A prospective study on drug utilization pattern of anti-diabetic drugs in a tertiary care teaching hospital of eastern Uttar Pradesh, India

Background: Diabetes mellitus is a metabolic disorder with common denominator of hyperglycemia, arising from a variety of pathogenic mechanisms. The aim of the study was to evaluate the drug utilization pattern of anti-diabetic drugs in diabetic patients and observe adverse drug events (ADEs) associated with anti-diabetic therapy in a prospective way.Methods: A prospective study was carried out in diabetic patients visiting the Departments of General Medicine in a tertiary care teaching hospital. Demographic data, drug utilization pattern and ADEs due to Anti-diabetic drugs were summarized.Results: In the present study, 153 (54%) of the 282 diabetic patients were males and 129 (46%) were females. Majority of patients were in the age group of 51-60 years (31.20%) and most of the patients (31.56%) had a diabetic history of 11-15 years. Metformin was the most commonly prescribed drug (64.89%). Majority of the patients (36.87%) were on multidrug therapy. Co-morbid condition was found in 232 patients (82.26%) where hypertension (22.69%) being the most common co-morbid condition. 32 ADRs were observed with Nausea being the most common ADR reported.Conclusions: The present study helps to find out current prescribing pattern of oral diabetic medications with different co-morbidities with respect to diagnosis, cost of treatment and it also highlight the need for comprehensive management of diabetic patients, including life style changes, dietary control, hypoglycemic agents, cardiovascular prevention, treatment of complications and co-morbidity. Therefore, through the existing prescribing patterns, attempts can be made to improve the quality and efficiency of drug therapy

Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI, Journal of Telecommunications and Information Technology, 2022, nr 4

In this paper, the performance of a deep learning based multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system is investigated for 5G radio communication networks. We consider independent and identically distributed (i.i.d.) Nakagami-m fading links to prove that when using MIMO with the NOMA system, the outage probability (OP) and end-to-end symbol error rate (SER) improve, even in the presence of imperfect channel state information (CSI) and successive interference cancellation (SIC) errors. Further more, the stacked long short-term memory (S-LSTM) algorithm is employed to improve the system’s performance, even under time-selective channel conditions and in the presence of terminal’s mobility. For vehicular NOMA networks, OP, SER, and ergodic sum rate have been formulated. Simulations show that an S-LSTM-based DL-NOMA receiver outperforms least square (LS) and minimum mean square error (MMSE) receivers. Furthermore, it has been discovered that the performance of the end-to-end system degrades with the growing amount of node mobility, or if CSI knowledge remains poor. Simulated curves are in close agreement with the analytical results

Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI

In this paper, the performance of a deep learningbased multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system is investigated for 5G radio communication networks. We consider independent and identically distributed (i.i.d.) Nakagami-m fading links to prove that when using MIMO with the NOMA system, the outage probability (OP) and end-to-end symbol error rate (SER) improve, even in the presence of imperfect channel state information (CSI) and successive interference cancellation (SIC) errors. Furthermore, the stacked long short-term memory (S-LSTM) algorithm is employed to improve the system’s performance, even under time-selective channel conditions and in the presence of terminal’s mobility. For vehicular NOMA networks, OP, SER, and ergodic sum rate have been formulated. Simulations show that an S-LSTM-based DL-NOMA receiver outperforms least square (LS) and minimum mean square error (MMSE) receivers. Furthermore, it has been discovered that the performance of the end-to-end system degrades with the growing amount of node mobility, or if CSI knowledge remains poor. Simulated curves are in close agreement with the analytical results