Arnebia euchroma (A. euchroma) is a traditional Chinese medicine (TCM) used for the treatment of blood diseases including leukemia. In recent years, many studies have been conducted on the anti-tumor effect of shikonin and its derivatives, the major active components of A. euchroma. However, the underlying mechanism of action (MoA) for all the components of A. euchroma on leukemia has not been explored systematically.A. euchroma on leukemia via network pharmacology approach. Firstly, the chemical components and their concentrations in A. euchroma as well as leukemia-related targets were collected. Next, we predicted compound-target interactions (CTIs) with our balanced substructure-drug-target network-based inference (bSDTNBI) method. The known and predicted targets of A. euchroma and leukemia-related targets were merged together to construct A. euchroma-leukemia protein-protein interactions (PPIs) network. Then, weighted compound-target bipartite network was constructed according to combination of eight central attributes with concentration information through Cytoscape. Additionally, molecular docking simulation was performed to calculate whether the components and predicted targets have interactions or not.In this study, we analyzed the MoA of A. euchroma were obtained and 27 of them with concentration information, which were involved in 157 targets and 779 compound-target interactions (CTIs). Following the calculation of eight central attributes of targets in A. euchroma-leukemia PPI network, 37 targets with all central attributes greater than the median values were selected to construct the weighted compound-target bipartite network and do the KEGG pathway analysis. We found that A. euchroma candidate targets were significantly associated with several apoptosis and inflammation-related biological pathways, such as MAPK signaling, PI3K-Akt signaling, IL-17 signaling, and T cell receptor signaling pathways. Moreover, molecular docking simulation demonstrated that there were eight pairs of predicted CTIs had the strong binding free energy.A total of 65 components of A. euchroma in the treatment of leukemia might be attributed to 10 targets and 14 components, which were associated with inhibiting leukemia cell survival and inducing apoptosis, relieving inflammatory environment and inhibiting angiogenesis.This study deciphered that the efficacy of  Leukemia is a common malignancy in children and adults originating from pluripotent hematopoietic stem cells. Due to various reasons, leukemia cells undergo enhanced self-renewal, over proliferation, and blocked differentiation and apoptosis. Abnormal immature cells proliferate and accumulate in bone marrow and other hematopoietic tissues, leading to the suppression of normal hematopoiesis and infiltration of other organs and tissues. According to the types of cells affected and the developmental stage of the originating cells, leukemia is classified into four major categories: acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), chronic myeloid leukemia (CML), and chronic lymphoblastic leukemia (CLL).The usual approach to treat leukemia is the use of toxic compounds to kill cancer cells, which will eventually destroy the immune system, and make patients susceptible to fatal bacterial and fungal infections. For that reason, it was suggested that approaches such as inducing leukemia cell differentiation and apoptosis other than killing cells may be more effective in the treatment of leukemia . TraditiArnebia euchroma (Royle) Jonst., also called Xinjiang Zicao, is a TCM used in the treatment of blood diseases including leukemia [A. euchroma is 10.3%, and the frequency of A. euchroma powder, a prescription that takes A. euchroma as the monarch herb is 53.1% [A. euchroma consist of two major categories: one is hydrophilic components, mainly a mixture of polysaccharides and glycoproteins; the other is lipophilic substances with a variety of biological activities, including naphthoquinones, alkaloids, monoterpene phenol and benzoquinones, organic acid ester, and so on. Among these, typical naphthoquinones include shikonin and a series of derivatives [R-configuration (named shikonin) and S-configuration   [http://119.3.41.228/tcmid/, entering at Apr 2019) [http://pharmdata.ncmi.cn/cnpc/, entering at Apr 2019), and Phytochemicals database , as well as related literature from PubMed , Web of Science  and CNKI . The structures of these compounds were obtained from NCBI PubChem Compound Database . Duplicates were removed.The components and their concentrations in pr 2019) , ChineseA. euchroma were collected from four databases, including BindingDB  [https://www.guidetopharmacology.org/, entering at Apr 2019) [https://www.ebi.ac.uk/chembl/, entering at Apr 2019) [https://pubchem.ncbi.nlm.nih.gov/, entering at Apr 2019) [Homo sapiens” by UniProt . Duplicated KCTIs were removed.Known compound-target interactions (KCTIs) for each component of pr 2019) , IUPHAR/pr 2019) , ChEMBL pr 2019) , and Pubpr 2019) . Target https://www.omim.org/, updated on Jan. 3, 2018) [https://www.pharmgkb.org/, updated on Dec. 28, 2017) [http://bidd.nus.edu.sg/group/cjttd/, updated on Sep. 15, 2017) [https://www.disgenet.org/search, entering at Apr 2019) [https://www.malacards.org/, entering at Apr 2019) [Leukemia related targets were obtained from five databases: OMIM  , PharmGk8, 2017) , TTD (htpr 2019) , and Malpr 2019) .A. euchroma. In bSDTNBI method, three parameters, namely α, β and γ, should be determined first. Among them, α controls the relative importance of substructure vs. target nodes, and β defines the importance of drug-substructure vs. drug-target edges, while γ determines the importance of hub nodes. The three parameters were optimized by grid search and 10-fold cross validation. First of all, α and β were optimized under the condition of ignoring the influence of hub nodes (γ = 0). Then, under the optimal values of α and β, the optimal value of parameter γ was searched. Therefore, with the optimal parameters α, β and γ, bSDTNBI method was used to predict 10 potential targets for each component of A. euchroma. The predicted targets were further standardized as official gene symbols. Eventually, the collected known and predicted CTIs were integrated.The bSDTNBI method was used to predict potential compound-target interactions (PCTIs) for each component of A. euchroma (TA) and targets related to leukemia (TL) were imported into Cytoscape 3.6.0, respectively. Protein-protein interactions (PPIs) of TA and TL were obtained by stringAPP, a Cytoscape plugin. High-confidence PPIs with scores above 0.7 were selected to construct A. euchroma PPI network (PPIA) and leukemia PPI network (PPIL). Then PPIA and PPIL were merged together to construct A. euchroma-leukemia PPI network.Targets of components of A. euchroma-leukemia PPI network was analyzed by CytoNCA, another Cytoscape plugin. Each node in the network has eight central attributes, including subgraph centrality (SC), degree centrality (DC), eigenvector centrality (EC), information centrality (IC), local average connectivity-based method (LAC), betweenness centrality (BC), closeness centrality (CC), and network centrality (NC). For all target nodes, the targets of TA that meet the screening criteria “SC > median SC & DC > median DC & EC > median EC & IC > median IC & LAC > median LAC & BC > median BC & CC > median CC & NC > median NC” were regarded as leukemia-related targets of A. euchroma (TAL) and were retained [retained .AL were obtained from all CTIs (KCTIs + PCTIs). Here the concentrations of components in A. euchroma were used to weight the importance of each component. Since the concentrations of components were determined under different experimental conditions and influenced by many factors, such as the origin and growth time of A. euchroma, we processed the original concentration data as follows. Firstly, all the concentrations of components were summarized and standardized to a uniform percentage (mass percentage). Then, the average value of percentage of each component was calculated after the maximum and minimum values were removed. Finally, with “percent concentration value = 0.1%” as the threshold, components were divided into three classes, and the formula was as following:Compound-target interactions for TAfter all components were weighted, a weighted compound-target bipartite network was constructed through Cytoscape.https://string-db.org/cgi/input.pl, entering at Apr 2019) [PSm refers to the score of the m-th KEGG pathway; NT refers to the number of targets involved in the m-th pathway; NC refers to the number of components that interact with the j-th target in the compound-target bipartite network; Xi refers to the weight of the i-th component; LFDRm refers to the negative logarithmic value of FDR of the m-th pathway; Norm refers to the min-max normalization method with normalize data from 0.1 to 1.KEGG pathway enrichment