Currently, one of the topical areas of application of artificial intelligence methods in industrial production is neural networks, which allow for predicting the performance properties of products and structures that depend on the characteristics of the initial components and process parameters. The purpose of the study was to develop and train a neural network and an ensemble model to predict the mechanical properties of lightweight fiber-reinforced concrete using the accumulated empirical database and data from construction industry enterprises, and to improve production processes in the construction industry. The study applied deep learning and an ensemble of regression trees. The empirical base is the result of testing a series of experimental compositions of fiber-reinforced concrete. The predicted properties are cubic compressive strength, prismatic compressive strength, flexural tensile strength, and axial tensile strength. The quantitative picture of the accuracy of the applied methods for strength characteristics varies for the deep neural network method from 0.15 to 0.73 (MAE), from 0.17 to 0.89 (RMSE), and from 0.98% to 6.62% (MAPE), and for the ensemble of regression trees, from 0.11 to 0.62 (MAE), from 0.15 to 0.80 (RMSE), and from 1.30% to 3.4% (MAPE). Both methods have shown high efficiency in relation to such a hard-to-predict material as concrete, which is so heterogeneous in structure and depends on many factors. The value of the developed models lies in the possibility of obtaining additional useful information in the process of preparing highly functional lightweight fiber-reinforced concrete without additional experiments. Currently, there is a need to introduce methods of digitalization of business and technological processes, including in the construction industry, which helps construction companies move towards the introduction of modern information technologies. There is a tendency to accumulate and digitize the available data with the aim of further applications of mining algorithms ,2,3,4,5 The modern construction industry, and in particular the production of complex composite materials such as concrete, is facing a serious problem that often affects the quality of buildings and structures being built, leads to an increased number of accidents in construction and, in general, significantly reduces the life cycle of buildings and structures ,7,8,9. AIn the study , predictIn a comparison of multilayer perceptron neural networks (MLPNNs), adaptive neural systems for fuzzy detection (ANFIS), and genetic programming (GEP) in terms of predicting compressive and tensile strengths, the highest efficiency was noted for GEP and the lowest for MLPNN. The order of accuracy for the compressive and tensile strength models is GEP > ANFIS > MLPNN. The advantage of GEP is that it provides a new mathematical equation that can be used to predict the properties of another database. Sensitivity analysis showed that water and cement are the determining factors in the development of the compressive strength model. However, these factors have the least impact on the development of a tensile strength model .Backpropagation neural network (BPNN) models have been used to predict the torsional strength of reinforced concrete beams , the uncThe Convolutional Neural Network (CNN) has found applications in the construction industry, especially for monitoring and inspection purposes, in particular, to identify areas covered by fresh and young concrete  and predA method for predicting the 28-day compressive strength of concrete using Multilayer Feed-forward Neural Networks (MFNNs), based on the inadequacy of existing methods dealing with multiple variables and non-linear problems, demonstrates that using a neural network to predict the strength of concrete is practical and beneficial ,28.A comparison of several methods, such as artificial neural networks, the support vector machine, classification and regression trees, the Gaussian regression process, and the extreme gradient boosting tree in terms of predicting the properties of fiber-reinforced concrete, showed the effectiveness of the latter, which had the least error. The tree model with an extreme gradient was the best imitator in predicting the properties of fiber-reinforced concrete .A study of the structural behavior of hollow concrete columns (HCC) reinforced with fiberglass, which performed detailed numerical simulations, is presented in . The autDepending on the actual production situation and performance requirements, it is proposed to choose one of the methods for developing high-strength concrete (UHPC) with the given requirements. Integrating two or more methods can serve as a good approach to addressing the challenges of UHPC development by taking advantage of the benefits of each method .All the above-mentioned machine learning methods and neural network models have been applied to a wide range of materials, such as heavy concrete without additives ,27,28, cHowever, it is known that the properties of lightweight concrete are much more difficult to predict than the properties of normal mass concrete, especially when the prediction concerns the thermal insulation properties of concrete with artificial lightweight aggregate (LWA) ,35. The As a result of experiments in the production of highly functional lightweight fiber-reinforced concrete, researchers have generated a large stream of data containing important information about the mechanical properties of the resulting material. Data such as the volume content of components, experimental results, description of experimental results (the class of concrete and its strength and density), and others often have an unstructured and complex form . The use of methods for the intelligent processing of accumulated data arrays will allow for structuring data, automating the solution of many problems that arise in practice, thus improving the quality of construction production technology, and optimizing costs by reducing the time required to determine the key properties of the building material in question.-Revealing the fundamental possibility of predicting the properties of lightweight fiber-reinforced concrete using artificial intelligence methods.-Determination of the quantitative and qualitative picture of the relationship between the initial data of the formulation and technology and the output parameters expressed in the properties of concrete.-The fundamental basis of structure formation and the formation of concrete properties, presented for the analysis of the constructed neural network and the issuance of applied forecasts to it on the operational characteristics of concrete.-Proof of the possibility of neural network control of the structure and properties of concrete and analysis of complex relationships that were previously inaccessible through the manual method.The scientific novelty lies in:In this regard, the main purpose of the study is to develop and train a deep neural network, as well as to use an ensemble regression tree model to determine the mechanical properties of lightweight fiber-reinforced concrete. It is possible to realize the set purpose using the accumulated empirical base and data available at the disposal of the construction industry enterprises. The ultimate purpose of the study is to improve the production processes in the construction industry.The main task of the study was to achieve the ability to control the properties of concrete using artificial intelligence methods using lightweight fiber-reinforced concrete with improved characteristics as an example. The next task of the authors in the future is to project the result obtained onto other types of concrete and establish the technical feasibility and rational ranges of such control.-Review and analysis of existing literature in the areas of improving the quality of the concrete industry and the introduction of new management and quality control systems in production, as well as a review of existing authors’ methods for the application of artificial intelligence methods in the concrete industry and manufacturing enterprises in general.-Selection of an empirical base built on physical experiments and accumulated experience with the results of testing concretes and the mutual dependencies between the input parameters of the initial components and the technological and output parameters of the resulting concretes established by us and other authors.-After processing and applying the data of a physical experiment, it is necessary to develop, train, and test a deep neural network, as well as apply an ensemble model of regression trees to process the empirical base with a further comparison of the results based on the values of the main metrics.-Evaluate the prospects of applying the developed methods in practice and evaluate the possibility of translating and projecting the results obtained to other types of concrete, as well as developing specific proposals for the construction industry.The stages of the study are as follows:Determination of the key mechanical properties of highly functional lightweight fiber-reinforced concrete, such as the density, cubic compressive strength, prism compressive strength, tensile strength in bending, and axial tensile strength, is of great importance in determining the quality and further performance properties of the material. Ideally, these properties should be obtained empirically. However, it is quite often difficult to carry out appropriate measurements after a series of experiments due to the high cost and time-consuming nature.The evaluation of the mechanical properties of highly functional lightweight fiber-reinforced concrete is an area in which artificial intelligence methods can be very effective due to saving material resources and time. In this work, the following methods will be applied: Deep learning and an ensemble of regression trees. The methods discussed represent an alternative way to obtain reliable and accurate results to determine the mechanical properties of a material, since there can be both linear and non-linear relationships between input data (features) and output (predicted) values.The initial dataset for the methods under consideration is the results of 153 experimental compositions for concrete with strength ranging from 40 to 50 MPa . The feaHere, the “glass fiber content” and “fiber distribution method” data are feature values (training sets), and the cubic compressive strength, prismatic compressive strength, flexural tensile strength, and axial tensile strength are prediction value data (prediction sets).i), the network receives a vector of features that describe the object—in this case, two features. In the inner layers , they are processed: The input vector is multiplied by the matrix of connections, and the vector of new features formed in this way is transferred to the next layer. The signal processing result is sent to the output layer of the network .The concept of “deep learning” describes the training of so-called “deep artificial neural networks”. The architecture of a deep artificial neural network is a structure  consistiFor the study, a neural network of the following architecture was selected : A fullyThe choice of neural network architecture  was justified by a small empirical base. In such cases, a network with an