , SEARCH Additionally, hybridizing existing FH risk assessment tools could achieve higher clinical and genetic detection rates than unadjusted, single tools . This wiData from 6,208 patients with ASCVD diagnosis at first discharge were collected from January 2012 to June 2020 in Peking Union Medical College Hospital (PUMCH). Data were then de-identified for anonymity. Cases with the following characteristics were excluded: (a) those without an LDL-c reading; (b) those with a history of ASCVD; (c) cases without total cholesterol (TC) record; and (d) those younger than 18 years, given the different criteria for the children.n = 3,993) were men, and the average BMI was 25.46 ± 3.32 kg/m2. Only 0.11% (n = 6) had tendon xanthomata. Overall, 24.71%  had been diagnosed as having premature coronary heart disease. We divided them into four groups according to the FH likelihood through the DLCN. The average age of four groups is decreased with the raising FH likelihood; for example, the patients with definite FH were 42.41 ± 9.19 years on average. More patients have been diagnosis with premature coronary atherosclerotic heart disease with a raising FH likelihood, and its prevalence in the definite FH patients is 100%. We have provided more information that is useful for describing our sample of 5,597 participants in In total, 5,597 cases made up the final dataset for criteria assessment and model development. Peking Union Medical College Hospital approved the study protocol, and all participants were provided with information regarding the objectives of the study and provided formal consent to participate.To establish an FH tool for the mainland Chinese population, we selected several tools to hybridize, using the following three steps:(I)We select the most frequently used tools in both Chinese and international populations. These included 11 risk assessment tools that intercalated varied items and cutoff values. The heads of these items can be divided into five levels, namely, lipid levels, physical examination, family history, clinical history, and genetic test. We have provided each of the factors included in the 11 tools in (II)We also select candidate tools for the hybrid model. In order to assess the tool performance, the 11 tools were assessed using the DLCN as the reference. As a four-level criterion, the DLCN determines “probable” and “definite” patients into a high-risk group. The remainder was considered low-risk; two of the 10 risk assessment tools were selected for high sensitivity and specificity. Additionally, the definition of the three variables has to be mentioned before the assessment and include untreated LDL-c, BMI, and the cutoff of premature age. Data lipid-lowering treatments and duration of interventions were recorded. Lipid-lowering treatments mainly included statins, ezetimibe, niacin, and fibrates. Based on dosage, the treatments could be divided into three levels: high, median, and low potency. Evidence of receiving lipid-lowering interventions provides insights into LDL-c adjustments , which i(III)We hybridize the selected tools. We combined the two aforementioned tools with DLCN using a voting strategy . To discIn order to establish the predictive model based on the dataset with a large number of variables and sample size, the ML models have been built as an FH risk predictor and to avoid personal biases during the multi-tool application, simultaneously. There were essentially three steps involved in establishing the predictive model.The first step in this process was variable selection. For this, 33 variables were collected directly through the electronic medical record (EMR) system. Variables were then divided into four categories namely demographics, patient histories, laboratory examinations, and family histories. Patient variables and family histories were based on the items from 11 frequently used risk assessment tools. Demographics, that is, age and gender and clinical characteristics including TC, LDL-c, HDL-c, and Lp(a) were included in respective laboratory indexes.After stratifying the dataset with HYR into two subgroups, the variable selection process was considered under univariate analysis and through a four-variable selection method. The variable selection method included the Lasso , elasticThe second step in this process was the FH risk predictor step which included establishing the Hybrid FH Risk Assessment Tool (HFHRAT). HYR was set as the outcome, with four ML methods taken as the main structure. These were then compared in terms of their performances using logistic models, including extreme gradient boosting (XGBoost) , RF 33)33), suppResults were integrated using several models using the same ML method. Stacking is a heterogeneous method that links the results of base-level models set by different methods and further inputs into the meta-level model as independent variables. In this study, base-level models included RF, SVM, and BPANN, while simultaneously taking the logistic as the meta-level model. In total, six indexes assessed the performance of classifiers, including accuracy, sensitivity, precision–recall F measure (F), AUC, root-mean-squared error (RMSE), and G-mean value.To simplify the use and explanation for participants, the probability model was also established using five indexes for performance assessment, consisting of AUC, RMSE, mean of calibration error (CAL mean) , and BriDuring the final step, we interpreted the HFHRAT. ML models are not best suited to describing relations between independent and dependent variables. Therefore, we implemented interpretative machine learning methods, such as individual conditional expectation (ICE) and partial dependence plot (PDP), which are two model agnostic methods . The ICEt-test, the Kruskal–Wallis test, and χ2 analysis were implemented for inferential analysis.To discover additional changes these hybrids present, we have compared all 33 variables for high-risk group predictions by five tools, including three existing tools, the HYR, and the HFHRAT. Continuous variables are presented as means with SDs. Categorical variables are presented as simple frequencies and percentages. Student’s Identifiers were constructed using R software (version 4.0.2). Refer to the “Tools were assessed according to performance using the final dataset and were compared with the DLCN using five clinical measures. According to the DLCN assessment, the FH prevalence in our sample was 2.57%, which has two distinct elements (herein referred to as levels). In order to ensure consistency, each of the remaining assessment tools to be analyzed here was also modified to have two distinct levels. Additionally, without the result of the genetic test, the Japanese FH Management Criteria (JFHMC) could not predict the FH diagnosis and therefore was excluded because it cannot effectively identify high-risk cases.The existing tools were selected in two aspects, namely sensitivity and specificity, to ensure the higher accuracy of the following hybrid tool. In terms of sensitivity, both the mDLCN and the LDL-C/TC tool have reached 97.22%, in which the latter assessed only laboratory test indexes, that is, LDL-C or TC, which yielded a high AUC (92.46%) and a slightly lower specificity (87.51%) compared to the mDLCN (AUC [95.06%] and specificity [92.90%]). In terms of specificity, TW and American Heart Association (AHA) reached 100%; however, the TW criteria had the highest sensitivity (41.54%) of the three. Therefore, the mDLCN and TW were the two selected tools. The Dutch Lipid Clinic Network, modified DLCN for China (mDLCN), and TW diagnostic criteria are three 4-level tools. With a voting strategy, their HYR was also a 4-level tool. Overall, 4,285 ASCVD patients were categorized as unlikely FH cases according to all three tools. However, different cutoff values for each tool led to an inconsistent risk-level assessment of a single sample, that is, a sample may be assessed by DLCN as probable FH, mDLCN as definite FH, and TW as possible FH. To discover all high-risk patients, we further grouped HYRs into two levels according to “possible,” “probable,” and “definite” FH cases as high risk, which means the aforementioned samples were identified as high-FH risk patients. For another group of samples, they were assessed by the DLCN as possible FH, mDLCN as probable FH, and TW as unlikely FH. According to the voting strategy, the results of the two tools owned to the high risk, and these samples were identified as the high-FH risk samples. For details of the voting strategy and the hybridizing process, see the The HYR modified the three existing tools into a robust single tool; however, the cutoff for every single item remained unknown using a crude voting strategy. When the HYP is applied, clinicians will have to learn several tools simultaneously, which may introduce personal biases into FH diagnosis. Therefore, the ML model was established based on the HYR, for better application and correlation clarification.The dataset was stratified according to HYR into high-risk and low-risk groups. In all, 33 significant variables remained between the groups for variable selection, while more than two variable selecting methods highlighted 14 candidate variables. LDL-c was identified as the most correlated variable by all methods, and the highest LDL-c during admission was included in the final model.We included three variables identified in the DLCN and TW: history of premature CHD, premature stroke, and the family history of premature CHD. To avoid problems caused by overlapping information, we also eliminated variables with smaller coefficients. Finally, nine variables were left for further model development which coefficient in each variable selection method has been displayed in The HFHRAT was based on the aforementioned nine variables as the independent variables and the HYR as the dependent variable. The HYR divided the predictor setting dataset into two groups: 1,112 high-risk participants, that is, level 1, and 4,485 low-risk participants, that is, level 0 participants. The number of participants in the low-risk group was predictably four times larger than that in the high-risk group. We applied ensemble learning to handle problems caused by imbalanced data. Under the process of 10-fold cross-validation, 7-fold was randomly selected and combined to create a training dataset, while testing the model performance with the remaining 3-fold.The HFHRAT is composed of the stacking models with the best performance in both classifier (AUC_class[94.85 ± 0.47]) and the probability predict model (AUC_prob[98.66 ± 0.27]). The BPANN also showed high AUC with AUC_class [90.65 ± 1.04] and AUC_prob [98.4 ± 0.27]), however, BPANN was not applied for further application, considering the lack of sensitivity [84.56 ± 2.19] and the predicted accuracy in the minority group can hardly be compromised. The XGboost model has shown high sensitivity [95.34 ± 1.52] but lacks separation in two classes, especially in the classifier . The performance of classifiers and probability models is given in Partial dependence plot was used to explain correlations between age, LDL-c, and lipid-lowering therapies with FH risk. According to our dataset, 1,947 patients were yet to receive any lipid-lowering medication before admission. In total, 312, 3,210, and 128 patients had received low-, medium-, and high-potency statins before admission, respectively.We divided the dataset into four subsets according to lipid-lowering therapies and analyzed correlations. According to PDP, the risk of FH correlated with an upward trend in all participants and across each subset. With the increasing level of lipid therapies, the risk of FH increased at