[Effects of MICT/HIIT on the ultrastructure of myocardium and soleus in rats with high-fat diet and its mechanisms].

Objective: To investigate the effects of moderate intensity continuous training (MICT) and high intensity intermittent training (HIIT) on the ultrastructure of myocardium and soleus in rats with high fat diet, and to explore the mechanisms. Methods: 5-week-old male SD rats were randomly divided into normal diet quiet group (C), high-fat diet quiet group (F), high-fat MICT group (M) and high-fat HIIT group (H), with 8 rats in each group, and the fat content of the high-fat dietary feed was 45%. The M and H groups were given 12 weeks of treadmill running with an incline of 25°. The M group was given continuous exercise with 70%VO2max intensity, and the H group was given intermittent exercise with 5 min 40%~45%VO2max and 4 min 95%~99%VO2max intensity successively. After the intervention, the contents of free fatty acid (FFA), triglyceride (TG), high density lipoprotein cholesterol (HDL) and low density lipoprotein cholesterol (LDL) in serum were detected. Transmission electron microscopy was used to observe the ultrastructure of myocardium and soleus in rats. Western blot was used to detect the protein expressions of AMPK, malonyl-CoA decarboxylase (MCD) and carnitine palmitoyl transterase 1 (CPT-1) in myocardium and soleus. Results: Compared with C group, the body weight, Lee's index, the contents of LDL, TG and FFA in serum were increased, the content of HDL was decreased (P＜0.05), the protein expressions of AMPK and CPT-1 in myocardium and soleus were increased, the protein expression of MCD was decreased (P＜0.05), and the ultrastructure was damaged in group F. Compared with F group, the body weight and Lee's index were decreased, the contents of LDL and FFA in serum were decreased (P＜0.01), the protein expressions of AMPK, MCD and CPT-1 in myocardium were increased, and the protein expressions of AMPK and MCD in soleus were increased (P＜0.05), and the ultrastructural damage was attenuated in M and H groups. Compared with M group, the content of HDL in serum was increased (P＜0.01), the protein expressions of AMPK and MCD in myocardium were increased, and the ultrastructural damage was mild, the protein expression of AMPK in soleus was decreased, the protein expression of MCD in soleus was increased (P＜0.05), and the ultrastructural damage was severe in group H. Conclusion: MICT and HIIT have different effects on the ultrastructure of myocardium and soleus in high-fat diet rats by intervening the protein expression of AMPK, MCD and CPT-1.

Which Is Safer, Chinese Medicine or Western Medicine? Comparative Analysis Based on Chinese Spontaneous Reporting Database.

OBJECTIVE: To compare the safety differences between Chinese medicine (CM) and Western medicine (WM) based on Chinese Spontaneous Reporting Database (CSRD). METHODS: Reports of adverse events (AEs) caused by CM and WM in the CSRD between 2010 and 2011 were selected. The following assessment indicators were constructed: the proportion of serious AEs (PSE), the average number of AEs (ANA), and the coverage rate of AEs (CRA). Further comparisons were also conducted, including the drugs with the most reported serious AEs, the AEs with the biggest report number, and the 5 serious AEs of interest (including death, anaphylactic shock, coma, dyspnea and abnormal liver function). RESULTS: The PSE, ANA and CRA of WM were 1.09, 8.23 and 2.35 times higher than those of CM, respectively. The top 10 drugs with the most serious AEs were mainly injections for CM and antibiotics for WM. The AEs with the most reports were rash, pruritus, nausea, dizziness and vomiting for both CM and WM. The proportions of CM and WM in anaphylactic shock and coma were similar. For abnormal liver function and death, the proportions of WM were 5.47 and 3.00 times higher than those of CM, respectively. CONCLUSION: Based on CSRD, CM was safer than WM at the average level from the perspective of adverse drug reactions.

A stratification method based on clustering for the minimization of data masking effect in signal detection.