excess number of elements would lose its generalization ability and would work well only on the training set.An ensemble of regression trees is a predictive model consisting of a combination of decision trees, each of which by itself gives a low quality of regression, but due to their large number, the result is satisfactory, thereby combining regression trees increases predictive efficiency.The study used an ensemble model of regression trees with a variety of parameters to select the best ones .The Number of Trees parameter specifies the total number of decision trees to create in the ensemble. Their number can vary from a few dozen to several thousand. Typically, an ensemble with a good predictive score requires several hundred to several thousand “weak learners”. However, in the case of small samples, one should start with a few dozen trees, evaluate the efficiency of the ensemble, and then, if necessary, increase their number. Another important hyperparameter that needs to be tuned is the depth of decision trees. The study will evaluate the effectiveness of depth from 1 to 3 levels.To solve the problem, the MATLAB 9.11 (Release 2021b) environment was used, which implements a deep neural network and the ensemble method of regression trees.The following metrics were used to assess the quality of the constructed models: Mean Absolute Error (MAE), Mean Square Error (MSE), Root-Mean-Square Error (RMSE), and Mean Absolute Percentage Error (MAPE).When training the neural network, the dataset was divided into training, validation, and test sets in the ratio of 70/10/20. For an ensemble of regression trees, the sample is divided into training and test in the ratio of 80/20; in turn, 10% of the test data, when building trees, go to validation.Consider the process of training a deep artificial neural network when predicting the parameter “cubic compressive strength” (MPa).From the graph , which sIt makes sense to build graphs of errors against the number of trees, taking into account the depth of the tree structure, and limit the size at the moment when the errors become the smallest.That is why, according to the figures, the smallest errors are observed when the model runs with a tree depth of 3 and the number of trees is equal to 20.Decision trees allow one to obtain easily interpretable models, which are a set of rules of the form “if … then …”. Interpretation is facilitated, among other things, by the ability to present these rules in the form of a visual tree structure. Prediction error plots  show theThe developed regression models can be evaluated using similar graphs, where a line at an angle of 45 degrees is highlighted, and the ratios between the real values of the predicted parameters and their calculated values are presented. The ensemble method of regression trees in this implementation demonstrated the best values of metrics compared to the neural network, since it is a more complex algorithm in terms of the number of parameters than the neural network. Thus, the ensemble method of regression trees had more opportunities to adjust to the data.As the experience of the application of machine learning methods shows, the use of algorithms for combining models—ensembles—is one of the most powerful methods of machine learning and often surpasses other models in quality. In this study, the ensemble method, which consists of using a committee of decision trees, demonstrated better metric values compared to a neural network due to the ability to work with categorical variables (the fiber distribution method parameter), as well as fine-tuning the key parameters of the model.Artificial intelligence methods make it possible to generalize the results of experiments for complex models with many parameters . It is not necessary to conduct a deep theoretical analysis to obtain an adequate forecast. Even after the derivation of theoretical formulas, their accuracy is often lower than that given by the neural network model. The considered methods are valuable for their ability to generalize the information coming to them during training, and not just memorize specific examples.Many works are known concerning the application of artificial intelligence methods for various high-tech industries, such as automotive, instrumentation, and various technological industries, as well as the service sector ,45.However, in the global space of scientific research and in practice, there is a deficit, expressed in a certain conservatism of the construction industry and the slow pace of the introduction of artificial intelligence methods and other innovative methods in this industry ,33,34,35The most important parameter of the operational reliability of lightweight concrete is the rational ratio of its strength and density. Due to the fact that the density directly depends on the pore structure and on the whole process of concrete structure formation, the strength indicator comes to the fore. In order to fully use the advantages of fiber reinforcement (or fibers) and rely on the created concrete as a running structural material, a range of strength values from 30 MPa to 40 MPa was adopted in our study. Fiber-reinforced concretes with such strength characteristics have great prospects, and after working out the neural network model on these concretes, it is supposed to project the study to even higher-strength concretes, and also, if necessary, turn to less durable concretes for less critical levels of construction work.Taking into account the fact that we have previously established objective opportunities to significantly improve the quality of lightweight fiber-reinforced concrete and reduce the percentage of rejects and losses due to the correct recipe and technological approaches, the introduction of artificial intelligence methods in the concrete industry will further improve these processes, achieve a significant increase quality, and ultimately lead to a reduction in the cost of concrete production by up to 20%, which has already been established according to preliminary estimates of industrial partners.As for scientific deficits, some pioneering studies were carried out earlier in Russia and the CIS, in particular, reference , the autThe quantitative characteristics of the work performed and the result obtained are as follows. We present the characteristics of accuracy for each method for each property. Thus, for the cubic compressive strength, MAE varied from 0.46 to 0.62, RMSE from 0.6 to 0.8, and MAPE from 0.98% to 1.30%. For prismatic compressive strength, the accuracy of the methods was somewhat different: MAE from 0.48 to 0.73, RMSE from 0.62 to 0.89, and MAPE from 1.33% to 2.11%. For bending tensile strength, the accuracy was MAE from 0.30 to 0.63, RMSE from 0.44 to 0.87, and MAPE from 3.40% to 6.62%. Finally, for axial tensile strength, MAE was from 0.11 to 0.15, RMSE from 0.15 to 0.17, and MAPE from 2.06% to 2.49%. Thus, the percentage characteristics of accuracy are very high, and the qualitative picture of the validity and expediency of using the studied methods of artificial intelligence in predicting the properties of concrete is quantitatively confirmed. Such high accuracy is ensured by the accepted large amount of empirical data, in-depth analysis of the results, and, as a result, the good correlation between artificial intelligence methods and standard methods for predicting the strength of concrete. Such high-precision characteristics of the methods are a significant achievement and open further opportunities for the development of the direction of introducing artificial intelligence methods for predicting the properties of concrete.The information obtained using the constructed models based on artificial intelligence methods makes it possible to obtain additional information for controlling and adjusting the process of preparing highly functional lightweight fiber-reinforced concrete and obtaining a material with specified strength and density characteristics without additional experiments.An analysis of the literature data and the results obtained in the course of our own experiments made it possible to formulate the following main conclusions.(1)Deep neural network and ensemble regression trees methods can be applied to determine the mechanical properties of highly functional lightweight fiber-reinforced concrete.(2)The developed models are adequate since the distribution of points on the graphs has no patterns and looks like a random scatter of points around zero. The coefficient of determination shows values from 0.94 to 0.99, which indicates a high correspondence of the model to the data.(3)The ensemble method of regression trees in this implementation demonstrated the best values of metrics compared to the neural network, since it is a more complex algorithm in terms of the number of parameters compared to the neural network. Therefore, the ensemble method of regression trees had more opportunities to adjust to the data.(4)The quantitative picture of the accuracy of the applied methods for strength characteristics varies: For the deep neural network method, it was from 0.15 to 0.73 (MAE), from 0.17 to 0.89 (RMSE), and from 0.98% to 6.62% (MAPE), and for the ensemble of regression trees, it was from 0.11 to 0.62 (MAE), from 0.15 to 0.80 (RMSE), and from 1.30% to 3.4% (MAPE). Both methods have shown fairly high efficiency in relation to such a hard-to-predict material as concrete, which is very heterogeneous in structure and depends on a large number of factors.(5)The result obtained in the form of a developed neural network for predicting the properties of lightweight fiber-reinforced concrete allows us to establish the fundamental role of the initial factors, namely the amount of fiber and the method of distribution of fibers in the formation of output parameters—the strength properties of fiber-reinforced concrete. Thus, with the help of the considered methods of artificial intelligence, the role of specific recipe-technological factors, which were previously evaluated only from the point of view of traditional recipes and technology, is revealed. Thus, artificial intelligence methods for building a neural network made it possible to reveal, establish, and manage this fundamental relationship, strengthening the role of the control technologist in the formation and prediction of the properties of specific building materials.These methods can be easily implemented in the process of development and production of unique building materials, since they do not require serious computing resources, and in the future, based on artificial intelligence, an expert system can be created to summarize all the accumulated experimental data, which can be located in the electronic environment of the university and provide data to interested workers and researchers for the development of the industry. Thus, the main goal of the study was achieved: it was shown that artificial intelligence methods are applicable to this task and have great potential in determining the mechanical characteristics of unique building materials.In further studies, we plan to focus on the study of new theoretical and practical dependencies that arise in the production of fiber-reinforced concrete made from various components and operated in various conditions, for even greater universalization of the proposed concept.


