The colonization of dairy herds and subsequent contamination of raw milk by Staphylococcus aureus (S. aureus), especially those expressing a multi-drug resistance (MDR), biofilm and toxins producing ability, remains an important issue for both the dairy producer and public health. In this study, we investigated the prevalence, antimicrobial resistance, virulence, and genetic diversity of S. aureus in raw milk taken from 2 dairy farms in Beijing, China. Ninety (46.2%, 90/195) samples were positive for S. aureus. Resistant to penicillin (PEN) (31.3%), ciprofloxacin (18.8%) and enrofloxacin (15.6%) were the most often observed. Isolates cultured from farm B showed significantly higher resistance to penicillin (73.9%), ciprofloxacin (34.8%), enrofloxacin (34.8%), tilmicosin (17.4%), and erythromycin (17.4%) than those from farm A (p < 0.05). Totally, 94.8% S. aureus harbored at least one virulence gene and the pvl (93.8%), sec (65.6%), and sea (60.4%) genes were the most frequently detected. The pvl and sec genes were more often detected in isolates from farm A (97.3% and 84.9% respectively) than those from farm B (p < 0.05). Of all 77 staphylococcus enterotoxin (SE)-positive isolates, more than 90% could produce enterotoxins and 70.1% could produce two types. Biofilm related genes (icaA/D, clf/B, can, and fnbA) were detected in all96 isolates. All 96 isolates could produce biofilm with 8.3, 70.8, and 18.8% of the isolates demonstrating weak, moderate and strong biofilm formation, respectively. A total of 5 STs, 7 spa types (1 novel spa type t17182), 3agr types (no agrII), and 14 SmaI-pulso-types were found in this study. PFGE cluster II-CC1-ST1-t127-agr III was the most prevalent clone (56.3%). Isolates of agr III (PFGE Cluster I/II-CC1-ST1-t127/2279) had higher detection of virulence genes than those of agr I and agr IV. TheMSSA-ST398-t1456-agr I clone expressed the greatest MDRbut with no virulence genes and weakly biofilm formation. Our finding indicated a relatively high prevalence of S. aureus with less antimicrobial resistance but often positive for enterotoxigenicity and biofilm formation. This study could help identify predominant clones and provide surveillance measures to eliminate and decrease the contamination of S. aureus in raw milk of dairy cows with mastitis.

Between January 2005 and January 2006, 25 (12.8%) of 195 Staphylococcus aureus isolates were positive for Panton-Valentine leukocidin (PVL) genes in a teaching hospital in Wenzhou, China. Nineteen (11.9%) of 160 hospital-acquired isolates, and six (17.1%) of 35 community-acquired isolates, harboured lukS/F-PV. Six sequence types (ST88, ST239, ST398, ST25, ST30 and ST59) were found among 18 PVL-positive methicillin-resistant isolates with SCCmec types I, III, IIIA or IV. Only ST88 was found among seven PVL-positive methicillin-susceptible S. aureus isolates. The PVL-positive isolates were associated with lung infection, bloodstream infection and soft-tissue pyogenic infection. Overall, there was a high prevalence of PVL genes in genetically diverse S. aureus isolates.

Patients with chronic liver diseases (CLD) including chronic hepatitis B and hepatic cirrhosis (CIR) are the major high-risk population of hepatocellular carcinoma (HCC). The differential diagnosis between CLD and HCC is a challenge. This work aims to study the related metabolic deregulations in HCC and CLD to promote the discovery of the differential metabolites for distinguishing the different liver diseases. Serum metabolic profiling analysis from patients with CLD and HCC was performed using a liquid chromatography-mass spectrometry system. The acquired large amount of metabolic information was processed with the random forest-recursive feature elimination method to discover important metabolic changes. It was found that long-chain acylcarnitines accumulated, whereas free carnitine, medium and short-chain acylcarnitines decreased with the severity of the non-malignant liver diseases, accompanied with corresponding alterations of enzyme activities. However, the general changing extent was smaller in HCC than in CIR, possibly due to the special energy-consumption mechanism of tumor cells. These observations may help to understand the mechanism of HCC occurrence and progression on the metabolic level and provide information for the identification of early and differential metabolic markers for HCC.

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Highlights Developed a data preprocessing strategy to cope with missing values and mask effects in data analysis from high variation of abundant metabolites.A new method- ‘x-VAST’ was developed to amend the measurement deviation enlargement.Applying the above strategy, several low abundant masked differential metabolites were rescued.Metabolomics is a booming research field. Its success highly relies on the discovery of differential metabolites by comparing different data sets (for example, patients vs. controls). One of the challenges is that differences of the low abundant metabolites between groups are often masked by the high variation of abundant metabolites. In order to solve this challenge, a novel data preprocessing strategy consisting of three steps was proposed in this study. In step 1, a ‘modified 80%’ rule was used to reduce effect of missing values; in step 2, unit-variance and Pareto scaling methods were used to reduce the mask effect from the abundant metabolites. In step 3, in order to fix the adverse effect of scaling, stability information of the variables deduced from intensity information and the class information, was used to assign suitable weights to the variables. When applying to an LC/MS based metabolomics dataset from chronic hepatitis B patients study and two simulated datasets, the mask effect was found to be partially eliminated and several new low abundant differential metabolites were rescued.

Metabolic markers are the core of metabonomic surveys. Hence selection of differential metabolites is of great importance for either biological or clinical purpose. Here, a feature selection method was developed for complex metabonomic data set. As an effective tool for metabonomics data analysis, support vector machine (SVM) was employed as the basic classifier. To find out meaningful features effectively, support vector machine recursive feature elimination (SVM-RFE) was firstly applied. Then, genetic algorithm (GA) and random forest (RF) which consider the interaction among the metabolites and independent performance of each metabolite in all samples, respectively, were used to obtain more informative metabolic difference and avoid the risk of false positive. A data set from plasma metabonomics study of rat liver diseases developed from hepatitis, cirrhosis to hepatocellular carcinoma was applied for the validation of the method. Besides the good classification results for 3 kinds of liver diseases, 31 important metabolites including lysophosphatidylethanolamine (LPE) C16:0, palmitoylcarnitine, lysophosphatidylethanolamine (LPC) C18:0 were also selected for further studies. A better complementary effect of the three feature selection methods could be seen from the current results. The combinational method also represented more differential metabolites and provided more metabolic information for a ''global'' understanding of diseases than any single method. Further more, this method is also suitable for other complex biological data sets.