By conducting an analysis of chloride migration in concrete, it is possible to enhance the durability of concrete structures and mitigate the risk of corrosion. In addition, the utilization of machine learning techniques that can effectively forecast the chloride migration coefficient of concrete shows potential as a financially viable and less complex substitute for labour-intensive experimental evaluations. The existing models for predicting chloride resistance encounter two primary challenges: the constraints imposed by a limited dataset and the absence of certain input variables. These factors collectively contribute to a decrease in the overall effectiveness of these models. Therefore, this study aims to propose an advanced approach for dataset cleaning, utilizing a comprehensive experimental dataset comprising 1073 pre-existing experimental outcomes. The proposed model for predicting the chloride diffusion coefficient incorporates various input variables, such as water content, cement content, slag content, fly ash content, silica fume content, fine aggregate content, coarse aggregate content, superplasticizer content, fresh density, compressive strength, age of compressive strength test, and age of migration test. The utilization of the artificial neural network (ANN) technique is also employed for the processing of missing data. The current supervised learning incorporates both regression and classification tasks. The efficacy of the proposed models for accurately predicting the chloride diffusion coefficient has been effectively validated. The findings indicate that the XGBoost and SVM algorithms exhibit superior performance compared to other regression prediction algorithms, as evidenced by their high R2 scores of 0.94 and 0.91, respectively. In relation to classification algorithms, the findings demonstrate that the Random Forest, LightGBM, and XGBoost models exhibit the highest levels of accuracy, specifically 0.93, 0.96, and 0.97, respectively. Furthermore, a website has been developed that is capable of predicting the chloride migration coefficient and chloride penetration resistance of concrete. Despite the mechanical properties, understanding the effect of concrete composition on durability is still challenging for researchers in this field3. The emergence of a new generation of concretes  has increased this concern about the durability of concrete structures. The durability of reinforced concrete (RC) structures is considerably influenced by chloride penetration, especially in coastal and chloride-rich environments, resulting in a severe corrosion issue for steel reinforcement or steel fibers4. This corrosion phenomenon causes considerable economic loss and environmental influence in the construction industry. Various resources cause chloride attacks, such as de-icing salt, seawater, and groundwater. Different parameters substantially affect the chloride resistance of RC members, including concrete composition, temperature, relative humidity, and carbonation. Among these variables, concrete composition plays a major role in controlling internal damage of chloride attack. Previous studies strongly emphasized that concrete composition directly affects the fresh, mechanical, and durability characteristics5. Concrete composition means the water-to-binder ratio (W/B), water content, cement content, cement type, aggregate content, aggregate type, fillers, nanomaterials, mineral additives, and chemical admixtures. Each of these ingredients affects the concrete porosity. Achieving a reliable concrete mixture with the lowest permeability to prevent the penetration of the chloride ions is one of the main practical solutions to mitigate internal chloride attack damages. For instance, different supplementary cementitious materials (SCMs), fillers, and nanomaterials can be used to obtain an efficient concrete mixture resistant to chloride attack. However, as innovative and new concrete generations have been introduced in recent years, standards recommended experimental rapid tests to determine the performance of these new cementitious composites against chloride attack, including salt ponding, bulk diffusion, and rapid chloride permeability tests. These tests are time-consuming (more than 28 days) and require an adequate material, researchers, and testing equipment budget. Hence, a practical solution and an extensive experimental program are necessary to determine the efficiency of a proposed mixture for concrete members exposed to chloride attack.The concrete composition considerably impacts concrete's mechanical and durability characteristics7, where 0 is the initial chloride concentration in concrete, and Cs is the apparent surface chloride concentration.Experimental tests considered different parameters to determine the chloride resistance of mixtures. As mentioned in Eq. , Fick’s 8. Many experimental test methods were used to determine the chloride diffusion coefficient. As recommended by ASTM C1556–119 and NT Build 44310, bulk diffusion tests are time-consuming tests where concrete samples are exposed to a chloride solution for a long period. In this context, Nordic standard NT Build 49211 proposed an accelerated test method, where chloride ions infuse the concrete at high rates caused by the applied electric field. The chloride diffusion coefficient measured by this method is denoted as the “non-steady-state migration coefficient (11 method compared to ASTM C120212 is mentioned in Table 12 considers five categories for chloride ion penetrability based on the passed electric charge (PEC), while 11. Although the literature severally confirmed the accuracy of this method, the test procedure needs a practiced operator and accurate resources and facilities. Although this method's experimental results are precise, conducting this test for each concrete mixture for RC projects is not feasible and practical due to the long-term immersion period and costs. Hence, an efficient and practical solution is needed to determine the chloride resistance of concrete mixture for each project. Accordingly, it is crucial to advance a feasible and accurate predicting model that specify the chloride diffusion coefficients using concrete composition.Among these empirical parameters, the chloride diffusion coefficient plays a critical role in showing the chloride resistance of concrete mixtures. Different parameters affect mentclass2pt{minim14 presented an effective predicting method for the strength of sustainable concrete utilizing rice husk ash (RHA) as supplementary cementitious materials using machine learning (ML) algorithms. Hosseinzadeh et al. (2023)15 used ML techniques , to forecast the mechanical properties of fly ash-based recycled aggregate concrete (FARAC). Jiao et al. (2023)16 employed ML to predict the compressive strength of concrete with carbon nanotubes as nanomaterials. In this context, Zheng et al. (2023)17 developed an ML model to predict the compressive strength of silica fume concrete. Ullah et al. (2022)19 used the ML method to predict dry density and compressive strength of lightweight foamed concrete. In this field, Khan et al. (2021)20 used the ML method to formulate the depth of wear of fly-ash concrete. Song et al. (2021)21 worked on presenting an ML model to predict the compressive strength of concrete with fly ash admixture. Farooq et al. (2021)22 used the ML method to formulate the strength of self-compacting concrete (SCC). Ahmad et al. (2021)23 employed the ML method to forecast the compressive strength of concrete at high temperatures considering 207 experimental datasets. Ahmad et al. (2021)24 presented an ML model along with gene expression programming (GEP) and artificial neural network (ANN) to predict the compressive strength of recycled coarse aggregate (RCA)-based concrete. Farooq et al. (2021)25 employed ML algorithms with individual learners and ensemble learners  to forecast the mechanical properties of high-performance concrete using waste materials. Ahmad et al. (2021)26 used ML to predict the compressive strength of fly ash-based concrete. In this field, Khan et al. (2021)27 used soft-computing techniques, including random forest regression and gene expression programming to present a prediction model for the compressive strength of fly ash-based geopolymer concrete. Other studies in the literature also used AI methods in predicting the mechanical characteristics of cementitious composites29.Recently, in the last few years, the use of artificial intelligence (AI) to predict cementitious composites' mechanical and durability characteristics has gained significant speed. However, specific types of concrete were considered for each study, whereas a comprehensive dataset was required in a unified AI model. Additionally, the number of datasets is another critical issue of these investigations. For instance, Amin et al. (2023)30 used the ANN method to predict chloride ions permeability using 86 datasets and 8 input variables for high-performance concrete (HPC) mixtures with compressive strength ranging from 30 to 120 MPa. They reported that the optimum cement content for chloride-resistant HPC is 450–500 kg/m3. Also, based on their model, the chloride resistance of HPC can be significantly improved using both SF and FA (around more than 20% cement replacement). Song & Kwon (2009)31 slightly increased the dataset (120 number) for HPC specimens and found that they added GGBFS along with other SCMs to the input variables to predict the diffusion coefficient of chloride ion using the ANN method. They also used the parameter “duration time in submerged condition” instead of “superplasticizer” for the input parameters. As the precision of the soft computing methods in AI is extremely dependent on the number of database, Hodhod & Ahmed (2013)32 used 300 datasets of HPC mixture for the ANN method. They considered 5 input variables of W/B, cement, FA, GGBFS, and curing age. Regarding self-consolidating concrete (SCC) mixtures, extensive AI models were also presented in the literature35. In this field, Ghafoori et al. (2013)36 used statistical and ANN models on their limited experimental database (only 24 SCC mixtures) to present a predicting model for the rapid chloride penetration test (RCPT) value choosing 6 input features of cementitious materials, W/B, coarse aggregate, fine aggregates, air-entraining admixture, and high range water reducer (HRWR). Although a general trend cannot be extracted based on their limited database, they reported that three independent parameters of cementitious materials content, W/B ratio, and coarse or fine aggregate are essential to predict the RCPT value of SCC. Accordingly, Mohamed et al. (2018)37 used 86 datasets of SCC mixture to present a predicting ANN model for chloride penetration level using comprehensive 11 input variables of W/C ratio, cement, GGBFS, FA, SF, water, superplasticizer, coarse aggregate, fine aggregate, age, and charge. Najimi et al. (2019)38 collected 72 datasets of SCC mixtures and used feed-forward ANN combined with an artificial bee colony algorithm to develop a chloride