analysis is usually used to describe the characteristics of query targets. Here, STRING 10.5 (pr 2019)  was usedAfter all pathways were scored, pathways that meet the role “score > 1.5 median score” were selected. Referring to the pathway classification standard of KEGG Pathway Database, here, these selected pathways were divided into four categories, including cell survival and death, immune system, endocrine system and specific human diseases. In order to facilitate analysis, we removed specific human disease pathways, which were not directly related to the treatment mechanism of leukemia, and the other pathways were remained.TSj refers to the score of the j-th target; Np refers to the number of pathways that the j-th target involved in; Nc refers to the number of components that have interactions with the j-th target; Xi refers to the weight of the i-th component; Norm refers to the min-max normalization method with normalize data from 0.1 to 1.Targets that were involved in the above qualified pathways were selected out and scored further by the following scoring function:After all targets were scored, targets that meet “score > 1.5 median score” were regarded as hub genes, and CTIs that were associated with these targets were selected out. The molecular docking simulation was further performed to detect whether the components and predicted targets form reasonable interactions and estimate their binding affinities.http://www.pdb.org/, updated on 2020-4-10) and selected the relatively higher resolution crystal structures with the ligands. Second, the structures of chemical components contained in A. euchroma were downloaded from the NCBI PubChem Compound Database. Then, protein preparation module of Schrödinger’s Maestro Molecular modeling suite (Schrödinger Release 2015–2) was utilized for preparation of the protein crystallographic structures. Water molecules were subsequently deleted from the structures, and the amide moieties in the side chain were adjusted to optimize their interactions with surrounding residues and groups of atoms. Force field OPLS_2005 was also added. A ligand grid generation was based on the ligand in the co-crystallographic structure. The docking region was centered on the ligand, and after the ligand grid was generated, the compounds were imported into Mastero. LigPrep module of the Maestro molecular modeling package was used to obtain the 3D structures and energy minimization of the identified compounds. Compounds were docked in the generated grid using the standard Glide docking mode.First, we collected the crystal structures of the selected protein from the RCSB Protein Data Bank  network establishment. A. euchroma-leukemia PPI network and weighted compound-target bipartite network were constructed through Cytoscape; (3) network analysis. Hub genes were selected out according to KEGG pathways analysis, and molecular docking was used to calculate whether the components from A. euchroma and predicted targets have interactions or not and to construct the critical compound-target bipartite network. Figure In this study, we took the concentrations of components in low Fig. : 1) dat datA. euA. euchroma were obtained, and 27 of them with concentration information  was calculated. Finally, 11 components with concentration greater than 0.1% had a weight of 3 , 16 components with concentration less than 0.1% but greater than 0 had a weight of 2 (X = 2), and the remaining 38 components without collected concentration data had a weight of 1 (X = 1).From OMIM, PharmGkb, TTD, DisGeNET, and MalaCards, a total of 332 targets were ultimately reserved after removing the duplicates.A. euchroma have known targets. Therefore, it is especially necessary to predict potential targets for components.A total of 129 KCTIs for 13 components  were collected from databases, which were involved in 92 targets  of 65 components in A. euchroma and 332 leukemia related targets (TL = 332) were imported to Cytoscape, respectively. When the confidence (score) cutoff was set to 0.7, 129 targets out of TA had 364 PPIs (PPIA = 364), while 270 targets out of TL had 1335 PPIs (PPIL = 1335). When they were merged, 1658 PPIs were obtained. Among these, there were 709 PPIs for the 129 targets of A. euchroma components. These 709 PPIs were selected out and re-submitted to Cytoscape to construct A. euchroma-leukemia PPI network. In the PPI network, eight central attributes were calculated by CytoNCA (see Supplementary Table SA. euchroma components (TAL) were qualified ; Class II components, which have a lower concentration (green node); and Class III components, which do not have concentration information (gray node). There were 10 Class I components, 13 Class II components and 32 Class III components. Among Class I components, only MOL16  and MOL18  had interactions with more than one target; the remaining ones were associated with only one target. As for Class II components, eight components had interactions with one target, the others were associated with more than one target. NFE2L2 as the target with the most components, it could be hit by 42 components.AL were enriched into 115 KEGG pathways. According to the scoring function, 34 pathways were qualified. We divided the 34 pathways into four categories, including four cell survival and death pathways, two immune system pathways, three endocrine system pathways and 25 specific human diseases pathways , PI3K-Akt signaling pathway (hsa04151), Sphingolipid signaling pathway (hsa04071), Apoptosis (hsa04210). These four pathways were related to cell growth, differentiation and apoptosis. MAPK signal transduction pathway plays a crucial role in cell proliferation, differentiation and other processes. As a caspase activator, MAPK also plays an important role on apoptosis. AKT1, CASP3, EGFR, HSPB1, MAPT, TNF, and TP53 participated in the MAPK signaling pathway, and 22 components could act on them. PI3K/Akt signaling pathway is extensively present in cells. It is believed to be one of the pivotal signaling pathways regulating cell growth, proliferation, differentiation and apoptosis. Akt (gene symbol as AKT1) is a direct downstream target of PI3K/Akt pathway. Activated Akt can inhibit the release of cytochrome c from the mitochondria, which blockade the apoptosis triggered by caspases. The PI3K/Akt signaling pathway enriched eight genes, including AKT1, EGFR, GSK3B, IL2, MCL1, PTEN, TLR2, TP53, and a total of 11 components could act on them. Apoptosis is also known as programmed cell death, which plays a critical role in maintaining development, homeostasis and defending against pathogens. In transmitting apoptotic signals, CASP3 is the major effect factor. The activation of CASP3 triggers apoptosis. MCL1 is a member of the Bcl-2 family, which is an apoptotic inhibitor. Inactivation of p53 functions also causes cancer cells to escape apoptosis. The p53 plays a pivotal role in leukemic hematopoiesis. A total of five genes were enriched in Apoptosis signaling pathway, they were AKT1, CASP3, MCL1, TNF, TP53, and 15 components were associated with them. Analysis of the above results indicates that induction of cell apoptosis may be a key factor in the treatment of leukemia by A. euchroma relieves inflammatory environment may be related to its treatment of leukemia.There were two immune system pathways, include IL-17 signaling pathway (hsa04657) and T cell receptor signaling pathway (hsa04660). The IL-17 signaling pathway were enriched by five genes, include CASP3, GSK3B, MMP1, MMP9 and TNF. As for T cell receptor signaling pathway (hsa04660), AKT1, FYN, GSK3B, IL2 and TNF were enriched. It’s well known that cytokines play a critical role in the context of inflammation, and extensive research has also proved that cytokines exert profound effects on the progression of hematopoietic malignancies including leukemia. It can be inferred that The other three pathways were endocrine system-related pathways, include Estrogen signaling pathway (hsa04915), Thyroid hormone signaling pathway (hsa04919) and Relaxin signaling pathway (hsa04926). A total of 12 genes enriched by these three pathways, they were AKT1, EGFR, ESR1, GSK3B, HDAC3, HIF1A, MMP1, MMP2, MMP9, RARA, SP1 and TP53. Of which, EGFR, one of the transmembrane tyrosine kinase receptors, serves as a stimulus for cancer growth, which is aberrantly activated in most common tumors, including leukemia.According to the scoring function, 10 hub targets  were qualified, and 18 components formed interactions with them in all CTIs. These components include arnebinone (MOL6), shikonofuran b (MOL8), acetylalkannin/acetylshikonin (MOL16), alkannin/shikonin (MOL18), arnebiabinone (MOL21), dehydroalkannin (MOL24), oleanolic acid (MOL47), shinjulactone k (MOL65), ursolic acid (MOL49), o9-angeloylretronecine (MOL41), caffeic acid (MOL42), linoleic acid (MOL53), daucosterol (MOL38), beta-sitosterol (MOL36), echimidine (Mol39), alkannin angelate (Mol19), 