Reinforced concrete structures’ (RCSs) ageing and early deterioration are some of the main challenges faced by the building sector today, and steel bar corrosion is one of the main problems. In this phenomenon, water and concrete’s electric resistivity play a fundamental role. Therefore, developing sensor systems capable of estimating any variations in these parameters in real time and remotely would represent considerable progress in sustainably maintaining RCSs. Many types of sensors capable of estimating humidity variation and electrical resistivity in concrete currently exist, but the variability of these sensors’ sensitivity can be extreme depending on several factors; for example, temperature or presence of ions and their incorporation into smart monitoring systems, which is difficult. As an alternative to today’s sensors, this study centered on developing two estimation models by means of the response of a novel voltammetric stainless steel (SS) sensor. The estimation models were one of humidity variation and another of concrete’s electric resistivity. These models were calibrated, fitted and validated. In the validation, both these models explained a percentage of variance over 80%. Given today’s socio-economic context, the sustainable development of cities is very important . Therefo2, CO2, etc.) ,25dl) XC=2 (The employed SS working electrode consisted of a U-shaped SS wire (0.8 mm diameter). The effective working area was limited by covering ends with waterproof polyurethane paint. The average sensor effective surface area was 2.99 ± 0.05 cm2 . The sen2.The pseudo-reference/CE employed to apply electro-analytical techniques was L-shaped SS wire meshing (0.5 mm diameter). Its effective surface area was 132 ± 11 cmThe estimation model of humidity variation: this was obtained with the correlation of the electric resistance (Rs) data obtained with the weight variation of samples after submitting them to different humidity conditions.The estimation model of concrete’s electric resistivity: this was obtained with the correlation of the empirical electric resistivity (ρ) and electric resistance (Rs) data obtained with the sensor system.The performed experimental study consisted in developing the following estimation models:To develop both these models, it was necessary to submit them to calibration, fitting and validation processes.3) were made with six conventional concrete types without additions, which are specified in In order to develop the estimation models, prismatic concrete samples  and six without sensors (SSNS) per concrete, which totaled 72 samples for this study.For both the CSNS and SSNS samples, four samples per concrete were used to calibrate models, with two samples for concrete validation purposes.the humidity variations in the environment would quickly affect the zone where sensors were;sensors were not affected by any uncontrolled defects on concrete surfaces .Sensors were placed in the position indicated in After concreting, samples were left for 48 days inside a curing chamber under conditions with relative humidity (RH close to 100%). After this time, they were taken out of the chamber and immersed in water to maintain the saturation conditions. Tests were carried out under these conditions on 3 consecutive days.When tests finished, the samples with sensors (CSNS) and without sensors (SSNS) were left inside an oven at T ≈ 50 °C for 48 h to be dried. After this 48 h period, all the samples were allowed to cool and were equilibrated in a drier with humidity-controlling salt to avoid samples absorbing water vapor while cooling. After 24 h, tests were run under these conditions.0). Tests began after samples’ weight stabilized.Next, all the samples were placed inside closed containers with saturated salt solution to maintain constant RH conditions with RH values of around 100% . The temFinally, samples were left under laboratory conditions for 72 h  and the last lot of tests was performed.In each state, three measurements were repeated 3 times per sample to ensure the statistical reliability of the results. Samples were weighed after each test.The direct method (UNE 83988-1:2008) was applied to obtain samples’ electric resistivity. The measurement was taken by a Knick Portavo 902 conductimeter. This device was connected to two metal plates placed aside samples. The electrical contact between plates and concrete was ensured using a damp cloth. 2); l is the distances between electrodes (m).Samples’ electrical conductance was obtained using the conductimeter. The inverse of this value was concrete’s electric resistance (R), given by the relation established for a uniform electric field between two rectangular parallel plates (Equation (2)) to obtain concrete’s electric resistivity :(2)ρ=R ∗Hardened concrete tests. To determine resistance to compression (UNE 12390-3:2009). Resistance to compression was determined at 28 days (fc). Two cylindrical samples were prepared  for each mass at each dose. Total number of samples: 36.Determining water absorption, density and accessible porosity for water (UNE 83980:2014). Testing was conducted at 28 days. Two cylindrical samples were prepared  for each mass at each dose. Total number of samples: 36.Determining air permeability (UNE 83981:2008). Testing was conducted at 28 days. Two cylindrical samples were prepared  for each mass at each dose. To run the test, sample sides were covered with sealing paint. The air permeability coefficient (k) was obtained. Total number of samples: 36.3) for each mass at each dose. Total number of samples: 36.Determining electrical resistivity (ρ): direct (reference) method (UNE 83988-1:2008). Progression over time. Two prismatic samples were prepared (4 × 4 × 16 cmDetermining the water cover depth under pressure (UNE 83-309-90). A test was conducted at 28 days. For this test, two samples were prepared  for each mass at each dose. Total number of samples: 36.To define the quality of the manufactured concretes, the following concrete characterization tests were conducted:Very low-durability concretes: w/c = 0.9 and w/c = 0.8. These concretes are not covered by Spanish Standard EHE-08 for their structural use. Their use is justified when the sensor needs to be characterized within a wide range of porosities.Low-durability concrete: w/c = 0.6.Medium-durability concrete: w/c = 0.5.High-durability concrete: w/c = 0.4 and w/c = 0.3.After taking into account the results of the characterization tests and the durability indicators set by the French Civil Engineering Association (AFGC) , the conIn line with this, different conventional concrete qualities were included in this study to obtain more reliable results.0 was estimated. This variation was established according to the definition in Equation (3):To first establish correlations, the variation in samples’ weight in relation to m0 is sample weight under these conditions: RH ≈ 100%, T = 22 ± 2 °C. Weight under these conditions was taken as a reference because in a real case, it is believed that the continuous measurement of the humidity state in structures would begin under similar conditions by taking into account the mean year-long temperature in Spain (kg).mi is the sample weight in a given measurement number (kg).mThis variation in the concrete sample’s weight is directly associated with the loss or gain of the water inside it because there are neither salts that can diffuse to a sample nor variations in the temperature that can affect its weight.0), Rs decreases.Taking into account the morphology of the correlation and its functions found in the bibliography, three models were used in the calibrating and fitting phases. The first of these was the “modified Stam model” (Equation (4)). This function derives from the equation proposed by Stam, A.J. , which wAnother fitting function proposed for calibration was the linear correlation between humidity variation and the electric resistance natural logarithm, the “natural logarithm model” (Equation (5)). Different authors resorted to this correlation in previous studies ,32. FinaThe proposed models were solved by the SOLVE tool and making iterations converge until the minimum root-mean-squared deviation (RMSE) value was obtained, as defined according to Equation (7):0 = f(Rs) for the different models. The Stam model seems to have best represented reality.2 value (which explains the percentage of variation in the estimated data vs. the measured data). The different models’ fit was good because the fitted lines came very close to the 1:1 line with little scattering for the dots   ). R2 wasll cases . This me2 value was chosen. This corresponded to the Stam model (Equation (8)):The model with the lowest RMSE value was selected, and the highest slope and RAfter selecting the model to which the experimental data best fitted, validation was undertaken using the data of the samples not utilized for the model’s calibration fit. In the sample group for validation, there were two samples per concrete, which were exposed to the same states as the samples used in fitting and calibrating.0 data, the Rs values obtained in the validation group of samples were substituted in Equation (7).2 indicates that the model represents 83.5% of the data variance  when electrical charges passed through it. This parameter was neither specific nor standardized, and depended on the cell constant of the electric field generated between the WE and the pseudo-reference CE. Thus, it could not be compared among different systems.Given the sensor system’s electrodes configuration, the applied field was not uniform and, thus, the expression defined in Equation (1) could not be used, which corresponded to a uniform electric field between two parallel plates.Therefore, to obtain a specific value that would allow us to compare different zones and configurations, a decision was made to empirically seek the existing correlation between ρ and Rs.0 vs. ρREAL and the graphic Δm/m0 vs. the Rs obtained with the sensor were compared (0 = f(ρ) α Δm/m0 = f(Rs)].To do so, first the graphical representation of the real Δm/mcompared . The mor0 = f(ρ), the similarity seen in 0 = f(Rs).Thus, to obtain the equation with the best fit Δm/m0 = f(ρ), the real values and those obtained with the models. The Stam model appears to better represent reality, as does model Δm/m0 = f(Rs).2 values , the modified Stam model was selected (Equation (9)).Bearing in mind the parameters selected to analyze the statistical goodness of fit  and (9) were derived. This equality was simplified by finding ρ according to Rs and solving. This was how Equation (10) was obtained. It defines the correlation between ρ and Rs. This correlation demonstrates that the field generated in the sensor system case was not a uniform field. Therefore, the simplifications related to this condition could not be applied.For validation, no samples in which