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Zeitschriftenartikel zum Thema „Glucose“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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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 (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

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Cembrowski, George, Joanna Jung, Junyi Mei, et al. "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 (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 wit

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Kiki, Prawiroredjo, and Shintadewi Julian Engelin. "Comparative study of 940 nm and 1450 nm near infrared sensor for glucose concentration monitoring." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 2 (2019): 981–85. https://doi.org/10.12928/TELKOMNIKA.v17i2.10149.

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In order to manage their blood glucose level, diabetics have to test their blood glucose level regularly. Unfortunately, the current blood glucose measurement device is uncomfortable, painful, event costly for the diabetics; therefore, a lot of effort is given to develop a noninvasive blood glucose meter. We studied the potential of two near infrared wavelength i.e. 1450 and 940 nm as glucose sensor. Each sensor consists of a light emitting diode (LED) as light source, a photodiode that is sensitive to that wavelength, transimpedance amplifier, and filters. An acrylic box size 5cmx1cmx5cm was

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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 (2010): 292–97. http://dx.doi.org/10.5352/jls.2010.20.2.292.

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5

Assistant, Professor Farida Rozy. "Comparison of Insulin and Glucagon in the Regulation of Blood Glucose Levels." ISRG Journal of Arts Humanities & Social Sciences (ISRGJAHSS) III, no. III (2025): 147–52. https://doi.org/10.5281/zenodo.15454908.

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<em>Maintaining blood glucose levels is of vital importance, as glucose serves as the primary energy source for body cells. Therefore, to ensure adequate energy supply, body tissues and cells—particularly brain cells—rely on glucose as a principal energy substrate. The objectives of this study are to examine and compare the roles of insulin and glucagon in regulating blood glucose levels, explore their metabolic impacts, identify the mechanisms of action of these two hormones, and investigate how they interact to maintain glucose homeostasis. Furthermore, the study assesses complic

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

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7

Harmayetty, Harmayetty, Ilya Krisnana, and Faida Anisa. "String Bean Juice Decreases Blood Glucose Level Patients with Diabetes Mellitus." Jurnal Ners 4, no. 2 (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 p

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Yanet, Medina Rojas, Vargas Campos Luis Eder, Vargas Campos Adriana, et al. "Comparación de las concentraciones de glucosa plasmática y saliva en sujetos sanos." Archives of Health 2, no. 5 (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 s

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Logel, Santhi N., Ellen L. Connor, David A. Hsu, Kristin M. Engelstad, and Darryl De Vivo. "Continuous Glucose Monitoring Facilitates Diazoxide Use in the Management of Glut1 Deficiency Syndrome." Journal of the Endocrine Society 5, Supplement_1 (2021): A698—A699. http://dx.doi.org/10.1210/jendso/bvab048.1422.

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Abstract Background: Glut1 deficiency syndrome (Glut1DS) is caused by mutations in SLC2A1 on chromosome 1p34.2, which impairs transmembrane glucose transport across the blood brain barrier resulting in hypoglycorrhachia and decreased glucose availability for brain metabolism. This causes a drug-resistant, metabolic epilepsy due to energy deficiency. Standard treatment for Glut1DS is the ketogenic diet (KD) but treatment options are limited if patients fail the KD. Diazoxide, which inhibits insulin release, was used sparingly in the past for a few Glut1DS patients to increase blood glucose leve

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Agung, Biworo, Rezki Amalia Dwi, Rizky Amalia Lisda, Halim Valentina, and Suhartono Eko. "In Vitro Diabetogenic Effect of Cadmium on Liver." International Journal of Current Pharmaceutical Review and Research 8, no. 1 (2017): 68–73. https://doi.org/10.5281/zenodo.12678459.

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The objectives of this study were to determine the effect of cadmium (Cd) on glucose metabolism disruption in liver cellshomogenate in vitro. The glucose metabolism disruption was analyzed by measuring the level of liver glucose, glycogenand methylglyoxal (MG), and the activity of glucokinase activity. In this experiment, a liver sample was taken from malerats (Rattus novergicus). Samples then homogenized and divided into four groups with; C served as control which containsliver homogenate only; T1 which contains liver homogenate + 0.03 mg/l of cadmium sulphate (CdSO4); T2 which containsliver

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

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12

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

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13

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

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14

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

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15

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

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16

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

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17

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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"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 (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 va

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"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 (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 fl

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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 (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, th

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

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22

"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 (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 lin

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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

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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

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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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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 (2019). http://dx.doi.org/10.18535/jmscr/v7i8.90.

