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This paper reviews self-powered medical devices integrated with advanced energy harvesting technologies. This article aims to explain the advantages of integrating self-powered medical devices with advanced energy harvesting technologies, outlining the transformation in healthcare system and patient experience. In today's world, we focus more on consuming energy harnessed from the environment and human body. This approach lowers down our emphasis on conventional power sources like batteries, power packs etc. As a result, the devices used in the medical sector have a longer lifespan, maintain continuous functioning, and improve patient comfort and mobility. Integrating advanced energy harvesting technologies (i.e., piezoelectric, thermal, solar, and electromagnetic) with medical devices plays a pivotal role in revolutionizing the healthcare sector. But there is still some research and development needed to enhance these technologies. This paper will set out by introducing some self-powered medical devices commonly used in healthcare, followed by their advantages, benefits, and challenges that the healthcare practitioners face. This review also discusses their biocompatibility factor which is crucial to use. Then there are examples of a few advanced energy harvesting methods that are being used which include: piezoelectric, solar, thermal, triboelectric and electromagnetic. As we go further, we will come across a table consisting of a comparison between these advanced energy harvesting technologies and their examples in the healthcare sector. The last section is future perspective and the conclusion highlights the transformative potential of this integration, followed by a future recommendation for advancing this field. • Self-powered medical devices and their prevalent uses in healthcare are reviewed. • Comparison of energy harvesting technologies i.e., piezoelectric, solar, thermal, and electromagnetic are presented. • Potential of integrating energy harvesting technologies with medical devices and future recommendations are discussed. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of the recent progress made in energy harvesting systems for wearable technology. An energy-harvesting system would be a useful strategy to address the issue of powering wearable electronic devices. This review presents different wearable energy harvesting methods based on the human body's heat and mechanical energy. To achieve continuous operation and high performance, reduce the requirement for external sources of energy, and enhance the lifespan of wearable devices, the invention of a sustainable and compatible power supply is required. In the human body, heat and mechanical motions are the two reliable and readily available energy sources. This study highlights the most recent research and advancements in energy harvesting from the human's mechanical motion and heat source. This article provides a detailed overview of the different wearable energy harvesters, their fabrication, working, and output results, which include piezoelectric, electrostatic, triboelectric, electromagnetic, thermoelectric, solar and hybrid wearable energy harvesters. The second part defines wearable energy harvesting using smart systems and artificial intelligence technology. Then the comparison of these energy harvesters is analyzed. Hybrid wearable energy harvesters provide the maximum power densities because they use two combined energy conversions. The advantages, limitations, and future perspectives of wearable energy harvesting technology are also discussed. Lastly, the wearable energy harvesters' market, and general developing and manufacturing cost of each wearable device is also presented functioning as a point of reference to comprehend the cost factors that are taken into account during the development and manufacturing processes. • The Wearable Energy Harvesters types, materials, and applications are reviewed. • The Comparison of Wearable Energy Harvesters are presented. • The Future Perspective of Energy Harvesting Systems for Wearable Technology are discussed. [ABSTRACT FROM AUTHOR]

Abstract A highly active cathode catalyst for oxygen reduction reaction (ORR) is the key to the efficient production of renewable energy from waste resources using microbial fuel cells (MFCs). Among non-noble metal catalysts, transition metals have shown promising activity towards ORR. In this study, synergy between cobalt (Co) and iron phthalocyanine (FePc) supported in carbide-derived carbon (CDC) is demonstrated in Co-FePc/CDC as the catalyst for ORR. Notably, CDC is synthesized from waste heating rods of a high temperature furnace. Several physicochemical characterization techniques, such as x-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy and Raman spectroscopy are used to confirm the successful doping of Co-FePc in the graphitic CDC. The polarization study using rotating disc electrode reveals Co-FePc/CDC to be a more efficient catalyst towards ORR than Co/CDC, the former material promoting a 4 electrons-pathway with the negligible formation of the intermediate H 2 O 2. The cyclic voltammetry (CV) analysis showed highly consistent multiple redox peaks with a relatively smaller overpotential over multiple CV cycles. As a result of the enhanced ORR kinetics, the Co-FePc/CDC-based MFC recovered a peak power density of 1.57 W/m2 with the coulombic efficiency of 43.6% from the acetate-based synthetic wastewater. Furthermore, the MFC demonstrated an excellent chemical oxygen demand removal efficiency of 86%. Graphical abstract Image 1 Highlights • CDC prepared from waste SiC heating rods was used as a support for Co-FePc. • Physical characterizations revealed a synergy between CDC and Co-FePc. • Co-FePc/CDC showed an excellent ORR characteristics. • Co-FePc/CDC cathode catalyst-based MFC recovered a power density of 1.5 W/m2. • MFC simultaneously demonstrated an excellent COD removal efficiency of 86%. [ABSTRACT FROM AUTHOR]

