WellExplorer.org), an interactive tool for researchers and community members to use for retrieving information regarding the hormonal, testosterone and estrogen modulators located at each well. We found that wells in Alabama use a disproportionately high number of ingredients targeting estrogen pathways, while Illinois, Ohio and Pennsylvania use a disproportionately high number of ingredients targeting testosterone pathways. Researchers can utilize WellExplorer to study health outcomes related to exposure to fracturing chemicals in their population-based cohorts. Community members can use this resource to search their home or work locations (e.g. town or zip code) to determine proximity between where they live or work and specific hormonal exposures.Exposure to hydraulic fracturing fluid in drinking water increases the risk of many adverse health outcomes. Unfortunately, most individuals and researchers are unaware of the health risks posed by a particular well due to the diversity of chemical ingredients used across sites. We constructed WellExplorer (http:// WhiFollowing this explosion of growth in the hydraulic fracturing industry, initial reports began to suggest that earthquakes were correlated with fracking activity  is useful to both researchers studying health outcomes related to proximity to hydraulic fracturing wells and also to community members deciding what chemicals they should test for in their private water wells.The purpose of WellExplorer is 2-fold: (i) to integrate information on hormonal, estrogen and testosterone pathways and the proteins that are targeted by chemicals used in hydraulic fracturing fluid to enable both researchers and community members to readily access this information when studying health outcomes and (ii) to include information about well locations in an easy-to-use manner so that community members can search their own zip codes to locate hydraulic fracturing wells in close proximity to them (ranked by distance from entered zip code). Therefore, WellExplorer , toxin mechanisms of actions and specific protein functions. Furthermore, we extracted the toxicity rankings of the top 275 most toxic ingredients from the Agency for Toxic Substances and Disease Registry (ATSDR), as well as a list of ingredients that were food additives as described by Substances Added to Food Inventory, managed by the Federal Drug Administration (FDA). Our overall linkage method is shown in .Once we had derived this cleaned and linked dataset of hydraulic fracturing chemicals from the aforementioned data sources, we looked to expand and simplify this data by identifying and annotating ingredients that affected certain hormonal pathways, and the proteins those ingredients targeted. To do this, we looked at the specific function, full gene name  and gene synonyms of all the targets in the T3DB dataset and if they involved interactions with ‘estrogen’, ‘testosterone’ or another ‘hormone’. Specifically, we looked to see if any of the targets in T3DB explicitly contained these three respective words in one of those fields , all genes falling into this category being subsequently labeled ‘estrogen gene’, ‘testosterone gene’ and/or ‘hormone gene’, depending on which of the three words their field(s) contained. To further identify genes involved in hormonal pathways, we used all of the Kyoto Encylopedia of Genes and Genomes (KEGG) pathways that contained an estrogen, testosterone and/or hormone gene and labeled those as ‘estrogen’, ‘testosterone’ or ‘hormone’ pathways. Therefore, for example, we labeled an ingredient as hitting an estrogen pathway if that ingredient targeted any of the genes/proteins in the same pathway. This labeling resulted in six new binary fields: estrogen gene, testosterone gene, hormone gene, estrogen pathway, testosterone pathway and hormone pathway. A diagram illustrating how T3DB annotated protein/gene information was labeled as belonging to estrogen, testosterone, hormone genes and their corresponding pathways is shown in Because part of our goal in cleaning and correlating this data was to make our conclusions and final data easily accessible to not only researchers but also to the layperson, we wanted to create a web interface through which these cleaned datasets could be easily browsed and interpreted. Since there are already sites that present in-depth hydraulic fracturing well data in an accessible way, we looked to specialize our interface more toward the unique aspects of the data analysis that we performed (as detailed above), particularly the biological and toxic properties of the ingredients contained inside these wells. In this way, we looked to include specific information about the proteins these ingredients target and the ultimate hormones and hormonal pathways that could be affected, as well as useful links regarding the ingredients and genes themselves. These genes encode proteins targeted by the chemicals.Well proximity is one of the most important and relevant considerations when discussing the potential effect these ingredients could have on an individual’s exposome; we also looked to include a Zip Searcher, or position locator function in the WellExplorer application. Specifically, we wanted the browser to not only be able to display wells close to the entered location but to also list the distances of these wells from the input position and to return them by absolute proximity to the location. Finally, we included options to narrow and shorten the number of well results by querying the data by its different fields, including well name/operator name, ingredient name/CAS number and affected gene name/affected pathways.In terms of developing this web application, we first cleaned, shortened and subsetted the data to create two newly compressed, usable and formatted .csv files that could be efficiently used in the deployment of the browsing site, ultimately dubbed WellExplorer. The first of these files is well_data, which contains a list of the wells and ingredients, which we aggregated by well name for ease of interpretation, with general well information as well as ingredient details that are specific to that well, such as supplier and ingredient purpose. The second is ingr_path_props, which consists of the more ingredient-specific data, as well as target proteins (and the genes encoding those proteins), and pathway-related information; we aggregated this dataset by ingredient name, with each ingredient having a corresponding list of targets.To develop the Zip Searcher function, we utilized the Geocoding Google Maps API to extract the latitude and longitude from the entered location. This position is then passed, in real time, to the Haversine Distance Formula along with the well latitude and longitude positions, to compute the absolute distances between the entered location and the well sites. The haversine distance formula is appropriate here because of its accuracy in calculating distances on a curved surface, such as the earth.uniprot.org, providing even more information about certain ingredients and proteins.We also developed the Data Explorer, a platform through which users can more directly interact, query and download these two datasets. To create this, we used the DataTables package in conjunction with the R Shiny package, which was the library ultimately used to develop the entire web application, both of which are offered freely by R Studio. Our initial version used comma separated (.csv) files linked together in our R script. Additionally, to deal with information bias, data warning flags were set for any well where the data seemed to be inaccurate, e.g. when the depth of the well was reportedly above 40 000 feet (which is the depth of the largest known well in the world), or the longitude and latitude do not fall within the stated county name. Finally, we created well landing page that contained much more in-depth information about the well itself and all of the relevant details about the ingredients used at the well. This included information regarding suppliers, toxicities, protein targets and pathways. We also linked the latitude and longitude positions of the well to Google Maps, so that the position of the wells could be seen more visually. In addition, we used the T3DB and UniProt IDs to link the ingredients and proteins to t3db.ca and .We cleaned the data from FracFocus and found that 1276 unique chemicals with CAS numbers were listed as ingredients in hydraulic fracturing fluids. We also found that 29 non-blank unique ingredients were not mapped to CAS numbers. Most of these non-CAS mappable chemicals contained the name ‘other’ in them (e.g. ‘other inorganic magnesium compound’) or were non-specific  or were vague (e.g. BE-9). We linked the 1276 unique chemicals with CAS numbers to our information on hormonal pathway, estrogen pathway and testosterone pathways obtained by linking with KEGG  with ill3.2.Linking the hydraulic fracturing wells to their hormonal modulators using the method outlined in Because WellExplorer has two purposes: one for researchers and the second for interested community members, we constructed the interface to have several features that are specifically designed to be used and interpreted by community members. The Zip Searcher page, in particular, was developed with community members in mind. As shown in Furthermore, the results themselves are presented in a readable manner. The first result that is displayed to the user (after they search a location) is an embedded interactive map that displays the first 10 wells on a satellite map . The welB) for viewing the raw data that can be downloaded for additional research purposes. Not only are these data tables very comprehensive, having 134 900 unique wells and 1467 unique ingredients, they also have a variety of different filtering and sorting features that can allow increased accessibility. Specifically, each data table can be sorted, alphabetically or numerically, depending on the data type, by each individual field. In addition, the search bars at the bottom of each column (highlighted with purple outline in C) allow the user to filter the data by each of these individual fields. This filter-by-field feature is especially applicable to researchers concerned with the proteins (and the genes that encode them) affected by these potentially toxic ingredients by allowing them to filter the ingredients in accordance with their particular gene/protein of interest.Additionally, WellExplorer