3-hydroxy-3-methyl butyric acid (Mol51), and tormentic acid (Mol48).A. euchroma and the obtained hub genes.There were 27 CTIs between the 18 components and 10 targets, and 11 of them were KCTIs, 16 were PCTIs. All of the 16 PCTIs were composed of CTIs related to HIF1A and TNF while all of the CTIs related to HIF1A and TNF were PCTIs. In this study, we used the molecular docking stimulation to identify the binding ability of the 16 pairs of PCTIs that between components of A. euchroma. The docking scores of 16 pairs of PCTIs were listed in Table SAs the above described, only HIF1A and TNF-related CTIs were predicted, the other hub genes and their associated CTIs were KCTIs. Therefore, the molecular docking simulation was further performed to determine the binding level between the two predicted targets (HIF1A and TNF) and their corresponding components containing in (green nodes), four  were naphthoquinones (gold node), three  were triterpenoids (blue nodes), one (MOL41) was alkaloids (cyan node), two (MOL38 and MOL36) were steroids (purple nodes), and the other two (MOL42 and MOL53) were organic acids (brown nodes). Structures of the 14 components were shown in Supplementary Figure SThe eight pairs of PCTIs and 11 pairs of KCTIs were used to critical compound-target bipartite network, which involved in 10 targets and 14 components, as shown in Fig. A. euchroma has been used to treat blood diseases, including leukemia in the clinical of Chinese medicine [A. euchroma have never been explored systematically. Network pharmacology focuses on “multi-constituents, multi-targets to treat diseases”, which coincides with the holistic and systematic concepts of TCM [A. euchroma on leukemia. We retrieved 65 components and 157 targets of A. euchroma from known databases and literature. Combined with network analysis and molecular docking simulation, we found that 14 components and 10 targets were potential critical components and targets, which might contribute to the effect of A. euchroma on leukemia treatment. The 14 components include alkannin/shikonin (MOL18), arnebinone (MOL6), shikonofuran b (MOL8), arnebiabinone (MOL21), acetylalkannin/acetylshikonin (MOL16), dehydroalkannin (MOL24), oleanolic acid (MOL47), shinjulactone k (MOL65), ursolic acid (MOL49), o9-angeloylretronecine (MOL41), caffeic acid (MOL42), linoleic acid (MOL53), daucosterol (MOL38), beta-sitosterol (MOL36). The 10 targets include AKT1, CASP3, EGFR, GSK3B, HIF1A, MMP2, MMP9, PTEN, TNF and TP53. Our results indicated that the possible MoA might be involved in three aspects: 1. A. euchroma inhibits leukemia cell survival and induces apoptosis; 2. A. euchroma relieves inflammatory environment; 3. A. euchroma inhibits angiogenesis.medicine . In recemedicine –11. Mosts of TCM . MoreoveApoptosis, also known as programmed cell death, is a physiological process or some pathological condition, in which the cells take part in the death process after they are triggered by a certain signal. Inhibiting leukemia cell survival and inducing leukemia cell apoptosis is a promising therapeutic approach for leukemia. There were four pathways , PI3K-Akt signaling pathway (hsa04151), Sphingolipid signaling pathway (hsa04071), Apoptosis (hsa04210)) were related to cell survival and apoptosis, and 13 targets, 26 components in weighted compound-target bipartite network were mapped onto the four pathways. When transmitting apoptotic signals, CASP3 is the major effect factor. The activation of CASP3 triggers apoptosis. As one of the caspase activators, MAPK is well known to play a crucial role in apoptosis. Extensive studies have shown that targeting to MAPK signaling cascades, alone or in combination with other drugs, results in enhanced anti-leukemic responses in AML , 35. It 10, PI3K-It is well known that inflammation is one of the major barriers to cancer therapy. In recent years, it has become evident that inflammation plays a key role in leukemia . There wAngiogenesis is a critical element to cancer cell survival, and increased vascularity was found in patients with AML . HIF1A, A. euchroma discovered by network pharmacology, molecular docking simulation was used to explore the binding ability of between components and proteins. This technique is structure-based and could help researchers discover the interactions between the components of TCM and network targets. In this study, HIF1A and TNF were selected for molecular docking studies because they were predicted targets for components. TNF was involved in five pathways, include IL-17 signaling pathway (hsa04657), MAPK signaling pathway (hsa04010), T cell receptor signaling pathway (hsa04660), Sphingolipid signaling pathway (hsa04071) and Apoptosis (hsa04210), which were related to both apoptosis and inflammation. The molecular docking results indicated that six components  had strong binding free energy with TNF, and two components (MOL8 and MOL41) had good binding affinity with HIF1A, since their docking scores were higher than the median value of all pairs. Among the eight components, MOL6 and MOL8 were monoterpene phenol and benzoquinones, MOL24 and MOL21 were naphthoquinones, which were the two main types of active components in A. euchroma.On the basis of the targets of A. euchroma, it was demonstrated to have anti-inflammatory and pro-apoptotic effects [According to the above analysis, we selected 10 targets and 14 components as critical targets and key components. Among these, shikonin (MOL18) was the most studied components of  effects . Acetyls effects .A. euchroma and related literature, we compared ATO and A. euchroma in terms of leukemia treatment, and we found some commonalities: (1) MAPK signaling pathway. ATO has been demonstrated to activate all three MAPKs pathways, ERKs, JNKs, and p38 kinases to exert its anti-leukemia effect [A. euchroma. (2) Angiogenesis. ATO was reported to inhibit angiogenesis by inhibiting the production of VEGF in leukemic cell [A. euchroma, seven components could act on HIF1A, which is responsible for increased expression of VEGF. Moreover, several studies have shown that some components of A. euchroma exhibited antiangiogenic activity by inhibiting VEGF [A. euchroma increased the expression of caspases to induce apoptosis [A. euchroma may be a potential anti-leukemia agent with less toxicity than ATO.As one of the most famous Chinese medicine for treating leukemia, ATO is an effective and relatively safe drug in the treatment of APL  . Combinia effect . The MAPmic cell . As for ing VEGF , 63. (3)ing VEGF . Extensipoptosis , 65. As A. euchroma on leukemia involves the inhibition of leukemia cell survival and induction of leukemia cell apoptosis, the relief of inflammatory environment, and the inhibition of angiogenesis. Concretely speaking, it includes three aspects: (1) A. euchroma impacts the PI3K/AKT and MAPK signaling pathway, which inhibit leukemia cell survival and induce leukemia cell apoptosis. (2) A. euchroma inhibits the expression of inflammatory medium, such as TNF, IL6, IL2 and so on to relieve inflammatory environment. (3) A. euchroma inhibits angiogenesis by regulating HIF1A, MMP2, MMP9 and other factors related to angiogenesis. The results preliminarily validated and explained the therapeutic material basis and mechanism of A. euchroma on leukemia, which provided a preliminary information and basis for further in-depth exploration of its MoA, and a reference for the study of the more complex components of TCM.In this study, we found that the MoA of A. euchroma and combined with the methods of network analysis and molecular docking simulation, we found that 55 active components and 37 targets might be related to the anti-leukemia effects of A. euchroma, of which 14 components  and 10 targets  were considered to be critical. It was demonstrated that A. euchroma has potential anti-leukemia activities because of its effects on inhibiting leukemia cell survival and inducing apoptosis, relieving inflammatory environment and inhibiting angiogenesis. We further realized that, not only shikonin, the most fully studied ingredient of A. euchroma, one of compound of naphthoquinones, other components among the 14 critical components of A. euchroma, including monoterpene phenol and benzoquinones, triterpenoids, alkaloids, steroids and organic acids would also have therapeutic effects on leukemia.Considering the concentration information of components in Additional file 1. Figure S1. Two configurations of naphthoquinones.Additional file 2. Figure S2. Structures of the 14 critical components.Additional file 3 Table S1. Components of A. euchroma and content score.Additional file 4. Table S2. Known and predicted component-target interactions.Additional file 5. Table S3. Eight central attributes of targets.Additional file 6. Table S4. Weighted component-important target interactions.Additional file 7. Table S5. 34 KEGG pathways.Additional file 8. Table S6. Docking scores of 16 pairs of PCTIs.


