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The present study conducted to investigate the effect of cortisol injection on growth indices, food intake and plasma cortisol in juvenile common carp (Cyprinus carpio). After 2 weeks adaptation, 240 fish with 19.5 ± 0.2 g average weight were randomly distributed in to 12 fiberglass tanks with four treatments and three replicates (20 fish per tank). Based on body weight, cortisol (mixed with oil) was injected to treatments with different dosages at 0 (C0), 1 (C1) and 10 (C10) µg/g. At the end of 21 days, fish were weighed and growth parameters showed significant reduction in C10. No significant change was observed in hepatosomatic index among different treatments. Food intake were recorded daily during the experiment and showed significant reduction in days 1 to 8, 16 and 19 in C10 compared to control group . Blood was taken to determine plasma cortisol at the start, day 3, day 7 and day 21 of the experiment. Cortisol concentrations showed significant reduction in C10 compared to C0 group three days after the initiation of the experiment. The results showed that increasing of cortisol in a short time after injection, affected by changes of blood plasma cortisol and reduction of food intake could negatively have an effect on growth.

Discharge of pollution loads into natural water systems remains a global challenge that threatens water and food supply, as well as endangering ecosystem services. Natural rehabilitation of contaminated streams is mainly influenced by the longitudinal dispersion coefficient, or the rate of longitudinal dispersion (D x ), a key parameter with large spatiotemporal fluctuations that characterizes pollution transport. The large uncertainty in estimation of D x in streams limits the water quality assessment in natural streams and design of water quality enhancement strategies. This study develops an artificial intelligence-based predictive model, coupling granular computing and neural network models (GrC-ANN) to provide robust estimation of D x and its uncertainty for a range of flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of D x estimated from the proposed GrC-ANN model was performed by alteration of the training data used to tune the model. Modified bootstrap method was employed to generate different training patterns through resampling from a global database of tracer experiments in streams with 503 datapoints. Comparison between the D x values estimated by GrC-ANN to those determined from tracer measurements shows the appropriateness and robustness of the proposed method in determining the rate of longitudinal dispersion. The GrC-ANN model with the narrowest bandwidth of estimated uncertainty (bandwidth-factor = 0.56) that brackets the highest percentage of true D x data (i.e., 100%) is the best model to compute D x in streams. Considering the significant inherent uncertainty reported in the previous D x models, the GrC-ANN model developed in this study is shown to have a robust performance for evaluating pollutant mixing (D x ) in turbulent environmental flow systems., (© 2022. The Author(s).)

The outbreak of the COVID-19 virus in 2020 has left many changes in the quality of life and environment, including air quality in different parts of the world. As a result of lockdown conditions, the level of air pollution has been changed considerably due to topographic, geographical, and cultural conditions as well as traffic restrictions. Thus, this study aimed to investigate the effect COVID-19 outbreak on improving air quality as a result of changes in traffic volume and traffic patterns in Queens, New York, using the moderation and mediation analysis model structure. In this model, COVID-19 outbreak periods were defined as a moderating variable, traffic volume (number of daily vehicles) as an independent variable and mediator, and air pollution concentration parameters (NO x , PM 2.5 , and O 3 ) individually as dependent variables. Three-time periods were selected, each representing the duration and severity of traffic restrictions and prohibitions, and these three periods corresponded to 1 February-4 March, 5 March-21 March, and 22 March-15 May. They represented the normal, aware, and lockdown periods, respectively. The result of the study showed that in 2020 compared to the last five consecutive years, PM 2.5 and NO x pollutants decreased by 39.2% and 35.8% as a result of the traffic ban due to the COVID-19, but an increase of 15.1% in O 3 pollutant was observed in the mentioned period. Although traffic restrictions reduced total traffic volume compared to the same period last year, there has been no significant reduction in the air quality index (AQI). The reduction in NO x concentration leads to more O 3 ground levels, and this caused the AQI not to decrease significantly. Finally, the moderation and mediation model results showed that the COVID-19 almost has no significant effect on the correlation between daily traffic and the concentration of NO x , PM 2.5 , and O 3 pollutants as moderator. However, the COVID-19 has a significant correlation with O 3 and PM 2.5 concentration, and the traffic volume mediation effect is negligible. Therefore, the statistical analysis and models show that the COVID-19 pandemic is an effective traffic volume and air quality parameter., (© The Author(s), under exclusive licence to Springer Nature B.V. 2021.)

