SERS‐based biosensor for Alzheimer disease evaluation through the fast analysis of human serum

Alzheimer disease (AD) is the most common form of dementia in the elderly, progressively affecting the cognitive functions with a complex diagnostic procedure that limits the time for a prompt intervention. In this study we optimized a reliable protocol for the analysis of AD patients and healthy subjects' serum using the Surface Enhanced Raman Spectroscopy (SERS), taking into consideration the effect of different variables on the final spectra, analyzed and compared through multivariate analysis and correlated with hippocampus volume. As results, we demonstrated a statistical difference between the spectra collected from the two investigated groups, with an accuracy, precision and specificity of respectively 83%, 86%, and 86%. The correlation of these data with those obtained from MRI, demonstrated a direct correlation between Raman spectra and hippocampus degeneration showing the Raman Spectroscopy (RS) as a potential tool for the monitoring of AD progression and rehabilitation treatments.     

HepG2细胞中线粒体形状的动态变化

目的:研究HepG2细胞中线粒体形状动态变化过程中的功能变化及其初步分子机制。方法:HepG2细胞经过HBSS缓冲液饥饿处理后,使用线粒体氧化磷酸化解偶联剂CCCP、脂肪酸受体GPR40/120激动剂GW9508、脂肪酸油酸OA和钙离子载体Ionomycin等4种不同药物处理,通过共聚焦显微镜观察和流式细胞分析的手段检测细胞中线粒体形状和功能发生的改变。然后,通过基因沉默Drp1,Mff或者Fis1蛋白,初步研究调控线粒体形状改变的分子机制。结果:经过CCCP和GW9508处理细胞中产生甜甜圈线粒体,而OA和Ionomycin处理产生球状线粒体。CCCP,OA和Ionomycin使线粒体去极化,CCCP、GW9508、OA或者Ionomycin单独处理在一定程度上影响细胞中活性氧化簇ROS。甜甜圈线粒体产生由Drp1介导,而球状线粒体形成依赖于Drp1和Mff。结论:线粒体的形态与其功能相互联系,Drp1和Mff蛋白对于细胞线粒体形状动态改变过程中形状的调整和适应具有很重要的作用。     

CT联合MRI在早期肝癌诊断临床价值分析

摘要 目的：探究CT联合MRI在早期肝癌诊断中的应用价值。方法：选择2016年6月至2019年6月于我院接受诊断治疗的78例已知或疑似肝癌患者,分别对其实施CT及MRI检测,以病灶部位病理学检查结果为金标准（50例确诊为早期肝癌,28例为良性病变）,分别评估CT、MRI、CT联合MRI对早期肝癌的诊断价值,将确诊为肝癌的50例患者按照病灶大小区分为直径≤3 cm组（21例）和>3 cm组（29例）,对比CT与MRI对不同直径肝癌诊断率。结果：（1）检测发现,CT对早期肝癌诊断一致性为73.08 %,灵敏度为72.00 %,特异度为75.00 %;（2）MRI对早期肝癌诊断一致性为82.05 %,灵敏度为82.00 %,特异度为82.14 %;（3）CT联合MRI检测对早期肝癌诊断一致性为93.59 %,灵敏度为92.00 %,特异度为96.43 %;（4）对比发现,对直径≤3 cm的早期肝癌患者,MRI诊断率明显高于CT（95.24 % vs 76.19 %,P<0.05）。结论：CT及MRI对早期肝癌均具有较好的诊断价值,但联合检测明显优于任一单独检测,同时对病灶直径≤3 cm的早期肝癌患者,MRI诊断准确率更高。     

DCE-MRI联合DWI对直肠癌患者术前T、N分期及系膜淋巴结良恶性的诊断价值

摘要 目的：探讨动态对比增强磁共振(DCE-MRI)联合弥散加权成像（DWI）诊断直肠癌术前T、N分期和系膜淋巴结良恶性的价值。方法：收集2017年2月至2019年10月中国医科大学附属本溪中心医院和中国医科大学附属盛京医院接诊的80例直肠癌患者,均进行常规核磁共振成像（MRI）、DCE-MRI、DWI扫描,获得DCE-MRI、DWI定量参数[转运常数（K ^trans ）、细胞外血管外空间的体积分数（V _e ）、速率常数（K _ep ）、表观扩散系数(ADC)],比较不同T、N分期、不同性质系膜淋巴结DCE-MRI、DWI参数,及其对T、N分期和系膜淋巴结性质的诊断效能。结果：直肠癌癌灶K ^trans 、 K _ep 、V _e 高于正常肠壁,ADC低于正常肠壁（P＜0.05）。TNM分期为T_Ⅲ～Ⅳ期的患者K ^trans 、 K _ep 、V _e 高于T_Ⅰ～Ⅱ期,ADC低于T_Ⅰ～Ⅱ期（P＜0.05）;TNM分期为N₁期的患者K ^trans 、K _ep 、V _e 高于N₀期,ADC低于N₀期（P＜0.05）。联合诊断的灵敏度、特异度、阳性预测值、阴性预测值较高。结论：DCE-MRI联合DWI对直肠癌术前T、N分期、系膜淋巴结性质诊断价值较高。     