Estrogen receptor positive (ER+) breast cancer patients are eligible for hormonal treatment, but only around half respond. A test with higher specificity for prediction of endocrine therapy response is needed to avoid hormonal overtreatment and to enable selection of alternative treatments. A novel testing method was reported before that enables measurement of functional signal transduction pathway activity in individual cancer tissue samples, using mRNA levels of target genes of the respective pathway-specific transcription factor. Using this method, 130 primary breast cancer samples were analyzed from non-metastatic ER+ patients, treated with surgery without adjuvant hormonal therapy, who subsequently developed metastatic disease that was treated with first-line tamoxifen. Quantitative activity levels were measured of androgen and estrogen receptor (AR and ER), PI3K-FOXO, Hedgehog (HH), NFκB, TGFβ, and Wnt pathways. Based on samples with known pathway activity, thresholds were set to distinguish low from high activity. Subsequently, pathway activity levels were correlated with the tamoxifen treatment response and progression-free survival. High ER pathway activity was measured in 41% of the primary tumors and was associated with longer time to progression (PFS) of metastases during first-line tamoxifen treatment. In contrast, high PI3K, HH, and androgen receptor pathway activity was associated with shorter PFS, and high PI3K and TGFβ pathway activity with worse treatment response. Potential clinical utility of assessment of ER pathway activity lies in predicting response to hormonal therapy, while activity of PI3K, HH, TGFβ, and AR pathways may indicate failure to respond, but also opens new avenues for alternative or complementary targeted treatments. Estrogen receptor (ER) positive breast cancer patients are treated routinely with surgical tumor resection, local radiation, and chemotherapy depending on stage and tumor subtype, followed by adjuvant hormonal therapy, either tamoxifen or an aromatase-inhibitor, for at least 5 years . UnfortuTo investigate the clinical utility of these signaling pathway models in terms of predicting therapy benefit, and of the ER pathway model in particular, we performed a multiple pathway model analysis on Affymetrix mRNA expression data from primary tumor tissue samples of a retrospective series of patients with ER positive breast cancer who did not receive adjuvant hormonal treatment and subsequently developed metastatic disease. Upon diagnosis of metastatic disease, these patients were treated with first-line endocrine therapy (tamoxifen). The primary purpose of this analysis was to investigate whether ER pathway activity in the primary tumor is a better predictor of response to tamoxifen or disease progression in this setting than mere ER protein expression. The second purpose was to investigate whether activity of specific signaling pathways is associated with a worse clinical outcome following start of endocrine therapy.http://www.federa.org/codes-conduct).The protocol to study biological markers associated with disease outcome in the current cohort was approved by the medical ethics committee of the Erasmus Medical Center Rotterdam, The Netherlands (MEC 02.953) and tissues were studied in accordance with the Code of Conduct of the Federation of Medical Scientific Societies in the Netherlands , and 15 had a partial remission (PR). There were 33 patients with progressive disease (PD), with an increase in tumor size of 25% or more or showing new tumor lesions within 3 months. The remaining 79 patients were considered to be patients with stable disease (SD), of which 69 patients had SD at > 6 months and 10 patients had SD ≤ 6 months. The median PFS times of the different response groups were: CR, 32 months; PR, 20 months; SD > 6 months, 14 months; SD ≤ 6 months, 5 months; and PD, 3 months. Because the patients with SD > 6 months had a PFS similar to patients with partial remission, we classified them together with CR and PR as responders to tamoxifen as advised by the European Organization for Research and Treatment of Cancer [ESR1, PGR and ERBB2 was performed using RT-qPCR as described before [PIK3CA mutation status  was assessed by SnaPshot multiplex analyses as described in [Routine ER and PR protein expression analysis was performed on this historical cohort on a tissue sample of the primary tumor by ligand binding assay or enzyme immunoassay as described before . The cutd before ,10. PIK3ribed in .RNA was isolated from 30 μm sections of a fresh frozen primary tissue sample with RNA-B  and DNAse treated as described before . AffymetFunctional signal transduction pathway activity levels were assessed by means of measuring and interpreting pathway-specific target gene expression levels. To this end, Bayesian network models were developed as originally presented for the Wnt and ER signaling pathways in . This apOnce built, the models were biologically validated on human cell line models for the respective signal transduction pathways and on tissue samples of cancer types that are well characterized with respect to activity of the specific signal transduction pathway ,3,4. TheAfter calibration, the computational signaling pathway models were frozen, and used to interpret microarray data of tumor samples included in this study. For each individual sample, the target genes’ expression measurements (c) were entered in each pathway model and then the model inferred backwards what the odds were that the respective TC must have been active vs. inactive. These odds are generally represented on a logarithmic scale (log2odds). Log2odds larger than zero  indicate that there is more evidence for an active TC, and log2odds smaller than zero that there is more evidence for an inactive TC, which corresponded well with the ‘ground truth’ status with respect to signaling pathway activity of calibration and biological validation samples. Therefore, we use a threshold of 0 on the log2odds scale in this paper to separate low from high pathway activity.SOD2 expression level, which is with high likelihood also associated with an active PI3K pathway as described in [SOD2 probesets similarly as in [For the pathway models, calculated log2odds scores represent activity of their respective TCs, which for most pathways is directly related to activity of the pathway. However, there is an inverse relation between PI3K pathway activity and FOXO activity, as the FOXO transcription factor is generally inactivated by an active PI3K pathway. Hence, an inactive FOXO transcription factor is indicative of an active PI3K pathway. Alternatively, FOXO activity can be induced by oxidative stress, indicated by a high ribed in . To thisly as in , but witUnivariate and multivariate Cox proportional hazards regression was used to associate the pathways’ activities with PFS on first-line tamoxifen treatment, without and with traditional response prediction factors. This was complemented by a Kaplan–Meier analysis of PFS. Fisher exact tests were used to analyze pathway co-activity, and significant patterns of pathway activity across response categories. All analyses were performed in R .All patients in the cohort were assessed as ER positive luminal breast cancer patients based on conventional pathology diagnostics, but we have shown before that this does not mean that they all had a high ER pathway activity, and that in tumors with low ER activity another signal transduction pathway may be more likely to drive tumor growth . This map = 0.40) and neither between ER activity and ESR1 RT-qPCR levels , these results confirm that ER expression did not necessarily imply ER transcription factor activation.An overview of the resulting pathway activity scores, in terms of log2odds, is given in SOD2 expression, indicating cellular oxidative stress as an alternative cause of FOXO activity [ERBB2 status , indicating that PI3K activity cannot be inferred from HER2/ERBB2 amplification status. It was also not significantly correlated with PIK3CA mutation status .The PI3K pathway was found high in 10 (8%) cases, as inferred from low FOXO transcription factor activity, while in 25 patients (19%), high FOXO activity was associated with elevated activity ,15. Althactivity ,16. AddiOf the other measured signaling pathways, the NFκB pathway was most often scored high, in 74 (57%) cases, and the ER pathway second most with 53 (41%) cases. The other pathways had a high activity in smaller numbers of tumors A. In 108Signaling pathways not only exert cellular functions independently, but also in concert. To obtain more insight into disease pathophysiology it is useful to investigate combinations of signaling pathway activities. SOD2) gene, which codes for a protein that protects against oxidative damage [SOD2-high PI3K active samples (96%), categorized as a cellular state of oxidative stress, the NFκB pathway also had a high activity . Similar to the FOXO-PI3K pathway, the NFκB pathway plays a role in protecting cells from oxidative stress caused by reactive oxygen species (ROS) . For thee damage . In 24 oSOD2 gene expression, being: (1) PI3K pathway activity low, as defined by high FOXO activity and low SOD2; (2) PI3K pathway activity high, defined by low FOXO activity; (3) putative high PI3K pathway activity with cellular oxidative stress, defined by high FOXO activity and high SOD2, also with high NFκB pathway activity (p = 0.02). Furthermore, high TGFβ pathway activity was also associated with high HH pathway activity  and low ER pathway activity .Next, we analyzed which signaling pathways were preferentially activated in the three subgroups of our ER positive patient cohort as defined by the combinations of the PI3K-FOXO pathway and activity , top rowOn the other hand, in the low FOXO activity (high PI3K pathway activity) subgroup, hardly any other high pathway activity was found , indicating that in general in this subgroup the PI3K pathway was the