the lower level with LDL-c. The decreasing trend of age to the FH risk was associated with limited changes among the different levels of therapies; however, the FH risk has been improved with the growing level of lipid-lowering therapy. The high-risk FH patients without lipid therapy were mostly patients aged <70 years and LDL-c > 4.5 mmol/L. As the level of lipid therapy increased, the range of the age and LDL-c have become more concentrated in the patients younger than 75 years with LDL-c > 4 mmol/L. See To further discover the use of these hybrids in the tools, we have further compared the features of the high-risk groups predicted by five tools, including three existing tools, HYR, and the HFHRAT. Based on the voting strategy, HYR has found the median of three tools. With the ML algorithms, the HFHRAT has followed the data distribution and further adjusted the median position by enlarging the difference between the high- and low-risk groups. Of all 5,597 participants, 1,076 have been predicted to be high risk by DLCN, while 1,234 by the mDLCN, 959 by TW, 1,112 by HYR, and 1,116 by HFHRAT. For family history of hypertension, 39.6% of the high-risk participants defined by the DLCN have, while 39.7% in mDLCN, 39.1% in TW, 39.3% in HYR, and 40.1% in HFHRAT. Analytical results for the remaining features have been displayed in the The hybrid FH risk assessment tool is a novel risk assessment tool based on hybridizing diagnostic with machine learning. This was designed for early identification of FH risk, specifically for mainland Chinese populations. The HFHRAT modified the median of the existing tools and avoided personal biases which can be a problem when applying hybridized tools. Despite having a rather heterogeneous population, this highlights a marked improvement and, of course, supports the need for a more accurate tool for mainland Chinese. It is hoped that this study will not only benefit those in China by identifying and preventing to development of ASCVD but will also help the global research community who are striving to improve risk assessment tools of this type.In our research, the overall prevalence of FH in patients with ASCVD (definite and probable) was 2.57%, of which definite FH accounted for 0.52% and probable FH was 2.05%, based on the DLCN. Although the FH prevalences are different between regions  and evenThe diagnosis of the study population originated from the hybrid result of the three existing tools. Each of them is applicable to diagnose both homozygous and heterozygous FH. Homozygous FH is an orphan disease, with a low prevalence of 1 in 300,000 individuals . HomozygThe 10 existing FH risk assessment tools we selected were from the former two categories and have estimated the FH risk in each participant in our dataset, which performed their consistency and difference with DLCN. In the consistency aspect, two modified DLCN criteria for Han Chinese specifically have left, including mDLCN and TW. The mDLCN has higher sensitivity than DLCN and the genetic test ; howeverTo eliminate the personal bias and get the cutoff value, the multi-variable models were settled as the classifier and probability predicting models with both the hybrid diagnosis and machine learning algorithms, named HFHRAT. The best performing model was finally set with the stacking ensemble algorithm, and nine variables yielded an AUC for the classifier, which was 94.85 ± 0.47%, and performed similarly to the previous ML models based on the participants from other regions , which rFor the number of the variables mentioned in the model, the FindFH model  for the Most of the previous classifiers preferred the tree model for easier interpretation, which may compromise the accuracy of the model. The stacking ensemble learning model performed the best in our research, and its black box feature may be a disadvantage before the appearance of interpretation machine learning compared to the tree models. As a data-derived method, the correlations in the machine learning models mostly originated from the training dataset directly and can hardly acquire from the domain expertise, which brings potential pitfalls, such as unwanted confounding or interaction effects. The black box feature is the result of these effects. With two interpretation machine learning methods, PDP and ICE, the correlations in the model have been unveiled and assessed whether they corresponded with the natural laws. We have unveiled correlations among age, LDL-c, and the level of lipid-lowering therapy on the FH risk assessments. For the normal value of LDL-c ranging from 2.07 to 3.11 mmol/L, the participants with LDL-c ≥ 5.0 mmol/L can be defined as the possible FH by DLCN and TW, so as it is with the LDL-c ≥ 3.5 mmol/L in mDLCN. In HFHRAT, the LDL-c has been modified through the voting strategy and with machine learning methods using LDL-c levels in the three existing risk assessment tools, including DLCN, mDLCN, and the Taiwan FH diagnostic criteria (TW). LDL-c in the HFHRAT machine learning model highlighted the highest low-density lipoprotein cholesterol in first-ever ASCVD admissions. With the interpretation of the PDP, the participants with untreated LDL-c > 4.5 mmol/L without any lipid-lowering therapy are the possible FH, which is the median of the aforementioned three tools. The PDP and ICE have first estimated the LDL-c level of the participants with various levels of lipid-lowering therapies.n = 1,234), higher than that in the DLCN , TW (n = 959), and modified HYR . The HYR has found the “median” position in these tools, while the HFHRAT has modified this “median” position based on the data distribution resulting from the features of the machine learning algorithms. For example, lp(a) is 199.21 ± 226.79 mg/L in the high-risk group of HYR, which is lower than that of the TW (201.52 ± 230.90 mg/L) but higher than that of the DLCN (198.39 ± 224.20 mg/L) and mDLCN (198.39 ± 225.05 mg/L). Although lp(a) is 198.04 ± 228.51 mg/L in the high-risk group of HFHRAT and the modified process has a lower lp(a) value, the high lp(a) still has a good effect on FH risk assessment  with premature CHD in the level 1 group defined using DLCN . Level 1 defined using HFHRAT may be higher in age, but this approach compromised the prevalence of premature CHD and several other specific clinical features of patients with FH. However, this provided the unique potential for HFHRAT in FH likelihood detection and highlights its effectiveness in early diagnosis. Nevertheless, FH still requires results from patients at a longer follow-up and with genetic testing. Other variables with levels between 1 and 0 were divided according to the five risk assessment tools, provided in the The hybrid FH risk assessment tool is a machine learning model based on the HYR, which is a majority testing result  of three tools, including DLCN, mDLCN, and TW. The performance of each tool could be tested by sensitivity and specificity. Majority testing avoids the trade-off and results in relatively high overall values for both test characteristics . In thisWhile there were some advantages and this study adds to the evidence base, there are few limitations to this study. First, 5,597 participants were all from a single center. This is likely to have skewed data to some extent, although further biases are introduced when analyzing multi-centric datasets. However, the final identifier , a voting strategy tool with three tools to avoid discrepancies among the DLCN, mDLCN, and TW. The mDLCN and TW were selected for consistency with the DLCN, which is most commonly used in clinical practice. The PDP and ICE revealed that the Chinese participants were younger than 75 years and untreated LDL-c > 4.5 mmol/L with the wFH risk. We would encourage these participants to assess their FH risk through the HFHRAT, and the high-risk ones shall further receive the genetic test in addition to the lipid-lowering therapy. We envisage that this risk assessment tool could perform efficiently in FH diagnosis in China and reduce the development of ASCVD and associated deaths.The original contributions presented in this study are included in the article/The studies involving human participants were reviewed and approved by Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China. The patients/participants provided their written informed consent to participate in this study.SZ, LW, and JG designed the work, while ZT and YJ provided valuable advice in terms of design and interpretation. LW and JG have collected and interpreted the dataset. LW created the codes and put them into practice. SS helped to write and edit this report. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: Celastrol, an important extract of Tripterygium wilfordii, shows strong antitumor activity in a variety of tumors including nasopharyngeal carcinoma (NPC). However, little is known about its targets in NPC. We aimed to screen the key gene targets of Celastrol in the treatment of NPC by means of in silico analyses (including network pharmacology and molecular docking) and experimental evaluations.Methods: The main target genes of Celastrol and the genes related to NPC were obtained by retrieving the relevant biological databases, and the common targets were screened. Protein-protein interaction analysis was used to screen the hub genes. Then, a “compound-target-disease” network model was created and molecular docking was used to predict the binding of Celastrol to the candidate hub proteins. Afterward, the expression changes of the candidate genes under the administration of Celastrol were verified in vitro and in vivo.Results: Sixty genes common to Celastrol and NPC were screened out, which may be related to numerous biological processes such as cell proliferation, apoptosis, and tube development, and enriched in various pathways such as PI3K- Akt, EGFR tyrosine kinase inhibitor resistance, and Apoptosis. The tight binding ability of the candidate hub proteins  to Celastrol was predicted by molecular docking . In vitro experiments showed that the expression of TNF and VEGFA decreased while the expression of IL6 increased in NPC cells (CNE2 and HONE1) treated with Celastrol. In vivo experiments suggested that Celastrol significantly reduced the weight and volume of the transplanted tumors in tumor-bearing mice in vivo. The expression of TNF, VEGFA, and IL6 in the transplanted tumor cells could be regulated by using Celastrol, and the expression trends were consistent with the in vitro model.Conclusion: Several gene targets have been filtered out as the core targets of Celastrol in the treatment of NPC, which might be involved in a variety of signaling pathways. Hence, Celastrol may exert its anti-NPC activity through multiple targets and multiple pathways, which will provide new clues for further research. Future experiments are warranted to validate the findings. Nasopharyngeal carcinoma (NPC) is a kind of malignant tumor that mainly occurs in nasopharyngeal epithelial cells. It is common in Southeast Asian countries and in Southeast China . The onsIn recent years, more attention has been focused on the efficacy of natural bioactive products for NPC treatment, which not only have strong biological activity but also are safe and lack systemic toxic effects . For exaTripterygium wilfordii (T. wilfordii), has strong anticancer activity. Celastrol is rich in the roots, stems, and leaves of T. wilfordii, and it has three terpenes in its structure , TCMIP , TCMIP , and othFirst, we obtained the target gene of Celastrol by using the TCMSP platform. Second, the smile structure of Celastrol was introduced into the Swisstarget platform, and the probability (probability >0) was set as the screening