Alzheimer’s disease (AD) has become a problem, owing to its high prevalence in an aging society with no treatment available after onset. However, early diagnosis is essential for preventive intervention to delay disease onset due to its slow progression. The current AD diagnostic methods are typically invasive and expensive, limiting their potential for widespread use. Thus, the development of biomarkers in available biofluids, such as blood, urine, and saliva, which enables low or non-invasive, reasonable, and objective evaluation of AD status, is an urgent task. Here, we reviewed studies that examined biomarker candidates for the early detection of AD. Some of the candidates showed potential biomarkers, but further validation studies are needed. We also reviewed studies for non-invasive biomarkers of AD. Given the complexity of the AD continuum, multiple biomarkers with machine-learning-classification methods have been recently used to enhance diagnostic accuracy and characterize individual AD phenotypes. Artificial intelligence and new body fluid-based biomarkers, in combination with other risk factors, will provide a novel solution that may revolutionize the early diagnosis of AD.
Objective: Quantification of angiographic images with two-photon laser scanning fluorescence microscopy (2PLSM) relies on proper segmentation of the vascular images. However, the images contain inhomogeneities in the signal-to-noise ratio (SNR) arising from regional effects of light scattering and absorption. The present study developed a semiautomated quantification method for volume images of 2PLSM angiography by adjusting the binarization threshold according to local SNR along the vessel centerlines.Methods: A phantom model made with fluorescent microbeads was used to incorporate a region-dependent binarization threshold.
Results:The recommended SNR for imaging was found to be 4.2-10.6 that provide the true size of imaged objects if the binarization threshold was fixed at 50% of SNR.However, angiographic images in the mouse cortex showed variable SNR up to 45 over the depths. To minimize the errors caused by variable SNR and a spatial extent of the imaged objects in an axial direction, the microvascular networks were threedimensionally reconstructed based on the cross-sectional diameters measured along the vessel centerline from the XY-plane images with adapted binarization threshold.The arterial volume was relatively constant over depths of 0-500 µm, and the capillary volume (1.7% relative to the scanned volume) showed the larger volumes than the artery (0.8%) and vein (0.6%).
Conclusions:The present methods allow consistent segmentation of microvasculature by adapting the local inhomogeneity in the SNR, which will be useful for quantitative comparison of the microvascular networks, such as under disease conditions where SNR in the 2PLSM images varies over space and time.
The present study was aimed to characterize 3-dimensional (3D) morphology of the cortical microvasculature (e.g., penetrating artery and emerging vein), using two-photon microscopy and automated analysis for their cross-sectional diameters and branching positions in the mouse cortex. We observed that both artery and vein had variable cross-sectional diameters across cortical depths. The mean diameter was similar for both artery (17 ± 5 μm) and vein (15 ± 5 μm), and there were no detectable differences over depths of 50-400 μm. On the other hand, the number of branches was slightly increased up to 400-μm depth for both the artery and vein. The mean number of branches per 0.1 mm vessel length was 1.7 ± 1.2 and 3.8 ± 1.6 for the artery and vein, respectively. This method allows for quantification of the large volume data of microvascular images captured with two-photon microscopy. This will contribute to the morphometric analysis of the cortical microvasculature in functioning brains.
Indocyanine green (ICG), a relatively nontoxic fluorescent compound, is known to bind to albumin after intravenous injection. 1 ICG is therefore used as a surrogate for blood plasma perfusion in human organs. ICG videoangiography (ICG-VA) is an imaging technique that is widely used during surgery because it provides real-time information on blood flow, the patency of blood vessels, and occlusion of aneurysms in a noninvasive and reliable manner. 2,3 Previous studies in patients undergoing neurosurgery quantified the time-intensity curves of ICG. The parameters quantified include maximum intensity, rise time, time to peak, time to half-maximal fluorescence, transit time, and blood flow index. 4 A growing body of research has shown that these values, measured from ICG-VA, characteristically reflect blood flow status in cerebrovascular diseases, such as subarachnoid hemorrhage, 5 stroke, 6 moyamoya disease, 7,8 traumatic brain injury, 9 atherosclerotic occlusive diseases, 10 and arteriovenous malformations. 11,12 Despite the widespread use of ICG-VA in neurosurgery, its use remains limited to relative measurements of the cerebral blood
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