Given the interest in accurately predicting the Longitudinal Dispersion Coefficient (D x ) within the fields of hydraulic and water quality modeling, a wide range of methods have been used to estimate this parameter. In order to improve the accuracy of D x predictions, this paper proposes the use of a Deep Convolutional Network (DCN), a sub-field of machine learning. The proposed deep neural network architecture consists of two parts; first, a one-dimensional convolutional neural network (CNN) to build informative feature maps, and second, a stack of deep, fully connected layers to estimate pollution dispersion (as D x ) in streams. To accurately predict D x the developed model draws upon a large and diverse array of datasets in the form of three dimensionless parameters: Width/Depth (W/H), Velocity/Shear Velocity (U/u*), and Longitudinal Dispersion Coefficient/(Depth * Shear Velocity) (D x /Hu*). The model's accuracy is compared to that of several empirical models using a number of statistical measures. In addition, the DCN model results are compared with artificial neural network (ANN) and support vector machine (SVM) models implemented in this research and also similar studies applying various machine learning models (ML) towards D x prediction. The statistical evaluation indicates that the DCN model outperforms the tested empirical, ANN, SVM and ML models with a significant difference. Additionally, five-fold cross-validation is performed to analyze the sensitivity and dependency of the DCN model's results on dataset selection, which shows that the dataset selection process does not significantly affect the model's accuracy. Since both ML and empirical models are, in general, poor predictors of the upper and lower ranges of D x values, the DCN model's predictions of D x in six different extreme-value ranges are assessed. The DCN model shows excellent accuracy in estimating D x over the full possible range of data. In comparison with the empirical and ML models mentioned above, the DCN model more accurately predicts D x values from river geometry and hydraulic datasets, with low errors across all ranges of D x . The most significant advantage of DCN is that it tries to learn high-level features from data in an incremental manner., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Over recent years, many different nanoparticle-based drug delivery systems (NDDSs) have been developed. Recently the development of stimulus-responsive NDDSs has come into sharper focus. Carbon dots (CDs) possess outstanding features such as useful optical properties, good biocompatibility, and the ability for easy surface modification. Appropriate surface modification can allow these NDDSs to respond to various chemical or physical stimuli that are characteristic of their target cells or tissue (frequently malignant cells or tumors). The present review covers recent developments of CDs in NDDSs with a particular focus on internal stimulus response capability that allows simultaneous imaging and therapeutic delivery (theranostics). Relevant stimuli associated with tumor cells and tumors include pH levels, redox potential, and different enzymatic activities can be used to activate the CDs at the desired sites.

