Background: Type 2 diabetes mellitus is a condition characterized by an elevation of oxidative stress, which has been implicated in diabetic progression and its vascular complications. Aim: Assessing the impact of gliclazide modified release (MR) versus glimepiride on oxidative stress markers, glycemic indices, lipid profile, and estimated glomerular filtration rate in uncontrolled type 2 diabetic patients on metformin monotherapy. Methods: This was an observational comparative study conducted in Thi-Qar specialized diabetic, endocrine, and metabolism center. Sixty-six patients were randomized into two groups based on the addition of the sulfonylureas (SUs). Group 1 (33 patients) was on gliclazide MR, whereas Group 2 (33 patients) was on glimepiride. The measured oxidative stress markers were reduced glutathione (GSH), superoxide dismutase (SOD), malondialdehyde (MDA), and protein carbonyl (PC) evaluated before and after 16 weeks of SUs addition. Results: There were significant drops in SOD (P < 0.001), MDA (P < 0.001), and PC (P = 0.001) and a significant increase in GSH (p = 0.029) levels after gliclazide MR add-on therapy. There were significant drops in SOD (P = 0.026) and MDA (P < 0.001) levels with non-significant changes in both GSH (P = 0.214) and PC (P = 0.538) after glimepiride add-on therapy. There was a significant difference in improvement of PC level (P = 0.048) in the gliclazide group compared to the glimepiride group, with a non-significant numerically higher improvement of GSH, SOD, and MDA in gliclazide MR than glimepiride. At the end of the study, there were no significant differences in glycemic control, lipid profile, or eGFR improvement between the two groups. Conclusion: Glycemic control plays a pivotal role in decreasing oxidative stress. The control of diabetes with the gliclazide-MR-metformin combination reduced oxidative stress more than the glimepiride-metformin combination, indicating its antioxidant property. Keywords: Oxidative Stress, T2DM, Gliclazide MR, Glimepiride, Metformin.
Background: Metabolic syndrome (Mets) is partially heritable. High mobility group AT-hook1 (HMGA1), an architectural transcription factor, affects the homeostasis of glucose. The marked inter-individual differences between T
... Show MoreThis paper proposes a hybrid artificial intelligence (AI) model that combines an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to predict the Quality of Service (QoS) in 5G networks. The model utilizes radio channel indicators such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), and Channel Quality Indicator (CQI) to forecast throughput and latency levels. These indicators are critical factors affecting network performance; however, the nonlinear relationship among them makes traditional analytical models inadequate for accurate QoS prediction. The importance of the current study lies in estimating QoS in 5G networks by combining
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