Day 2 :
Gerald C. Hsu, eclaireMD Foundation, USA
Time : 09:30-10:15
Gerald C Hsu has received his PhD in Mathematics and majored in Engineering at MIT. He has attended different universities over 17 years and studied seven academic disciplines. He has spent a huge time research in T2D research. His approach is “Math-Physics and Quantitative Medicine” based on mathematics, physics, engineering modeling, signal processing, computer science, big data analytics, statistics, machine learning and AI. His research focus is on preventive medicine using prediction tools.
Based on his research, the author has developed two glucose prediction tools and he was able to reduce his FPG from 185 mg/dL to 119.6 mg/dL (28 lbs weight reduction), daily glucose from 279 mg/dL to 117 mg/dL and A1C from 10% to 6.1%. He examined correlations between FPG and PPG, carbs and sugar intake and exercise amount but found all were below 7% (very low) and finally discovered the major cause: It is weight. Based on 25,000 data of 1,449 days (1/1/2014 - 12/20/2017), he found 85% correlation between FPG and weight. In time series diagram, there are two high peak periods and two low valley periods of weight and the FPG curve followed the weight curve like its twin. In spatial analysis diagram of BMI vs. FPG (without time factor), there is a quasi-linear equation existing between two coordinates of BMI and FPG: From point A (24.5, 98.0) to point B (27.0,148.0). The stochastic (random) distribution of data has 2 clear concentration bands stretched from lower left corner toward upper right corner. The ±10% band covers 65% of total data and the ±20% band covers 93% of total data. Only the remaining 7% of total data are influenced by other 5 secondary factors. After capturing basic characteristics, he then developed a practical tool to predict each day’s FPG value. The final prediction accuracy is 98.3% with 85% correlation between predicted and actual FPG values.
Abdul Malek Ukil Medical College, Bangladesh
Keynote: Antimicrobial sensitivity pattern of uropathogens in Diabetic patients with urinary tract infection at Bangladesh
Time : 10:35-11:20
Mohammad Saifuddin is a young eminent Endocrinologist of Bangladesh who dedicated himself in service of humanity towards rural people of Bangladesh. He has received his MBBS from Dhaka Medical College at 2004 and obtained Fellowship in Medicine (FCPS) from Bangladesh College of Physicians and Surgeons in 2012 and passed MD (Endocrinology) from BIRDEM Academy at 2013. He had worked in Bangladesh Civil Service for the last 10 years and currently working as an Assistant Professor (Endocrinology) in Abdul Malek Ukil Medical College, Noakhali, Bangladesh. He has 14 publications in national and international level. His research interests are in diabetes and complications, adrenal disorders, osteoporosis, thyroid disorders and menstrual abnormalities.
Patients with Diabetes Mellitus (DM) are prone to develop infection, especially Urinary Tract Infection (UTI) in comparison with non-diabetics. Due to the emergence of Multidrug Resistant (MDR) uropathogenic strains, the choice of antimicrobial agent is sometimes difficult. This study is designed to reveal the distribution of uropathogens in diabetic patients and corresponding sensitivity patterns and to correlate the microbiological results with various clinical parameters. A nine-month retrospective review of 100 urine culture reports of Diabetic patients from January 2015 to September 2015 from semi-urban multispecialty hospital of Feni, Bangladesh were analyzed. Only diabetic patients were included in this study who were clinically diagnosed as UTI patients with a corresponding urine culture showing a bacterial count of ˃105 CFU/ml. Out of 100 patients with UTI, 39 (39%) were male and 61 (61%) were female. Organisms grown in urine culture were Escherichia coli (64) followed by Klebsiella (11), Proteus (7), Staphylococcus aureus (4), Pseudomonas (4), Acinetobacter (3), Streptococcus (3), Enterococcus (2) and one each of Enterobacter and Fungi. Overall sensitivity pattern in decreasing order of various commonly used antibiotics were Meropenem (89%), Nitrofurantoin (86%), Amikacin (81%), Ceftriaxone (68%), Cefuroxime (61%), Cefixime (39%), Quinolones (28%) and Amoxicillin (16%). The significance of the study lies in the determination of common pathogens in diabetic patients with UTI and the resistance pattern of antibiotics so that physicians and pharmacists get the proper information rationalizing the rational use of antibiotics.