dominant identified driving pathway.p = 0.005), and that high PI3K pathway activity in the primary tumor was associated with a shorter time to progression, with a hazard ratio of 1.8 (p = 0.004); see p = 0.05 and 0.051, respectively). Notice that only two patients were classified as having high AR pathway activity, in combination with low ER and PI3K pathway activity. All seven patients with high HH pathway activity also had low ER pathway activity. A multivariate analysis of these four pathways showed similar results, although the p-value for the HH pathway increased to 0.12  showed that high ER pathway activity in the primary tumor was associated with a longer time to progression of metastases, with a hazard ratio of 0.58 ( to 0.12 B.p = 0.004), and decreased from 12.4 months for PI3K pathway inactive patients to 5.2 months for PI3K pathway active patients (logrank p = 0.003). Median PFS decreased from 10.1 months for AR inactive to 4.0 months for the two AR pathway active patients (logrank p = 0.033), and it decreased from 10.6 months for HH pathway inactive patients to 6.6 months for HH pathway active patients.ESR1 and PGR RT-qPCR expression, HER2/ERBB2 RT-qPCR status and PIK3CA mutation status; see ERBB2 status are also significant prognostic factors for PFS in this cohort ; see p = 0.038, 0.035 and 0.015, respectively), while HER2/ERBB2 status did not ; see ERBB2 status, which was unknown for ten patients, the results were even slightly better (To exclude the possibility that the correlation between ER or PI3K pathway activity and PFS was due to a dependency on other (known) predictors for PFS, a multivariate analysis was performed for PFS with ER and PI3K pathway activity in combination with known predictive factors: disease-free interval (DFI), dominant site of relapse, menopausal status at start 1st line therapy, log y better E,F alreaFinally, we assessed whether pathway activity in this study was associated with the clinical response categories. While ER pathway activity was clearly associated with a longer PFS, there was no significant relation with the five response categories , showing fewer patient samples with high PI3K pathway activity in the response group  than in the non-response group ; see In this study, we analyzed whether the molecular phenotype of a cancer is predictive for therapy response. To this end, we used novel computational models that measure functional activity of oncogenic signaling pathways, which have been developed over the past decade for activity assessment of the AR, ER, PI3K-FOXO, HH, NFκB, TGFβ and Wnt pathways based on analysis of mRNA from cancer tissue ,3,4. In In this study, of 130 ER positive patients, the ER pathway model identified a subgroup of 53 (41%) patients in which functional ER pathway activity was measured as high, using a preset threshold of 0 on the log2odds score . The obsERBB2 expression is used in routine diagnostics to obtain information on activity of the HER2-induced PI3K pathway, and more importantly to predict response to HER2 targeting drugs such as trastuzumab [ERBB2 mRNA expression status as measured by RT-qPCR and activity of the downstream PI3K pathway, suggesting that HER2 overexpression alone does not necessarily indicate PI3K pathway activity. This is in agreement with the emerging consensus that the predictive value of HER2 protein expression for response to PI3K pathway targeting drugs has been disappointing, and the biomarker seems to lack both sensitivity and specificity [PIK3CA mutation status was not significantly correlated with PI3K activity. Like HER2 overexpression, a mutation upstream in a signaling pathway does not necessarily lead to activation downstream.Measurement of HER2/stuzumab . In the cificity . A possicificity ,22. AlsoSOD2. The NFκB pathway is similarly activated in the presence of oxidative stress [SOD2 expression, is strongly indicative of cellular oxidative stress [SOD2; (2) high PI3K pathway activity, defined by low FOXO activity; (3) putative PI3K pathway activity with cellular oxidative stress, defined by high FOXO activity and high SOD2, in the presence of high NFκB pathway activity. In the oxidative stress group, 17 (68%) patients had low ER pathway activity, five of which with high TGFβ and two with high HH pathway activity, suggesting a tumor subtype with some characteristics of epithelial mesenchymal transition [Signal transduction pathways can interact to regulate cellular processes such as cell division, differentiation, and migration. We described previously that the well-known inverse relationship between FOXO transcription factor activity and PI3K pathway activity can be broken by the presence of cellular oxidative stress, which activates the FOXO transcription factor irrespective of PI3K pathway activity. However, the presence of cellular oxidative stress itself probably reflects growth factor pathway activity, specifically activity of the PI3K pathway ,23. Undee stress ,25,26. Te stress ,28. Suche stress ,29,30. Bansition ,32,33. OComparing these three PI3K pathway activity groups to the pathway activity profiles found in other sets of breast cancer tumors , group 1SOD2 levels, thus without the protective effect of SOD2. This may be indicative of apoptosis [Other potentially relevant signaling pathway combinations were found in the seven samples with high HH pathway activity, which all had high NFκB and low ER pathway activity. The HH/NFκB pathway activity combination may reflect the synergistic crosstalk between these two pathways . Anotherpoptosis ,39,40, oThe pathway models have been developed with the main goal of predicting response to targeted therapy, e.g., hormonal therapy. The fact that patients in this cohort had not received any endocrine treatment prior to the tamoxifen treatment of metastatic disease may have improved the predictive value of ER pathway activity in the primary tumor for the PFS, since no secondary endocrine treatment resistance mechanisms were in play . PersistOn the other hand, the predictive value of primary tumor ER pathway activity may have been limited by several factors, among which differences between the primary tumor and metastases. For instance, an active ER pathway in the primary tumor need not imply activity in the metastases, potentially resulting in disease progression, possibly worsened by the above-mentioned agonistic effect of tamoxifen. The other way around may also occur, as one patient had low ER activity in the primary tumor, while she had a complete response on first-line tamoxifen treatment of the metastasis. Heterogeneity in ER staining between primary tumor and metastases is known to occur frequently, as well as between various metastases of the same patient, suggesting variations in ER activity ,45,46,47SOD2 may confer resistance to tamoxifen [To investigate a prognostic role for PI3K pathway activity in primary tumors with respect to PFS in the tamoxifen treated metastatic patients, the groups of low FOXO-associated PI3K pathway activity and oxidative stress-associated PI3K pathway activity were taken together and compared with low PI3K pathway activity patients. This revealed PI3K pathway activity with or without cellular oxidative stress in the primary tumor as a predictive factor associated with shorter PFS in the metastatic patients. In addition, PI3K pathway activity was associated with poor response to tamoxifen treatment. The two clinical outcome measurements are related and PI3K pathway activity as well as cellular redox stress countered by amoxifen . These famoxifen .TGFβ pathway activity was also associated with resistance to tamoxifen in our cohort, and this association has been described before for in vitro cancer model systems . Indeed,Finally, the two patients with low ER but high AR pathway activity in the primary tumor showed rapid tumor progression under tamoxifen. AR staining was shown in a large clinical study to be associated with a relatively favorable outcome in ER positive patients while being a poor prognostic factor in ER negative breast cancer patients . If primThis unique patient cohort of endocrine treatment-naïve patients with metastatic disease also has inherent limitations, since only patients who developed metastatic disease were included. Unfortunately, Affymetrix HG-U133Plus2.0 or +PM expression data were not available from a comparable group of endocrine treatment-naïve patients that did not develop metastases. Clinical confirmation would require a study in which patients, similarly treated, with and without development of metastases could be compared. Due to lack of a patient set in which patients did not receive tamoxifen treatment, it was also not possible to define the value of primary tumor ER pathway activity to predict response to tamoxifen therapy in the metastatic setting. On the other hand, clinical studies in the neoadjuvant hormonal treatment setting may serve as proper validation studies to prove the value of the ER pathway model to predict therapy response. Indeed, results of a neoadjuvant endocrine treatment study  provide Based on the presented evidence, the most important conclusions of the current study are as follows. (1) Positivity for ER protein in a breast cancer sample does not always mean that the ER pathway is in the active state. (2) Despite expected heterogeneity between primary and metastatic tumors, ER pathway activity in the primary tumor retained predictive value for PFS under tamoxifen, suggesting that at least several metastases had an active ER pathway. This provides additional support for the expectation that ER pathway activity analysis has clinical utility as a predictor of hormonal therapy response in ER positive breast cancer patients in general. (3) PI3K-FOXO, HH, AR and TGFβ pathway analysis may be useful to identify patients with ER positive tumors who may benefit from alternative targeted therapeutics. Clinical studies are in progress to provide further clinical validation.


