Comprehensive assessment of the effects of operating conditions on membrane intrinsic parameters of forward osmosis (FO) based on principal component analysis (PCA)

Evaluating the Physicochemical Properties-Activity Relationship and Discovering New 1,2-Dihydropyridine Derivatives as Promising Inhibitors for PIM1-Kinase: Evidence from Principal Component Analysis, Molecular Docking, and Molecular Dynamics Studies

In this study, we evaluated the physicochemical properties related to the previously reported anticancer activity of a dataset comprising thirty 1,2-dihydropyridine derivatives. We utilized Principal Component Analysis (PCA) to identify the most significant influencing factors. The PCA analysis showed that the first two principal components accounted for 59.91% of the total variance, indicating a strong correlation between the molecules and specific descriptors. Among the 239 descriptors analyzed, 18 were positively correlated with anticancer activity, clustering with the 12 most active compounds based on their IC50 values. Six of these variables-LogP, Csp3, b_1rotN, LogS, TPSA, and lip_don-are related to drug-likeness potential. Thus, we then ranked the 12 compounds according to these six variables and excluded those violating the drug-likeness criteria, resulting in a shortlist of nine compounds. Next, we investigated the binding affinity of these nine shortlisted compounds with the use of molecular docking towards the PIM-1 Kinase enzyme (PDB: 2OBJ), which is overexpressed in various cancer cells. Compound 6 exhibited the best docking score among the docked compounds, with a docking score of -11.77 kcal/mol, compared to -12.08 kcal/mol for the reference PIM-1 kinase inhibitor, 6-(5-bromo-2-hydroxyphenyl)-2-oxo-4-phenyl-1,2-dihydropyridine-3-carbonitrile. To discover new PIM-1 kinase inhibitors, we designed nine novel compounds featuring hybrid structures of compound 6 and the reference inhibitor. Among these, compound 31 displayed the best binding affinity, with a docking score of -13.11 kcal/mol. Additionally, we performed PubChem database mining using the structure of compound 6 and the similarity search tool, identifying 16 structurally related compounds with various reported biological properties. Among these, compound 52 exhibited the best binding affinity, with a docking score of -13.03 kcal/mol. Finally, molecular dynamics (MD) studies were conducted to confirm the stability of the protein-ligand complexes obtained from docking the studied compounds to PIM-1 kinase, validating the potential of these compounds as PIM-1 kinase inhibitors.

On the evaluating membrane flux of forward osmosis systems: Data assessment and advanced intelligent modeling

As an emerging desalination technology, forward osmosis (FO) can potentially become a reliable method to help remedy the current water crisis. Introducing uncomplicated and precise models could help FO systems' optimization. This paper presents the prediction and evaluation of FO systems' membrane flux using various artificial intelligence-based models. Detailed data gathering and cleaning were emphasized because appropriate modeling requires precise inputs. Accumulating data from the original sources, followed by duplicate removal, outlier detection, and feature selection, paved the way to begin modeling. Six models were executed for the prediction task, among which two are tree-based models, two are deep learning models, and two are miscellaneous models. The calculated coefficient of determination (R2 ) of our best model (XGBoost) was 0.992. In conclusion, tree-based models (XGBoost and CatBoost) show more accurate performance than neural networks. Furthermore, in the sensitivity analysis, feed solution (FS) and draw solution (DS) concentrations showed a strong correlation with membrane flux. PRACTITIONER POINTS: The FO membrane flux was predicted using a variety of machine-learning models. Thorough data preprocessing was executed. The XGBoost model showed the best performance, with an R2 of 0.992. Tree-based models outperformed neural networks and other models.

Forward Osmosis Application for the Removal of Emerging Contaminants from Municipal Wastewater: A Review

Forward osmosis (FO) has attracted special attention in water and wastewater treatment due to its role in addressing the challenges of water scarcity and contamination. The presence of emerging contaminants in water sources raises concerns regarding their environmental and public health impacts. Conventional wastewater treatment methods cannot effectively remove these contaminants; thus, innovative approaches are required. FO membranes offer a promising solution for wastewater treatment and removal of the contaminants in wastewater. Several factors influence the performance of FO processes, including concentration polarization, membrane fouling, draw solute selection, and reverse salt flux. Therefore, understanding and optimizing these factors are crucial aspects for improving the efficiency and sustainability of the FO process. This review stresses the need for research to explore the potential and challenges of FO membranes to meet municipal wastewater treatment requirements, to optimize the process, to reduce energy consumption, and to promote scalability for potential industrial applications. In conclusion, FO shows promising performance for wastewater treatment, dealing with emerging pollutants and contributing to sustainable practices. By improving the FO process and addressing its challenges, we could contribute to improve the availability of water resources amid the global water scarcity concerns, as well as contribute to the circular economy.

