Glutathione S-Transferase T1 (GSTT1) Null Polymorphism, Smoking, and Their Interaction in Coronary Heart Disease: A Comprehensive Meta-Analysis.

BACKGROUND: The association between glutathione S-transferase T1 (GSTT1) null polymorphism and coronary heart disease (CHD) is inconsistent among studies, and data on the GSTT1 null genotype-smoking interplay in CHD is lacking. We conducted this meta-analysis to investigate the relationship between GSTT1 null polymorphism and CHD and to assess the potential interaction between GSTT1 null genotype and smoking. METHODS: PubMed and EMBASE databases were searched up to 27 January 2016 using the appropriate terms. Odds ratios were pooled using either fixed-effects or random-effects models. RESULTS: Twenty-nine articles including 31 studies with 15,004 cases and 35,597 controls were eligible. The random-effects model showed that the GSTT1 null genotype was associated with increased CHD risk (OR=1.213, 95%CI: 1.004-1.467; I2=90.4%). After excluding 10 studies detected by Galbraith plot, the fixed effects summary estimate also showed an increased risk of CHD (OR=1.14, 95% CI: 1.06-1.22; I2=27.7%). A case-only analysis including eight studies showed a statistically significant positive interaction between GSTT1 null polymorphism and smoking status on CHD (OR=1.34, 95% CI: 1.09-1.64; I2=0%). Sensitivity analyses further supported the associations. No publication bias was observed. CONCLUSIONS: This meta-analysis suggests that GSTT1 null polymorphism is associated with the risk of CHD. To our knowledge, this is the first meta-analysis to prove a positive effect of the interaction between GSTT1 null genotype and smoking status on the risk of CHD. Future studies with detailed individual information are needed to confirm our findings.

Merging RFID and Blockchain Technologies to Accelerate Big Data Medical Research Based on Physiological Signals.

The proliferation of physiological signals acquisition and monitoring system, has led to an explosion in physiological signals data. Additionally, RFID systems, blockchain technologies, and the fog computing mechanisms have significantly increased the availability of physiological signal information through big data research. The driver for the development of hybrid systems is the continuing effort in making health-care services more efficient and sustainable. Implantable medical devices (IMD) are therapeutic devices that are surgically implanted into patients' body to continuously monitor their physiological parameters. Patients treat cardiac arrhythmia due to IMD therapeutic and life-saving benefits. We focus on hybrid systems developed for patient physiological signals for collection, storage protection, and monitoring in critical care and clinical practice. In order to provide medical data privacy protection and medical decision support, the hybrid systems are presented, and RFID, blockchain, and big data technologies are used to analyse physiological signals.

[Determination of two non-phthalates plasticizers in edible oil by gas chromatography-tandem mass spectrometry].

A gas chromatography-tandem mass spectrometry (GC-MS/MS) method was developed for the simultaneous determination of di (2-ethylhexyl) terephthalate and tris (2-ethylhexyl) trimellitate in edible oils. The plasticizers in the samples were extracted with acetonitrile. The extract was purified by freezing at -20 ℃, and determined by GC-MS/MS in selective reaction monitoring mode. For the two compounds, the limit of detection and limit of quantification were 0.03 mg/kg and 0.1 mg/kg, respectively, and the linear range was 0.1-10 mg/kg. The recoveries of the two compounds ranged from 81.04% to 108.31% at three spiked levels (0.1, 0.3, and 1.0 mg/kg), and the relative standard deviation ranged from 0.70% to 9.91%. The method is simple, accurate, and suitable for the determination of di (2-ethylhexyl) terephthalate and tris (2-ethylhexyl) trimellitate in edible oils.