Association Between the Aldehyde Dehydrogenase-2 rs671 G>A Polymorphism and Head and Neck Cancer Susceptibility: A Meta-Analysis in East Asians.

BACKGROUND: Aldehyde dehydrogenase-2 (ALDH2) plays an important role in the alcohol detoxification and acetaldehyde metabolism. Published studies have demonstrated some inconsistent associations between ALDH2 rs671 G>A polymorphism and head and neck cancer (HNC) risk. METHODS: A meta-analysis was performed to provide pooled data on the association between the ALDH2 rs671 G>A polymorphism and HNC risk. Electronic databases were searched to identify relevant studies. Odds ratios and 95% confidence intervals (CIs) were used to examine the pooled effect size of each genetic model. In addition, heterogeneity test, accumulative analysis, sensitivity analysis, and publication bias were conducted to test the statistical power. RESULTS: Thirteen publications (14 independent case-control studies) involving 10,939 subjects were selected. The stratified analysis indicated that both light/moderated drinking (e.g., GA vs. GG: OR = 1.47, 95% CI = 1.16 to 1.86, p < 0.01, I2  = 81.1%) and heavy drinking would increase HNC risk with rs671 G>A mutation (e.g., GA vs. GG: OR = 2.30, 95% CI = 1.11 to 4.77, p = 0.03, I2  = 81.9%). CONCLUSIONS: In summary, this meta-analysis suggested that the ALDH2 rs671 G>A polymorphism may play an important synergistic effect in the pathogenesis of HNC development in East Asians.

lncRNA-NRF is a Potential Biomarker of Heart Failure After Acute Myocardial Infarction.

Long non-coding RNAs (lncRNAs) are a new focus in cardiovascular diseases. The necrosis-related factor (NRF) is a newly discovered lncRNA, which is increased in myocardial injury. We investigated the role of lncRNA-NRF in heart failure (HF) after acute myocardial infarction (AMI) to find a biomarker for early HF detection. This was a cross-sectional study of 76 AMI patients with HF and 58 AMI patients without HF. lncRNA-NRF was shown to be increased in AMI patients with HF compared with AMI patients without HF and had predictive value for diagnosis of HF. It had a high diagnostic value for HF (AUC, 0.975), while the AUC for N-terminal pro-brain natriuretic peptide was 0.720. Our findings suggest that lncRNA-NRF may represent a marker of risk for development of HF post-AMI.

In silico screening of Chinese herbal medicines with the potential to directly inhibit 2019 novel coronavirus.

OBJECTIVE: In this study we execute a rational screen to identify Chinese medical herbs that are commonly used in treating viral respiratory infections and also contain compounds that might directly inhibit 2019 novel coronavirus (2019-nCoV), an ongoing novel coronavirus that causes pneumonia. METHODS: There were two main steps in the screening process. In the first step we conducted a literature search for natural compounds that had been biologically confirmed as against sever acute respiratory syndrome coronavirus or Middle East respiratory syndrome coronavirus. Resulting compounds were cross-checked for listing in the Traditional Chinese Medicine Systems Pharmacology Database. Compounds meeting both requirements were subjected to absorption, distribution, metabolism and excretion (ADME) evaluation to verify that oral administration would be effective. Next, a docking analysis was used to test whether the compound had the potential for direct 2019-nCoV protein interaction. In the second step we searched Chinese herbal databases to identify plants containing the selected compounds. Plants containing 2 or more of the compounds identified in our screen were then checked against the catalogue for classic herbal usage. Finally, network pharmacology analysis was used to predict the general in vivo effects of each selected herb. RESULTS: Of the natural compounds screened, 13 that exist in traditional Chinese medicines were also found to have potential anti-2019-nCoV activity. Further, 125 Chinese herbs were found to contain 2 or more of these 13 compounds. Of these 125 herbs, 26 are classically catalogued as treating viral respiratory infections. Network pharmacology analysis predicted that the general in vivo roles of these 26 herbal plants were related to regulating viral infection, immune/inflammation reactions and hypoxia response. CONCLUSION: Chinese herbal treatments classically used for treating viral respiratory infection might contain direct anti-2019-nCoV compounds.

Network pharmacology suggests biochemical rationale for treating COVID-19 symptoms with a Traditional Chinese Medicine.

Chinese herbal formulas including the lung-cleaning and toxicity-excluding (LCTE) soup have played an important role in treating the ongoing COVID-19 pandemic (caused by SARS-CoV-2) in China. Applying LCTE outside of China may prove challenging due to the unfamiliar rationale behind its application in terms of Traditional Chinese Medicine. To overcome this barrier, a biochemical understanding of the clinical effects of LCTE is needed. Here, we explore the chemical compounds present in the reported LCTE ingredients and the proteins targeted by these compounds via a network pharmacology analysis. Our results indicate that LCTE contains compounds with the potential to directly inhibit SARS-CoV-2 and inflammation, and that the compound targets proteins highly related to COVID-19's main symptoms. We predict the general effect of LCTE is to affect the pathways involved in viral and other microbial infections, inflammation/cytokine response, and lung diseases. Our work provides a biochemical basis for using LCTE to treat COVID-19 and its main symptoms.

Meta-analysis of the CYP1A2 -163C>A polymorphism and lung cancer risk.

Many published studies have concerned associations between the CYP1A2 -163 C>A polymorphism and risk of lung cancer, but the results have been inconsistent. Therefore, we performed a meta-analysis to obtain a more precise estimate. We searched the PubMed database up to March 1, 2013 for relevant cohort and case-control studies. Supplementary search was conducted manually by searching the references of the included studies and relevant meta-analyses. A meta-analysis was performed using RevMan 5.2 software for calculation of pooled odds ratios (ORs) and relevant 95% confidence intervals (CIs) after data extraction. Finally, seven case-control studies and one nested case-control study involving 1,675 lung cancer patients and 2,393 controls were included. The meta-analysis showed that there was no association of CYP1A2 -163 C>A polymorphism with risk of lung cancer overall [(OR=0.89, 95%CI= 0.74-1.07) for C vs. A; (OR=0.73, 95%CI= 0.50-1.07) for AA vs. CC ; (OR=0.82, 95%CI= 0.62-1.09) for AC vs. CC; (OR=0.79, 95%CI= 0.58-1.07) for (AC+AA) vs. CC; and (OR=0.87, 95%CI= 0.67-1.13) for AA vs. (CC+AC)]. Subgroup analysis indicated that there was an associationbetween CYP1A2 -163C>A polymorphism and lung cancer risk for population-based controls, a trend risk for SCCL (squamous cell carcinoma of lung) and Caucasians. These results suggested that -163 C>A polymorphism is likely to be associated with risk of lung cancer compared with population-based controls.