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"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 (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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"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

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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 (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 dete

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Mastra, Nyoman. "GAMBARAN KADAR GLUKOSA DARAH SEWAKTU PADA LANSIA DI DESA ADAT TAMPUAGAN KECAMATAN KARANGASEM KABUPATEN KARANGASEM." Meditory : The Journal of Medical Laboratory 10, no. 2 (2022). http://dx.doi.org/10.33992/meditory.v10i2.2273.

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Background: The aging process is a factor that causes a decrease in pancreatic function cells and insulin secretion in the elderly. As a result of this aging process, the risk of increased blood glucose levels and impaired glucose tolerance will be higher.Purpose: This study aimed to find out the description of random blood glucose levels in the elderly in the Traditional Village of Tampuagan, Karangasem District, Karangasem Regency.Method: The type of research used is descriptive research with simple random sampling techniques. The sample used 39 samples obtained through interviews and checki

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Myndrul, Valerii, Emerson Coy, Nataliya Babayevska, et al. "MXene nanoflakes decorating ZnO tetrapods for enhanced performance of skin-attachable stretchable enzymatic electrochemical glucose sensor." March 6, 2022. https://doi.org/10.1016/j.bios.2022.114141.

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ABSTRACT Continuous painless glucose monitoring is the greatest desire of more than 422 million diabetics worldwide. Therefore, new non-invasive and convenient approaches to glucose monitoring are more in demand than other tests for microanalytical diagnostic tools. Besides, blood glucose detection can be replaced by continuous glucose monitoring of other human biological fluids (e.g. sweat) collected non-invasively. In this study, a skin-attachable and stretchable electrochemical enzymatic sensor based on ZnO tetrapods (TPs) and a new class of 2D materials - transition metal carbides, known a

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"Estimation of Organ Impact by Relative Energy Associated with Higher-Frequency Glucose Components using GH-Method: Math-Physical Medicine (No. 290)." Journal of Applied Material Science & Engineering Research 4, no. 4 (2020). http://dx.doi.org/10.33140/jamser.04.04.15.

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This paper describes the author’s estimation of the relative energy associated with higher-frequency glucose components. During this research, he attempted to develop a simplified yet practical “equation” for calculating his estimated relative energy. His ultimate goal is to identify the degree of impact or damage to the human internal organs due to excessive energy caused by hyperglycemia in diabetes patients. He has applied his developed GH-Method: math-physical medicine (MPM) approach to conduct this medical research. By using his own ~33,000 glucose data for the past 129 days; he has ident

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"Comparison of High Range and Low Range of Sensor eAG and 24-Hour Daily GF Using 3+ years of Continuous Glucose Monitoring Sensor Device Collected Data Based on GH-Method: Math-Physical Medicine (No. 458)." Advances in Bioengineering and Biomedical Science Research 4, no. 3 (2022). http://dx.doi.org/10.33140/abbsr.04.03.003.

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Since 5/5/2018, the author utilized a continuous glucose monitoring (CGM) sensor device to collect his glucoses 96 times each day. He then calculates his average daily sensor glucoses (eAG) and sensor glucose fluctuation (GF) within a 24-hour period each. His GF is defined as the maximum glucose value minus the minimum glucose value within a day. The definition of “eAG” is the mean value of glucose data that is similar to HbA1C which is useful in diabetes control. Moreover, the glucose excursion or GF has noticeable influences on various diabetes complications. During the period of 1,218 days

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"Accuracy of Three HbA1C Equations and their Predicted Results in Comparison with the Lab-Tested A1C on 7/22/2021 Based on GH-Method: Math-Physical Medicine (No. 485)." Advances in Bioengineering and Biomedical Science Research 4, no. 3 (2022). http://dx.doi.org/10.33140/abbsr.04.03.006.

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Since 7/1/2015, the author has utilized his collected data of finger pierced glucose readings 4 times daily, carbs/sugar intake amount, and post-meal walking steps for each meal to calculate the predicted daily HbA1C values (the “daily finger A1C”). Over the past 5.5 years, the predicted HbA1C values were calculated 12 times within the same timeframe of the12 different lab-tested dates. During the 12 continuous 5-month time periods, he achieved a 100% prediction accuracy using his daily finger A1C model. Starting from 5/5/2018, along with finger glucose levels, he has been collecting 96 glucos

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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)." 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 s

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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 s

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

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

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39

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

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40

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

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41

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

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42

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

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43

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

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44

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

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45

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

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46

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

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47

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

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48

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

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49

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

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50

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

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