Cantú syndrome (CS), a multisystem disease with a complex cardiovascular phenotype, is caused by gain-of-function (GoF) variants in the Kir6.1/SUR2 subunits of ATP-sensitive potassium (KATP) channels and is characterized by low systemic vascular resistance, as well as tortuous, dilated, vessels, and decreased pulse-wave velocity. Thus, CS vascular dysfunction is multifactorial, with both hypomyotonic and hyperelastic components. To dissect whether such complexities arise cell autonomously within vascular smooth muscle cells (VSMCs) or as secondary responses to the pathophysiological milieu, we assessed electrical properties and gene expression in human induced pluripotent stem cell-derived VSMCs (hiPSC-VSMCs), differentiated from control and CS patient-derived hiPSCs, and in native mouse control and CS VSMCs. Whole-cell voltage clamp of isolated aortic and mesenteric arterial VSMCs isolated from wild-type (WT) and Kir6.1[V65M] (CS) mice revealed no clear differences in voltage-gated K+ (Kv) or Ca2+ currents. Kv and Ca2+ currents were also not different between validated hiPSC-VSMCs differentiated from control and CS patient-derived hiPSCs. While pinacidil-sensitive KATP currents in control hiPSC-VSMCs were similar to those in WT mouse VSMCs, they were considerably larger in CS hiPSC-VSMCs. Under current-clamp conditions, CS hiPSC-VSMCs were also hyperpolarized, consistent with increased basal K conductance and providing an explanation for decreased tone and decreased vascular resistance in CS. Increased compliance was observed in isolated CS mouse aortae and was associated with increased elastin mRNA expression. This was consistent with higher levels of elastin mRNA in CS hiPSC-VSMCs and suggesting that the hyperelastic component of CS vasculopathy is a cell-autonomous consequence of vascular KATP GoF. The results show that hiPSC-VSMCs reiterate expression of the same major ion currents as primary VSMCs, validating the use of these cells to study vascular disease. Results in hiPSC-VSMCs derived from CS patient cells suggest that both the hypomyotonic and hyperelastic components of CS vasculopathy are cell-autonomous phenomena driven by KATP overactivity within VSMCs .

Retinoic acid is a metabolic product derived from vitamin A, acting at a nuclear level to maintain the proper transcriptional activity. Moreover, this molecule contributes to the development and maturation of the cerebral vascular system, playing a pivotal role in development and maintenance of neurovascular unit integrity. This physiological structure is comprised of glial cells, vascular cells, and neurons, ensuring the correct function of the blood-brain barrier and, at last instance, the homeostasis of the central nervous system. Therefore, retinoic acid ensures the physiological structure integrity of the neurovascular unit, decreasing the development of neurological disorders. Furthermore, retinoic acid can modulate the physiological function of the neurovascular unit cells, which is crucial to the maintenance of this physiological structure. The deletion of this molecule leads to the development of neurodegenerative diseases such as Alzheimer\'s disease, multiple sclerosis, Parkinson\'s disease. In addition, impaired signaling of this molecule contributes to a worse prognosis in the recovery after ischemic stroke. This review characterizes the cellular components that constitute the neurovascular unit and analyzes the effect of retinoic acid on these cellular components that, in a coordinated manner, are responsible for homeostasis of the central nervous system. Through this description, it seems apparent that retinoic acid administration might be an essential pharmacological tool in the near future.

Blood perfusion is an important index for the function of the cardiovascular system and it can be indicated by the blood flow distribution in the vascular tree. As the blood flow in a vascular tree varies in a large range of scales and fractal analysis owns the ability to describe multi-scale properties, it is reasonable to apply fractal analysis to depict the blood flow distribution. The objective of this study is to establish fractal methods for analyzing the blood flow distribution which can be applied to real vascular trees. For this purpose, the modified methods in fractal geometry were applied and a special strategy was raised to make sure that these methods are applicable to an arbitrary vascular tree. The validation of the proposed methods on real arterial trees verified the ability of the produced parameters (fractal dimension and multifractal spectrum) in distinguishing the blood flow distribution under different physiological states. Furthermore, the physiological significance of the fractal parameters was investigated in two situations. For the first situation, the vascular tree was set as a perfect binary tree and the blood flow distribution was adjusted by the split ratio. As the split ratio of the vascular tree decreases, the fractal dimension decreases and the multifractal spectrum expands. The results indicate that both fractal parameters can quantify the degree of blood flow heterogeneity. While for the second situation, artificial vascular trees with different structures were constructed and the hemodynamics in these vascular trees was simulated. The results suggest that both the vascular structure and the blood flow distribution affect the fractal parameters for blood flow. The fractal dimension declares the integrated information about the heterogeneity of vascular structure and blood flow distribution. In contrast, the multifractal spectrum identifies the heterogeneity features in blood flow distribution or vascular structure by its width and height. The results verified that the proposed methods are capable of depicting the multi-scale features of the blood flow distribution in the vascular tree and further are potential for investigating vascular physiology.