penetration model. They added air-entraining admixture and HRWR to their input variables. Kumar et al. (2020)39 gathered 360 datasets of SCC mixtures to predict chloride penetration using MARS and minimax probability machine regression (MPMR). They only considered three input variables of FA, SF, and temperature. In this context, the model presented by Yuan et al. (2022)34 revealed that temperature and SF considerably affect the model performance. In the field of high-strength concrete (HSC), Inthata et al. (2013)40 used 270 datasets of mixtures containing ground pozzolans to predict chloride penetration and chloride penetration depth. They added for the first time the actual concrete compressive strength to the input parameters in ANN models. Their model revealed that 40% of cement replacement by ground pozzolans shows the highest resistance to chloride penetration, especially for rice husk ash-contained concrete mixtures. In line with this research, Boğa et al. (2013)41 used 162 datasets of concrete containing GGBFS and calcium nitrite-based corrosion inhibitors to develop a predicting model for chloride ion permeability using a four-layered ANN and adaptive neuro-fuzzy inference system. They considered Cure type, curing period, GGBFS ratio, and calcium nitrite-based corrosion inhibitor ratio independent variables. Based on the dataset's limited range of concrete types, no general report can be extracted by their study. Regarding concrete containing high calcium FA, Marks et al. (2015)42 gathered 56 dataset and used ML method to predict the concrete resistance to chloride penetration. Their input parameters were water, cement, high calcium FA, and the specific surface of fly ash. Their model showed that to have a reliable chloride-resistance FA concrete, the water content should be controlled in the w ≤ 158 L/m3 range. However, they emphasize that the experimental mode dataset should be checked due to their limited dataset number. In the context of the limited dataset, Slika & Saad (2016)43 used Ensemble Kalman Filter (EnKF) on the limited dataset of NC to predict Chloride concentration. Regarding SF concrete, 162 datasets were collected by Asghshahr et al. (2016)44, and ANN, along with classification and regression tree (CART) methods, were utilized to predict the Chloride concentration. Only 4 input variables were considered in their models, including environmental condition, penetration depth, W/B ratio, and SF. Their models showed that the penetration depth is the most vital factor impacting the chloride concentration. However, they could not find the critical role of SF in their model. The literature also used AI models to predict chloride ion diffusion. For instance, Hoang et al. (2017)45 considered the ML model with the multi-gene Genetic programming (MGGP) and multivariate adaptive regression splines (MARS) to predict the chloride ion diffusion of cement mortars using mortar age, depth of measured point, diffusion dimension, the reinforcement presence as input variables. However, along with ML methods, valuable soft computing models have still been used using the ANN method on different types of concrete. In the field of AI techniques, genetic programming (GP) was also used by Gao et al. (2019)46 on 25 experimental databases of NC to predict chloride-ion diffusion. They found that the GP method was more efficient than the ANN model. However, based on their limited dataset, more studies should check their finding. Some previous studies also concentrated on predicting AI models of chloride resistance for recycled aggregate concrete (RAC). Ahmad et al. (2021)47 employed an ML model to predict the surface chloride concentration in concrete containing waste material, including gene expression programming (GEP), the decision tree (DT), and an ANN. In this field, Liu et al. (2022)48 used the ensemble ML method to predict the PEC gathering 226 dataset. Their soft computing technique found that water content is the most vital parameter controlling the chloride ion permeability of RAC. In this context, Jin et al. (2022)49 developed an ANN model to predict the chloride diffusion coefficient using 112 dataset. They considered 9 input variables consisting of cement, water, coarse aggregate, sand, water reducing agent, recycled aggregate replacement rate, W/C ratio, water absorption rate of coarse aggregate, and apparent density of coarse aggregate. However, they ignored two important parameters of recycled fines and mineral admixtures due to the lack of an appropriate dataset. Also, unnoticed the effect of cement type. Their model demonstrated that the natural fine aggregate content, along with recycled coarse aggregate, has the most substantial influence on the penetrability of RAC chloride ions. Regarding metakaolin-contained concrete, Alabdullah et al. (2022)50 gathered 201 dataset to develop ML method chloride migration coefficient. They used concrete aging, binder content, W/B ratio, metakaolin, sand, coarse aggregate, and concrete compressive strength as input features. Their model revealed that concrete compressive strength is the most critical parameter in predicting the chloride migration coefficient. However, they found that aggregate content showed the minimum importance. Also, it was deduced from their ML model that using metakaolin has a vital influence on chloride penetration resistance with the optimum dosage of 15%. In this regard, Amin et al. (2022)51 used the same dataset and input features of metakaolin-based concrete to develop a GEP model of predicting RCPT. Their model showed that the concrete age is the most noteworthy factor, along with aggregate content.Accordingly, there has also been a growing interest in using AI techniques for predicting chloride diffusion coefficients, such as ANN, fuzzy inference systems, and ML methods. Table entclass1pt{minima54. For instance, Delgado et al. (2020)52 used 243 datasets without concrete type limitations to develop ANN models of chloride depth penetration and diffusion coefficient. They considered cement type as an additional input variable for the first time. Based on their model, curing time is the most important input feature for the chloride penetration ANN model. In this field, Cai et al. (2020)53 collected 642 datasets for different types of concrete to predict surface chloride concentration using the ensemble ML method. Twelve input variables were considered in their model, including cement, water, FA, GGBFS, SF, Superplasticizer, fine aggregate, coarse aggregate, exposure time, Annual mean temperature, chloride content in seawater, and exposure type. Their predicting model showed that the exposure condition  and W/B ratio seem to be the greatest important factors affecting the surface chloride concentration. Ahmad et al. (2021)47 gathered a dataset of concrete containing waste material to predict the surface chloride concentrations using gene expression programming (GEP). According to these studies, Liu et al. (2021)4 collected 653 datasets of different concrete types to develop an ANN model for predicting the chloride diffusion coefficient. They also considered the concrete compressive strength, curing mechanism, and common input variables. Tran et al. (2022)57 used an ensemble decision tree boosted (EDT Boosted) model to predict the surface chloride concentration considering the 386 datasets. Their model indicated that the fine aggregate content is the key parameter influencing the Cs. Tran (2022)58 concentrated on 127 concrete data-containing SCMs to develop an ML model for the chloride diffusion coefficient. They ignored using temperature and curing time as input parameters. It can be deduced from their model that water content and FA dosage have the most and least effect on chloride diffusion coefficient precision, respectively. They reported that GGBFS content has also a low impact on the output. Based on their model, water content and W/B ratio has negative relationship with the chloride diffusion coefficient. Guo et al. (2022)60 developed ML and Fuzzy logic system methods on two datasets (366 and 495 numbers) to predict surface chloride concentration without considering a specific type of concrete. Their models indicated that by increasing the W/B ratio, Cs increases. Moreover, mineral admixture and the W/B ratio affect the surface chloride concentration. Based on their model, FA and GGBFS cause higher and lower surface chloride concentration, respectively. Finally, the most recently studied in this field was conducted by Taffese & Espinosa-Leal (2022)55, where the ML method was used to predict the migration coefficient of different types of concrete in a unique dataset. Although they gathered an extensive experimental database, four separate models were used for different input variable groups due to some missing input variables. Each group has less than 200 datasets, including (1) first group with 134 dataset containing W/B ratio, Cement, Slag, FA, SF, fine aggregate, coarse aggregate, superplasticizer, migration test age, and cement types; (2) the second group uses 131 datasets with the same as group first input variables along with fresh density; (3) third group has 176 datasets considering the same input variables of the first group and additional parameters of compressive strength test age and compressive strength; and finally, (4) fourth group having all input variables with 91 datasets, showing that a limited number of the dataset mentioned both concrete compressive strength and fresh density in their studies. Hence, they couldn’t use the ML method on all 834 datasets due to the huge amount of missing input data. Similarly, Taffese & Espinosa-Leal (2022)61 only used the final 204 datasets for the ML method, considering input variables of W/B ratio, cement, water, slag, FA, SF, fine aggregate, coarse aggregate, total aggregate, superplasticizer, and air-entraining agent, and migration test age. Their finding from the proposed model showed that binder content and aggregate content considerably affect the predicting model. They also found that cement type has no impact, which is considered a weak predictor for classifying concrete chloride resistance and can be removed from the model. In this filed, Tran et al. (2022)56 used the ML technique to predict chloride content based on 404 dataset. Based on their model, exposure condition and depth of measurement are the most important parameters for the prediction of chloride content. In this context, Golafshani et al. (2022)8 developed a marine creatures-based metaheuristic artificial intelligence model to predict the apparent chloride diffusion using 216 dataset. Their model indicated that the exposure time and curing conditions considerably control the performance of the predicting model.Regarding the second category (models with no limitations), many studies gathered many types of concrete data in a specific database52 reported the critical role as input illustrative in predicting chloride penetration in concrete, Taffese et al. (2022)61 showed its lack of influence as a predictor.One of the main criteria for evaluating