GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • American Physiological Society  (3)
  • 1
    Online Resource
    Online Resource
    Hyperglycemia, maturity-onset obesity, and insulin resistance in NONcNZO10/LtJ males, a new mouse model of type 2 diabetes
    American Physiological Society ; 2007
    In:  American Journal of Physiology-Endocrinology and Metabolism Vol. 293, No. 1 ( 2007-07), p. E327-E336
    Details
    In: American Journal of Physiology-Endocrinology and Metabolism, American Physiological Society, Vol. 293, No. 1 ( 2007-07), p. E327-E336
    Abstract: As a new mouse model of obesity-induced diabetes generated by combining quantitative trait loci from New Zealand Obese (NZO/HlLt) and Nonobese Nondiabetic (NON/LtJ) mice, NONcNZO10/LtJ (RCS10) male mice developed type 2 diabetes characterized by maturity onset obesity, hyperglycemia, and insulin resistance. To metabolically profile the progression to diabetes in preobese and obese states, a 2-h hyperinsulinemic euglycemic clamp was performed and organ-specific changes in insulin action were assessed in awake RCS10 and NON/LtJ (control) males at 8 and 13 wk of age. Prior to development of obesity and attendant increases in hepatic lipid content, 8-wk-old RCS10 mice developed insulin resistance in liver and skeletal muscle due to significant decreases in insulin-stimulated glucose uptake and GLUT4 expression in muscle. Transition to an obese and hyperglycemic state by 13 wk of age exacerbated insulin resistance in skeletal muscle, liver, and heart associated with organ-specific increases in lipid content. Thus, this polygenic mouse model of type 2 diabetes, wherein plasma insulin is only modestly elevated and obesity develops with maturity yet insulin action and glucose metabolism in skeletal muscle and liver are reduced at an early prediabetic age, should provide new insights into the etiology of type 2 diabetes.
    Type of Medium: Online Resource
    ISSN: 0193-1849 , 1522-1555
    URL: Article
    DOI: 10.1152/ajpendo.00376.2006
    Language: English
    Publisher: American Physiological Society
    Publication Date: 2007
    detail.hit.zdb_id: 1477331-4
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Computational analysis of airflow dynamics for predicting collapsible sites in the upper airways: a preliminary study
    American Physiological Society ; 2019
    In:  Journal of Applied Physiology Vol. 126, No. 2 ( 2019-02-01), p. 330-340
    Details
    In: Journal of Applied Physiology, American Physiological Society, Vol. 126, No. 2 ( 2019-02-01), p. 330-340
    Abstract: The present study aimed to detail the relationship between the flow and structure characteristics of the upper airways and airway collapsibility in obstructive sleep apnea. Using a computational approach, we performed simulations of the flow and structure of the upper airways in two patients having different facial morphologies: retruding and protruding jaws, respectively. First, transient flow simulation was performed using a prescribed volume flow rate to observe flow characteristics within upper airways with an unsteady effect. In the retruding jaw, the maximum magnitude of velocity and pressure drop with velocity shear and vortical motion was observed at the oropharyngeal level. In contrast, in the protruding jaw, the overall magnitude of velocity and pressure was relatively small. To identify the cause of the pressure drop in the retruding jaw, pressure gradient components induced by flow were examined. Of note, vortical motion was highly associated with pressure drop. Structure simulation was performed to observe the deformation and collapsibility of soft tissue around the upper airways using the surface pressure obtained from the flow simulation. At peak flow rate, the soft tissue of the retruding jaw was highly expanded, and a collapse was observed at the oropharyngeal and epiglottis levels. NEW & NOTEWORTHY Aerodynamic characteristics have been reported to correlate with airway occlusion. However, a detailed mechanism of the phenomenon within the upper airways and its impact on airway collapsibility remain poorly understood. This study provides in silico results for aerodynamic characteristics, such as vortical structure, pressure drop, and exact location of the obstruction using a computational approach. Large deformation of soft tissue was observed in the retruding jaw, suggesting that it is responsible for obstructive sleep apnea.
    Type of Medium: Online Resource
    ISSN: 8750-7587 , 1522-1601
    URL: Article
    DOI: 10.1152/japplphysiol.00522.2018
    RVK:
    WA 15000
    RVK:
    XA 52094
    Language: English
    Publisher: American Physiological Society
    Publication Date: 2019
    detail.hit.zdb_id: 1404365-8
    SSG: 12
    SSG: 31
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Computational analysis of airflow dynamics for predicting collapsible sites in the upper airways: machine learning approach
    American Physiological Society ; 2019
    In:  Journal of Applied Physiology Vol. 127, No. 4 ( 2019-10-01), p. 959-973
    Details
    In: Journal of Applied Physiology, American Physiological Society, Vol. 127, No. 4 ( 2019-10-01), p. 959-973
    Abstract: Obstructive sleep apnea (OSA) is a common sleep breathing disorder. With the use of computational fluid dynamics (CFD), this study provides a quantitative standard for accurate diagnosis and effective surgery based on the investigation of the relationship between airway geometry and aerodynamic characteristics. Based on computed tomography data from patients having normal geometry, 4 major geometric parameters were selected and a total of 160 idealized cases were modeled and simulated. We created a predictive model using Gaussian process regression (GPR) through a data set obtained through numerical method. The results demonstrated that the mean accuracy of the overall GPR model was ~72% with respect to the CFD results for the realistic upper airway model. A support vector machine model was also used to identify the degree of OSA symptoms in patients as normal-mild and moderate and severe. We achieved an accuracy of 82.5% with the training data set and an accuracy of 80% with the test data set. NEW & NOTEWORTHY There have been many studies on the analysis of obstructive sleep apnea (OSA) through computational fluid dynamics and finite element analysis. However, these methods are not useful for practical medical applications because they have limited information for OSA symptom. This study employs the machine learning algorithm to predict flow characteristics quickly and to determine the symptoms of the patient's OSA. The overall Gaussian process regression model's mean accuracy was ~72%, and the accuracy for the classification of OSA was 〉 80%.
    Type of Medium: Online Resource
    ISSN: 8750-7587 , 1522-1601
    URL: Article
    DOI: 10.1152/japplphysiol.01033.2018
    RVK:
    WA 15000
    RVK:
    XA 52094
    Language: English
    Publisher: American Physiological Society
    Publication Date: 2019
    detail.hit.zdb_id: 1404365-8
    SSG: 12
    SSG: 31
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...