Monitoring the mass of liquid absorption in laminated composite structures, that have a direct contact surface with working liquid-like pipes, is very important in order to prevent the sudden collapse of the structures because of the degradations in strength and mechanical properties over time. An electrical capacitance sensor technique has been applied for monitoring the mass of liquid absorption, over the time, in laminated composite pipelines by measuring the change of dielectric characteristics of composite pipelines subjected to an internal hydrostatic pressure load of water and thermal effect. Results show the technique is very effective. However, a major difficulty in utilizing this technique is that it is highly time consuming to be used for monitoring, in addition to the detection efforts that it requires to calculate the mass of liquid absorption that exerts high cost and loss of additional time in monitoring. In this paper, a deep neural network model is used to estimate the mass of liquid absorption in glass fiber reinforced epoxy laminated composite pipelines by extracting the features from datasets from experimental and numerical measurements of the electrical capacitance sensor. The experimental and numerical data used in this paper to train and test the new deep neural network model are collected from the literature and the finite element model of the electrical capacitance sensor system respectively. The results show an excellent agreement between the finite element model data, available experimental data, and those predicted by a deep neural network with an average error of 0.067%, and show that the proposed method achieves satisfactory performance with 86.34% accuracy, 82.83% regression rate and 83.74% F-score. The proposed approach overcomes the difficult problem of saving time and effort to accurately detect the mass of liquid absorption over the time, and provides a promising approach for a wider application of this intelligent model. • A FEM is established for monitoring the mass of water absorption over time (M%) of the composite pipeline. • The M% of the composite pipeline is analyzed using the electrical capacitance sensor (ECS) technique. • The ECS results are verified using a dataset of experimental work adapted from literature. • Trained and tested the Convolutional Neural Network (CNN) by the electrical potential difference between ECS electrodes. • The M% over time of composite pipeline is predicted and compared with the FEM outputs and available experimental results. [ABSTRACT FROM AUTHOR]

Background: It is conjectured that providing feedback on physicians' prescribing behavior improves quality of drug prescriptions. However, the effectiveness of feedback provision and mode of feedback delivery is not well understood. The objective of this study was to assess and compare the effect of traditional paper letters (TPL) and short text message (STM) feedback on general practitioners' prescribing behavior of parenteral steroids (PSs).Methods: In a single-blind randomized controlled trial, 906 general practitioners (GPs) having at least 10 monthly prescriptions were randomly recruited into two interventions and one control study arms with 1:1 allocation, stratified by percentage of prescriptions. The intervention was the provision of 3 feedback messages containing prescribing indices in TPL and STM (in the first two arms) versus the control arm (CG) with an interval of 3 months between these messages. We calculated the PS Defined Daily Dose (DDD) for every GP, every month, and compared between the 3 arms, before and after the interventions. The expected primary outcome was to reduce prescription of parenteral steroids by participants. The study was performed in the Kerman Social Security Organization in Iran.Results: A total of 906 GPs were selected for the trial, but only 721 of them (TPL=191, STM=228, CG=302) were recruited for the 1st feedback. The mean age of GPs was 44 and 59% of them were male. The prescribed parenteral steroid DDDs at baseline were similar (TPL=121.62, STM=127.49, CG=115.68, P>0.5). At the end of the study, DDDs in the TPL and STM arms were similar (TPL=104.38, STM=101.90, P>0.9) but DDDs in each intervention arm was statistically significantly lower than in CG (CG=156.17, P<0.0001). Being in TPL and STM arms resulted in 36.1 and 41.7 units of decrease in DDD respectively, compared to the control arm (P<0.02 and P<0.005) after the one-year duration of the study.Conclusion: Feedback by TPLs and STMs on prescribing performance effectively reduced prescribing PSs by GPs. STM, being a cheap and fast tool, is potentially powerful and efficient for drug prescription rationalization. [ABSTRACT FROM AUTHOR]

