Biography:

Gerald C Hsu has completed his PhD in Mathematics and majored in Engineering at MIT. He attended different universities over 17 years and studied seven academic disciplines. He has spent 20,000 hours in T2D research. His approach is quantitative medicine based on mathematics, physics, optical and electronics physics, engineering modeling, signal processing, computer science, big data analytics, statistics, machine learning and artificial intelligence. His main focus is on preventive medicine using prediction tools. He believes that the better the prediction, the more control you have.

Abstract:

Methodology of MPM: Initially, the author spent four years of self-studying six chronic diseases and food nutrition to gain in-depth medical domain knowledge. During 2014, he defined metabolism as a nonlinear, dynamic, and organic mathematical system having 10 categories with ~500 elements. He then applied topology concept with partial differential equation and nonlinear algebra to construct a metabolism equation. He further defined and calculated two variables, metabolism index and general health status unit. During the past 8.5 years, he has collected and processed 1.5 million data. Since 2015, he developed prediction models, i.e. equations, for both postprandial plasma glucose (PPG) and fasting plasma glucose (FPG). He identified 19 influential factors for PPG and five factors for FPG. Each factor has a different contribution margin to the glucose formation. He developed PPG model using optical physics and signal processing. Furthermore, by using both wave and energy theories, he extended his research into the risk probability of heart attack or stroke. In this risk assessment, he applied structural mechanics concepts, including elasticity, dynamic plastic, and fracture mechanics, to simulate artery rupture and applied fluid dynamics concepts to simulate artery blockage. He further decomposed 12,000 glucose waveforms with 21,000 data and then re-integrated them into three distinctive PPG waveform types which revealed different personality traits and psychological behaviors of type 2 diabetes patients. For single time-stamped variables, he used traditional time-series analysis. For interactions between two variables, he used spatial analysis. Furthermore, he also applied Fourier Transform to conduct frequency domain analyses to discover some hidden characteristics of glucose waves. He then developed an AI Glucometer tool for patients to predict their weight, FPG, PPG, and A1C. It uses various computer science tools, including big data analytics, machine learning (self-learning, correction, and simplification), and artificial intelligence to achieve very high accuracy (95% to 99%).

Results: In 2010, his average glucose was 280 mg/dL and A1C was >10%. Now, his glucose value is 116 mg/dL and A1C is 6.5%. Since his health condition is stable, he no longer suffers from repetitive cardiovascular episodes.

Conclusion: Instead of utilizing traditional biology, chemistry, and statistics the methodology of GH-Method: math-physical medicine uses advanced mathematics, physics concept, engineering modeling, and computer science tools (big data analytics, artificial intelligence), which can be applied to other branches of medical research in order to achieve a higher precision and deeper insight.

Biography:

Xuanxian Peng is a Professor in the School of Life Sciences at Sun Yat-Sen University, China. He has completed his PhD from Xiamen University, China and studied at McGill University, Canada as a Postdoctoral Fellow. He was the first Dean of School of Life Sciences, Xiamen University, China from 1999 to 2003. He was a Member of the Fifth Chinese Education Ministry for Sciences and Technology and Vice Chairmen of Chinese Society for Marine Biochemistry and Molecular Biology. His research focuses on functional metabolomics for antibiotic resistance, which have recently been published in Cell Metabolism and PNAS.

Abstract:

