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Peritoneal adhesion formation is a challenging postoperative complication. We aim to evaluate the effect of orally administered sirolimus, prednisolone, and their combination to prevent this entity.Eighty female albino underwent intraperitoneal injection of 3 mL of 10% sterile talc solution to induce peritoneal adhesion, and were subsequently and randomly divided into four groups (each n = 20); including a control group; 1 mg/kg oral prednisolone daily in the morning; 0.1 mg/kg oral sirolimus daily; and a combination group which received both drugs, with the same dosage. On the 29th day, abdominal cavities were explored, and classification was done based on Nair classification.All rats were healthy on the 29th day, in which exploration was performed. The rats in the control group had extensive intra-abdominal adhesions, while 17 (85%) rats in the control group had substantial adhesion; however, the prednisolone, sirolimus, and combination group had lesser adhesion formation. Also, 14 (70%) rats of prednisolone group, 13 (65%) of sirolimus group, and 16 (80%) of combination group had insubstantial adhesion. The decrease in the grade of peritoneal adhesion bands was highly significant in the combination group (P 0.001).The combination of sirolimus and prednisolone was effective for preventing peritoneal adhesions in rats.

Renal artery stenosis is caused by a heterogeneous group of diseases, including atherosclerosis and fibromuscular dysplasia, which can be treated medically, via endovascular techniques, or by open revascularization; however, satisfactory and effective results are not always obtained. We aimed to assess the possibility of renal revascularization by a pedicled intestinal segment wrapping the kidney. Five dogs were operated on at three steps. At the first step, laparotomy was performed, and the right kidney was released. Subsequently, an 8–10 cm segment of jejunum was separated longitudinally, and mucosectomy was done. This intestinal patch wrapped up the kidney. After eight weeks, the kidney and the intestinal patch were analyzed, and the renal artery was ligated. After four weeks, the kidney and the intestinal patch were sent for pathological evaluation. At the 12th week of evaluation, no evidence of abscess formation or collection was seen. All kidneys had a normal color, consistency, and size. All renal cells were alive, and neither atrophy nor necrosis was seen. Glomerulus and tubules were intact, and no inflammatory change was visible. Furthermore, thick wall vasculature was inspected in a fibromuscular tissue, rising from the intestinal flap toward the kidney. One of the dogs expired due to peritonitis and sepsis in the fifth week. In our study, indirect perfusion of the kidney by an intestinal patch was achieved successfully. This represents new hope in patients suffering from chronic renal failure who underwent former medical and surgical interventions with undesirable results.

Viruses as intracellular pathogens take over the host metabolism and reprogram to facilitate optimal virus production. DNA viruses can cause alterations in several metabolic pathways, including aerobic glycolysis also known as the Warburg effect, pentose phosphate pathway activation, and amino acid catabolism such as glutaminolysis, nucleotide biosynthesis, lipid metabolism, and amino acid biosynthesis. The available energy for productive infection can be increased in infected cells via modification of different carbon source utilization. This review discusses the metabolic alterations of the DNA viruses that will be the basis for future novel therapeutic approaches.

Dairy wastewater contains high levels of organic matter that would be a serious threat if being discharged to the environment without any treatment. This study investigated the influence of various parameters including the concentration of poly aluminum chloride (PAC) on the chemical coagulation process and the concentration of H2O2, pH of the solution, reaction time, voltage and distance of electrodes in the electro-Fenton (EF) process. The optimum conditions obtained in this study included PAC concentration of 100 mg/L, pH of 3, reaction time of 60 min, voltage of 20 V, H2O2 concentration of 1.5 g/L and electrode distance of 2 cm. This resulted in the removal efficiency of 90.3%, 87.25%, and 87% for chemical oxygen demand (COD), biochemical oxygen demand (BOD5), and total suspended solids, respectively. The estimated cost of the process in terms of 1 kg COD removal was about $1.16. Two experimental models, called the feed-forward artificial neural network (FF-ANN) and partial least squares (PLS), were developed to predict the behavior of the process in COD removal. The results confirmed that both FF-ANN and PLS were powerful tools in predicting COD removal in the EF process, as the correlation coefficients between the predicted COD and the actual COD for the PLS and FF-ANN models were 0.854 and 0.971, respectively. Sensitivity analysis of both models showed that the reaction time and electrode distance had the most influence on the output of PLS and FF-ANN, respectively. Therefore, this combined process can be regarded as a thought-provoking one for dairy wastewater treatment, and the FF-ANN model was a more powerful tool than the PLS one.