also caters to researchers and academics. A Data Explorer page was specially designed with researchers in mind and it contains advanced functionality. As shown in The Zip Searcher page also has some advanced functionality for researchers. In particular, the expanded search interface  allows uuniprot.org)—as well as some other fields specific to each of these affected proteins. This functionality of linking this well data to the toxin information is one of the hallmarks of WellExplorer, and it sets it apart from other interfaces.The well landing page also contains some more advanced functionality that could be utilized by a researcher—namely, in the ingredients section. Not only does each ingredient contain a plethora of information about the general properties of that ingredient —specificUsers can search for hydraulic fracturing wells that are using ingredients targeting estrogen (or testosterone or other hormones) by selecting estrogen in the ‘genes and pathways affected’ option in the dropdown . This wiet al. compared 1021 chemicals used in hydraulic fracturing fluid to REPROTOX (a database containing information on reproductive toxins), and they found that 43% of chemicals with known toxicity information were reproductive toxins . For example, Alabama has a disproportionately large amount of hydraulic fracturing wells that are using ingredients that target estrogen pathways. Whereas, Ohio, Pennsylvania and Illinois have hydraulic fracturing wells that are using a disproportionately large amount of ingredients that target testosterone pathways. On the other hand, Nevada has a disproportionately large amount of hydraulic fracturing wells that are targeting hormonal pathways in general. This information can be important for researchers studying exposure to hydraulic fracturing fluids  and close proximity to hydraulic fracturing wells in Pennsylvania (Currie sylvania . It is ahttp://www.wellexplorer.org/) is the ease with which regular community members and individuals can query and interact with WellExplorer to learn more about exposures near them or their loved ones. All prior health outcomes studies have used pre-existing health data to link with proximity to hydraulic fracturing fluids. However, these studies do not provide individuals in the community with the information that they need to understand their own health risk. By creating WellExplorer, we have made a tool that can be useful to researchers to understand the proteins that these hydraulic fracturing fluid ingredients target and the hormonal implications of this targeting, including the genes encoding these protein targets. In addition, we have made a tool that community members can utilize to learn more about the areas where they live and the potential for inadvertent exposure to hormonal modulators in their communities. By providing community members with the information in WellExplorer, we are disseminating scientific research back to those whom are the most impacted by its results. Furthermore, we are providing a sense of autonomy and control to environmental justice communities, disproportionately burdened by fracking. With WellExplorer, concerned citizens can take note of the chemicals that are found nearest to their water source and test their wells or drinking water for these chemicals. It provides them with a scientific basis for them to then make decisions from. These decisions include not only testing for chemicals but also decisions about whether they will drink their water, which can be critical to the health of families. A lack of feeling in control breeds fear and uncertainty; WellExplorer provides an actionable resource to vulnerable communities.A major contribution of WellExplorer , which contains information on hydraulic fracturing wells and also detailed information on the ingredients used in the hydraulic fracturing fluid. This includes whether the ingredient is a toxin, a food additive, known to target a hormonal pathway, known to target an estrogen pathway, or known to target a testosterone pathway. We also include specific information on the gene targeted by the ingredient, if that information is available. Use of WellExplorer enables two ends: (i) to integrate information on hormonal, estrogen and testosterone pathways and genes that hydraulic fracturing fluid ingredients target so that researchers and community members can readily access this information when studying health outcomes and (ii) to include information about well locations in an easy-to-use manner so that community members can search their own zip codes to locate hydraulic fracturing wells in close proximity to them (ranked by distance from entered zip code). WellExplorer is useful to both researchers studying health outcomes related to proximity to hydraulic fracturing wells and also to community members deciding what chemicals they should test for in their private water wells. Researchers using WellExplorer can also search for specific gene names that they may be interested in to locate wells that are using ingredients targeting those genes. This could be very important for future research questions investigating health outcomes following exposure to hydraulic fracturing fluids.In conclusion, we have developed WellExplorer (Database online.Hydraulic_Fracturing_Paper_Supplemental_DatabaseClick here for additional data file.


A new type of HFRP hybrid bars (hybrid fiber reinforced polymer) was introduced to increase the rigidity of FRP reinforcement, which was a basic drawback of the FRP bars used so far. Compared to the BFRP  bars, modification has been introduced in HFRP bars consisting of swapping basalt fibers with carbon fibers. One of the most important mechanical properties of FRP bars is compressive strength, which determines the scope of reinforcement in compressed reinforced concrete elements . The compression properties of FRP bars are currently ignored in the standards . The article presents compression properties for HFRP bars based on the developed compression test method. Thirty HFRP bars were tested for comparison with previously tested BFRP bars. All bars had a nominal diameter of 8 mm and their nonanchored (free) length varied from 50 to 220 mm. Test results showed that the ultimate compressive strength of nonbuckled HFRP bars as a result of axial compression is about 46% of the ultimate strength. In addition, the modulus of elasticity under compression does not change significantly compared to the modulus of elasticity under tension. A linear correlation of buckling load strength was proposed depending on the free length of HFRP bars. The last 40 years are characterized by an extremely dynamic development of construction materials and a rapid increase in the performance parameters of composite materials. It is said that the engineer has more than 50,000 materials at his disposal. The engineer can and should combine the best properties of these materials to create composites (the most known and widespread is fiberglass) that offer particularly attractive sets of properties . The comThe economic aspect: The global composite market is expected to grow from USD 90.6 billion in 2019 to USD 131.6 billion in 2024 [ in 2024 . The estAt present, depending on the type, the price of basalt fibers varies in the range of 2–3 USD/kg. On the other hand, the price of carbon fibers is higher and currently amounts to 10–16 USD/kg .The purpose of the new approach to assess the compressive strength of composite bars is to use them as reinforcement in compressed RC elements (columns), as well as in the compressed zone of bent elements . RC compressed elements are the main load-bearing vertical structures of buildings and structures with various functions. They take over the load from floors and shells, transferring them to the foundation and further to the ground under the foundation. Hence, the reinforcement in these elements must have high physical and mechanical indexes and be resistant to corrosion. On the one hand, HFRP bars have a much better combination of mechanical properties than FRP bars with homogeneous fibers. On the other hand, they are corrosion resistant and have significantly better strength characteristics than conventional steel reinforcements.Empirical research is only possible if it can be reasonably assumed that the examined process is structurally stable. Otherwise there would be no guarantee that small external influences do not significantly change the indications of the measuring instruments. The applied author’s method of anchoring the tested specimens means that the measurement disturbance due to improper mounting is minimized and does not affect the structural stability of the specimens.The tests carried out according to the proprietary compression test method determine the scope of the application of HFRP reinforcement and its standardization in compressed RC elements. In addition, they allow a rational spacing of transverse reinforcement in compressed RC elements to effectively suppress lateral deformations. At the same time, they allow the optimization of HFRP compressive strength based on the determined correlation strength—free length of a bar.Characteristics of basalt and carbon fibers: The properties of basalt and carbon fibers included in HFRP bars have a number of significant advantages, which are presented below.Basalt fibers have an excellent resistance to acids and alkalis. The weight loss in the environment is three times less than that of glass fibers in the acidic environment . The redIn contrast, carbon fibers have a high modulus of elasticity and high tensile strength, which are maintained at temperatures exceeding 2000 °C, creep resistance, and relatively high values of tensile strength . Carbon −6/°C and a relatively high thermal conductivity. These features, combined with high strengths, cause a relatively low susceptibility to thermal shock. Their main disadvantage is the tendency to oxidize at high temperature. Compressive strength decreases with increasing the tensile modulus [G12) increases, then the strength of carbon fibers also increases [In addition, carbon fibers have a negative coefficient of thermal expansion (−0.6 to −1.45) ∙ 10 modulus . Howeverncreases .Bars Hybridization. An HFRP bar is a hybrid that is obtained by using two or more different types of fibers in one matrix. Hybrid bars have a better combination of properties than composites containing only one type of fiber. Various