Stroke is a major disease that leads to high mortality and morbidity. Given the ageing population and the potential risk factors, the prevalence of stroke and socioeconomic burden associated with ...stroke are expected to increase. During the past decade, both prophylactic and therapeutic strategies for stroke have made significant progress. However, current therapies still cannot adequately improve the outcomes of stroke and may not apply to all patients. One of the significant advances in modern medicine is cell-derived neurovascular regeneration and neuronal repair. Progress in stem cell biology has greatly contributed to ameliorating stroke-related brain injuries in preclinical studies and demonstrated clinical potential in stroke treatment. Mesenchymal stem cells (MSCs) have the differentiating potential of chondrocytes, adipocytes, and osteoblasts, and they have the ability to transdifferentiate into endothelial cells, glial cells, and neurons. Due to their great plasticity, MSCs have drawn much attention from the scientific community. This review will focus on MSCs, stem cells widely utilized in current medical research, and evaluate their effect and potential of improving outcomes in ischemic stroke.
In urban environments, Global Navigation Satellite Systems (GNSS) signals are frequently attenuated, blocked or reflected, which degrades the positioning accuracy of GNSS receivers significantly. To ...improve the performance of GNSS receiver for vehicle urban navigation, a GNSS/INS deeply-coupled software defined receiver (GIDCSR) with a low cost micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) ICM-20602 is presented, in which both GPS and BDS constellations are supported. Two key technologies, that is, adaptive open-close tracking loops and INS aided pseudo-range weight control algorithm, are applied in the GIDCSR to enhance the signal tracking continuity and positioning accuracy in urban areas. To assess the performance of the proposed deep couple solution, vehicle field tests were carried out in GNSS-challenged urban environments. With the adaptive open-close tracking loops, the deep couple output carrier phase in the open sky, and improved pseudo-range accuracy before and after GNSS signal blocked. Applying the INS aided pseudo-range weight control, the pseudo-range gross errors of the deep couple decreased caused by multipath. A popular GNSS/INS tightly-coupled vehicle navigation kit from u-blox company, M8U, was tested side by side as benchmark. The test results indicate that in the GNSS-challenged urban areas, the pseudo-range quality of GIDCSR is at least 25% better than that of M8U, and GIDCSR’s horizontal positioning results are at least 69% more accurate than M8U’s.
Induced pluripotent stem cells (iPS cells) are promising cell source for stem cell replacement strategy applied to brain injury caused by traumatic brain injury (TBI) or stroke. Neural stem cell ...(NSCs) derived from iPS cells could aid the reconstruction of brain tissue and the restoration of brain function. However, tracing the fate of iPS cells in the host brain is still a challenge. In our study, iPS cells were derived from skin fibroblasts using the four classic factors Oct4, Sox2, Myc, and Klf4. These iPS cells were then induced to differentiate into NSCs, which were incubated with superparamagnetic iron oxides (SPIOs) in vitro. Next, 30 TBI rat models were prepared and divided into three groups (n = 10). One week after brain injury, group A&B rats received implantation of NSCs (labeled with SPIOs), while group C rats received implantation of non-labeled NSCs. After cell implantation, all rats underwent T2*-weighted magnetic resonance imaging (MRI) scan at day 1, and 1 week to 4 weeks, to track the distribution of NSCs in rats’ brains. One month after cell implantation, manganese-enhanced MRI (ME-MRI) scan was performed for all rats. In group B, diltiazem was infused during the ME-MRI scan period. We found that (1) iPS cells were successfully derived from skin fibroblasts using the four classic factors Oct4, Sox2, Myc, and Klf4, expressing typical antigens including SSEA4, Oct4, Sox2, and Nanog; (2) iPS cells were induced to differentiate into NSCs, which could express Nestin and differentiate into neural cells and glial cells; (3) NSCs were incubated with SPIOs overnight, and Prussian blue staining showed intracellular particles; (4) after cell implantation, T2*-weighted MRI scan showed these implanted NSCs could migrate to the injury area in chronological order; (5) the subsequent ME-MRI scan detected NSCs function, which could be blocked by diltiazem. In conclusion, using an in vivo MRI tracking technique to trace the fate of iPS cells-induced NSCs in host brain is feasible.
