Polydopamine-Decorated Microcomposites Promote Functional Recovery of an Injured Spinal Cord by Inhibiting Neuroinflammation

Neuroinflammation following spinal cord injury usually aggravates spinal cord damage. Many inflammatory cytokines are key players in neuroinflammation. Owing largely to the multiplicity of cytokine targets and the complexity of cytokine interactions, it is insufficient to suppress spinal cord damage progression by regulating only one or a few cytokines. Herein, we propose a two-pronged strategy to simultaneously capture the released cytokines and inhibit the synthesis of new ones in a broad-spectrum manner. To achieve this strategy, we designed a core/shell-structured microcomposite, which was composed of a methylprednisolone-incorporated polymer inner core and a biocompatible polydopamine outer shell. Thanks to the inherent adhesive nature of polydopamine, the obtained microcomposite (MP-PLGA@PDA) efficiently neutralized the excessive cytokines in a broad-spectrum manner within 1 day after spinal cord injury. Meanwhile, the controlled release of immunosuppressive methylprednisolone reduced the secretion of new inflammatory cytokines. Benefiting from its efficient and broad-spectrum capability in reducing the level of cytokines, this core/shell-structured microcomposite suppressed the recruitment of macrophages and protected the injured spinal cord, leading to an improved recovery of motor function. Overall, the designed microcomposite successfully achieved the two-pronged strategy in cytokine neutralization, providing an alternative approach to inhibit neuroinflammation in the injured spinal cord.Peer reviewe

Novel Multi-Scale Filter Profile-Based Framework for VHR Remote Sensing Image Classification

Publisher's version (útgefin grein).Filter is a well-known tool for noise reduction of very high spatial resolution (VHR) remote sensing images. However, a single-scale filter usually demonstrates limitations in covering various targets with different sizes and shapes in a given image scene. A novel method called multi-scale filter profile (MFP)-based framework (MFPF) is introduced in this study to improve the classification performance of a remote sensing image of VHR and address the aforementioned problem. First, an adaptive filter is extended with a series of parameters for MFP construction. Then, a layer-stacking technique is used to concatenate the MPFs and all the features into a stacked vector. Afterward, principal component analysis, a classical descending dimension algorithm, is performed on the fused profiles to reduce the redundancy of the stacked vector. Finally, the spatial adaptive region of each filter in the MFPs is used for post-processing of the obtained initial classification map through a supervised classifier. This process aims to revise the initial classification map and generate a final classification map. Experimental results performed on the three real VHR remote sensing images demonstrate the effectiveness of the proposed MFPF in comparison with the state-of-the-art methods. Hard-tuning parameters are unnecessary in the application of the proposed approach. Thus, such a method can be conveniently applied in real applications.This research was funded by the National Science Foundation China (61701396 and 41501378) and the Natural Science Foundation of Shaan Xi Province (2018JQ4009).Peer Reviewe

Changes of Circulating Transforming Growth Factor-²1 Level During Radiation Therapy Are Correlated with the Prognosis of Locally Advanced Non-small Cell Lung Cancer

IntroductionWe hypothesized that plasma transforming growth factor-²1 (TGF-²1) level and its dynamic change are correlated with the prognosis of locally advanced non-small cell lung cancer (NSCLC) treated with radiation therapy (RT).MethodsPatients with stage IIIA or IIIB NSCLC treated with RT with or without chemotherapy were eligible for this study. Platelet poor plasma was collected from each patient within 1 week before RT (pre-RT) and at the 4th week during RT (during-RT). TGF-²1 level was measured with enzyme-linked immunosorbent assay. The primary end point was overall survival (OS) and the secondary end point was progression-free survival (PFS). Kaplan-Meier and Cox regression were used for risk factor evaluation.ResultsA total of 65 patients were eligible for the study. The median OS and PFS were 17.7 and 13.7 months, respectively. In univariate analysis, performance status, weight loss, radiation dose, and TGF-²1 ratio (during-RT/pre-RT TGF-²1 level) were all significantly correlated with OS. In the multivariate analysis, performance status, radiation dose, and TGF-²1 ratio were still significantly correlated with OS. The median OS was 30.7 months for patients with TGF-²1 ratio ≤1 versus 13.3 months for those with TGF-²1 ratio more than 1 (p = 0.0029); and the median PFS was 16.8 months versus 7.2 months, respectively (p = 0.010).ConclusionsIn locally advanced NSCLC, the decrease of TGF-²1 level during RT is correlated with favorable prognosis

Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification from VHR Imagery

© 1980-2012 IEEE. Imbalanced training sets are known to produce suboptimal maps for supervised classification. Therefore, one challenge in mapping land cover is acquiring training data that will allow classification with high overall accuracy (OA) in which each class is also mapped onto similar user's accuracy. To solve this problem, we integrated local adaptive region and box-and-whisker plot (BP) techniques into an iterative algorithm to expand the size of the training sample for selected classes in this article. The major steps of the proposed algorithm are as follows. First, a very small initial training sample (ITS) for each class set is labeled manually. Second, potential new training samples are found within an adaptive region by conducting local spectral variation analysis. Lastly, three new training samples are acquired to capture information regarding intraclass variation; these samples lie in the lower, median, and upper quartiles of BP. After adding these new training samples to the ITS, classification is retrained and the process is continued iteratively until termination. The proposed approach was applied to three very high-resolution (VHR) remote-sensing images and compared with a set of cognate methods. The comparison demonstrated that the proposed approach produced the best result in terms of OA and exhibited superiority in balancing user's accuracy. For example, the proposed approach was typically 2%-10% more accurate than the compared methods in terms of OA and it generally yielded the most balanced classification