combinations of fibers and matrix materials are used, but it has been decided to combine the beneficial properties of carbon and basalt fibers with a polymer resin. When the HFRP bars are extended, failure is usually not catastrophic . Carbon fibers are the first to fail, and at this point the load is transferred to the basalt fibers. Carbon fibers are strong and relatively stiff and provide a low-density reinforcement; however, they are expensive. Basalt fibers are relatively cheap but do not have carbon stiffness. The basalt-carbon hybrid is stronger and harder, has a higher impact resistance, and can be produced at lower costs than comparable composites reinforced entirely with carbon or entirely with basalt.Vb, Vc, Vm—volume fraction of basalt, carbon roving fibers, and polymer matrix, respectively; fb, fc, fm—ultimate strength of basalt, carbon roving fibers, and polymer matrix respectively; Eb, Ec, Em—modulus of elasticity of basalt, carbon roving fibers, and polymer matrix, respectively; αf = Vc/Vb.The volume fraction of fibers in FRP composites can reach up to 80% . The proHowever, the modulus of elasticity at the load perpendicular to the direction of the fibers is much smaller and can be described by Equation (2):αf = 1:9, 1:4, 1:3, 1:2, and 1:1 . Thanks to this substitution, HFRP bars are characterized by a much higher stiffness, which enables more efficient application as reinforcement of concrete elements subjected to bending and compression. The tensile strength of HFRP bars is more than two times higher than the yield stress of steel [HFRP bars are composed of epoxy matrix and basalt fibers, carbon fibers with a volume ratio of 1:4  . SimilarASTM D695-15 recommends carrying out the test by placing the sample between the cylindrical heads in a compression device. The strength test specimen should have a diameter equal to the diameter of the bar and a length twice that of the bar. The guidelines state that the ends of the sample are cut perpendicular to the loaded axis and as flat as possible .However, this procedure did not take into account crushing and unintentional rotation of the bar at both its ends, which significantly affects the assessment of strength and deformation of the bar under compression. The assessment of compressive strength is necessary to understand and predict the behavior of eccentrically bent and compressed reinforced FRP elements. Despite standard restrictions, recent studies have shown that neglecting the use of GFRP compression reinforcement in concrete columns is too conservative.Tavassoli et al. examined nine columns reinforced with GFRP bars under cyclic loading and showed that the columns had significant strength and ductility. The failure was initiated by crushing the compressed concrete followed by crushing of the longitudinal reinforcement GFRP. Although this revealed the general applicability of GFRP bars as compression reinforcement in columns, the mechanism of destruction of the bars themselves was still unknown .Afifi et al. showed that the strength of GFRP reinforced columns with sufficient transverse reinforcement spacing is only 7% lower than that similar in structural columns with steel reinforcement . Tests oBased on the research, it can be concluded that compressed elements with composite reinforcement should be designed by other methods than compressed elements with steel reinforcement. Different spacing of transverse reinforcement should be used in comparison with steel reinforcement. Currently, it is recommended to ignore the share of FRP fibers along the axial compression force of concrete elements due to the lack of conclusive data on this topic .Tests on FRP bars with a length to diameter ratio of 1:1 to 2:1 showed that the compressive strength is much lower than the tensile strength . FailureFujisaki and Kobayashi tested FRP bars embedded in concrete anchors at both ends of the sample with a free length of 5 mm under compression. The compressive strength of the AFRP, CFRP, and GFRP bars was determined as 10%, 30–50%, and 30–40% tensile strength. However, the free unbraced length between two concrete anchorages of samples with diameters from 6 to 10 mm was only 5 mm . This caLf/db < 7.33 (Lf < 110 mm) were destroyed by crushing. The second failure mode for samples with 7.33 < Lf/db < 14 of free length was a combination of crushing and buckling failures. For samples with free length of Lf > 210 mm (Lf/db > 14), the failure mode consisted of almost clean buckling of the free length bar. Samples showed a wide spread due to crushing, while those which were damaged due to buckling showed a small spread of values. Based on the limited tests of the three samples, it was found that the modulus of elasticity under compression is approximately equal to the modulus of elasticity under tension. In addition, it was found that for unbuckled reinforcing bars with a free length of Lf < 110 mm, the ultimate compressive strength is about 50% of the final tensile strength. [Lf < 10db are characterized by too large a dispersion, which makes it difficult to infer as to the value of the results.Dietz et al. conducted tests of 45 GFRP reinforcing bars, 15 mm in diameter and 50–380 mm free (unbraced) length to determine their ultimate strength and compressive modulus of elasticity. Three different failure modes were observed, namely crushing, buckling and crushing, and buckling. Failure modes were dependent on the free length of the samples. Individual samples were mounted in two 135 mm long steel cylinders with an external diameter of 50 mm, intended for screwing into the UTM heads of the testing machine. A hole 65 mm long and 17.5 mm in diameter was drilled in the center of each threaded cylinder so that any possible rotation of the sample ends would not lead to uncertainty of the results. GFRP samples with a ratio of free bar length to diameter trength. . Due to Lf from 2 to 40 bar diameters . It was found that GFRP bars with a Lf/db < 9.2 (Lf < 230 mm) are damaged by crushing, while with a Lf/db > 12.6 (Lf > 315 mm) the failure is carried out due to buckling. The mechanism of failure in the transition zone of 230 mm < Lf < 315 mm was not recognized [Brunn tested 34 samples of 25 mm GFRP bars for compression. Specimens of GFRP bars were cut perpendicular to the longitudinal axis and then placed in UTM holders. The specimens were loaded at a rate of about 100 MPa/min. The tested samples had a free length cognized . Since tKhan et al. performed the tensile test of three GFRP and three CFRP bars in addition to five CFRP bars and five GFRP bars with 15.9 and 15.0 mm in diameter and 80 and 60 mm, respectively. Each compression specimen with flat parallel ends was placed directly and vertically between the UTM heads and tested under the load with controlled displacement at a speed of 1.0 to 1.3 mm per min until damaged. The compressive strength values obtained for the GFRP bars were 1.4 times higher than the CFRP bars, and the final compressive stress corresponding to the final compressive strength was 1.65 times higher in GFRP bars than in CFRP bars. The final compressive stress of the tested FRP bars obtained from the tests were reported as registered in the compression test because there is no slip and the values are reliable. The compression modulus of elasticity of CFRP bars was 1.17 times greater than that of GFRP bars. The modulus of elasticity at compression of GFRP bars was 42.0 GPa and was close to the value of the module given by Dietz .Khorramian and Sadeghian studied GFRP bars with a diameter of 13 to 21 mm in a modified design device. The compressed specimen was mounted in a device consisting of two steel plates, two steel rings, two anchor adhesives, and a GFRP bar. The steel rings had a depth equal to the diameter of the bar, a diameter twice as large as the diameter of the GFRP bar, and a minimum thickness of 3.3 mm. The GFRP bar was then fastened with a fast-drying adhesive. It has been proposed that the total length of the GFRP bar is four times larger than its nominal diameter, which means that the free length of the bar is 2 diameters . This leFailure Mechanism. To properly assess the effect of compressive forces on the behavior of HFRP bars, you must first know the mechanism of destruction of FRP composites. The compressive strength of composites is less than the tensile strength. This is due to the fact that the fibers undergo buckling, or more precisely—they break down—a type of simultaneous buckling, shown in During compression of composites with a parallel , relative to the load, orientation of the fibers often occur in their elastic micro-buckling b, which The mechanism of this type of failure in the form of micro-buckling in FRP composites is most often associated with a change in the direction of crack propagation at the fiber-matrix phase, in which the gap changes direction, spreading deeper into the matrix. At the initial stage of loading, elastic bending of FRP fibers occurs a. Then, Microscopic Analysis: Before starting the compression tests, it was decided to conduct a microscopic analysis to determine the effect of chemical composition on the properties of HFRP bars.2 silica plays the dominant role in the chemical composition of basalt fibers, constituting approximately 43% by weight, the Al2O3 content is 17%, while FeO, CaO, and MgO are present in comparable amounts of several percent. Unlike glass fibers, basalt fibers contain Fe2O3 and FeO, which affect density (2.73 g/cm3), strength, and are responsible for the olive-brown color of the fibers  provided important information on the composition of HFRP bars. While carbon fibers are homogeneous in terms of chemical composition, the chemical composition of basalt fibers has a significant impact on the strength properties of HFRP bars. SiOe fibers .By examining HFRP bars using the BSE (backscattered electrons) method, significant data on the configuration of basalt and carbon fibers inside the samples was obtained. Breakage of the main load carrier of the composite structure (fibers) is considered the final state in the process of its damage b. It leaLf)” means the bar length between the sample anchorages. The variation in free bar lengths reflects the behavior of composite longitudinal reinforcement limited by the stirrup spacing. By specifying the buckling load strength for various free bar lengths, it is possible to optimize the transverse reinforcement spacing depending on the type of FRP