Bio-electroreduction of CO 2 to formate is carried out in a double-chambered microbial electrolysis system (MES) using the iron phthalocyanine (FePc) dispersed carbide-derived carbon (CDC) cathode. Formate dehydrogenase (FDH) released by prokaryotic E. coli catalyzes the CO 2 reduction and acts as a promotor for selective formate generation. The prepared FePc-CDC composite catalyst is characterized by several physico- and electro-chemical characterization techniques including scanning electron microscopy, surface area analysis, X-ray diffraction, and infrared spectroscopy to corroborate the sustainability of as-synthesized catalyst material in MES. The FePc-CDC-based MES shows a maximum formate production rate of ∼30 mg/L.h from CO 2 (120 mg/L.h) at a poised potential of −1.0 V (Ag/AgCl) using E. coli and neutral red mediator. The study clearly demonstrates that the electrofermentation of CO 2 to formate in a FePc-CDC cathode-based MES can be a selective and viable green process for the utilization of CO 2 as a sole carbon feed-stock. Image 1 • FePc-CDC facilitated efficient immobilization of E.Coli on the electrode surface. • FePc-CDC triggered a selective reduction of CO 2 to formate in MES. • Maximum formate production rate of ∼30 mg/L-h with FE of 58% was achieved. • Application of NR as mediator for E. coli enhanced formation rate of formate. [ABSTRACT FROM AUTHOR]

Over the past two decades, an increasing number of large-scale structures have been built around the world. Constructing these structures has been a time consuming and highly expensive process. Thus, providing a structural health monitoring system to guarantee their proper functionality is important. In recent years, the advancement of technology and artificial intelligence methods based on signal processing and machine learning has attracted the attention of researchers. The challenges currently exist in the field of structural health monitoring to identify and classify damages to achieve high accuracy in a health-monitoring program. The presence of noise in measurement, various exciting load types, and varying environmental conditions cause difficulty in the practical identification and classification of damage in structures. Recent studies have employed finite element modeling to test the effectiveness of proposed methods for identifying damages in structures. However, detecting damage in real-world structures as mentioned above, presents unique difficulties, and the effectiveness of the proposed methods for damage detection in real-world structures remains uncertain. In order to improve the performance of damage detection methods and increase the accuracy of these methods as much as possible, the most important action is to identify damage sensitive data in the structure. The next challenge is to choose a high performance algorithm for damage identification and classification. One of the advanced algorithms, which has a very high ability to extract the desired features from the measured data, is the XGBoost algorithm. This algorithm has recently attracted the attention of researchers and has been used in different fields. So far, the ability of this algorithm has not been examined in the field of damage detection in order to extract desirable features. This article deals with the identification, classification, and severity of damages in the SMC benchmark bridge, which is an existing megastructure in the real world, as well as the IASC-ASCE benchmark structure, whose responses were taken under applied loads in the laboratory environment. First, using the XGBoost algorithm, the importance of the features extracted from the sensors' data is evaluated, and then the features, which are effective in the damage detection process, are selected. The results of this algorithm indicate that only by selecting 6 features from a large volume of data, the best performance can be achieved and selecting more does not help increase efficiency. In the next step, the Stacking method, which is a hybrid machine learning algorithm for damage classification, is evaluated and compared with some conventional machine learning algorithms that have been used in previous studies. The Stacking method stands out as the top performer with an average accuracy rate of 93.1%, leading to the conclusion that it is the most effective approach. Finally, by applying the presented algorithm to the two mentioned structures, its validation is appraised. [ABSTRACT FROM AUTHOR]