the performance of an AI model is simplicity. Accordingly, the number of input variables and accessibility should be considered in a predicting model. Hence, based on the experience of other proposed models and SHapley Additive exPlanations (SHAP) results, some input variables can be ignored to predict the chloride diffusion coefficient. Predominant input variables reported for each literature on AI methods are summarized in Table 49. Accordingly, most developed AI models ignored this variable as an input feature. Also, only a few studies used concrete compressive strength as an input variable in AI methods51. Similarly, only Taffese et al. (2022)55 considered concrete density as one of the input variables to predict chloride resistance. Most studies also ignored the temperature, while many models considered it an input feature60. It may be due to the missing data in the experimental research. As mentioned in Table Moreover, the greatest numbers of the dataset did not precisely mention the cement type in the experimental programDeveloping a unique ML method to predict the chloride diffusion coefficient for all types of concrete.Introducing a developed dataset-cleaning technique (DCT) to cover the missed database of all experimental works using ANN.Compare classification algorithms with regression ones in the supervised learning method.Finding the most and least influencing parameters controlling the predicting model for chloride diffusion coefficient.As reviewed in this section, introducing a unique model for each concrete type is impractical. Accordingly, a significant research gap should be filled by developing a novel AI method using all available datasets. Additionally, due to the lack of consistency and exitance of missing data in the details of mixtures tested by the literature, most of the previous predicting AI models ignored these valuable experimental datasets in their model, causing a significant reduction in the accuracy and comprehensiveness of the existing predicting models for all types of concrete. Accordingly, the present study uses the ANN method as a dataset arranging technique to practically predict missing details in datasets for the ML models. In other words, the ANN method helps to prepare an accurate and efficient dataset for ML input and prevents the deletion of valuable data. Additionally, prior studies have examined the forecasting of concrete's chloride migration coefficient, but there has been a lack of research on the integration of regression and classification tasks. Moreover, there is a scarcity of scholarly investigations pertaining to the advancement of artificial neural networks and machine learning techniques for the purpose of constructing a robust predictive model using a comprehensive dataset comprising over 1000 data points. Therefore, it is imperative to address a notable deficiency in research by devising an innovative artificial intelligence (AI) approach that effectively utilizes all accessible datasets. Hence, the present study intends to address this issue by following the objectives:To achieve these objectives, a comprehensive experimental database containing 1037 datasets was gathered in the present study to predict the Generally, the ML method is the branch of AI that deals with the application of algorithms that allow computers to advance patterns using the experimental database. The main intention of the ML method is to automatically learn to identify complicated patterns (relations between variables) and then make ingenious judgments based on datasets. The dataset is a set of logical (laboratory) records with unique characteristics called machine input data or features. Each of these input data is also called a predictor. Moreover, the intelligent decision that the machine should make after learning this data is considered a prediction model for a particular output (or target). In other words, the machine has reached intellectual maturity by finding a logical connection between the input data and the results. It provides a logical model of a real physical phenomenon. The main problem exists where the set of all possible behaviors given all potential inputs is too enormous to be covered by the set of observed examples (training data). Accordingly, the learner should generalize from the training data to produce expedient target data for new conditions out of the datasets. Pattern recognition, commonly accompanied by classification, is the most popular use case for the ML method. Although the quantity and quality of datasets play a significant role in the training process, selecting appropriate features or input variables (unique dataset characteristics) affects the ML model's performance considerably.62. The ANN model in Figure 63 in this study. The flowchart of the ML methodology proposed in the present study is shown in Fig. The present study comprehensively assesses the potential of using regression and classification algorithms in the ML method to predict the non-steady-state migration coefficients  m2/s was gathered in the present study. However, only a few contents of datasets are in the range of 3 were removed from the datasets. A range of 200–1500 kg/m3 was kept as the main database of fine aggregates, while experimental databases out of this range were considered outliers  high number, so datasets belong to the first group where concrete samples have a density of 220–2400 kg/m3, and (2) some portion of samples has a density ranging from 1800–2000 kg/m3 , employing Seaborn, a Python library developed specifically for data visualization. Distplotm or also distribution plot is used to characterize data in histogram form. Distplotm represents a univariant set of gathered data, describing the data distribution of each variable compared to another one. As shown in Fig. To illustrate the relations between variables within a dataset and reveal valuable details from the datasets, the pairplot is shown in Fig. mentclass2pt{minimTo illustrate the relationships between two variables, the heatmap plot is shown in Fig. For each pair of values  in the dataset, the formula subtracts the mean of X from each Xi, and the mean of Y from each Yi. This step represents the deviation of each data point from its respective mean. Then, the formula multiplies the deviations of X and Y for each data point and sums up these products across the dataset. This step captures the covariation between X and Y. The resulting sum of products is divided by the product of the standard deviations of X and Y. The standard deviation of X is calculated by summing the squared deviations of X from its mean and taking the square root. Similarly, the standard deviation of Y is obtained by summing the squared deviations of Y from its mean and taking the square root. Changing cell’s color for each axis shows the patterns in value for one or both variables ranging from -1.0, as a perfect negative linear correlation between two variables, to 1.0 representing a perfect positive linear correlation. The value of 0 designates no linear correlation between the two features. On the other hand, the heatmap plot demonstrates the independence of the variables. For instance, the first row of this plot shows that water content seems to be positively correlated with the cement content, while it is negatively correlated with the slag content. For the second raw, it can be deduced from the plot that the cement content has a strong positive correlation with the water content + 0.57), along with negative correlations with the slag content (− 0.68) and the fresh density (− 0.43). Also, analysis of the heatmaps plot shows that coarse aggregate content has negatively correlated with the cement content (− 0.41) and significantly has a positive correlation with the fresh density value of mixtures. This is a significant finding for the coarse aggregate content in predicting , along w3 to 400 kg/m3 and a water content domain of 150–180 kg/m3  shows better correlation as compared to FA and SF. Moreover, it can be deduced from the KDE jointplot that highly resistant concrete mixtures have fine aggregate and coarse range of 700–850 kg/m3 and 900–1200 kg/m3, respectively. However, no clear trend was obtained by the KDE jointplot between SP content and 3 to resist the chloride attack. Finally, the KDE distribution jointplot revealed that high chloride-resistant concrete samples have a concrete compressive strength of 45–60 MPa.Kernel Density Estimation (KDE) jointplot for all variables against the Data visualization plots revealed that the number of datasets for Prior to commencing the machine learning model in data visualization, it is imperative to conduct a thorough examination of the feature importance plots for both regression and classification methodologies, as depicted in Fig. ssm Fig. a. Fresh As shown in Fig. mentclasspt{minimamentclasspt{minimamentclasspt{minimamentclasspt{minimamentclasspt{minimamentclasspt{minima1, accuracy, and specificity.Different statistical criteria are used in the present study to determine the efficiency of regression and classification models. For the regression models, different coefficients are used in the present study, as follows:ues Fig. . Generalentclass1pt{minimaues Fig. . DiffereThe performance of the regression model in the ML method is summarized in Fig. 1 scores higher than 0.90. The XGBoost model also has a high accuracy of 0.97, the maximum accuracy among other classification models  approach is used in the present study to determine the sensitivity of the predicting model considering different variables. SHAP is a game hypothetical and mathematical method to clarify the ML model's output by measuring each feature's influence on the prediction. As shown in Fig. ent Fig. c. SHAP aent Fig. d. As shodel Fig. f. In thidel Fig. h, so thadel Fig. i.Figure The present study incorporated an additional validation process to assess the efficacy of the current research. The outcomes of this validation procedure are concisely presented in Table After conducting an analysis of feature importance and data visualization, as well as evaluating various models, a new dataset consisting of a 28-day test age was chosen as the input dataset for the website. This test age was selected due to its prevalence among civil engineers. In order to validate and confirm that certain data points were not observed by the algorithm, a subset of 12 numbers from the datasets were completely isolated from the dataset utilized for the final validation procedure.As indicated in Table http://materialai.ir". Researchers and engineers involved in the development of various concrete mixtures have the ability to verify the durability of their mixtures through online platforms. The utilization of extensive datasets and the inclusion of multiple validation stages in the current study make this resource applicable to a wide range of concrete types.The present study has developed a free-access machine learning predictive model for concrete durability checking, as depicted in Fig. To present a reliable and efficient prediction model for Supplementary Information.


