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Journal articles on the topic 'Glucose'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 November 2024

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1

Foley, J. E., P. Thuillez, S. Lillioja, J. Zawadzki, and C. Bogardus. "Insulin sensitivity in adipocytes from subjects with varying degrees of glucose tolerance." American Journal of Physiology-Endocrinology and Metabolism 251, no. 3 (September 1, 1986): E306—E310. http://dx.doi.org/10.1152/ajpendo.1986.251.3.e306.

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Previous studies showed that the sensitivity of glucose transport to insulin is lower in adipocytes isolated from subjects with noninsulin-dependent diabetes mellitus and impaired glucose tolerance compared with subjects with normal glucose tolerance. This study analyzed the relationship between insulin sensitivity of glucose transport and glycemia in a large group of nondiabetic-nonglucose-intolerant subjects with a wide range of glycemic response to oral glucose. Seventy-four Pima Indians with 2-h postglucose load glucoses between 77 and 197 mg/100 ml, fasting plasma glucoses between 76 and 108 mg/100 ml, and no postload glucoses less than 199 mg/100 ml were studied. Isolated adipocytes were prepared in vitro after an abdominal fat biopsy, ED50 of insulin for glucose transport was correlated with 2-h postload glucoses, but not between insulin binding per cell or per cell surface area or in ED50 of insulin for antilipolysis and 2-h postglucose load glucoses. Although only 17% of the variation in glucose tolerance could be explained by a change in the sensitivity of glucose transport to insulin, the data suggests that a postinsulin-binding defect in the coupling of insulin binding to glucose transport may be an early step in the development of insulin resistance in human adipocytes.

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2

Cembrowski, George, Joanna Jung, Junyi Mei, Eric Xu, Tihomir Curic, RT Noel Gibney, Michael Jacka, and Hossein Sadrzadeh. "Five-Year Two-Center Retrospective Comparison of Central Laboratory Glucose to GEM 4000 and ABL 800 Blood Glucose: Demonstrating the (In)adequacy of Blood Gas Glucose." Journal of Diabetes Science and Technology 14, no. 3 (November 5, 2019): 535–45. http://dx.doi.org/10.1177/1932296819883260.

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Purpose: To evaluate the glucose assays of two blood gas analyzers (BGAs) in intensive care unit (ICU) patients by comparing ICU BGA glucoses to central laboratory (CL) glucoses of almost simultaneously drawn specimens. Methods: Data repositories provided five years of ICU BGA glucoses and contemporaneously drawn CL glucoses from a Calgary, Alberta ICU equipped with IL GEM 4000 and CL Roche Cobas 8000-C702, and an Edmonton, Alberta ICU equipped with Radiometer ABL 800 and CL Beckman-Coulter DxC. Blood glucose analyzer and CL glucose differences were evaluated if they were both drawn either within ±15 or ±5 minutes. Glucose differences were assessed graphically and quantitatively with simple run charts and the surveillance error grid (SEG) and quantitatively with the 2016 Food and Drug Administration guidance document, with ISO 15197 and SEG statistical summaries. As the GEM glucose exhibits diurnal variation, CL-arterial blood gas (ABG) differences were evaluated according to time of day. Results: Compared to the GEM glucoses measured between 0200 and 0800, the run charts of (GEM-CL) glucose demonstrate significant outliers between 0800 and 0200 which are identified as moderate to severe clinical outliers by SEG analysis ( P < .002 and P < .0005 for 5- and 15-minute intervals). Over the entire 24-hour period, the rates of moderate to severe glucose clinical outliers are 3.5/1000 (GEM) and 0.6/1000 glucoses (ABL), respectively, using the 15-minute interval ( P < .0001). Discussion: The GEM ABG glucose is associated with a higher frequency of moderate to severe glucose clinical outliers, especially between 0800 and 0200, increased CL testing and higher average patient glucoses.

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3

Darwin. "Determination of Glucose Concentration in Anaerobic Acidification Cultures by Portable Glucose Monitoring System." Asian Journal of Chemistry 31, no. 4 (February 27, 2019): 763–66. http://dx.doi.org/10.14233/ajchem.2019.21593.

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In mixed microbial fermentation, sugar concentration should be monitored regularly in order to evaluate the effectiveness of fermentation process. Anaerobic acidification fermentation is the process involving microbes to convert the soluble carbohydrate (e.g. glucose) derived from the hydrolysis of insoluble carbohydrates (e.g. starch). The determination of glucose during the fermentation is essential in order to evaluate the mass balance of electron transfer from the oxidation of glucose to the fermentation end-products (e.g. organic acids and alcohols). The fast and practical measurements of glucose concentration in the fermentation broth are highly required to evaluate and ensure the stability of fermentation process. The results showed that once glucose as a soluble sugar is available in the fermentation broth, it was accurately measured by GlucoDr blood glucose biosensor. The standard curve of glucose solution showed the linear relationship between glucometer reading and glucose concentration in which the coefficient determination obtained was at about 0.99. This indicated that glucose analysis with using GlucoDr blood glucose biosensor was accurate and reproducible.