Introduction: Otorrhagia is not a common manifestation of petrous internal carotid artery (ICA) aneurysm. Besides idiopathic cases, different etiologies, including trauma, infection, and radiation, have been suggested for petrous ICA aneurysms/pseudoaneurysms. However, there is limited experience in the management of this rare aneurysm/pseudoaneurysm with coil embolization. Case Presentation: A 58-year-old man, who was a known case of chronic otitis media, was referred from the otorhinolaryngology ward with intractable bleeding from the right ear. A pseudoaneurysmal lesion (7 mm × 5 mm) was detected in the petrous segment of the internal carotid artery. The patient underwent coil embolization with no complications. Conclusion: To the best of our knowledge, this is the first case of petrous ICA pseudoaneurysm, presenting with only intractable otorrhagia. The patient was successfully treated with coil embolization, with no need for further interventions. Besides, a review of petrous ICA aneurysms or pseudoaneurysms presenting with otorrhagia was performed.

Human urine contains high concentration of pharmaceuticals, a concern that must be addressed if used as a fertiliser. This study systematically evaluated granular activated carbon (GAC) adsorption in removing five most commonly found pharmaceuticals in the environment – naproxen (NAP), carbamazepine (CBZ), ibuprofen (IBP), acetaminophen (APAP) and metronidazole (MTZ) from hydrolysed urine. Fixed-bed column experiments were conducted to obtain breakthrough curves and assess GAC (1000 m2g−1) performance in the adsorption of pharmaceuticals at different adsorbent mass (4–12 g·L−1), flow rate (1.15–4.32 L·d−1) and adsorption/contact time at ambient room temperature and pH 9. The highest adsorption capacity was observed at a lower adsorbent mass (4 g·L−1) and lower flow rate (1.15 L·d−1) for all micropollutants. The breakthrough curves showed the highest GAC adsorption capacity for CBZ (56.1 mg·g−1) while MTZ (32.2 mg·g−1) with the lowest adsorption will be the design limiting for column adsorption application. Thomas and Yoon-Nelson models fitted well for predicting empirical breakthrough curves for fixed-bed GAC column adsorption. The artificial neural network (ANN) modelling was able to predict the removal effectiveness of over 99% except for APAP at 84.5%. The study showed that the potential application of GAC column adsorption for micropollutant removal is significant although this study was limited in the range of parameters studied.

The most valuable resource on the planet is water, and the survival of life on earth is directly proportional to water, among other essential resources. Water also plays a critical role in the human body, wherein it adjusts the temperature of the body, cushions joints, conveys oxygen and nutrients to cells, defends tissues and organs, and eliminates wastes. While water covers 70% of the earth’s surface, nearly 97% of water resources are saline and inappropriate for direct human consumption. Around 2% is out of the way in glaciers and ice caps. Thus, it leaves only about 1% of total water resources available for human needs, such as farming, housing, industrial, municipal, and private necessities. Artificial intelligence (AI) is defined as the exhibition of human intelligence by machines, where machines can think and act like humans. AI was invented by John McCarthy in 1956 and obtained the greatest successes in the 90s and early 21st century, and now it is considered one of the emerging sciences. There are six general subsets for AI, i.e., machine learning, deep learning, robotics, expert systems, fuzzy logic, and natural language processing. Among those subsets, machine and deep learning and fuzzy logic have been more applied in water process engineering. The AI-assisted procedures can be applied in different aspects of process engineering for maintenance and operation, design, plant planning and optimization, configuration, training, computer-aided instruction, operator decision support, process control, simulation, alarm management, troubleshooting, and scheduling. Since there is quick simultaneous progress in both AI and molecular sciences, more importantly, there is a considerable convergence between these two fields; water process engineering is experiencing an unexpected transition from traditional procedures to AI-assisted procedures. In this chapter, the use of AI-assisted removal and sensings for four main emerging and traditional water contaminates including per- and polyfluoroalkyl substances, endocrine-disrupting chemicals/pharmaceuticals, disinfection by-products, and heavy metals will be discussed.