G1 for pH-, photo-, and cation- responsiveness. Previously, we reported a novel hydrogen-bonded azo-macrocycle 1. The size of this host can be controlled through light-induced E↔Z photo-isomerization of the constituent azo-benzenes. The host is found in this work to be capable of forming stable complexes with bipyridinium/pyridinium salts, and implementing guest capture and release with G1 under light in a controlled manner. The binding and release of the guest in the complexes can also be easily controlled reversibly by using acid and base. Moreover, the cation competition-induced dissociation of the complex 21a⊃G1 is achieved. These findings are expected to be useful in regulating encapsulation for sophisticated supramolecular systems.Research on stimuli-responsive host–guest systems is at the cutting edge of supramolecular chemistry, owing to their numerous potential applications such as catalysis, molecular machines, and drug delivery. Herein, we present a multi-responsive host–guest system comprising azo-macrocycle 1 and 4,4′-bipyridinium salt  Uncovering the mystery of biomolecules such as proteins for controlling the capture and release process has aroused extensive research interests ,2. HowevThere are mainly three types of host–guest-responsive systems used in the field of supramolecular chemistry according to external stimuli: (a) one relying on guest molecules that are switched between capture and release according to chemical actions , (b) the second type accomplishing the capture and release process using light irradiation—a clean, easily manipulated approach—for controlling complexation, and (c) the third type modulating the host–guest binding ability using electrochemical methods to change the charge and electron distributions by implementing electron transfer reactions . Most ofα-amino esters was found to induce chiroptical amplification, showing the pronounced enantiomeric and structural differentiation of α-amino esters [Two-dimensional hydrogen-bonded aramide macrocycles with shape persistency have exhibited plenty of unique features for guest binding ,42,43 coo esters . The supo esters . Notablyo esters .1 and protonated 4,4′-bipyridine G1 and its analogues G3–G5  (4.1 Å in width) [1H NMR, HRMS, and UV-vis spectroscopy. Hydrogen-bonded aramide macrocycle n width) . The hos1a and guest G1 in solution by 1H NMR spectroscopy. At room temperature, the 1H NMR spectra of a mixture of host 1a and guest G1 recorded in CDCl3-CD3CN show signals corresponding to the formation of a host–guest complex  molar ratio  provided binding constants of K1 = 6.55 × 104 M−1, K2 = 7.20 × 104 M−1, and K12 = 4.72 × 109 M−2 for 21a⊃G1  were obtained in CHCl3-CH3CN  due to the low solubility in CHCl3-CH3CN  by fitting the experimental data from the UV-vis spectroscopy   and aK = 4.02 × 104 M−1 (G5) in CHCl3-CH3CN , respectively  . Since πotaxanes , G4/G5 womplexes . The Jobsolution . HR-MS snd G4/G5 , but no ectively .G1 and 1a was probed by two-dimensional nuclear overhauser effect spectroscopy (2D NOESY) in CDCl3-CD3CN. The sizable cross-peaks,  and , are apparently a consequence of the interactions through space between the macrocycle and guest G1 , azo-macrocycle 1a exhibited pronounced light-induced E→Z isomerization. At the photo-stationary state, three isomers were identified as E,E-1a (10%), E,Z-1a (25%), and Z,Z-1a (65%), and the relative percentages were calculated by integrating the area of different stereoisomers  [1a to E,E-1a can be completed in less than 10 min when the sample solution is irradiated with blue light  /6-31G ), the size of the macrocyclic cavity decreases substantially upon azo-benzene photo-isomerization  . The lig 450 nm) , indicatrization , which l1a and G1 (21a⊃G1 complex) was exposed to photo-irradiation, the signals from protons H1 and H2 of E,Z-1a and Z,Z-1a appeared upfield  relative to the proton resonance of the 21a⊃G1 complex before UV irradiation, and the signal from the other proton H2 also shifted upfield to a great extent (Δδ = −0.39 ppm), strongly suggesting that the guest was released  under blue light irradiation with respect to the same protons in solution after irradiation. It should be noted that not all the signals of protons H1 and H2 returned to the original resonances because the conversion from Z,Z-1a to E,E-1a was incomplete, which is normal in photo-isomerization for most azo-based compounds [1a nor Z,Z-1a has an appreciable affinity for G1 via complexation within the cavity. Upon the exposure of the 21a⊃G1 complex to UV light irradiation, the E→Z transformation of the macrocycle occurred, leading to the reduced size of the cavity and, thus, the release of G1. Exposure of the irradiated mixture to light at 450 nm reset the system back to the guest-trapped E,E-21a⊃G1-enriched state. Indeed, when a mixture of  upfield . The chareleased c. Reversompounds . On the 1c and protonated 4,4′-bipyridine at the B3LYP/6-31 level  level . The resp) level a. For ex complex a. An ind complex  revealedE-1c2⊃G1 c. These 21a⊃G2, 21a⊃G3, 1a⊃G4, and 1a⊃G5 were not observed under photo-responsive conditions  salt (G2–G5) with bipyridine (or pyridine), only a signal set of signals  for the 2:1 mixture of host and guest in CDCl3-CD3CN   for 1a experienced an upfield shift by Δδ = −0.49, −0.16, −0.18, and −0.11 ppm, respectively  is added to the solution above, deprotonation occurs on G1, and as a consequence, the complex 21a⊃G1 dissociates  . We atte complex ; howevers failed , indicattermined . Therefoectively c,d, whilectively c,f. Compectively c of the  in the presence of G6 as a competitive ion to release protonated 4,4′-bipyridine G2  was mixed with guest G2 (1.0 mM) in CDCl3-CD3CN , a stable 21a⊃G2 complex was formed . HPLC CHCl3 and CH3CN were purchased from Chron Chemicals . All reagents purchased from commercial suppliers were used without further purification.4,4′-Bipyridine, 2,2′-bipyridine, 4-phenylpyridine, 2-phenylpyridine, CDCl1H: 400 MHz; 2D: 600 MHz). Chemical shifts were reported in δ values in ppm using tetramethylsilane (TMS) or residual solvent as internal standard, and coupling constants (J) are denoted in Hz. Multiplicities are denoted as follows: s—singlet, d—doublet, t—triplet, dd—double doublet, and m—multiplet. High-resolution mass (HRMS) data were collected by WATERS Q-TOF Premier. UV-vis spectra were measured by SHIMADZU UV-2450 .NMR Analytical NMR spectra were recorded on Bruker AVANCE AV II-400/600 MHz at a temperature of 298 K (1 (1a and 1b) and guests G1–G6 were synthesized according to previous references [Azo-macrocycles ferences ,67,72,731 and R2) on the periphery of azo-macrocycle 1 were replaced by methyl groups to provide compound 1c for simplicity. The counterion CF3COO− of guest G1 was also omitted for clarity.DFT calculations for the geometrical optimizations were performed in Gaussian 09 program package . All subIndependent gradient model (IGM) analysis is an approach  based on1 and protonated 4,4′-bipyridine. The complexation and dissociation of the complexes were explored under UV/BL irradiation and acid/base conditions. Switchable complexation was corroborated and rationalized by using the results from 1H-NMR, HR-MS, binding constant, and titration experiments. Moreover, control can also be achieved using cation competition. The multi-responsive host–guest systems in this work may serve as a potential platform to construct multi-component and complicated molecular machines. Correlational research is underway in our laboratory. In summary, we demonstrated a photo-responsive and acid/base-controllable host–guest system based on hydrogen-bonded azo-macrocycle


This paper presents an experimental study on the dynamic strength of concrete by using a split Hopkinson pressure bar. The stress–strain relationship and fragmentation degree of concrete were analyzed. The change process of the incident energy, reflection energy, transmission energy and consumption energy of concrete was calculated. The corresponding relationship between the variation of each energy and the stress state of concrete was studied. The law of energy evolution during the concrete fracture process was determined and the mechanism of concrete dynamic strength increase was revealed from the perspective of energy. The results show that the higher the strain rate, the higher the fragmentation degree of concrete, the smaller the grain diameter of fragments, the easier cracks are to pass directly through the aggregate, and the more regular the fragment shape. The change process of increasing amplitude of concrete consumption energy can reflect four mechanical states of concrete: stress increase, stress slow releasing, stress rapid releasing, and return-to-zero stress. Since the increase in reflected energy does not increase immediately with the increase in strain rate, it leads to the hysteresis of energy release in concrete, resulting in an increase in the dynamic strength of concrete. As the most common building material, concrete has been widely used in various industries, such as urban construction, national defense, water conservancy, etc. As the international situation changes, the probability of buildings being subjected to missiles, explosives and other impact and explosive loads increase rapidly. The mechanical properties of concrete under dynamic loading are quite different from those under static loading, therefore, only when the mechanical properties of concrete under impact loading are accurately described can we more accurately predict the stability of buildings under impact loading.The most common method for studying the dynamic mechanical properties of concrete under impact loading is to use a split Hopkinson pressure bar (SHPB) ,2 apparaThe main reason why the mechanism of the dynamic strength increase in concrete has not been unified is the limitations of existing test methods. Studying the strength and deformation characteristics alone cannot effectively reveal the change mechanism of dynamic mechanical properties, and the stress–strain relationship describing the specific mechanical state of concrete is only one aspect of the thermodynamic state of concrete . The theAt present, the study of concrete in terms of energy mainly includes two aspects: fracture energy and energy absorption rate. In fracture mechanics, it is assumed that the energy required for fracture propagation is converted from the energy released by the system . TherefoAlthough some previous studies on the dynamic mechanical characteristics of concrete from the perspective of energy have been carried out, the existing research emphasizes the total amount of energy absorption, and there are few studies on the process of energy conversion. However, the improvement