Objective To systematically review the correlation between epidermal growth factor (EGF) 61A/G polymorphism and the risk of esophageal carcinoma. Methods Such databases as PubMed, EMbase, CJFD, CBM, CNKI, VIP and WanFang Data were electronically searched from inception to January 1st, 2013, to collect case-control studies on the correlation between epidermal growth factor (EGF) 61A/G polymorphism and the risk of esophageal carcinoma. Two reviewers independently identified the literature according to inclusion and exclusion criteria, extracted data, and assessed the quality of the included studies. Then, meta-analysis was performed using RevMan 5.1 and Stata 12.0 software. Results A total of six studies involving 1 448 cases and 1 728 control subjects were included. The results of meta-analysis showed that, there was no significant association between EGF 61A/G polymorphism and the risk of esophageal carcinoma (dominant model: AG+GG vs. AA: OR=1.22, 95%CI 0.91 to 1.65; and recessive model: GG vs. AG+AA: OR=1.35, 95%CI 0.94 to 1.94; AG vs. AA: OR=1.12, 95%CI 0.93 to 1.35; GG vs. AA: OR=1.43, 95%CI 0.83 to 2.47). The results of subgroup analysis grouped by ethnicity showed that, EGF 61A/G polymorphism increased the risk of esophageal carcinoma of the White population (dominant model: AG+GG vs. AA: OR=1.39, 95%CI 1.14 to 1.71; and recessive model: GG vs. AG+AA: OR=1.75, 95%CI 1.37 to 2.25; GG vs. AA: OR=1.93, 95%CI 1.47 to 2.55). However, it had no correlation to the risk of esophageal carcinoma of Asian population. Conclusion Current studies showed that, EGF 61A/G polymorphism is not associated with susceptibility to esophageal carcinoma , but it may increase the risk of esophageal carcinoma in White population. Due to limited quality and quantity of the included studies, the above conclusion needs to be verified by more studies with large sample size.
ObjectiveExploring the potential causal effects and directions of insulin resistance (IR) and chronic airway inflammatory diseases, including asthma and chronic obstructive pulmonary disease (COPD), through two sample Mendelian randomization (MR). MethodsA total of 53 validated single nucleotide polymorphisms (SNPs) associated with IR were selected as instrumental variables. The inverse variance-weighted (IVW) method was used to model the causal association, and sensitivity analyses through leave-one-out analysis and pleiotropy testing were conducted to assess the relationship between IR and asthma and COPD. ResultsMR analysis revealed no significant causal effect of IR on asthma (IVW: OR=1.067, 95%CI 0.871 to 1.306, P=0.531) or COPD (IVW: OR=0.906, 95%CI 0.686 to –1.196, P=0.557). The results were consistent across sensitivity analyses and multiple pleiotropy tests, with no evidence of horizontal pleiotropy detected. ConclusionNo causal association was found between IR and the development of asthma or COPD. The relationship between these conditions may be influenced indirectly through complex interactions between metabolic and inflammatory pathways affecting disease progression.
Partial least square (PLS) combining with Raman spectroscopy was applied to develop predictive models for plasma paclitaxel concentration detection. In this experiment, 312 samples were scanned by Raman spectroscopy. High performance liquid chromatography (HPLC) was applied to determine the paclitaxel concentration in 312 rat plasma samples. Monte Carlo partial least square (MCPLS) method was successfully performed to identify the outliers and the numbers of calibration set. Based on the values of degree of approach (Da), moving window partial least square (MWPLS) was used to choose the suitable preprocessing method, optimum wavelength variables and the number of latent variables. The correlation coefficients between reference values and predictive values in both calibration set (Rc2) and validation set (Rp2) of optimum PLS model were 0.933 1 and 0.926 4, respectively. Furthermore, an independent verification test was performed on the prediction model. The results showed that the correlation error of the 20 validation samples was 9.36%±2.03%, which confirmed the well predictive ability of established PLS quantitative analysis model.