longitudinal reinforcement.Due to the free length of longitudinal FRP bars that may vary in the column depending on the spacing of the spiral or stirrup chosen by the designer, it is necessary to establish the relationship between the buckling load strength and the nonanchored length for these bars. Therefore, it is extremely important to optimize transverse reinforcement spacing, which significantly affects the possibility of local buckling of FRP longitudinal reinforcement. Therefore, it was decided to carry out buckling load strength tests for HFRP bars based on the newly developed compression test method. HFRP bars with six different free lengths of five pieces were tested. The term “free length— was times the diameter of the tested bars and was Lf = n ∙ db. The tests were carried out for another six values of n = 6, 10, 14, 20, 22, 26. The steel sleeves had a length of 120 mm and an external diameter of 42 mm. The test bars were centrally attached to the sleeves after degreasing with a special adhesive with a hardener. First, the bar was anchored in one sleeve and then, after the connection was cured, the other end of the sample was anchored  testing machine with a maximum load of 200 kN. Sampling was performed at 10 measurements per second including axial strain, axial load, and axial stroke displacement from the machine. The study was considered complete just after obtaining the maximum compressive force.Fu is the maximum force registered during the test; Af,min is the minimum bar diameter measured immediately before installing the strain gauges on the surface of the bar being tested.The compressive strength of HFRP bar samples is determined according to the Equation:db; , the onset of failure occurred as a result of buckling, and with further loading, the fibers were simultaneously split and the tested bar crushed  and determining the strain as the mean strain value recorded from two strain gauges attached on opposite sides in the middle of the sample. The average buckling load strength for samples with successive values of free lengths and their standard deviation and the coefficient of variation are presented in db free length) and decreases to 15% as the anchor length increases (for 26db free length) (εuc), in terms of absolute value, they reach from 1/3 to 2/3 of the tensile deformation (εut), with no apparent effect of free length on the deformation value  of the stress–strain curve is not negligible. For this nonlinear behavior, the tangent or secant modulus of elasticity is usually used. The average values of the modulus of elasticity are shown for specimens of successive free (unbraced) lengths , but for end values in the range from 0.2 to 0.5 of the ultimate force.The modulus of elasticity under compression for subsequent HFRP bar specimens can be determined in several ways. The first way is to determine the modulus of elasticity under compression based on procedure B2 of the ACI 440.3R-12  standardEft for the same HFRP bar diameter increases slightly as the load increases. The comparison of the tangent tensile modulus of elasticity Eft and tangent compressive modulus of elasticity Efc for the whole range of absolute stress values (from 0.1 to 1.0 ultimate stress) fut and fuc, respectively is shown in Efc,min is the proper (best) measure of the stiffness of the tested bars due to compression, which should be taken into account when designing due to compression. In secondary structural elements, the compressive modulus of elasticity of HFRP bars can be determined based on the modulus of elasticity in tension. However, determination of the buckling load strength based on the compression test may be necessary, due to the large disproportion of the buckling load strength depending on the unbraced free length of HFRP bars.For subsequent unanchored lengths, the minimum value of the compressed tangent modulus of elasticity varies from 0.95 to 1.00 of the maximum value (for 0.2 ultimate force) , the value of the coefficient of determination is 0.9904. This means that the vast majority of the variability of buckling load strength is highly correlated with the change in the length of the unbraced HFRP bars  from the three-phase relationship for BFRP bars of a similar diameter.R2 = 0.99. This allows a simple and reliable selection of the transverse reinforcement spacing depending on the effort of compressed HFRP bars.The buckling load strength monotonically decreases with the increasing length of the nonanchored HFRP bar according to the linear relationship established with a correlation of The compressive tangent modulus of elasticity in the range of 0.2–0.5 the ultimate load decreases by 4 GPa (5%) in contrast to the tangent elastic modulus at tension, the value of which increases in the analogous range for the ultimate load at 4 GPa (2.5%).Efc,min is the correct (best) measure of the stiffness of the tested bars due to the buckling load, which should be taken into account when designing due to compression.A representative value of the modulus of elasticity (according to the author) should be the minimum value of the tangent modulus of elasticity for the range of 0.2–0.5 ultimate load. The minimum tangent modulus of elasticity Ef,c) for nonbuckled bars are similar to the modulus of elasticity under tension . For bars with larger free lengths, the tangent modulus of elasticity under compression reached slightly higher values.The values of the modulus of elasticity under compression (According to the author, HFRP bars can be used as compressive reinforcement, but under the following specified conditions:Further research should be carried out to determine the relationship of the buckling load strength–free length for the remaining HFRP bar diameters used to propose a general relationship for the design of reinforcement in the compressed zone.An important factor affecting the reduction of the longitudinal compressive strength of HFRP bars is the misalignment of basalt and carbon fibers or their bending.Factors that improve the longitudinal compressive strength of HFRP anisotropic bars are: Increasing the value of the matrix shear modulus, tensile modulus, and thus compression, limit deformation and matrix interfacial strengths.


Eucommia ulmoides (EU) and Radix Achyranthis Bidentatae (RAB) have potential in the treatment of osteoarthritis, and this is associated with their multi-target and multi-link action characteristics. Although their potential anti-arthritic activity has been reported, the exact mechanism of EU-RAB action in osteoarthritis remains unexplored. Therefore, this study explores the mechanism of EU-RAB against osteoarthritis using network pharmacology and molecular docking technology.Osteoarthritis is a disabling disease, which seriously affects the quality of life of patients. Increasing evidence has indicated that Chinese herbal medicine including Public databases including TCMSP、BATMAN-TCM、OMIM and Genecards were used to predict the bioactive ingredients and putative targets of EU-RAB against osteoarthritis. Enrichment analysis was performed to expound the biological functions and associated pathways of the hub targets. Cytoscape software was used to construct a “compounds-targets-pathways” network for elucidating the comprehensive molecular mechanism of EU-RAB against osteoarthritis. Molecular docking was used to verify the correlation between the main active ingredients and hub targets.Network pharmacological analysis of EU-RAB in the treatment of osteoarthritis, identified 50 active ingredients including quercetin, kaempferol, wogonin, and baicalein with important biological effect. A total of 68 key targets were screened, including IL-6, EGFR, MAPK8, etc., and they were found to be enriched in a series of signaling pathways, such as apoptosis, TNF, MAPK, PI3K/AKT, and IL-17 signaling pathways. Moreover, molecular docking analysis showed that the main ingredients were tightly bound to the core targets, further confirming the anti-arthritic effects.Based on network pharmacology and molecular docking analysis, the present study provides insights into the potential mechanism of EU-RAB in osteoarthritis after successfully screening for associated key target genes and signaling pathways. These findings further provide a theoretical basis for further pharmacological research into the potential mechanism of EU-RAB in osteoarthritis. Osteoarthritis (OA) is characterized by progressive degeneration and wear of cartilage as well as subchondral osteosclerosis, pain, swelling, and stiffness in the joints. Globally, osteoarthritis affects approximately 250 million people . ChronicEucommia ulmoides  and Radix Achyranthis Bidentatae , are natural herbs reported to nourish kidney and strengthen the bone and are recorded in the classic of Herbal medicine Shennong Ben Cao Jing, which is one of China’s earliest pharmaceutical works [Achyranthes bidentata polysaccharides on chondrocytes proliferation [In the field of Traditional Chinese Medicine (TCM), OA is referred to as Arthralgia Syndrome, which originated from the ancient book “Huangdi Neijing”. Valuable research over the years has explored the role of TCM in the treatment of OA, via the mechanisms of anti-inflammation, anti-apoptosis, and anti-catabolism . Eucommial works , 6. Reseal works . Moreoveal works , 9. Xie al works . Zhang eal works . Ecdysteal works , 13. Wenferation . TherefoTCM has a complex mechanism of action and multi-pathway interactions. Over the past few decades, Chinese medicine was regarded as empirical medicine, which was neglected due to a lack of modernized research approaches in related disciplines such as molecular biology, pharmacology, and bioinformatics, etc. . Networkhttp://lsp.nwu.edu.cn/tcmsp.php), which is a unique platform for the analysis of active ingredients of Chinese herbal medicine and their interaction in specific diseases [http://bionet.ncpsb.org/batman-tcm/), which is the first online bioinformatics analysis tool for screening of the molecular mechanism of TCM [The compounds-related data of EU and RAB were retrieved from Traditional Chinese Medicine Systems Pharmacology (TCMSP) database . The DL threshold is determined based on the average DL value of the Drugbank being 0.18.In this equation, X represents the molecular descriptors of a new ingredient, and Y represents the average molecular properties of 6511 molecules in the DrugBank database .TCMSP database not only allows users to analyze to identify active