The transport of pollutants inside the groundwater system is profoundly affected by absorption and transmission via colloid or soil particles. Therefore, it is essential to investigate the significant pollutants (Such as hexavalent chromium (Cr(VI))) transfer in the presence of colloid particles that can facilitate or retain this transfer. For this purpose, an experiment is carried out in a saturated porous media column to study the bentonite concentration, flow velocity and sand grain size effects on co-transport of Cr(VI) with bentonite. The results of this study demonstrated that the colloid particles facilitate the transfer of Cr(VI) by 30% in 200 mg/l bentonite colloids concentration. The amount of transmitted Cr(VI) is decreased by increasing the bentonite colloids concentration from 200 mg/l to 300 mg/l. As the flow velocity increased from 2 cm/min to 3.3 cm/min, the amount of transferred Cr(VI) increased by 7%. The results show that with reducing the sand grain size, the amount of transmitted bentonite and Cr(VI) is reduced that this effect is more sensible in bentonite transport. As a result, it can be noted that the bentonite colloidal particles according to its concentration and experimental conditions, may facilitate or retain the Cr(VI) transport and sand gradation has a significant impact on colloid and pollutant transmission., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The synthetic analogue to biogenic apatite, hydroxyapatite (HA) has a number of physicochemical properties that make it an attractive candidate for diagnosis, treatment of disease and augmentation of biological tissues. Here we describe some of the recent studies on HA, which may provide bases for a number of new medical applications. The content of this review is divided to different medical application modes utilizing HA, including tissue engineering, medical implants, controlled drug delivery, gene therapies, cancer therapies and bioimaging. A number of advantages of HA over other biomaterials emerge from this discourse, including (i) biocompatibility, (ii) bioactivity, (iii) relatively simple synthesis protocols for the fabrication of nanoparticles with specific sizes and shapes, (iv) smart response to environmental stimuli, (v) facile functionalization and surface modification through noncovalent interactions, and (vi) the capacity for being simultaneously loaded with a wide range of therapeutic agents and switched to bioimaging modalities for uses in theranostics. A special section is dedicated to analysis of the safety of particulate HA as a component of parenterally administrable medications. It is concluded that despite the fact that many benefits come with the usage of HA, its deficiencies and potential side effects must be addressed before the translation to the clinical domain is pursued. Although HA has been known in the biomaterials world as the exemplar of safety, this safety proves to be the function of size, morphology, surface ligands and other structural and compositional parameters defining the particles. For this reason, each HA, especially when it comes in a novel structural form, must be treated anew from the safety research angle before being allowed to enter the clinical stage.

The co-transport of pollutants with colloidal particles to lower depths of groundwater and porous environments has been demonstrated in many studies in recent three decades. Despite the numerous researches, all experimental and numerical studies of pollutant transfer in the presence of colloidal particles have been carried out in one dimension, which causes significant errors in this phenomenon. In this study, the two-dimensional transfer experiment of chromium in the presence of bentonite colloidal particles is done in saturated porous media. In order to conduct the experiment in two-dimensional conditions, the sampling was done in central and lateral of the last experiment column section. The results have been demonstrated that the transmission along the longitudinal direction is higher than lateral in the three tests of the transfer of chromium, bentonite, and chromium in the presence of bentonite colloidal particles at the beginning of the experiment, and due to completed mixing in the section, it reached to a constant value as lateral samples. While the presence of bentonite colloidal particles facilitates the transfer of chromium in both longitudinal and lateral directions, increasing the bentonite colloidal particle concentration causes more getting stuck of colloid particles between the sand grains and reduction of the chromium transfer in both longitudinal and lateral directions. So, it can be concluded that transfer in the lateral direction is lower in bentonite colloidal particles compared with chromium, and the reason is the bentonite colloidal particles getting stuck between sand grains, which is exacerbated by increasing the concentration of the bentonite. Also, due to the chromium co-transport with colloid particles in the fraction of chromium total transport, increasing the bentonite concentration causes decreasing the chromium lateral transfer.

Background: Hypertension is a major cause of noncommunicable diseases such as cardiovascular disease. Therefore, this study aimed to estimate the prevalence of hypertension in cardiovascular patients in Iran by meta-analysis., Methods: The search was carried out using authentic Persian and English keywords in national and international databases including IranMedex, Scientific Information Database (SID), Magiran, IranDoc, Medlib, ScienceDirect, PubMed , Scopus, Cochrane, Embase, Web of Science, and Google Scholar search engine without any time limitation until 2017. Heterogeneity of studies was assessed using I2 statistic . Data were analyzed using STATA 11.1., Results: In 66 reviewed studies with a sample of 111,406 participants, the prevalence of hypertension was 44% in Iranian patients with cardiovascular disease 67%(95%CI: 38%-49%) in women and 42% in men. The prevalence of systolic hypertension in cardiac patients was 25%, diastolic 20%, diabetes 27%, and overexposure 43%. The prevalence of hypertension was 44% in patients with coronary artery disease, 50% in myocardial infarction, 33% in aortic aneurysm, and 44% in cardiac failure., Conclusions: Hypertension has a higher prevalence in women with cardiovascular disease than men, and it increases with age. Among patients with cardiovascular disease, myocardial infarction patients have the highest levels of hypertension. The prevalence of systolic hypertension in cardiac patients is higher than diastolic hypertension., Competing Interests: There are no conflicts of interest., (Copyright: © 2020 International Journal of Preventive Medicine.)