in the dynamic strength of concrete is closely related to the failure process of concrete. Therefore, studying the law of energy evolution in the process of concrete dynamic failure and the relationship between energy evolution and dynamic strength are of great significance for revealing the mechanism of concrete dynamic strength increase.In this paper, from the perspective of energy, the stress–strain relationship of concrete under different strain rates was studied by using the SHPB apparatus, and the effect of the strain rate on the crushing model and fragmentation distribution of concrete was analyzed. The energy analysis method is used to study the energy evolution process of concrete under dynamic loading and analyze the relationship between the energy evolution process and the stress–strain change process of concrete. Finally, by comparing the energy evolution process of concrete under different strain rates, the mechanism of the dynamic strength increase in concrete is revealed from the perspective of energy.Considering the heterogeneity of concrete materials, the SHPB apparatus with a small diameter is not suitable for concrete material experiments. Therefore, impact compression tests on concrete are carried out by a SHPB apparatus with a diameter of 100 mm in this paper. The effectiveness of the SHPB experiment is based on the following two assumptions: (1) the propagation of the stress wave in the bar is a one-dimensional stress wave; (2) the stress of the specimen is evenly distributed along its length. A schematic of the SHPB is shown in In the SHPB impact loading process, because both the incident bar and transmission bar are made of steel, most of them are in the elastic deformation range during impact. The incident energy is divided into three parts while the bar is in contact with the specimen: the first part is the energy of reflected wave in the incident bar; the second part is the energy of transmitted wave in the transmission bar; the third part is consumption energy in specimen during the loading process, the energy mainly consumed by the cracking and the crack propagation of the specimen. During the impact, the stress amplitude generally does not exceed the elastic limit of the steel bar, and the stress–strain relationship of the bar is linear. If the strain can be obtained, the stress can be converted and the incident, transmitted and reflected waves can be integrated to calculate the incident energy Assuming that the energy loss at the surface between the specimen and the incident bar and the transmission bar is ignored, according to the law of energy conversation, the consumption energy The loading devices of the SHPB test and concrete specimens are shown in 3. Two strain gauges with a 1.9 sensitivity coefficient and 1000 Ω electric resistance value are employed to record stress waves. The change in the strain rate can be reflected by changing the velocity of the striker. The red copper is chosen to be pulse shaper. In addition, the lengths and diameter of the incident bar are 6000 mm and 100 mm, and the length and diameter of the transmission bar are 4000 mm and 100 mm, respectively.The bars used in this SHPB system are steel bars with an elastic modulus of 210 GPa, a longitudinal wave velocity of 5200 m/s, Poisson’s ratio of 0.25 and a density of 7840 kg/m−1, 67.2 s−1 and 90.1 s−1, the dynamic compressive strength of concrete was 100.89 MPa, 115.36 MPa and 126.40 MPa, respectively. It can be seen that with the increase in loading rate and strain rates, the dynamic compression strength of concrete also increases, which is the same as the test results of a large number of scholars.The SHPB compression test was conducted on concrete samples with an aggregate volume ratio of 37%, and the gas gun pressure was selected as 0.25 MPa, 0.3 MPa and 0.325 MPa, respectively. Because of the mesoscopic heterogeneity of concrete, the mesostructure of every specimen is different, which leads to differences in mechanical characteristics. Therefore, five tests were conducted for each pressure, and the specimen that was the closest to their average test strength was selected for study, and the stress–strain curves of concrete under different strains were obtained, as shown in The failure process of concrete is as follows: the micro-cracks expand and connect to form macro-cracks, and the macro-cracks expand to form through-cracks until the concrete loses its bearing capacity. Therefore, the damage of concrete has a direct relationship with crack propagation, and the degree of damage can reflect the mechanical properties of concrete.−1, and some areas are not broken along the loading direction. However, with increasing strain rate, the fragment degree of concrete becomes greater. In particular, when the strain rate is 90.1 s−1, there are only four pieces with a diameter of more than 20 mm, and the diameter is only slightly longer than 20 mm. It can be seen that the higher the strain rate is, the greater the fragment degree will be.The concrete specimens damaged in each test were collected and screened using a screening device. The fragmentation distribution after screening is shown in Compared with the characteristics of the cracks developing around the aggregates in the static test, no complete aggregate was found in the concrete fragments under dynamic loading; that is, in the dynamic failure process of concrete, the cracks spread through the aggregates. The main reasons are as follows: cracking will stop when it expands to the aggregate and will expand again after accumulating enough energy. There are two propagation paths: spread around the aggregate or directly through the aggregate. During quasi-static loading, due to the low loading rate, the cracks have enough time to expand, so they will propagate along the low-strength mortar. However, during dynamic loading, the loading rate and the energy input rate are high, and the time of crack propagation is short. When the energy accumulated at the tip of the crack is sufficient, the crack will directly expand along the aggregates and along the shortest path of failure, as shown in −1, the particle size range of concrete fragments with the highest proportion is more than 20 mm, accounting for 82.7%. When the strain rate is 67.2 s−1, the particle size range of concrete fragments with the highest proportion is 10~20 mm, accounting for 40.7%. When the strain rate is 90.1 s−1, the particle size range of concrete fragments with the highest proportion is 10~20 mm, accounting for 37%. When the particle size is less than 10 mm, the proportion of each particle size range increases with an increase in the strain rate. It can be seen that the higher the strain rate is, the higher proportion of small particles in the concrete and the more new surfaces formed after the concrete failure.By comparing the screening results, the distribution curve of concrete fragmentation under different strain rates is obtained in To more intuitively describe the fragmentation distribution of concrete specimens after being impacted, fragmentation degree −1 is shown in Under the condition that the distance between the strain gauges on the incident bar and transmission bar and the end face of the specimen and the stress wave velocity in the bar are known, the change process of the incident wave, reflected wave and transmission wave at the end face of the specimen can be calculated. Combined with Formulas (4)~(7), the change process of the incident energy, reflected energy, transmission energy and consumption energy can be obtained. The energy change of concrete when the strain rate of 67.2 sAccording to the energy variation process of concrete under impact loading, the mutual conversion process of energy can reflect the mechanical properties of concrete, among which the change in consumption energy variation amplitude can directly reflect the four stages of concrete from being loaded to failure.Compared with the variation process of incident energy, reflected energy, transmission energy, consumption energy and stress change with time under three strain rates, as shown in Before the peak stress, under different strain rates, the variation laws of the time-history curve of reflection energy are similar, and the values of reflection energy are close. At the initial stage of loading, the change in reflection energy is less affected by the strain rate, but the increasing amplitude of reflection energy after peak stress increases with the increase in loading rate. This shows that the increasing-amplitude of the reflection energy does not increase immediately with increasing strain rate and has a hysteresis effect. This effect leads to the enhancement of the energy storage capacity in concrete, and the extra energy input into concrete is not released in time, thus enhancing the strength of concrete.According to the characteristics of concrete compressive strength increasing with strain rate, dynamic compression tests of concrete under different strain rates have been carried out. From the perspective of energy, the mechanism of concrete dynamic compressive strength improvement is studied, and the following conclusions are given:The higher the strain rate of concrete loading, the greater the degree of fragmentation, the smaller the size of the fragments, the larger the new surface area formed after the crushing, and the greater the energy used for fracture. The higher the strain rate is, the higher the probability of crack propagation that the cracks pass directly through the aggregate is, and the more regular the fragments are, which are close to cuboid. Since it takes more energy for crack propagation through the aggregate than along the interface, the increase in the dynamic compressive strength of concrete is related to the increase in consumption energy.By comparing the energy conversion process with the concrete failure process, it is found that the conversion process of each energy in the test can reflect the mechanical properties of concrete in the process of load-bearing failure, in which the change in the variation amplitude of consumption energy corresponds to the four stages of loading failure.According to the variation process of reflected energy under different impact velocities, the mechanism of dynamic strength increase in concrete is proposed: the variation amplitude of reflected energy that changes with the strain rate before peak stress is small, and it can be considered that the increased amplitude of reflected energy lags behind the increased amplitude of incident energy. As a result, the energy stored in concrete is not released in time through reflected energy, resulting in an increase in energy storage and an increase in dynamic strength.


