Broad-Spectrum Profiling of Drug Safety via Learning Complex Network.

Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug-gene-adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene-ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlert-gene at http://www.bio-add.org/ADRAlert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage.

Visual and efficient immunosensor technique for advancing biomedical applications of quantum dots on Salmonella detection and isolation.

It is a great challenge in nanotechnology for fluorescent nanobioprobes to be applied to visually detect and directly isolate pathogens in situ. A novel and visual immunosensor technique for efficient detection and isolation of Salmonella was established here by applying fluorescent nanobioprobes on a specially-designed cellulose-based swab (a solid-phase enrichment system). The selective and chromogenic medium used on this swab can achieve the ultrasensitive amplification of target bacteria and form chromogenic colonies in situ based on a simple biochemical reaction. More importantly, because this swab can serve as an attachment site for the targeted pathogens to immobilize and immunologically capture nanobioprobes, our mAb-conjugated QD bioprobes were successfully applied on the solid-phase enrichment system to capture the fluorescence of targeted colonies under a designed excitation light instrument based on blue light-emitting diodes combined with stereomicroscopy or laser scanning confocal microscopy. Compared with the traditional methods using 4-7 days to isolate Salmonella from the bacterial mixture, this method took only 2 days to do this, and the process of initial screening and preliminary diagnosis can be completed in only one and a half days. Furthermore, the limit of detection can reach as low as 10(1) cells per mL Salmonella on the background of 10(5) cells per mL non-Salmonella (Escherichia coli, Proteus mirabilis or Citrobacter freundii, respectively) in experimental samples, and even in human anal ones. The visual and efficient immunosensor technique may be proved to be a favorable alternative for screening and isolating Salmonella in a large number of samples related to public health surveillance.

Rapid Assessment of Adverse Drug Reactions by Statistical Solution of Gene Association Network.

Adverse drug reaction (ADR) is a common clinical problem, sometimes accompanying with high risk of mortality and morbidity. It is also one of the major factors that lead to failure in new drug development. Unfortunately, most of current experimental and computational methods are unable to evaluate clinical safety of drug candidates in early drug discovery stage due to the very limited knowledge of molecular mechanisms underlying ADRs. Therefore, in this study, we proposed a novel na€ive Bayesian model for rapid assessment of clinical ADRs with frequency estimation. This model was constructed on a gene-ADR association network, which covered 611 US FDA approved drugs, 14,251 genes, and 1,254 distinct ADR terms. An average detection rate of 99.86 and 99.73 percent were achieved eventually in identification of known ADRs in internal test data set and external case analyses respectively. Moreover, a comparative analysis between the estimated frequencies of ADRs and their observed frequencies was undertaken. It is observed that these two frequencies have the similar distribution trend. These results suggest that the naive Bayesian model based on gene-ADR association network can serve as an efficient and economic tool in rapid ADRs assessment.

[Application of niche theory in evaluation of main tourism scenic areas in Zhangjiajie City].

Five tourism scenic areas in Zhangjiajie City were selected as research objects, and fifty kinds of resource conditions affecting the development of tourism scenic area were taken as evaluation indices. Through disposing and consolidating the indices level by level, an analysis was made on the niche breadth and niche overlap of the five tourism scenic areas at three levels (I, II, and III). In the five scenic areas, index level had significant effects on the niche breadth (F = 10.278, P = 0.006), but less effects on the relative niche breadth, suggesting that in the evaluation of the development potential of tourism scenic area, relative niche breadth was more reasonable than absolute niche breadth. From level III to level I, the niche overlap of the five scenic areas was increasing, indicating that level choice would affect the evaluation of the actual niche overlap of the scenic areas. With the progressive refinement of the indices to certain level, and when the difference between observed and Monte Carlo-simulated Pianka indices achieved to significant level, this index level could be used as the minimum standard of the refinement, and the simulated niche overlap could be taken as an important reference in the competition evaluation of tourism scenic area.