condition to obtain the predicted target genes of Celastrol. Third, the target genes with scores >20 were screened using the BATMAN-TCM tool.www.uniprot.org) to obtain the target gene name. Finally, the target genes obtained by the above two approaches were combined to obtain the predicted targets gene of Celastrol.After obtaining the target gene/protein name, the protein name was corrected using the UniProt database (www.genecards.org)  . The keyTo obtain the common target genes of Celastrol and NPC, we analyzed the intersection of Celastrol target genes obtained in the above steps and NPC-related genes. The common related targets were obtained based on the Venn diagram. These genes can be considered potential target genes of Celastrol on NPC.www.string-db.org) (Homo sapiens” was selected to construct the protein-protein interaction network (PPI network).Interactions may be observed between proteins. We used the STRING database (-db.org)  to prediThe cytohubba plug-in of the Cytoscape tool was used to calculate the gene network and sorted according to the degree value, and the hub genes were screened.To understand the biological functions and the possible enriched signaling pathways of the common target genes, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to learn the possible molecular mechanism of Celastrol in the treatment of NPC.For GO analysis, the c5.go.bp.v7.4.symbols.gmt subset from the Molecular Signatures Database was used as the background, while for KEGG analysis, the KEGG rest API was used to obtain the latest gene annotation of the KEGG pathway as the background. The genes were mapped to the backgrounds. Then, the R software package clusterprofiler (version 3.14.3) for enrichment analysis was utilized for analysis. The false discovery rate (FDR) < 0.05 was considered statistically significant.Cytoscape 3.2.0 software was used to construct the “compound-target-disease” network interaction model, in which the “node” represents the compound, disease, or target and the “edge” represents the interaction relationship.To verify the possible binding force between Celastrol and the hub proteins, we used molecular docking technology.www.rcsb.org)  , and thecsb.org) , and theG  value, the binding force between the drug and protein was determined. A negative value indicates that they can be combined. If the negative value is below −7, the drug and protein can be very tightly coupled.According to the estimated Δ2 at 37 °C. The cells were respectively separated into two groups: the Celastrol-treated group (10 μM Celastrol) and the control group (without Celastrol)  containing 5% fetal bovine serum in a humidified atmosphere with 5% COlastrol) .After intervention for 24 h, the mRNAs of the top hub genes  were detected by RT-PCR assay as described previously . Each saTNF ; VEGFA ; IL-6 ; GAPDH The protein expression of the three top genes was detected by western blot assay as previously described . In brieThe sources of antibodies were as follows: Tumor necrosis factor alpha (TNF/TNFa) mouse Monoclonal, Vascular endothelial growth factor A (VEGFA) rabbit polyclonal, Interleukin-6 (IL6) rabbit polyclonal, and GAPDH rabbit polyclonal . The bands were visualized and quantified using the Image-Pro Plus 6.0 software .3 in the logarithmic growth phase. After a specific length of time after each group of cells had been cultured, CCK-8 reagent was added to the plates and OD450 values were monitored.Cell viability was evaluated by Cell counting kit-8 assay . Cells were reseeded into a 96-well culture plate at a density of 4×10To learn the efficacy of Celastrol on the expression of the candidate genes, animal experiments were further carried out. All animal experimental protocols were in accordance with the principles established by the Animal Care and Use Committee of our institute. Female BALB/c nude mice between 4 and 5 weeks of age were purchased from Charles River Laboratories .From February to April 2022, the observation was performed. For the nude mice xenograft model, HONE1 cells were subcutaneously injected. Twelve mice were divided randomly into two groups (the Celastrol-treated and the control). Then, 2 mg/kg Celestrol  or saline  was intraperitoneally administered every 2 days in each group . The tumt-test, Analysis of Variances, or a Wilcoxon Rank Sum Test according to the concrete types of the data. These analyses were performed by using the MedCalc software . p < 0.05 was considered statistically significant.Differences between groups were assessed with a 29H38O4, and its relative molecular weight is 450.6 .The intersection of the two target gene sets above was obtained based on the Venn diagram, which contained 60 possible target genes of Celastrol for NPC treatment .To obtain the pivotal genes that play an important role in the biological process, the common targets obtained in the above process were imported to the STRING tool for protein-protein interaction network construction.The network contains 59 nodes and 1,050 edges. Each node represents a gene/protein. Each edge represents the interaction between the proteins. The more edges represent the closer the interaction, as shown in To determine the key genes in the network, we used the Cytoscape software to calculate the degree value and sort them according to the value. The top 10 genes were ranked according to the degree value in GO enrichment analysis mainly includes “biological process” (BP), “cellular component”, and “molecular function”. In the present study, we selected the most commonly used BP for analysis.The results of GO enrichment showed that these genes were enriched in more than 1000 GO entries by screening according to the standard of FDR <0.05. The top 10 GO terms are listed in KEGG pathway enrichment results showed that these genes were enriched in 105 pathways. The interaction relationship among Celastrol, NPC, and gene targets was visualized using the Cytoscape software. G  less than 0 indicates that the molecules could be docked, and a score close to −7 indicated that the docking was good. The docking energy of TNF was −6.08, that of VEGFA was −6.76, and IL6 was −6.91.The docking of Celastrol with the top three hub proteins, namely, TNF, VEGFA, and IL6 was shown in p < 0.05, p < 0.05) while the expression of IL-6 was increased relative to the controls, respectively (p < 0.05, NPC cells were treated with or without Celastrol for 24 h. The cell viability of the cells in the Celastrol-treated group was significantly lower than that of the cells in the control group (in vivo experiments showed that both the volume and the weight of the transplanted tumors were smaller in the Celastrol-treated group than those in the control group (p < 0.05, in vivo. Celastrol treatment did not markedly induce changes in body weight in mice (in vitro (Further  in mice . Next, tin vitro .T. wilfordii is a natural plant as well as a traditional Chinese medicine that has been widely used for the treatment of various diseases, such as nephritic syndrome, inflammatory bowel disease, systemic lupus erythematosus, rheumatoid arthritis, and other diseases (T. wilfordii, and its antitumor activity has gradually attracted people’s attention (in silico analysis to understand the targets and signaling pathways of Celastrol on NPC from multiple angles and levels. Sixty common targets were screened out, which are mainly enriched in multiple cell functions, such as cell proliferation, apoptosis, cytokines, and tube development. These genes may also participate in multiple signaling pathways, such as PI3K/Akt, EGFR tyrosine kinase inhibitor resistance, apoptosis, and AGE-RAGE signaling pathway in diabetic complications. Through the construction of a protein−protein interaction network, key genes such as TNF, VEGFA, and IL6 were identified.diseases . Celastrttention . This stCelastrol may exert anticancer activity through various mechanisms. For example, in pancreatic cancer cells, Celastrol plays a cytotoxic role by regulating various gene expressions, mainly through ATF3/DDIT3 overexpression and RRM2/MCM4 downregulation . CelastrAmong the common targets, some genes such as TNF, VEGFA, IL6, Myc, and CCND1 are in a pivotal position. These genes play a very important role in the occurrence and development of tumors. For example, in glioma, some non-coding RNAs promote tumor cell growth and tumor angiogenesis by regulating VEGFA expression . Moreove+ T cell activation. Instead of observing a tumor-suppressive effect, accelerated tumor progression was monitored in melanoma mice treated with IL6-blocking antibodies (We examined the changes in TNF, VEGFA, and IL6 proteins after Celastrol treatment. Several studies have found that VEGFA is highly expressed in NPC cells and is associated with cell proliferation, invasion and metastasis, chemosensitivity, radiosensitivity, and other phenotypes of cancer cells . High extibodies . The eviAlthough computer-simulated molecular docking technology suggests that there may be a direct binding relationship between Celastrol and the candidate proteins, the direct relationship needs further confirmation by experiments using specific methods, such as fluorescence polarization immunoassay , isotherDL can reflect whether a compound has the physical, chemical, and biological characteristics of becoming a drug, which is related to clinical efficacy . The grein vivo experimental part, only one type of cell was used for transplantation tumor experiments. In future experiments, more experiments need to be performed with other cell lines to eliminate the bias of the results brought by the use of a single cell. In addition, whether the core genes have reciprocal relationships with each other needs to be investigated in future studies. However, the method and process of network pharmacology analysis used in this study are relatively mature, and the results obtained by this method are of great relevance as they point the way and provide clues for subsequent basic experiments. It is also important to note that female mice were used for animal experiments in this study, and although the experimental animals were not deliberately selected in terms of sex, this may have biased the experimental results in some ways. Moreover, the sample size of the experimental animals was only referred to other literature (This study has some limitations. First, in exploring the targets of Celastrol, not all commonly used databases can be searched for relevant drugs and targets, and different databases contain different targets because of different algorithms. Second, in the animal experimental part, some serological indicators can be considered, and also, the damage of Celastrol on solid organs such as the liver and kidney can be assessed. Third, considering the complexity of biological processes, the target genes obtained in this study are not necessarily the main genes of Celastrol acting in NPC. More experimental results, literature mining, or algorithm updates may reveal more gene targets and help us understand the molecular mechanism of Celastrol in the treatment of NPC more comprehensively. Therefore, the results of this study should be interpreted carefully. Fourth, in the terature  without This study uses bioinformatics methods to analyze the potential molecular mechanism of Celastrol for the treatment of NPC from multiple angles. The results suggest that Celastrol may play an intervention role in NPC through multiple targets, pathways, and angles. The results of this study will provide an important reference for subsequent basic and drug experiments. However, further experiments and repeated verification are needed to provide more information and thus uncover the anticancer mechanism of Celastrol.


