Prevention of influenza virus shedding and protection from lethal H1N1 challenge using a consensus 2009 H1N1 HA and NA adenovirus vector vaccine

Vaccines against emerging pathogens such as the 2009 H1N1 pandemic virus can benefit from current technologies such as rapid genomic sequencing to construct the most biologically relevant vaccine. A novel platform (Ad5 [E1-, E2b-]) has been utilized to induce immune responses to various antigenic targets. We employed this vector platform to express hemagglutinin (HA) and neuraminidase (NA) genes from 2009 H1N1 pandemic viruses. Inserts were consensuses sequences designed from viral isolate sequences and the vaccine was rapidly constructed and produced. Vaccination induced H1N1 immune responses in mice, which afforded protection from lethal virus challenge. In ferrets, vaccination protected from disease development and significantly reduced viral titers in nasal washes. H1N1 cell mediated immunity as well as antibody induction correlated with the prevention of disease symptoms and reduction of virus replication. The Ad5 [E1-, E2b-] should be evaluated for the rapid development of effective vaccines against infectious diseases.     

支持向量机在建立冠心病早期诊断模型中的应用

目的 探索支持向量机方法在建立冠心病早期诊断模型中的应用,为冠心病危险因素在早期诊断中的合理应用提供理论依据.方法 首先应用logistic回归分析方法筛选冠心病危险因素,将有统计学意义的危险因素与24 h动态心电图检查结果共同构建支持向量机模型,并应用测试数据集对各模型的诊断能力进行评价.结果 24 h动态心电图检查结果与危险因素共同构建的支持向量机模型较单独应用24 h动态心电图诊断有更好的诊断准确率和灵敏度,特异度较低.对应用不同变量构建的模型进行比较,应用24 h动态心电图,结合年龄、性别、糖尿病、高血压构建的模型诊断效果较好,准确率为70.35%,灵敏度为90.27%,特异度为34.76%.结论 应用支持向量机可以建立合适的冠心病早期诊断模型;结合主要危险因素进行冠心病的早期诊断可以提高诊断准确率.     

人源S100A1蛋白的表达纯化与鉴定

目的 构建pBV220-S100A1表达栽体,表达并纯化人源S100A1蛋白,为进一步研究人源S100A1蛋白相关的生物学功能及应用于临床诊断奠定基础.方法 采用全基因合成法合成人源S100A1基因;通过GeneBank查询人源S100A1蛋白的氨基酸序列,进行序列优化设计,同时引入Ecor Ⅰ 和BamH Ⅰ 酶切位...     

Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms

PurposeMachine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data.MethodsThe hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set.ResultsThe appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range: 78.1 %–88 % vs. 66 %–83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods.ConclusionsA subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes.     

Genomic targeting by recombinase‐mediated cassette exchange in the spotted wing drosophila,Drosophila suzukii

Drosophila suzukii is a significant pest of stone and small fruits. The genome of this species has been sequenced and manipulated by transposon‐mediated transformation and CRISPR/Cas9 gene editing. These technologies open a variety of possibilities for functional genomics and genetic modifications that might improve biologically based population control strategies. Both of these approaches, however, would benefit from genome targeting that would avoid position effects and insertional mutations associated with random transposon vector insertions, and the limited DNA fragment insertion size allowed by gene editing. Here, we describe an efficient recombinase‐mediated cassette exchange (RMCE) system for D. suzukii in which heterospecific lox recombination sites were integrated into the genome by transposon‐mediated transformation and subsequently targeted for double recombination by a donor vector in the presence of Cre recombinase. Three loxN/lox2272 landing site lines have previously been created in D. suzukii, and quantitative PCR determined that polyubiquitin‐regulated enhanced green fluorescent protein expression is least susceptible to position effect suppression in the 443_M26m1 line. We presume that RMCE target sites may also be inserted more specifically into the genome by homology‐directed repair gene editing, thereby avoiding position effects and mutations, while eliminating restrictions on the size of donor constructs for subsequent insertion.     