Dynamic control of toxic natural product biosynthesis by an artificial regulatory circuit

To mimic the delicately regulated metabolism in nature for improved efficiency, artificial and customized regulatory components for dynamically controlling metabolic networks in multiple layers are essential in laboratory engineering. For this purpose, a novel regulatory component for controlling vanillin biosynthetic pathway was developed through directed evolution, which was responsive to both the product vanillin and substrate ferulic acid, with different capacities. This regulatory component facilitated pathway expression via dynamic control of the intracellular substrate and product concentrations. As vanillin is an antimicrobial compound, low pathway expression and vanillin formation levels enabled better cell growth at an early stage, and the product feedback-activated pathway expression at later stages significantly improved biosynthesis efficiency. This novel multiple-layer dynamic control was demonstrated effective in managing the trade-off between cell growth and production, leading to improved cell growth and vanillin production compared to the conventional or quorum-sensing promoter-controlled pathway. The multiple-layer dynamic control enabled by designed regulatory components responsive to multiple signals shows potential for wide applications in addition to the dynamic controls based on biosynthetic intermediate sensing and quorum sensing reported to date.     

Phenotyping of hypertensive heart disease and hypertrophic cardiomyopathy using personalized 3D modelling and cardiac cine MRI

Differential diagnosis of hypertensive heart disease (HHD) and hypertrophic cardiomyopathy (HCM) is clinically challenging but important for treatment management. This study aims to phenotype HHD and HCM in 3D + time domain by using a multiparametric motion-corrected personalized modeling algorithm and cardiac magnetic resonance (CMR). 44 CMR data, including 12 healthy, 16 HHD and 16 HCM cases, were examined. Multiple CMR phenotype data consisting of geometric and dynamic variables were extracted globally and regionally from the models over a full cardiac cycle for comparison against healthy models and clinical reports. Statistical classifications were used to identify the distinctive characteristics and disease subtypes with overlapping functional data, providing insights into the challenges for differential diagnosis of both types of disease. While HCM is characterized by localized extreme hypertrophy of the LV, wall thickening/contraction/strain was found to be normal and in sync, though it was occasionally exaggerated at normotrophic/less severely hypertrophic regions during systole to preserve the overall ejection fraction (EF) and systolic functionality. Additionally, we observed that hypertrophy in HHD could also be localized, although at less extreme conditions (i.e. more concentric). While fibrosis occurs mostly in those HCM cases with aortic obstruction, only minority of HHD patients were found affected by fibrosis. We demonstrate that subgroups of HHD (i.e. preserved and reduced EF: HHDpEF & HHDrEF) have different 3D + time CMR characteristics. While HHDpEF has cardiac functions in normal range, dilation and heart failure are indicated in HHDrEF as reflected by low LV wall thickening/contraction/strain and synchrony, as well as much reduced EF.     

Effects of MRI image normalization techniques in prostate cancer radiomics

The variance in intensities of MRI scans is a fundamental impediment for quantitative MRI analysis. Intensity values are not only highly dependent on acquisition parameters, but also on the subject and body region being scanned. This warrants the need for image normalization techniques to ensure that intensity values are consistent within tissues across different subjects and visits. Many intensity normalization methods have been developed and proven successful for the analysis of brain pathologies, but evaluation of these methods for images of the prostate region is lagging.In this paper, we compare four different normalization methods on 49 T2-w scans of prostate cancer patients: 1) the well-established histogram normalization, 2) the generalized scale normalization, 3) an extension of generalized scale normalization called generalized ball-scale normalization, and 4) a custom normalization based on healthy prostate tissue intensities. The methods are compared qualitatively and quantitatively in terms of behaviors of intensity distributions as well as impact on radiomic features.Our findings suggest that normalization based on prior knowledge of the healthy prostate tissue intensities may be the most effective way of acquiring the desired properties of normalized images. In addition, the histogram normalization method outperform the generalized scale and generalized ball-scale methods which have proven superior for other body regions.     

Modeling of dynamical systems through deep learning

This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics. Advanced deep learning methods like autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are used in modeling of dynamical systems.