Improving the Cpf1-mediated base editing system by combining dCas9/dead sgRNA with human APOBEC3A variants

正Base editor-mediated C-to-T base conversion obviates the requirements of double-strand breaks, thereby showing promise as a tool for disease modeling and gene therapy (Gaudelli et al., 2017;Rees and Liu 2018). The most actively used base editor comprises a Cas9 nickase (nCas9) with cytidine deaminase and fused uracil DNA glycosylase inhibitor at the carboxy terminus of nCas9 to inhibit uracil N-glycosylase effects (Pearl, 2000; Kunz et al., 2009;     

20例葡萄球菌感染所致化脓性脊柱炎的临床特征分析

为探讨葡萄球菌感染所致化脓性脊柱炎(pyogenic vertebral osteomyelitis,PVO)的临床及影像学特征,本研究回顾性分析了2009年1月-12月上海交通大学附属第六人民医院感染病科收治的20例葡萄球菌感染所致PVO患者的临床特征、实验室检查指标、影像学资料及治疗效果。结果显示,20例PVO患者中金黄色葡萄球菌感染较为多见,85%患者病变在腰椎,50%患者白细胞计数升高,13例发热(65%),12例(60%)出现椎旁脓肿,所有患者C反应蛋白、红细胞沉降率、铁蛋白均升高。计算机断层扫描(computed tomography,CT)平扫示感染椎体骨质破坏;磁共振成像(magnetic resonance imaging,MRI)示病变椎体及椎间盘破坏区异常信号灶,增强后可见明显强化;单光子发射计算机断层扫描(emission computed tomography,ECT)示病变椎体不均匀放射性摄取增高。金黄色葡萄球菌对青霉素耐药率达77.8%。12例椎旁脓肿患者经CT引导下穿刺置管引流加敏感抗生素治疗,临床结局良好。结果提示,金黄色葡萄球菌是葡萄球菌感染所致PVO的主要致病菌,其对青霉素普遍耐药,炎症指标和影像学检查可用于疗效评估及随访,内科保守治疗对PVO有效。     

A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.     

PSMA Ligand Conjugated PCL-PEG Polymeric Micelles Targeted to Prostate Cancer Cells

In this content, a small molecular ligand of prostate specific membrane antigen (SMLP) conjugated poly (caprolactone) (PCL)-b-poly (ethylene glycol) (PEG) copolymers with different block lengths were synthesized to construct a satisfactory drug delivery system. Four different docetaxel-loaded polymeric micelles (DTX-PMs) were prepared by dialysis with particle sizes less than 60 nm as characterized by dynamic light scattering (DLS) and transmission electron microscope (TEM). Optimization of the prepared micelles was conducted based on short-term stability and drug-loading content. The results showed that optimized systems were able to remain stable over 7 days. Compared with Taxotere, DTX-PMs with the same ratio of hydrophilic/hydrophobic chain length displayed similar sustained release behaviors. The cytotoxicity of the optimized targeted DTX-PCL_12K-PEG_5K-SMLP micelles (DTX-PMs2) and non-targeted DTX-PCL_12K-mPEG_5K micelles (DTX-PMs1) were evaluated by MTT assays using prostate specific membrane antigen (PSMA) positive prostate adenocarcinoma cells (LNCaP). The results showed that the targeted micelles had a much lower IC50 than their non-targeted counterparts (48 h: 0.87±0.27 vs 13.48±1.03 µg/ml; 72 h: 0.02±0.008 vs 1.35±0.54 µg/ml). In vitro cellular uptake of PMs2 showed 5-fold higher fluorescence intensity than that of PMs1 after 4 h incubation. According to these results, the novel nano-sized drug delivery system based on DTX-PCL-PEG-SMLP offers great promise for the treatment of prostatic cancer.     

