scholarly journals Investigation of Two Glucose Coefficients of GH.f-Modulus and GH.p-Modulus based on the Data of Three Clinical Cases During the COVID-19 Period using linear Elastic Glucose Theory of GHMethod: Math-Physical Medicine, Part 8 (No. 360)

2021 ◽  
pp. 1-6
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Health Conditions ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Linear Elastic ◽  
Behavior Study ◽  
Obesity And Diabetes ◽  
Near Future

This article is Part 8 of the author’s linear elastic glucose behavior study which focuses on the deeper understanding of these two newly defined glucose coefficients, GH.f-modulus and GH.p-modulus. Findings have shown the sensitive relationships with health conditions such as obesity and diabetes and certain lifestyle components, e.g. carbs/sugar intake amount and post-meal walking steps. Hopefully in the near future, he will be able to develop a reasonable and applicable numerical ranges of these two glucose coefficients. He used his measured glucose data and predicted glucose models for both fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) for three patients with chronic diseases over the COVID-19 quarantined period, from 3/1/2020 to 11/10/2020.

scholarly journals Coefficient of GH.F-Modulus in the Linear Elastic Fasting Plasma Glucose Behavior Study Based on Health Data of Three Diabetes Patients Using the GH-Method: Math-Physical Medicine, Part 4 (No. 356)

2020 ◽  
pp. 1-7
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Health Data ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Linear Elastic ◽  
Fasting Plasma ◽  
Behavior Study ◽  
Diabetes Patients

This article is Part 4 of the author’s linear elastic glucose behavior study, which focuses on fasting plasma glucose (FPG) component. It is the continuation of his previous three studies, Parts 1, 2, and 3, on linear elastic postprandial plasma glucose (PPG) behaviors

scholarly journals Accuracy of Predicted Glucose using both Natural Intelligence and Artificial Intelligence via GH-Method: Math-Physical Medicine (No. 320)

2021 ◽  
pp. 1-4
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Artificial Intelligence ◽  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Daily Average ◽  
Fasting Plasma ◽  
Average Glucose

This paper describes the accuracy of using natural intelligence (NI) and artificial intelligence (AI) methods to predict three glucoses, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and daily average glucose, in comparison with the actual measured PPG by using the finger-piercing (Finger) method. The entire glucose database contains 7,652 glucoses (4 glucose data per day) over 1,913 days from 6/1/2015 through 8/27/2020

scholarly journals Applying Segmentation Pattern Analysis to Investigate Postprandial Plasma Glucose Characteristics and Behaviors of the Carbs/Sugar Intake Amounts In Different Nations (GH Method: Math-Physical Medicine)

2020 ◽  
Vol 5 (1) ◽  
Keyword(s):  
Plasma Glucose ◽  
Pattern Analysis ◽  
Communication Model ◽  
Physical Medicine ◽  
Internal Organs ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Liver And Pancreas ◽  
Segmentation Pattern ◽  
The Brain

In this paper, the author presents the results of his national segmentation pattern analysis of the sensor PPG data based on both high-carb and low-carb intake amounts. It also verified his earlier findings on the communication model between the brain and internal organs such as the stomach, liver, and pancreas.

scholarly journals A Neurocommunication Model between the Brain and Liver Regarding Glucose Production and Secretion in Early Morning Using GH-Method: Math-Physical Medicine (No. 324)

2020 ◽  
pp. 1-5
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Glucose Production ◽  
Early Morning ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Fasting Plasma ◽  
The Difference ◽  
The Brain

This article address is the author’s hypothesis on the neurocommunication model existing between the brain and liver regarding production and glucose secretion in the early morning. This is based on the observation of the difference between glucose at wake up moment in the morning for the fasting plasma glucose (FPG), and glucose at the first bite of breakfast for the glucose at 0-minute or “open glucose” of postprandial plasma glucose (PPG)

scholarly journals Using GH-Method: Math-Physical Medicine to Investigate Different Contribution Margins of Fasting Plasma Glucose vs. Postprandial Plasma Glucose on HbA1C

2020 ◽  
Vol 3 (3) ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Medical Research ◽  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Fasting Plasma ◽  
Metabolic Conditions

This paper displays results of the contribution margin calculation of fasting plasma glucose (FPG) vs. postprandial plasma glucose (PPG) on HbA1C. The dataset is provided by the author, who uses his own type 2 diabetes metabolic conditions control, as a case study via the “math-physical medicine” approach of a nontraditional methodology in medical research.

Guesstimate Probable Partial Self-Recovery of Pancreatic Beta Cells Using Calculations of Annualized Glucose Data Using GH-Method: Math-Physical Medicine

2020 ◽  
pp. 1-4
Author(s):  
Gerald C. Hsu
Keyword(s):  
Type 2 Diabetes ◽  
Beta Cells ◽  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Pancreatic Beta Cells ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Fasting Plasma ◽  
Regeneration Capability

In this paper, the author describes his hypothesis on the probable partial self-recovery of some insulin regeneration capability of pancreatic beta cells on a Type 2 Diabetes (T2D) patient via his collected data of both Postprandial Plasma Glucose (PPG) and Fasting Plasma Glucose (FPG) during the period of 1/1/2014 to 11/23/2019.