Predicting potential drug-drug interactions (DDIs) can effectively mitigate unforeseen interactions throughout the entire drug development process, playing a pivotal role in ensuring drug safety. ...However, traditional methods are laborious and require specific expert knowledge. This paper proposes RPDAnet, a novel molecular substructure-aware net work based on R einforced P ooling and D eep A ttention mechanism, to investigate the interactive relationships between drugs and predict the potential DDIs. Particularly, RPDAnet leverages reinforcement learning to dynamically select informative molecular fragments, thus enhancing its generalization capacity without relying on prior knowledge. Subsequently, RPDAnet develops C ommunicative M essage M assing N eural N etwork (CMPNN) to enhance the representation of molecular structures by reinforcing message interactions between nodes and edges through a communicative kernel. Finally, RPDAnet aggregates the interactions between substructures of drugs to predict the DDI between a pair of drugs. The experimental results on two real-world datasets demonstrate that our proposed RPDAnet outperforms the state-of-the-art methods with more than 5% performance gains in DDI prediction.
Deep learning denoising methods are often constrained by the high cost of acquiring real-world noisy images and the labor-intensive process of dataset construction. Our self-supervised Multi-Scale ...Blind-Spot Network with Adaptive Feature Fusion (MA-BSN) addresses these issues, offering an efficient solution for image denoising. MA-BSN mitigates the challenges of spatial noise correlation preservation and limited receptive fields, which are prevalent in existing self-supervised denoising approaches. The network employs a blind-spot architecture that generates sub-images at multiple scales, enhancing denoising beyond the capabilities of pixel-shuffle downsampling. A depth-wise convolutional Transformer network (DTN) extracts features across a global receptive field, addressing the convolutional neural networks' (CNNs) limitations. An adaptive feature fusion module (AFF) is introduced to refine feature learning for specific regions in the denoised images, leveraging attention mechanisms for improved performance. Our network's efficacy is validated through experiments on the SIDD and DND real-world noise benchmark datasets. Results on the DND dataset show a PSNR/SSIM of 38.41 dB/0.940, surpassing state-of-the-art self-supervised methods and underscoring our approach's superior denoising capability.
Purpose
The aim of this study was to thoroughly analyze the clinical characteristics of a large cohort of spinal meningioma (SM) from a single neurological center and identify risk factors associated ...with worse progression free survival and neurological function outcome.
Methods
Clinical information was retrieved from 483 SM and 9806 cranial meningioma cases who were operated in our center between 2003 and 2013. 194 SM patients who were followed at the main branch were used for prognostic analyses that included both recurrence free survival and neurological functions based on Modified McCormick scale (MMS).
Results
Females were predominant (
P
< 0.001). High grade tumors were not common (WHO grade II, 2.9%; grade III, 1.7%), while the clear cell subtype was frequent within grade II SMs (6/14, 42.9%). Macroscopic total resection was achieved in all SMs (Simpson grade I, 30.9%; grade II, 65.5%; grade III, 3.6%) with a low complications rate (4.6%) and provided neurological improvement in 80 patients (41.2%). Recurrence was seen in 9 cases (4.6%) and associated with high WHO grade, male, prior recurrence, and Simpson grade III. High WHO grade and high Ki-67 index were identified to be independent factors predictive of both neurological function deterioration and impaired post-operative neurological status.
Conclusions
Our analysis of the largest SM cohort in scale from a single institution offers a comprehensive view of the clinical characteristics of surgically treated SM, revealing the distinct biology of SM in comparison to its cranial counterparts, and providing guidance to improve surgical management of SM.
Despite the past two decades of research progress, glioma still remains the most challenging of all primary central nervous system (CNS) tumors. The complexity of its pathogenesis makes the disease ...hard to deal with, especially glioblastoma multiforme (GBM, WHO grade IV), the most aggressive brain tumor entity. This Special Issue collect studies that focus on the pathogenesis of glioma, such as its cell origin, the role of GSCs, genomic alterations, animal models, etc. We have collected 10 high-quality papers, and we welcome all researchers to read.
Auroras are bright occurrences when high-energy particles from the magnetosphere and solar wind enter Earth's atmosphere through the magnetic field and collide with atoms in the upper atmosphere. The ...morphological and temporal characteristics of auroras are essential for studying large-scale magnetospheric processes. While auroras are visible to the naked eye from the ground, scientists use deep learning algorithms to analyze all-sky images to understand this phenomenon better. However, the current algorithms face challenges due to inefficient utilization of global features and neglect the excellent fusion of local and global feature representations extracted from aurora images. Hence, this paper introduces a Hash-Transformer model based on Vision Transformer for aurora retrieval from all-sky images. Experimental results based on real-world data demonstrate that the proposed method effectively improves aurora image retrieval performance. It provides a new avenue to study aurora phenomena and facilitates the development of related fields.
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