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In-vivo, non-invasive detection of hyperglycemic states in animal models using mm-wave spectroscopy

View Article: PubMed Central - PubMed

ABSTRACT

Chronic or sustained hyperglycemia associated to diabetes mellitus leads to many medical complications, thus, it is necessary to track the evolution of patients for providing the adequate management of the disease that is required for the restoration of the carbohydrate metabolism to a normal state. In this paper, a novel monitoring approach based on mm-wave spectroscopy is comprehensively described and experimentally validated using living animal models as target. The measurement method has proved the possibility of non-invasive, in-vivo, detection of hyperglycemia-associated conditions in different mouse models, making possible to clearly differentiate between several hyperglycemic states.

No MeSH data available.


Scores of the different mice groups for the two first principal components.
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f3: Scores of the different mice groups for the two first principal components.

Mentions: Principal Component Analysis (PCA) techniques were applied to the data matrix from Fig. 2 in order to reduce the dimensionality of the data (see Methods section for further details). The scores of the two first Principal Components (PCs) that were found to explain the 94.75% and the 4.86% of the variance, respectively, (with a cumulative explained variance of 99.61% between them), are shown in Fig. 3. From this figure it is clear how the score of PC 1 (first PC), accounting for the highest variance in the measurements, can be directly used to differentiate the control animals from hyperglycemic mice. In this way, the measurement set-up together with the previously presented data analysis is capable of providing a single indicator (score of PC1) that differentiates between control and hyperglycemic mice. It is also equally worth noting the behavior of PC 2 (second PC), that differentiates between control animals (including all the mice strains detailed below) that are grouped together, obese, and diabetized and genetically diabetic mice. Another very interesting observation is that our measurement set-up is capable of detecting the changes taking place in obese mice after leptin replacement for 25 days, classifying them as control animals.


In-vivo, non-invasive detection of hyperglycemic states in animal models using mm-wave spectroscopy
Scores of the different mice groups for the two first principal components.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037419&req=5

f3: Scores of the different mice groups for the two first principal components.
Mentions: Principal Component Analysis (PCA) techniques were applied to the data matrix from Fig. 2 in order to reduce the dimensionality of the data (see Methods section for further details). The scores of the two first Principal Components (PCs) that were found to explain the 94.75% and the 4.86% of the variance, respectively, (with a cumulative explained variance of 99.61% between them), are shown in Fig. 3. From this figure it is clear how the score of PC 1 (first PC), accounting for the highest variance in the measurements, can be directly used to differentiate the control animals from hyperglycemic mice. In this way, the measurement set-up together with the previously presented data analysis is capable of providing a single indicator (score of PC1) that differentiates between control and hyperglycemic mice. It is also equally worth noting the behavior of PC 2 (second PC), that differentiates between control animals (including all the mice strains detailed below) that are grouped together, obese, and diabetized and genetically diabetic mice. Another very interesting observation is that our measurement set-up is capable of detecting the changes taking place in obese mice after leptin replacement for 25 days, classifying them as control animals.

View Article: PubMed Central - PubMed

ABSTRACT

Chronic or sustained hyperglycemia associated to diabetes mellitus leads to many medical complications, thus, it is necessary to track the evolution of patients for providing the adequate management of the disease that is required for the restoration of the carbohydrate metabolism to a normal state. In this paper, a novel monitoring approach based on mm-wave spectroscopy is comprehensively described and experimentally validated using living animal models as target. The measurement method has proved the possibility of non-invasive, in-vivo, detection of hyperglycemia-associated conditions in different mouse models, making possible to clearly differentiate between several hyperglycemic states.

No MeSH data available.