Oral submucous fibrosis (OSF) is a chronic, progressive, and precancerous condition mainly caused by chewing areca nut. Currently, OSF therapy includes intralesional injection of corticosteroids with limited therapeutic success in disease management. Therefore, a combined approach of in silico, in vitro and in vivo drug development can be helpful. Polyphenols are relatively safer than other synthetic counterparts. We used selected polyphenols to shortlist the most suitable compound by in silico tools. Based on the in silico results, epigallocatechin-3-gallate (EGCG), quercetin (QUR), resveratrol, and curcumin had higher affinity and stability with the selected protein targets, transforming growth factor beta-1 (TGF-β1), and lysyl oxidase (LOX). The efficacy of selected polyphenols was studied in primary buccal mucosal fibroblasts followed by in vivo areca nut extract induced rat OSF model. In in vitro studies, the induced fibroblast cells were treated with EGCG and QUR. EGCG was safer at higher concentrations and more efficient in reducing TGF-β1, collagen type-1A2 and type-3A1 mRNA expression than QUR. In vivo studies confirmed that the EGCG hydrogel was efficient in improving the disease conditions compared to the standard treatment betamethasone injection with significant reduction in TGF-β1 and collagen concentrations with increase in mouth opening. EGCG can be considered as a potential, safer and efficient phytomolecule for OSF therapy and its mucoadhesive topical formulation help in the improvement of patient compliance without any side effects.
Graphical abstract
Highlights
Potential polyphenols were shortlisted to treat oral submucous fibrosis (OSF) using in silico tools
Epigallocatechin 3-gallate (EGCG) significantly reduced TGF-β1 and collagen both in vitro and in vivo
EGCG hydrogel enhanced antioxidant defense, modulated inflammation by reducing TGF-β1 and improved mouth opening in OSF rat model.
Electrochemical action and subsequent discharges are utilized in electrochemical discharge machining (ECDM) for the fabrication of components by subtracting undesired material. However, as the process progresses, localized electrolyte vaporization (machining zone) and its leading effects limit the process performance. Controlled delivery of fresh electrolyte into the machining zone to replenish the vaporized electrolyte improves ECDM process performance, utilized in the electrolyte injection-ECDM (EI-ECDM) process. Apart from control strategies, the literature lacks a detailed investigation of the phenomena involved in deteriorating the ECDM’s machining performance, with few researchers investigating how the deposition of the machined by-products on the tooltip might be a significant factor. Therefore, the present work was carried out to investigate the influence of deposition of the machined by-products on outcomes of the ECDM process at different parametric conditions. Various scientific tools and techniques were used to explore the underlying phenomena of machined by-products deposition. This shows that deposition significantly alters the geometry, surface texture, and properties of tool-electrode, which interns affect the ECDM’s performance. Further, experimental results and subsequent characterization reveal that EI-ECDM can significantly control the deposition and enhance the process performance. Thus, a multi-response optimization was performed to increase the applicability of the EI-ECDM process.
Structural integrity is a crucial aspect of engineering components, particularly in the field of additive manufacturing (AM). Surface roughness is a vital parameter that significantly influences the structural integrity of additively manufactured parts. This research work focuses on the prediction of the surface roughness of additive-manufactured polylactic acid (PLA) specimens using eight different supervised machine learning regression-based algorithms. For the first time, explainable AI techniques are employed to enhance the interpretability of the machine learning models. The nine algorithms used in this study are Support Vector Regression, Random Forest, XGBoost, AdaBoost, CatBoost, Decision Tree, the Extra Tree Regressor, the Explainable Boosting Model (EBM), and the Gradient Boosting Regressor. This study analyzes the performance of these algorithms to predict the surface roughness of PLA specimens, while also investigating the impacts of individual input parameters through explainable AI methods. The experimental results indicate that the XGBoost algorithm outperforms the other algorithms with the highest coefficient of determination value of 0.9634. This value demonstrates that the XGBoost algorithm provides the most accurate predictions for surface roughness compared with other algorithms. This study also provides a comparative analysis of the performance of all the algorithms used in this study, along with insights derived from explainable AI techniques.
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