concrete’s electric resistivity was measured by the direct method, and in which electric resistivity was measured with sensors, were available because the presence of the sensor system would have disturbed the measurement taken by the direct method. This is why the average resistivity value obtained in the SSNS samples  was employed for the validation per concrete and per state . In the 2 indicates that the model represents 94% of the data variance  and Δm/m0 = f(Rs) functions have the same curve morphology, their empirical data fit the same function typology.As Δm/mThis study demonstrates how the electric field generated in the sensor system cannot be considered uniform because no uniform linear correlation exists between electric resistivity and electric resistance. This means that uniform field simplifications cannot be used to achieve electric resistivity by means of the sensor system.0 = f(ρ) and Δm/m0 = f(Rs), the estimation model of electric resistivity by the parameter is obtained with the sensor system’s Rs. This model offers good reproducibility of reality by accounting for 94% of data variance.With the correlation between functions Δm/mWorking with two electrodes implies there are no limitations to the developed models’ reliability and reduces the energy needed for the system.This paper reviewed research on developing models with an SS sensor system for hardened concrete’s humidity variations and concrete’s electric resistivity. These parameters play a key role in RCS durability. Based on the above review, the following conclusions can be drawn:•The sensor system presented with the developed models can be coupled to a multi-sensor system, developed according to the smart sensor network concept. Depending on data requirements about the state, this sensor network is capable of collecting interesting data at the necessary points to optimize the system’s data resources, and its economic and energy uses.•It is possible to collect further information from the electric resistivity estimation to characterize the degree of corrosion with these relations:✓The correlation between the electric resistivity value and the presence of chlorides. Initial tests can be conducted to check if the model slightly varies when chloride anions are present in the concrete’s porous solution.✓The correlation between concrete’s electric resistivity and the corrosion electric current, which are correlated by Ampère’s Law.Based on the above review, the following opportunities for future research into this sensor to monitor the RCS state can be identified:


Background: Gut microbiota can be associated with COVID-19 vaccine immunogenicity. We investigated whether recent antibiotic use influences BNT162b2 vaccine immunogenicity. Methods: BNT162b2 recipients from three centers were prospectively recruited. Outcomes of interest were seroconversion of neutralising antibody (NAb) at day 21, 56 and 180 after first dose. We calculated the adjusted odds ratio (aOR) of seroconversion with antibiotic usage (defined as ever use of any antibiotics within six months before first dose of vaccine) by adjusting for covariates including age, sex, smoking, alcohol, and comorbidities. Results: Of 316 BNT162b2 recipients  recruited, 29 (9.2%) were antibiotic users. There was a trend of lower seroconversion rates in antibiotic users than non-users at day 21  and day 56 , but not at day 180 (93.3% vs. 94.1%). A multivariate analysis showed that recent antibiotic usage was associated with a lower seroconversion rate at day 21 . Other factors associated with a lower seroconversion rate after first dose of the BNT162b2 vaccine included age ≥ 60 years  and male sex . There were no significant factors associated with seroconversion after two doses of BNT16b2, including antibiotic use . Conclusions: Recent antibiotic use may be associated with a lower seroconversion rate at day 21 (but not day 56 or 180) among BNT162b2 recipients. Further long-term follow-up data with a larger sample size is needed to reach a definite conclusion on how antibiotics influence immunogenicity and the durability of the vaccine response. As of March 2022, COVID-19 has affected more than 480 million people and caused more than six million deaths worldwide. Among different measures to contain the spread of the virus, vaccination is of paramount importance in preventing infection, severe symptoms, and death , while tYounger age and female sex are associated with higher vaccine efficacy and adverse reactions for influenza . OverweiAs age, sex, overweight/obesity are associated with vaccine efficacy against SARS-CoV-2 as in influenza , other fStudies have shown that neutralizing antibody levels are surrogate markers of vaccine effectiveness , which aThis is a prospective multi-center cohort study recruiting adult subjects receiving BNT162b2 vaccine from three vaccination centers in Hong Kong . Exclusion criteria included age < 18 years, history of gastrointestinal surgery, inflammatory bowel disease, immunocompromised status including post-transplantation and immunosuppressives/chemotherapy, other medical diseases , those with prior COVID-19 infection (identified from history taking or presence of antibodies to SARS-CoV-2 nucleocapsid (N) protein. This antibody is not inducible by SARS-CoV- vaccines and therefore is an indicator of past infection. Two doses of intramuscular BNT162b2 (0.3 mL) were administered at three weeks apart as recommended. Their blood samples were collected at four time-points: (i) before vaccination (baseline); (ii) 21 days after first dose; (iii) 56 days after first dose; and (iv) 180 days after first dose. A prior study showed that BNT162b2 protection against infection peaked in the first month after the second dose . The bloSubjects recorded any adverse reactions daily for seven days after vaccination, including systemic reactions  and local reactions . The severity of reactions were graded as 1, 2, 3, and 4, according to toxicity grading scale by the U.S. Department of Health and Human Services .The study was approved by the Institutional Review Board of the University of Hong Kong (HKU) and Hong Kong West Cluster (HKWC) of Hospital Authority. All patients provided written informed consent for participation in this study.Primary outcomes of interest were seroconversion rates at three time points after the first dose of BNT162b2 . Testing for NAb against SARS-CoV-2 RBD was performed using the new version of the iFlash-2019-nCoV Nab kit   vs. 248 [86.4%], p = 0.59; systemic reaction: 16 [55.2%] vs. 179 [62.4%], p = 0.45). A lower rate of systemic adverse reaction of vomiting was observed in antibiotic non-users .There was no significant difference in frequency of adverse reactions between antibiotic users and non-users , underpower may be an issue due to the low number of patients with recent antibiotics use. Nonetheless, after multivariable analysis including potential variables that may affect vaccine response , we found that antibiotic usage was associated with a 74% lower chance of seroconversion after one dose of BNT162b2. All antibiotics users had broad-spectrum antibiotics, and therefore the effect of narrow-spectrum antibiotics could not be studied in the current study. Similarly, on day 56, a trend towards a lower seroconversion rate was observed for antibiotic users versus non-users  with an OR of 0.03 (95% CI: 0.001–1.15).Our current study is the first to report an association between the usage of antibiotics and impaired early vaccine immunogenicity after one dose of BNT162b2. The seroconversion rate of BNT162b2 was 82.8% and 91.3% for antibiotic users and non-users, respectively. Although the comparison was statistically non-significant , any stratified analysis according to duration of antibiotic use and the time interval from last antibiotic use will likely suffer from underpower. The relatively small sample size of this cohort also precludes stratified analysis according to different variables.One of the limitations of the current study is the definition of antibiotic use, including the duration of exposure and the time interval between last antibiotic use and vaccination. We used an arbitrary cutoff of ever use of any antibiotics within six months before vaccination , as antiOther limitations should be noted. First, confounding by indication of antibiotics may exist, in which those who received antibiotics may have an underlying impaired immune system. However, this bias was minimized by the exclusion of study subjects with cancer, rheumatological/autoimmune diseases, and those receiving immunosuppressives/chemotherapy. Recent antibiotic use may be associated with a lower seroconversion rate at day 21 (but not day 56 or 180) among BNT162b2 recipients. Further long-term follow-up data with a larger sample size and a stratified analysis according to duration of antibiotic exposure and time interval from last antibiotic use will be helpful to reach a definite conclusion and to further delineate how antibiotics influence immunogenicity and the durability of the vaccine response.


To study the influence of mineral admixtures on concrete’s mechanical properties after a low-temperature exposure, green concrete was prepared by mixing fly ash and slag at different replacement rates. By analysing the changes to concrete’s mechanical properties and the damage layer thickness under different ambient temperatures , the change rule of concrete at low temperatures was explored. The results revealed that the compressive strength of concrete, containing either fly ash or slag, peaked at 30 °C; moreover, the slag concrete’s flexural and splitting tensile strength peaked at −40 °C. The best mechanical properties were observed for a fly ash-to-slag ratio of 1:2  and its compressive strength at different temperatures was higher than that of concrete, containing 30% fly ash (F30) or 30% slag (S30), but the flexural and splitting tensile strength was lower than S30. Further, as the temperature decreased, the fly ash concrete’s damaged layer thickness gradually increased. When the content of fly ash and slag were both 15% (F15S15), the damaged layer thickness was minimal at different low temperatures, especially at −30 °C, where the thickness was only 8.31 mm. Concrete, as a primary building material, is widely used in construction projects, bridges, tunnels, hydraulic engineering projects, and other engineering fields. With the emergence of the construction industry in China, the proportion of constructed areas in winter is notably rising. According to incomplete statistics, the construction areas in winter currently account for 30–40% of the total constructed areas . ConsideThe performance of concrete at room temperature and at a high temperature has been fully explored in previous research ,4,5,6,7.Dahmani  discoverThe pore volume of concrete expands, owing to the freezing of water ,17. Jian2 during the production process; to reduce cement consumption, adding mineral admixtures will improve the density of the test pieces and improve the utilisation of industrial solid wastes. Therefore, to investigate the performance changes of concrete at the ambient temperatures of 20 °C, −10 °C, −20 °C, −30 °C, and −40 °C, fly ash and slag are used as the mineral admixtures to partially replace the cement through single and multiple admixtures to make green concrete. By analysing the mechanical properties of the green concrete specimens at a low temperature, including the compressive strength, flexural strength, and the splitting tensile strength, and by measuring the thickness of the damaged layer of the specimens at ultrasonic testing points, the mechanical properties and deterioration damage of the concrete with different mineral admixtures at a low temperature are characterised.Cement generates a large amount of COThe cement used in this study is ordinary Portland cement , and the fineness (retained on a 45 μm sieve) is 3.6%. The coarse aggregate was continuously graded with natural crushed stone (520 mm). The fine aggregate was river sand, with a fineness modulus of 2.71, and the water used was laboratory tap water. The mineral admixture was fly ash and S95 slag, with a fineness (retained on 45 μm sieve) of 23.7% and 4.7%, respectively. The water reducer used is polycarboxylic acid super-plasticiser. The chemical compositions of the cement, fly ash, and slag were tested via X-ray fluorescence spectroscopy (XRF). The results are summarised in To explore the influence of the mineral admixtures on the performance of the concrete after exposure to la ow temperature, ordinary concrete was considered the benchmark group, which was recorded as OPC, and fly ash and slag were separately employed to partially replace the cement through single and compound admixtures, of which the single substitution rate of the fly ash and slag was 15, 30, 45, and 60%. The concrete with the single fly ash was recorded as F15, F30, F45, and F60, and the concrete with the single slag was recorded as S15, S30, S45, and S60, respectively. For the concrete mixed with fly ash and slag, the replacement rate of the compound mixing is 30%, in which the proportions of fly ash and slag are 2:1, 1:1, and 1:2, respectively, which were recorded as F20S10, F15S15, and F10S20, respectively. The detailed mix proportions are listed in First, the cementing material and river sand were poured into the concrete mixing pot and mixed for 30 s. Subsequently, water and the water reducing agent were added and mixed for 60 s to yield a uniformly distributed mixture. Finally, the coarse bone aggregate was added and mixed for 90 s, and thereafter, the mixture was discharged and installed into the mould. Following the vibrating and moulding, the mixture was cured at room temperature for 24 h. Subsequently, the mould was removed, and the set mixture was placed into a standard curing room. Following the curing of the mixture, for 28 d under standard conditions , it was retrieved from the room. The surface was allowed to completely dry, and a low-temperature test was conducted thereafter.The DW-50 low-temperature box, produced by the China Cangzhou Xinxing Experimental Instrument Co. Ltd., is used to freeze the concrete at a low temperature, as shown in The mechanical property test refers to BS EN 12390-4 (2000) , in whicfh is the thickness of the damaged layer (mm); The ultrasonic testing of the concrete surface’s damaged layer is an indirect testing method. As per the basic principle of this method, sound waves travel at different speeds in the media with different densities, and the refraction and reflection occur when they pass through the interface of the media with different densities. When the concrete structure experiences early frost damage, the low temperature will gradually penetrate the concrete from the outside to the inside, which invariably causes varying degrees of damage on the concrete surface, thereby loosening the surface concrete and yielding damage layers of different thicknesses. For a large thickness of the concrete’s damaged layer, this parameter will affect the durability and bearing capacity of the concrete, so the ultrasonic test is an important parametric index in the concrete non-destructive testing technology . In accoThe results plotted in As observed in For the single-slag specimen, the splitting tensile strength reaches its peak value at 30%. At this time, the exposure to −40 °C exerts the most significant effect on the splitting tensile strength of the slag concrete, followed by the effects at −20, −10, and −30 °C. Excluding the development that the splitting tensile strength of the S60 group after exposure to −30 °C is slightly lower than that of the OPC, the splitting tensile strength of the slag concrete after exposure to different low temperatures, is higher than that of the OPC, indicating that the addition of slag has a positive effect on the splitting tensile strength of concrete.The splitting tensile strength of the concrete mixed with fly ash and slag is illustrated in 2 is consumed, greatly reducing the compactness of the test pieces [The thickness of the damaged layer of each group of test pieces at different temperatures is depicted in t pieces ,37,38. TAs illustrated in The change rule of the thickness of the damaged concrete layer caused by the fly ash and slag is illustrated in (1)As the fly ash content was increased (1560%), the compressive strength of the concrete gradually decreased, and the strength of the concrete (F15) was maximal when the content was 15%. The flexural strength and the splitting tensile strength of the concrete increased first and then decreased as the fly ash content was increased, and the strength was maximal when the fly ash content was 30% (F30). With the decrease in temperature, the strength of the fly ash concrete fluctuated greatly. In terms of the compressive strength, the strength of fly-ash concrete at different low temperatures was higher than that of the concrete at room temperature, especially at −30 °C, followed by those at −40, −10, and −20 °C. In terms of the flexural strength, the strength of the fly ash concrete at −20 °C was significantly higher than that at other temperatures. For the −40 ℃ variant, the flexural strength and splitting tensile strength attained the maximum peak.(2)The positive influence of the slag on the mechanical properties of the concrete was considerable. When the slag amount was 30% (S30) within the test range, the splitting tensile strength was maximal, and when the slag amount was 45% (S45), the compressive strength and flexural strength of the concrete were the highest. Slag concrete, exposed to a low temperature of −30 °C, yielded the maximum compressive strength; for the −40 °C variant, the flexural strength and splitting tensile strength attained the maximum peak.(3)Under the condition of the double mixing of the fly ash and slag, the concrete strength at room temperature was greatly improved, compared with the OPC due to the synergistic effect of the fly ash and slag. When fly ash: slag = 2:1 (F10S20), the mechanical properties are superior to the rest, and the compressive strength is maximal, which is 127.2, 129.6, and 103.6% of the OPC, F30 and S30, respectively. The compressive strength at different temperatures was higher than that of the F30 and S30 groups, and the optimum temperature was −40 °C. However, its flexural strength at different temperatures was lower than that of the S30 group, but the flexural strength at other temperatures was higher than that of F30 group, except at −20 °C. Notably, its splitting strength is less than that of the S30 group at different temperatures.(4)During low temperatures, the thermal stress will be generated in the concrete. During thermal stress, the damage layer thickness of the concrete will increase as the temperature decreases. For the single fly ash blending, the maximum value of the damage layer thickness is 43.3 mm at −40 °C. As the slag content increases, the damage layer thickness first decreases and then increases, and as the temperature decreases, the damage layer thickness of the concrete increases. For the concrete specimen containing both the fly ash and slag, when the fly ash-to-slag ratio is 1:1 (F15S15), the damage layer thickness is the smallest, and is superior to the OPC, F30, and S30. At −30 °C, the damage layer thickness of the concrete is 8.31 mm, which is 32.8%, 33.3%, and 25.3% of the OPC, F30 and S30, respectively.This paper mainly focused on the influence of different temperatures on the macro-performance of concrete under low-temperature conditions. However, factors, such as the pore distribution, the structure of concrete at low temperatures, and the relationship between the pore size and the freezing point will affect the mechanical properties and damage of concrete at low temperatures. Therefore, in future research, it is necessary to further explore the micro-morphology and mechanism of the concrete at low temperatures.


Periprosthetic joint infection (PJI) is characterized by biofilm infection, which is difficult to alleviate while preserving implant integrity. Furthermore, long-term antibiotic therapy may increase the prevalence of drug-resistant bacterial strains, necessitating a non-antibacterial approach. Adipose-derived stem cells (ADSCs) exert antibacterial effects; however, their efficacy in PJI remains unclear. This study investigates the efficacy of combined intravenous ADSCs and antibiotic therapy in comparison to antibiotic monotherapy in a methicillin-sensitive Staphylococcus aureus (MSSA)-infected PJI rat model. The rats were randomly assigned and equally divided into 3 groups: no-treatment group, antibiotic group, ADSCs with antibiotic group. The ADSCs with antibiotic group exhibited the fastest recovery from weight loss, with lower bacterial counts  and less bone density loss around the implants . The modified Rissing score was used to evaluate localized infection on postoperative day 14 and was the lowest in the ADSCs with antibiotic group; however, no significant difference was observed between the antibiotic group and ADSCs with antibiotic group . Histological analysis revealed a clear, thin, and continuous bony envelope, a homogeneous bone marrow, and a defined, normal interface in the ADSCs with antibiotic group. Moreover, the expression of cathelicidin expression was significantly higher , whereas that of tumor necrosis factor (TNF)-α and interleukin(IL)-6 was lower in the ADSCs with antibiotic group than in the no-treatment group . Thus, the combined intravenous ADSCs and antibiotic therapy induced a stronger antibacterial effect than antibiotic monotherapy in a MSSA-infected PJI rat model. This strong antibacterial effect may be related to the increased cathelicidin