An Analysis of Injury Trends and Disease Burden From Three Surveillance Hospitals in Urumqi From 2006 to 2018.

Objective: To investigate injury trends, injury distribution, and disease burden from three surveillance hospitals in Urumqi from 2006 to 2018. Method: Injury data from the National Injury Surveillance System (NISS) from three hospitals in Urumqi (2006 to 2018) were collected to analyze changes in the characteristics of outpatient injury cases. Years of potential life lost (YPLL) were calculated to determine the disease burden of the injury cases. Results: A total of 161,400 injury cases were recorded over 13 years, and the average age of the patient seeking medical attention was 32.4 years old. Male patients outnumbered female patients with a ratio of 1.6:1, but the proportion of female patients was greater after 45 years of age. The highest number of cases occurred in patients 15-29 years of age, accounting for 26.8% of all injury cases. Injury in females occurred most frequently in the home. A total of 41.4% of injury cases occurred while doing housework. The top three causes of injury were falls (49.7%), blunt force of an object, (13.7%), and motor vehicle accidents (MVA) (13.5%). Years of potential life lost from injury accounted for 7.39% of the total YPLL in the three hospitals. Conclusion: Males should be targeted for injury prevention and intervention in Urumqi. The prevention of falls, blunt force of objects, and MVA should be made a priority. Injury prevention strategies and targeted projects should be developed to reduce the disease burden of injury.

Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM.

Protein-Protein Interactions (PPIs) play vital roles in most biological activities. Although the development of high-throughput biological technologies has generated considerable PPI data for various organisms, many problems are still far from being solved. A number of computational methods based on machine learning have been developed to facilitate the identification of novel PPIs. In this study, a novel predictor was designed using the Rotation Forest (RF) algorithm combined with Autocovariance (AC) features extracted from the Position-Specific Scoring Matrix (PSSM). More specifically, the PSSMs are generated using the information of protein amino acids sequence. Then, an effective sequence-based features representation, Autocovariance, is employed to extract features from PSSMs. Finally, the RF model is used as a classifier to distinguish between the interacting and noninteracting protein pairs. The proposed method achieves promising prediction performance when performed on the PPIs of Yeast, H. pylori, and independent datasets. The good results show that the proposed model is suitable for PPIs prediction and could also provide a useful supplementary tool for solving other bioinformatics problems.