Exposure to water-pipe smoking, whether flavored or unflavored, has been shown to instigate inflammation and oxidative stress in BALB/c mice. This consequently results in alterations in the expression of inflammatory markers and antioxidant genes. This study aimed to scrutinize the impact of Epigallocatechin gallate (EGCG)-a key active component of green tea-on inflammation and oxidative stress in BALB/c mice exposed to water-pipe smoke. The experimental setup included a control group, a flavored water-pipe smoke (FWP) group, an unflavored water-pipe smoke (UFWP) group, and EGCG-treated flavored and unflavored groups (FWP + EGCG and UFWP + EGCG). Expression levels of IL-6, IL1B, TNF-α, CAT, GPXI, MT-I, MT-II, SOD-I, SOD-II, and SOD-III were evaluated in lung, liver, and kidney tissues. Histopathological changes were also assessed. The findings revealed that the EGCG-treated groups manifested a significant decline in the expression of inflammatory markers and antioxidant genes compared to the FWP and UFWP groups. This insinuates that EGCG holds the capacity to alleviate the damaging effects of water-pipe smoke-induced inflammation and oxidative stress. Moreover, enhancements in histopathological features were observed in the EGCG-treated groups, signifying a protective effect against tissue damage induced by water-pipe smoking. These results underscore the potential of EGCG as a protective agent against the adverse effects of water-pipe smoking. By curbing inflammation and oxidative stress, EGCG may aid in the prevention or mitigation of smoking-associated diseases.
CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T2DM) and 173 control subjects to study the involvement of CD36 gene rs1761667 (G>A) and rs1527483 (C>T) polymorphisms in the pathogenesis of T2DM and dyslipidemia among Jordanian population. Lipid profile, blood sugar, gender and age were measured and recorded. Also, genotyping analysis for both polymorphisms was performed. Following statistical analysis, 10 different neural networks and machine learning (ML) tools were used to predict subjects with diabetes or dyslipidemia. Towards further understanding of the role of CD36 protein and gene in T2DM and dyslipidemia, a protein-protein interaction network and meta-analysis were carried out. For both polymorphisms, the genotypic frequencies were not significantly different between the two groups (p > 0.05). On the other hand, some ML tools like multilayer perceptron gave high prediction accuracy (≥ 0.75) and Cohen's kappa (κ) (≥ 0.5). Interestingly, in K-star tool, the accuracy and Cohen's κ values were enhanced by including the genotyping results as inputs (0.73 and 0.46, respectively, compared to 0.67 and 0.34 without including them). This study confirmed, for the first time, that there is no association between CD36 polymorphisms and T2DM or dyslipidemia among Jordanian population. Prediction of T2DM and dyslipidemia, using these extensive ML tools and based on such input data, is a promising approach for developing diagnostic and prognostic prediction models for a wide spectrum of diseases, especially based on large medical databases.