The receptor tyrosine kinase c-Met is elaborated in embryogenesis, morphogenesis, metabolism, cell growth, and differentiation. JNJ38877605 (JNJ) is an inhibitor of c-Met with anti-tumor activity. The c-Met expression and its role in adipocyte differentiation are unknown. Here, we investigated the c-Met expression and phosphorylation, knockdown (KD) effects, and pharmacological inhibition of c-Met by JNJ on fat accumulation in murine preadipocyte 3T3-L1 cells. During 3T3-L1 preadipocyte differentiation, strikingly, c-Met expression at the protein and mRNA levels and the protein phosphorylation on Y1234/1235 and Y1349 is crucial for inducing its kinase catalytic activity and activating a docking site for signal transducers were increased in a time-dependent manner. Of note, JNJ treatment at 20 μM that strongly inhibits c-Met phosphorylation without altering its total expression resulted in less lipid accumulation and triglyceride (TG) content with no cytotoxicity. JNJ further reduced the expression of adipogenic regulators, including CCAAT/enhancer-binding protein-α (C/EBP-α), peroxisome proliferator-activated receptor-γ (PPAR-γ), fatty acid synthase (FAS), acetyl CoA carboxylase (ACC), and perilipin A. Moreover, JNJ treatment increased cAMP-activated protein kinase (AMPK) and liver kinase B-1 (LKB-1) phosphorylation but decreased ATP levels. Significantly, KD of c-Met suppressed fat accumulation and triglyceride (TG) quantity and reduced the expression of C/EBP-α, PPAR-γ, FAS, ACC, and perilipin A. Collectively, the present results demonstrate that c-Met is a novel, highly conserved mediator of adipogenesis regulating lipid accumulation in murine adipocytes. Obesity is a significant factor that gives rise to noncontagious diseases, particularly hyperlipidemia, type 2 diabetes, and cancer . IncreasAdipocyte differentiation (adipogenesis) is the fibroblast-like morphology of the preadipocyte development process into adipocyte-like cells . AdipogeMET proto-oncogene [To explore the regulation of transcriptional circumstance in white preadipocyte differentiation and function, we have recently conducted RNA sequencing in differentiating 3T3-L1 mouse white preadipocytes from the preadipocytes (day 0 (D0)) to D2 and D7 of the early and late stages of cell differentiation and found that the expression levels of multiple RNAs including mesenchymal-epithelial transition factor (Met or c-Met) are increased by the time difference. Of interest, c-Met was reported to be a novel immensely upregulated during adipogenesis. c-Met, also called hepatocyte growth factor receptor (HGFR), is a receptor tyrosine kinase encoded by the oncogene . c-Met ioncogene ,16.However, c-Met expression, phosphorylation, and its role in white preadipocyte differentiation remain undisclosed. Our study demonstrated the expression and c-Met function in 3T3-L1 cell adipogenesis using c-Met knockdown and pharmacological inhibition with JNJ38877605 (JNJ) .We scrutinized cellular fat deposition and TG levels in day 0 (D0), D2, D5, and D8 of 3T3-L1 preadipocyte differentiation by utilizing Oil Red O staining and AdipoRed assays. There was a marked increase in fat deposition and TG levels in 3T3-L1 cells on D5 and D8 A,B folloNext, we evaluated the effect of JNJ A, an inhNext, we scrutinized the effect of JNJ (20 µM) on c-Met Y1234/5 and Y1349 phosphorylation throughout 3T3-L1 preadipocyte differentiation over time. Treatment by 20 µM JNJ strongly repressed c-Met Y1234/5 and Y1349 phosphorylation on D5 and D8. Triplicate experiments confirmed the capability of JNJ to almost completely inhibit c-Met Y1234/5 and Y1349 phosphorylation on D8 of 3T3-L1 preadipocyte differentiation B,C.Next, we tested the effect of JNJ (20 µM) on the expression and phosphorylation levels of C/EBP-α, PPAR-γ, and STAT-3/5, known adipogenic transcription factors, throughout 3T3-L1 preadipocyte differentiation over time. JNJ (20 µM) treatment caused the reduced expression of C/EBP-α and PPAR-γ at their protein and mRNA levels on D5 and D8 of the cell differentiation A,B; furtAdditionally, we tested the effect of JNJ (20 µM) on the expression levels of FAS and perilipin A throughout 3T3-L1 preadipocyte differentiation over time. JNJ treatment (20 µM) was shown to inhibit the protein expression of FAS and perilipin A A as wellNext, we investigated the effect of JNJ (20 µM) on the expression and phosphorylation levels of AMPK and LKB-1 throughout 3T3-L1 preadipocyte differentiation over time. Notably, treatment with JNJ (20 µM) instigated a time-dependent surge in AMPK phosphorylation while maintaining total AMPK protein expression in D2, D5, and D8 of 3T3-L1 cell differentiation A. ACC isTo directly see the role of c-Met in fat accumulation during 3T3-L1 preadipocyte differentiation, we established a stably pooled 3T3-L1 preadipocytes that were transfected with control or c-Met shRNA and selected in the presence of puromycin. Apparently, c-Met shRNA-transfected cells (Met #1 and 2) had significantly lower expression levels of endogenous c-Met on D8 of the cell differentiation D8 in opposition to control shRNA-transfected cells (con #1 and 2) A. NotablEvidence suggests that c-Met is pivotal in metabolic diseases, including obesity, insulin resistance, and type 2 diabetes . Thus, cIn the beginning, we illustrated that c-Met expression and phosphorylation are very low or not detectable in D0 and D2 of 3T3-L1 cell differentiation, but their levels are markedly elevated on D5 and D8. It is known that upon HGF binding, c-Met is phosphorylated on Y1234/5, which induces its kinase activity , and on It has been previously shown that capmatinib, a selective inhibitor of c-Met with anti-cancer activity, attenuates adipogenesis  and miR-AMPK is a heterotrimeric protein complex comprising α, β, and γ subunits, which take a specific role in stability and regulation . It is eIn addition to JNJ, we utilized c-Met gene silencing to affirm the role of c-Met in fat accumulation during adipogenesis of 3T3-L1 cells. Importantly, we showed that the knockdown of c-Met leads to less fat accumulation and TG levels throughout 3T3-L1 preadipocyte differentiation. These findings indicate that the expression and activation of c-Met are essential for fat accumulation during adipogenesis of 3T3-L1 cells. Moreover, considering that c-Met knockdown further leads to the reduction in C/EBP-α, PPAR-γ, FAS, ACC, and perilipin A during adipogenesis of 3T3-L1 cells, It appears that c-Met acts as an upstream regulator of C/EBP-α, PPAR-γ, FAS, ACC, and perilipin A during the adipogenic process, and that the lipid-reducing effect by c-Met knockdown in 3T3-L1 preadipocyte differentiation was further due to downregulation of these adipogenesis/lipogenesis/fat storage-related markers. Compared to previous studies that capmatinib, another c-Met inhibitor, attenuates adipogenesis/lipogenesis, which is mediated via AMPK phosphorylation and the reduced expression of SREBP1, C/EBP-α, FAS, and SCID1  and miR-In conclusion, this is the first study to show that c-Met is highly expressed and phosphorylated during the adipocyte differentiation of 3T3-L1 preadipocytes and that gene silencing of c-Met or pharmacological inhibition of c-Met by JNJ leads to a strong reduction in lipid accumulation during the preadipocyte differentiation. The lipid-reducing mechanism during the 3T3-L1 preadipocyte differentiation by c-Met knockdown or JNJ is mediated via control of the intracellular expression and phosphorylation levels of C/EBP-α, PPAR-γ, FAS, ACC, perilipin A, AMPK, and LKB-1 as well as ATP content. This work advocates c-Met and JNJ as potential novel targets for new anti-obesity drugs.JNJ38877605 (JNJ) was obtained from Selleckchem . Primary antibodies for anti-C/EBP-α, anti-PPAR-γ, anti-phospho (p)-STAT-3 (Y705), and anti-STAT-3 also secondary goat anti-Rabbit and goat anti-Mouse IgG antibodies were purchased from Santa Cruz Biotechnology . Anti-p-AMPK (T172), anti-AMPK, anti-p-ACC (S79), and anti-ACC antibodies were ordered from Cell Signaling Technology . Anti-perilipin A antibody was bought from Bio Vision . Anti-FAS antibody was from BD Bioscience . Anti-β-actin antibody, 3-isobutyl-1-methylxanthine (IBMX), dexamethasone, and insulin were acquired from Sigma .3T3-L1 preadipocyte  were grown in DMEM containing 10% heat-inactivated fetal calf (FCS)  and 1% penicillin/streptomycin . To induce 3T3-L1 differentiation, DMEM was supplemented with 10% heat-inactivated fetal bovine serum (FBS) (Welgene) with the addition of a cocktail of hormones (MDI): 0.5 mM IBMX (M), 0.5 µM dexamethasone (D), and 5 µg/mL insulin (I). Consequently, the differentiation medium was replaced after 48 h with Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% FBS and 5 µg/mL insulin for further 3 days. The cells were nourished every other day with DMEM containing 10% FBS until day 8. In certain experiments, the media was also added at all time points with various concentrations  of JNJ.TM 2000 . The shRNA plasmids also contain a puromycin resistance cassette. In the transfected cells, the culture medium was taken out and refreshed with DMEM containing 10% FCS, followed by 48 h incubation. The transfected cells were then selected for plasmid expression in DMEM containing 1 μg/mL puromycin for two weeks. The differentiation of stably transfected 3T3-L1 cells harboring control or c-Met shRNA was induced as above.3T3-L1 preadipocytes seeded into 6-well plates were transfected for 6 h with control or c-Met shRNA  using LipofectamineOil Red O working solution was composed by mixing 6 mL of 0.1% Oil Red O in isopropanol and 4 mL of double-distilled water. On the differentiation day 8, differentiated 3T3-L1 cells were washed with PBS, fixed with 10% formaldehyde for 2 h at room temperature (RT), washed with 60% isopropanol, and dried. The fixed cells were stained with Oil Red O working solution  for 1 h at RT and washed with distilled water. Lipid droplets were then visualized under light microscopy .3T3-L1 preadipocytes were seeded in 24-well plates and differentiated as explained above. On day 8 of differentiation, cells were stained with trypan blue dye, and those that did not take up the dye were counted under a light microscope.On day 8 of differentiation, intracellular TG contents were quantified using the AdipoRed Assay Reagent kit in accordance with the manufacturer’s instructions . Fluorescence was defined after a 10 min incubation on Victor3  with excitation at 485 nm and emission at 572 nm.3T3-L1 cells were washed with PBS and lysed in a modified radioimmunoprecipitation assay buffer (Sigma) at appointed time points. The cell lysates were extracted and centrifuged at 12,000 rpm for 15 min at 4 °C, and the supernatant protein concentration was concluded using the bicinchoninic acid assay kit .v/v) Tween 20 (TBST) followed by blocking in TBST supplemented with 5% (w/v) non-fat dried milk. The membranes were incubated overnight with certain primary antibodies at 4 °C. The membranes were then rinsed with TBST and further incubated with an HRP-conjugated secondary antibody  for 2 h at RT. Membranes were then rinsed three times with TBST and proceeded with enhanced chemiluminescent reagents. β-Actin expression levels were utilized as a control for uniform protein loading.The separation of protein was done by SDS-PAGE electrophoresis and moved onto nitrocellulose membranes . The membranes were washed with Tris-buffered saline (TBS)  containing 0.05% . As previously described, three micrograms of total RNA were converted using a random hexadeoxynucleotide primer and reverse transcriptase . Single-3T3-L1 preadipocytes were seeded in 96-well plates and grown in the presence of differentiation media with or without JNJ or 2-deoxyglucose (2-DG). On days 2, 5, and 8 of differentiation, intracellular ATP levels were quantified by luciferase activity as explained by the manufacturer’s protocol . After a 2 min incubation, luminescence was assessed on a Victor3 (PerkinElmer).p < 0.05 was contemplated to indicate statistically significant differences.Cell count analysis was carried out in triplicates and repeated three times. Data are expressed as mean ± standard error (SE). One-way ANOVA followed by Dunnett’s post hoc test was done using SPSS 11.5 software .


