PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397
Browse
2 results
Search Results
Article Citation - WoS: 22Citation - Scopus: 26Suppression of Inflammatory Cytokines Expression With Bitter Melon (Momordica Charantia) in TNBS-Instigated Ulcerative Colitis(Sciendo, 2020-09-01) Semiz, Asli; Acar, Ozden Ozgun; Cetin, Hulya; Semiz, Gurkan; Sen, AlaattinBackground and Objective: This study was aimed to elucidate the molecular mechanism of Momordica charantia (MCh), along with a standard drug prednisolone, in a rat model of colitis induced by trinitrobenzene sulfonic acid (TNBS). Methods: After the induction of the experimental colitis, the animals were treated with MCh (4 g/kg/day) for 14 consecutive days by intragastric gavage. The colonic tissue expression levels of C-C motif chemokine ligand 17 (CCL-17), interleukin (IL)-1 beta, IL-6, IL-23, interferon-gamma (IFN-gamma), nuclear factor kappa B (NFkB), and tumor necrosis factor-alpha (TNF-alpha), were determined at both mRNA and protein levels to estimate the effect of MCh. Besides, colonic specimens were analyzed histopathologically after staining with hematoxylin and eosin. Results: The body weights from TNBS-instigated colitis rats were found to be significantly lower than untreated animals. Also, the IFN-gamma, IL-1 beta, IL-6, Il-23, TNF-alpha, CCL-17, and NF-kB mRNA and protein levels were increased significantly from 1.86-4.91-fold and 1.46-5.50-fold, respectively, in the TNBS-instigated colitis group as compared to the control. Both the MCh and prednisolone treatment significantly reduced the bodyweight loss. It also restored the induced colonic tissue levels of IL-1 beta, IL-6, IFN-gamma, and TNF-alpha to normal levels seen in untreated animals. These results were also supported with the histochemical staining of the colonic tissues from both control and treated animals. Conclusion: The presented data strongly suggests that MCh has the anti-inflammatory effect that might be modulated through vitamin D metabolism. It is the right candidate for the treatment of UC as an alternative and complementary therapeutics.Article Citation - WoS: 9Citation - Scopus: 15MicroBiomeGSM: The Identification of Taxonomic Biomarkers From Metagenomic Data Using Grouping, Scoring and Modeling (G-S-M) Approach(Frontiers Media S.A., 2023-11-22) Bakir-Gungor, Burcu; Temiz, Mustafa; Jabeer, Amhar; Wu, Di; Yousef, MalikNumerous biological environments have been characterized with the advent of metagenomic sequencing using next generation sequencing which lays out the relative abundance values of microbial taxa. Modeling the human microbiome using machine learning models has the potential to identify microbial biomarkers and aid in the diagnosis of a variety of diseases such as inflammatory bowel disease, diabetes, colorectal cancer, and many others. The goal of this study is to develop an effective classification model for the analysis of metagenomic datasets associated with different diseases. In this way, we aim to identify taxonomic biomarkers associated with these diseases and facilitate disease diagnosis. The microBiomeGSM tool presented in this work incorporates the pre-existing taxonomy information into a machine learning approach and challenges to solve the classification problem in metagenomics disease-associated datasets. Based on the G-S-M (Grouping-Scoring-Modeling) approach, species level information is used as features and classified by relating their taxonomic features at different levels, including genus, family, and order. Using four different disease associated metagenomics datasets, the performance of microBiomeGSM is comparatively evaluated with other feature selection methods such as Fast Correlation Based Filter (FCBF), Select K Best (SKB), Extreme Gradient Boosting (XGB), Conditional Mutual Information Maximization (CMIM), Maximum Likelihood and Minimum Redundancy (MRMR) and Information Gain (IG), also with other classifiers such as AdaBoost, Decision Tree, LogitBoost and Random Forest. microBiomeGSM achieved the highest results with an Area under the curve (AUC) value of 0.98% at the order taxonomic level for IBDMD dataset. Another significant output of microBiomeGSM is the list of taxonomic groups that are identified as important for the disease under study and the names of the species within these groups. The association between the detected species and the disease under investigation is confirmed by previous studies in the literature. The microBiomeGSM tool and other supplementary files are publicly available at: https://github.com/malikyousef/microBiomeGSM.