GOLM1 Stimulation of Glutamine Metabolism Promotes Osteoporosis via Inhibiting Osteogenic Differentiation of BMSCs

Background/Aims: Bone marrow mesenchymal stem cells (BMSCs) play an essential role in osteoporosis. However, the molecular mechanisms and the involvement of glutamine metabolism in osteogenic BMSCs differentiation and osteoporosis remain largely unclear. In this study, we investigated the role of Golgi membrane protein 1 (GOLM1) and glutamine metabolism in BMSCs differentiation and osteoporosis. Methods: Osteogenic differentiation-inducing media (Odi) was used to induce the osteogenic differentiation of BMSCs. The mRNA expression of GOLM1, ALP, Runx2, Osx, BSP and OCN was determined by qRT-PCR assay. Western blot assay was used to analyze GOLM1, p-mTOR, mTOR, p-S6 and S6 abundance in GOLM1 silencing and over-expressed BMSCs. Glutamine uptake, intracellular glutamine, glutamate and α-KG level was detected using indicated Kits. GOLM1 antibody, glutamine metabolism inhibitors EGCG and BPTES were used to treat ovariectomy (OVX)-induced osteoporosis. Bone mineral density and bone volume relative to tissue volume (%) were analyzed by micro-CT. Serum was collected from osteoporosis patients and healthy participants and subjected to GOLM1 determination using ELISA Kit. Results: GOLM1 expression and glutamine metabolism were suppressed by Odi. GOLM1 blockage or inhibition of glutamine metabolism promoted the osteogenic differentiation of BMSCs induced by Odi. GOLM1 activated glutamine metabolism depending on the mTOR signaling pathway. In vivo, GOLM1 antibody or combination of glutamine inhibitor EGCG and BPTES rescued the osteoporosis in an OVX-operated mouse model. Serum GOLM1 level was increased in the patients of osteoporosis compared with healthy people. Conclusion: GOLM1 stimulates glutamine metabolism to suppress the osteogenic differentiation of BMSCs and to promote osteoporosis. Therefore, GOLM1 activation of glutamine metabolism is a potential target for osteoporosis

Silk Fibroin-Based Biomaterials for Tissue Engineering Applications

Tissue engineering (TE) involves the combination of cells with scaffolding materials and appropriate growth factors in order to regenerate or replace damaged and degenerated tissues and organs. The scaffold materials serve as templates for tissue formation and play a vital role in TE. Among scaffold materials, silk fibroin (SF), a naturally occurring protein, has attracted great attention in TE applications due to its excellent mechanical properties, biodegradability, biocompatibility, and bio-absorbability. SF is usually dissolved in an aqueous solution and can be easily reconstituted into different forms, including films, mats, hydrogels, and sponges, through various fabrication techniques, including spin coating, electrospinning, freeze drying, and supercritical CO2-assisted drying. Furthermore, to facilitate the fabrication of more complex SF-based scaffolds, high-precision techniques such as micro-patterning and bio-printing have been explored in recent years. These processes contribute to the diversity of surface area, mean pore size, porosity, and mechanical properties of different silk fibroin scaffolds and can be used in various TE applications to provide appropriate morphological and mechanical properties. This review introduces the physicochemical and mechanical properties of SF and looks into a range of SF-based scaffolds that have recently been developed. The typical applications of SF-based scaffolds for TE of bone, cartilage, teeth and mandible tissue, cartilage, skeletal muscle, and vascular tissue are highlighted and discussed followed by a discussion of issues to be addressed in future studies

A Modified MPS Method with a Split-Pressure Poisson Equation and a Virtual Particle for Simulating Free Surface Flows

As a Lagrangian mesh-free method, the moving particle semi-implicit (MPS) method can easily handle complex incompressible flow with a free surface. However, some deficiencies of the MPS method, such as inaccurate results, unphysical pressure oscillation, and particle thrust near the free surface, still need to be further resolved. Here, we propose a modified MPS method that uses the following techniques: (1) a modified MPS scheme with a split-pressure Poisson equation is proposed to reproduce hydrostatic pressure stably; (2) a new virtual particle technique is developed to ensure the symmetrical distribution of particles on the free surface; (3) a Laplacian operator that is consistent with the original gradient operator is introduced to replace the original Laplacian operator. In addition, a two-judgment technique for distinguishing free surface particles is introduced in the proposed MPS method. Four free surface flows were adopted to verify the proposed MPS method, including two hydrostatic problems, a dam-breaking problem, and a violent sloshing problem. The enhancement of accuracy and stability by these improvements was demonstrated. Moreover, the numerical results of the proposed MPS method showed good agreement with analytical solutions and experimental results

Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can meet the requirements of clinical diagnosis and have excellent quality and efficiency. Moreover, a more comprehensive understanding of applications and opportunities would help to implement AI-based methods in an ethical and sustainable manner. This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images. Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer’s disease (AD), Parkinson’s disease (PD), major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Finally, the limitations of the existing research are discussed, and possible future research directions are proposed

The Role of Autoimmunity in the Pathogenesis of Sudden Sensorineural Hearing Loss

Sudden sensorineural hearing loss (SSHL) is a clinically common acute symptom in otolaryngology. Although the incidence of SSHL has increased around the world in recent years, the etiology of the disease is still unclear. It has been reported that infections, ototoxic drugs, membrane labyrinth rupture, carcinomas, circulatory system diseases, autoimmune diseases, brain lesions, mental diseases, congenital or inherited diseases, and so on, are all risk factors for SSHL. Here, we discuss the autoimmune mechanisms behind SSHL, which might be induced by type II–IV allergic reactions. We also introduce the main immunosuppressive medications that have been used to treat SSHL, which will help us to identify potential targets for immune therapy