In primary care, urinary tract infections (UTIs) account for the majority of antibiotic prescriptions. Comments from microbiologists on interpreting the antimicrobial susceptibility testing (AST) profile for urinalysis were made to improve the prescription of antibiotics. We aimed to explore the added value of these comments on the quality of antibiotic prescribing by a superior double-blind digital randomized case-vignette trial among French general practitioners (GPs). One case vignette with (intervention) or without (control) a ‘comment’ after AST was randomly assigned to GPs. Among 815 participating GPs, 64.7% were women, at an average age of 37 years. Most (90.1%) used a computerized decision support system for prescribing antibiotics. Empirical antibiotic therapy was appropriate in 71.9%  of the cases, without differences between arms. The overall appropriateness of targeted antibiotic therapy (primary outcome) was not significantly increased when providing ‘comments’: 83.4% vs. 79.9% . With the multivariate analysis, the appropriateness was improved by 2-folds  among physicians working in healthcare facilities. Among digital-affine young general practitioners, the adjunction of a ‘comment’ by a microbiologist to interpret urinalysis in community-acquired UTIs did not improve the overall level of appropriateness of the targeted antibiotic. The burden of antimicrobial resistance has dramatically increased over the last few years worldwide ,2,3. In GPs are therefore a key target for Antibiotic Stewardship Programs. However, hospital-based interventions that have been developed over the past decade are not directly scalable to the primary care setting, and innovative interventions tailored to these settings are needed. To that end, computerized decision support systems (CDSSs) , or otheIn primary care, urinary tract infections (UTIs) account for most antibiotic prescriptions ,12,13,14The urinalysis is the key diagnostic test for identifying the bacterium/bacteria involved in a UTI and to establish its/their antimicrobial susceptibility testing (AST) profile. A urinalysis allows GPs to select a targeted and effective documented antibiotic therapy, ideally with the narrowest spectrum possible, to reduce the risk of emerging resistance . HoweverWe aimed at exploring the impact of comments for interpreting urinalysis AST, issued by primary care microbiologists, on the validity of prescribed antibiotics for community-acquired UTIs managed by GPs, assuming that it would guide GPs toward better antibiotic prescribing. Herein, we assess the comparative effect of such a comments-based versus standard urinalysis/AST report in regard to the appropriateness of targeted antibiotic therapy initiated by GPs, adjusting for confounding factors, including the use of a CDSS.Ten out of one hundred general medical councils accepted our request to broadcast our trial to their mailing list between 24 November 2020 and 6 June 2021. We enrolled 1015 GPs in the trial, mostly from the Auvergne-Rhône-Alpes area (52.3%), of whom 815 completed an interpretable questionnaire for empirical antibiotic therapy (intention-to-treat set) and 716 for targeted antibiotic therapy (by-the-protocol set). The GPs’ mean age was 37 years, and a majority (64.7%) of them were women. Most (73.8%) of the GPs were either established or replacing GPs, with an average professional experience of 11 years. The demographic and professional characteristics of GPs enrolled in the trial were similar between arms and are described in The appropriateness of empirical antibiotic therapy on the whole (intention to treat) trial’s population with interpretable questionnaire was 71.9% , without differences between arms.p-value = 0.230).We assessed the appropriateness of documented antibiotic therapy on the by-protocol population. This subset population included all GPs with an interpretable questionnaire for the primary outcome, excluding 99 (12.1%) GPs with missing information about the targeted antibiotic therapy. A total of 716 GPs were included in the primary outcome analysis: 343 in the intervention arm and 373 in the control arm. The overall appropriateness of targeted antibiotic therapy was not significantly increased when providing ‘comments’ for urinalysis interpretation: 83.4% vs. 79.9% .In the multivariable sensitivity analysis conducted on 710 GPs with complete data (complete cases), the effect of intervention was similar for the two arms, when adjusting for GPs’ sex, age group, main mode of practice, professional experience, student mentoring status, CDSS use on appropriateness, and the type of UTI. The imputation of missing data by the MICE (multiple imputation by chained equation) method generated a larger number of inappropriate prescriptions and tended toward a more even distribution between the two randomization arms, without difference in point estimates.GPs working in hospital settings had higher appropriateness than those working in primary care , p-value = 0.108).The use of ‘comments’ did not reduce the deviation from the antibiotic regimen recommended in the first line, yielding a 0.07 non-significant absolute reduction and corresponding to a relative reduction by 6% in favor of the intervention arm , especially those from the WATCH list [In addition, adding a ‘comment’ did not reduce the prescribing rate of broad-spectrum antibiotics  compared to the 20–60% reported in the literature ,17,23,28During the last decade, many studies showed that selective AST reporting may improve the appropriateness of documented antibiotic therapy in community-acquired UTIs ,35,36,37Additional limitations were due to software for setting the randomization, as it was not possible to stratify randomization regarding CDSS use. However, the concealment of allocation was ensured, and the randomization process allowed us to properly balance case vignettes between arms. Moreover, we did not use a user-centered approach in our pilot study conducted before the trial to ensure that the understanding of ‘comments’ was optimal . FinallyWe conducted a randomized (ratio 1:1) double-blind digital case-vignette controlled superiority trial among French GPs between 24 November 2020 and 6 June 2021.Any French GPs, including residents, were eligible for single participation in the trial. Other medical specialists and medical students were excluded.We sent an advertising email presenting the trial to the 100 French general medical councils, 30 regional residents’ unions, and 13 regional seniors’ physician unions. We invited organizations to broadcast the offer for participation in the trial to their mailing lists, using a preformatted email. The email included a web-link redirecting to a questionnaire hosted on the LimeSurvey web platform. The web link was active over the whole study period, and a dunning email was sent out 3 weeks after the initial broadcasting.Upon reaching the trial’s online platform, the respondents answered a captcha (Turing test) to ensure that they were not bots. They were then screened for eligibility for the trial. We attached a unique anonymized tracking number to each participant and internet protocol address to ensure data confidentiality and prevent duplicates. Participants underwent two-step randomization that was centralized and operated by the trial platform. Allocation was concealed. The first step allocated participants to one either the comments (intervention) arm or the control arm. The second step allocated participants within arms to one of the six case vignettes designed for the trial. GPs were aware of the clinical diagnosis associated with the case vignette. The case vignettes are summarized in After randomization, we asked GPs to fill out a web self-questionnaire divided into three parts. The first part (Part I) was common to any participant in the trial. In Part I, the GPs had to report information about their demographics, their experience in using and interpreting AST, and their use of the ‘Antibioclic’ web-based CDSS for prescribing antibiotics in primary care . The secThe ‘comments’ consisted of predefined texts to be displayed below the crude report results of the AST . The cruThe primary outcome was the overall appropriateness of the targeted antibiotic therapy prescribed according to previously established 2018 French guidelines for the management of community-acquired UTIs . The tarBefore the trial started, we estimated that a minimum of 500 answers would allow us to reach 90% power to detect an increase in overall appropriateness, from 65% in the control arm to 78% in the intervention arm.p-value < 0.050) to indicate statistical significance. We performed the analysis using the R software version 4.03 .We conducted the main analysis on the by-the-protocol population, enrolling all participants for which the primary outcome was available. We used binomial logistic regression to compare the levels of appropriateness of targeted antibiotic therapy between arms. We also conducted a set of sensitivity analyses by (i) conducting a multivariable binomial logistic regression assessing the overall appropriateness among GPs with complete data, by adjusting for potential confounders—demographic characteristics, use of CDSS, and assigned case vignette; and (ii) by imputing missing data for the primary outcome, using baseline characteristics and empirical antibiotic therapy in multiple imputation by chained equation (MICE). We used a log-transformation to report the difference between the general linear models as the odds ratio (OR) and its 95% confidence interval (95% CI). All tests were two-sided at a 5% threshold (The comments made by microbiologists regarding urinalysis and the interpretation of antimicrobial susceptibility testing in community-acquired UTIs did not improve the selection of an adequate targeted antibiotic therapy in a young and digital-affine population of GPs extensively using a web-based CDSS to prescribe antibiotics. Such approaches deserve randomized real-life investigations before being prioritized as a component of national antimicrobial action plans.