Perfluorooctanoic acid (PFOA) is used in a variety of industries and is highly persistent in the environment, with potential human health risks. Photocatalysis has been extensively used for the decomposition of various organic pollutants, yet its simulation and modeling are challenging. This research aimed to establish different machine learning (ML) algorithms which can simulate and predict the photocatalytic degradation of PFOA. The published results were used to estimate and predict the photocatalytic degradation of PFOA. Statistical criteria including the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) were considered in assessing the best method of modeling. Among the seven ML algorithms pre-screened, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) showed the best performance and were chosen for deep modeling and analysis. Grid search was used to optimize the models developed by AdaBoost, GBM, and RF; and permutation variable importance (PVI) was used to analyze the relative importance of different variables. Based on the modeling results, GBM model (R2 = 0.878, MSE = 106.660, MAE = 6.009) and RF model (R2 = 0.867, MSE = 107.500, MAE = 6.796) showed superior performances compared with AdaBoost model (R2 = 0.574, MSE = 388.369, MAE = 16.480). Furthermore, the PVI results suggested that the GBM model provided the best outcome, with the light irradiation time, type of catalyst, dosage of catalyst, solution pH, irradiation intensity, initial PFOA concentration, oxidizing agents (peroxymonosulfate, ammonium persulfate, and sodium persulfate), irradiation wavelength, and solution temperature as the most important process variables in decreasing order.

Microbubbles are typically 0.5–10 μm in size. Their size tends to make it easier for medication delivery mechanisms to navigate the body by allowing them to be swallowed more easily. The gas included in the microbubble is surrounded by a membrane that may consist of biocompatible biopolymers, polymers, surfactants, proteins, lipids, or a combination thereof. One of the most effective implementation techniques for tiny bubbles is to apply them as a drug carrier that has the potential to activate ultrasound (US); this allows the drug to be released by US. Microbubbles are often designed to preserve and secure medicines or substances before they have reached a certain area of concern and, finally, US is used to disintegrate microbubbles, triggering site-specific leakage/release of biologically active drugs. They have excellent therapeutic potential in a wide range of common diseases. In this article, we discussed microbubbles and their advantageous medicinal uses in the treatment of certain prevalent disorders, including Parkinson's disease, Alzheimer's disease, cardiovascular disease, diabetic condition, renal defects, and finally, their use in the treatment of various forms of cancer as well as their incorporation with nanoparticles. Using microbubble technology as a novel carrier, the ability to prevent and eradicate prevalent diseases has strengthened the promise of effective care to improve patient well-being and life expectancy.

Introduction: Stroke is one of the most serious and debilitating diseases in our society. Stroke survivors can suffer several neurological impairments. They typically show pathological changes in neural functions of brain areas. Transcranial direct current stimulation (tDCS) can be applied to modify cerebral excitability and has been recently applied in the treatment of neuropsychological and neurological disorders. The aim of the study herein was to evaluate changes in movement and cognitive functions of chronic ischemic stroke patients using anodic and cathodic tDCS. Method: In this clinical trial, 100 patients with chronic ischemic stroke were divided in 4 groups (control, sham, anodic tDCS, and cathodic tDCS; n=25 per group). The patients underwent routine treatment and received the same tDCS protocol (3 times per week of 30-min sessions during one month). Movement and cognitive functions were evaluated using the NIHSS Scale & Trail Making Test and the Beck Test, before and after 1 and 3 months of tDCS sessions. All data were analyzed by 2-way repeated measures ANOVA using SPSS program (version 18). Results: The results showed that there were significant increases (P