With the continuous development of production technology, the performance of glass-fiber-reinforced polymer (GFRP) bars is also changing, and some design codes are no longer applicable to new materials based on previous research results. In this study, a series of durability tests were carried out on a new generation of GFRP bars in laboratory-simulated seawater and a concrete environment under different temperatures and sustained loads. The durability performance of GFRP bars was investigated by analysing the residual tensile properties. The degradation mechanism of GFRP bars was also analysed by scanning electronic microscopy (SEM). Furthermore, the long-term performance of GFRP bars exposed to concrete pore solution under different stress levels was predicted using Arrhenius theory. The research results show that the degradation rate of GFRP bars was increased significantly at a 40% stress level. By comparing the test results, design limits, and other scholars’ research results, it is demonstrated that the GFRP bars used in this test have a good durability performance. It is found that the main degradation mechanism of the GFRP bars is the debonding at the fiber-matrix interface. In the range test, the effects of a 20% stress level on the degradation of GFRP bars were not obvious. However, the long-term performance prediction results show that when the exposure time was long enough, the degradation processes were accelerated by a 20% stress level. The steel bars in traditional reinforced concrete (RC) structures are subject to increasing corrosion due to the continuous intrusion of chloride ions in seawater, which reduces the durability of the RC structures. Costly remedial measures, such as coating the surface of the steel bar with epoxy or galvanized steel using stainless steel bars, have failed to improve the long-term durability performance of RC structures . In receThe degradation rate of GFRP bars depends on the rate of the corrosion ions entering the interior of the GFRP bars and the rate of the corrosion ions reacting with the matrix and fibers. Therefore, the durability of GFRP bars tends to be affected by many factors, such as 1) environmental factors, such as the pH ,4, exposIn GFRP-RC structures, GFRP bars normally work under stress. Stress may cause micro-cracks in the matrix. These micro-cracks may result in the invasion of the surrounding environment (such as hydroxyl ions (OH–) and moisture, etc.), which in turn may corrode the fibers ,16. AccoAlthough the long-term performance of unstressed GFRP bars exposed to corrosion environments has been studied ,19,20,21The residual tensile properties of stressed GFRP bars in a corrosive environment were evaluated and the obtained results were compared with the literature and the design limits given by ACI 440.1R-15 ;The experimental phenomena and SEM analysis results were analysed to investigate the degradation mechanism of GFRP bars exposed to simulated seawater solution and concrete pore solution;The long-term performance of GFRP bars exposed to concrete pore solution under different stress levels was predicted based on Arrhenius theory.In summary, the main work of this research is summarized as follows:The GFRP bars used in this study were prepared by the pultrusion process with E44 epoxy resin and fibers. The detailed production parameters are shown in Sustained tensile stresses: 20% and 40% of the ultimate tensile strength were selected in this test. The corresponding strains were 5200 and 10,400 με, respectively. These levels of strain are about 1.68–3.35 times the values recommended by ACI 440.1R-15 for creep rupture strain . This waSurrounding environment: Two different corrosive mediums were used to simulate the seawater and concrete environment. The compositions of the two simulated solutions are shown in Temperature: Both ambient and elevated temperatures were used, with the ambient temperature being 23 °C and elevated temperatures being 40 and 60 °C.This study mainly includes three parameters:Test specimen: The total length of the GFRP bar used for testing was 1000 mm, and each end was anchored with 275 mm-long and 18 mm-inner diameter steel tubes for applying axial tension. The middle of the specimens was instrumented with two strain gauges to monitor longitudinal strains. In order to expose the GFRP to a specific solution, a plastic pipe  with a diameter of 63 mm and a length of 250 mm was inserted into the middle of the bar, which served as a reservoir. A PVC pipe was inserted into the gap between the two ends of the plastic pipe and the GFRP bar, and then glued them together. An opening with a diameter of 15 mm was left in the PVC pipe at one end of the plastic pipe, to allow for frequent replacement of the solution when the pH of the solution was lower than the set value. The unstressed specimen is shown in Loading device: The loading device was composed of a reaction force frame and a spring. The reaction frame was connected by two 18 mm thick steel plates and four steel rods (diameter = 8 mm) through bolts, and the stiffness of the spring was 790 kN/mm. The loading procedures mainly consisted of two steps: the first step was to fix the specimen on the reaction frame, and the second step was to compress the spring by constantly tightening the nut (2) to provide a reaction force. Then, the force was applied to the test specimen through the reaction frame until the compression of the spring reached the target length and the strain gauge reached the corresponding strain. Finally, the nut (3) was tightened so that the GFRP bar was under sustained stress. During the test, when the compression length of the spring dropped below the calculated length, it was compressed to the target length. In the subsequent test process, the strains of the GFRP bar were checked every 5 days to observe the creep process. The stressed specimen is shown in Temperature control: A glass fiber heating belt  was used to wrap the plastic tube and heat the solution in the plastic tube. A temperature sensor between the plastic tube and the heating belt was installed, with a US-controlled TN-99 temperature controller , to monitor the temperature. During the test, the temperature error was controlled within ±2 °C, as shown in Test procedure: After reaching a defined duration under the controlled testing condition, the preload on specimens was released and the PVC container was removed. Then, the specimen was subjected to the tensile test according to ACI 440.3R B.2-04 .In this research, three specimens were tested for each condition, and a total of 162 specimens were tested. The specimen numbering was defined as follows.The numbering of conditioned specimens consists of five parts: The first letter G stands for GFRP, and the second letter stands for the solution type. For example, S stands for seawater solutions, and C stands for concrete pore solutions. Then, the first number represents the exposure temperature, the second number represents the sustained stress level, and the third number represents the exposure time. For a typical conditioned specimen, G/S/40/20/30 indicates that a GFRP bar with a stress level of 20% is exposed to a simulated seawater solution at 40 °C for 30 days. A JEOL 7001F field emission scanning electron microscope  was used to observe the cross section of the GFRP bar samples to understand the degradation mechanism of GFRP bars. The samples observed with the SEM were reference specimens and specimens conditioned in 60 °C solutions for the longest duration, which was 90 days. To prepare the samples, the GFRP bars were embedded in epoxy resin, cut with a low-speed saw, and polished with sandpaper and fluffy cloth.Columns 3 and 4 in The tensile strength retentions of GFRP bars are summarized in In order to verify the applicability of the GFRP bars investigated in this study, this paper compares the test data with results presented in literature. The selected literature includes similar test methods, compositions of solutions, and pH levels, to ensure reliability. Due to the low elastic modulus of GFRP bars, under the same reinforcement ratio, GFRP-RC members tend to exhibit larger deformation and result in wider cracks compared to steel-RC members ,26,27. Tffu (ffu is the design tensile strength of GFRP).Creep tensile strain: According to the changes in the strain of GFRP