Small peptides can be absorbed by the bovine mammary gland for the synthesis of milk protein, but the absorption mechanism still needs further study. In this study, the role of peptide transporters in small peptide uptake by bovine mammary epithelial cells (BMECs) was studied. First, BMECs were obtained and cultured in a transwell chamber. After 5 days of culture, the FITC‐dextran permeability of the cell layer was detected. Then, 0.5 mM methionyl‐methionine (Met‐Met) was added to the medium of the lower and upper transwell chambers, respectively. The culture medium and BMECs were collected after 24 h of treatment. Liquid chromatography‐mass spectrometry (LC–MS) was used to detect the concentration of Met‐Met in the culture medium. Real‐time PCR was used to detect the mRNA abundance of β‐casein, oligopeptide transporter 2 (PepT2), and small peptide histidine transporter 1 (PhT1) in BMECs. Then, the BMECs were transfected with siRNA‐PepT2 and siRNA‐PhT1, respectively, and the uptake of β‐Ala‐Lys‐N‐7‐amino‐4‐methylcoumarin‐3‐acetic acid (β‐Ala‐Lys‐AMCA) in BMECs was detected. The results showed that, after 5 days of culture, the FITC‐dextran permeability of BMECs was 0.6%, which was significantly lower than that of the control group. The absorption rates of Met‐Met in the culture medium of the upper and lower chambers were 99.99% and 99.95%, respectively. The addition of Met‐Met to the upper chamber significantly increased the mRNA abundance of β‐casein and PepT2. The addition of Met‐Met to the lower chamber significantly improved the mRNA abundance of β‐casein, PepT2, and PhT1. The uptake of β‐Ala‐Lys‐AMCA significantly decreased in BMECs transfected with siRNA‐PepT2. These results suggested that the BMECs were successfully cultured in the transwell chamber and formed a cell layer with little permeability. The small peptides in both the upper and lower chambers of the transwell can be absorbed by BMECs in different ways. PepT2 plays an important role in the uptake of small peptides on both the basal and apical sides of BMECs, and PhT1 may be involved in the uptake of small peptides on the basal side of BMECs. Therefore, the addition of small peptides in dairy cow diets may be an effective dietary manipulation to increase milk protein concentration or yield. In this study, role of oligopeptide transporter 2 (PepT2) and small peptide histidine transporter 1 (PhT1) on small peptide uptake in bovine mammary epithelial cells (BMECs) cultured in transwell chamber was studied for the first time. The results suggest that the small peptides in both upper and lower chambers of transwell can be absorbed and used for milk protein synthesis by BMECs in different ways; PepT2 plays an important role in the uptake of small peptides in both basal and apical side of BMECs, and PhT1 may be involved in the uptake of small peptides in the basal side of BMECs. The cells were cultured at 37°C and the medium was changed every other day.Mammary tissues were obtained from three healthy mid‐lactation Chinese Holstein dairy cows. Then, the BMECs were isolated and purified according to the method of Zhou et al. . The priThe cells of passages 4–5 were used in this study. After cells cultured in transwells formed a cell layer, the tightness of the cell layer was detected by evaluating the FITC‐dextran 4000 permeability. The treatment was divided into a cell‐free group (Control) and cell layer group (BMECs) with three repetitions each. The cells were washed three times with PBS, then 1.5 mL of 100 μM FITC‐dextran 4000 was added to the lower chamber, and 0.5 mL of medium was added to the upper chamber. The cells were incubated at 37°C for 2 h. At the end of incubation, 200 μL of upper chamber culture medium was taken, and the fluorescence value was measured by fluorescence microplate reader.2.2+ LC–MS/MS . First, the samples were pretreated to remove impurities and extract Met‐Met: acetonitrile (400 μL) was added to the cell culture medium (100 μL), stirred for 1 min, and then centrifuged at 15,000× g for 10 min; the supernatant (400 μL) was concentrated and evaporated, 150 μL of 50% acetonitrile aqueous solution was added to redissolved the solute, and then centrifuged at 15,000× g for 10 min; and the supernatant was used for analysis. The LC–MS MRM mode was used to detect and collect the chromatographic information of Met‐Met. Then, MultiQuant™ software  was used for Met‐Met data analysis. After obtaining the mass spectrum peak area of the Met‐Met sample, the concentration of Met‐Met in the cell culture medium was calculated according to the Met‐Met standard solution.The concentration of Met‐Met in the cell culture medium was determined by SCIEX Triple Quad 6500Chromatographic conditions: the chromatographic column was Waters Xbridge Amide , aqueous phase A was water (containing 0.1% formic acid), organic phase B was acetonitrile (containing 0.1% formic acid), and the running time, injection volume, and column temperature were 12 min, 5 μL, and 35°C, respectively. The elution gradient is shown in Table Mass spectrometry conditions: positive ion (ESI+) MRM scanning mode was used. The specific mass spectrum parameters are shown in Tables 2.3Total RNA was extracted from BMECs with TRIzol® Reagent  and first‐strand cDNA was synthesized with the 5X All‐In‐One RT MasterMix kit . The relative mRNA abundance of β‐casein, PepT2, and PhT1 normalized to glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH) was detected by real‐time PCR according to the method of Zhou et al. . The pri2.4After BMECs in transwells reached 70–80% confluence, the cells were transfected with various siRNAs. The siRNA interference sequences and transfection method were performed according to Wang et al. . The siR2.5Transport studies of PepT2 and PhT1 in BMECs were determined using the β‐Ala‐Lys‐AMCA method .Data were analyzed using one‐way ANOVA and Duncan's multiple‐range tests using SAS software . Values are shown as the means ± SDs. Bars with different superscripts are significantly different , and the lower chamber represents the basal membrane side of epithelial cells (blood). This barrier model is convenient for accurately studying the uptake mechanism of small peptides in BMECs. The results showed that BMECs were successfully cultured in transwells, and the cell barrier was successfully established, which could be used for the transmembrane transport test. Further experimental results showed that both upper and lower chambers Met‐Met uptake by BMECs increased the expression of β‐casein. The PepT2 was involved in the absorption of small peptides on both the apical and basal membrane sides of BMECs. A study by Wang et al.  showed t5The BMECs were successfully cultured in a transwell chamber and formed a cell layer. Both acinar and basal Met‐Met can be absorbed by BMECs through small peptide transporters and used for milk protein synthesis. Thus, the addition of small peptides in dairy cow diets may be an effective dietary manipulation to increase milk protein concentration or yield.The authors declare that they have no conflict of interest.The authors confirm that the ethical policies of the journal, as noted on the journal's author guidelines page, have been adhered to, and the appropriate ethical review committee approval has been received. The authors confirm that they have followed guidelines for the care and use of laboratory animals approved by the Special Committee on Scientific Research Ethics of Liaocheng University, Liaocheng, China (No. 2022112007).