Glucoses and HbA1C Comparison Study Between Pre-COVID-19 and COVID-19 Using GH-Method: Math-Physical Medicine(No. 318)

2020 ◽  
Keyword(s):  
Quantitative Analysis ◽  
Diabetes Control ◽  
Comparison Study ◽  
Physical Medicine

The author utilizes quantitative analysis results based on diabetes control for two periods: the pre-COVID-19 period, from 5/5/2018 to 1/18/2020, and the COVID-19 period, from 1/19/2020 to 8/25/2020, within a duration of 2.3 years. Special attention has been placed on the comparison of quantitative glucose results, including FPG, PPG, daily glucose, and HbA1C, especially the COVID-19 quarantine period from 1/19/2020 to 8/25/2020.

scholarly journals Glucoses and HbA1C Comparison Study between preCOVID-19 and COVID-19 using GH-Method: MathPhysical Medicine (No. 318)

2020 ◽  
pp. 1-7
Keyword(s):  
Big Data ◽  
Quantitative Analysis ◽  
Diabetes Control ◽  
Comparison Study ◽  
Lower Glucose

The author utilizes quantitative analysis results based on diabetes control for two periods: the pre-COVID-19 period, from 5/5/2018 to 1/18/2020, and the COVID-19 period, from 1/19/2020 to 8/25/2020, within a duration of 2.3 years. Special attention has been placed on the comparison of quantitative glucose results, including FPG, PPG, daily glucose, and HbA1C, especially the COVID-19 quarantine period from 1/19/2020 to 8/25/2020. From his collected big data, it is obvious that the COVID-19 period has lower glucose values which are expressed in the following format, all in the unit of mg/dL: (pre-period, COVID-19 period, glucose difference).

scholarly journals Quantitative Analysis and Comparison Study of [18F]AlF-NOTA-PRGD2, [18F]FPPRGD2 and [68Ga]Ga-NOTA-PRGD2 Using a Reference Tissue Model

PLoS ONE ◽  
2012 ◽  
Vol 7 (5) ◽  
pp. e37506 ◽  
Author(s):  
Ning Guo ◽  
Lixin Lang ◽  
Weihua Li ◽  
Dale O. Kiesewetter ◽  
Haokao Gao ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Comparison Study ◽  
Reference Tissue ◽  
Tissue Model ◽  
Reference Tissue Model

scholarly journals Comparison of effective health ages between the sophisticated model for researchers and simplified model for patients using GH-Method: math-physical medicine (No. 323)

2020 ◽  
Vol 4 (3) ◽  
Keyword(s):  
Input Data ◽  
Computer Software ◽  
Comparison Study ◽  
Physical Medicine ◽  
Age Related ◽  
Sophisticated Model ◽  
The Subject ◽  
The Difference ◽  
Arithmetical Formula ◽  
Metabolism Model

This is the fourth article the author has written regarding the subject of effective health age (“Health Age”) related to the medical branch of geriatrics. Originally, he used his metabolism indexes data which were collected and processed via a sophisticated software for researchers. Later, he developed a simplified APP on the iPhone for other patients. This specific article discusses the differences of health input data and output results based on metabolism indexes and estimated health ages between these two different software versions. A comparison study between the difference of estimated health ages by using two different computer software versions was completed. The finding indicates that the complex metabolism model of his chronic software version would gain an extra 1.4% of accuracy on estimating his health age when compared to the simplified APP version. The author is not a fortune teller who uses a crystal ball to predict his or other people’s future life expectancy. Rather, he is a scientist who applies solid and sophisticated scientific techniques, such as math-physical medicine with biomedical evidence, to develop a simple arithmetical formula which can serve as a useful tool for the general population to maintain their health and achieve their desired longevity.

scholarly journals Comparison of effective health ages between the sophisticated model for researchers and simplified model for patients using GH-Method: math-physical medicine (No. 323)

2020 ◽  
Vol 5 (3) ◽  
Keyword(s):  
Input Data ◽  
Computer Software ◽  
Comparison Study ◽  
Physical Medicine ◽  
Age Related ◽  
Sophisticated Model ◽  
The Subject ◽  
The Difference ◽  
Arithmetical Formula ◽  
Metabolism Model

This is the fourth article the author has written regarding the subject of effective health age (“Health Age”) related to the medical branch of geriatrics. Originally, he used his metabolism indexes data which were collected and processed via a sophisticated software for researchers. Later, he developed a simplified APP on the iPhone for other patients. This specific article discusses the differences of health input data and output results based on metabolism indexes and estimated health ages between these two different software versions. A comparison study between the difference of estimated health ages by using two different computer software versions was completed. The finding indicates that the complex metabolism model of his chronic software version would gain an extra 1.4% of accuracy on estimating his health age when compared to the simplified APP version. The author is not a fortune teller who uses a crystal ball to predict his or other people’s future life expectancy. Rather, he is a scientist who applies solid and sophisticated scientific techniques, such as math-physical medicine with biomedical evidence, to develop a simple arithmetical formula which can serve as a useful tool for the general population to maintain their health and achieve their desired longevity.

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

Quantification of Silica Uptake by Alveolar Macrophages - An Emperical Scanning Electron Microprobe Method

1982 ◽  
Vol 40 ◽  
pp. 332-333
Author(s):  
V. V. Damiano ◽  
R. P. Daniele ◽  
H. T. Tucker ◽  
J. H. Dauber
Keyword(s):  
Quantitative Analysis ◽  
Alveolar Macrophages ◽  
Bulk Sample ◽  
Silica Particles ◽  
Empirical Method ◽  
Phagocytic Cells ◽  
Calibration Standards ◽  
X Ray ◽  
Accurate Quantitative Analysis ◽  
Scanning Electron

An important example of intracellular particles is encountered in silicosis where alveolar macrophages ingest inspired silica particles. The quantitation of the silica uptake by these cells may be a potentially useful method for monitoring silica exposure. Accurate quantitative analysis of ingested silica by phagocytic cells is difficult because the particles are frequently small, irregularly shaped and cannot be visualized within the cells. Semiquantitative methods which make use of particles of known size, shape and composition as calibration standards may be the most direct and simplest approach to undertake. The present paper describes an empirical method in which glass microspheres were used as a model to show how the ratio of the silicon Kα peak X-ray intensity from the microspheres to that of a bulk sample of the same composition correlated to the mass of the microsphere contained within the cell. Irregular shaped silica particles were also analyzed and a calibration curve was generated from these data.

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