Successful application of one-dimensional advection-dispersion models in rivers depends on the accuracy of the longitudinal dispersion coefficient (LDC). In this regards, this study aims to introduce an appropriate approach to estimate LDC in natural rivers that is based on a hybrid method of granular computing (GRC) and an artificial neural network (ANN) model (GRC-ANN). Also, adaptive neuro-fuzzy inference system (ANFIS) and ANN models were developed to investigate the accuracy of three credible artificial intelligence (AI) models and the performance of these models in different LDC values. By comparing with empirical models developed in other studies, the results revealed the superior performance of GRC-ANN for LDC estimation. The sensitivity analysis of the three intelligent models developed in this study was done to determine the sensitivity of each model to its input parameters, especially the most important ones. The sensitivity analysis results showed that the W/H parameter (W: channel width; H: flow depth) has the most significant impact on the output of all three models in this research.

Background: Hypertension is a risk factor for renal disease. Therefore, this study was aimed at estimating the prevalence of hypertension in renal patients in Iran through meta-analysis., Methods: The search was carried out using authentic Persian and English keywords in national and international databases including IranMedex, SID, Magiran, IranDoc, Medlib, ScienceDirect, Pubmed, Scopus, Cochrane, Embase, Web of Science, Medline, and Google Scholar search engine without any time limitation until 2017. Heterogeneity of studies was assessed using the I 2 index. Data were analyzed using STATA ver 11., Results: In 35 reviewed studies with a sample of 39,621 subjects, the prevalence of hypertension in renal patients was 35% (95% CI: 29%-41%) (25% in women and 18% in men). The prevalence of systolic hypertension in renal patients was 5%, diastolic hypertension 26%, and diabetes 23%. The prevalence of hypertension in hemodialysis patients was 34%, 27% in peritoneal dialysis, 43% in kidney transplantation, and 26% in chronic renal failure. In addition, meta-regression showed that the prevalence of hypertension in renal patients did not significantly decrease during the years 1988-2017., Conclusions: More than a third of kidney patients in Iran suffer from high blood pressure. The diastolic blood pressure of these patients is about five times higher than their systolic blood pressure. Moreover, the age group under 30 is a high-risk group. The prevalence of hypertension in women with kidney disease is higher than in men. In addition, patients who have kidney transplants are more likely to have high blood pressure than other kidney patients., Competing Interests: There are no conflicts of interest.

Introduction: Prostate cancer is the most common malignant cancer in men worldwide and after lung cancer, it is the second leading cause of cancer mortality in men. The purpose of this study was to investigate the relationship between prostate cancer and metformin consumption in men., Methods: The current study is a systematic and meta-analysis review based on the PRISMA statement. To access the studies of domestic and foreign databases, Iran Medex, SID, Magiran, Iran Doc, Medlib, ProQuest, Science Direct, PubMed, Scopus, Web of Science and the Google Scholar search engine were searched during the 2009- 2018 period for related keywords. In order to evaluate the heterogeneity of the studies, Q test and I2 indicator were used. The data were analyzed using the STATA 15.1 software., Results: In 11 studies with a sample size of 877058, the odds ratio of metformin consumption for reducing prostate cancer was estimated at 0.89 (95%CI: 0.67-1.17). Meta-regression also showed there was no significant relationship between the odds ratio and the publication year of the study. However, there was a significant relationship between the odds ratio and the number of research samples., Conclusion: Using metformin in men reduces the risk of prostate cancer but it is not statistically significant., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Introduction: Renal diseases are among the major health problems around the world that cause major changes in patients' lifestyle and affect their quality of lives. The aim of this study was to evaluate the quality of life of patients with chronic kidney disease (CKD) in Iran through a meta-analysis., Materials and Methods: This study was conducted using authentic Persian and English keywords in the national and international databases including IranMedex, SID, Magiran, IranDoc, Medlib, Science Direct, Pubmed, Scopus, Cochrane, Embase, Web of Science, and Medline. The data were analyzed using meta-analysis (random effects model). Heterogeneity of studies was assessed using I2 index. In this study, SF-36: 36-Item Short Form health-related quality of life (HRQOL), kidney disease quality of life-SF (KDQOL-SF), KDQOL and KDQOL-SFTM questionnaires were used. Data were analyzed using STATA Version 11 software., Results: A total of 17200 individuals participated in 45 reviewed studies, and the mean score of CKD patients' quality of life was estimated by SF-36 (60.31), HRQOL (60.51), and KDQOL-SF (50.37) questionnaires. In addition, meta-regression showed that the mean score of CKD patients' quality of life did not significantly decrease during the past years., Conclusion: The mean score of quality of life of patients with CKD was lower in different dimensions in comparison with that of normal people. Therefore, interventional measures should be taken to improve the quality of life of these patients in all dimensions., Competing Interests: There are no conflicts of interest.