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4

Park, Ji-Yeon, Sung-Chool Park, and Jae-Ho Pyee. "Functional Analysis of a Grapevine UDP-Glucose Flavonoid Glucosyl Transferase (UFGT) Gene in Transgenic Tobacco Plants." Journal of Life Science 20, no. 2 (February 28, 2010): 292–97. http://dx.doi.org/10.5352/jls.2010.20.2.292.

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5

Hargreaves, M., A. Rose, K. Howlett, and D. S. King. "GLUCOSE KINETICS FOLLOWING GLUCOSE INGESTION." Medicine & Science in Sports & Exercise 33, no. 5 (May 2001): S97. http://dx.doi.org/10.1097/00005768-200105001-00548.

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6

Harmayetty, Harmayetty, Ilya Krisnana, and Faida Anisa. "String Bean Juice Decreases Blood Glucose Level Patients with Diabetes Mellitus." Jurnal Ners 4, no. 2 (July 23, 2017): 116–21. http://dx.doi.org/10.20473/jn.v4i2.5022.

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Introduction: Type 2 diabetes mellitus is deficiency of insulin and caused by decreases of insulin receptor or bad quality of insulin. As a result, insulin hormone does not work effectively in blood glucose regulation. String bean juice contains thiamin and fiber may regulate blood glucose level. The aim of this study was to analyze the effect of string bean juice to decrease blood glucose level of patients with type 2 diabetes mellitus. Method: This study employed a quasy-experimental pre-post test control group design and purposive sampling. The population were all type 2 diabetes mellitus patients in Puskesmas Pacar Keling Surabaya. Sample were 12 patients who met inclusion criteria. The independent variable was string bean juice and dependent variable was blood glucose level. Data were analyzed by using Paired T-test with significance level of α≤ 0.05 and Independent T-test with significant level of α≤0.05. Result: The results showed that string bean juice has an effect on decreasing blood glucose between pre test and post test for blood glucose with independent T-test is p=0.003.In conclusion, string bean juice has an effect on blood glucose level in patients with type 2 diabetes mellitus.Discussion: The possible explanation for this findings is string bean juice contains two ingredients: thiamine and fiber. Thiamine helps support insulin receptors and glucose transporter in cells hence GLUT-4 could translocated to the cell membrane brought glucouse enter to the intracellular compartment, that leads to blood glucouse level well regulated. Dietary fiber reduces food transit time so slowing the glucose absorption. Therefore blood glucose level will be decreased.

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7

Yanet, Medina Rojas, Vargas Campos Luis Eder, Vargas Campos Adriana, Rivera Ramírez Ana Bertha, Gallardo Montoya Juan Manuel, Luna Gomez Juan Manuel, Adams Ocampo Julio Cesa, and Vargas Zuñiga Luis Martin. "Comparación de las concentraciones de glucosa plasmática y saliva en sujetos sanos." Archives of Health 2, no. 5 (July 28, 2021): 1429–40. http://dx.doi.org/10.46919/archv2n5-005.

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RESUMEN INTRODUCCIÓN: La saliva pueda ser utilizada como un líquido de diagnóstico para evaluar el estado de salud. OBJETIVO: Comparar la glucosa salival con la glucemia plasmática en sujetos sanos. MATERIAL / MÉTODOS: Se analizaron saliva no estimulada de 99 mujeres y 47 hombres aparentemente sanos. RESULTADOS: Al comparar la glucosa plasmática vs. la saliva en mujeres encontramos que hay una fuerte diferencia estadística 68.723 ± 7.302 mg/dL plasmática vs 24.44 ± 2.095 mg/dL salival (p= 0.0001), de manera similar ocurrió en los hombres 70.393 ± 9.00 mg/dL plasmática vs 24.93 ± 2.643 salival (p ≤ 0.0001). CONCLUSION: Que la concentración de glucosa en la saliva pudiera ser un buen método para evaluar de manera no invasiva la concentración de glucosa en el organismo. ABSTRACT INTRODUCTION: Saliva can be used as a diagnostic fluid to assess health status. OBJECTIVE: To compare salivary glucose with plasma glucose in healthy subjects. MATERIAL/METHODS: Unstimulated saliva from 99 apparently healthy women and 47 apparently healthy men was analyzed. RESULTS: Comparing plasma glucose vs saliva in women we found that there is a strong statistical difference 68.723 ± 7.302 mg/dL plasma vs 24.44 ± 2.095 mg/dL saliva (p= 0.0001), similarly it occurred in men 70.393 ± 9.00 mg/dL plasma vs 24.93 ± 2.643 saliva (p ≤ 0.0001). CONCLUSION: That glucose concentration in saliva could be a good method to evaluate in a non-invasive way the glucose concentration in the organism.