Highlights • An effective strategy to use metamodels (surrogate models), for model updating in structural health monitoring. • A comparative study of ten most common metamodeling techniques used for damage detection and the severity of damage; the first such study in SHM research. • A novel optimization algorithm, Colliding Bodies, CBO, with and without surrogate models for damage severity and computational efficiency. • Proposed application of this approach for general hysteretic degradation of a structure subjected to external excitations. • Identified LS-SVM as the most promising tool, combined with metamodels, for damage severity assessment. Abstract Despite advances in computer capacity, the enormous computational cost of running complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of structural health monitoring. To cut down the cost, surrogate models, also known as metamodels, are constructed and then used in place of the actual simulation models. In this study, structural damage detection is performed using two approaches. In both cases ten popular metamodeling techniques including Back-Propagation Neural Networks (BPNN), Least Square Support Vector Machines (LS-SVMs), Adaptive Neural-Fuzzy Inference System (ANFIS), Radial Basis Function Neural network (RBFN), Large Margin Nearest Neighbors (LMNN), Extreme Learning Machine (ELM), Gaussian Process (GP), Multivariate Adaptive Regression Spline (MARS), Random Forests and Kriging are used and the comparative results are presented. In the first approach, by considering dynamic behavior of a structure as input variables, ten metamodels are constructed, trained and tested to detect the location and severity of damage in civil structures. The variation of running time, mean square error (MSE), number of training and testing data, and other indices for measuring the accuracy in the prediction are defined and calculated in order to inspect advantages as well as the shortcomings of each algorithm. The results indicate that Kriging and LS-SVM models have better performance in predicting the location/severity of damage compared with other methods. In the second approach, after locating precisely the eventual damage of a structure using modal strain energy based index (MSEBI), to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, the MSEBI of structural elements is evaluated using a properly trained surrogate model. The results indicate that after determining the damage location, the proposed solution method for damage severity detection leads to significant reduction of computational time compared to finite element method. Furthermore, engaging colliding bodies optimization algorithm (CBO) by efficient surrogate model of finite element (FE) model, maintains the acceptable accuracy of damage severity detection. [ABSTRACT FROM AUTHOR]

A single step anodizing process is used to produce micro-nano structures on Aluminum (1050) substrates with sulfuric acid as electrolyte. Therefore, surface energy of the anodized layer is reduced using stearic acid modification. Undoubtedly, effects of different parameters including anodizing time, electrical current, and type and concentration of electrolyte on the final contact angle are systemically studied and optimized. Results show that anodizing current of 0.41 A, electrolyte (sulfuric acid) concentration of 15 wt.% and anodizing time of 90 min are optimal conditions which give contact angle as high as 159.2° and sliding angle lower than 5°. Moreover, the study reveals that adding oxalic acid to the sulfuric acid cannot enhance superhydrophobicity of the samples. Also, scanning electron microscopy images of samples show that irregular (bird's nest) structures present on the surface instead of high-ordered honeycomb structures expecting from normal anodizing process. Additionally, X-ray diffraction analysis of the samples shows that only amorphous structures present on the surface. The Brunauer–Emmett–Teller (BET) specific surface area of the anodized layer is 2.55 m 2  g −1 in optimal condition. Ultimately, the surface keeps its hydrophobicity in air and deionized water (DIW) after one week and 12 weeks, respectively. [ABSTRACT FROM AUTHOR]

• Propose a novel deep learning model based on Runge-Kutta recurrent neural network (RKRNN) with prior knowledge to realize structural system identification and seismic response prediction. • Formulate a partition training strategy to train the proposed neural network to improve the efficiency of training. • Utilize three numerical examples to valid the feasibility of RKRNN model including a linear three degrees of freedom system, a nonlinear single degree of freedom system with Bouc-Wen hysteresis and a simply supported bridge. • Site monitoring data from a bridge located in California has been used to further validate the proposed approach. In the seismic analysis of structural systems, dynamic response prediction is an essential problem and is significant in every stage during the structural life cycle. Conventionally, response analysis is carried out by numerical analysis. However, when the structural parameter is unknown, the establishment of a numerical model will be difficult. Enlightened by the Runge-Kutta (RK) numerical algorithm, this paper proposes a novel recurrent neural network named Runge-Kutta recurrent neural network (RKRNN) to realize the seismic response prediction. A partition training strategy is formulated to train the proposed neural network and to improve the efficiency of training. The proposed model can be trained by using a limited number of samples. Three numerical examples are utilized to validate the feasibility of RKRNN model including a linear three degrees of freedom (DOFs) system, a nonlinear single DOF system with Bouc-Wen hysteresis, and a numerical reinforced concrete bridge model. Additionally, the site monitoring data from a real-world bridge is utilized to further validate the proposed network. The results show that the proposed RKRNN model can effectively and efficiently predict the structural response under seismic load and exhibits robustness to noise, with good potential for applications in engineering practice. [ABSTRACT FROM AUTHOR]

