Lead is a ubiquitous pollutant that constitutes an environmental hazard worldwide. Although lead has been known as a carcinogenic factor in animal models, its role in human carcinogenesis is still a topic of debate with limited epidemiological evidence. Moreover, the association between urinary lead, as the most non-invasive and accessible way for lead measurement in human, and cancer mortality in general population has never been explored. We addressed this subject using continuous National Health and Nutrition Examination Survey 1999–2010 data and its Mortality Follow-Up Study. Of 5,316 subjects in study population, 161 participants died due to cancer. Cancer-specific mortality was associated with urinary lead levels after multivariable adjustment. Kaplan–Meier survival curve and cubic regression spline analyses indicated that high concentration of urinary lead exhibited significant association with raised death rate of cancer. Despite the marked decrease in environmental lead levels over the past three decades, lead exposure is still the significant determinant of cancer mortality in general population in U.S., and quantification of urinary lead may serve as a non-invasive approach to facilitate biomarker discovery and clinical translational research.

This research aimed to develop an FTIR-based method for rapid and low-cost integrated quality assessment of organic-inorganic composite herbs, which are kinds of herbs composed of both organic and inorganic active ingredients or matrix components. A two-step quality assessment route was designed and verified using the example of Indigo Naturalis (Qing Dai). First, the FTIR spectra were used as global chemical fingerprints to identify the true and fake samples. Next, the contents of the organic and inorganic marker components were estimated by FTIR quantification models to assess the quality of the true samples. Using the above approaches, all the 56 true samples and five fake samples of Indigo Naturalis could be identified correctly by the correlation threshold of the FTIR chemical fingerprints. Furthermore, the FTIR calibration models provided an accurate estimation of the contents of marker components with respect to HPLC and inductively coupled plasma optical emission spectrometry (ICP-OES). The coefficients of determination (R²) for the independent validation of indigo, indirubin, and calcium were 0.977, 0.983, and 0.971, respectively. Meanwhile, the mean relative errors (MRE) for the independent validation of indigo, indirubin, and calcium were 2.2%, 2.4%, and 1.8%, respectively. In conclusion, this research shows the potential of FTIR spectroscopy for the rapid and integrated quality assessment of organic-inorganic composite herbs in both chemical fingerprints identification and marker components quantification.

Atherosclerosis (AS) is the leading cause of coronary heart disease, cerebral infarction, peripheral vascular disease, and other cardiovascular diseases, making it a major risk factor for high morbidity and mortality. Although long non-coding RNAs (lncRNAs) have been reported to play a role in AS, the specific effects of lncRNAs on AS remain largely unknown. Thus the purpose of this study was to explore the roles of mRNAs and lncRNAs in atherosclerosis via an ox-low-density lipoprotein induced macrophage-derived foam cell model. Microarray analysis identified a total of 50 688 mRNAs and 1514 lncRNAs, including 51 lncRNAs and 1730 mRNAs that were significantly dysregulated in the model group (p-adjust < 0.05 and |log 2FC| > 2). The results of gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses demonstrated that the dysregulated genes were associated with cell proliferation, cell apoptosis, and inflammatory responses. An lncRNA–mRNA co-expression network was created to further analyze the key regulatory genes. The lncRNAs Brip1os, Gm16586, AU020206, 9430034N14Rik, 2510016D11Rik, LNC_000709, Gm15472, Gm20703, and Dubr were identified as potential biomarkers in macrophage-derived foam cells. Based on 9 lncRNAs and 13 mRNAs, key genes influencing the degree of cell proliferation and cell apoptosis and the subsequent development of AS were identified. Q-PCR verified the key dysregulated genes. Thus, our results suggest potential therapeutic targets for AS and provide avenues for further research on AS pathogenesis.

Near-infrared spectroscopy (NIRS), a rapid and efficient tool, was used to determine the total amount of nine ginsenosides inPanax ginseng. In the study, the regression models were established using multivariate regression methods with the results from conventional chemical analytical methods as reference values. The multivariate regression methods, partial least squares regression (PLSR) and principal component regression (PCR), were discussed and the PLSR was more suitable. Multiplicative scatter correction (MSC), second derivative, and Savitzky-Golay smoothing were utilized together for the spectral preprocessing. When evaluating the final model, factors such as correlation coefficient (R²) and the root mean square error of prediction (RMSEP) were considered. The final optimal results of PLSR model showed that root mean square error of prediction (RMSEP) and correlation coefficients (R²) in the calibration set were 0.159 and 0.963, respectively. The results demonstrated that the NIRS as a new method can be applied to the quality control ofGinseng Radix et Rhizoma.