生物电阻抗矢量法评估血液透析合并高血压患者的容量负荷

目的应用生物电阻抗矢量法评估血液透析合并高血压患者的容量负荷,探讨高容量负荷状态对高血压患者预后的影响。方法研究对象来自南京医科大学附属明基医院透析前收缩压(6次透析治疗前平均收缩压值)>160 mmHg的血液透析患者。用生物电阻抗矢量法评估患者容量状态,并根据患者容量状态分为容量增加组和非容量增加组(包括容量正常和容量下降的患者)。比较两组患者临床资料、实验室指标、细胞内液比例(ICW)、细胞外液比例(ECW)、体细胞质量、瘦体重、干瘦体重及其占总体重的百分比、脂肪含量占总体重的百分比、阻抗/身高、容抗/身高、相位角、疾病指数的差异。采用Kaplan⁃Meier生存曲线比较两组患者生存率的差异。结果共51例血液透析合并高血压患者入选本研究,容量增加组19例,非容量增加组32例(容量正常27例,容量下降5例)。容量增加组患者的血白蛋白、前白蛋白、血红蛋白、血细胞比容、血磷较非容量增加组显著下降,淋巴细胞比例明显升高,组间比较差异均有统计学意义(均P<0.05)。容量增加组的细胞外液比例、疾病指数显著高于非容量增加组(均P<0.01);相位角、阻抗/身高、容抗/身高显著低于非容量增加组(均P<0.01)。临床追踪20个月,容量增加组患者血压达标率(透析前收缩压<160 mmHg)低于非容量增加组(26.3%比43.8%),但差异无统计学意义。容量增加组患者全因死亡率高于非容量增加组(26.3%比15.6%),Kaplan⁃Meier生存曲线分析提示两组生存率的差异尚无统计学意义。结论容量负荷增加的血液透析合并高血压患者细胞外液增加明显,营养状态评估指标较非容量增加的高血压患者明显下降,而淋巴细胞比例升高,可能与患者微炎症状态有关。容量负荷增加的高血压患者血压更加难以控制,临床预后可能不及非容量增加的患者。     

EEG functional connectivity contributes to outcome prediction of postanoxic coma

ObjectiveTo investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.MethodsProspective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5).ResultsWe included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity.ConclusionFunctional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma.SignificanceFunctional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.     

Identification of microbes in wounds using near-infrared spectroscopy

BackgroundRapid diagnosis of microbes in the burn wound is a big challenge in the medical field. Traditional biochemical detection techniques take hours or days to identify the species of contaminating and drug-resistant microbes. Near-infrared spectroscopy (NIRS) is evaluated to address the need for a fast and sensitive method for the detection of bacterial contamination in liquids.MethodsHerin, we developed a novel technique which by using NIRS together with supporting vector machine (SVM), to identify the microbial species and drug-resistant microbes in LB medium, and to diagnose the wound colonization and wound infection models of pigs.ResultsThe device could recognize 100% of seven kinds of microbes and 99.47% of the multi-drug resistant Staphylococcus aureus (S. aureus), with a concentration of 10⁹ cfu ml^?1 in LB medium. The accuracy of the microbial identification in colonized and infected wounds in-situ was 100%. The detection limit of NIRS with SVM for the detection of S. aureus and Escherichia coli (E. coli) was 10¹ cfu ml^?1 in LB medium. Identification time was less than 5 s.ConclusionOur findings validate for the first time a novel technique aimed at the rapid, noncontacted, highly sensitive, and specific recognition of several microbial species including drug-resistant ones. This technique could represent a promising approach to identify diverse microbial species and a potential bedside device to rapidly diagnose infected wounds.     

脂质纳米粒-mRNA递送系统及其在嵌合抗原受体T细胞治疗中的应用

尽管嵌合抗原受体（CAR）T细胞治疗在血液系统恶性肿瘤患者中取得了显著的临床疗效，但需要进一步优化。脂质纳米粒（LNP）-信使核糖核酸（mRNA）递送系统作为一种非病毒性基因载体运用于CAR-T细胞治疗研究中，一方面通过LNP将密封蛋白-6 mRNA靶向递送至抗原提呈细胞，从而实现抗原提呈细胞辅助性增强密封蛋白-6靶向的CAR-T细胞的功能，以进一步诱导对实体瘤的清除；另一方面，通过LNP将成纤维细胞激活蛋白（FAP）CARmRNA靶向递送至T细胞，实现体内FAP靶向的CAR-T细胞的制备，以通过阻断心脏纤维化过程达到治疗急性心肌损伤的目的。在CAR-T细胞研究和治疗中，LNP-mRNA递送系统具有不与细胞基因组整合、价格便宜、毒副作用小及可修饰等优点，亦存在蛋白瞬时表达导致调控细胞功能的持久性不足及制备等方面的技术局限性。本文综述了LNP-mRNA递送系统及其在CAR-T细胞治疗中的应用研究。