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8

Pane, Gregg A., and Frederick B. Epstein. "Glucose." Emergency Medicine Clinics of North America 4, no. 1 (February 1986): 193–205. http://dx.doi.org/10.1016/s0733-8627(20)30991-3.

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9

&NA;. "Glucose." Reactions Weekly &NA;, no. 1090 (February 2006): 13–14. http://dx.doi.org/10.2165/00128415-200610900-00039.

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10

Sieber, Frederick E., David S. Smith, Richard J. Traystman, and Harry Wollman. "Glucose." Anesthesiology 67, no. 1 (July 1, 1987): 72–81. http://dx.doi.org/10.1097/00000542-198707000-00013.

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11

&NA;. "Glucose." Reactions Weekly &NA;, no. 1343 (March 2011): 18. http://dx.doi.org/10.2165/00128415-201113430-00063.

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12

Nesto, Richard W., and Rodrigo M. Lago. "Glucose." Circulation 117, no. 8 (February 26, 2008): 990–92. http://dx.doi.org/10.1161/circulationaha.107.757450.

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13

Peters, F. P. A. M. N. "Glucose." Bijzijn XL 7, no. 2 (February 2014): 16–20. http://dx.doi.org/10.1007/s12632-014-0021-1.

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14

Kang, Yanggyo. "Glucose Management Using Continuous Glucose Monitors." Journal of Korean Diabetes 20, no. 1 (2019): 42. http://dx.doi.org/10.4093/jkd.2019.20.1.42.

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15

SUNDARAM, P. V., and P. G. HILL. "Glucose Oxidase Impette?Serum Glucose Estimation." Annals of the New York Academy of Sciences 672, no. 1 Enzyme Engine (November 1992): 608–12. http://dx.doi.org/10.1111/j.1749-6632.1992.tb35679.x.

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16

Hsu, Gerald C. "Accuracy of Predicted Glucose using both Natural Intelligence and Artificial Intelligence via GH-Method: Math-Physical Medicine (No. 320)." Journal of Diabetes Research Reviews & Reports, January 31, 2021, 1–4. http://dx.doi.org/10.47363/jdrr/2021(3)144.

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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

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17

"A Neurocommunication Model between the Brain and Liver Regarding Glucose Production and Secretion in Early Morning Using GH-Method: Math-Physical Medicine (No. 324)." Advances in Neurology and Neuroscience 3, no. 3 (July 25, 2020). http://dx.doi.org/10.33140/an.03.03.04.

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This article address 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). All of the eight identified glucoses of breakfast PPG are higher than the eight glucoses at time of wake up by a difference of an average of 8 mg/dL. The value difference using Method B of CGM sensor glucoses during the COVID-19 period offers the most accurate picture and credible glucose difference of 8 mg/dL between his FPG at wake-up moment and PPG at the first bite of breakfast. The author believes that the brain senses when a person wakes up due to different kinds of stimuli from many sources, including eye, environment, and even internal organs, which will alert the body to be in “active” mode requiring “energy” through glucose. Even though the person has not eaten anything or is not actively moving, the brain issues a marching order to the liver to produce or release glucose for the body to use in the forthcoming day. This hypothesis can currently explain why his glucose of eating his breakfast is ~8 mg/dL higher than his FPG at wakeup.

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18

Sadek, Dr Hassan. "Glucose Estimation: The most suitable blood collection tube for glucose estimation." Journal of Medical Science And clinical Research 7, no. 1 (January 15, 2019). http://dx.doi.org/10.18535/jmscr/v7i1.69.

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19

"Using Distributional Data Analysis Tools to Investigate the Glucose Density Distribution of the Mean Daily Glucose Values (eAG) for Three Type 2 Diabetes Patients Over an 18-Month Period Based on GH-Method: Math-Physical Medicine (No. 510)." Journal of Applied Material Science & Engineering Research 5, no. 3 (September 28, 2021). http://dx.doi.org/10.33140/jamser.05.03.11.