Xenopus laevis. Xenopus has long been used to study developmental and cell biology, and is an increasingly important model for human birth defects and disease, genomics, proteomics and toxicology. Scientists utilize Nieuwkoop and Faber's classic ‘Normal Table of Xenopus laevis (Daudin)’ and accompanying illustrations to enable experimental reproducibility and reuse the illustrations in new publications and teaching. However, it is no longer possible to obtain permission for these copyrighted illustrations. We present 133 new, high-quality illustrations of X. laevis development from fertilization to metamorphosis, with additional views that were not available in the original collection. All the images are available on Xenbase, the Xenopus knowledgebase (http://www.xenbase.org/entry/zahn.do), for download and reuse under an attributable, non-commercial creative commons license. Additionally, we have compiled a ‘Landmarks Table’ of key morphological features and marker gene expression that can be used to distinguish stages quickly and reliably (https://www.xenbase.org/entry/landmarks-table.do). This new open-access resource will facilitate Xenopus research and teaching in the decades to come.Normal tables of development are essential for studies of embryogenesis, serving as an important resource for model organisms, including the frog  Summary: A new open-access Normal Table of Xenopus development is presented that integrates new embryo images with gene expression markers in Xenbase, the Xenopus biomedical research knowledgebase. Xenopus development depends upon the temperature of the water in which they grow, and these temperatures vary throughout the day and between individual laboratories, standardizing experiments to allow comparisons between laboratories and between experiments is inherently problematic. Since 1956, the standard reference for Xenopus laevis development has been the Normal Table of Xenopus laevis (Daudin) , edited by Normal tables of development are essential to allow different researchers to compare their results and to make experimental replication possible. Because the speed of Xenopus ‘from the fertilized egg till the end of metamorphosis’, as the subtitle of the Nieuwkoop and Faber Normal Table states, started with field work to collect X. laevis from ponds near Stellenbosch in South Africa. The resulting work was an international collaboration of 28 embryologists, zoologists and field biologists, including the editors, from nine countries, all experts in the different organ systems in Anurans and other vertebrates. The primary data in the Normal Table was included in Chapter VI, expanded into 21 ‘divisions’, which detailed the temporal development of organs and systems including the nervous systems and sense organs; the early axial system; the skeleton and musculature of head, trunk and tail; the lateral line, skin and pigmentation; brain; cephalic nerves; spine and spinal ganglia; eye; head; heart and vasculature; gonads, adrenal glands; and the intestinal tract. The last two divisions (XX and XXI) focused on the gross anatomical and histological changes in the alimentary system, such as intestinal gut coiling. Chapter VII then summarized all external and internal criteria in detail for each developmental stage in chronological order from Nieuwkoop and Faber (NF) stage 1 (fertilized egg) to NF stage 66 (the froglet)  , 1994. Ifroglet) . A latereference .Xenopus species, including X. tropicalis, a physically smaller species widely used in disease modeling owing to its simpler diploid genome (X. laevis is pseudotetraploid)  , and eveody size . AlthougOne of the most frequently used parts of the Normal Table is the set of 125 drawings by J. J. Prijs. Created in the style of embryologists of the era, these were based on pencil drawings Job Faber made on his South African field trip. These illustrations were initially used to great advantage by the Nieuwkoop laboratory during the pre-genomic era to characterize the timing of inductive signaling involved in the formation of the mesoderm germ layer and the neural ectoderm . Others Now, more than 65 years after the original publication, J. J. Prijs' classic drawings are considered an essential resource and are used extensively in teaching and research. There has always been a demand to reuse these drawings in publications, yet it is no longer possible to obtain copyright permission. Nieuwkoop and Faber personally held copyright as editors, and this was reinstated in subsequent reprintings in 1967, 1975, and lastly in 1994. Xenbase was given permission to display the entire series NF drawings in 2008, and these can be downloaded for personal use; however, to the best of our knowledge, the Nieuwkoop and Faber images are not in the public domain and still require permission for subsequent reuse in publications. Herein lies the conundrum: it is no longer possible to obtain re-use permission as the copyright holders are both deceased, and the publisher of the latest edition, Garland Publishing, Inc. (New York and London), is out of business. There is literally no one from whom to request permission.X. laevis development, from the fertilized egg to metamorphosing tadpoles and froglets, adding to a set of 30 previously released Zahn drawings that focused on anterior views of craniofacial development from the XenHead project (Table S1).Aside from the copyright issues, a few limitations of the Normal Table drawings have become apparent over the years. A number of important views of embryos and tadpoles are missing from the collection. For example, there are no anterior views after NF stage 21, no ventral views of neurula or early tadpoles at NF stages 13-40, dorsal views from NF stages 28-59 are not included, and views of larger tadpoles and metamorphic stages are truncated, omitting the tail. In addition, the gut-coiling diagrams that accompanied NF stage 43-45 lack sufficient detail to reflect accurately stage-specific changes in digestive system development. To address these limitations, we generated a new, open-source graphical resource to be used in conjunction with the original Normal Table text. This new resource includes 133 new high-quality illustrations of  project . ImportaXenopus knowledgebase (http://www.xenbase.org/entry/zahn.do), and can be downloaded and reused under an attributable, non-commercial creative commons license. This new open-access resource will enable Xenopus research and teaching in the decades to come.All the images in the Zahn series are available on Xenbase, the Xenopus embryonic stages (NF stages 1-66) . To define a scope of work we prioritized: (1) standard views ; (2) stages most frequently assessed in Xenopus research (based on curation of Xenopus literature in Xenbase); while (3) attempting to cover comprehensively development from fertilization to metamorphosis; and (4) adding additional views not included in the 1956 Normal Table, but deemed useful to current Xenopus research . The result was a scope of work with 133 illustrations of 56 developmental stages, spanning fertilized egg to froglet stages, including a composite illustration of hindlimb and forelimb development and new gut-coiling diagrams. Of these drawings, 100 are fully rendered and shaded, and 33 are unshaded line drawings. In total, the Zahn collection now consists of 198 images of X. laevis, covering 60 of the 66 NF developmental stages, 73 more than the original 1956 artwork by J. J. Prijs .We first compared the existing 125 Nieuwkoop and Faber illustrations, and the 30 Zahn drawings from the XenHead project , to all Xenopus embryos is a skill that takes considerable practice to master. Our concept for a series of ‘staging landmarks’ arose from students’ comments highlighting the need for a simplified ‘staging for beginners’ chart. Our goal was to generate a list of external anatomical features that experienced bench scientists look for when staging embryos, which could be used by students at the bench and by newcomers to Xenopus development, in addition to seasoned experts. We liken the concept of Xenopus staging landmarks to ‘birding field marks’, a simple yet distinctive combination of just a few traits that birders use to identify morphologically similar bird species in the field . With input from experienced Xenopus researchers, and with careful reference to descriptions from the 1994 edition of the Normal Table  and immunohistochemistry (IHC) data curated in Xenbase, the Landmarks Table includes key gene expression markers of specific tissues and organs, data that was unknown at the time of the original Normal Table's publication, but which are now standard tools in most research laboratories. Importantly, molecular markers often indicate tissues and organ primordia prior to the overt formation of anatomical structures. For example, expression of the homeobox gene nkx2-1 can be detected by ISH in the respiratory epithelium as early as NF stage 33-34, prior to the emergence of lung buds at NF stage 40 (Leveraging decades of stage 40 .Xenopus staging Landmarks Table (Table S1) will be maintained on Xenbase (https://www.xenbase.org/entry/landmarks-table.do) as a living document, updated over time with community input and new research findings. Extensive gene expression data are also searchable on Xenbase using either the Expression Search tool (http://www.xenbase.org/geneExpression/geneExpressionSearch.do?method=display) or the Xenopus Anatomy Ontology (XAO)/Search Anatomy menu (http://www.xenbase.org/anatomy/anatomy.do?method=display&tabId=2). Curated