ABSTRACT
OBJECTIVE: To investigate and analyze the genotyping, virulence genes and drug-resistant genes of methicillin resistant Staphylococcus aureus (MRSA) strains isolated from skin and soft tissue infections in this area. METHODS: The skin secretions of 204 patients with skin and soft tissue infections in the Fifth Central Hospital of Tianjin between Jan. 2019 and Dec. 2020 were collected, and MRSA strains identified as non-repetitive strains were isolated. The Staphylococcal cassette chromosome mec (SCCmec) and Staphylococcal protein A gene (spa) genotyping and Panton-valentine leukocidin (PVL) gene carrying status were analyzed among the MRSA strains, and their relationship with drug resistance was analyzed. RESULTS: Totally 82 strains of S. aureus were isolated from the skin secretions of 204 patients with skin and soft tissue infections, including 44 strains of MRSA (53.66%). The most common SCCmec genotype was genotype III (accounting for 84.09%) and the most common spa genotype was genotype t030 (accounting for 84.09%). PVL genes encoding virulence factors were amplified in 5 strains (11.36%). The drug resistance rates of 44 MRSA strains to vancomycin and compound sulfamethoxazole were 0.00%, and all the strains were drug-resistant to penicillin. Different SCCmec and spa genotypes were highly resistant to erythromycin, cefazolin, clindamycin and levofloxacin, but the differences in drug resistance rates of different SCCmec genotypes to clindamycin and levofloxacin were significant (P < 0.05). The resistance rates of strains with PVL positive genes to chloramphenicol, gentamicin and tetracycline were significantly higher than those with PVL negative genes (P < 0.05). CONCLUSION: Strains carrying SCCmec III and spa t030 genotypes may be the dominant strains of MRSA in skin and soft tissue infections in this area. Spa genotypes and PVL gene have certain impact on drug resistance of MRSA, and the isolated MRSA strains are all sensitive to vancomycin and compound sulfamethoxazole, which can provide a reference for anti-MRSA treatment in this area.
ABSTRACT
The COVID-19 pandemic caused severe economic contraction and paralyzed industrial activity. Despite a growing body of literature on the impacts of COVID-19 mitigation measures, scant evidence currently exists on the impacts of lockdowns on the economic and industrial activities of developing countries. Our study provides an empirical assessment of lockdown measures using 298,354 data points on daily electricity consumption in 396 sub-industries. To infer causal relationships, we employ difference-in-differences models that compare cities with and without lockdown policies and provide quantitative evidence on whether the long-term gain of lockdowns outweighs the short-term loss. The results show that lockdown policies led to a significant short-term drop in electricity consumption of 15.2% relative to the control group. However, the electricity loss under the no-lockdown scenario is 2.6 times larger than that under the strict lockdown scenario within 4 months of the outbreak. Discrepancies in the impacts among industries are identified, and even within the same industry, lockdowns have heterogeneous effects. The impact of lockdowns on small and medium-sized enterprises in developing countries is seriously underestimated, raising concerns about the distributional impact of subsidy measures. This study serves as a crucial reference for the government when facing public health emergencies and shocks to support better policies.
ABSTRACT
BACKGROUND: Virus screening and viral genome reconstruction are urgent and crucial for the rapid identification of viral pathogens, i.e., tracing the source and understanding the pathogenesis when a viral outbreak occurs. Next-generation sequencing (NGS) provides an efficient and unbiased way to identify viral pathogens in host-associated and environmental samples without prior knowledge. Despite the availability of software, data analysis still requires human operations. A mature pipeline is urgently needed when thousands of viral pathogen and viral genome reconstruction samples need to be rapidly identified. RESULTS: In this paper, we present a rapid and accurate workflow to screen metagenomics sequencing data for viral pathogens and other compositions, as well as enable a reference-based assembler to reconstruct viral genomes. Moreover, we tested our workflow on several metagenomics datasets, including a SARS-CoV-2 patient sample with NGS data, pangolins tissues with NGS data, Middle East Respiratory Syndrome (MERS)-infected cells with NGS data, etc. Our workflow demonstrated high accuracy and efficiency when identifying target viruses from large scale NGS metagenomics data. Our workflow was flexible when working with a broad range of NGS datasets from small (kb) to large (100 Gb). This took from a few minutes to a few hours to complete each task. At the same time, our workflow automatically generates reports that incorporate visualized feedback (e.g., metagenomics data quality statistics, host and viral sequence compositions, details about each of the identified viral pathogens and their coverages, and reassembled viral pathogen sequences based on their closest references). CONCLUSIONS: Overall, our system enabled the rapid screening and identification of viral pathogens from metagenomics data, providing an important piece to support viral pathogen research during a pandemic. The visualized report contains information from raw sequence quality to a reconstructed viral sequence, which allows non-professional people to screen their samples for viruses by themselves (Additional file 1).