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.

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.

Body mass index and health-related quality of life in adults: a population based study in five cities of China.

BACKGROUND: To investigate the relationship between obesity and health-related quality of life (HRQL) in a randomly selected Chinese sample. METHODS: A total of 3600 residents aged 18-80 years were sampled in five cities of China using a randomized stratified multiple-stage sampling method to receive the interview, with a self-completed questionnaire to collect demographic information, and the Mandarin version of Short Form 36 Health Survey questionnaire (SF-36) to assess HRQL, followed by height and weight measurements for calculating body mass index (BMI). Cross-sectional association between BMI and HRQL was analysed. RESULTS: Among the 3207 participants (mean age 42 years) suitable for analysis, BMI differed by age and gender. Based on the international or the Asian BMI categories, in women, meaningful impairments were seen between obese and normal weight participants in four physical health scales, and only one scale of the four mental health scales--vitality scale was affected by obesity; in men, impairments by obesity were not found in all of the eight SF-36 scales, and better HRQL in two mental health scales were observed in obese participants compared to normal weight ones; after adjusting related variables, several physical but not mental health scales were found impaired by obesity. CONCLUSION: Obesity impaired physical but not mental health, and the impairments varied between genders. Public health agencies and government should emphasize the impairments of obesity on physical health.

Prediction of pattern recognition receptor family using pseudo-amino acid composition.

Pattern recognition receptors (PRRs) play a key role in the innate immune response by recognizing pathogen associated molecular patterns derived from a diverse collection of microbial pathogens. PRRs form a superfamily of proteins related to host health and disease. Thus, prediction of PRR family might supply biologically significant information for functional annotation of PRRs and development of novel drugs. In this paper, a computational method is proposed for predicting the families of PRRs. The prediction was performed on the basis of amino acid composition and pseudo-amino acid composition (PseAAC) from primary sequences of proteins using support vector machines. A non-redundant dataset consisted of 332 PRRs in seven families was constructed to do training and testing. It was demonstrated that different families of PRRs were quite closely correlated with amino acid composition as well as PseAAC. In the jackknife test, overall accuracies of amino acid composition-based and PseAAC-based classifiers reached 96.1% and 97.9%, respectively. The results indicate that families of PRRs are predictable with high accuracy. It is anticipated that this computational method might be a powerful tool for the automated assignment of families of PRRs.

Steroids in the treatment of IgA nephropathy to the improvement of renal survival: a systematic review and meta-analysis.

BACKGROUND: Studies have shown that steroids can improve kidney survival and decrease the risk of proteinuria in patients with Immunoglobulin A nephropathy, but the overall benefit of steroids in the treatment of Immunoglobulin A nephropathy remains controversial. The aim of this study was to evaluate the benefits and risks of steroids for renal survival in adults with Immunoglobulin A nephropathy. METHODOLOGY AND PRINCIPAL FINDINGS: We searched the Cochrane Renal Group Specialized Register, Cochrane Controlled Trial Registry, MEDLINE and EMBASE databases. All eligible studies were measuring at least one of the following outcomes: end-stage renal failure, doubling of serum creatinine and urinary protein excretion. Fifteen relevant trials (n = 1542) that met our inclusion criteria were identified. In a pooled analysis, steroid therapy was associated with statistically significant reduction of the risk in end-stage renal failure (RR: 0.46, 95% CI: 0.27 to 0.79), doubling of serum creatinine (RR = 0.34, 95%CI = 0.15 to 0.77) and reduced urinary protein excretion (MD = -0.47 g/day, 95%CI = -0.64 to -0.31). CONCLUSIONS/SIGNIFICANCE: We identified that steroid therapy was associated with a decrease of proteinuria and with a statistically significant reduction of the risk in end-stage renal failure. Moreover, subgroup analysis also suggested that long-term steroid therapy had a higher efficiency than standard and short term therapy.

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.

Improving the classification of nuclear receptors with feature selection.

Nuclear receptors are involved in multiple cellular signaling pathways that affect and regulate processes. Because of their physiology and pathophysiology significance, classification of nuclear receptors is essential for the proper understanding of their functions. Bhasin and Raghava have shown that the subfamilies of nuclear receptors are closely correlated with their amino acid composition and dipeptide composition [29]. They characterized each protein by a 400 dimensional feature vector. However, using high dimensional feature vectors for characterization of protein sequences will increase the computational cost as well as the risk of overfitting. Therefore, using only those features that are most relevant to the present task might improve the prediction system, and might also provide us with some biologically useful knowledge. In this paper a feature selection approach was proposed to identify relevant features and a prediction engine of support vector machines was developed to estimate the prediction accuracy of classification using the selected features. A reduced subset containing 30 features was accepted to characterize the protein sequences in view of its good discriminative power towards the classes, in which 18 are of amino acid composition and 12 are of dipeptide composition. This reduced feature subset resulted in an overall accuracy of 98.9% in a 5-fold cross-validation test, higher than 88.7% of amino acid composition based method and almost as high as 99.3% of dipeptide composition based method. Moreover, an overall accuracy of 93.7% was reached when it was evaluated on a blind data set of 63 nuclear receptors. On the other hand, an overall accuracy of 96.1% and 95.2% based on the reduced 12 dipeptide compositions was observed simultaneously in the 5-fold cross-validation test and the blind data set test, respectively. These results demonstrate the effectiveness of the present method.

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.

Classification of amine type G-protein coupled receptors with feature selection.