compounds of Chinese herbal medicine but to also search for related genes targets of the active compounds. After obtaining the active ingredients by limiting OB and DL, the targets of each ingredient were collected and the repeating parts were removed to obtain the EU-RAB related targets. The species of target proteins were set to “https://www.genecards.org/, ver. 4.9.0) database is a human gene online analysis platform which is based on abundant gene resources, it integrates and predicts gene targets associated with diseases and also used to conduct Gene-Centered “OMIC” research [http://www.omim.org/, updated February 19, 2019) database is centered on the connection between the gene and phenotype, maintained timely updating [Osteoarthritis targets were acquired from two databases. GeneCards  database, mined the key regulatory genes. The potential targets were inputted to “Multiple proteins” in STRING and the organism was limited to “Homo sapiens”. A confidence score with correlation degree ≥0.400 was set using the default threshold, and this enabled the enhanced interaction between targets while ensuring a positive rate and extracted the PPI network after removing the discrete genes.Protein-protein interaction (PPI) analysis helped in the identification of key targets belonging to both EU-RAB and OA, and also highlight the related hub genes of EU-RAB anti-OA. The active ingredient of EU-RAB relevant to OA targets was retrieved via the STRING  and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to explore the core mechanism of action and biological pathways associated with the drug. After the PPI network was successfully constructed, hub co-targets were imported into R software and the clusterprofile, a bio-conductor package, was used to perform the GO and KEGG enrichment analysis, to obtain the biological processes, cellular components, molecular function and key signaling pathways. Only functional annotations with enrichment Network construction was performed using Cytoscape (ver. 3.7.2), a network visualization software to intuitively display the compound-target-pathway relationship between EU-RAB and OA, including 3 networks, namely: 1) compound-target network of EU-RAB; 2) potential compounds-potential targets network of EU-RAB for anti-osteoarthritis; 3) potential compounds-potential targets–pathways network of EU-RAB for anti-osteoarthritis.http://www.rcsb.org/). The imported crystal structure was imported into MOE for use in constructing the receptor protein grid, which was associated with the processes of protonation, removal of water molecules and repetitive structure, structure preparation, and energy minimization. The receptor grid construction was used to select the binding site with ligands. Lastly, the three-dimensional compound structure was edited in ChemBioDraw software, imported into MOE, and docked with protein construct to obtain the graphical results of molecular docking.Molecular docking technology was based on the “Key-Lock principle” to reflect the small molecule ligands-protein receptors association. Further, the best binding mode and site were searched based on the conformation change and energy matching process. In this study, the Molecular Operating Environment (MOE) (v2015.10) was performed to validate compound-protein target interaction. The crystal structure of the protein targets was downloaded from the RCSB PDB database  were obtained by further integrating all the targets.A total of 2498 OA related gene targets were retrieved from the OMIM and Genecards databases. Further, 68 putative targets of EU-RAB were obtained after integrating and connecting 110 putative targets of EU-RAB with 2498 OA gene targets. IL-6, EGFR, GSK3β, ESR, etc. are some of the densely related targets identified between EU-RAB and OA, and details are shown in Table p-value < 0.05. As shown in Fig. p = 3.91E− 17, FDR = 1.99E− 17), regulation of apoptotic signaling pathway , response to oxidative stress  and cellular response to oxidative stress  were the closely related to OA biological processes. In regard to cellular components, higher enrichment was found in membrane raft , membrane microdomain  and membrane region . The main OA-related terms in molecular functions contained DNA-binding transcription activator activity, RNA polymerase II-specific  and cysteine-type endopeptidase activity involved in apoptotic process  and estrogen receptor binding .GO and KEGG pathway enrichment analysis contributed to further analysis of cellular localization, biological effect, pathway, and function of the 68 gene targets of EU-RAB anti-OA. In GO analysis, the 68 potential genes were highly enriched in 1272 biological processes (BP), 31 cellular components (CC), and 95 molecular functions (MF) with KEGG enrichment analysis focused on the anti-osteoarthrits pathways of EU-RAB. Results demonstrated that “Apoptosis”, “TNF signaling pathway”, “PI3K-Akt signaling pathway”, “MAPK signaling pathway” and “IL-17 signaling pathway” were highly associated with the onset and progression of osteoarthritis , PI3K/Akt signaling pathway (degree = 15), Cysteinyl aspartate-specific proteinase 3 , inhibitor of nuclear factor kappa-B kinase subunit beta  and Glycogen Synthase Kinase 3 beta  were identified. The details of the compound-target-pathway network are shown in Table A total of 68 hub gene targets were imported into STRING for the construction of a protein-protein interaction network. A PPI network containing 68 nodes and 699 edges Fig. a was obtMCODE analysis of PPI network based on the STRING database and Cytoscape software resulted in 2 Clusters networks  was the lowest, indicating that the binding ability and stability of quercetin-IKBKB complex was higher compared with the other targets.Due to lack of corresponding ligands of a few targets in the PDB, EGFR (PDB id: 1 m17), ESR1 (PDB id: 1r5k), MAPK8 PDB id: 3pze), GSK3β (PDB id: 1q3d) and IKBKB (PDB id:4kik) were selected based on the core genes, signal pathway and research of osteoarthritis as successfully constructed proteins receptor for molecular docking verification. In molecular docking, if the ligand can bind to one or more amino acid residues with an H bond, H-π bond or π-π bond in the active site  of the receptor and participates in the process of conformation change, energy complementation, etc., then the small molecule ligand can combine with the receptor to form a stable structure . The binpze, GSK3Network pharmacology is a rational approach used to analyze the potential biological mechanism of TCM in the treatment and prevention of various diseases, and especially in chronic and frequently-occurring disease, such as osteoarthritis , 31. PreThrough the screening of active ingredients and analysis of compound-target network, quercetin, kaempferol, wogonin, and baicalein were identified to be the most important active ingredients of EU-RAB in the treatment of OA. Previous studies have shown that quercetin promotes chondrocytes proliferation and acts on inflammatory injury to accelerate cartilage repair . IncreasBy integrating all the targets of EU-RAB and OA, 68 targets related to OA were identified. The 68 targets were used to construct PPI for use in highlighting pivotal targets and interaction degree with others, such as IL6, EGFR, VEGFA, CSAP3, ESR1, GSK3β, etc. In the entire target network of EU-RAB against OA, these targets showed a rich interaction with others and were more likely to produce a cascade effect for exerting antagonistic effects on OA. Moreover, in the C-T-P network, gene targets acting on multiple pathways at the same time were also found, indicating the importance of specific targets in the entire OA bioinformatics network. For instance, MAPK8 (degree = 14) appeared in apoptosis, TNF, and MAPK signaling pathways, and both PI3/Akt and IL-17 signaling pathways contained GSK3β (degree = 16). This also reflected the “multiply targets-multiply pathways” regulation mechanism of EU-RAB. IL-6 is a key pro-inflammatory mediator cytokine related to bone resorption and degradation of the extracellular matrix . VEGFR sGO and KEGG analysis revealed that “apoptosis”, “TNF signal”, “IL-17 signal”, “PI3K/Akt signal pathway” and “MAPK signal pathway” were related to the mechanism of EU-RAB in the treatment of OA. In this study, “apoptosis” mapped the most key targets and possessed the highest degree of enrichment, and was the most dominant signaling way of EU-RAB against OA. Combined with hub targets of BP and MF analysis, the anti-apoptotic effect of EU-RAB reflected on the mediation of oxidative stress  and apoptotic signaling . Besides, the active ingredients of EU-RAB were found to possess a good anti-apoptosis effect via inhibition of inflammatory induction or directly suppressing cellular stress state –35. SimuIn this study, network pharmacology was used to elaborate on the potential anti-osteoarthritis effect of EU-RAB and this effect was visualized by molecular docking for verification. This scientific and systematic theoretical study provides a basis for future pharmacological studies to further explore the mechanism of EU-RAB in the treatment of OA.Traditional Chinese medicine has a long history for thousands of years in the treatment of osteoarthritis. As a typical representative, EU-RAB has been reported to play a protective effect through multiple targets and pathways in OA. For the first time, this study systematically expounds on the mechanism of action of EU-RAB anti-OA using network pharmacology. Besides, this effect is visualized through molecular docking to verify this relationship. In this study, 50 main bioactive compounds of EU-RAB anti-OA are identified. Moreover, IL-6, EGFR, VEGFA, ESR1, CASP3, GSK3β, MAPK8, etc., are the pivotal genes enriched to signaling transduction pathways  and for EU-RAB to exert its anti-apoptosis and anti-inflammatory effect on OA. Therefore, the analysis of molecular biological networks based on multiply target-pathway interaction using network pharmacology not only contributes to deepening the understanding of EU-RAB against OA but also provides a pharmacology basis for further research.Additional file 1: Table S1. Active compounds of Eucommia ulmoides (EU)-Radix Achyranthis Bidentatae (RAB). Table S2. Potential targets of Eucommia ulmoides (EU)-Radix AchyranthisBidentatae (RAB). Table S3. Cluster network of target genes of Eucommia ulmoides (EU)-Radix AchyranthisBidentatae (RAB) against Osteoarthritis (OA).