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The author read an article recently, “Glucodensities: a new representation of glucose profiles using distributional data analysis,” dated August 19, 2020, from arxiv.org (see Reference 1). Incidentally, he has also made two further improvements on his glucose data analysis with his collected big data of sensor glucoses via a continuous glucose monitoring sensor device (CGM). First, in addition to using the HbA1C, which is the mean value of the past 115 days of red blood cells carried glucoses, of a patient is the golden standard in evaluating diabetes conditions. He investigates the glucose fluctuation or GF (glucose excursion or glycemic variability) and then transforms the GF values from a wave’s time-space into an energy’s frequency-space via Fourier transform operations. Using this approach, he can then guesstimate the degree of damage on internal organs caused by the energies associated with glucose fluctuations. Although the GF research is one step deeper compared to the study of mean value of glucoses, such as HbA1C, it is still not deep enough to provide additional details and useful information hidden within the glucose waves. Second, he realized that the average values or mean values of glucoses defined by the American Diabetes Association (ADA) such as the HbA1C or Time in Range (average glucose within a range) can only provide partial overviews of diabetes conditions. However, these basic biomarkers are still missing some hidden internal turmoil, i.e. glucose vibrations or severe stimulations, throughout certain selected timeframes due to all types of external and/or internal stimulators. Therefore, he has defined another term known as the glucose density (GD) in order to explore more and different information hidden within the glucose data and their waveforms. GD is defined as the occurrence frequency at a specific glucose value, for example 2.1% occurrence rate at 110 mg/dL glucose value over a selected time period of collected sensor glucoses. In this way, he can then calculate and examine each glucose value’s occurrence rate within a glucose range that is suitable to a specific patient. If this glucose examination method is accepted by the medical community, it would be an extremely beneficial tool for doctors to be able to quickly study the conditions of their diabetes patients. Furthermore, the author has also programmed his algorithm into an iPhone APP software. Through the combination of his published papers and medical books along with a widely distributed APP for patient’s use in the future, he believes that worldwide type 2 diabetes (T2D) patients can benefit from his research work.

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"Comparison of Waves and Energies between the 3-Hours Versus 24-Hours of Glucose Fluctuation using 3+ Years of Continuous Glucose Monitoring Sensor Device Collected Data Based on GH-Method: Math-Physical Medicine (No. 457)." Advances in Bioengineering and Biomedical Science Research 4, no. 3 (September 28, 2022). http://dx.doi.org/10.33140/abbsr.04.03.002.

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Since 2017, the author utilized his collected data of finger pierced glucoses 4x per day, along with the data of 10 metabolism index (MI) categories including 4 medical conditions and 6 lifestyle details over a 9.5-year period, from 2012 to 2021, to estimate his risk probabilities of having diabetic complications. They include macro-vascular and micro-vascular diseases such as cardiovascular disease, stroke, diabetic kidney disease, diabetic retinopathy, foot ulcer, Alzheimer’s disease, and certain cancers. In addition to the mean value of glucoses, namely the average glucose such as HbA1C, the actual glucose excursion or glucose fluctuation (GF) has noticeable influences on these diabetic complications. Starting from 5/5/2018, along with the finger glucoses, he collected 96 data of glucose values per day for 1,120 days using a continuous glucose monitoring (CGM) sensor device for a total of 107,520 glucose data. Thus far, he has accumulated 3+ years of sensor glucose data; therefore, he would like to enhance his medical research work by using them. Especially with 96 glucose data collected per day, he is now able to easily study the phenomenon of glucose excursion, glucose wave vibration, or glucose data oscillation. The medical community has used the term “glycemic variability (GV)” to describe the glucose excursion which involves several defined GV equations with some inconclusive findings. The author believes that the word “variability” could mean many things; therefore, he decided to apply the same basic concept of glucose excursion or GF without using the other defined GV equations in order to deeply understand and precisely describe the basic biophysical phenomenon of “glucose excursion”. The author has been utilizing glucose fluctuation known as “Daily GF or 24-hour GF” over a 24-hour period in his research work each day. The definition of GF is the maximum glucose (usually around 60-minutes after a meal) minus the minimum glucose (usually around 3am to 4am during sleep) within 24-hours or another selected time period. Recently, he noticed the extremely high and extremely low glucoses frequently occurring within a shorter duration of 3 hours. Therefore, he has inserted a new algorithm of computation into his software program to dynamically calculate the difference between the maximum glucose and the minimum glucose within the moving duration of 3 hours, at15-minute increments throughout the day. By the end of a day, the largest number of GF, which is defined as 3-hour GF, is selected and stored on the cloud server

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21

"GH.p-Modulus Study using both Finger and Sensor Glucoses and Linear Elastic Glucose Theory of GH-Method: Math-Physical Medicine, Part 16 (No. 370)." Journal of Applied Material Science & Engineering Research 4, no. 4 (November 19, 2020). http://dx.doi.org/10.33140/jamser.04.04.09.