marker gene lists, based on published and community submitted gene expression images, are also provided for hundreds of XAO terms on Xenbase  is the standard marker for mesoderm.Early tage 6.5 A, cell dtage 6.5 B, the fltage 6.5 C from NFtage 6.5 D. This itage 6.5 . At NF ssox2 expression in the dorsal ectoderm , a process which proceeds in a posterior to anterior direction. Accurate staging during early neurulation A-E, NF sst cells , 2009. Nst cells F is a po vesicle , tekt3 apidermis , and paxsenchyme .Fig. 3.myod1 can clarify the boundary between the formed somites and the presomitic mesoderm from NF stages 22  and the beginning of the pericardial cavity. The otic vesicles (the auditory system primordia) detach from the epidermis at NF stage 28, yet are difficult to see under light microscopes, although they are commonly detected by sox3, sox9 and eya1 expression . The developing eye cup forms a gray disc at NF stage 29-30 , as is the angle formed between the tail and the posterior end of the gut (the future proctodeum) to distinguish NF stage 37-38 (140°), NF stage 39 (125°) and NF stage 40 (90°). Additional external landmarks include the extent of migration of the pigmented melanophores over the head and trunk starting at NF stage 33, and along the tail by NF stage 39. NF 35-36 marks a major behavioral transition as tadpoles naturally break free from their vitelline membrane to become free-swimming larvae/tadpoles.As embryos lengthen as a result of tail growth, their abdominal area simultaneously ‘shortens’ as undifferentiated gut endoderm begins to form distinct fore-, mid- and hindgut domains. The ‘tail length to gut length’ ratio is used as an external landmark , and aplnr for blood vessels , nephrostomes (which collect the urine/filtrate) and pronephric duct (which drains the filtrate) form from the nephrogenic intermediate mesoderm by NF stage 28-30 . The proand lhx1 . Once epsporters .en2 , pax2 and fgf8 mark the midbrain-hindbrain boundary  and the hindbrain rhombomeres  at NF stage 31-32 . At NF stage 47, the ‘atria’, where forelimb will develop, is formed; however, forelimb buds are not visible until NF stage 50  focus on limb growth and increasing numbers of pigmented melanophores , the respiratory system changes from gills to lungs and the limb musculature and skeleton mature. The relative proportions of limb segments and the position of the growing limbs are used as key staging landmarks. At NF stage 55 (not shown), the forelimb hand rotates 90° and the digits/fingers are as long as they are wide. At NF stage 56 C, the hi4) and 3,5,3′-l-triiodothyronine (T3) are the key driver of metamorphosis (Table S1). First detectable at NF stage 47, TH levels dramatically increase from NF stage 54 (Levels of the thyroid hormones (TH) thyroxine , and the claws form on the tips of hindlimb digit tips (they are white at first). At NF stage 59  provides a succinct guide that can be used by experts and the inexperienced alike, to stage embryos quickly and reliably. The Landmarks Table not only includes external morphology, but also internal anatomical landmarks based on descriptions in the Nieuwkoop and Faber Normal Table , an industry standard used to assess the developmental toxicity of pharmaceuticals and food supplements , with term definitions being directly adapted from the Normal Table (e.g. NF stage 42 is XAO ID:l1000054) (Xenopus Phenotype Ontology (the XPO), which is used to describe experimental phenotypes and link these phenotypes to human disease (https://www.xenbase.org/entry/landmarks-table.do) has live links that direct researchers to (1) gene pages, with links to extensive gene-specific expression data, genomic resources and orthologs, and to (2) XAO pages, with tissue-specific gene expression data . Researchers can use Xenbase search tools to interrogate and efficiently connect anatomical development to curated data  and primary literature sources  . The XAO disease . On Xenb sources . ImportaXenopus development. We anticipate that the new Zahn images and their integration on Xenbase will facilitate the ongoing renaissance of Xenopus research in the genomics of human disease modeling. Finally, we hope that releasing the Zahn images as an open-access resource will support educators teaching the next generation of embryology students and Xenopus researchers.In summary, we hope that these new open-source online resources will encourage and enhance exploration of the classic Nieuwkoop and Faber Normal Table of The method of producing this new set of illustrations began with observations of live specimens under a stereomicroscope, sketching and photographing specimens to produce reference images.X. laevis (NXR_0.0024), accessed at the National Xenopus Resource  at the Marine Biology Laboratory (MBL) at Woods Hole, MA, USA. Animals were handled following the ARRIVE and NIH Guidelines for Use and Care of Laboratory Animals that were approved by the Institutional Animal Care and Use Committee of the MBL. Representative embryos from several different clutches were used. The developmental stages were prior to overt sexual identification. Vitelline membranes were removed from embryos at NF stages 13-15, because if not removed, the membrane flattens the neural plate, making it more difficult to tell NF stages 13-15 apart  provided dorsal views of late-stage tadpoles showing brain morphology; and we used Kirschner Laboratory  and other images posted in the Xenopus community Slack forum]. In addition to the fully shaded illustrations, simple line drawings and outlines for many of the stages were also drawn . New diagrams of the gut coiling process (by J.G. and N.Z.) accompany ventral views of NF stages 41-46, illustrating the shape and directionality of this dynamic process. Gut coiling diagrams were based on anatomical studies of the Xenopus alimentary system, for which tadpoles were anesthetized and/or euthanized in accordance with North Carolina State University Institutional Animal Care and Use Committee regulations. Final versions of all illustrations were produced in a digital format using applications from Adobe's Creative Suite and a Wacom Cintiq Pro 32 digital drawing display. All drawings are provided as 300 dpi, high-resolution pixel-based art, and include a scale bar and, where applicable, a compass rose, indicating size and orientation, respectively.Draft drawings were assessed by the authors , all experienced domain experts, to ensure that they illustrated stereotypical anatomical features, and additional reference images were all provided to the illustrator from our labs . Additional high-resolution reference images of staged embryos were kindly provided by the http://www.xenbase.org/entry/zahn.do). Additionally, the Zahn drawings are used to illustrate the XAO pages (http://www.xenbase.org/anatomy/xao.do?method=display) for relevant NF stages .The Zahn drawings presented here and those from the XenHead project  are avai


This study aimed to investigate the antimigraine potential of quercetin in migraine pain induced by nitroglycerin (NTG), 10 mg/kg, intraperitoneal injection in rats. Quercetin was administered orally for 1 week, and behavioral parameters associated with pain were assessed 30 min after NTG injection. At the end of the study, the rats were killed so that immunohistochemical examination of their brains could be performed. The time and frequency of rearing and sniffing in the category of exploratory behavior, walking in the category of locomotor behavior, and total time spent in the light chamber were reduced in the disease control group compared with the normal group during the assessment of behavioral parameters. Pathologic migraine criteria, such as increased levels of calcitonin gene-related peptide and increased release of c-fos cells, were more prominent in the caudal nucleus triceminalis of the NTG control group. In the treatment groups, behavioral and pathological measures were less severe after pretreatment with quercetin at doses of 250 and 500 mg/kg. Therefore, it was concluded that quercetin improved the pain behavior of migraine patients in the NTG-induced migraine rat model. Quercetin is thought to have antimigraine effects due to its antioxidant and anti-inflammatory potential. Quercetin may therefore be a novel agent that can treat or prevent migraine pain and associated avoidance behaviors. Most often, any type of pain is due to inflammation or nerve damage. Migraine is a recurrent neurological pain disorder that affects the health, social, and economic situation of patients. According to epidemiological reports, migraine affects 1 in 10 people worldwide, and its incidence continues to increase inconspicuously . MigrainAll over the world, pain treatment with classical drugs (NSAIDs) is a challenge. This is because such treatments are associated with many side effects and are iatrogenic. In clinical trials, these NSAIDs showed numerous side effects and led to an increase in gastrointestinal ulcers, as well as cardiac and renal diseases ,5. Thus,Quercetin is a rich flavonol found in plants, fruits, onions, vegetables, bark, tea, and so on. It is a powdery, yellowish compound that dissolves completely in alcohol and lipids. Quercetin is not produced/formed in the human body ,10. TherQuercetin , nitroglycerin, normal saline, and sumatriptan . All chemicals and kits used in this study were laboratory grade.Sprague Dawley rats weighing 210–260 g were used in the present study, as shown in The rats were divided