bars over time at sustained stress, the creep characteristics can be analysed. Tensile strength and strain: The degradation rate of GFRP bars is relatively more rapid when exposed to a solution at 60 ° C. Therefore, only the microstructure analysis of GFRP bars after 90 days of exposure to a solution at 60 ° C was performed to understand the degradation mechanism of GFRP bars. Y is the tensile strength retention (%), t is the exposure time, τ is the fitted parameter, and Y∞ is the tensile strength retention (%) at the exposure time of infinity. As the GFRP bars were exposed to the solution for only a short time in this study, the tensile strength of the GFRP bars was still decreasing after exposure, so it is impossible to know the tensile strength retention Y∞ after an infinite exposure time. Therefore, Equation (2) was used to predict the long-term performance of GFRP bars. Previous research  has conk is the degradation rate (1/time), A is a constant related to the material degradation process, Ea is the activation energy, R is the universal gas constant, and T is the temperature in Kelvin.According to Arrheneius theory, the relationship between the degradation rate and the temperature can be expressed asIt should be noted that the primary assumption of Arrheneius theory is that the single dominant degradation mechanism of the material does not change with time and temperature during the exposure, but the rate of degradation is accelerated with temperature . TherefoT with the slope of the Ea/R value.From Equation (4), the logarithm of time needed for a material property to reach a given value is a linear function of 1/AF) for a range of high temperature (T1) to low temperature (T0) can be calculated asAF is the acceleration coefficient; t1 and t0 are the times required to reach a given tensile strength retention value at temperatures of T1 and T0, respectively; c is a constant; and k1 and k0 are the degradation rates at temperatures of T1 and T0, respectively.According to Equation (3), the acceleration coefficient .τ and correlation coefficient R2 were as shown in The long-term performance prediction of GFRP bars consists of four steps. Step 1: As shown in τ (t) against the inverse of temperature (1/T), and the data were fit using Equation (4), as shown in Ea/R and correlation coefficient R2 are presented in Step 2: The parameter τ  could beAF values for other temperatures  based on the reference temperature of 20 °C. The obtained AF values are listed in Step 3: 20 °C was taken as a reference temperature and Equation (5) was used to calculate the AFs for 23, 40, and 60 °C were obtained, AFs. Then, Equation (2) was used to fit τ values in τ values obtained at 23 °C presented in τ values listed in Step 4: Once the As shown in From the test results in Ea) is defined as the energy required for a chemical reaction. The lower the required activation energy of a GFRP bar, the faster the GFRP bar degradation rate. For example, the activation energy of GFRP bars at a 40% stress level is 28.6% and 14% lower compared to lower stress levels of 0% and 20%, respectively, as shown in The activation energy ((1)The degradation rate of GFRP bars in seawater solution is significantly lower than that in concrete pore solution, and the difference in degradation rate becomes more obvious as the temperature and stress level increase;(2)A stress level of 40% is able to cause micro-cracks in the resin matrix of the GFRP bars, accelerating the degradation rate of the GFRP bar. In the design of GFRP-RC structures, the stress level in the GFRP bars should be limited to under 40% of the ultimate tensile strength;(3)It has been found that a lower stress level of 20% does not affect the degradation rate of GFRP bars. However, based on the prediction analysis of the service life of GFRP bars, it has been found that for the same tensile strength retention, the time required for a stress level of 20% is significantly reduced compared to a 0% stress level. With the increase of the exposure time of GFRP bars, the damage of GFRP bars increases continuously and the degradation rate of damage GFRP bars is accelerated by a 20% stress level;(4)The elastic modulus of GFRP bars is not affected by the corrosive environment and stress level, and the degradation rate for tensile strength and tensile strain tends to remain at the same level;(5)The residual tensile properties of all GFRP bars can meet the requirements of ACI 440.1R-15, except for those tested under a 40% stress level in 60 °C concrete pore solution for 90 days;(6)Since the GFRP bars were directly exposed to the corrosion solution, the long-term performance prediction results of the GFRP bars obtained using Arrheneius theory are considered to be conservative. Future research is needed to establish the correlation between the degradation of GFRP bars in laboratory-simulated and field conditions, in order to provide practical guidelines for GFRP-RC structures.This experimental research has investigated the durability of new GFRP bars exposed to laboratory-simulated seawater and concrete environments. The main conclusions are as follows:


Bombyx mori, is an important economic insect. Viruses cause serious economic losses in sericulture; thus, the economic importance of these viruses heightens the need to understand the antiviral pathways of silkworm to develop antiviral strategies. Insect innate immunity pathways play a critical role in the outcome of infection. The RNA interference (RNAi), NF-kB-mediated, immune deficiency (Imd), and stimulator of interferon gene (STING) pathways, and Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway are the major antiviral defense mechanisms, and these have been shown to play important roles in the antiviral immunity of silkworms. In contrast, viruses can modulate the prophenol oxidase (PPO), phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt), and the extracellular signal-regulated kinase (ERK) signaling pathways of the host to elevate their proliferation in silkworms. In this review, we present an overview of the current understanding of the main immune pathways in response to viruses and the signaling pathways modulated by viruses in silkworms. Elucidation of these pathways involved in the antiviral mechanism of silkworms furnishes a theoretical basis for the enhancement of virus resistance in economic insects, such as upregulating antiviral immune pathways through transgenic overexpression, RNAi of virus genes, and targeting these virus-modulated pathways by gene editing or inhibitors.The lepidopteran model silkworm,  Bombyx mori, is economically important for silk production. Sericulture is one of the main sources of income for farmers in many developing countries (Bombyx mori nucleopolyhedrovirus (BmNPV), Bombyx mori cytoplasmic polyhedrosis virus (BmCPV), or the Bombyx mori bidensovirus (BmBDV)  .Insects mainly rely on innate immunity to defend against invading pathogens, and immune pathways play an important role in this process. Although some host signaling pathways can be modulated by viruses to elevate virus proliferation, targeting these pathways can also inhibit virus infection. In this review, we present an overview of the main pathways involved in the antiviral mechanism of silkworms. Such knowledge could provide a theoretical basis for strategies for control of viral diseases in economic insects.Cypovirus genus of the Reoviridae family, and its genome consists of ten discrete double-stranded RNA (dsRNA) segments , NF-kB-mediated pathways, and Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway , 21–24. There are three RNAi-related pathways in insects, including the small interfering RNA (siRNA) pathway, microRNA (miRNA) pathway, and the PIWI-associated RNA (piRNA) pathway . When chP6.9 expression, respectively; BmCPV-miR-1 could facilitate target gene BmIAP expression and BmCPV replication  genes transcription mediated by two distinct orthologs of the NF-kB transcription factor , 25, 44.osophila –48. AMPse needed .BmPGRP-S2 was induced by BmCPV in the silkworm midgut , phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt), and the extracellular signal-regulated kinase (ERK) pathways have been reported to be modulated by viruses to elevate viral proliferation. For example, baculovirus induces g BmSpry –60. It iin vitro  belongs to the RTK family , 81. ThemE cells  (Figure mE cells . Furtheroduction . AG1478 oduction  and the oduction . The twooduction , but theBmPGRP-S2 and Imd to activate Relish whereas BmNPV activates cGAMP and STING to process Relish. Additionally, RNAi inhibits viral replication by cleaving the viral RNA while NF-kB-dependent antiviral immunity may based on AMPs. The multi-level response is beneficial to antiviral defense of host.Antiviral mechanisms are a worldwide problem and research hotspot. Insect-virus interactions may provide information on a vast repertoire of antiviral immune mechanisms . ResultsIt is now apparent that these antiviral pathways are integrated and are responsive to one another, providing a pathogen-specific response. For example, the ERK and PI3K/Akt pathways have all been reported to interact with the JAK/STAT pathway , and theCoevolution between hosts and viruses favors the development of immune evasion mechanisms through modulation of the host signaling pathways by the pathogen . TargetiLJ: drew figure, wrote the article, and supervision.