BACKGROUND: Data masking is an inborn defect of measures of disproportionality in adverse drug reactions (ADRs) signal detection. Many previous studies can be roughly classified into three categories: data removal, regression and stratification. However, frequency differences of adverse drug events (ADEs) reports, which would be an important factor of masking, were not considered in these methods. The aim of this study is to explore a novel stratification method for minimizing the impact of frequency differences on real signals masking. METHODS: Reports in the Chinese Spontaneous Reporting Database (CSRD) between 2010 and 2011 were selected. The overall dataset was stratified into some clusters by the frequency of drugs, ADRs, and drug-event combinations (DECs) in sequence. K-means clustering was used to conduct stratification according to data distribution characteristics. The Information Component (IC) was adopted for signal detection in each cluster respectively. By extracting ADRs from drug product labeling, a reference database was introduced for performance evaluation based on Recall, Precision and F-measure. In addition, some DECs from the Adverse Drug Reactions Information Bulletin (ADRIB) issued by CFDA were collected for further reliability evaluation. RESULTS: With stratification, the study dataset was divided into 21 clusters, among which the frequency of DRUGs, ADRs or DECs followed the similar order of magnitude respectively. Recall increased by 34.95% from 29.93 to 40.39%, Precision reduced by 10.52% from 54.56 to 48.82%, while F-measure increased by 14.39% from 38.65 to 44.21%. According to ADRIB after 2011, 5 DECs related to Potassium Magnesium Aspartate, 61 DECs related to Levofloxacin Hydrochloride and 26 DECs related to Cefazolin were highlighted. CONCLUSIONS: The proposed method is effectively and reliably for the minimization of data masking effect in signal detection. Considering the decrease of Precision, it is suggested to be a supplement rather than an alternative to non-stratification method.

Traditional Chinese medicine pharmacovigilance in signal detection: decision tree-based data classification.

BACKGROUND: Traditional Chinese Medicine (TCM) is a style of traditional medicine informed by modern medicine but built on a foundation of more than 2500 years of Chinese medical practice. According to statistics, TCM accounts for approximately 14% of total adverse drug reaction (ADR) spontaneous reporting data in China. Because of the complexity of the components in TCM formula, which makes it essentially different from Western medicine, it is critical to determine whether ADR reports of TCM should be analyzed independently. METHODS: Reports in the Chinese spontaneous reporting database between 2010 and 2011 were selected. The dataset was processed and divided into the total sample (all data) and the subsample (including TCM data only). Four different ADR signal detection methods-PRR, ROR, MHRA and IC- currently widely used in China, were applied for signal detection on the two samples. By comparison of experimental results, three of them-PRR, MHRA and IC-were chosen to do the experiment. We designed several indicators for performance evaluation such as R (recall ratio), P (precision ratio), and D (discrepancy ratio) based on the reference database and then constructed a decision tree for data classification based on such indicators. RESULTS: For PRR: R1-R2 = 0.72%, P1-P2 = 0.16% and D = 0.92%; For MHRA: R1-R2 = 0.97%, P1-P2 = 0.20% and D = 1.18%; For IC: R1-R2 = 1.44%, P2-P1 = 4.06% and D = 4.72%. The threshold of R,Pand Dis set as 2%, 2% and 3% respectively. Based on the decision tree, the results are "separation" for PRR, MHRA and IC. CONCLUSIONS: In order to improve the efficiency and accuracy of signal detection, we suggest that TCM data should be separated from the total sample when conducting analyses.

A novel method for signal detection of adverse drug reactions based on proportional reporting ratios.

OBJECTIVE: The proportional reporting ratio (PRR) is a statistical method for signal detection of adverse drug reactions (ADRs) based on unbalanced proportions. Although effective, this method only takes into account the proportional relation based on target adverse reactions and ignores the relation between a given ADR and the others for the same drug. Therefore, it is necessary to improve the calculation deviation in PRR. In this study, we developed a novel PRR (NPRR) method and compared it with the original PRR method for the purpose of a combined application of these two methods for ADR signal detection. METHODS: NPRR is also based on unbalanced proportions, in which the proportion for a given ADR is linked to a specific drug (or all other drugs), and then divided by the corresponding proportion for all other ADRs. RESULTS: Applying this method to the ADR data of Jiangsu Province, China in 2008 and 2009, we detected 3,021 signals. Compared with the PRR method, the sensitivity of our method is 0.99, the specificity is 0.97, and the Youden index is 0.96. CONCLUSION: NPRR is an excellent method supplementary to PRR. The combination of these two methods can reduce calculation deviation and detect ADRs more effectively.