Voxel- and tensor-based morphometry with machine learning techniques identifying characteristic brain impairment in patients with cervical spondylotic myelopathy

AimThe diagnosis of cervical spondylotic myelopathy (CSM) relies on several methods, including x-rays, computed tomography, and magnetic resonance imaging (MRI). Although MRI is the most useful diagnostic tool, strategies to improve the precise and independent diagnosis of CSM using novel MRI imaging techniques are urgently needed. This study aimed to explore potential brain biomarkers to improve the precise diagnosis of CSM through the combination of voxel-based morphometry (VBM) and tensor-based morphometry (TBM) with machine learning techniques.MethodsIn this retrospective study, 57 patients with CSM and 57 healthy controls (HCs) were enrolled. The structural changes in the gray matter volume and white matter volume were determined by VBM. Gray and white matter deformations were measured by TBM. The support vector machine (SVM) was used for the classification of CSM patients from HCs based on the structural features of VBM and TBM.ResultsCSM patients exhibited characteristic structural abnormalities in the sensorimotor, visual, cognitive, and subcortical regions, as well as in the anterior corona radiata and the corpus callosum [P < 0.05, false discovery rate (FDR) corrected]. A multivariate pattern classification analysis revealed that VBM and TBM could successfully identify CSM patients and HCs [classification accuracy: 81.58%, area under the curve (AUC): 0.85; P < 0.005, Bonferroni corrected] through characteristic gray matter and white matter impairments.ConclusionCSM may cause widespread and remote impairments in brain structures. This study provided a valuable reference for developing novel diagnostic strategies to identify CSM

Methylation of WTH3, a possible drug resistant gene, inhibits p53 regulated expression

<p>Abstract</p> <p>Background</p> <p>Previous results showed that over-expression of the <it>WTH3 </it>gene in MDR cells reduced <it>MDR1 </it>gene expression and converted their resistance to sensitivity to various anticancer drugs. In addition, the <it>WTH3 </it>gene promoter was hypermethylated in the MCF7/AdrR cell line and primary drug resistant breast cancer epithelial cells. <it>WTH3 </it>was also found to be directly targeted and up regulated by the <it>p53 </it>gene. Furthermore, over expression of the <it>WTH3 </it>gene promoted the apoptotic phenotype in various host cells.</p> <p>Methods</p> <p>To further confirm <it>WTH3</it>'s drug resistant related characteristics, we recently employed the small hairpin RNA (shRNA) strategy to knockdown its expression in HEK293 cells. In addition, since the <it>WTH3 </it>promoter's p53-binding site was located in a CpG island that was targeted by methylation, we were interested in testing the possible effect this epigenetic modification had on the <it>p53 </it>transcription factor relative to <it>WTH3 </it>expression. To do so, the <it>in vitro </it>methylation method was utilized to examine the <it>p53 </it>transgene's influence on either the methylated or non-methylated <it>WTH3 </it>promoter.</p> <p>Results</p> <p>The results generated from the gene knockdown strategy showed that reduction of <it>WTH3 </it>expression increased <it>MDR1 </it>expression and elevated resistance to Doxorubicin as compared to the original control cells. Data produced from the methylation studies demonstrated that DNA methylation adversely affected the positive impact of <it>p53 </it>on <it>WTH3 </it>promoter activity.</p> <p>Conclusion</p> <p>Taken together, our studies provided further evidence that <it>WTH3 </it>played an important role in MDR development and revealed one of its transcription regulatory mechanisms, DNA methylation, which antagonized <it>p53</it>'s positive impact on <it>WTH3 </it>expression.</p

Vacancy modification of Prussian-blue nano-thin films for high energy-density microsupercapacitors with ultralow RC time constant

In-plane micro-supercapacitors (MSCs), as promising power candidates for micro-devices, typically exhibit high power densities, large charge/discharge rates, and long cycling lifetimes. The high areal/volumetric capacitances, high energy/power densities, high rate capability, as well as flexibility are the main scientific pursue in recent years. Among diverse electrode materials for MSCs, coordination polymer frameworks are emerging due to the designable porous structure and tunable functionality. However, the unsatisfied electrochemical performance still hinders their practical applications. In this work, we demonstrate the first time an efficient in-situ growth approach to precisely modify the vacancy of Prussian-blue nano-thin films with pyridine by coordination reaction for high energy-density MSCs. Confirmed by the experimental results and density functional theory calculation, the vacancy modification within Prussian-blue network improved the film-forming property, hydrophilicity, and electrochemical activity of the thin films. The resultant MSCs based on pyridine-modified Prussian-blue exhibited an ultrahigh energy density of up to 12.1 mWh cm⁻³ and an ultra-low time constant (t₀) of 0.038 ms, which are the best values among the state-of-the-art in-plane MSCs. This work provides an attractive solution for structural engineering of promising active materials on molecule level toward high-performance micro-energy devices

Data_Sheet_1_Voxel- and tensor-based morphometry with machine learning techniques identifying characteristic brain impairment in patients with cervical spondylotic myelopathy.docx

AimThe diagnosis of cervical spondylotic myelopathy (CSM) relies on several methods, including x-rays, computed tomography, and magnetic resonance imaging (MRI). Although MRI is the most useful diagnostic tool, strategies to improve the precise and independent diagnosis of CSM using novel MRI imaging techniques are urgently needed. This study aimed to explore potential brain biomarkers to improve the precise diagnosis of CSM through the combination of voxel-based morphometry (VBM) and tensor-based morphometry (TBM) with machine learning techniques.MethodsIn this retrospective study, 57 patients with CSM and 57 healthy controls (HCs) were enrolled. The structural changes in the gray matter volume and white matter volume were determined by VBM. Gray and white matter deformations were measured by TBM. The support vector machine (SVM) was used for the classification of CSM patients from HCs based on the structural features of VBM and TBM.ResultsCSM patients exhibited characteristic structural abnormalities in the sensorimotor, visual, cognitive, and subcortical regions, as well as in the anterior corona radiata and the corpus callosum [P ConclusionCSM may cause widespread and remote impairments in brain structures. This study provided a valuable reference for developing novel diagnostic strategies to identify CSM.</p

Metal (M = Ru, Pd and Co) embedded in C2N with enhanced lithium storage properties

The improvement of the high-efficiency electrode materials for lithium ion batteries is one of the research priorities. The research on the carbon with M ??? N active sites (M-N-C) as anodes for lithium ion batteries is still quite rare. Herein, we report a series of highly crystalline M@C2N hybrids in which the metal (M = Ru, Pd and Co) are uniformly embedded in the two-dimensional C2N networks. Together with the unique structural features (e.g., uniform two-dimensional nanohole structure, high surface area and enlarged distance between C2N nanosheets), rich N-M coordination homogenously distributing through the M@C2N structure are favorable for the brisk infiltration of electrolyte, swift transportation of Li+/electrons and more storage of Li+, thereby leading to the superior lithium storage properties