into different groups and each group contained 6 rats. Quercetin (250 and 500 mg/kg) and sumatriptan 50 mg/kg were administered orally daily for 7 days at the same time. NTG (10 mg/kg) was administered intraperitoneally in the different groups on the eighth day to induce an acute migraine attack. Then, rats were exposed to a sequence of behavioral tests after induction of migraine ,30,31. Module 1—Acute spontaneous migraine-like behavior recognition test.Module 2—Light-aversive behavior in light/dark test.Module 3—Rough and smooth surface apparatus test.Module 4—Open-field test.Rats were sacrificed, and their trigeminal nucleus caudalis (TNC) was stored in 10% formalin solution for immunohistochemical staining. Then, the calcitonin-gene-related peptide (CGRP) and the amount of positive staining were analyzed.The experimental procedure is shown in After 30 min of NTG administration, the rat was placed in a transparent box, and migraine-like behaviors were exhibited, such as running and jumping (locomotor behavior), retrieving and sniffing (exploratory behavior), immobile posture or sleeping (resting behavior), twitching (freezing behavior), and preening face or body (grooming behavior). For the above activities, the rat spent the entire time performing each behavior, except for the twitching behavior. For the twitching behavior, only the frequency was recorded .3). One of the two chambers is illuminated at 1000 lx, and the other has walls painted black and a lid to observe the activity of the rats. There is a small opening between the two chambers through which the rats can move freely between the chambers. First, the rat was positioned in the center of the light chamber, and then its movement in the chambers was observed for 10 min. When assessing light-shy behavior, the total distance and total time the rats spent in the light chamber and the number of transitions between the two chambers were noted [After 90 min of NTG administration, rats were placed in a light/dark box and their light-aversive behavior was analyzed. The light/dark box consists of two identical chambers . Initially, the rat was positioned in the center of the chamber and then allowed to move freely over the surfaces for 5 min. When analyzing spontaneous tactile allodynia behavior, the parameters, i.e., total time spent on the rough and smooth surfaces, were noted ,32.3) with a black wooden floor. Initially, the rat was placed in the center of the apparatus and allowed to freely explore the central area (30 cm × 30 cm) for 5 min. The total distance traveled and the total time the rat spent in the central area served as the anxiety or depression index [After 160 min of NTG administration, the anxiety- or depression-like behaviors of the rats were assessed. The rat was placed in an open field apparatus  was used for immunohistochemical analysis. After 180 min of NTG administration, three rats from each group were sacrificed. The brains of the rats were removed and stored in 10% formalin for IHC analysis. Immunohistochemical staining of TNC was performed with the assistance of a pathological expert and followed Max Labs protocols for paraffin-embedded sections. After slide preparation, sections were counterstained with hematoxylin for 5 min and then rinsed 2–3 times with double-distilled water. Subsequently, the positive C-Fos and CGRP immunostainings were detected under the light microscope. The number of positive staining was evaluated by experts .p value of <0.05 was considered statistically significant, and Graphpad Prism software was used.All data were generated as mean ± SEM. All data were analyzed by one-way analysis ANOVA, followed by Tukey’s post hoc test. A p < 0.001). The quercetin-treated groups showed significantly more rearing behavior compared to the control group. Similarly, there was sniffing behavior in the disease control group compared to the normal control group (p < 0.001). Quercetin-treated rats were more involved in sniffing behavior. In the disease control group, the total time spent rearing and sniffing was significantly lower than in the normal control group (p < 0.001). The maximum time spent rearing and sniffing was found in the quercetin 500 mg/kg group (p < 0.05).To investigate acute spontaneous migraine-like behavior in rats, the behavioral parameters, i.e., frequency and duration of rearing and sniffing, were measured see a. The dip < 0.01), as shown in p < 0.01). When analyzing the total time spent jumping, no statistically significant difference was found between the groups.The disease control group showed a lower number of walking and jumping activities and less locomotor behavior compared with the normal control group (p > 0.01). The quercetin groups caused a significantly higher number of twitches compared to the disease control group (p < 0.01)  see a.p < 0.01) greater frequency of resting behaviors, including immobile posture or sleeping, compared with the normal control group (see p < 0.01). The quercetin-treated groups were less engaged in grooming behaviors compared to the disease control group.The disease control group showed statistically (roup see b. The qup > 0.001) compared to the normal control group. In addition, the disease control group spent the least total time in the light chamber compared to the normal control group (p < 0.01). It was found that the quercetin-treated groups spent statistically significantly more time in the light chamber than the disease control group. In addition, the number of 10 min transitions between groups was found to be not statistically significant.After 90 min of NTG administration, the mildly aversive behavior was recorded see a. Statisp < 0.001) more time on the smooth surface analyzed for 5 min than the normal control group (see The disease control group spent significantly (roup see b. The qup < 0.001). Quercetin 500 mg/kg effectively increased the movement of rats in the open field maze (p < 0.01). However, the time spent by the rats in the inner zone of the maze did not differ significantly among the different groups. The rats in the disease control group spent more time in the corners of the outer zone than the control group. However, the rats in the quercetin 500 mg/kg group spent significantly more time in both the outer and inner zones (see Rats in the disease control group covered shorter distances in an open field maze compared to the control group (ones see a.p < 0.001). However, in the quercetin group, the number of immunoreactive c-fos cells in TNCs was reduced. Moreover, in the disease control group, the amount of positive staining of immunoreactive CGRP cells in TNC increased compared to the normal control group. However, in the 500 mg/kg quercetin group, the number of immunoreactive positive CGRP cells in TNC was significantly reduced (see The number of immunoreactive c-fos cells in TNC was higher in the disease control group than in the normal control group (uced see b.Migraine is a sapping illness. Its primary features are recurrent severe headaches associated with sounds or light, vomiting, dizziness, etc. It is a common and multifaceted neurological disorder that affects approximately 18% of women and 6–7% of men. . In the Some recent reports have claimed that NTG administration can induce behavioral changes such as head scratching, increased cage climbing, body shaking, etc. . These bTraditionally, quercetin has been used to relieve various types of pain and as an anti-inflammatory agent. In addition, preclinical studies with quercetin showed a good analgesic effect until the effect on behavior was not analyzed. The results showed that pretreatment with quercetin improved the frequency and total time of rearing and sniffing during exploratory behavior. The rats’ locomotor behavior also changed, and the frequency and total time of walking increased. One report found allodynia of approximately 63% in migraine patients, mimicking tactile allodynia in the migraine rat model. Numerous pharmacological reports have found that assessment of allodynia is a useful parameter for neuropathic pain in animals . HoweverIn addition, quercetin-treated rats spent more time in the light chamber and also traveled a longer total distance in the light chamber. Photophobia is a common symptom in migraine, and this symptom was similarly induced by light-shy behavior in the light–dark box . QuercetQuercetin has analgesic and anti-inflammatory effects, probably due to the inhibition of prostaglandin production. . In anotCommon hypotheses suggest that migraine-specific triggers are responsible for the initial brain dysfunction, which then leads to dilatation of cranial blood vessels innervated by the TSN fiber . These dTherefore, it can be concluded that quercetin has a protective effect in migraine pain through its analgesic effect and inhibition of inflammatory factors such as nitric oxide, proinflammatory cytokines, CGRP, C-fos, and so on.Pretreatment with quercetin can improve locomotor, exploratory, resting, grooming, freezing, light-shy behavior, and allodynia in the NTG-induced migraine rat model. All these results suggest that quercetin can be used in the treatment of behavioral disorders and migraine pain. Mechanistically, quercetin reduces CGRP, immunoreactive C-fos cells, and other inflammatory factors involved in the development of migraine headaches. Nevertheless, there are several mechanisms and procedural limitations that we will need to overcome in further experiments to determine the exact role of quercetin in supporting the study.