To use network pharmacology and molecular docking technology in predicting the main active ingredients and targets of Qushi Huayu Decoction (QHD) treatment in Nonalcoholic Fatty Liver Disease (NAFLD) and explore the potential mechanisms of its multi-component-multi-target-multi-pathway.  The main chemical components of QHD were searched using traditional Chinese medicine system pharmacology technology platform (TCMSP) and PubChem database. The main chemical components of the prescription were ADMET screened by the ACD/Labs software. The main active ingredient was screened by 60% oral bioavailability, and 60% of “bad” ingredients were removed from the drug-like group. Swiss Target Prediction, the SEA, and HitPick systems were sequentially used to search for the target of each active ingredient, and a network map of the QHD's target of the active ingredient was constructed. Genome annotation database platforms  were used to predict action targets related to fatty liver disease. “Drug-Disease-Target” network diagram could be visualized with the help of Cytoscape (3.7.1) software. UniProt and STRING database platforms were used to build a protein interaction network. The KEGG signal pathway and DAVID platform were analyzed for biological process enrichment.  A total of 128 active ingredients and 275 corresponding targets in QHD were discovered through screening. 55 key target targets and 27 important signaling pathways were screened, such as the cancer pathway, P13K-AKT signaling pathway, PPAR signaling pathway, and other related signaling pathways.  The present study revealed the material basis of QHD and discussed the pharmacological mechanism of QHD in fatty liver, thus providing a scientific basis for the clinical application and experimental research of QHD in the future. Over recent years, the incidence of fatty liver has been increasing, especially among the younger population . Fatty lInflammation has an essential role in the pathological process. NAFLD has become one of the risk factors affecting liver health. It has also become the second largest liver disease in China after viral hepatitis. Therefore, NAFLD has attracted increased attention; nonetheless, there is still no effective drug for the treatment of NAFLD. The commonly used drugs for the treatment of NAFLD are statins, which have a poor curative effect and may increase the risk of liver burden . TraditiPolygonum cuspidatum , Yinchen , gardenia , turmeric , and Tianjihuang  [α [α [QHD is a traditional Chinese medicine prescription for the treatment of NAFLD, which includes the following herbaceous plants: nb, TJH) . Previounb, TJH) . In this TJH) [α , IL-6, aH) [α [α , and usePolygonum cuspidatum, Herba Yinchen, turmeric, Gardenia jasminoides, and Tianjihuang) were retrieved from the TCMSP database. The chemical composition of QHD was established, and it was as follows: Polygonum cuspidatum had 52 components, turmeric had 39 components, Yinchen had 49 components, gardenia had 85 components, and Tianjihuang had 38 components. All ingredients were confirmed through PubChem (http://pubchem.ncbi.nih.gov) database. In addition, the PubChem ID and canonical SMILES of each component were collected.The main active components in the prescription of QHD  were taken as the effective components. Metabolic stability is an indicator used to measure the stability of drug metabolism. In ADMET evaluation, “unstable in HLM” was removed. Ames and hERG are, respectively, genotoxicity and cardiotoxicity indicators, and the components with clear toxicity were removed after screening.http://sea.bkslab.org/), HitPick (http://mips.helmholtz-muenchen.de/proj/hitpick), Swiss Target Prediction, and other databases were used to predict the main effective chemical components of QHD. Among them, the score value max was selected from the SEA database. The prediction results of TC > 0.6 and HitPick online prediction system precision value > 50 were used as potential targets of QHD.First of all, the canonical SMILES data on QHD, SEA (http://www.gengcards.org/), OMIM (http://omim.org/), and DisGeNET (http://disgenet.org/home/). The key words “NAFLD” were input into GeneCards, OMIM, and DisGeNET databases, respectively, to search for genes related to NAFLD.Then, the disease genes related to NAFLD were collected from GeneCards (http://www.uniprot.org/).Finally, the genes related to NAFLD collected from the three databases were intersected with the predicted potential targets of effective components of QHD. All targets were searched and confirmed by UniProt KB of UniProt (http://string-db.org/) database, where the organism was set to Homo sapiens, and the combination score threshold was 0.7 to obtain the protein-protein interaction relationship. Then, the PPI network of the target protein was constructed and analyzed using the Cytoscape software (3.7.1). The node size in the network was used to reflect the degree size, and the thickness of the edge was used to reflect the comprehensive score size. At the same time, the core submodules of the PPI network were obtained by module mining with the MCODE tool, in which the parameters were set as the default values.The related target of QHD in the treatment of NAFLD was imported into the STRING (http://david.ncifcrf.gov) was used to analyze the GO function and KEGG pathway enrichment of protein targets; the results were screened using P value < 0.05 and FDR < 0.05. According to the results of KEGG pathway enrichment analysis, we established the data file and constructed the target pathway network by using the Cytoscape software (3.7.1).To further explore the molecular mechanism of QHD in the treatment of NAFLD, DAVID 6.8  database, and protein structure files of key targets were collected from PDB (http://www.rcsb.org/) database according to UniProt ID. Then, PyMOL software (https://pymol.org/) was used to preprocess all small molecules and key targets to remove water molecules and other impurities, all of which were saved as PDB files. Next, the target protein was input into the POCASA 1.1 system  to calculate the optimal binding site (region) of each protein ligand. The best binding site (region) was used as a reference for the docking site. Finally, AutoDock Vina software (http://vina.scripps.edu/) was used for semiflexible molecular docking, and the affinity of all small molecules to their target was calculated and expressed as value (affinity). The smaller the affinity value is, the more stable the interaction between the target protein and the active component is.First, SDF format files of 3D structures of these optimized active compounds were downloaded from PubChem  and KEGG signal pathway were screened according to GO enrichment analysis showed that NAFLD, as a complex disease, is involved in multiple biological functions. The main chemical components of QHD can adjust these biological functions, thus developing therapeutic effects on nonalcoholic fatty liver. According to the KEGG pathway annotation analysis, as shown in According to the target degree value in the PPI network, the active ingredients were arranged in degrees. It is generally believed that the higher the degree value, the more disease targets correspond to the components, having a stronger effect . HoweverThe concepts of “disease” and “syndrome” have been well known in TCM since ancient times. The identification of disease is based on the overall condition of the disease, the pathogenesis of the disease, the syndrome, and the location of the disease. Accordingly, the potential occurrence and development of the disease are understood from a macroperspective . NAFLD b4-induced liver fibrosis in rats. Li and Yang [Based on experience from clinical practice, QHD has been used as a representative and effective prescription for NAFLD for a long time . In thisand Yang  found thGO enrichment analysis revealed that the key targets of QHD involved biological processes, such as positive regulation of RNA polymerase II promoter transcription, redox processes, and inflammation and negative regulation of apoptosis, protein heterodimers activity, lipid binding, transcription factor binding, heme binding, and similar. Our results revealed biological processes were closely related to cellular DNA replication, proliferation, and signal transduction , thus indicating that QHD had potential therapeutic effects on NAFLD, possibly by affecting biological processes, such as proliferation and apoptosis, inflammation, and oxidative stress. Finally, KEGG analysis revealed that the active ingredients of QHD affected NAFLD, involving cancer pathways, P13K-AKT signaling pathway, PPAR signaling pathway, cAMP signaling pathway, HIF-1 signaling pathway, HTLV-1 infection, and other pathways.α partnership in hepatocytes can coordinate the NAFLD process in mice and humans. It has also been reported that GPS2 acts as an epigenetic genome modifier with selective repressor PPARα hepatocytes and that its inhibitory effect reverses the NASH process to fibrosis [α expression after transcription, thereby limiting the progress of NAFLD in triglyceride synthesis. In the present study, we found that liver biliverdin reductase A (BVRA) inhibits the protective effect of glycogen synthase kinase 3 (GSK3β) on hepatic steatosis by enhancing serine 9 phosphorylation. Hinds and colleagues [β-PPARα axon that can regulate liver lipid metabolism and provide a unique target for NAFLD treatment. Antrodan (Ant) is a purified glucan extracted from cinnamon β-, which has good biological activities, including liver protection, hypolipidemia, antiliver cancer, and anti-inflammatory effects. It has been reported that Ant can effectively alleviate NAFLD through the AMPK/SIRT1/CREB-1C/PPARγ pathway [α activates lipid synthesis by upregulating the PI3K-AKT-mTOR pathway in the hypoxic microenvironment, thus promoting the progression of fatty liver cancer. Therefore, HIF-2α can be used as a biomarker for the diagnosis of NAFLD-HCC and a treatment target [Moreover, previous studies have shown that upregulating the level of miR-373 in liver cells can effectively inhibit AKT signaling and can be used as a strategy for the treatment of fatty liver degeneration . AKT dysfibrosis . In theifibrosis  found thlleagues  discover pathway . In our  pathway . NAFLD it target .Previous pharmacological experiments have found that QHD delayed the lipid deposition in liver cells and also suppressed the inflammatory response and improved liver injury . At the in vivo and in vitro.Huang et al.  also conBy using network pharmacology analysis and molecular docking technology, we found that QHD has a potential therapeutic effect on NAFLD, which was consistent with our previous experimental results. Moreover, our results were a reminder that network pharmacology can be used to predict the mechanism of action for a drug in the treatment of the disease.α expression after transcription, which in turn limited the synthesis of triglycerides during the occurrence of NAFLD. Liver BVRA inhibited glycogen synthase kinase 3β (GSK3β) by enhancing the phosphorylation of serine 9 that inhibited its activity and had a protective role against liver fatty degeneration. The mechanism of QHD treatment in NAFLD may be related to the regulation of the BVRA level.The mechanism of action of QHD in the treatment of NAFLD may be related to the following three aspects: QHD controlled lipid metabolism through the AKT signaling pathway. QHD further inhibited the synthesis of triglycerides by regulating PPAR


