Effects of genetically modified human skin fibroblasts,stably overexpressing hepatocyte growth factor,on hepatic functions of cocultured C3A cells

Diseases leading to terminal hepatic failure are among the most common causes of death worldwide. Transplant of the whole organ is the only effective method to cure liver failure. Unfortunately, this treatment option is not available universally due to the serious shortage of donors. Thus, alternative methods have been developed that are aimed at prolonging the life of patients, including hepatic cells transplantation and bridging therapy based on hybrid bioartificial liver devices. Parenchymal liver cells are highly differentiated and perform many complex functions, such as detoxification and protein synthesis. Unfortunately, isolated hepatocytes display a rapid decline in viability and liver‐specific functions. A number of methods have been developed to maintain hepatocytes in their highly differentiated state in vitro, amongst them the most promising being 3D growth scaffolds and decellularized tissues or coculture with other cell types required for the heterotypic cell‐cell interactions. Here we present a novel approach to the hepatic cells culture based on the feeder layer cells genetically modified using lentiviral vector to stably produce additional amounts of hepatocyte growth factor and show the positive influence of these coculture conditions on the preservation of the hepatic functions of the liver parenchymal cells' model—C3A cells.     

人体动脉血气波浪式变化特点的实验证据—同时间动、静脉血波浪式变化幅度的不同*

目的: 人动脉血来源是右心系统并在肺脏进行气体交换的静脉血,右心系统的静脉血是否存在波浪式信号目前尚没有证据支持,本研究旨在对比同时间动、静脉血中信号的连续变化特点。方法: 选择心功能正常,需要连续监测动脉血流动力学变化的患者6 例,4男2女,年龄（59.00±16.64） 岁,体质量（71.67±10.37）kg,左心射血分数（LVEF）（61.33±2.16)%。患者签署知情同意书后,选择心功能正常需要监测动、静脉血流动力学变化的患者6 例,连续同时桡动脉、颈内静脉逐搏取血,测定PaO₂。选取2个典型呼吸周期,用于分析同时段动、静脉血气的波浪式变化。分别比较患者血氧分压最高和最低值,以验证同时段动、静脉血气是否都存在周期性波浪式信号变化。此外,将患者动脉、静脉血气周期性波浪式信号的变化幅度进行统计学t 检验分析,比较有无差异。结果: 共6例患者,抽取动、静脉血液充满肝素化细长塑化管需要15~16次心跳,即取血需要15~16次心跳,全部覆盖超过2个呼吸周期。所有患者动脉血气中PaO₂均呈现明显的波浪式变化（P<0.05）,幅度是（9.96±5.18）mmHg,是均值的（8.09±2.43）%。患者静脉血气中PaO₂波动幅度并不明显,为（1.63±0.41）mmHg,是均值的（3.91±1.22）%,与动脉血气组相比有明显统计学差异（P<0.05）。结论: 采用同时连续逐搏动、静脉取血血气分析法证实,患者自主呼吸时动脉血气有明显的周期性波浪式变化信号,而静脉血气几乎没有周期性波浪式变化信号（很弱）,说明动脉血气波浪式信号主要是由于肺通气过程中吸气和呼气期产生肺泡中氧分压规律性上升和下降,通过离开肺毛细血管与肺泡氧气压力平衡的动脉化血液,经过左心室搏血进入动脉血管系统所致。     

体外转录的自我复制型mRNA疫苗研究进展*

自我复制型mRNA是一种灵活的疫苗平台,该平台的开发基于甲病毒表达载体,其中复制必需基因得以完整保留,而结构蛋白基因则被来自病原的抗原基因替换。由于避免了病原培养、毒力返强和现存免疫的干扰,使其成为疫苗快速设计的理想平台。大量研究数据显示,此类疫苗可应用在人、小鼠、兔、猪、禽甚至鱼类体内诱导体液免疫和细胞免疫。过去,自我复制mRNA疫苗的研究采用重组单载体的模式,基因组骨架来源于辛德毕斯病毒、塞姆利基森林病毒和委内瑞拉马脑炎病毒。现在,反式复制型RNA和核酸修饰的反式复制型RNA作为下一代技术平台被寄予厚望。对基于甲病毒表达载体的mRNA疫苗技术的研究进展进行概述,重点介绍针对以流感病毒、新型冠状病毒和寨卡病毒等为代表的自我复制型mRNA疫苗研究现状,并探讨了该技术平台的未来发展方向。     

基于深度学习与多层次信息融合的药物靶标亲和力预测*

药物研发是非常重要但也十分耗费人力物力的过程。利用计算机辅助预测药物与蛋白质亲和力的方法可以极大地加快药物研发过程。药物靶标亲和力预测的关键在于对药物和蛋白质进行准确详细地信息表征。提出一种基于深度学习与多层次信息融合的药物靶标亲和力的预测模型,试图通过综合药物与蛋白质的多层次信息,来获得更好的预测表现。首先将药物表述成分子图和扩展连接指纹两种形式,分别利用图卷积神经网络模块和全连接层进行学习;其次将蛋白质序列和蛋白质K-mer特征分别输入卷积神经网络模块和全连接层来学习蛋白质潜在特征;随后将4个通道学习到的特征进行融合,再利用全连接层进行预测。在两个基准药物靶标亲和力数据集上验证了所提方法的有效性,并与其他已有模型作对比研究。结果说明提出的模型相比基准模型能得到更好的预测性能,表明提出的综合药物与蛋白质多层次信息的药物靶标亲和力预测策略是有效的。     