Corynebacterium pseudotuberculosis is the causative bacterial agent of the zoonotic disease known as caseous lymphadenitis, and it presents several mechanisms of response to host defenses, including the presence of virulence factors (VFs). The genomes of these bacteria have several polymorphic markers known as microsatellites, or simple sequence repeats (SSRs), that can be used to characterize the genome, to study possible polymorphisms existing among strains, and to verify the effects of such polymorphic markers in coding regions and regions associated with VFs. In this study, several SSRs were identified within coding regions throughout the 54 genomes of this species, revealing possible polymorphisms associated with coding regions that could be used as strain-specific or serotype-specific identifiers of C. pseudotuberculosis. The similarities associated with SSRs amongst the different serum variants of C. pseudotuberculosis, biovars equi and ovis, were also evaluated, and it was possible to identify SSRs located in coding regions responsible for a VF enrolled in pathogenesis known to mediate bacterial adherence (SpaH-type pili virulence factor). Phylogenetic analyses revealed that strains sharing SSR patterns, including the possible polymorphisms identified in the same position of gene-coding regions, were displayed by strains with a common ancestor, corroborating with the Genome Tree Report of the NCBI. Statistical analysis showed that the microsatellite groups belonging to equi and ovis biovars have a significance of 0.006  in similarity, thus indicating them as good biomarker candidates for C. pseudotuberculosis. Corynebacterium pseudotuberculosis causes a chronic infectious disease called caseous lymphadenitis, manifested by the presence of necrosis in the lymphatic glands  perfect microsatellites, presenting perfect repetitions, for e.g., (AT) 20; (II) imperfect microsatellites, presenting interruptions in the repetition caused by different nucleotides than those that occur in the repetitive pattern, for e.g., (AT) 12 GC (AT) 8; and (III) compound microsatellites, containing different motifs (two or more) repeated in tandem, for e.g., (AT) 7 (GC) 6 .Microsatellites are widely used to identify a particular molecular sequence in an unknown DNA pool. Previous studies suggested that the origin of microsatellites in microbial genomes is not random. Several mechanisms can stimulate the presence of SSRs in gene regions, such as insertions, deletions, recombination, transpositions, and horizontal gene transfer . These mSSRs are more than just repetitive strings, as they can play an important role in several biological pathways and be inserted within genes responsible for virulence in several pathogenic bacteria. They might also alter the expression of genes involved in the host–pathogen interaction. In addition, the presence of trinucleotide and hexanucleotide repetitions in genes encoding proteins may be highly relevant to the protein 3D structure. In humans, tri- and tetra-motifs located in protein-coding regions are often associated with genetic diseases .C. pseudotuberculosis are an important asset in the study of its pathogenicity by identifying possible markers associated with virulence genes.Due to high mutation rates and next-generation sequencing (NGS) technologies, microsatellites are useful molecular markers that can be easily detected by low-cost PCR techniques . The scrCorynebacterium pseudotuberculosis available in the National Center for Biotechnology Information’s RefSeq database (NCBI) was carried out using the software package of IMEx tools  files as input, allowing the identification of SSRs located in genetic and intergenic regions. We established the perfect microsatellite search parameters in the IMEx tool as follows (size of motifs—the minimum number of repetitions): 1-12, 2-6, 3-4, 4-3, 5-3, and 6-3; based on research by C. pseudotuberculosis C231 belonging to the ovis biovar and C. pseudotuberculosis MB154 from the equi biovar.The WEB BedSect tool  was usedhttps://www.r-project.org/)  . The outct.org/) .® software  and neighbor-joining (NJ) algorithms. The ML analysis was carried out in RAxML 8.2.10 software . We applied a Euclidean distance method between the two groups to measure their similarity and represent the two microsatellite categories through an information content matrix. This calculation is based on Shannon’s entropy .Through the IMEx tool, it was possible to locate all microsatellites fully inserted in coding regions, in non-coding regions, partially inserted in coding regions on the left (coding left overlap), and partially inserted in coding regions on the right (coding right overlap) . All genC. pseudotuberculosis MEX30, C. pseudotuberculosis CIP 52.97, C. pseudotuberculosis 38, and C. pseudotuberculosis 258, with a count of 57 SSRs each. All the other genomes in the study had a count of fewer than 57 microsatellites . The bar plot also displays the motif occurring within a coding region for a virulence factor, which is marked in red , 3.equi biovar, we identified that the strains C. pseudotuberculosis 31, 32, 33, 34, 35, 36, 38, and 39 presented the same pattern, which can be observed in C pseudotuberculosis 31  occurred in the genome of C. Pseudotuberculosis 36 in which the CGT motif was not found  and intra-biovars , comparing exclusively the regions where SSRs were found for every genome of this study. The following graph shows a remarkable separation between biovars  that were not amongst the 54 initial genomes in this study were selected, and the CAC and GGAA motifs were used to identify the biovars by the position and type of microsatellite observed. Thus, other strains out of the scope of this work had their biovars successfully identified only by the coordinates of SSRs found in this study  than to the equi ones (p-distance from 0.82 to 0.91%). However, the genetic divergences between almost all samples of ovis were less than 0.09%, except for the sample Cp_267, which ranged from 0.13 to 0.18% . The probability of each base for each microsatellite position observed was estimated. These results are represented through an information content matrix where it is possible to evaluate which positions are the most important, as each position’s total information indicates the conservation level. Thus, we can graphically observe this matrix in the form of a sequence logo, highlighting a difference between the equi and ovis biovars in position 3, indicating the possibility of using them as markers for the different biovars  showed 100% similarity in all microsatellite regions observed, except for the C. pseudotuberculosis 36 strain in which the CGT motif was not found , encoded by many bacteriophage and prophage genomes next to their cohesive end site and terminase genes  have a common ancestor.While looking at the flanking regions in the genomes, the following arrangement of bases was detected: TGG CGT CGT CGT CGT GAA. However, in the genome of the NCBI , as wellthe NCBI  where thC. pseudotuberculosis 43  did not show the virulence factor SpaH and, consequently, did not show any microsatellites with GGCT or CAGC motifs, which are the ones that occur within this VF, once again showing the potential of using such SSRs to screen for possible changes in the genome as well as for the absence of some genes.Regarding the genomes of the C. pseudotuberculosis PA02, where the observed motif within the SpaH gene was AGCC (C. pseudotuberculosis PA02 (TTGG AGCC AGCC AGCC AGCC TT), where the change from cytosine to guanine (highlighted in red) changed the pattern of tandem nucleotides, as seen in the strains PA01, PA04, and PA07 (TTGG AGG CAGC CAGC CAGC CTT).The major difference between the motifs found in VFs occurred in the genome of was AGCC . This faC. pseudotuberculosis C231 strain presented the CAC motif at coordinates 56941–56952 bp, as expected for a genome belonging to the ovis biovar  were also identified, being all of the SSR motifs related to the same SpaH-like pili VF in all genomes. These VR-related SSRs can serve as indicators of the genome organization and identify polymorphisms among the strains evaluated here.Different patterns of microsatellites, or simple short repeats (SSRs), were observed for different strain groups of In addition, microsatellites are important evolutionary markers and can be isolated by NGS technology from the genome of a model and non-model species, allowing the tracking of SSR length variations, such as point mutations and duplications across the entire genome to identify similarities and differences among strains.C. pseudotuberculosis.Finally, the results in this work demonstrated an unexplored potential for using these molecular markers not only for the identification of species and strains but also in the screening of specific biovars. Therefore, the study of SSRs has been proven crucial to the understanding of the genomic content, dynamics, and structure of bacterial pathogens, such as


The accuracy of the model was r = 0.47 (p = 0.002). The positively and negatively associated network strengths were then compared among FES subjects, individuals at genetic high risk  for schizophrenia, and healthy controls . The results indicated that the positively associated network contained more cross-subnetwork connections (96.02% of 176 edges), with a focus on the default-mode network (DMN)-salience network (SN) and the DMN-frontoparietal task control (FPT) network. The negatively associated network had fewer cross-subnetwork connections (71.79% of 117 edges) and focused on the sensory/somatomotor hand (SMH)-Cingulo opercular task control (COTC) network, the DMN, and the visual network with significantly decreased connectivity in the COTC-SMH network in FES . Additionally, the connectivity strengths of the right supplementary motor area (SMA) (p < 0.001) and the right precentral gyrus (p < 0.0001) were reduced in FES. To the best of our knowledge, this is the first study to generate two brain networks associated with positive symptoms by utilizing CPM in FES. Abnormal segregation, interactions of brain subnetworks, and impaired SMA might lead to salience attribution abnormalities and, thus, as a result, induce positive symptoms in schizophrenia.Positive symptoms are marked features of schizophrenia, and emerging evidence has suggested that abnormalities of the brain network underlying these symptoms may play a crucial role in the pathophysiology of the disease. We constructed two brain functional networks based on the positive and negative correlations between positive symptom scores and brain connectivity in drug-naive patients with first-episode schizophrenia (FES,  Positive symptoms are distinguishing characteristics of schizophrenia; a severe mental disorder with diverse symptoms. Among these positive symptoms, the most prominent include hallucinations, delusions, and disordered thinking and behavior. The hallucination-delusion syndrome can lead to serious impairment of social function in patients. Currently, the primary goal of antipsychotic drugs in the treatment of schizophrenia is to reduce the occurrences and severity of positive symptoms. Exploring the underlying mechanisms of positive symptoms can assist in expediting the search for the etiology and potential therapies for the disease.The “disconnection” hypothesis in schizophrenia is widely accepted. Both the abnormal salience monitoring theory of schizophrenia , 2 and tWith the development of technology, machine learning (ML) has significantly impacted the field of neuroscience. Thus, we aim to explore brain network connections based on a whole-brain, ML approach. Connectome-based predictive modeling (CPM) is one such method used for developing predictive models of brain-behavior relationships from whole-brain functional connectivity, with