In this study, granular activated carbon (GAC) was examined for the removal of five of the most commonly detected pharmaceuticals (naproxen, carbamazepine, acetaminophen, ibuprofen and metronidazole) from a nitrified urine to make the urine-derived fertiliser nutrient safe for food crops. Batch experiments were conducted to investigate the adsorption kinetics that described the removal of micropollutants (equal concentrations of 0.2 mM) from the synthetic nitrified urine at different GAC dosages (10–3000 mg/L). Artificial neural network modelling was also used to predict and simulate the removal of pharmaceuticals from nitrified urine. Langmuir and Freundlich isotherm models described the equilibrium data, with the Langmuir model providing slightly higher correlations. At the highest dose of 3000 mg/L GAC, all the pharmaceuticals showed a removal rates of over 90% after 1 h of adsorption time and 99% removal rates after 6 h of adsorption time. This study concludes that GAC is able to remove the targeted xenobiotics without affecting the concentration of N and P in the urine, suggesting that nitrified urine could be safely used as a nutrient product in future.

Renal artery stenosis is caused by a heterogeneous group of diseases, including atherosclerosis and fibromuscular dysplasia, which can be treated medically, via endovascular techniques, or by open revascularization; however, satisfactory and effective results are not always obtained. We aimed to assess the possibility of renal revascularization by a pedicled intestinal segment wrapping the kidney.Five dogs were operated on at three steps. At the first step, laparotomy was performed, and the right kidney was released. Subsequently, an 8-10 cm segment of jejunum was separated longitudinally, and mucosectomy was done. This intestinal patch wrapped up the kidney. After eight weeks, the kidney and the intestinal patch were analyzed, and the renal artery was ligated. After four weeks, the kidney and the intestinal patch were sent for pathological evaluation.At the 12th week of evaluation, no evidence of abscess formation or collection was seen. All kidneys had a normal color, consistency, and size. All renal cells were alive, and neither atrophy nor necrosis was seen. Glomerulus and tubules were intact, and no inflammatory change was visible. Furthermore, thick wall vasculature was inspected in a fibromuscular tissue, rising from the intestinal flap toward the kidney. One of the dogs expired due to peritonitis and sepsis in the fifth week.In our study, indirect perfusion of the kidney by an intestinal patch was achieved successfully. This represents new hope in patients suffering from chronic renal failure who underwent former medical and surgical interventions with undesirable results.

Photocatalysis is widely used for the degradation of organic pollutants, with TiO2 and ZnO as the best candidates with unique properties. However, agglomeration and recycling are major challenges in practical photocatalysis applications. Advanced deposition processes can provide nanotubular or hierarchical structures that are more promising than suspended particles. More importantly, higher efficiency of photoelectrocatalysis than photocatalysis for the degradation of persistent organic pollutants including perfluorooctanoic acid (PFOA) necessitates catalyst immobilization. Photoelectrocatalysis exhibited remarkably higher efficiency (56.1%) than direct photolysis (15.1%), electrocatalysis (5.0%) and photocatalysis (18.1%) for PFOA degradation. This paper aims to review the progress in the application of anodizing and thermal spraying as two major industrial surface engineering processes to bridge the gap between laboratorial and practical photocatalysis technology. Overall, thermal spraying is considered as one of the most efficient methods for the deposition of TiO2 and ZnO photocatalytic films.