Introduction: The prognostic value of serum Uric Acid (UA) levels in Traumatic Brain Injury (TBI) is unclear., Aim: To investigate the relationship between serum UA levels and prognosis of patients with TBI when in hospital and at six months after discharge., Materials and Methods: All patients attended our emergency department during July 2014 and December 2015 and were consecutively entered into the study and among 890 evaluated candidates based on inclusion criteria we finally investigated the serum UA levels of 725 TBI patients. Computed Tomography (CT) images of the brain were obtained within the first 24 hours of hospitalization. Outcome was assessed using the Glasgow Outcome Scale (GOS) score at discharge and at six months after discharge., Results: Data of 725 patients (42.89% men; mean age: 54.69±12.37 years) were analyzed. Mean±Standard Deviation (SD) of Glasgow Coma Scale (GCS) scores was 4.65±1.76. Serum levels of UA, when in hospital and at six months after discharge, among those who died were lower than those who survived (in hospital: 0.126±0.026 vs. 0.243±0.942 mmol/l, p = 0.000; 6 months post-discharge: 0.130±0.044 vs. 0.286±0.069 mmol/l, p<0.001). The mean UA plasma was significantly different between deceased and alive patients according to GOS scores (p<0.001 and p=0.030, respectively). The UA levels showed a significant relationship with GCS scores and severity of brain injury assessed using the Marshall Classification Score (p=0.005)., Conclusion: Our results showed a strong relationship between UA levels and patients' outcomes either in hospital or at six months after discharge. Serum UA level could be considered as a valuable marker for evaluating the severity of brain injury and outcomes of TBI.

Introduction: Development of delayed graft function is more prevalent in patients receiving a kidney allograft from brain-dead than living donors. This study aimed to evaluate the association between urine neutrophil gelatinase-associated lipocalin (NGAL) levels in brain-dead donors and subsequent allograft function., Materials and Methods: Urine NGAL concentration was measured in urine samples obtained from 24 brain-dead kidney allograft donors before organ retrieval. The 24 kidney recipients were followed for 6 months. The immunosuppressive therapy was similar for all of the recipients. Following transplantation, plasma creatinine was recorded daily during the recipient's stay in the hospital and then at 1, 3, and 6 months after transplantation. Delayed graft function was defined as the need for dialysis in the first 7 days after transplantation., Results: The mean age of the donors was 28.7 ± 11.2 years and 70.8% were men. Their median urine NGAL level was 7.4 ng/ml (range, 2 ng/mL to 45 ng/mL). Urine NGAL levels were only associated with the need for cardiopulmonary resuscitation (P = .007). On the 1st day after transplantation, 16.7% of the recipients developed delayed graft function, which was declined to 12.5% on the 2nd day and to 8.3% during the 3rd day and the following days. No significant association was observed between the donor's urine NGAL levels and graft function (P = .86)., Conclusions: Our results did not show any association between urine NGAL levels and outcome of allograft function obtained from brain-dead donors. Larger studies are required to confirm this finding.