expression and decreased inflammatory cytokine expression at the site of infection. Moreover, prolonging antibiotic therapy to quell infection while preserving the integrity of the implant is extremely difficult, and revision surgeries are often required2. Furthermore, with an increase in the number of arthroplasty procedures being performed, an increase in associated infections will also inevitably occur, posing a significant threat to society3. In clinical practice, removing implants might be impossible as this process can negatively impact quality of life, and the development of bacterial resistance during long-term antibiotic therapy is also a concern4. Therefore, a non- antibiotics strategy is needed to prevent biofilm infection while preserving implant integrity.Periprosthetic joint infection (PJI), an implant-related infection, is one of the most serious complications that can occur following total joint arthroplasty. PJI is characterized by biofilm infections, wherein biofilm-forming bacteria escape the host’s immune response and become resistant to antibiotics6 or induce the release of antimicrobial peptides from mesenchymal stem cells (MSCs)8. MSCs have been studied in various fields and have exhibited therapeutic efficacy against coronavirus disease 2019 infection, sepsis, and pneumonia9. Additionally, MSCs, including adipose-derived stem cells (ADSCs), may be effective for treating biofilm infections and infection-related sepsis when administered intravenously in combination with antibiotics11. However, this therapy has not yet been evaluated in detail for PJI. Furthermore, the antibacterial efficacy of the combined intravenous ADSCs and antibiotic therapy, as well as their effect on host physiology, including bone and soft tissue structure, remain unknown. Therefore, in the current study, we examined whether combined ADSCs and antibiotic therapy is superior to antibiotic monotherapy in a methicillin-sensitive Staphylococcus aureus (MSSA)-infected PJI rat model.Previous studies have focused on strategies that activate and modulate the immune systemFidelity of the ADSCs was confirmed using flow cytometry. Flow cytometric analysis showed that the subgroup of CD90+ ADSCs had the highest population. Moreover, the cells were spindle-shaped, a typical morphological feature of ADSCs Fig. . All ratThe modified Rissing score was used to evaluate localized infection on POD14. This score was the lowest in the ADSCs with antibiotic group; however, no significant difference was observed between the antibiotic group and ADSCs with antibiotic group  was 506.6 ± 12.7 mg/cm3  in the no-treatment group, 516.2 ± 23.1 mg/cm3 (95% CI 491.9–540.5) in the antibiotic group, and 589.2 ± 46.8 mg/cm3 (95% CI 540.0–638.3) in the ADSCs with antibiotic group. The ADSCs with antibiotic group exhibited the highest BMD  rendering and qualitative evaluation of the μCT images revealed a clear difference in bone quality among the three groups. The 3D μCT axial slice images showed osteolysis and an increased bone size associated with periosteal reaction, which was clearly observed in the distal region of the infected femurs in the no-treatment group and antibiotic group Fig. b.Ex vivo colony-forming units (CFUs) from sonicated Kirschner wires (K-wires) were enumerated on POD14. The implants from the no-treatment group had a mean CFU count of 150.7 ± 72.1 (95% CI 75.0–226.3) × 104 CFU/mL; those from the antibiotic group had a mean of 55.0 ± 72.1 (95% CI 30.3–79.7) × 104 CFU/mL; and from the ADSCs with antibiotic group had a mean of 17.0 ± 15.3 (95% CI 0.9–33.1) × 104 CFU/mL. Although bacteria were detected in the ADSCs with antibiotic group, the bacterial burden was significantly lower than that in the no-treatment or antibiotic groups  revealed a significant increase in the expression of the rCRAMP gene in the ADSCs with antibiotic group  cells were distributed through the trabecular bone around the K-wire Fig. . The obs15. We selected ADSCs as they are abundant in the subcutaneous adipose tissue and can be readily harvested in clinical settings using syringes or minimally invasive liposuction.The combined intravenous ADSCs and antibiotic therapy exhibited good antibacterial effects in the MSSA-infected PJI rat model. Moreover, the combined therapy was superior to antibiotic monotherapy in reducing weight loss, bacterial counts in implant biofilms, and abscess formation. The combined therapy also effectively minimized peri-implant osteolysis and reduced BMD, significantly increasing cathelicidin expression at the site of infection. These results emphasize that ADSCs do not interfere with, but rather enhance, the effects of antibiotic agents and that the underlying mechanism may include increased cathelicidin expression at the site of infection. MSCs, including ADSCs, exert antimicrobial activity through multiple complementary mechanisms of action: indirectly through immunomodulators and directly through the release of antimicrobial peptides17. The systemic response to infection can be determined by measuring weight change because slow, subtle weight loss represents the earliest reliable sign of a worsening systemic condition18. A recent study has shown that intravenous ADSCs act synergistically with antibiotics to reduce organ damage within the urinary system and mortality in a rat model of sepsis in which enteric bacteria were intraperitoneally injected, suggesting that intravenous ADSCs therapy is effective against systemic infections11. Similarly, in present study, the combined intravenous ADSCs and antibiotic therapy prevented the exacerbation of systemic conditions caused by PJI. Although no significant difference was observed between the ADSCs with antibiotic group and antibiotic group, the combined intravenous ADSCs and antibiotic therapy suppressed the local infection score and expression of inflammatory cytokines (TNF-α and IL-6) at the site of infection, suggesting an association between ADSCs and an improved antibacterial effect.PJI has systemic consequences with negative adverse effects on patient mortality and quality of life20. In clinical practice, the reduction of bone stock around PJI is a major problem21. Our results suggest that the combined intravenous ADSCs and antibiotic therapy inhibits bone stock reduction around infected implants and facilitates two-stage reconstruction, which is necessary in clinical practice. A previous study has shown that ADSCs regulate B cells, promote osteoblast formation, and inhibit osteoclasts, thereby restoring the regenerative capacity of bone defects after infection22. However, even in the intravenous ADSCs with antibiotic group, the femoral BMD was lower than that in normal rats.PJI causes periarticular osteolysis and periosteal osteogenesis, which may adversely affect clinical outcomes. Established infections worsen bone quality over time, resulting in a decreased BMD and cortical thickeningin vivo subcutaneous mesh mouse model, showing a reduction in the number of bacteria in the peri-implant biofilm due to the interaction with antibiotics10. In our MSSA-infected PJI rat model, the combined intravenous ADSCs and antibiotic therapy significantly reduced the number of bacteria in the biofilm and enhanced the antibacterial effect compared with the results of antibiotic monotherapy. Furthermore, pathological evaluation revealed that the extent of the abscess was reduced.Johnson et al. reported a significant antimicrobial effect of intravenous ADSCs in an 15. Bacteria are less likely to develop resistance to antimicrobial peptides than to antibiotics; therefore, antimicrobial peptides have attracted considerable attention in recent years23. Cathelicidins comprise a major antimicrobial peptide family in mammals that exerts its killing effect by disrupting bacterial membrane integrity and inhibiting biofilms26. Thus, cathelicidin at the site of infection may have enhanced the antibacterial effects. Additionally, systemically administered MSCs tend to remain in the lungs; however, they can be distributed to multiple organs and tissues away from the administration site27. Particularly, compared to locally administered ADSCs, intravenously administered ADSCs effectively suppress deep-seated bacterial infections by migrating from the lungs to the site of infection over several days, resulting in lower bacterial counts in the wounds10. ADSCs then accumulate around implants, where direct administration is difficult, leading to an increase in cathelicidin expression at the site of infection. Therefore, our study findings suggest that the increase in cathelicidin expression at the site of infection is related to the accumulation of ADSCs. Intravenous ADSCs may, therefore, prove effective as a treatment against PJI.Notably, we found that the cathelicidin expression at the site of infection was significantly increased in the ADSCs with antibiotic group. ADSCs and other MSCs secrete various antimicrobial peptides, including cathelicidin29. MSCs are devoid of hematopoietic and endothelial markers, such as CD11b, CD14, CD31, CD34 and CD4528. Our ADSCs were consistent with the characteristics of the MSCs. In addition, in our study, ADSCs localization to the site of infection was confirmed by in vivo cell tracking; furthermore, the combined therapy increased the expression of cathelicidin and decreased that of inflammatory cytokines at the site of infection. However, further investigation is needed to elucidate the relationship and mechanism between the antibacterial activity of the combined therapy and the expression of inflammatory cytokines and cathelicidin. Furthermore, the data were collected only after 14 days, whereas results obtained at 3 and 7 days would provide additional information regarding the evolution of the antibacterial effect of ADSCs combined with antibiotic treatment. Therefore, additional studies are required encompassing larger total numbers of animals to permit evaluation at multiple time points. Finally, only the effects of ciprofloxacin were examined; different results may occur using other antibiotics. However, quinolones are excellent antimicrobial agents in terms of their bioavailability, antibacterial activity, and tolerability30. Moreover, ciprofloxacin has been extensively tested for the long-term treatment of implant-related staphylococcal infections and can be applied in clinical practice31.Several limitations were noted in the current study. The study was conducted exclusively in rats; therefore, the results may differ in large mammals, including humans. The PJI rat model was created by inserting K-wires retrogradely, which may not reproduce the actual loading environment of the artificially infected joint. Moreover, our results may differ from those of other studies owing to differences in the expression markers of ADSCs. At present, no specific marker or combination of markers has been