We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel trees and networks and corresponding features in 3-D angiographic volumes using deep learning. We discuss the problems of low execution speed and high memory requirements associated with full 3-D networks, high-class imbalance arising from the low percentage (<3%) of vessel voxels, and unavailability of accurately annotated 3-D training data-and offer solutions as the building blocks of DeepVesselNet. First, we formulate 2-D orthogonal cross-hair filters which make use of 3-D context information at a reduced computational burden. Second, we introduce a class balancing cross-entropy loss function with false-positive rate correction to handle the high-class imbalance and high false positive rate problems associated with existing loss functions. Finally, we generate a synthetic dataset using a computational angiogenesis model capable of simulating vascular tree growth under physiological constraints on local network structure and topology and use these data for transfer learning. We demonstrate the performance on a range of angiographic volumes at different spatial scales including clinical MRA data of the human brain, as well as CTA microscopy scans of the rat brain. Our results show that cross-hair filters achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy that does not differ from full 3-D filters. Our class balancing metric is crucial for training the network, and transfer learning with synthetic data is an efficient, robust, and very generalizable approach leading to a network that excels in a variety of angiography segmentation tasks. We observe that sub-sampling and max pooling layers may lead to a drop in performance in tasks that involve voxel-sized structures. To this end, the DeepVesselNet architecture does not use any form of sub-sampling layer and works well for vessel segmentation, centerline prediction, and bifurcation detection. We make our synthetic training data publicly available, fostering future research, and serving as one of the first public datasets for brain vessel tree segmentation and analysis.

The chick embryo includes the area vasculosa is subdivided into 2 concentric zones, the inner transparent area pellucida vasculosa and the surrounding less transparent area opaca vasculosa, peripherally limited by the sinus terminalis. In this study, we have analyzed by a modern morphometric approach the total length of the vascular network, the number of vascular branches, of the branching points density, the modality of vessel ramification, and spatial arrangement of the vascular network in four consecutive stages of development of the area vasculosa. The results have shown that there is a significant 15% increase in the total length of the vascular network associated with a progressive increase of the number of vascular branches and of the branching points density. Moreover, the results indicated that vascular spatial disorder significantly decreased during development in area vasculosa, suggesting a more uniform occupancy of the tissue by the vascular pattern. Finally, a more regular pattern of branching was observed, as indicated by the significant decrease of topological disorder of the vascular tree.

This study proposes a novel geometrical force constraint method for 3-D vasculature modeling and angiographic image simulation. For this method, space filling force, gravitational force, and topological preserving force are proposed and combined for the optimization of the topology of the vascular structure. The surface covering force and surface adhesion force are constructed to drive the growth of the vasculature on any surface. According to the combination effects of the topological and surface adhering forces, a realistic vasculature can be effectively simulated on any surface. The image projection of the generated 3-D vascular structures is simulated according to the perspective projection and energy attenuation principles of X-rays. Finally, the simulated projection vasculature is fused with a predefined angiographic mask image to generate a realistic angiogram. The proposed method is evaluated on a CT image and three generally utilized surfaces. The results fully demonstrate the effectiveness and robustness of the proposed method.

A proper analysis of blood flow is contingent upon accurate modelling of the branching pattern and vascular geometry of the network of interest. It is challenging to reconstruct the entire vascular network of any organ experimentally, in particular the pulmonary vasculature, because of its very high number of vessels, complexity of the branching pattern and poor accessibility in vivo. The objective of our research is to develop an innovative approach for the reconstruction of the full pulmonary vascular tree from available morphometric data. Our method consists of the use of morphometric data on those parts of the pulmonary vascular tree that are too small to reconstruct by medical imaging methods. This method is a three-step technique that reconstructs the entire pulmonary arterial tree down to the capillary bed. Vessels greater than 2 mm are reconstructed from direct volume and surface analysis using contrast-enhanced computed tomography. Vessels smaller than 2 mm are reconstructed from available morphometric and distensibility data and rearranged by applying Murray\'s laws. Implementation of morphometric data to reconstruct the branching pattern and applying Murray\'s laws to every vessel bifurcation simultaneously leads to an accurate vascular tree reconstruction. The reconstruction algorithm generates full arterial tree topography down to the first capillary bifurcation. Geometry of each order of the vascular tree is generated separately to minimize the construction and simulation time. The node-to-node connectivity along with the diameter and length of every vessel segment is established and order numbers, according to the diameter-defined Strahler system, are assigned. In conclusion, the present model provides a morphological foundation for future analysis of blood flow in the pulmonary circulation.