Bumetanide promotes neural precursor cell regeneration and dendritic development in the hippocampal dentate gyrus in the chronic stage of cerebral ischemia

Bumetanide has been shown to lessen cerebral edema and reduce the infarct area in the acute stage of cerebral ischemia. Few studies focus on the effects of bumetanide on neuroprotection and neurogenesis in the chronic stage of cerebral ischemia. We established a rat model of cerebral ischemia by injecting endothelin-1 in the left cortical motor area and left corpus striatum. Seven days later, bumetanide 200 μg/kg/day was injected into the lateral ventricle for 21 consecutive days with a mini-osmotic pump. Results demonstrated that the number of neuroblasts cells and the total length of dendrites increased, escape latency reduced, and the number of platform crossings increased in the rat hippocampal dentate gyrus in the chronic stage of cerebral ischemia. These findings suggest that bumetanide promoted neural precursor cell regeneration, dendritic development and the recovery of cognitive function, and protected brain tissue in the chronic stage of ischemia.

Computational Intelligence in Remote Sensing Image Registration:A survey

In recent years,computational intelligence has been widely used in many fields and achieved remarkable performance.Evolutionary computing and deep learning are important branches of computational intelligence.Many methods based on evolutionary computation and deep learning have achieved good performance in remote sensing image registration.This paper introduces the application of computational intelligence in remote sensing image registration from the two directions of evolutionary computing and deep learning.In the part of remote sensing image registration based on evolutionary calculation,the principles of evolutionary algorithms and swarm intelligence algorithms are elaborated and their application in remote sensing image registration is discussed.The application of deep learning in remote sensing image registration is also discussed.At the same time,the development status and future of remote sensing image registration are summarized and their prospects are examined.

Correlation of In Vivo and In Vitro Methods in Measuring Choroidal Vascularization Volumes Using a Subretinal Injection Induced Choroidal Neovascularization Model

Background： In vivo quantification of choroidal neovascularization （CNV） based on noninvasive optical coherence tomography （OCT） examination and in vitro choroidal flatmount immunohistochemistry stained of CNV currently were used to evaluate the process and severity of age-related macular degeneration （AMD） both in human and animal studies. This study aimed to investigate the correlation between these two methods in murine CNV models induced by subretinal injection. Methods： CNV was developed in 20 C57BL6/j mice by subretinal injection of adeno-associated viral delivery of a short hairpin RNA targeting sFLT-1 （AAV.shRNA.sFLT- 1）, as reported previously. After 4 weeks, CNV was imaged by OCT and fluorescence angiography. The scaling factors for each dimension, x, y, and z （ktm/pixel） were recorded, and the corneal curvature standard was adjusted from human （7.7） to mice （1 .4）. The volume of each OCT image stack was calculated and then normalized by multiplying the number of voxels by the scaling factors for each dimension in Seg3D software （University of Utah Scientific Computing and Imaging Institute, available at http：//www.sci.utah.edu/cibc-software/seg3d.html）. Eighteen mice were prepared for choroidal flatmounts and stained by CD31. The CNV volumes were calculated using scanning laser confocal microscopy after immunohistochemistry staining. Two mice were stained by Hematoxylin and Eosin for observing the CNV morphology. Results： The CNV volume calculated using OCT was, on average, 2.6 times larger than the volume calculated using the laser confocal microscopy. The correlation statistical analysis showed OCT measuring of CNV correlated significantly with the in vitro method （R： = 0.448, P = 0.001, n = 18）. The correlation coefficient for CNV quantification using OCT and confocal microscopy was 0.693 （n = 18, P = 0.001 ）. Conclusions： There is a fair linear correlation on CNV volumes between in vivo and in vitro methods in CNV models induced by subretinal injection. The result might provide a useful evaluation of CNV both for the studies using CNV models induced by subretinal injection and human AMD studies.