Shedding light on the transfer of tetracycline in forward osmosis through experimental investigation and machine learning modeling

Tetracycline in wastewater can pose adverse impacts on the environment and human health. Forward osmosis (FO) is a promising method to reject antibiotics due to its low energy demand and high rejection rate. Tetracycline rejection during FO is a complicated process. Mechanistic models have been developed to describe antibiotic rejection by the FO membrane under ideal conditions but cannot be applied to real wastewater. Herein, the effects of draw concentration, pH, and solute type on the fate of tetracycline during FO were investigated by combining experimentation, factor analysis, and artificial neural network (ANN) modeling. High draw concentrations led to high convection that favored tetracycline diffusion. Low draw pH helped reject antibiotics potentially due to the decreased tortuosity and pore size of the FO membrane. When different draw solutes were tested, both convection and electrostatic interaction exerted effects on tetracycline retention on the FO membrane surface, and steric hindrance could further affect the amount of tetracycline in the draw solution. Exploratory factor analysis (EFA) showed that tetracycline rejection was a combined result of convection, steric hindrance, and electrostatic interactions. Path analysis revealed the significant roles of initial conductivity and draw pH in tetracycline rejection. Eight representative input variables were selected from 13 observed explanatory variables using redundancy analysis (RDA), based on which an ANN was trained and successfully predicted tetracycline diffusion and transfer through the FO membrane. These results have provided practical and predictive insights in the development of FO processes for efficient treatment of pharmaceutical wastewater.

Understanding the combination of fractional factorial design and chemometrics analysis for screening super-saturable quercetin-self nano emulsifying components

Quercetin is formulated in a super saturable - self-nano emulsifying (SS-SNE) to increase its stability and bioavailability. This study focuses on the screening design for SS-SNE components with a fractional factorial design (FrFD) approach and chemometric analysis. The FrFD method was chosen because it provides comprehensive benefits. The oil components used are canola and grape seed oil. Croduret 50-SS was selected as a surfactant and PEG 400 as a co-surfactant. The interaction of SNE components was evaluated using FTIR-ATR instrumentation. SNE droplet morphology was observed using a transmission electron microscope (TEM). The selected formulas were grape seed oil as oil phase at 19.6%, croduret at 60%, and PEG 400 as co-surfactant with a concentration of 16.6%. The selected formula has a droplet size of 133.27 nm, PDI of 0.181, the zeta potential of 17.00 mV, electrophoretic mobility of 1.332 µmcm/Vs, emulsification time of 10.05 seconds, a viscosity of 370.147 mPa.s, and a drug load of 31.70 mg/mL. The components of grape seed oil, croduret, and PEG 400 resulted in a quercetin carrier SNE formula that met the criteria. FrFD design and chemometric analysis in the screening process can help determine the selected formula very effectively and efficiently.

The Application of Principal Component Analysis (PCA) for the Optimization of the Conditions of Fabrication of Electrospun Nanofibrous Membrane for Desalination and Ion Removal

Nowadays, acquiring a water supply for urban and industrial uses is one of the greatest challenges facing humanity for ensuring sustainability. Membrane technology has been considered cost-effective, encompasses lower energy requirements, and at the same time, offers acceptable performance. Electrospun nanofibrous membranes (ENMs) are considered a novel and promising strategy for the production of membranes that could be applied in several treatment processes, especially desalination and ion removal. In this study, we apply an unsupervised machine-learning strategy, the so-called principal component analysis (PCA), for the purpose of seeking discrepancies and similarities between different ENMs. The main purpose was to investigate the influence of membrane fabrication conditions, characteristics, and process conditions in order to seek the relevance of the application of different electrospun nanofibrous membranes (ENMs). Membranes were majorly classified into single polymers/layers, from one side, and dual multiple layer ENMs, from another side. For both classes, variables related to membrane fabrication conditions were not separated from membrane characterization variables. This reveals that membranes' characteristics not only depend on the chemical composition, but also on the fabrication conditions. On the other hand, the process conditions of ENM fabrication showed an extensive effect on membranes' performance.