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Glioblastoma (GBM) is the most common malignant brain tumor and its malignant phenotypic characteristics are classified as grade IV tumors. Molecular interactions, such as protein–protein, protein–ncRNA, and protein–peptide interactions are crucial to transfer the signaling communications in cellular signaling pathways. Evidences suggest that signaling pathways of stem cells are also activated, which helps the propagation of GBM. Hence, it is important to identify a common signaling pathway that could be visible from multiple GBM gene expression data. microRNA signaling is considered important in GBM signaling, which needs further validation. We performed a high-throughput analysis using micro array expression profiles from 574 samples to explore the role of non-coding RNAs in the disease progression and unique signaling communication in GBM. A series of computational methods involving miRNA expression, gene ontology (GO) based gene enrichment, pathway mapping, and annotation from metabolic pathways databases, and network analysis were used for the analysis. Our study revealed the physiological roles of many known and novel miRNAs in cancer signaling, especially concerning signaling in cancer progression and proliferation. Overall, the results revealed a strong connection with stress induced senescence, significant miRNA targets for cell cycle arrest, and many common signaling pathways to GBM in the network. Glioblastoma (GBM) is the most common aggressive brain tumor, rendering it incurable by surgery ,2,3,4. TOn the other hand, small non-coding RNA and microRNA (miRNA) signaling tend to have an essential role in GBM signaling, requiring further validation and understanding ,7. miRNASenescence is one of the prominent factors in various cancers, including GBM ,14. In pWe identified novel senescence-related pathways involved in GBM through micro array expression data. This helps to present new signaling interactions governed by miRNA mediated molecular mechanisms. Differentially expressed miRNAs identified were significantly expressed from the matrix , which wReliability and coverage of functional annotation for over expressed genes were further retrieved and confirmed by hypergeometric distribution analysis in two levels: Gene Ontology and Reactome pathway. From the GO based enrichment analysis, we obtained 90 GO terms  showing Reactome pathway database targeted hypergeometric distribution analysis resulted in13 pathways  having 6(3) Formation of senescence-associated heterochromatin foci (SAHF) (REACT_169121) pathway. The processes of these pathways culminate in the formation of senescence-associated heterochromatin foci (SAHF). These foci represent facultative heterochromatin formed in the senescent cells. They contribute to the repression on the proliferation promoting genes and play an important role in the permanent cell cycle exit that finally leads to senescence ,28. (4) (9) Constitutive signaling by NOTCH1 HD+PEST domain mutants (REACT_160254). Functionality is highly similar to the previous complex and the roles observed were mainly from the T cell acute lymphoblastic leukemia . (10) NOIn total, 31 genes were found as the functional base of annotations resulting from the 90 GO terms identified from the network. The functional relationship among these genes was measured in all three levels of GO annotation: biological process, cellular component, and molecular function. CDKN1A (Cyclin-Dependent Kinase Inhibitor 1A), CDKN2A (cyclin-dependent kinase inhibitor 2A), CDKN2B cyclin-dependent kinase inhibitor 2B , CEBPB (CCAAT/enhancer binding protein (C/EBP), beta), CXCL8 (interleukin 8), EED (embryonic ectoderm development), EZH2 (enhancer of zeste homolog 2), IGFBP7 (insulin-like growth factor binding protein 7), IL1A , IL6 , KDM6B (lysine (K)-specific demethylase 6B), MIR3074 (microRNA 3074), and SUZ12 (SUZ12 polycomb repressive complex 2 subunit). Extended network analysis of these target proteins and associated miRNA reveals the signaling pathways from transcriptional regulation, cell cycle control, and cancer related molecular mechanisms . The isocitrate dehydrogenase 1 (IDH1) gene is an example that is known to frequently mutate in different types of cancer and influence EGFR expression ,42. SincAgilent single channel (green) microarray data of 574 samples each containing 12,033 probes was retrieved from The Cancer Genome Atlas (TCGA) repository  using “Dp-value cut-off of 0.05 from the linear model fit matrix. Adjustment method “BH” was used to control the expected false discovery rate (FDR) with the specified p-value, which is the most appropriate criteria for microarray studies.Primary array data from 574 samples were merged into an expression matrix and pre-processing was performed by normalization and differential expression analysis using series of R/Bioconductor  packagesGene Ontology  annotatiWe performed hypergeometric distribution analysis targeting the Reactome  pathway x significant genes, the p-value of enrichment of the pathway p with m genes is calculated by the following Equation (1)sK is the length of the significant genes list, K is total number of the genes for the evaluation. This test assumes that the list of significant genes is random and conditions on a fixed pathway. This is a one-sided test thatallows to check if the pathway is enriched/over-represented within the list of most significantly associated genes for the given phenotype . BMA provides the average similarity between the best-matching terms [To express the quantitative functional relationships among the GO terms observed , in relar (MICA) ,58,59.sng terms . Resnik ng terms ,62. Semang terms .RNAseq analysis of cancer data can aid in identifying tumor-related miRNA that function as an oncogene or tumor suppressor. In our analysis, the levels of certain miRNA in glioma tissues were notably underexpressed or overexpressed compared to corresponding non-tumor tissues. The differentially expressed miRNA targeted genes can be studied by using cellular assays to understand the blocking of cell cycle and proliferation. Altogether, these experiments reveal that certain miRNA loss facilitates malignant phenotype of glioma cells through a specific biochemical pathway. GBM have strong communications with aging induced stress and it is common among elderly males, which get aggressive as age increases. In the present work, we performed a differential expression analysis between all 574 glioblastoma tumor samples obtained from the TCGA database, with the goal of identifying significant associations between miRNA targets, their enriched functional categories, and tumor etiologies. Our analysis revealed the overexpressed Differentially Expressed Genes (DEGs) and GO annotations related to many senescence-linked molecular mechanisms in GBM. A major proportion of the genes identified as miRNA targets are the inhibitors of cell cycle and proliferation. Blocking such inhibitors will make cells into an aggressive proliferate condition, and virtually immortal, which is the hallmark mechanism of any tumor, including GBM. Observation of other cancer pathways, including bladder cancer and retinoblastoma in the network suggests the possible overlapping of signals among these cancer types.