15 figures, 4 tables.-- © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/, Internal concentration polarization (ICP) is currently a major bottleneck in the forward osmosis process. Proper modelling of the internal concentration polarization is therefore vital for improving the process performance and efficiency. This study assessed the feasibility of several machine learning methods for internal concentration polarization prediction, including artificial neural networks, extreme gradient boosting (XGBoost), Categorical boosting (CatBoost), Random forest, and linear regression. Among the many algorithms evaluated, the CatBoost regression outperformed other methods in terms of coefficient of determination (R2) and the mean square error. The CatBoost algorithm's prediction power was then evaluated using non-training (user-provided) data and compared to solution diffusion models. The results indicated that the machine learning algorithms could predict ICP in the process with high accuracy for the provided dataset and excellent generalizability for future testing data. Furthermore, machine learning algorithms may offer insights into the input features that majorly affect ICP modelling in the forward osmosis process., Thanks to the Australian government for providing a research training scholarship to Ibrar. © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

This study aims to analyze and model cathodic H2 recovery (rcat), coulombic efficiency (CE) with inputs of voltage, electrical conductivity (EC) and anode potential, and H2 production rate and total energy recovery with inputs of rcat and CE in a microbial electrolysis cell using artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) procedures. Both ANN and ANFIS models demonstrated great goodness of fit for rcat, CE, H2 production rate and total energy recovery prediction with high R2 values. The sum square error values for rcat (0.0017), CE (0.0163), H2 production rate (0.1062) and total energy recovery (0.0136) in ANN models were slightly higher than those in ANFIS models at 0.0005, 0.0091, 0.1247 and 0.0148 respectively. Sensitivity analysis by ANN models demonstrated that voltage, EC, rcat and rcat were the most effective factors for rcat, CE, H2 production rate and total energy recovery, respectively.

Osmotic Membrane Bioreactor (OMBR) is an emerging technology for wastewater treatment with membrane fouling as a major challenge. This study aims to develop Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models in simulating and predicting water flux in OMBR. Mixed liquor suspended solid (MLSS), electrical conductivity (EC) and dissolved oxygen (DO) were used as model inputs. Good prediction was demonstrated by both ANFIS models with R2 of 0.9755 and 0.9861, and ANN models with R2 of 0.9404 and 0.9817, for thin film composite (TFC) and cellulose triacetate (CTA) membranes, respectively. The root mean square error for TFC (0.2527) and CTA (0.1230) in ANFIS models was lower than in ANN models at 0.4049 and 0.1449. Sensitivity analysis showed that EC was the most important factor for both TFC and CTA membranes in ANN models, while EC (TFC) and MLSS (CTA) are key parameters in ANFIS models.

This study aimed to assess the prediction efficiencies of response surface methodology (RSM) and artificial neural network (ANN)-based models in terms of benzene, toluene, ethylbenzene, and xylenes (BTEX) removal from a polluted airstream using non-thermal plasma (NTP). The effect that key elements of the NTP process, including temperature, BTEX concentration, voltage and flow rate, had on the BTEX elimination efficiency was investigated using a central composite RSM design along with three ANN models including Feed-Forward Back Propagation Neural Network (FFBPNN), Cascade-Forward Back Propagation Neural Network (CFBPNN) and Elman-Forward Back Propagation Neural Network (EFBPNN) with the topology of 4-h-1. The RSM and ANN models were statistically compared using some indicators including Sum of Squared Errors (SSE), adjusted R2, determination coefficient (R2), Root Mean Squared Error (RMSE), Absolute Average Deviation (AAD). According to the RSM output, voltage was the most efficient variable with a coefficient proportion of 8.28. Besides, FFBPNN was the best model among the considered ANN models. Also, the R2 achieved for ANN (FFBPNN) and RSM models were 0.9736 and 0.9656 correspondingly. Therefore, it was concluded that the ANN (FFBPNN) represents a powerful tool for modeling the BTEX removal.