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This article is Part 16 of the author’s linear elastic glucose behavior study. It focuses on a deeper investigation of GH.p-modulus over the period from 8/5/2018 through 11/27/2020 using both finger-piercing measured glucoses (finger) and continuous glucose monitor (CGM) sensor collected glucoses (sensor). The author plans to conduct additional studies on linear elastic glucose behavior theory in order to obtain a solid and better understanding on the glucose coefficient of GH.p-modulus. Here is the step-by-step explanation for the predicted postprandial plasma glucose (PPG) equation using linear elastic glucose theory as described in References 9 through 22: (1) Baseline PPG equals to 97% of fasting plasma glucose (FPG) value, or 97% * (weight * GH.f-Modulus). (2) Baseline PPG plus increased amount of PPG due to food, i.e., plus (carbs/sugar intake amount * GH.p-Modulus). (3) Baseline PPG plus increased PPG due to food, and then subtracts reduction amount of PPG due to exercise, i.e., minus (post-meal walking k-steps * 5). (4) The Predicted PPG equals to Baseline PPG plus the food influences, and then subtracts the exercise influences. The linear elastic glucose equation is: Predicted PPG = (0.97 * GH.f-modulus * Weight) + (GH.p-modulus * Carbs&sugar) - (post-meal walking k-steps * 5) Where, (1) Incremental PPG = Predicted PPG - Baseline PPG + Exercise impact (2) GH.f-modulus = FPG / Weight (3) GH.p-modulus = Incremental PPG / Carbs intake Therefore, GH.p-modulus = (PPG - (0.97 * FPG) + (post-meal walking k-steps * 5)) / (Carbs&Sugar intake) The study in this article calculates and analyzes the glucose coefficient of GH.p-modulus values over the period from 8/5/2018 through 11/27/2020 using both finger glucoses and sensor glucoses. The calculated GH.p-modulus values are 2.0 for finger glucoses, and 3.3 for sensor glucoses. This paper investigates the likely situations of the author’s health conditions and lifestyle details based on two different glucose measuring methods. These two GH.p-modulus values have a relatively small and insignificant variance of 1.2, which is between 2.0 and 3.2. Actually, any number located between the range of 1.8 to 3.3, even if it skews toward the higher side of this scale, can be used as an application to the GH.p-modulus for PPG prediction. This study utilizes a step-by-step illustration, moving from the difference between PPG and FPG, going through the Incremental PPG, and then arriving at the Predicted PPG. In the described steps, the most important variable of the linear elastic glucose behaviors is the coefficient of GH.p-modulus (similar to Young’s modules in theory of engineering elasticity). That is why the author has conducted a massive amount of research on linear elastic glucose behaviors theory in order to acquire a good and solid understanding for the GH.p-modulus.

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22

MD., Dr P. Sureshkumar. "Impaired Glucose Regulation in Cirrhosis Liver – The Utility of Oral Glucose Tolerance Test." Journal of Medical Science And clinical Research 7, no. 8 (August 17, 2019). http://dx.doi.org/10.18535/jmscr/v7i8.90.

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23

"High Glucose Predication Accuracy of Postprandial Plasma Glucose and Fasting Plasma Glucose During the COVID-19 Period Using Two Glucose Coefficients of GH-Modulus from Linear Elastic Glucose Theory Based on GH-Method: Math-Physical Medicine, Part 7 (No. 359)." Journal of Applied Material Science & Engineering Research 4, no. 4 (November 19, 2020). http://dx.doi.org/10.33140/jamser.05.01.02.

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This article is Part 7 of the author’s linear elastic glucose behavior study, which focuses on the prediction accuracy of the postprandial plasma glucose (PPG) and fasting plasma glucose (FPG) over the COVID-19 quarantined period, from 1/1/2020 to 11/8/2020. This research is the continuation of his previous six studies on linear elastic glucose behaviors.

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24

Hsu, Gerald C. "Comparison of Glucose and HBA1C Values Between Finger-Piercing and Continuous Glucose Monitoring Sensor Using Gh-Method: Math-Physical Medicine (no. 293)." Current Investigations in Clinical and Medical Research 1, no. 1 (2021). http://dx.doi.org/10.53902/cicmr.2021.01.000503.

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This article discusses the fundamental characteristics of measured glucose levels and predicted glycated hemoglobin A1c (HbA1c) values among three sets of collected data, measured finger-piercing and continuous glucose monitoring (CGM) sensor device collected glucose levels at 15-minute (15-min) and 5-minute (5-min) intervals. The average glucose (in milligram per deciliter-mg/dL) is listed below: Finger glucose: 109 mg/dL (100%) Sensor at 15-min: 120 mg/dL (109%) Sensor at 5-min: 117 mg/dL (107%) Using candlestick chart, the comparison of average glucoses during this period between two sensor glucose (mg/dL) data (15-min/5-min) are as follows: Open glucose: 108/111 Close glucose: 115/115 Maximum (max) glucose: 170 /175 Minimum (min) glucose: 85/83 Average glucose: 120/117 Additional analysis of time above range (TAR)≥140 mg/dL for hyperglycemia, time within the range (TIR) from 70-140 mg/dL for normal, time below range (TBR)≤70 mg/dL for hypoglycemia based on two sensor candlesticks revealing the following information in a specific format of TAR%/ TIR%/TBR%. 15-min:18.3%, 80.5%, 1.2% 5-min: 17.0%, 81.9%, 1.1% By evaluating the results of the TIR analysis, the 5-min glucose levels appear to be marginally healthier (1.4%) than the 15-min ones. During the coronavirus pandemic (COVID 19) quarantine period, the author lived a rather unique lifestyle which is extremely calm with regular routines, such as eating home-cooked meals and exercising on a regular basis. As a result, his HbA1c has decreased from 6.6% to 6.3% with an average A1c of 6.4% without taking any diabetes medications. However, these three different measurement methods still provide three different sets of glucoses levels which are within a 10% margin of differences, while the HbA1c values are particularly close to each other between the finger-piercing and CGM 15-min.