In recent years, the application of structural control strategies to attenuate the dynamic response of civil engineering structures subjected to human-induced and environmental loads has once again received much attention. The hardware involved in providing effective control, the stochastic nature of the dynamic excitation, the number of distinct variables required to determine the dissipating forces, and the nature of the control approach are a few of the parameters involved in determining the efficacy of structural control systems. However, the primary parameter to determine the overall performance of a smart adaptive structure is the implementation of a control algorithm that would conveniently counteract the external known and/or unknown excitation by providing additional force input through control devices. The control algorithm to be adopted should be robust, multi-purpose, and simple to design and implement. Moreover, the control algorithm should provide the flexibility in selecting performance objectives for a holistically effective response reduction. This paper focuses on providing a comprehensive review of control algorithms implemented in structural control engineering. This article first provides an overview of the fundamental concepts pertaining to the vibration control of structures subjected to dynamic excitations. Next, an exhaustive review of different types of control algorithms that feed the input signal to active-type control devices is conducted. Thereafter, important benchmark applications in the field of structural control in civil engineering are presented. Finally, conclusions are presented on the advantages and drawbacks of each control strategy and recommendations are presented for further research in this area. [ABSTRACT FROM AUTHOR]

• Investigated uncertainties in FE model updating from measurement and modeling errors. • Employed information entropy measure criterion for optimum sensor placement in system identification. • Presented a novel nonlinear FE model updating framework using EKF and UKF. • Investigated uncertainties in nonlinear FE model updating framework. • Used information entropy measure criterion to determine optimal response utilization in FE model updating. In this work, we developed a framework for identifying frame-type structures regarding the measurement uncertainty and the uncertainty involved in inherent and structural parameters. The identification process is illustrated and examined on a one-eight-scale four-story moment-resisting steel frame under seismic excitation using two well-known recursive schemes: the Extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) methods. The nonlinear system equations were assessed by applying a first-order instantaneous linearization approach through the EKF method. In contrast, the UKF algorithm employs several sample points to estimate moments of random variables' nonlinear transformations. A nonlinear transformation is applied to distribute sample points to derive the precise mean and covariance up to the second order of any nonlinearity. Accordingly, it is theoretically expected that the UKF algorithm is more capable of identifying the nonlinear systems and determining the unknown parameters than the EKF algorithm. The capability of the EKF and UKF algorithms was assessed by considering a 4-story moment-resisting steel frame with several inherent uncertainties, including the material behavior model, boundary conditions, and constraints. In addition to these uncertainties, the combination of acceleration and displacement responses of different structural levels is employed to evaluate the capability of the algorithms. The information entropy measure is used to investigate further the uncertainty of a group of established model parameters. As highlighted, a good agreement is observed between the results using the information entropy measure criterion and those using the UKF and EKF algorithms. The results illustrate that using the responses of fewer levels placed in the proper positions may lead to improved outcomes than those of more improperly positioned levels. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proposes a novel deep learning model that utilizes both 1DCNN and RNN variants. • Uses the attention mechanism to improve the performance of the 1DCNN-RNN variant. • Uses raw acceleration time-series type of sequential data as the input data. • Compares the proposed model with equivalent dl -based model architectures. • Moreover, examines the environmental variable which affects structural response. In recent years, by emerging deep learning (DL) based algorithms, researchers have been exploring dl -based models to identify structural damage through data-driven approaches. dl -based data-driven techniques using autonomous feature extraction from raw sequential data are more robust under environmental variations. Extraction of robust features while considering the sequential dependencies will significantly improve the accuracy of damage identification by these techniques. In this regard, various architectures of dl -based models have been proposed. This study presents a novel dl -based model that utilizes both one-dimensional convolutional neural network (1DCNN) and recurrent neural network (RNN) variants using an attention mechanism. Attention mechanism improves the performance of the 1DCNN-RNN variants model precisely when its input data is raw acceleration time-history, as a kind of sequential data. The IASC-ASCE phase II and Qatar University grandstand simulator benchmarks are used to evaluate the proposed model by comparing its performance with dl -based neural network architectures that could be equivalent to this combination. Moreover, the environmental variable which affects structural response is also examined. Results demonstrate that the CNN-ATT-biGRU model architecture has the best accuracy and appropriate training time and model size among nine compared architectures. [ABSTRACT FROM AUTHOR]

