scholarly journals Effect of Insulin Glulisine on Microvascular Blood Flow and Endothelial Function in the Postprandial State

Diabetes Care ◽  
2008 ◽  
Vol 31 (5) ◽  
pp. 1021-1025 ◽  
Author(s):  
C. Hohberg ◽  
T. Forst ◽  
M. Larbig ◽  
M. Safinowski ◽  
S. Diessel ◽  
...  
Keyword(s):  
Blood Flow ◽  
Endothelial Function ◽  
Microvascular Blood Flow ◽  
Postprandial State ◽  
Insulin Glulisine

Assessment of microvascular endothelial function in human skin

2001 ◽  
Vol 101 (6) ◽  
pp. 567-572 ◽  
Author(s):  
David J. NEWTON ◽  
Faisel KHAN ◽  
Jill J.F. BELCH
Keyword(s):  
Blood Flow ◽  
Substance P ◽  
Endothelial Function ◽  
Animal Studies ◽  
Doppler Imaging ◽  
Microvascular Blood Flow ◽  
Coefficients Of Variation ◽  
Forearm Skin ◽  
Invasive Techniques ◽  
Dose Dependent

Endothelial dysfunction is an important factor in many cardiovascular diseases, and is commonly associated with impaired endothelium-mediated vasodilatation. Information about the mechanisms behind this dysfunction has come largely from animal studies or, in humans, through invasive techniques that are not specific to one vascular bed. We have developed protocols to assess endothelial function non-invasively in the cutaneous microcirculation by measuring blood flow responses to four receptor-specific vasoactive compounds. Cumulative doses of acetylcholine, methacholine, bradykinin and substance P were administered iontophoretically to the forearm skin of healthy volunteers on two to three occasions. Dose-dependent increases in skin microvascular blood flow in response to these drugs were measured with laser Doppler imaging. Vascular responses to acetylcholine and methacholine were reasonably consistent, with coefficients of variation of approx. 17%. The coefficients of variation for bradykinin and substance P were much poorer, as high as 70% for some doses. This might partly be a consequence of the more unpredictable effects of histamine release in the vasoactive behaviour of these two agonists. Although it might be advantageous to find other agonists with which to test the function of different receptor pathways, we have shown that just acetylcholine and methacholine can currently be used with iontophoresis to allow sensitive and reproducible assessment of endothelial function.

1715-P: Impaired Postprandial Adipose Tissue Microvascular Blood Flow in Healthy People with a Family History of Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1715-P
Author(s):  
KATHERINE ROBERTS-THOMSON ◽  
RYAN D. RUSSELL ◽  
DONGHUA HU ◽  
TIMOTHY M. GREENAWAY ◽  
ANDREW C. BETIK ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Blood Flow ◽  
Adipose Tissue ◽  
Family History ◽  
Healthy People ◽  
Microvascular Blood Flow ◽  
History Of

Nitric oxide release in rat skeletal muscle capillary

1996 ◽  
Vol 270 (5) ◽  
pp. H1696-H1703 ◽  
Author(s):  
D. Mitchell ◽  
K. Tyml
Keyword(s):  
Nitric Oxide ◽  
Blood Flow ◽  
Leukocyte Adhesion ◽  
Microvascular Blood Flow ◽  
Capillary Length ◽  
Nitric Oxide Release ◽  
Longus Muscle ◽  
Potent Vasodilator ◽  
Capillary Bed ◽  
Synthase Inhibitor

Nitric oxide (NO) has been shown to be a potent vasodilator released from endothelial cells (EC) in large blood vessels, but NO release has not been examined in the capillary bed. Because the capillary bed represents the largest source of EC, it may be the largest source of vascular NO. In the present study, we used intravital microscopy to examine the effect of the NO synthase inhibitor, NG-nitro-L-arginine methyl ester (L-NAME), on the microvasculature of the rat extensor digitorum longus muscle. L-NAME (30 mM) applied locally to a capillary (300 micron(s) from the feeding arteriole) reduced red blood cell (RBC) velocity [VRBC; control VRBC = 238 +/- 58 (SE) micron/s; delta VRBC = -76 +/- 8%] and RBC flux (4.4 +/- 0.7 to 2.8 +/- 0.7 RBC/s) significantly in the capillary, but did not change feeding arteriole diameter (Dcon = 6.3 +/- 0.7 micron, delta D = 5 +/- 7%) or draining venule diameter (Dcon = 10.1 +/- 0.6 micron, delta D = 4 +/- 2%). Because of the VRBC change, the flux reduction was equivalent to an increased local hemoconcentration from 1.8 to 5 RBCs per 100 micron capillary length. L-NAME also caused an increase in the number of adhering leukocytes in the venule from 0.29 to 1.43 cells/100 micron. L-NAME (30 mM) applied either to arterioles or to venules did not change capillary VRBC. Bradykinin (BK) locally applied to the capillary caused significant increases in VRBC (delta VRBC = 111 +/- 23%) and in arteriolar diameter (delta D = 40 +/- 5%). This BK response was blocked by capillary pretreatment with 30 mM L-NAME (delta VRBC = -4 +/- 27%; delta D = 5 +/- 9% after BK). We concluded that NO may be released from capillary EC both basally and in response to the vasodilator BK. We hypothesize that 1) low basal levels of NO affect capillary blood flow by modulating local hemoconcentration and leukocyte adhesion, and 2) higher levels of NO (stimulated by BK) may cause a remote vasodilation to increase microvascular blood flow.

scholarly journals Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ossama Mahmoud ◽  
Mahmoud El-Sakka ◽  
Barry G. H. Janssen
Keyword(s):  
Machine Learning ◽  
Blood Flow ◽  
Human Error ◽  
Image Data ◽  
Video Microscopy ◽  
Microvascular Blood Flow ◽  
Image Processing Algorithm ◽  
Step Algorithm ◽  
3D Cnn

AbstractMicrovascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcirculation in health and sickness. Microvascular blood flow is generally investigated with Intravital Video Microscopy (IVM), and the captured images are stored on a computer for later off-line analysis. The analysis of these images is a manual and challenging process, evaluating experiments very time consuming and susceptible to human error. Since more advanced digital cameras are used in IVM, the experimental data volume will also increase significantly. This study presents a new two-step image processing algorithm that uses a trained Convolutional Neural Network (CNN) to functionally analyze IVM microscopic images without the need for manual analysis. While the first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures, the second step uses a 3D-CNN to assess whether the vessel-like structures have blood flowing in it or not. We demonstrate that our two-step algorithm can efficiently analyze IVM image data with high accuracy (83%). To our knowledge, this is the first application of machine learning for the functional analysis of microvascular blood flow in vivo.

On the poro-elastic models for microvascular blood flow resistance: An in vitro validation

2021 ◽  
Vol 117 ◽  
pp. 110241
Author(s):  
Alberto Coccarelli ◽  
Supratim Saha ◽  
Tanjeri Purushotham ◽  
K. Arul Prakash ◽  
Perumal Nithiarasu
Keyword(s):  
Blood Flow ◽  
Flow Resistance ◽  
Microvascular Blood Flow
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