cross-validation to improve statistical efficacy and generalizability . In prevThis study included patients with FES as the subjects. As FES subjects had never been treated, unlike patients with chronic schizophrenia, connectivity abnormalities may be distinguished without the confounding effects of medications. CPM was utilized here to extract the most relevant features (connections) associated with positive symptoms in patients with FES across the brain and for the construction of two functional networks. The connections between different functional subnetworks were then observed by using the networks identified previously with CPM. Meanwhile, we chose subjects at genetic high risk (GHR) for schizophrenia and healthy controls (HCs) and compared their network connectivity with that of the FES group. Compared to HCs, GHR subjects were at a higher risk of developing schizophrenia  and showThe study participants comprised 48 drug-naive patients with FES, 41 GHR subjects, and 50 HCs. The FES subjects were recruited from the Department of Psychiatry of the Second Xiangya Hospital of Central South University in China, while the GHR subjects and HCs were recruited from the local community. The FES subjects were diagnosed through a structured clinical interview according to the DSM-5 criteria. The GHR subjects had at least one first-degree relative who suffered from schizophrenia. All participants were drug-naive, 13–35 years old, right-handed, and Han Chinese. Subjects with a history of neurological or severe physical disease, substance abuse, or with an IQ of <70, which was determined by the WASI-IV , were exThe Positive and Negative Syndrome Scale (PANSS)  was used2 × 256 mm2, slice thickness = 3.4 mm, and voxel size = 3.4 mm3 × 3.4 mm3 × 3.4 mm3. For each participant, one functional run contained 250 image volumes within a 508 s scanning time. For registration of functional images, a high-resolution structural image was acquired using a high-resolution sequence: TR = 2,530 ms, TE = 2.33 ms, flip angle = 7°, slice number = 192, FOV = 256 mm2 × 256 mm2, slice thickness = 1 mm, and voxel size = 1 mm3 × 1 mm3 × 1 mm3.For each participant, magnetic resonance imaging (MRI) data were acquired by using a 3.0T magnetic resonance imager , equipped with a 16-channel array coil at Hunan Children’s Hospital, Changsha, China. During the scan, the participants were required to remain motionless and awake with their eyes closed. Foam pads and earplugs were provided to minimize head motion. Rest data was collected with single-shot full k-space echo-planar imaging (EPI) and the sequence parameters were as follows: TR/TE = 2,000/30 ms, slice number = 36, flip angle = 90°, field of view (FOV) = 256 mm3 × 4 mm3 × 4 mm3 full-width at half-maximum (FWHM) Gaussian kernel with the linear trends removed. Finally, the MRI data were bandpass filtered (0.01–0.1 Hz) to reduce the effects of low-frequency drift and high-frequency physiological noises. To control the quality of the fMRI data, pictures for normalization from each participant were presented and scored during preprocessing. One HC with serious normalization problems (score < 3) was excluded. To control the head motion, the discarded subjects were defined as mean FD (Jenkinson) of >0.2 mm . The fMRI data of each subject were then divided into 264 brain regions by utilizing the Power264 atlas  data preprocessing was performed by using Data Processing Assistant for rs-fMRI (running in MATLAB R2013b) . The dat >0.2 mm  and comp64 atlas . A correp-value. By setting a threshold value of p = 0.01, the most positively and negatively relevant edges were selected for model-building in the training set. The strengths of all positively associated connections, as well those of the negative connections, were summed. The two total values were entered into a linear model with the total positive symptom score. The generated model was used to predict the positive symptom scores of individuals in the test set. After 45 iterations, the predicted score of each patient was obtained. The connections that survived in each iteration were aggregated to form a final and positively associated network and a negatively associated network. The predicted scores were correlated with the actual scores to measure the model’s accuracy . Statistical significance for the model accuracy was assessed by using 10,000 permutation tests. Positive symptom scores were randomly reassigned to different subjects. Then, new label assignments were input to build a new model and obtain a new correlation coefficient. This process was repeated 10,000 times to generate an empirical null distribution to assess the statistical significance of the true model’s accuracy. The statistical threshold was set at p < 0.05.In this study, CPM used the leave-one-out cross-validation approach, where in each iteration, one FES subject formed the test set and the remaining FES subjects formed the training set. First, a 264 × 264 correlation matrix was obtained for each subject in the training set with each number representing the strength of the connection between the two brain regions. The total positive symptom scores of the FES subjects were normally distributed. Pearson correlation was used to correlate each value in the correlation matrix with the total positive symptom score and to obtain the statistical p = 0.01 has been used in several previous studies which utilized CPM . The accuracies of the remaining models are presented in the Threshold selection: As mentioned above, the selection of significantly related connections is important in CPM and a threshold value of ized CPM , 10. HowIn all three groups , the total strength of the final positively/negatively associated network was calculated by summing all connections. First, in each group, the total strength of the positively associated network was compared with that of the negatively associated network. Second, the total strength of both the positively and negatively associated networks was compared among the three groups. Multiple comparison correction was then performed with Bonferroni correction.We calculated the connection strengths between the different subnetworks according to the main subnetworks involved. In the positively associated network, the strengths of connections between the DMN and the FPT network along with the DMN-SN connections were summed. In the negatively associated network, the connection strengths of the SMH-Cingulo opercular task control (COTC) network, the DMN, and the visual network were calculated. These connection strengths were compared among the three groups. Multiple comparison correction was performed with Bonferroni correction.q < 0.05). Age, gender, years of education, and head motion (mean FD) were treated as covariates.Furthermore, the strength of each node in the positively/negatively associated network was calculated by summing the weights of all edges connected to the node. The nodes were then compared among the three groups and corrected with FDR  were applied. The network strength was examined by using an F-test with Bonferroni correction. Group differences in node strength were identified by ANOVA with FDR correction. Age, gender, years of education, and head motion . Mean FD, demographic, and clinical variables of the three groups were compared by using analysis of variance (ANOVA) or chi-squared test. For p = 0.671), age (p = 0.602), and mean FD (p = 0.57) among the three groups. HC had a higher educational level than the other groups  and GHR subjects (p < 0.001). The difference between the two networks was not observed in the FES subjects. Although the negatively associated networks had fewer edges, they had higher connectivity strength in all individuals except for FES subjects.The total strength of the negatively associated network was significantly higher than that of the positively associated network in HCs , P21 , and P54  were significantly reduced in FES subjects compared to that in GHR subjects. No significant difference was observed in positively associated nodes.In the negatively associated network, the node strengths of P28 (Precentral_R, Schizophrenia is considered a disorder of the brain’s network connection , 25. RecThe small-world property , 29 is aAccording to a previously proposed hypothesis, a resting-state organization represents an optimized state of metabolic energy demands and maintains segregation at rest to allow each functional system to respond rapidly and flexibly to the processing of tasks and goals . Thus, tAmong the mainly involved subnetworks in positively associated connections, The DMN, one of the primary subnetworks involved in positively associated connections, focuses on different aspects of self-referential processing . The FPTAnother important mechanism of the triple network model is the aberrant bottom-up detection of salient events. In this study, reduced connectivity in the COTC-SMH network was found in the negatively associated network, indicating that positive symptoms are associated with low-level sensory signal changes in cognitive processing. The COTC network shares similar functions and anatomical locations with the SN , 45 and Another important result of this study was the reduced connectivity strength of the right SMA and the right precentral gyrus in the FES group. In patients with schizophrenia, the precentral gyrus is involved in auditory-verbal imagery , and itsBoth the affected interactions between subnetworks and the decreased connectivity of SMA indicate that the inappropriate salience assignment is closely related to the positive symptoms in patients with schizophrenia. From a pharmacological perspective, midbrain dopamine receptors are the therapeutic target for positive symptoms . AbnormaA limitation of this study was that another independent set was not used to validate the CPM model. Therefore, the positively and negatively associated networks extracted in this study might not apply to all individuals with schizophrenia. On one hand, it was difficult to ensure consistency in PANSS assessment across studies. The derived brain network based on FES in this study might not be able to accurately predict the PANSS scores of subjects in other studies. On the other hand, in clinical practice, clinicians do not need to use CPM to assess the positive symptoms of schizophrenia. Given the strong interpretability of CPM, we sought to extract brain networks that were significantly associated with positive symptoms. After rigorous cross-validation and permutation tests, we concluded that the functional network extracted by CPM is significantly correlated with positive symptoms. Another limitation of this study was that the age of the FES group was between 13 and 35 years. Although age was treated as a covariate, it was difficult to remove any potential influence. In future studies, more subjects should be included to cover all age stages.In this study, we found that increased communication among different functional modules is related to higher positive symptoms scores. Abnormal interactions among the DMN, the SN, and the CEN along with decreased connectivity in the COTC-SMH network could be involved in the development of positive symptoms. The hypofunction of SMA might cause abnormal attributions of the internal language. These might interfere with the patient’s recognition of internal and external stimuli, contributing to the bias of thought. Thus, more in-depth neurobiological studies need to be conducted to explore the function and interactions of large-scale brain networks.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Ethics Committee of the Second Xiangya Hospital of Central South University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.All authors have contributed to and have approved of the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.