Cancer signaling pathways were the most upregulated ones in T2D  = 32.3; 88.5% (n = 906) of the total differentially expressed genes located in these pathways). In particular, integrin  = 2.03) and paxillin  = 1.46) signaling pathways were predicted to be upregulated, whereas the Rho guanosine diphosphate (Rho-GDP) dissociation inhibitor signaling pathway was predicted to be downregulated in T2D individuals  = 2.41). Our results suggest that, at transcriptional expression level, elderly individuals with T2D present an increased activation of signaling pathways related to neoplastic processes, T-cell activation and migration, and inflammation.We aimed to explore the differences in the whole transcriptome of peripheral blood mononuclear cells between elderly individuals with and without type 2 diabetes (T2D). We conducted a microarray-based transcriptome analysis of 19 individuals with T2D and 15 without. Differentially expressed genes according to linear models were submitted to the Ingenuity Pathway Analysis system to conduct a functional enrichment analysis. We established that diseases, biological functions, and canonical signaling pathways were significantly associated with T2D patients when their logarithms of Benjamini–Hochberg-adjusted  Omics technologies, including transcriptomics, proteomics, and metabolomics, have emerged as promising tools in this field [Type 2 diabetes (T2D) is a metabolic disorder characterized by chronic insulin resistance and subsequent hyperglycemia. It is a major risk factor for fatal and non-fatal cardiovascular disease , neurodeis field . Transcris field . Neverthis field . To overis field . They aris field  and playis field  and its is field . In the The aim of the present work was to comprehensively analyze the transcriptome of PBMC from individuals with and without T2D, and subsequently conduct a functional enrichment analysis to determine the association between T2D and the activation of diseases, biological functions, and canonical signaling pathways.n = 34) were randomly selected from a total of 584 recruited at the Instituto Hospital del Mar de Investigaciones Médicas (Barcelona) PREDIMED center. They were community-dwelling men (55–80 years) and women (60–80 years) free from cardiovascular disease at enrollment but presenting either T2D or >3 of the following risk factors: current smoking, hypertension, high levels of low-density lipoprotein cholesterol, low concentrations of high-density lipoprotein cholesterol, overweight/obesity, or a family history of premature coronary heart disease. They were then grouped according to whether they presented T2D (n = 19) or not  at baseline. T2D was defined by clinical diagnosis or use of antidiabetic medication [http://www.isrctn.com/ISRCTN35739639). All participants provided written informed consent before joining the trial. Full details of the study design, inclusion and exclusion criteria, and dietary intervention have been published elsewhere [This research followed a baseline, cross-sectional study design and included a subset of 34 participants from the PREDIMED (Prevención con Dieta Mediterránea) study, a clinical trial aimed at assessing the effectiveness of the traditional Mediterranean diet on the primary prevention of cardiovascular disease. Between June 2007 and October 2008, subjects of the current study , p value, and log2-fold changes (log2-FC) were calculated for each gene. Genes were considered differently expressed when they presented absolute t-statistics ≥ 2.0 and p values ≤ 0.05. All the statistical procedures included in this stage were performed using the computing environment R version 2.15.1 .We included all 22,277 probe sets represented in the array in the comparison analyses . The rawhodology . Subsequ package . In thisP and log2-FC values was loaded into the software. The core-expression analysis function, with the “Ingenuity Knowledge Base (Genes only)” as reference set, was then applied. The statistical significance and direction of activation for each disease, biological function, and signaling canonical pathway were determined using two metrics: the z-score and the adjusted p value. The z-score is a statistical measure that provides predictions about whether a biological process is upregulated (positive z-score) or downregulated (negative z-score). The p value, according to the right-tailed Fisher’s exact test, reflects the likelihood of the association between a set of genes and a determined biological process being significant. Furthermore, to control for a false discovery rate among biological process modulations, the software provides an adjustment of raw p values through the Benjamini–Hochberg (BH) procedure. In the current work, we considered that a disease, biological function, or canonical pathway was significantly associated with T2D when it showed both a z-score ≥ 2.0 and a BH-adjusted p value ≤ 0.05 (−log(BH-P) ≥ 1.30) [Functional enrichment analysis, including the prediction of diseases, biological functions, and signaling canonical pathways associated with T2D, was performed using Ingenuity Pathway Analysis software  . An inpu ≥ 1.30) .2-FC ≥ 2.0We estimated that a total sample of 34 participants allowed ≥80% of power to detect at least 5% of true differentially expressed genes between non-T2D and T2D conditions. Calculations were based on the algorithm proposed by Lin et al. , assumint-tests and chi-squared tests, respectively. Statistical analysis of anthropometric and clinical data was conducted with the SPSS 22.0 software . A p ≤ 0.05 value was considered to be statistically significant in all tests.The Kolmogorov–Smirnov test and normal probability plots were used to determine the distribution of anthropometric and clinical data. Variables with a non-normal distribution were log-transformed prior to analysis. Differences in continuous and categorical variables between T2D and non-T2D groups were assessed by The characteristics of the participants are presented in We found 1024 differentially expressed genes between T2D and non-T2D individuals (569 were upregulated and 455 downregulated). Gene expression linked to 11 diseases and biological functions was predicted to be activated in T2D patients a,b. P) = 32.3), cellular movement  = 19.4), gene expression  = 8.88), inflammatory response  = 8.03), cell-to-cell signaling and interaction  = 7.36), cell morphology  = 6.69), and cellular function and maintenance  = 5.17) were predicted to be upregulated. In contrast, cellular development  = 14.2), hematological system development and function  = 10.0), cell death and survival  = 8.81), and connective tissue disorders  = 7.24) were predicted to be downregulated. According to the number of genes involved (n = 906) and the overlapping percentage (88.5%) of all differentially expressed genes, cancer signaling was the most activated in T2D patients  = 2.03; genes: 18 upregulated, 4 downregulated) and paxillin  = 1.46; genes: 11 upregulated, 1 downregulated) signaling pathways were predicted to be upregulated, whereas the Rho guanosine diphosphate ssociation inhibitor (RhoGDI)signaling pathway was predicted to be downregulated in T2D  = 2.41; genes: 16 upregulated, 5 downregulated)  system also predicted the activation of three signaling canonical pathways in T2D. Integrin  .Distribution of gene expression changes among these signaling pathways is shown in According to the IPA, the upregulation of integrin and paxillin signaling pathways, and the downregulation of RhoGDI signaling pathway, in T2D would lead to the activation of cytoskeletal organization, rearrangement and reorganization, cell activation, adhesion, mobility and polarity, lamellipodia and filopodia formation, and actin polymerization and linkage, and the inhibition of actin stabilization .In the current study, we conducted a microarray-based transcriptome analysis in elderly individuals with and without T2D, and identified diseases, biological functions, and canonical pathways differentially expressed in T2D. We found that cancer signaling and biological functions, and canonical pathways related to cell dynamics, were upregulated in T2D patients.T2D has been related with an increased risk of developing a wide set of malignancies ,21,22. IObesity and the use of glucose-lowering medications, two T2D-associated conditions, have been shown to be relevant modulators of the transcriptome in PBMC ,26. ObesOur results showing that PBMC transcriptome could be a suitable model to unravel the relationship between T2D and cancer are consistent with others previously reported in literature. In a recent study conducted by Calimlioglu and colleagues (2015), transcriptomics data from different tissues including beta-cells, pancreatic islets, arterial tissue, PBMC, liver, and skeletal muscle of 228 samples were integrated with protein–protein interaction data and genome-scale metabolic models to unravel the molecular and tissue-specific biomarker signatures of T2D . InteresIntegrin, paxillin, and RhoGDI signaling pathways are intimately inter-related and involved in cell dynamics . IntegriOur study has some limitations. First, its design was cross-sectional, which only allowed associations between T2D and differences in transcriptome to be established. Our conclusions and their clinical implications regarding cancer should, therefore, be corroborated in further studies in humans. Second, it had a relatively small sample size, which may have limited our statistical power. In order to counteract this limitation and add robustness to our results, we employed systematic bioinformatic procedures and functional enrichment analyses, in addition to adjusting for multiple comparisons. Finally, the presence of other cardiovascular risk factors beyond T2D complicates the extrapolation of our conclusions to other populations. In summary, T2D was associated with the upregulation of pathways related to cancer signaling, T-cell activation and migration, and inflammation. Our findings provide, from a transcriptomic point of view, insights concerning T2D pathophysiology and complications in a group of elderly adults at high cardiovascular risk. The modulation of present signaling pathways would be pharmacological targets in the primary prevention of development of neoplastic processes under T2D condition.