This work presents an experimental and a numerical studies on the effect of the phenomenon of porosity on the mechanical properties of Glass Fiber Reinforced Polymer (GFRP). In a first part, material elaboration, as well as its characterization using a three-point bending test to extract the basic mechanical properties of the material, is considered. In a second part, a finite element model is created to simulate the problem of air bubbles broadly. Several cases of different shapes and sizes are simulated. The results show a significant effect on the reduction of load in both tensile and bending cases as the size of the bubbles increases. Furthermore, the second part includes the application of the Artificial Neural Network-Enhanced Jaya Algorithm (ANN-E JAYA) to predict the reduction of the tensile load as a function of different crack lengths obtained from an Extended Finite Element Method (XFEM) model. Next, to verify the accuracy of provided application , a comparison is made with two other applications such as Artificial Neural Network-Jaya Algorithm (ANN-JAYA) and Artificial Neural Network-Particle Swarm Optimization (ANN-PSO). The results of the three algorithms show good convergence, with a slight increase in accuracy for ANN-E JAYA. MATLAB code and data used in this article can be found at https://github.com/Samir-Khatir/GFRP-ANN-E-JAYA.git. [ABSTRACT FROM AUTHOR]

• A novel hybrid strategy (HJDEA) based on Jaya and differential evolution algorithm is proposed. • A noise insensitive objective function is established based on adjacent acceleration correlation function. • An attractive combination of HJDEA and gradient search method is proposed to further improve performance. • Alterations of both stiffness and mass parameters are considered in the damage model. • Studies on shear-type frame, cantilever beam and ASCE benchmark structure are conducted to verify the effectiveness of the proposed methods. To solve the optimization-based structural damage identification problem, a novel hybrid algorithm based on Jaya and differential evolution algorithm (HJDEA) is proposed to detect, locate and quantify structural damages by effectively incorporating the powerful local exploitation capacity of Jaya algorithm and global exploration capability of differential evolution. Meanwhile, Hammersley sequence initialization and Lévy flight search mechanism are introduced into HJDEA to further improve convergence rate and refining the quality of the best solution. Four different algorithms, genetic algorithm, particle swarm optimization, Jaya and the proposed HJDEA are employed for comparative study. In addition, the objective function is established by adjacent acceleration correlation function so as to avoid false identification caused by defining improper reference point. The performance of the proposed damage identification strategy based on HJDEA and adjacent acceleration correlation function is investigated with numerical examples involving an 8-DOF lumped mass model and a cantilever beam, as well as an experimental study of the ASCE benchmark structure under white noise excitation. Results show that the proposed hybrid identification method is accurate, efficient and robust in the identification of the damage existence, location and severity of stiffness and mass parameters even with partial output-only responses and 20% noise-polluted measurements. [ABSTRACT FROM AUTHOR]

Objective To evaluate if addition of l -carnitine therapy to standard varicocelectomy adds any extra benefit in terms of improvement in semen parameters or deoxyribonucleic acid (DNA) damage. Materials and Methods One hundred patients enrolled in this study and were randomly divided into 2 groups (50 patients in each group). In group 1, standard inguinal varicocelectomy and, in group 2, standard inguinal varicocelectomy plus oral antioxidant therapy (oral l -carnitine, 250 mg 3 times a day) were performed for 6 months. For all patients, routine semen analysis and DNA damage test of spermatozoa (by 2 methods of terminal deoxynucleotidyl transferase dUTP nick end labeling and protamine damage assay) were performed at baseline and at 3 and 6 months postoperatively. Results In both groups, the improvement in semen analysis parameters and DNA damage was observed, but there was not any statistically significant difference between the 2 groups in these parameters, although the slope of improvement in DNA damage was slightly better in group 2 (that was not statistically significant). Conclusion We observed that addition of 750 mg of l -carnitine orally daily to standard inguinal varicocelectomy does not add any extra benefit in terms of improvement in semen analysis parameters or DNA damage. [ABSTRACT FROM AUTHOR]

