Molecular characteristics of methicillin resistant Staphylococcus aureus isolated from Chinese children / 中华儿科杂志

<p><b>OBJECTIVE</b>To investigate the molecular characteristics of methicillin-resistant Staphylococcus aureus (MRSA) isolates from Chinese children in seven cities.</p><p><b>METHOD</b>A total of 134 MRSA isolates were collected from nine hospitals. Multilocus sequence typing and spa typing were analyzed by polymerase chain reaction (PCR), and staphylococcal chromosomal cassette mec (SCCmec) type was analyzed by multiplex PCR. The Panton-Valentine leukocidin (pvl) gene was also detected.</p><p><b>RESULT</b>Most MRSA strains were isolated from pneumonia and skin and soft tissue infection (SSTIs) patients, accounting for 82.1%. Overall, 16 sequence types (STs) were obtained, and CC59 (51.7%) was found to be the most prevalent, which included ST 59 and ST 338, followed by ST239 (16.4%). SCCmec types II, III, IV, and V were also identified in the current study. SCCmec type IV was the most predominant type at 50.0%, followed by SCCmec type V at 23.9% and III at 23.9%. SCCmec subtypes IVa, IVc, and IVg were found among SCCmec type IV strains, whereas IVa was the main subtype at 77.6%. Twenty-six spa types were also identified, among which the predominant type was t437 (47.8%). The prevalence of pvl genes and the SCCmec type of strain was relevant, and the pvl gene positive rate was higher in SCCmec type IV and V-type strains than in SCCmec type II and III strains (58.6% vs. 14.3%, P < 0.05); there was a significant difference between them. In the strains isolated from pneumonia and SSTIs, ST59-MRSA-IVa(t437) was the predominant clone. There were five clones detected from the strains isolated from septicemia, with ST59-MRSA-IVa(t437) and ST59-MRSA-V(t437) as the main clones (57.1%). Various predominant clones existed in different regions. ST59-MRSA-IVa(t437) was the prevalent clone in the Guangzhou, Beijing, Chongqing, and Shenzhen areas, whereas ST239-MRSA-III(t037) was the prevalent clone in the Shanghai area. Fifty percent of the isolates from the Wenzhou area belonged to ST910-MRSA-V(t318), whereas three clinical strains isolated from the Shenyang region belonged to three different types.</p><p><b>CONCLUSION</b>The results indicate that MRSA isolates from Chinese children are largely associated with the ST59-MRSA-IV(t437) and ST239-MRSA-III(t037) clones. These two may belong to community-acquired MRSA and hospital-acquired ones, respectively. Different prevalent clones were detected in different diseases and different regions. Therefore, there is a need to conduct further research on clinical isolates, which can guide the choice of antibiotic treatment and the examination of MRSA prevalence.</p>

Using support vector machine to predict eco-environment burden: a case study of Wuhan, Hubei Province, China / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>The human socio-economic development depends on the planet's natural capital. Humans have had a considerable impact on the earth, such as resources depression and environment deterioration. The objective of this study was to assess the impact of socio-economic development on the ecological environment of Wuhan, Hubei Province, China, during the general planning period 2006-2020.</p><p><b>METHODS</b>Support vector machine (SVM) model was constructed to simulate the process of eco-economic system of Wuhan. Socio-economic factors of urban total ecological footprint (TEF) were selected by partial least squares (PLS) and leave-one-out cross validation (LOOCV). Historical data of socio-economic factors as inputs, and corresponding historical data of TEF as target outputs, were presented to identify and validate the SVM model. When predicted input data after 2005 were presented to trained model as generalization sets, TEFs of 2005, 2006,..., till 2020 were simulated as output in succession.</p><p><b>RESULTS</b>Up to 2020, the district would have suffered an accumulative TEF of 28.374 million gha, which was over 1.5 times that of 2004 and nearly 3 times that of 1988. The per capita EF would be up to 3.019 gha in 2020.</p><p><b>CONCLUSIONS</b>The simulation indicated that although the increase rate of GDP would be restricted in a lower level during the general planning period, urban ecological environment burden could not respond to the socio-economic circumstances promptly. SVM provides tools for dynamic assessment of regional eco-environment. However, there still exist limitations and disadvantages in the model. We believe that the next logical step in deriving better dynamic models of ecosystem is to integrate SVM and other algorithms or technologies.</p>