Association of Genetic Polymorphism rs 77630697(Gly64Asp) of Multidrug and Toxin Extrusion -1 with glycemic response to metformin in patients with Type 2 Diabetes Mellitus.

Objective: To determine the relationship between Gly64Asp (rs77630697) polymorphism of multidrug and toxin extrusion-1 (MATE-1) and therapeutic response of metformin in Type-2 diabetic patients. Methods: A longitudinal study was conducted at Riphah International Hospital, Islamabad from June 2020 to December 2021. Type-2 diabetic patients (n=200) on metformin monotherapy fulfilling the inclusion criteria were enrolled and followed up till three months. Based on change in HbA1c, they were divided into responders and non-responders. DNA was extracted and genotyping was done by TETRA ARMS PCR. Data was entered and association was analyzed by SPSS 22. Results: Out of 200 participants, 104 were categorized as responders and 96 as non-responders. The genotype and allelic distribution of rs77630697 was significantly different between responders and non-responders. The variant genotype (GG) was most prevalent among the study population and among responders. After follow up of three months, difference in glycemic response was found to be statistically significant (p < 0.05) among three genotypes (GG, GA and AA). The decline in HbA1c was highest in GG genotype with almost two-fold reduction in comparison with GA and AA. Carriers of allele A were significantly associated with impaired response to metformin. Conclusion: The variable therapeutic response to metformin in the responders and non-responders may be contributed to rs77630697 isoform variation of MATE-1.

IoT Based Expert System for Diabetes Diagnosis and Insulin Dosage Calculation.

High blood glucose levels are the defining characteristic of diabetes. Uncontrolled blood glucose levels in diabetic patients might result in mortality. As a result, there is a dire need to control blood glucose levels by constantly monitoring them and delivering the appropriate amount of insulin. However, insulin consumption is affected by several variables, including age, calorific intake, and body weight. The patient must see the doctor on a regular basis in order to determine the appropriate dose. Nonetheless, hospital facilities are finding it increasingly difficult to treat patients as the number of patients rises; thus, the healthcare industry is searching for an efficient method that can alleviate their burden by assisting patients with chronic conditions through remote patient care. In this work, we have developed an expert system to provide remote treatment for diabetic patients. Our expert system consists of two distinct components: one for the patient and one for the hospital. The sole requirement for the patient will be a wearable device that captures and transmits all relevant data to the cloud. On the hospital side, there should be a system in place to process that data in the cloud. The system employs a fuzzy system to handle data in two stages. A fuzzy system is initially employed to identify whether or not a patient is diabetic. In the second stage, a fuzzy system is utilized to determine the insulin dosage for a diabetic patient. Using sensors and the ESP8266 platform, we have developed a prototype of patient-side hardware. The MATLAB fuzzy toolbox is used for the processing part, which includes fuzzy systems, and the results of the MATLAB analysis are presented in the form of simulation results to demonstrate the accuracy of the proposed system in terms of determining insulin dosage. The results of the simulation using the fuzzy toolbox for the insulin dose of the diabetic patient are significantly close to the amount of dosage prescribed by the endocrinologist.

Association Of Vitamin B12 Deficiency With Intake Of Oral Metformin In Diabetic Patients.

BACKGROUND: It is estimated that 450 million people worldwide have diabetes mellitus Objective of the study was to determine effect of Metformin on vitamin B12 level among patients with type 2 diabetes mellitus. It is a cross sectional study conducted at Medicine department, Shalamar Hospital Lahore. METHODS: Seventy-two patients of diabetes mellitus were included in the study. All patients who could have any other disease process that can lead to Vitamin B12 deficiency were excluded from the study. They were segregated into two groups, first one was getting metformin for the last 2 years and the second group was not taking metformin. Vitamin B12 levels were checked in all the patients and value of 150 pmol/L was taken as normal. RESULTS: Out of 72 patients who participated in our study, 40 patients (55.5%) were taking metformin for more than two years while 32 patients (44.5%) were not taking metformin for the last two years. Mean age of patients taking metformin and not taking metformin was 59±9.12 years and 60±5.47 years respectively. Mean vitamin B12 level was 360±185.2 pg/ml in patients taking metformin while among non-metformin group mean vitamin B12 level was 619±176 pg/ml (p value=0.0001). Furthermore, HbA1c and MCV were significantly different among metformin group and nonmetformin group, i.e., p=0.034 and 0.039 respectively. Among metformin group, age and MCV were also significantly different in those having vitamin B12 deficiency and those having normal Vitamin B12 levels, i.e., p=0.042 and 0.001 respectively. CONCLUSIONS: It was concluded from our study that long term (>2 years) use of metformin is significantly associated with vitamin B12 deficiency.

Optimized Energy Harvesting, Cluster-Head Selection and Channel Allocation for IoTs in Smart Cities.

This paper highlights three critical aspects of the internet of things (IoTs), namely (1) energy efficiency, (2) energy balancing and (3) quality of service (QoS) and presents three novel schemes for addressing these aspects. For energy efficiency, a novel radio frequency (RF) energy-harvesting scheme is presented in which each IoT device is associated with the best possible RF source in order to maximize the overall energy that the IoT devices harvest. For energy balancing, the IoT devices in close proximity are clustered together and then an IoT device with the highest residual energy is selected as a cluster head (CH) on a rotational basis. Once the CH is selected, it assigns channels to the IoT devices to report their data using a novel integer linear program (ILP)-based channel allocation scheme by satisfying their desired QoS. To evaluate the presented schemes, exhaustive simulations are carried out by varying different parameters, including the number of IoT devices, the number of harvesting sources, the distance between RF sources and IoT devices and the primary user (PU) activity of different channels. The simulation results demonstrate that our proposed schemes perform better than the existing ones.

Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques.

The Internet of Things (IoT) has gained an incredible importance in the communication and networking industry due to its innovative solutions and advantages in diverse domains. The IoT' network is a network of smart physical objects: devices, vehicles, buildings, etc. The IoT has a number of applications ranging from smart home, smart surveillance to smart healthcare systems. Since IoT consists of various heterogeneous devices that exhibit different traffic patterns and expect different quality of service (QoS) in terms of data rate, bit error rate and the stability index of the channel, therefore, in this paper, we formulated an optimization problem to assign channels to heterogeneous IoT devices within a smart building for the provisioning of their desired QoS. To solve this problem, a novel particle swarm optimization-based algorithm is proposed. Then, exhaustive simulations are carried out to evaluate the performance of the proposed algorithm. Simulation results demonstrate the supremacy of our proposed algorithm over the existing ones in terms of throughput, bit error rate and the stability index of the channel.