G-protein coupled receptors (GPCRs) are involved in various physiological processes. Therefore, classification of amine type GPCRs is important for proper understanding of their functions. Though some effective methods have been developed, it still remains unknown how many and which features are essential for this task. Empirical studies show that feature selection might address this problem and provide us with some biologically useful knowledge. In this paper, a feature selection technique is introduced to identify those relevant features of proteins which are potentially important for the prediction of amine type GPCRs. The selected features are finally accepted to characterize proteins in a more compact form. High prediction accuracy is observed on two data sets with different sequence similarity by 5-fold cross-validation test. The comparison with a previous method demonstrates the efficiency and effectiveness of the proposed method.

EZ-Entry: a clinical data management system.

In this paper, a new data management system named EZ-Entry is introduced. Five major functions are enclosed in this system: (1) user authentication; (2) database construction; (3) double data entry with instant alignment; (4) revision tracking; (5) query management. The practical application performed on two clinical trials indicates that EZ-Entry meets the requirements of clinical data management with high efficiency and security. This software is freely available on request from the authors for academic purposes.

Classification of G-protein coupled receptors at four levels.

G-protein coupled receptors (GPCRs) are transmembrane proteins which via G-proteins initiate some of the important signaling pathways in a cell and are involved in various physiological processes. Thus, computational prediction and classification of GPCRs can supply significant information for the development of novel drugs in pharmaceutical industry. In this paper, a nearest neighbor method has been introduced to discriminate GPCRs from non-GPCRs and subsequently classify GPCRs at four levels on the basis of amino acid composition and dipeptide composition of proteins. Its performance is evaluated on a non-redundant dataset consisted of 1406 GPCRs for six families and 1406 globular proteins using the jackknife test. The present method based on amino acid composition achieved an overall accuracy of 96.4% and Matthew's correlation coefficient (MCC) of 0.930 for correctly picking out the GPCRs from globular proteins. The overall accuracy and MCC were further enhanced to 99.8% and 0.996 by dipeptide composition-based method. On the other hand, the present method has successfully classified 1406 GPCRs into six families with an overall accuracy of 89.6 and 98.8% using amino acid composition and dipeptide composition, respectively. For the subfamily prediction of 1181 GPCRs of rhodopsin-like family, the present method achieved an overall accuracy of 76.7 and 94.5% based on the amino acid composition and dipeptide composition, respectively. Finally, GPCRs belonging to the amine subfamily and olfactory subfamily of rhodopsin-like family were further analyzed at the type level. The overall accuracy of dipeptide composition-based method for the classification of amine type and olfactory type of GPCRs reached 94.5 and 86.9%, respectively, while the overall accuracy of amino acid composition-based method was very low for both subfamilies. In comparison with existing methods in the literature, the present method also displayed great competitiveness. These results demonstrate the effectiveness of our method on identifying and classifying GPCRs correctly. GPCRsIdentifier, a corresponding stand-alone executable program for GPCR identification and classification was also developed, which can be acquired freely on request from the authors for academic purposes.

Using Nearest Feature Line and Tunable Nearest Neighbor methods for prediction of protein subcellular locations.

The subcellular location of a protein is closely correlated with it biological function. In this paper, two new pattern classification methods termed as Nearest Feature Line (NFL) and Tunable Nearest Neighbor (TNN) have been introduced to predict the subcellular location of proteins based on their amino acid composition alone. The simulation experiments were performed with the jackknife test on a previously constructed data set, which consists of 2,427 eukaryotic and 997 prokaryotic proteins. All protein sequences in the data set fall into four eukaryotic subcellular locations and three prokaryotic subcellular locations. The NFL classifier reached the total prediction accuracies of 82.5% for the eukaryotic proteins and 91.0% for the prokaryotic proteins. The TNN classifier reached the total prediction accuracies of 83.6 and 92.2%, respectively. It is clear that high prediction accuracies have been achieved. Compared with Support Vector Machine (SVM) and Nearest Neighbor methods, these two methods display similar or even higher prediction accuracies. Hence, we conclude that NFL and TNN can be used as complementary methods for prediction of protein subcellular locations.

Prediction of protein subcellular location using a combined feature of sequence.

To understand the structure and function of a protein, an important task is to know where it occurs in the cell. Thus, a computational method for properly predicting the subcellular location of proteins would be significant in interpreting the original data produced by the large-scale genome sequencing projects. The present work tries to explore an effective method for extracting features from protein primary sequence and find a novel measurement of similarity among proteins for classifying a protein to its proper subcellular location. We considered four locations in eukaryotic cells and three locations in prokaryotic cells, which have been investigated by several groups in the past. A combined feature of primary sequence defined as a 430D (dimensional) vector was utilized to represent a protein, including 20 amino acid compositions, 400 dipeptide compositions and 10 physicochemical properties. To evaluate the prediction performance of this encoding scheme, a jackknife test based on nearest neighbor algorithm was employed. The prediction accuracies for cytoplasmic, extracellular, mitochondrial, and nuclear proteins in the former dataset were 86.3%, 89.2%, 73.5% and 89.4%, respectively, and the total prediction accuracy reached 86.3%. As for the prediction accuracies of cytoplasmic, extracellular, and periplasmic proteins in the latter dataset, the prediction accuracies were 97.4%, 86.0%, and 79.7, respectively, and the total prediction accuracy of 92.5% was achieved. The results indicate that this method outperforms some existing approaches based on amino acid composition or amino acid composition and dipeptide composition.