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25

Rahman, Mohammad Arifur. "Outcome of Patients with ST-T Changes in Non STSegment Elevation Myocardial Infarction." Current Investigations in Clinical and Medical Research 1, no. 1 (2021). http://dx.doi.org/10.53902/cicmr.2021.01.000504.

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This article discusses the fundamental characteristics of measured glucose levels and predicted glycated hemoglobin A1c (HbA1c) values among three sets of collected data, measured finger-piercing and continuous glucose monitoring (CGM) sensor device collected glucose levels at 15-minute (15-min) and 5-minute (5-min) intervals. The average glucose (in milligram per deciliter-mg/dL) is listed below: Finger glucose: 109 mg/dL (100%) Sensor at 15-min: 120 mg/dL (109%) Sensor at 5-min: 117 mg/dL (107%) Using candlestick chart, the comparison of average glucoses during this period between two sensor glucose (mg/dL) data (15-min/5-min) are as follows: Open glucose: 108/111 Close glucose: 115/115 Maximum (max) glucose: 170 /175 Minimum (min) glucose: 85/83 Average glucose: 120/117 Additional analysis of time above range (TAR)≥140 mg/dL for hyperglycemia, time within the range (TIR) from 70-140 mg/dL for normal, time below range (TBR)≤70 mg/dL for hypoglycemia based on two sensor candlesticks revealing the following information in a specific format of TAR%/ TIR%/TBR%. 15-min:18.3%, 80.5%, 1.2% 5-min: 17.0%, 81.9%, 1.1% By evaluating the results of the TIR analysis, the 5-min glucose levels appear to be marginally healthier (1.4%) than the 15-min ones. During the coronavirus pandemic (COVID 19) quarantine period, the author lived a rather unique lifestyle which is extremely calm with regular routines, such as eating home-cooked meals and exercising on a regular basis. As a result, his HbA1c has decreased from 6.6% to 6.3% with an average A1c of 6.4% without taking any diabetes medications. However, these three different measurement methods still provide three different sets of glucoses levels which are within a 10% margin of differences, while the HbA1c values are particularly close to each other between the finger-piercing and CGM 15-min.

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26

Díaz Hernández, Diana Patricia, and Luis Carlos Burgos Herrera. "¿Cómo se transporta la glucosa a través de la membrana celular?" Iatreia, March 9, 2002. http://dx.doi.org/10.17533/udea.iatreia.3957.

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La glucosa es el principal sustrato energético de la célula y para su ingreso requiere una proteína transportadora en la membrana celular. Se han descrito dos sistemas de transporte de glucosa y de otros monosacáridos: los transportadores de sodio y glucosa llamados SGLT (sodium-glucose transporters) y los transportadores de glucosa llamados GLUT (glucose transporters). En este artículo se presenta una revisión de las principales características moleculares, bioquímicas y funcionales de los transportadores de monosacáridos que se han descrito hasta el momento.

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27

"Viscoelastic Glucose Theory (VGT #1): Applying the Concept of Viscoelasticity Theory to Conduct a “Glucose Analogy” Study and Illustrate Certain Viscoelastic Characteristics of Time-Dependent Glucose Using Continuous Glucose Monitoring (CGM) Sensor Device Collected Postprandial Plasma Glucose (PPG) Data of 4,056 Elastic Glucoses (<180 mg/dL) within 3.7 Years from 5/8/2018 to 1/10/2022 Based on GH-Method: Math-Physical Medicine (No. 578)." Advances in Bioengineering and Biomedical Science Research 5 (May 21, 2022). http://dx.doi.org/10.33140/abbsr.05.024.