identified that specifically defines MSCs. Phenotypically, ex vivo-expanded MSCs express several nonspecific markers, including CD90, CD105, CD73, CD166, CD44, and CD29In conclusion, the combined intravenous ADSCs and antibiotic therapy induces a stronger antibacterial effect than antibiotic monotherapy in a MSSA-infected PJI rat model, with earlier recovery from weight loss, reduced peri-implant bacterial counts, and reduced peri-implant BMD. This strong antibacterial effect may be related to the increased expression of cathelicidin and decreased expression of inflammatory cytokines at the site of infection. Our results suggest that the combined intravenous ADSCs and antibiotic therapy may be used to treat patients with PJI who show an inadequate response to conventional antibiotic monotherapy.33. MSSA was streaked onto plates containing tryptic soy broth and Bacto agar  and grown overnight in 5 mL of tryptic soy broth at 37 °C in a shaking incubator. MSSA cells in the incubation medium were grown to the early exponential growth phase , corresponding to 5.0 × 107 CFU/mL.MSSA strain ATCC29213  was used as it tends to form biofilms34. Further details can be found in the Supplementary file. Cellular characteristics (i.e. expression of stem cell surface markers) were determined using flow cytometric analysis after labeling ADSCs with appropriate antibodies of cultivation.Adipose tissue (~1.5 g) was obtained from Wistar rats . ADSCs were prepared by modifying previously reported methodsad libitum access to a certified diet  and water . The drinking, feeding behavior, and body weight of the rats were monitored regularly. The animals were acclimatized for 7 days before undergoing the implant operation.Wistar rats  were housed under specific pathogen-free conditions with a 12-h light/dark cycle and 7 CFU. The K-wire was surgically placed into the distal femur as previously described36. Briefly, the skin overlying the leg was shaved and cleaned with iodine solution. A medial parapatellar approach was used, and the patella was dislocated laterally to access the knee joint. The femoral medullary canal was reamed with an 18-gauge needle and the K-wire was placed in a retrograde fashion with 1 mm of the wire protruding into the joint space. The quadriceps-patellar complex was reduced to the anatomic position, and the wound was closed with nylon 4-0 sutures. Rats were randomly assigned and equally divided into three groups: no-treatment, antibiotic (ciprofloxacin [3.0 mg/kg per day intravenously]), and ADSCs ) with antibiotic , medetomidine , and butorphanol tartrate . To establish infection, A medical-grade K-wire  was incubated in an overnight culture with MSSA strain ATCC29213 and then air-dried for 20min prior to insertion. This MSSA strain exposure coats the screw with 5×10ex vivo analyses.After evaluating the general overall condition and soft tissue swelling, the rats were euthanized on POD 14 using thiopental sodium (100 mg/kg body weight). Tissues from the knee joint space, femur, and implant were harvested in a sterile manner for Weight change (n = 6 rats per group) was calculated as a percentage change based on the preoperative weight to quantitatively measure the systemic response to infection. Preoperative baseline measurements were performed on the day before surgery. The weight of the rats was also evaluated on PODs 1, 3, 7, and 14.39. Further details can be found in Supplementary file.Soft tissue and bone damage (n = 6 rats per group) on POD14 was scored by three examiners  blinded to the rats according to a modified Rissing scoringμCT imaging (n = 6 rats per group) was performed on POD14 to determine the degree of infection within the femoral region of interest. Considering that image artifacts from the K-wires may cause artifacts in the reconstructed μCT images, isolated femurs from rats with the wire removed were subjected to μCT scanning , operating at 50 kV and 0.5 mA (radiation exposure remained below 40 mSv). BMD was calculated automatically using LaTheta software (version 3.51). Reconstructed μCT images were initially visualized in three dimensions (3D) to evaluate changes in bone morphology resulting from implant infection. A threshold-limited 3D rendering was created to visualize bone damage.41. To quantify living bacteria adherent to the implant within the biofilm, the removed implants were placed individually into 1.5-mL microtubes containing PBS (1 mL at 4 °C), vortexed for 15 s and sonicated for 5 min at 40 Hz in a water bath , followed by an additional 1 min of vortexing. The spread plate method was used to quantitatively evaluate the biofilm; the solution containing each bacterium from the biofilm was serially diluted 10-fold with PBS, followed by culturing on an agar plate at 37 °C for 24 h. MSSA was cultured on tryptic soy broth agar plates. The bacterial CFUs obtained from the implant were determined by counting the CFUs after culturing on plates overnight.Implants were harvested (n = 6 rats per group) from each group. Based on a previous report, the bacterial burden on the implants was determined using a CFU assayAt the established endpoint (POD14), the femurs isolated from the rats were fixed in 10% neutralized formalin solution and dehydrated using an ethanol gradient . The fixed specimens were decalcified in 10% formic sodium citrate solution, embedded in paraffin, and sectioned in the coronal plane. The sections were stained with hematoxylin and eosin, and the slides were observed using an optical microscope .−ΔΔCt method42. Further details can be found in the Supplementary file (Supplementary Table At the established endpoint (POD14), total RNA was extracted from the knee tissue of the rats (n = 6 rats per group). The mRNA expression of rCRAMP, TNF-α, IL-6 and IL-1b was evaluated by quantitative PCR. All values were normalized to the level of the GAPDH gene, and relative gene expression levels were calculated using the 25 cells/mL; DiI (5 μL/mL) was dissolved in the cell culture media and incubated for 15 min at 37 °C in a 5% CO2 incubator for ADSCs labeling. The filtrate was centrifuged at 180×g for 5 min at 25 °C and the supernatant was removed to separate the DiI from the filtrate. The ADSCs were centrifuged twice with Dulbecco’s modified Eagle medium under the same conditions and the supernatant was removed. We used separate rats for this experiment (n = 3 rats per antibiotic group and DiI-labeled ADSCs with antibiotic group). On day 14 post-injection, a frozen section was prepared using Kawamoto’s film method in the sagittal plane43. For identification of tissues following DiI labeling, the gray-scale scale (16 bit) of the DiI-labeled section was used.Tissue sections were evaluated to determine the location of ADSCs following injection. To confirm the location of the injected ADSCs, they were labeled with the fluorescent dye DiI  before injection. DiI binds to cellular thiols and has long-term stability, enabling the tracing of DiI-labeled transplanted cells in the host tissue. The concentration of ADSCs was adjusted to 5.0 × 10All continuous variables were assessed for normality using the Shapiro–Wilk test. Normally distributed data were expressed as the mean ± standard error. Data were analyzed using SPSS software . Multiple groups were compared using the Welch ANOVA followed by Tukey HSD or Games-Howell post-hoc test. For all analyses, results were considered statistically significant at p < 0.05.The investigational protocol was approved by the Kanazawa University Advanced Science Research Centre , and all animals were treated in accordance with Kanazawa University Animal Experimentation Regulations. The study was carried out in compliance with the ARRIVE guidelines.Supplementary Information.


IgG4-related respiratory disease (IgG4-RRD) is a chronic autoimmune disease that affects the respiratory system and organs outside the respiratory system. This study explored the diagnosis and treatment of a case of IgG4-RRD with unilateral pleural effusion diagnosed using medical thoracoscopy, and provides an associated literature review. This report summarizes the clinical characteristics of IgG4-RRD involving the pleura to improve the diagnosis of this disease.A 39-year-old man presented with a 2-week history of cough and chest tightness. Both physical examination and imaging supported the presence of left pleural effusion.Medical electronic thoracoscopy was performed to obtain a pleural biopsy, which showed lymphoplasmacytic infiltration, 40 IgG4+ plasma cells per High Power Field (HPF) on microscopy, IgG4/IgG ratio >50%, phlebitis obliterans, and storiform fibrosis. The final diagnosis was IgG4-RRD.The patient was treated with methylprednisolone, after which his symptoms improved, and he was discharged. Oral hormone therapy was continued outside the hospital. After 4 months, the patient returned to the hospital and his condition had improved significantly.Pleural involvement in IgG4-RRD is rare, and its diagnosis depends on pleural biopsy. Thoracoscopy usually reveals pleural thickening, pleural nodules, and milky white plaques. IgG4-RRD involving the pleura is rare in clinical practice. Therefore, the present study analyzed the diagnosis and treatment process of a patient diagnosed with IgG4-RRD following medical thoracoscopy with unilateral pleural effusion as the major manifestation and associated with relevant literature review, so as to improve the understanding of IgG4-RRD and explore its diagnosis.IgG4-related disease (IgG4-RD) is a chronic systemic and progressive autoimmune disease involving multiple organs or tissues. It is characterized by an increase in serum IgG4 level, infiltration of tissue IgG4-positive plasma cells, and involvement of all tissues and organs in the whole body. IgG4-RD involving the respiratory organs and their accessory structures in the thoracic cavity is collectively referred to as IgG4-related respiratory disease (IgG4-RRD).12/L, white blood cell count of 8.49 × 1012/L, hemoglobin of 132 g/L, erythrocyte sedimentation rate of 92.00 mm/h, C-reactive protein of 97.00 mg/L, procalcitonin of 0.080 ng/mL, complement C3 of 2.09 g/L , and complement C4 of 0.41 g/L ; antinuclear antibody quantitative determination, antinuclear antibody profile, anti-keratin antibody, and alpha fetoprotein results were all negative. Mycobacterium tuberculosis-specific T cells were not seen. The patient was provided underwent medical electronic thoracoscopy, which revealed a large number of light red effusions in the chest; multiple curtain-like adhesions with honeycomb-like appearance in some parts; congestion and edema of the parietal layer, diaphragm, and pleura; and uneven surface thickening. Multiple biopsies were taken from the parietal pleural lesions of the patient , CD20 (+), IgG (+), IgG4 (+), CK (−), κ (+), λ (+), CD21 (+), and Ki-67 ; and IgG4/IgG ratio >50% following pathological