SYBR Green Ⅰ-based product-enhanced reverse transcriptase assay for quantification of retroviral PFV and detection of the divalent cation preference of PFV RT

Dear Editor, Prototype foamy virus (PFV) belongs to the genus Spumavirus in the Spumaretrovirinae subfamily of Retroviridae.Although PFV and HIV have much in common,research into PFV has lagged far behind that into HIV,as PFV appeared to be non-pathogenic both in accidentally infected humans and in experimentally infected animals.In recent decades,however,more attention has been focused on PFV because it seems to be a promising candidate vector for gene therapy in clinical applications.Compared with other retroviral vectors,vectors derived from PFV appear to be safer (Sastry L,et al.,2005).In addition,it was found that PFV vectors are efficient vehicles for stable gene transfer in proliferating cells,and that the transferred genes are able to achieve stable integration and long-term expression in the target cells.     

A High-Density Genetic Map Identifies a Novel Major QTL for Boron Efficiency in Oilseed Rape (Brassica napus L.)

Low boron (B) seriously limits the growth of oilseed rape (Brassica napus L.), a high B demand species that is sensitive to low B conditions. Significant genotypic variations in response to B deficiency have been observed among B. napus cultivars. To reveal the genetic basis for B efficiency in B. napus, quantitative trait loci (QTLs) for the plant growth traits, B uptake traits and the B efficiency coefficient (BEC) were analyzed using a doubled haploid (DH) population derived from a cross between a B-efficient parent, Qingyou 10, and a B-inefficient parent, Westar 10. A high-density genetic map was constructed based on single nucleotide polymorphisms (SNPs) assayed using Brassica 60 K Infinium BeadChip Array, simple sequence repeats (SSRs) and amplified fragment length polymorphisms (AFLPs). The linkage map covered a total length of 2139.5 cM, with 19 linkage groups (LGs) and an average distance of 1.6 cM between adjacent markers. Based on hydroponic evaluation of six B efficiency traits measured in three separate repeated trials, a total of 52 QTLs were identified, accounting for 6.14–46.27% of the phenotypic variation. A major QTL for BEC, qBEC-A3a, was co-located on A3 with other QTLs for plant growth and B uptake traits under low B stress. Using a subset of substitution lines, qBEC-A3a was validated and narrowed down to the interval between CNU384 and BnGMS436. The results of this study provide a novel major locus located on A3 for B efficiency in B. napus that will be suitable for fine mapping and marker-assisted selection breeding for B efficiency in B. napus.     

Path Analysis to Identify Factors Influencing Health Skills and Behaviors in Adolescents: A Cross-Sectional Survey

Background

Studies conducted in the past mostly rely on models of functional health literacy in adult populations. However, such models do not satisfy the need for health intervention in adolescents. The identification of key factors influencing adolescents'' health literacy is essential in developing effective prevention and intervention measures. This study aimed to test a theoretical model of predictors on health skills and health behaviors in adolescents.

Methods

A cross-sectional survey was conducted in Guangdong using a multi-stage stratified cluster sample design. A representative random sample of 3821 students aged 13–25 years was selected using multi-stage stratified cluster sampling. The path analysis was used to test a hypothesized model of health literacy.

Results

The path analysis showed that knowledge of infectious disease (β = 0.26), health skills (β = 0.22), health concept (β = 0.20), general health knowledge (β = 0.15), gender (β = 0.12), and school performance (β = 0.06) had positive direct effect on health behaviors in adolescents. The explanatory variables accounted for 43% of the variance in explaining health behaviors. Knowledge of infectious disease (β = 0.30), health concept (β = 0.17), general health knowledge (β = 0.13), and school performance (β = 0.05) had positive indirect effect on health behaviors through the impacts on health skills.

Conclusion

This study identified several direct and indirect factors influencing health skills and health behaviors in adolescents. These findings will assist health professionals designing effective health interventions that aim to improve health skills and health behaviors in adolescents.     

Adverse Drug Reaction Prediction Using Scores Produced by Large-Scale Drug-Protein Target Docking on High-Performance Computing Machines

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60–0.69 and 0.61–0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.