Qing-Bin Gao

Proton pump inhibitors therapy and risk of hip fracture: a systematic review and meta-analysis

Background and aims Previous studies have reported inconsistent findings that proton pump inhibitors (PPIs) therapy might increase the risk of hip fracture. We investigated the association between PPIs therapy and hip fracture by a systematic review and meta-analysis. Methods We systematically searched PubMed, EMBASE, and the Cochrane Library. We included studies assessing the effects of PPIs on hip fracture. Data from the studies about odds ratio and 95% confidence interval were gathered and summarized. Results Seven studies met the inclusion criteria. PPIs therapy was associated with a statistically significant increase of hip fracture risk (pooled odds ratio=1.24; 95% confidence interval: 1.15–1.34; P<0.00001) under a random model. Meanwhile, we found that the effect of PPIs on hip fracture differs in different duration groups. Conclusion These results indicate that PPIs therapy might have the potential risk of hip fracture. Different effects on hip fracture in the subgroup analysis do not support a causal relationship between PPIs and hip fracture. Whether the risk exists warrants further investigation.

Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition.

Integral membrane proteins are central to many cellular processes and constitute approximately 50% of potential targets for novel drugs. However, the number of outer membrane proteins (OMPs) present in the public structure database is very limited due to the difficulties in determining structure with experimental methods. Therefore, discriminating OMPs from non-OMPs with computational methods is of medical importance as well as genome sequencing necessity. In this study, some sequence-derived structural and physicochemical features of proteins were incorporated with amino acid composition to discriminate OMPs from non-OMPs using support vector machines. The discrimination performance of the proposed method is evaluated on a benchmark dataset of 208 OMPs, 673 globular proteins, and 206 alpha-helical membrane proteins. A high overall accuracy of 97.8% was observed in the 5-fold cross-validation test. In addition, the current method distinguished OMPs from globular proteins and alpha-helical membrane proteins with overall accuracies of 98.2 and 96.4%, respectively. The prediction performance is superior to the state-of-the-art methods in the literature. It is anticipated that the current method might be a powerful tool for the discrimination of OMPs.

Prediction of nuclear receptors with optimal pseudo amino acid composition

Nuclear receptors are involved in multiple cellular signaling pathways that affect and regulate processes such as organ development and maintenance, ion transport, homeostasis, and apoptosis. In this article, an optimal pseudo amino acid composition based on physicochemical characters of amino acids is suggested to represent proteins for predicting the subfamilies of nuclear receptors. Six physicochemical characters of amino acids were adopted to generate the protein sequence features via web server PseAAC. The optimal values of the rank of correlation factor and the weighting factor about PseAAC were determined to get the appropriate descriptor of proteins that leads to the best performance. A nonredundant dataset of nuclear receptors in four subfamilies is constructed to evaluate the method using support vector machines. An overall accuracy of 99.6% was achieved in the fivefold cross-validation test as well as the jackknife test, and an overall accuracy of 98.4% was reached in a blind dataset test. The performance is very competitive with that of some previous methods.

A computerized system for signal detection in spontaneous reporting system of Shanghai China

We developed a computerized system for signal detection in spontaneous reporting system (SRS) of Shanghai. Data acquisition, data mining could be carried out automatically and the process of data preprocessing and cleaning could be facilitated. This system was expected to detect signals from SRS after drug licensing with minimum patient exposure.

A computerized system for detecting signals due to drug–drug interactions in spontaneous reporting systems

UNLABELLED WHAT IS ALREADY KNOWN ABOUT THE SUBJECT: * Concomitant use of different drugs may yield excessive risk for adverse drug reactions and it is a challenging task to do surveillance on the safety profile of the interaction between different drugs. * Currently, several methods are used by pharmacoepidemiologists and statisticians to detect possible drug-drug interactions in spontaneous reporting systems. * However, with the increasing number of reports in the system, there is a growing need for a computerized system that could facilitate the process of data arrangement and detection of drug interaction. WHAT THIS STUDY ADDS * We had already developed a computerized system to detect adverse drug reaction signals due to single drugs. * After the development of this system, interaction between different drugs could also be detected automatically and intelligently. AIMS In spontaneous reporting systems (SRS), there is a growing need for the automated detection of adverse drug reactions (ADRs) resulting from drug-drug interactions. In addition, special attention is also needed for systems facilitating automated data preprocessing. In our study, we set up a computerized system to signal possible drug-drug interactions by which data acquisition and signal detection could be carried out automatically and the process of data preprocessing could also be facilitated. METHODS This system was developed with Microsoft Visual Basic 6.0 and Microsoft Access was used as the database. Crude ADR reports submitted to Shanghai SRS from January 2007 to December 2008 were included in this study. The logistic regression method, the Omega shrinkage measure method, an additive model and a multiplicative model were used for automatic detection of drug-drug interactions where two drugs were used concomitantly. RESULTS A total of 33 897 crude ADR reports were acquired from the SRS automatically. The 10 drug combinations most frequently reported were found and the 10 most suspicious drug-drug ADR combinations for each method were detected automatically after the performance of the system. CONCLUSIONS Since the detection of drug-drug interaction depends upon the skills and memory of the professionals involved, is time consuming and the number of reports is increasing, this system might be a promising tool for the automated detection of possible drug-drug interactions in SRS.

Pharmacovigilance in traditional Chinese medicine safety surveillance

To give an overview of the current status including problems and efforts about pharmacovigilance in Traditional Chinese Medicine (TCM) safety surveillance.