基因治疗慢病毒载体的转导增强策略*

基于慢病毒载体的体外基因治疗已在临床试验中取得良好的效果,有望治愈一些造血系统的单基因遗传病。通过提高靶细胞转导效率和减少转导中病毒载体量,基因治疗有效性、安全性和成本都可以得到改善。不同包膜糖蛋白伪型慢病毒载体通过与细胞膜表面的不同受体结合,促进病毒黏附和入胞,增强不同靶细胞的病毒转导效率。此外病毒转导增强剂可以在病毒进入细胞过程或进入后发挥作用,在提高转导效率的同时使靶转导基因在体内长期稳定表达。通过对这两类方法的总结回顾,旨在为慢病毒载体的转导效率提供新的优化策略,使基因治疗得到更广泛的应用。     

The SSV-Seq 2.0 PCR-Free Method Improves the Sequencing of Adeno-Associated Viral Vector Genomes Containing GC-Rich Regions and Homopolymers

Adeno-associated viral vectors (AAV) are efficient engineered tools for delivering genetic material into host cells. The commercialization of AAV-based drugs must be accompanied by the development of appropriate quality control (QC) assays. Given the potential risk of co-transfer of oncogenic or immunogenic sequences with therapeutic vectors, accurate methods to assess the level of residual DNA in AAV vector stocks are particularly important. An assay based on high-throughput sequencing (HTS) to identify and quantify DNA species in recombinant AAV batches is developed. Here, it is shown that PCR amplification of regions that have a local GC content >90% and include successive mononucleotide stretches, such as the CAG promoter, can introduce bias during DNA library preparation, leading to drops in sequencing coverage. To circumvent this problem, SSV-Seq 2.0, a PCR-free protocol for sequencing AAV vector genomes containing such sequences, is developed. The PCR-free protocol improves the evenness of the rAAV genome coverage and consequently leads to a more accurate relative quantification of residual DNA. HTS-based assays provide a more comprehensive assessment of DNA impurities and AAV vector genome integrity than conventional QC tests based on real-time PCR and are useful methods to improve the safety and efficacy of these viral vectors.     

Synaptic dendritic activity modulates the single synaptic event

Synaptic transmission is the key system for the information transfer and elaboration among neurons. Nevertheless, a synapse is not a standing alone structure but it is a part of a population of synapses inputting the information from several neurons on a specific area of the dendritic tree of a single neuron. This population consists of excitatory and inhibitory synapses the inputs of which drive the postsynaptic membrane potential in the depolarizing (excitatory synapses) or depolarizing (inhibitory synapses) direction modulating in such a way the postsynaptic membrane potential. The postsynaptic response of a single synapse depends on several biophysical factors the most important of which is the value of the membrane potential at which the response occurs. The concurrence in a specific time window of inputs by several synapses located in a specific area of the dendritic tree can, consequently, modulate the membrane potential such to severely influence the single postsynaptic response. The degree of modulation operated by the synaptic population depends on the number of synapses active, on the relative proportion between excitatory and inbibitory synapses belonging to the population and on their specific mean firing frequencies. In the present paper we show results obtained by the simulation of the activity of a single Glutamatergic excitatory synapse under the influence of two different populations composed of the same proportion of excitatory and inhibitory synapses but having two different sizes (total number of synapses). The most relevant conclusion of the present simulations is that the information transferred by the single synapse is not and independent simple transition between a pre- and a postsynaptic neuron but is the result of the cooperation of all the synapses which concurrently try to transfer the information to the postsynaptic neuron in a given time window. This cooperativeness is mainly operated by a simple mechanism of modulation of the postsynaptic membrane potential which influences the amplitude of the different components forming the postsynaptic excitatory response.     

An Approach Based on Mutually Informed Neural Networks to Optimize the Generalization Capabilities of Decision Support Systems Developed for Heart Failure Prediction

Available clinical methods for heart failure (HF) diagnosis are expensive and require a high-level of experts intervention. Recently, various machine learning models have been developed for the prediction of HF where most of them have an issue of over-fitting. Over-fitting occurs when machine learning based predictive models show better performance on the training data yet demonstrate a poor performance on the testing data and the other way around. Developing a machine learning model which is able to produce generalization capabilities (such that the model exhibits better performance on both the training and the testing data sets) could overall minimize the prediction errors. Hence, such prediction models could potentially be helpful to cardiologists for the effective diagnose of HF. This paper proposes a two-stage decision support system to overcome the over-fitting issue and to optimize the generalization factor. The first stage uses a mutual information based statistical model while the second stage uses a neural network. We applied our approach to the HF subset of publicly available Cleveland heart disease database. Our experimental results show that the proposed decision support system has optimized the generalization capabilities and has reduced the mean percent error (MPE) to 8.8% which is significantly less than the recently published studies. In addition, our model exhibits a 93.33% accuracy rate which is higher than twenty eight recently developed HF risk prediction models that achieved accuracy in the range of 57.85% to 92.31%. We can hope that our decision support system will be helpful to cardiologists if deployed in clinical setup.