scholarly journals A simple formula based on postprandial plasma glucose prediction using 5,640 meals data via GH-Method: math-physical medicine (No. 301)

2020 ◽  
Vol 5 (5) ◽  
Keyword(s):  
Plasma Glucose ◽  
Research Work ◽  
The Other ◽  
Diabetes Research ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Glucose Testing ◽  
The Status ◽  
Food Nutrition

This article is based on the continuation of the author’s research work, a simple and practical, yet highly accurate postprandial plasma glucose (PPG) prediction formula for type 2 diabetes (T2D) patients. His methodology is the developed GH-Method: math-physical medicine (MPM) which has been utilized repeatedly in the past decade. The predicted PPG formula-based on the status of fasting plasma glucose (FPG), carbs/sugar intake amount, and postmeal walking steps are as follows: Predicted PPG = 0.97 * FPG + (carbs/sugar grams * 1.8) - (post-meal walking steps in thousand * 5) The conclusive results have the order of values m1 / m2 /m3 / prediction accuracy %. Case A: 1.8 / 5.0 / 0.97 / 99.8% Case B: 2.0 / 5.0 / 0.945 / 99.9% Case C: 2.2 / 5.0 / 0.92 / 99.9% Exercise is important, contributing ~3% higher than food, is easily achieved compared to the required knowledge of diet. As a result, the author spent four years to study food nutrition. Most T2D patients are seniors; therefore, he suggests that walking is the best form of exercise. However, the most difficult part of exercise is the behavior psychology related to the issue of “discipline and persistence”. T2D patients need to walk between 2,000 to 4,000 steps after each meal. The author walks an average of 4,300 steps after each meal. On the other hand, diet (carbs/sugar amount and nutritional balance) requires much more and deeper knowledge of food nutrition in order to control diabetes. Therefore, the author developed an AI-based tool to assist T2D patients. For non-tech patients, the following simple guidelines can assist with meal intake: Starchy food: Eat an amount half of your fist or hand at most Colorful vegetables: Eat an amount limited to one fist or hand. Green vegetables: Eat an amount limited to 2.5 fists or hands. Please note: you must combine two types of vegetable together in order to get the total intake limitation. The author highly recommends the patients to measure their FPG at least several times a quarter, in order to get a quarterly average FPG value. The other three PPG values can then utilize the formula-based predicted PPG to control their overall diabetes conditions. The described method mentioned above in regard to the predicted PPG formula along with the post-meal walking exercise and carbs/sugar intake amount can help patients control their diabetes without painful and troublesome fingerpiercing glucose measurements. The author has been measuring his glucoses for 8.5 years (3,126 days) with fingerpiercing glucose testing combined with his 10-years of diabetes research work. He hopes this article can provide useful guidelines to other diabetes patients to take back their lives from this dreadful chronic disease.

scholarly journals Segmentation and pattern analyses for three meals of postprandial plasma glucose values and associated carbs/sugar amounts using GH-Method: math-physical medicine (No. 326)

2020 ◽  
Vol 4 (4) ◽  
Keyword(s):  
Plasma Glucose ◽  
Exercise Program ◽  
Management Program ◽  
Physical Medicine ◽  
Lifestyle Management ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Glucose Levels ◽  
The Past ◽  
Segmentation Analysis

The author describes the results of segmentation and pattern analyses of postprandial plasma glucose levels (PPG) and carbs/sugar intake amount (carbs), which are associated with his three daily meals. In this paper, there are three consistent ranges of low, medium, and high for PPG values and carbs/sugar amounts that are used for each meal but with different units. One of the final objectives for this analysis is to calculate the most reasonable and effective conversion ratio between measured PPG in mg/dL and carbs/sugar intake amount in grams, by discovering how much PPG amount would be generated from 1 gram of carbs/sugar intake. This investigation utilized the PPG data and carbs/sugar amount collected during a period of 2+ years from 5/5/2018 to 9/6/2020 with a breakdown of 855 days, including 2,565 meals, 33,345 glucose data, and 33,345 carbs/sugar data. By using the segmentation analysis of his 33,345 PPG data and 2,565 carbs/sugar data, the author has conducted a pattern recognition and segmentation analysis from his PPG profiles with its associated carbs/sugar intake of his food and meals in the past 855 days. Since 12/8/2015, he ceased taking any diabetes medications. In other words, his diabetes control is 100% dependent on his lifestyle management program with no chemical intervention from any medications. Subsequently, he has maintained a stringent exercise program after each meal; therefore, the development of his simplified PPG prediction model, excluding the exercise factor, can be expressed solely with carbs/sugar intake amount.

High Accuracy of Predicted Postprandial Plasma Glucose Using Two Coefficients of GH.F-Modulus and GH.P-Modulus from Linear Elastic Glucose Behavior Theory Based on GH-Method: MathPhysical Medicine, Part 5 (No. 357)

2020 ◽  
pp. 1-6
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Plasma Glucose ◽  
High Accuracy ◽  
Behavior Theory ◽  
Postprandial Plasma Glucose ◽  
Linear Elastic ◽  
Behavior Study

This article is Part 5 of the author’s linear elastic glucose behavior study, which focuses on the predicted postprandial plasma glucose (PPG). This study is the combination and continuation of his previous four studies, Parts 1, 2, 3, and 4, on linear elastic glucose behaviors

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