The emergence and ongoing spread of multidrug-resistant bacteria puts humans and other species at risk of potentially lethal infections. Thus, novel antibiotics or alternative approaches are needed to kill the drug-resistant bacteria. Here, the mechanism by which multidrug-resistant Edwardsiella tarda evades killing by the traditional antibiotic kanamycin is explored using a reprogramming metabolomics-based approach. The results demonstrate that exogenous glutamate restores the ability of kanamycin to kill E. tarda in vitro and in vivo. It stimulates the P cycle containing the TCA cycle, which stimulates production of NDAH, increases proton-motive force and stimulates antibiotic uptake. Elimination of non-TCA P cycle enzymes blocks TCA metabolism even when there are ample other carbon sources to support the TCA. These results reveal a metabolic mechanism of the glutamate-potentiated killing, and lead to a novel understanding for the TCA cycle and the energy-generated chemical reaction cycle, suggesting a general mechanism for central carbon metabolism. Furthermore, the P cycle is tested in a model of bacterium, Escherichia coli. As E. tarda, the enzymes that feed pyruvate into the TCA cycle are also essential for energy homeostasis. Compounds that inhibit or deplete the enzymes in this pathway shut down the TCA cycle even in the presence of excess carbon sources. In contrast to pyruvate recycling in mammalian cells, which is limited to specific cells/tissues, the P cycle operates routinely as a general mechanism for energy production and for regulating the TCA cycle in several bacterial species. These findings address fundamental questions about bacterial biochemistry and energy metabolism.

Biography:

Bo Peng has his expertise in metabolic regulation of antibiotic resistance. His research focuses on the elucidation of the metabolic features antibiotic-resistant bacteria. He proposed that antibiotic-resistant bacteria have their metabolomes, naming antibiotic-resistance metabolome (ARM).

Abstract:

Statement of the Problem: Many infectious pathogens are susceptible to killing by antibiotics; however, mechanisms exist whereby susceptible pathogens as well as commensal bacteria can acquire resistance to antibiotics, especially after long-term, high-dose, or otherwise inappropriate exposure to one or more growth-inhibiting or cytotoxic drugs. This is the rational explanation for the recent surge in appearance of multidrug-resistant (MDR) bacterial strains, especially in the hospital environment, leading to increased human mortality. Therefore, new drugs and/or approaches are needed for treating such infections in the clinic. One possible approach would be to enhance the innate immune response of the infected host, recruiting endogenous host defense mechanisms to kill bacterial pathogens in a relatively risk-free manner.

Methodology & Theoretical Orientation: A systems biological approach was used to examine the host-bacterium interaction with the goal of identifying agents that could enhance the innate response to pathogens but limit tissue injury.

Findings: High levels of L-alanine promote phagocytosis of clinically-relevant pathogens. And more importantly, the downstream catabolite, palmitic acid could attenuate the tissue injury by excessive immune response through downregulating pyroptosis.

Conclusion & Significance: Host clearance of multidrug-resistant microbes is strongly associated with metabolic states, and that specific metabolic profiles are correlating with certain host defense strategy. Our study proposed a novel approach to identify metabolic modulator through investigation of metabolomics, by which crucial modulators can be used for therapeutic purpose

Biography:

Hui Li is a Professor, School of Life Sciences, Sun Yat-Sen University, China. She received her Ph.D. from Sichuan University, China and studied at Sun Yat-sen University,China as a postdoctoral fellow. Her research focuses on functional metabolomics for antibiotic resistance, which have recently been published in Cell Metabolism and PNAS.

Abstract:

The development of antibiotic resistance in Vibrio alginolyticus represents a threat to human health and fish farming. Environmental NaCl regulation of bacterial physiology is well documented, but whether the regulation contributes to antibiotic resistance remains unknown. To explore this, we compared minimum inhibitory concentration (MIC) of V. alginolyticus cultured in different media with 0.5% to 10% NaCl, and found that the MIC increased as the NaCl concentration increased, especially for aminoglycoside antibiotics. Consistent with this finding, internal NaCl also increased, while intracellular gentamicin level decreased. GC-MS-based metabolomics showed different distributions of pyruvate cycle intermediates among 0.5%, 4% and 10% NaCl. Differential activity of enzymes in the pyruvate cycle and altered expression of Na(+)-NQR led to a reducing redox state, characterized by decreased levels of NADH, proton motive force (PMF) and ATP. Meanwhile, NaCl negatively regulated PMF as a consequence of the reducing redox state. These together are responsible for the decreased intracellular gentamicin level with the increased external level of NaCl. Our study reveals a previously unknown redox state-dependent mechanism regulated by NaCl in V. alginolyticus that impacts antibiotic resistance.

