The smoking spectrum: review of the existing evidence and future directions

A New Reference Plasmid “pGMT27” Provides an Efficient Transgenic Detection Method for Flue-Cured Tobacco

Owing to the economic value of its foliage, tobacco (Nicotiana tabacum) is cultivated all across the world. For the detection of genetically modified (GM) tobacco, there is a lack of universal standard material which ultimately limits the detection methods because the accuracy and comparability of the results cannot be ensured. Here, we prepared a reference plasmid “pGMT27” for the detection of GM tobacco, which was 18,296 bp in length harboring two of the tobacco endogenous and seven exogenous genes. By using qualitative PCR test for the nine genes, 10 copies were used for plasmid sensitivity. In the quantitative real-time PCR (qPCR) assays with pGMT27 as a calibrator, the reaction efficiencies for P-35S and NR were 101.427% and 98.036%, respectively, whereas the limit of detection (LOD) and limit of quantification (LOQ) were 5 copies and 10 copies per reaction. For standard deviation (SD) and relative standard deviation (RSD) of the Ct values, the repeatability values were from 0.04 to 0.42 and from 0.18% to 1.29%, respectively; and the reproducibility values were from 0.04 to 0.39 and from 0.18% to 1.14%, respectively. For the unknown sample test, the average conversion factor (Cf) was 0.39, and the accuracy bias was from −15.55% to 1.93%; for precision, the SD values ranged from 0.02 to 0.62, while RSD values were from 1.34% to 10.6%. We concluded that using the pGMT27 plasmid as a calibrator provided a highly efficient transgenic detection method for flue-cured tobacco.

A novel model for regional indoor PM2.5 quantification with both external and internal contributions included

PM_2.5 (particulate matter with an aerodynamic size ≤ 2.5 μm) of indoor origins is a dominant contributor to the overall air pollution exposure. Although some sophisticated models have been developed to simulate indoor air quality for individual households, it is still challenging to quantify indoor PM_2.5 on a regional scale, which is critical for health impact assessments. In this study, a new model was developed to predict indoor PM_2.5 concentrations by quantifying the external penetration, as well as the internal contributions. The model was parameterized based on a set of simultaneously measured indoor and outdoor PM_2.5 concentrations at five-second temporal resolution for 53 households in Beijing. This study found that indoor PM_2.5 concentrations were significantly correlated with those in the outdoor environment with an approximately 1-h lag-time on average. Outdoor-to-indoor penetration dominated the contribution to indoor PM_2.5 during polluted hours with relatively high ambient PM_2.5 concentrations, while the indoor PM_2.5, during clean hours, was contributed by internal sources, including smoking, cooking, incense burning, and human disturbance. The influence of windows, house area, and air purifier use was addressed in the new model. The model was applied to evaluate indoor PM_2.5 concentrations in six urban districts of Beijing via an uncertainty analysis. The model was developed based on and applied to households using clean residential energy, and it would be interesting also important to evaluate it in households using solid fuels.