To detect the development, function and therapeutic potential of epicardial adipose tissue (EAT); analyze a related gene expression dataset, including data from neonates, infants, and children with congenital heart disease (CHD); compare the data to identify the codifferentially expressed (DE) mRNAs and lncRNAs and the corresponding miRNAs; generate a potential competitive endogenous RNA (ceRNA) network; and assess the involvement of immunocyte infiltration in the development of the EAT.Multiple algorithms for linear models for microarray data algorithms (LIMMA), CIBERSORT, gene-set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were used. The miRcode, miRDB, miRTarBase, and TargetScan database were used to construct the ceRNA network. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DE mRNAs were performed.MIR210HG, hsa-miR-449c-5p, and CACNA2D4 was generated and shown to target monocyte infiltration.Thirteen co-DE mRNAs and 47 co-DE lncRNAs were subsequently identified. The related categories included negative regulation of myoblast differentiation, regulation of ion transmembrane transport, and heart development, which were primarily identified for further pathway enrichment analysis. Additionally, the hub ceRNA network in EAT development involving MIR210HG, and CACNA2D4, which were shown to target the RIG-I-like receptor signaling pathway and PPAR signaling pathway, indicating that these factors may be novel regulators and therapeutic targets in EAT development.These findings suggest that the pathways of myoblast differentiation and ion transmembrane transport may be potential hub pathways involved in EAT development in CHD patients. In addition, the network includes monocytes,  EAT is known to have a protective role as a mechanical buffer and a rapid source of free fatty acids (FFAs) in the myocardium and vascular arteries . There apathways . Anatomipathways . Researcpathways , 7, 8. Rpathways . Of note2 [For adult patients, Patel et al. revealed that EAT could secrete multiple adipokines, including apelin, adiponectin, adrenomedullin, angiotensinogen, leptin, and visfatin, coupled with the potential paracrine function of regulating inflammation, immune response, coronary plaques, ventricular remodeling, and vascular smooth muscle cell growth and migration . Sakamot2 . Additio2 . Chechi 2 , 13.IGF1R) was shown to be stimulated after myocardial damage and then promotes the differentiation of epicardial cells into adipocytes to protect cardiomyocytes and ensure the survival of cardiac progenitors [In this scenario, EAT, an immune and endocrine organ, may function as a key inflammatory mediator for cardiovascular system development. Here, the insulin-like growth factor 1 receptor | greater than 1.5. Herein, these genes were identified as having significantly increased (log2FC > 1.5) or decreased (log2FC < − 1.5) expression. Figure We performed a differential expression (DE) analysis based on “affy” package of R software that was curated by data processing principles includes expression data background correction, log2 transformation, data normalization and linear model and DE analyses. Here, the data normalization, missing value procession, and powerful DE analyses were performed by a function of the robust multi-array average (RMA), K-nearest neighbor (KNN), and linear models for microarray data algorithms (LIMMA), respectively . We defihttps://cibersort.stanford.edu/) to estimate the burden of immune infiltration subtypes in the EAT development process, based on a linear support vector regression (SVR) algorithm with gene expression data with systems-level gene expression data in the R toolkit [p-value < 0.05; (4) multiple comparisons among the neonatal, infant and child groups were tested by one-way ANOVA, and the p-value was adjusted by the Bonferroni method.In this routine, we applied the CIBERSORT algorithm ( toolkit . We choshttp://metascape.org/), which is a web-based analytics tool used for gene functional enrichment analysis [p-value = 0.05; (3) the min enrichment score = 1.5.Gene Ontology (GO) term enrichment analyses were carried out separately for the DE genes using the MetaScape database  was performed [p-value < 0.05; (2) the permutation test number = 1000; (3) the gene profile was defined as continuous phenotypes with time series experiments for EAT development and defined as class labels in response to the expression level of hub mRNAs/immunocyte infiltration; (4) enrichment statistic was used for weighting; (5) the metric for ranking genes selected “Pearson” for time series analysis and defined the “Signal2Noise” for categorical analysis.Based on the time series-related Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and biological process term enrichment, a gene-set enrichment analysis  [http://mirdb.org/) [http://mirtarbase.mbc.nctu.edu.tw/php/index.php) [http://www.targetscan.org/mamm_31/) databases [The ceRNA networks of lncRNAs, miRNAs, and mRNAs were predicted based on the miRcode (de.org/) , miRDB (db.org/) , miRTarBdex.php) , and Taratabases . Here, tatabases ; (2) prePearson statistical method [After grouping by the median expression value of the target gene, lncRNA, and infiltrating immunocytes, we applied the gene set enrichment analysis (GSEA) to detect the downstream pathways. The normalized enrichment score (NES) was obtained via cluster analysis based on the l method . In addil method .Ovis aries. The CIBERSORT algorithm was used to compare the EAT sample immune infiltration between 1 and 4 weeks of age. Furthermore, the candidate pathway variation was assessed by the GSVA package. The mRNA and lncRNA expression values between the two groups were compared with powerful DE analyses with the LIMMA algorithm.Finally, we also verified the landscape of immunocyte infiltration, regulatory networks and hub gene expression related to EAT development based on the GSE115799 dataset for p-value = 0.001), melanogenesis , and hedgehog signaling pathway  were negatively correlated with EAT development, while other categories including the VEGF signaling pathway , glycosaminoglycan biosynthesis heparan sulfate , and renal cell carcinoma  were positively associated with EAT development , cyclic nucleotide binding , and neuronal action potential , which were negatively correlated with EAT development, and negative regulation of receptor mediated endocytosis , acidic amino acid transport , and establishment or maintenance of monopolar cell polarity , which were positively correlated with EAT development . Here, for the comparison among the neonate, infant, and child EAT samples, to illustrate the transcription and expression profiles in response to EAT development, we performed a powerful differential analysis across the comparison groups. The results showing that 270, 889, and 1069 DE mRNAs were detected in the comparisons of infant p-value = 7.96E−05), regulation of ion transmembrane transport , and heart development  , ion channel activity , and substrate-specific channel activity  , regulation of cell proliferation involved in tissue homeostasis , and positive regulation of electron transfer activity  for cellular components  and NK cells activated  were identified  1742 links, were calculated based on the 47 co-DE lncRNAs and targeted miRNAs, (2) and 27 lncRNAs and 7 miRNAs were selected after removing the duplicate nodes, (3) and 4 targeted mRNAs were selected after determination of the miRNAs targeting mRNAs and co-DE mRNAs in multiple group comparisons. As a consequence, the ceRNA network including 27 lncRNAs, 7 miRNAs, and 4 mRNAs was accordingly constructed for EAT development for CHD patients  , CYP4A11 (54%), SLC27A2 (30%), and CYP27A1 (28%), were identified in the PPAR signaling pathway , TRAF2 (64%), IL12A (59%), IFNK (64%), MAPK12 (59%), and TBKBP1 (62%) were detected in the retinoic acid-inducible gene-I (RIG-I) like receptor signaling pathway  in protein-coding mRNAs, microRNAs, long noncoding RNAs, pseudogenic RNAs, and circular RNAs , 26. RecMIR210HG was identified as an important competing endogenous RNA of hsa-miR-449c-5p to regulate the CACNA2D4 expression level and thus regulate monocyte infiltration during the EAT development of CHD patients in early life. The PPAR signaling pathway was positively correlated with MIR210HG, CACNA2D4 expression, and monocyte infiltration, while the RIG-I like receptor signaling pathway was inversely correlated.Based on the complex bioinformatic analysis, our study demonstrated a framework to systematically characterize a regulatory map involving DE mRNAs, DE lncRNAs, immunocyte infiltration, and potentially regulated pathways. In the comparisons among the neonate, infant, and child EAT samples from CHD patients, a total of 13 co-DE mRNAs and 47 co-DE lncRNAs were identified as core regulators that are primarily involved in the pathways of myoblast differentiation, ion transmembrane transport, and heart development. We subsequently constructed a ceRNA regulatory network for these co-DE mRNAs and co-DE lncRNAs as presented above. As a result, CACNA2D4 encodes a protein of the voltage-dependent calcium channel complex that serves as a mediator leading to the influx of calcium ions through the cytomembrane. Based on the PathCards database (https://pathcards.genecards.org/) [CACNA2D4 may be correlated with the pathways of T cell receptor signaling and dilated cardiomyopathy (DCM). Weeke et al. showed that the calcium channel subunit genes CACNB2 and CACNA2D4 were significantly correlated with familial atrial fibrillation [MIR210HG, as reported by recent studies, may function as an oncogenic regulator that promotes tumor metastasis, proliferation, and invasion in breast cancer, glioma, hepatocellular carcinoma, and non-small cell lung cancer [MIR210HG may be involved in the pathway of hypoxic and inflammatory stress of endothelial cells [Functionally, ds.org/) , we founillation . While tillation . The long cancer –34. In aal cells , 36. Hsaal cells –39. Xu eal cells .The PPARs, including PPARα, PPARβ/δ, and PPARγ, were reported to regulate adipogenesis, lipid metabolism, and immunoinflammatory responses, especially in the maintenance of metabolic homeostasis . InteresMIR210HG, hsa-miR-449c-5p, and CACNA2D4 was detected in EAT development and may serve as a primary regulator controlling thermogenesis and immunoinflammatory response. These results might be used in novel strategies to develop effective therapies targeting myocardial energy supply and development in CHD patients. Our findings could further elucidate the molecular mechanisms involved in EAT, thereby enabling the exploration of EAT development and CHD pathophysiology from a new perspective, which may help improve the current therapies.Related studies have proved that EAT development was regulated by the immune-inflammatory microenvironment and that the genetic regulation was complicated. In our study, these results further revealed the potential effects and research value of myoblast differentiation and ion transmembrane transport in EAT development during early childhood, and thus, these related DE genes were further assessed in the follow-up study. Additionally, a network including monocytes, Additional file 1: Table S1. The epicardial adipose tissue (EAT) development was correlated with pathways based on time series-related GSEA.Additional file 2: Table S2. The differentially expressed mRNAs and lncRNAs were identified in multiple comparisons among the EAT samples at different developmental stages.Additional file 3: Table S3. The Gene Ontology enrichment terms for codifferentially expressed mRNAs among the neonatal, infant and child EAT samples.Additional file 4: Table S4. The immunocyte infiltration detection of the GSE82155 and GSE115799 datasets.Additional file 5: Table S5. The competing endogenous RNA (ceRNA) regulatory network construction.Additional file 6: Table S6.Pearson’s correlation coefficient values of hub miRNAs and monocyte infiltration level.Additional file 7: Table S7. The gene set enrichment analysis after grouping by the monocyte infiltration level, CACNA2D4 and MIR210HG expression value.