Biography:

Gerald C Hsu has completed his PhD in Mathematics and majored in Engineering at MIT. He attended different universities over 17 years and studied seven academic disciplines. He has spent 20,000 hours in T2D research. His approach is quantitative medicine based on mathematics, physics, optical and electronics physics, engineering modeling, signal processing, computer science, big data analytics, statistics, machine learning and artificial intelligence. His main focus is on preventive medicine using prediction tools. He believes that the better the prediction, the more control you have.

Abstract:

Introduction: This paper discusses glucose measurement results and their impact on health from two different methods, finger piercing and testing strip (Finger) and a continuous glucose monitoring system (Sensor).

Method: The author has been collecting a total of 9,490 glucose data by finger measurement, including both fasting plasma glucose (FPG) once a day since 1/1/2014 (1,825 days) and postprandial plasma glucose (PPG) three times a day since 1/1/2012 (2,555 days). Recently, he has further collected 17,046 glucose data by applying a sensor on his upper arm to collect his glucose values continuously. This sensor measurement is conducted in parallel with his routine finger-piercing measurements. During the period of 5/5/2018 to 12/13/2018 (241 days), he has collected and recorded his glucose values about 70 times per day. The measurement rate is approximately every 15 minutes during the day and every hour during the night. In summary, he has collected a total of 17,046 glucose data and 964 waveforms (241 FPG and 723 PPG). Other waveforms generated between meals or from eating snack/fruit are not included in this analysis.

Results: All glucose units are mg/dL. Finger’s Average FPG/PPG: 110/116 mg/dL (as 100% baseline) Daily Average Sensor vs. Daily Average Finger: 130/115 (113%); Peak FPG Sensor vs. Average FPG Finger: 132/110 (120%); Average FPG Sensor vs. Average FPG Finger: 112/110 (102%); Peak PPG Sensor vs. Average PPG Finger: 159/116 (138% & +43); Average PPG Sensor vs. Average PPG Finger: 135/116 (117% & +19); Sensor’s Time of Peak PPG Glucose: ~ 60 minutes after first-bite; PPG rising speed: 33 mg/dL per hour; PPG decaying speed: 20 mg/dL per hour (~ 60% of rising); PPG rising speed is 190% (takes ~60 minutes) of decaying speed (takes ~100 minutes); FPG (period - from 00:00 to 07:00): Overall FPG waveform: Average FPG: 112 mg/dL; Peak (crest): 121 mg/dL; Valley (trough): 106 mg/dL; Period of Trough (from 3am to 5am); PPG (period - from first-bite to 180 minutes later, total 3 hours) Overall PPG waveform: Average PPG: 135 mg/dL; Peak (crest): 144 mg/dL; Valley (trough): 127 mg/dL; Differential Energy (Sensor / 120 mg/dL): 117%; which provides 6.4% increase of cardiovascular disease (CVD) and stroke risk probability from 26.4% to 28.1% (based on 2017 data of medical conditions) ; Differential Energy (Finger / 120 mg/dL): 93%; which indicates this patient’s type 2 diabetes condition is well controlled.

Conclusion: In average, PPG peak occurs around one hour after first-bite of meal, not two hours afterward as medical community said. PPG decaying speed is almost twice as slow than its rising speed; Average Sensor’s PPG is 17% higher (+19 mg/dL) than the Average Finger’s PPG. Peak Sensor’s PPG is 38% higher (+43 mg/dL) than the Average Finger’s PPG. FPG wave is similar to ocean wave which is much calmer than PPG wave that is similar to tsunami wave. FPG’s lowest trough range happens during the deepest sleeping hours (3am to 5am). FPG starts to rise near wake-up time in the morning. Higher glucose values from sensor provide excessive (leftover) energy and increase moderate risk probability of CVD and stroke.