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The author has studied the strength of materials and theory of elasticity from undergraduate courses at the University of Iowa. He also conducted research work to earn a master’s degree in Biomechanics under Professor James Andrews. He remembers that he used both spring and dashpot models to simulate the behaviors of human bone, muscle, and tendon to investigate the human-weapon interactions. Later on, he went to MIT to pursue his Ph.D. study under Professor Norman Jones who taught him the theory of plasticity and dynamic plastic behaviors of various structural elements. Furthermore, he took some graduate courses at MIT in the field of fluid dynamics and thermodynamics. Since then, biomechanics has made advancements in a few application areas, especially tissues of the human body which possess viscoelastic characteristics, such as bone, muscle, cartilage, tendon (connect bone to muscle), ligament (connect bone to bone), fascia and skin. For example, the night splint dorsiflexes forefoot on rear foot increasing plantar fascia tension to offer stress-relaxation of plantar fascia pain. This model of muscles and tendons connecting lower-leg and foot is a kind of viscoelastic problem. However, when we deal with human internal organs, it is not easy to conduct live experiments to obtain some accurate measurements of material properties. Although blood itself is a viscous material which viscosity factor may sit between water and honey, syrup, or gel. But, the author’s research subject is “glucose”, the sugar amount inside of blood or carried by blood cells, not the blood itself. It is near impossible to measure the material geometry or engineering properties of glucose, for example, the viscosity of “glucose”. Therefore, the best he could do is to apply the concept of viscoelasticity and viscoplasticity to construct an analogy motor to study the glucose behaviors which are time-dependent. The author’s background covers mathematics, physics, and various engineering disciplines, not including biology and chemistry. As a result, he can only investigate the observed biomedical phenomena using his ready-learned math-physical tools.

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"Viscoelastic Glucose Theory (VGT #1): Applying the Concept of Viscoelasticity Theory to Conduct a “Glucose Analogy” Study and Illustrate Certain Viscoelastic Characteristics of Time-Dependent Glucose Using Continuous Glucose Monitoring (CGM) Sensor Device Collected Postprandial Plasma Glucose (PPG) Data of 4,056 Elastic Glucoses (<180 mg/dL) within 3.7 Years from 5/8/2018 to 1/10/2022 Based on GH-Method: Math-Physical Medicine (No. 578)." Journal of Applied Material Science & Engineering Research 6 (March 14, 2022). http://dx.doi.org/10.33140/jamser.06.01.020.

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The author has studied the strength of materials and theory of elasticity from undergraduate courses at the University of Iowa. He also conducted research work to earn a master’s degree in Biomechanics under Professor James Andrews. He remembers that he used both spring and dashpot models to simulate the behaviors of human bone, muscle, and tendon to investigate the human-weapon interactions. Later on, he went to MIT to pursue his Ph.D. study under Professor Norman Jones who taught him the theory of plasticity and dynamic plastic behaviors of various structural elements. Furthermore, he took some graduate courses at MIT in the field of fluid dynamics and thermodynamics. Since then, biomechanics has made advancements in a few application areas, especially tissues of the human body which possess viscoelastic characteristics, such as bone, muscle, cartilage, tendon (connect bone to muscle), ligament (connect bone to bone), fascia and skin. For example, the night splint dorsiflexes forefoot on rear foot increasing plantar fascia tension to offer stress-relaxation of plantar fascia pain. This model of muscles and tendons connecting lower-leg and foot is a kind of viscoelastic problem. However, when we deal with human internal organs, it is not easy to conduct live experiments to obtain some accurate measurements of material properties. Although blood itself is a viscous material which viscosity factor may sit between water and honey, syrup, or gel. But, the author’s research subject is “glucose”, the sugar amount inside of blood or carried by blood cells, not the blood itself. It is near impossible to measure the material geometry or engineering properties of glucose, for example, the viscosity of “glucose”. Therefore, the best he could do is to apply the concept of viscoelasticity and viscoplasticity to construct an analogy motor to study the glucose behaviors which are time-dependent. The author’s background covers mathematics, physics, and various engineering disciplines, not including biology and chemistry. As a result, he can only investigate the observed biomedical phenomena using his ready-learned math-physical tools.

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29

"A Comparison of the Combined HbA1C values from Two Predicted HbA1C Results Against 13 Lab-Tested HbA1C Results Within a 41-Month Period Based on GH-Method: Math-Physical Medicine (No. 522)." Advances in Bioengineering and Biomedical Science Research 4 (December 15, 2021). http://dx.doi.org/10.33140/abbsr.04.005.

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Since 1/3/2012, the author utilized his collected data of finger pierced glucose readings 4 times each day to estimate the predicted daily HbA1C value also known as the “Daily finger A1C” by dividing the daily average finger glucose value by a factor of 18.7. Starting from 5/5/2018, along with his finger glucoses, he has been collecting 96 glucoses each day using a continuous glucose monitoring (CGM) sensor device until present day. Based on the collected CGM sensor glucoses, he further estimated another predicted HbA1C value known as the “daily sensor A1C” by dividing the daily average sensor glucose value by a factor of 16.7. Finally, in this article, he merges the above-mentioned two HbA1C values into a “Combined HbA1C” with different assigned weighting factors for each A1C value. His simple equation of the combination is as follows: Combined A1C= 0.4*Finger A1C + 0.6*Sensor A1C Although his initial dates for his finger glucoses and CGM sensor glucose are different, he has chosen an overlapping period of 1,233 days, 41 months, or 3.4 years from 5/8/2018 to 9/22/2021. In addition, he accumulated his 13 lab-tested HbA1C values within the same period approximately 3.2 months with one lab-test done quarterly. He then compares his combined A1C waveform against the lab-tested A1C waveform to check the suitability of the developed equation for the combined HbA1C.