immunofluorescence investigation of the pleural biopsy sample, with 40 IgG4+ plasma cells per high power field (HPF) on microscopy  chest imaging changes including any of hilar/mediastinal lymphadenopathy, bronchial wall/bronchovascular bundle thickening, interlobular septal thickening, nodular shadow, infiltration shadow, pleural thickening, and/or pleural effusion; (2) increase in the concentration of serum IgG4 to >135 mg/dL;(3) tissues and organs in the thoracic cavity with >2 pathological features  of (i) a large number of lymphoplasmacytic infiltration around the bronchial vessels, interlobular septum, and/or pleura; (ii) IgG4+/IgG+ cell count >40% and/or >10 IgG4+ cells/HPF; (iii) phlebitis obliterans or arteritis obliterans; and (iv) storiform fibrosis or fibrosis consisting of spindle cells proliferating around infiltrating lymphocytes;(4) lesions in extrathoracic organs that meet the diagnostic criteria for IgG4-RD, such as sclerosing dacryocystitis/sialadenitis, autoimmune pancreatitis, IgG4-related sclerosing cholangitis, IgG4-related renal disease, and retroperitoneal fibrosis; and (5) hypocomplementaemia. A definite diagnosis was made if (1), (2), and (3)a or (1), (2), (3)b, and (4) were present; a definite diagnosis was made histologically if (1) and all 4 items of (3) were present. A highly possible diagnosis was considered if (1), (2), and (4) or (1), (2), (3)b, and (5) were present. Possible diagnosis was considered if (1), (2), and (3)b were present. CT examination of revealed pleural effusion and pleural thickening. Additionally, and increased concentration of serum IgG4 of 1.36 g/L; Pleural biopsy found: lymphoplasmacytic infiltration, 40 IgG4+ plasma cells per HPF on microscopy, an IgG4/IgG ratio >50%, phlebitis obliterans, and storiform fibrosis, which met the diagnostic criteria of (1), (2), and (3)a. The patient was confirmed to have IgG4-RRD after joint consultation and discussion between the Department of Respiratory Medicine, Department of Rheumatology and Immunology, and Department of Pathology. The patient was given methylprednisolone  as treatment, and discharged from the hospital with improved symptoms. After discharge, the patient continued oral hormone treatment and maintained a stable condition during follow-up. After 4 months, the patient underwent hospital follow-up and chest computerized tomography (CT) examination; disappearance of the pleural effusion, significant reduction of the enlarged mediastinal lymph nodes .–12 were reported to have been diagnosed using medical thoracoscopy following literature review in China National Knowledge Infrastructure, the PubMed database, and Google Scholar. The patient we reported is the 11th case. The clinical data of these patients  and nine cases of chronic onset. Symptoms included dyspnea after exercise (9/11), cough (5/11), fever (1/11), chest pain (1/11), and abdominal pain (1/11). Signs included pleural effusion (11/11), palpable superficial lymph nodes (0/11) and hepatosplenomegaly (0/11). Blood tests revealed an increase in CRP levels  (5/7) and increased serum IgG4 levels (10/11). Imaging examination revealed pleural effusion(11/11), enlargement of the hilar or mediastinal lymph nodes (4/8), and involvement of additional extrapulmonary organs (6/11). Pleural effusion examination found that the effusion was exudate (11/11), and predominated by monocytes/lymphocytes (9/9), increased in adenylate deaminase (ADA)  (3/6), and increase in lactate dehydrogenase  (2/4). Diseased pleura manifestations under medical thoracoscopy: pleural thickening (7/11), milky white plaque (4/11), nodular change (3/11), adhesions (4/11), vascular proliferation (4/11), vascular congestion and edema, and inflammatory changes (6/11). Treatment and prognosis included hormone therapy (11/11) and a length of 2 weeks to 2 years from onset to final diagnosis, with an average of approximately 6 months.IgG4-RRD is a systemic autoimmune disease closely related to IgG4. The disease mainly involves the respiratory organs in the chest and their accessory tissues, and can be accompanied by the involvement of different extrathoracic tissues and organs, showing diverse and non-specific symptoms. Thus far, there is a rare report of patients with IgG4-RRD involving the pleura who presented with pleural effusion. Here, we reported a case of IgG4-RRD, and reviewed the literature on clinical cases of IgG4-RD involving the pleura presenting with pleural effusion to improve the understanding of the diagnosis of IgG4-RRD. Moreover, there may be no abnormality in the serum IgG4 level in patients with IgG4-RRD. In thoracoscopy, patients may have diffuse or local pleural thickening, pleural nodules; milky white pleural plaques, pleural vascular hyperplasia and congestion, and even pleural adhesion. Clinically, in the cases of unexplained pleural effusion, electronic thoracoscopy can be regarded as an important examination. The possibility of IgG4-RRD should be suspected when patients are identified to have pleural thickening, nodular changes, milky white plaques, vascular hyperemia, hyperplasia and inflammatory changes on microscopy.Our study summarized the clinical features of 11 cases of IgG4-RD involving the pleura. This disease commonly has a chronic onset commonly and shows a high incidence in middle-ages and elderly men, with an average age at onset of 61.5 years. It is characterized by diverse clinical manifestations, including intrathoracic manifestations such as dyspnea and cough more frequently, as well as the manifestations of pleurisy such as low fever and pain. Additionally, it may also progress to other tissues and organs  outside of the respiratory system. Patients with IgG4-RD may present with pleural effusion and pleural thickening through imaging, as well as another image feature of mediastinal lymph node enlargement. Furthermore, the biochemical examination of the pleural effusion fluid typically identifies it as exudation, mainly lymphocytic exudate, which can be accompanied by an increase in ADA and lactate dehydrogenase, but relatively low level of the tumor marker CEA, and normal or increased of serum CRP. The increased of IgG4 serum level is an important but nonspecific index for the diagnosis of IgG4-RRD, which can be found in other diseases, such as tumors and chronic infection. The corresponding results revealed that there was only one case of phlebitis obliterans; the other 10 cases showed fibrosis on pathological examination, with two cases of storiform fibrosis. Both phlebitis obliterans and storiform fibrosis are rare in patients with IgG4-RRD. Meanwhile, the principle of individualization should be followed in the medical treatment of IgG4-RRD. Glucocorticoid is the first-line drug for treating patients with IgG4-RRD. Immunosuppressive therapy can be considered as an auxiliary strategy when there is poor disease control. The prognosis is good in most patients with IgG4-RRD.The diagnosis of IgG4-RRD depends primarily on the pathological results of the pleural biopsy. Massive lymphoplasmacytic infiltration. phlebitis obliterans or arteritis obliterans; and storiform fibrosis are the major pathological changes seen in IgG4-RRD. Phlebitis obliterans was only found in the present case among the 11 cases we summarized. According to the literature review, Shota Okamoto reported 17 cases of IgG4-related pleural diseases diagnosed using pleural histopathology.Furthermore, there is a need to differentiate IgG4-RRD from the following diseases: Under medical thoracoscopy, there may be pleural necrosis, diffuse miliary tubercles, single or multiple pleural nodules, congestion, edema, thickening and adhesion. Significantly, patients with IgG4-RRD involving the pleura have different thoracoscopic manifestations, without the appearance of symptoms of tuberculosis poisoning.(1) Tuberculous pleurisy: Tuberculous pleurisy is the most common cause of pleural effusion in China. Common symptoms include chest pain, hot flashes, night sweats and other symptoms of tuberculosis poisoning. In addition, patients may be found to have local thickening of the pleural cavity, encapsulated pleural effusion, extensive pleural adhesion, and calcification on CT. In addition, there may be specific changes in the exfoliative cytology of pleural effusion and on pathological examination.(2) Metastatic tumor of pleura: The possibility of the metastatic malignant tumor should be considered when there is a bloody pleural effusion. Malignant tumors are more common in middle-ages and elderly people over 45 years old, with clinical manifestations of chest pain, dyspnea, and weight loss. CT findings generally include thickening of the parietal pleura (>1 cm), nodular and peripheral pleural thickening, thickening of the mediastinal pleura, and destruction of multiple ribs. and clinical manifestations of progressive dyspnea, chest pain and cachexia. The imaging findings generally are annular pleural thickening and involvement of the mediastinal pleura and pleural nodules. The differential diagnosis can be made in combination with the detection of markers, such as the serum or pleural mesothelin, calretinin and WT-1. Pleural biopsy and pathological examination may provide the major defining characteristics for differentiation.(3) Malignant pleural mesothelioma: Most patients with malignant pleural mesothelioma have a history of occupational exposure to asbestos, mostly unilateral pleural effusion, which makes it difficult to be differentiated from IgG4-RD. Clinically, a lymph node or pleural biopsy can be performed to achieve a diagnosis according to the pathological results.(4) Lymphoma: Painless lymphadenopathy is generally the first symptom for most patients with lymphoma. Some lymphomas, especially inert lymphomas, can be detected based on elevated serum IgG4 levels,To summarize, IgG4-RRD involving the pleura is rare clinically. Affected patients mainly have clinical symptoms of cough and dyspnea, and may have pleural effusion, pleural thickening, and mediastinal lymph node enlargement on imaging. Furthermore, these patients may also be characterized by exudative pleural effusion, and normal or increased ADA levels. It is difficult to diagnose IgG4-RRD based on clinical manifestations and imaging features, and through pleural effusion examination. Its final diagnosis depends on pathological examination. Findings in our study support that the possibility of IgG4-RRD should be considered in cases of pleural thickening, pleural nodules, and milky white plaques on medical thoracoscopy.All authors read and approved the final manuscript.Collect and analyze pathological examination:Quanyi Wang.Collected and summarized the data: Yue Ren and Hongyun Pei.Data curation: Hongyun Pei, Quanyi Wang, Yue Ren.Funding acquisition: Shenghua Jiang.Writing – original draft: Qing Guo.Writing – review & editing: Qing Guo and Shenghua Jiang.