Removal of antibiotics from aqueous environments is of great importance because of their potential adverse effects on living organisms and enhancement of bacterial resistance. The present study deals with tetracycline (TC) removal using sawdust modified with different agents (CaCl2, NaHCO3, HCl, and FeCl3) as a function of TC concentration (5.0–25.0 mg L−1), solution pH (3.0–12.0), adsorbent dose (1.0–5.0 g L−1), and contact time (15.0–120.0 min). Fourier Transform Infrared Spectroscopy and Scanning Electron Microscopy were employed to determine the characteristics of the adsorbent. The results showed that modification with FeCl3 was the most efficient way to increase adsorptive properties of sawdust. Optimization showed the highest removal efficiency is obtained as 98.40% at initial TC concentration 12.5 mg L−1, pH 7.9, adsorbent dose = 4.8 g L−1, and contact time 118.3 min. The Fe-modified adsorbent could reduce TC in real hospital wastewater from 0.25 mg L−1 to levels not detectable by HPLC. The equilibrium data for TC adsorption were better fitted to the Freundlich model (R2 = 0.9891), while both models of pseudo-first-order and pseudo-second-order could well describe the kinetics of adsorption (AIC values of −18.74 and −18.32, respectively). Finally, an extensive experiment was conducted to determine single-stage regeneration of used adsorbent, showing that among 10 different regenerating solutions, distilled water (pH = 4.0) and methanol demonstrated the best results with nearly 88% reclamation of water. Based on the results, Fe-modified sawdust could be an efficient adsorbent for TC removal from aqueous solutions, even from real wastewater.

This study aimed to assess, optimize and model the efficiencies of Fenton, photo-Fenton and ozonation/Fenton processes in formaldehyde elimination from water and wastewater using the response surface methodology (RSM) and artificial neural network (ANN). A sensitivity analysis was used to determine the importance of the independent variables. The influences of different variables, including H2O2 concentration, initial formaldehyde concentration, Fe dosage, pH, contact time, UV and ozonation, on formaldehyde removal efficiency were studied. The optimized Fenton process demonstrated 75% formaldehyde removal from water. The best performance with 80% formaldehyde removal from wastewater was achieved using the combined ozonation/Fenton process. The developed ANN model demonstrated better adequacy and goodness of fit with a R2 of 0.9454 than the RSM model with a R2 of 0. 9186. The sensitivity analysis showed pH as the most important factor (31%) affecting the Fenton process, followed by the H2O2 concentration (23%), Fe dosage (21%), contact time (14%) and formaldehyde concentration (12%). The findings demonstrated that these treatment processes and models are important tools for formaldehyde elimination from wastewater.

Renewable energy, water conservation, and environmental protection are the most important challenges today. Osmotic membrane bioreactor (OMBR) is an innovative process showing superior performance in bioenergy production, eliminating contaminants, and low fouling tendency. However, salinity build-up is the main drawback of this process. Identifying the microbial community can improve the process in bioenergy production and contaminant treatment. This review aims to study the recent progress and challenges of OMBRs in contaminant removal, microbial communities and bioenergy production. OMBRs are widely reported to remove over 80% of total organic carbon, PO43-, NH4+ and emerging contaminants from wastewater. The most important microbial phyla for both hydrogen and methane production in OMBR are Firmicutes, Proteobacteria and Bacteroidetes. Firmicutes' dominance in anaerobic processes is considerably increased from usually 20% at the beginning to 80% under stable condition. Overall, OMBR process has great potential to be applied for simultaneous bioenergy production and wastewater treatment.

Treatment with a non-thermal plasma (NTP) is a new and effective technology applied recently for conversion of gases for air pollution control. This research was initiated to optimize the efficient application of the NTP process in benzene, toluene, ethyl-benzene, and xylene (BTEX) removal. The effects of four variables including temperature, initial BTEX concentration, voltage, and flow rate on the BTEX elimination efficiency were investigated using response surface methodology (RSM). The constructed model was evaluated by analysis of variance (ANOVA). The model goodness-of-fit and statistical significance was assessed using determination coefficients (R 2 and R 2 adj) and the F-test. The results revealed that the R 2 proportion was greater than 0.96 for BTEX removal efficiency. The statistical analysis demonstrated that the BTEX removal efficiency was significantly correlated with the temperature, BTEX concentration, voltage, and flow rate. Voltage was the most influential variable affecting the dependent variable as it exerted a significant effect (p