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"Quantification of the CVD/Stroke Risk Probability Due to the Extra Input of an Adjustment Factor of Glycemic Variability or Glucose Fluctuation Using Three Years of the Continuous Glucose Monitoring Sensor Device Collected Data Based on GH-Method: Math-Physical Medicine (No. 456)." Advances in Bioengineering and Biomedical Science Research 4, no. 3 (September 28, 2022). http://dx.doi.org/10.33140/abbsr.04.03.009.

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Since 2017, the author utilized his collected data of finger pierced glucoses 4x per day, along with data of 10 metabolism index (MI) categories including 4 medical conditions and 6 lifestyle details over the period of 9.5 years, from 2012 to 2021. This is to estimate his annual risk probabilities of having a stroke, cardiovascular disease, diabetic kidney disease, diabetic retinopathy, Alzheimer’s disease, and certain cancers. Most of his research articles using the MI model have been published in various medical journals. The purpose of his previous risk assessment studies were aimed at determining his own risk reduction rates from improvements achieved to overall health conditions and MI, since he is a severe diabetes patient. Starting on 5/5/2018, along with the finger glucoses, he collects 96 data of glucose values per day for 1,095 days using a continuous glucose monitor (CGM) sensor device for a total of ~105,120 glucose data. Currently, he has accumulated 3+ years of sensor glucose data; therefore, he has enhanced his research work using his collected sensor glucose data.

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"Glucose." Reactions Weekly 1854, no. 1 (May 2021): 168. http://dx.doi.org/10.1007/s40278-021-95499-6.

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"Glucose." Reactions Weekly 1837, no. 1 (January 2021): 292. http://dx.doi.org/10.1007/s40278-021-88891-6.

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"Glucose." Reactions Weekly 1837, no. 1 (January 2021): 290. http://dx.doi.org/10.1007/s40278-021-88889-7.

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"Glucose." Reactions Weekly 1837, no. 1 (January 2021): 291. http://dx.doi.org/10.1007/s40278-021-88890-6.

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"Glucose." Reactions Weekly 1844, no. 1 (February 2021): 198. http://dx.doi.org/10.1007/s40278-021-91645-6.

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"Glucose." Reactions Weekly 1862, no. 1 (July 2021): 223. http://dx.doi.org/10.1007/s40278-021-98341-4.

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"Glucose." Reactions Weekly 1923, no. 1 (September 10, 2022): 234. http://dx.doi.org/10.1007/s40278-022-23097-4.

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"Glucose." Reactions Weekly 1858, no. 1 (June 2021): 175. http://dx.doi.org/10.1007/s40278-021-96893-8.

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"Glucose." Reactions Weekly 1885, no. 1 (December 2021): 233. http://dx.doi.org/10.1007/s40278-021-06992-3.

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"Glucose." Reactions Weekly 1860, no. 1 (June 2021): 182. http://dx.doi.org/10.1007/s40278-021-97637-2.

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"Glucose." Reactions Weekly 1870, no. 1 (August 2021): 148. http://dx.doi.org/10.1007/s40278-021-01358-8.

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"Glucose." Reactions Weekly 1878, no. 1 (October 2021): 254. http://dx.doi.org/10.1007/s40278-021-04224-4.

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"Glucose." Reactions Weekly 1878, no. 1 (October 2021): 255. http://dx.doi.org/10.1007/s40278-021-04225-4.

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"Glucose." Reactions Weekly 1902, no. 1 (April 2022): 223. http://dx.doi.org/10.1007/s40278-022-13367-5.

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"Glucose." Reactions Weekly 1926, no. 1 (October 1, 2022): 244. http://dx.doi.org/10.1007/s40278-022-24522-y.

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"Glucose." Reactions Weekly 1583, no. 1 (January 2016): 538. http://dx.doi.org/10.1007/s40278-016-13101-1.

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"Glucose." Reactions Weekly 1713, no. 1 (August 2018): 182. http://dx.doi.org/10.1007/s40278-018-50016-y.

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"Glucose." Reactions Weekly 1713, no. 1 (August 2018): 183. http://dx.doi.org/10.1007/s40278-018-50017-y.

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"Glucose." Reactions Weekly 1720, no. 1 (September 2018): 124. http://dx.doi.org/10.1007/s40278-018-51918-3.

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"Glucose." Reactions Weekly 1629, no. 1 (November 2016): 126. http://dx.doi.org/10.1007/s40278-016-23398-x.

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