Risk Factors and Imaging Mechanisms of Fatigue After Mild Ischemic Stroke: An Exploratory Study From a Single Chinese Center

Objective: To explore the biochemical risk factors and imaging mechanisms of post fatigue after mild ischemic stroke among a Chinese population.Methods: Forty consecutive patients with mild ischemic stroke within onset of 14 ± 2 days were enrolled between March and June 2018. The clinical information, scale data, biomarkers in peripheral venous blood, and imaging data during hospitalization and follow-up period were collected.Results: Patient age (range 34–78) was positively correlated with the prevalence of fatigue (p = 0.009). Both blood norepinephrine and serotonin levels during hospitalization were negatively correlated to the prevalence of post-stroke fatigue (model 1 p = 0.009 and model 2 P = 0.043, respectively). Infarct of right cerebral hemisphere is positively correlated with the occurrence of fatigue after mild ischemic stroke (p = 0.020). Compared to non-fatigue patients, amplitude of low-frequency fluctuation (ALFF) was lower in several areas of brain in stroke patients with fatigue, including the right orbital inferior frontal, right inner orbital frontal, right frontal, right triangular frontal inferior, right anterior and lateral cingulate, and right medial frontal gyruses. Analysis of the difference in functional connectivity between the fatigue and non-fatigue groups found no cluster.Conclusions: Frontal lobe-related neural pathways may play an essential role in the regulation of fatigue after mild ischemic stroke. Abnormal neural circuits may reduce the levels of neurotransmitters such as serotonin and norepinephrine and lead to post-stroke fatigue.

Nano-Neurotheranostics: Impact of Nanoparticles on Neural Dysfunctions and Strategies to Reduce Toxicity for Improved Efficacy

Nanotheranostics is one of the emerging research areas in the field of nanobiotechnology offering exciting promises for diagnosis, bio-separation, imaging mechanisms, hyperthermia, phototherapy, chemotherapy, drug delivery, gene delivery, among other uses. The major criteria for any nanotheranostic-materials is 1) to interact with proteins and cells without meddling with their basic activities, 2) to maintain their physical properties after surface modifications and 3) must be nontoxic. One of the challenging targets for nanotheranostics is the nervous system with major hindrances from the neurovascular units, the functional units of blood-brain barrier. As blood-brain barrier is crucial for protecting the CNS from toxins and metabolic fluctuations, most of the synthetic nanomaterials cannot pass through this barrier making it difficult for diagnosing or targeting the cells. Biodegradable nanoparticles show a promising role in this aspect. Certain neural pathologies have compromised barrier creating a path for most of the nanoparticles to enter into the cells. However, such carriers may pose a risk of side effects to non-neural tissues and their toxicity needs to be elucidated at preclinical levels. This article reviews about the different types of nanotheranostic strategies applied in nervous dysfunctions. Further, the side effects of these carriers are reviewed and appropriate methods to test the toxicity of such nano-carriers are suggested to improve the effectiveness of nano-carrier based diagnosis and treatments.

A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery

Image fusion is an effective complementary method to obtain information from multi-source data. In particular, the fusion of synthetic aperture radar (SAR) and panchromatic images contributes to the better visual perception of objects and compensates for spatial information. However, conventional fusion methods fail to address the differences in imaging mechanism and, therefore, they cannot fully consider all information. Thus, this paper proposes a novel fusion method that both considers the differences in imaging mechanisms and sufficiently provides spatial information. The proposed method is learning-based; it first selects data to be used for learning. Then, to reduce the complexity, classification is performed on the stacked image, and the learning is performed independently for each class. Subsequently, to consider sufficient information, various features are extracted from the SAR image. Learning is performed based on the model’s ability to establish non-linear relationships, minimizing the differences in imaging mechanisms. It uses a representative non-linear regression model, random forest regression. Finally, the performance of the proposed method is evaluated by comparison with conventional methods. The experimental results show that the proposed method is superior in terms of visual and quantitative aspects, thus verifying its applicability.

Thermal Injuries during Intraoperative Magnetic Resonance Imaging: Mechanisms and Prevention

Background Thermal injuries in a patient undergoing magnetic resonance imaging (MRI) are rare; more so, when the patient in question is being operated upon. We attempt to elucidate the various factors that may predispose to such an unfortunate circumstance, through a series of four cases. Materials and Methods We conducted a retrospective review of our experience with intraoperative MRI and found four cases of burns attributed to MRI. Factors leading to possible injury were examined after other causes were ruled out. Results Collection of moisture between the leads and the patient's skin was the most common factor for the burns. There were no instances of closed loop formation or injury due to direct contact of cables to the skin. Conclusion Awareness of the causative factor can lead to prevention. Proper education of all concerned personnel involved in the conduction of the intraoperative MRI is paramount to prevention of the same.

Fusion of SAR and Multispectral Images Using Random Forest Regression for Change Detection

In order to overcome the insufficiency of single remote sensing data in change detection, synthetic aperture radar (SAR) and optical image data can be used together for supplementation. However, conventional image fusion methods fail to address the differences in imaging mechanisms and cannot overcome some practical limitations such as usage in change detection or temporal requirement of the optical image. This study proposes a new method to fuse SAR and optical images, which is expected to be visually helpful and minimize the differences between two imaging mechanisms. The algorithm performs the fusion by establishing relationships between SAR and multispectral (MS) images by using a random forest (RF) regression, which creates a fused SAR image containing the surface roughness characteristics of the SAR image and the spectral characteristics of the MS image. The fused SAR image is evaluated by comparing it to those obtained using conventional image fusion methods and the proposed method shows that the spectral qualities and spatial qualities are improved significantly. Furthermore, for verification, other ensemble approaches such as stochastic gradient boosting regression and adaptive boosting regression are compared and overall it is confirmed that the performance of RF regression is superior. Then, change detection between the fused SAR and MS images is performed and compared with the results of change detection between MS images and between SAR images and the result using fused SAR images is similar to the result with MS images and is improved when compared to the result between SAR images. Lastly, the proposed method is confirmed to be applicable to change detection.