Adenovirus-delivered CIAPIN1 small interfering RNA inhibits HCC growth in vitro and in vivo

Hepatocellular carcinoma (HCC) is an aggressive cancer with a poor prognosis. The specific cellular gene alterations responsible for hepatocarcinogenesis are not well known. Cytokine-induced antiapoptotic molecule (CIAPIN1), a recently reported antiapoptotic molecule which plays an essential role in mouse definitive hematopoiesis, is considered a downstream effecter of the receptor tyrosine kinase–Ras signaling pathway. However, the exact function of this gene in tumors is not clear. In this study, we reported that CIAPIN1 is highly expressed in HCC as compared with non-tumor hepatic tissue (P < 0.05). We employed adenovirus-mediated RNA interference technique to knock down CIAPIN1 expression in HCC cells and observed its effects on HCC cell growth in vitro and in vivo. Among the four HCC and one normal human liver cell lines we analyzed, CIAPIN1 was highly expressed in HCC cells. Knock down of CIAPIN1 could inhibit HCC cell proliferation by inhibiting the cell cycle S-phase entry. Soft agar colony formation assay indicated that the colony-forming ability of SMMC-7721 cells decreased by ∼70% after adenovirus AdH1-small interfering RNA (siRNA)/CIAPIN1 infection. In vivo experiments showed that adenovirus AdH1-siRNA/CIAPIN1 inhibited the tumorigenicity of SMMC-7721 cells and significantly suppressed tumor growth when injected directly into tumors. These results suggest that knock down of CIAPIN1 by adenovirus-delivered siRNA may be a potential therapeutic strategy for treatment of HCC in which CIAPIN1 is overexpressed

Hydrothermal conversion of biomass to 5-hmf

Bibliography: p. 84-99A few pages are in colour

AGTML: A novel approach to land cover classification by integrating automatic generation of training samples and machine learning algorithms on Google Earth Engine

The timely, accurate, and automatic acquisition of land cover (LC) information is a prerequisite for detecting LC dynamics and performing ecological analyses. Cloud computing platforms, such as the Google Earth Engine, have substantially improved the efficiency and scale of LC classification. However, the lack of sufficient and representative training samples hinders automatic and accurate LC classification. In this study, we propose a new approach that integrates the automatic generation of training samples and machine learning algorithms (AGTML) for LC classification in Heilongjiang Province, China. After optimal focal radii were determined for different LC types using Landsat 8 based on focal statistics and unique phenology. Then target training samples were automatically generated based on the improved distance measure SED (a composite of Spectral angle distance (SAD) and Euclidean distance (ED)). Furthermore, LC classification was performed using four feature combinations and three machine learning algorithms. According to independent validation data, the automatically generated training samples demonstrated good representativeness and stability among all three classifiers, with an overall accuracy (OA) of classification higher than 86%, and showed high consistency in the landscape pattern of classification. RF yielded the highest classification accuracy (92.99% OA). AGTML outperformed GLC-FCS30 in identifying large fragmentation and small patch regions in the landscape types. The AGTML approach was subsequently applied to the Guanzhong Plain using different satellite imagery. Results were consistent and accurate (>96.50% OA), demonstrating that the AGTML approach can be applied to various regions and sensors, and has immense potential for automated LC classification across regional and global scales

Reinforced education improves the quality of bowel preparation for colonoscopy: An updated meta-analysis of randomized controlled trials.

BACKGROUND AND AIMS:Inadequate bowel preparation (BP) is an unfavorable factor that influence the success of colonoscopy. Although standard education (SE) given to patients are proved useful to avoid inadequate BP. Studies concerning the effects of reinforced education (RE) on the quality of BP were inconsistent. The aim of this updated meta-analysis of randomized controlled trial was to compare the quality of BP between patients receiving RE in addition to SE and those receiving SE alone. METHODS:MEDLINE, EMBASE, Web of Science and the Cochrane Library were systemically searched to identify the relevant studies published through April 2019. The primary outcome was the rate of adequate BP. Subgroup analyses were conducted. Secondary outcomes included BP score, adenoma detection rate (ADR), polyp detection rate (PDR), insertion time, withdrawal time, adverse events, >80% purgative intake and diet compliance. Dichotomous variables were reported as odds ratio (OR) with 95% confidence interval (CI). Continuous data were reported as mean difference (MD) with 95%CI. Pooled estimates of OR or MD were calculated using a random-effects model. Statistical heterogeneity was accessed by calculating the I2 value. A P value less than 0.05 was considered significant. RESULTS:A total of 18 randomized controlled trails (N = 6536) were included in this meta-analysis. Patients who received RE had a better BP quality than those only receiving SE (OR 2.59, 95%CI: 2.09-3.19; P80% purgative intake (OR 2.17; 95%CI, 1.09-4.32; P = 0.030) and were compliant with diet restriction (OR 2.38; 95%CI: 1.79-3.17; P<0.001) in the RE group. CONCLUSION:RE significantly improved BP quality, increased ADR and PDR, decreased insertion and withdrawal time and adverse events

Additional file 1: of Controllable Preparation of V2O5/Graphene Nanocomposites as Cathode Materials for Lithium-Ion Batteries

XRD patterns of vanadium precursors, CV curves, charge/discharge profiles of the V@GO-II composite. Discharge/charge voltage profiles of the V@GO-I composite. Raman peaks and their assignments of V2O5. Figure S1. XRD patterns of the nanosheet-assembled vanadium precursor/GO composite (blue line) and the nanoparticle-assembled vanadium precursor/GO composite (red line). Figure S2. CV curves of the V/GO-II composite at a scan rate of 0.1 mV s−1. Figure S3. Charge/discharge profiles of the V@GO-II composite at different densities. Figure S4. Discharge/charge voltage profiles of the V@GO-I composite (a) and the V@GO-I composite (b) at a current rate of 2C. (DOC 4760 kb

Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases

Metallic glasses attract considerable interest due to their unique combination of superb properties and processability. Predicting their formation from known alloy parameters remains the major hindrance to the discovery of new systems. Here, we propose a descriptor based on the heuristics that structural and energetic ‘confusion' obstructs crystalline growth, and demonstrate its validity by experiments on two well-known glass-forming alloy systems. We then develop a robust model for predicting glass formation ability based on the geometrical and energetic features of crystalline phases calculated ab initio in the AFLOW framework. Our findings indicate that the formation of metallic glass phases could be much more common than currently thought, with more than 17% of binary alloy systems potential glass formers. Our approach pinpoints favourable compositions and demonstrates that smart descriptors, based solely on alloy properties available in online repositories, offer the sought-after key for accelerated discovery of metallic glasses