A Retrospective Survey of Research Design and Statistical Analyses in Selected Chinese Medical Journals in 1998 and 2008

Background High quality clinical research not only requires advanced professional knowledge, but also needs sound study design and correct statistical analyses. The number of clinical research articles published in Chinese medical journals has increased immensely in the past decade, but study design quality and statistical analyses have remained suboptimal. The aim of this investigation was to gather evidence on the quality of study design and statistical analyses in clinical researches conducted in China for the first decade of the new millennium. Methodology/Principal Findings Ten (10) leading Chinese medical journals were selected and all original articles published in 1998 (N = 1,335) and 2008 (N = 1,578) were thoroughly categorized and reviewed. A well-defined and validated checklist on study design, statistical analyses, results presentation, and interpretation was used for review and evaluation. Main outcomes were the frequencies of different types of study design, error/defect proportion in design and statistical analyses, and implementation of CONSORT in randomized clinical trials. From 1998 to 2008: The error/defect proportion in statistical analyses decreased significantly ( = 12.03, p<0.001), 59.8% (545/1,335) in 1998 compared to 52.2% (664/1,578) in 2008. The overall error/defect proportion of study design also decreased ( = 21.22, p<0.001), 50.9% (680/1,335) compared to 42.40% (669/1,578). In 2008, design with randomized clinical trials remained low in single digit (3.8%, 60/1,578) with two-third showed poor results reporting (defects in 44 papers, 73.3%). Nearly half of the published studies were retrospective in nature, 49.3% (658/1,335) in 1998 compared to 48.2% (761/1,578) in 2008. Decreases in defect proportions were observed in both results presentation ( = 93.26, p<0.001), 92.7% (945/1,019) compared to 78.2% (1023/1,309) and interpretation ( = 27.26, p<0.001), 9.7% (99/1,019) compared to 4.3% (56/1,309), some serious ones persisted. Conclusions/Significance Chinese medical research seems to have made significant progress regarding statistical analyses, but there remains ample room for improvement regarding study designs. Retrospective clinical studies are the most often used design, whereas randomized clinical trials are rare and often show methodological weaknesses. Urgent implementation of the CONSORT statement is imperative.

Classifying G-protein-coupled receptors to the finest subtype level.

G-protein-coupled receptors (GPCRs) constitute a remarkable protein family of receptors that are involved in a broad range of biological processes. A large number of clinically used drugs elicit their biological effect via a GPCR. Thus, developing a reliable computational method for predicting the functional roles of GPCRs would be very useful in the pharmaceutical industry. Nowadays, researchers are more interested in functional roles of GPCRs at the finest subtype level. However, with the accumulation of many new protein sequences, none of the existing methods can completely classify these GPCRs to their finest subtype level. In this paper, a pioneer work was performed trying to resolve this problem by using a hierarchical classification method. The first level determines whether a query protein is a GPCR or a non-GPCR. If it is considered as a GPCR, it will be finally classified to its finest subtype level. GPCRs are characterized by 170 sequence-derived features encapsulating both amino acid composition and physicochemical features of proteins, and support vector machines are used as the classification engine. To test the performance of the present method, a non-redundant dataset was built which are organized at seven levels and covers more functional classes of GPCRs than existing datasets. The number of protein sequences in each level is 5956, 2978, 8079, 8680, 6477, 1580 and 214, respectively. By 5-fold cross-validation test, the overall accuracy of 99.56%, 93.96%, 82.81%, 85.93%, 94.1%, 95.38% and 92.06% were observed at each level. When compared with some previous methods, the present method achieved a consistently higher overall accuracy. The results demonstrate the power and effectiveness of the proposed method to accomplish the classification of GPCRs to the finest subtype level.

Misuse of Statistical Methods in 10 Leading Chinese Medical Journals in 1998 and 2008

Statistical methods are vital to biomedical research. Our aim was to find out whether progress has been made in the last decade in the use of statistical methods in Chinese medical research. We reviewed 10 leading Chinese medical journals published in 1998 and in 2008. Regarding statistical methods, using a multiple t-test for multiple group comparison was the most common error in the t-test in both years, which significantly decreased in 2008. In contingency tables, no significant level adjustment for multiple comparison significantly decreased in 2008. In ANOVA, over a quarter of articles misused the method of multiple pair-wise comparison in both years, and no significant difference was seen between the two years. In the rank transformation nonparametric test, the error of using multiple pair-wise comparison for multiple group comparison became less common. Many mistakes were found in the randomised controlled trial (56.3% in 1998; 67.9% in 2008), non- randomised clinical trial (57.3%; 58.6%), basic science study (72.9%; 65.5%), case study or case series study (48.4%; 47.2%), and cross-sectional study (57.1%; 44.2%). Progress has been made in the use of statistical methods in Chinese medical journals, but much is yet to be done.

Reporting patterns of adverse drug reactions over recent years in China: analysis from publications

Purpose: The goal of this study was to clarify the reporting patterns of self-reported adverse drug reactions (ADRs) in China. Methods: A variety of sources were searched, including the official website of China FDA, the national center for ADR monitoring center, publications from PubMed, and so on. We retrieved the relevant information and made descriptive and comparative analysis from the year 2009 to 2013. Results: The ADR reporting numbers were 638,996, 692,904, 852,799, 1,200,000 and 1,317,000 from 2009 to 2013, respectively. Healthcare professionals contributed significantly, and their proportion always exceeded 80% before 2012. The average report per million inhabitants has increased from 479 to 983 from 2009 to 2013. However, the proportion of new or serious report was always below 25%. The reports mainly concern anti-infective agents and traditional Chinese medicine (TCM), especially TCM injection. The proportion of ADR reports in geriatric patients has increased for 4 consecutive years. Conclusions: ADR report numbers and reporting rates in China are on the rise. However, the proportion of new or serious reports as well as the proportion of reports contributed by consumers and pharmaceutical companies are still quite low. More attention should be paid to the elderly, anti-infective agents and TCM, especially TCM injections.