• The molecular mechanisms of preeclampsia have not been fully understood and multifactorial processes are involved in PE pathogenesis. • The level of miRNA-326 increases in preeclamptic pregnancy, especially in Th17 and CD4+ T cells. • The CD4+ T-cell subset could be an extra source of antiangiogenic factors for the maintenance of this hypertension disorder. Alongside many complications in understanding the etiology of Preeclampsia (PE), several determinants, such as the imbalanced proportion of anti-angiogenic/proangiogenic T-cell subsets, especially CD4+ (Th17/Treg), as well as alterations in the expression profile of related cytokines, miRNAs, and transcription factors might have been implicated in PE pathogenesis. After sample collection and preparation, CD4+ cells were isolated from PE and non-PE pregnant woman and were cultured. Furthermore, analysis such as flow cytometry, real-time PCR, western blotting, and ELISA were performed to assess determinants related to PE manifestation, including sFlt-1, sEng, STAT-3, RORγt, SMAD-7, Foxp3, IL-17, IL-22, Ets-1, and miRNA-326. Our results showed that the miRNA-326 expression level increased in CD4+ Cells and Th17 in PE patients which downregulated Ets-1 expression that acts as a negative control for Th17 development. Furthermore, we showed that the number and expression level of Th17 s and transcription factor ROR γ t escalated, respectively. While Treg and its related transcription factor (Foxp3) demonstrated a decrease. Flow cytometry analysis illustrated that the Th17/Treg ratio increased in PE. Additionally, we demonstrated that expression and concentration levels of cytokines (IL-17 and IL22) and anti-angiogenic molecules (sEng and sFlt-1) soared in isolated CD4+ cells from PE patients, which could be correlated with PE pathogenicity. In conclusion, we comprehensively evaluated immunological factors and molecules involved in PE manifestation. Interestingly, the CD4+ T-cell subset could be an extra source of antiangiogenic factors for the maintenance of this hypertension disorder. [ABSTRACT FROM AUTHOR]

• Statistical analysis showed that the water-rock interaction contributed more to the chemistry of water in the Sarein-Nir area than anthropogenic factors. • Based on stable isotope (δ18O and δD) and geothermometry techniques, the majority of thermal water samples were immature. • Results demonstrated that springs of the Sarein region showed high temperatures compared to those of the Nir region. In recent years, water management in Iran has mostly focused on monitoring, understanding and mitigating issues of water quality. To understand the processes that can affect water quality, this study carried out a geochemical study on cold (5 well water samples and 6 cold springs) and thermal (9 hot springs) waters in the Sarein-Nir area in Iran. A total of 20 water samples were collected in July 2016, and physicochemical variables such as pH, electrical conductivity (EC) and major, minor and trace element concentrations were determined. Using multivariate statistics, the main geochemical processes and possible origin of selected trace elements were investigated. The maturity and mixing processes of the waters were investigated using stable isotope (δ 18O and δ D) and geothermometry techniques. It was found that water-rock interaction was the predominant process, and that denitrification and ion exchange processes also took place in the groundwater system of the area. Thermal waters had an elevated EC, whereas cold waters showed elevated pH and NO 3 values, indicating the impact of anthropogenic activities on water quality. Alkalinity and temperature were recognized as the most important variables that control the release of trace elements into the groundwater. Most thermal water samples were immature and belonged to peripheral water types, indicating that these samples came in part from deep circulation. Geothermometry of the thermal waters showed that the springs of the Sarein region had high temperatures compared to the springs of the Nir region. Considering that the Sarein spring waters had lower deuterium ratios than spring waters in the Nir region, it was concluded that the recharge elevation of the Sarein springs was higher than that of the Nir springs. [ABSTRACT FROM AUTHOR]