Background:
Complicated urinary tract infections (cUTI) are universal reasons for hospitalization, and highly likely to develop into sepsis or septic shock. Carbapenem antibiotics with potentially higher efficacy or with fewer and milder side effects have increased in popularity, but evidence is limited by a scarcity of randomized controlled trials (RCTs) comparing different carbapenem antibiotics for cUTI. Network meta-analysis is a useful tool to compare multiple treatments when there is limited or no direct evidence available.
Objective:
The aim of this study is to compare the efficacy and safety of different carbapenems with alternative antibiotics for the treatment of cUTI.
Methods:
Pubmed, Medline, CENTRAL, and Embase were searched in November 2018. Studies of cUTI patients receiving carbapenem were included. We performed network meta-analysis to estimate the risk ratio (RR) and 95% credible interval (CrI) from both direct and indirect evidence; traditional meta-analysis was also performed. Primary outcomes were clinical and microbiological treatment success.
Results:
A total of 19 studies and 7380 patients were included in the analysis. Doripenem (DOPM) was associated with lower clinical treatment success rates than other carbapenems. Although the efficacy of other carbapenems by RRs with 95% CrIs did not show statistical differences, the cumulative rank probability indicated that meropenem/vaborbactam (MV), ertapenem (ETPM), and biapenem (BAPM) had higher clinical and microbiological treatment success rates; imipenem/cilastatin (IC) and MV showed higher risk of adverse events (AEs).
Conclusions:
MV was associated with higher treatment success rates for cUTI, especially for cUTI caused by carbapenem-resistant uropathogens, but also with higher risk of AEs. Our findings suggest MV as a first-choice treatment of carbapenem-resistant cUTI. ETPM, BAPM, and meropenem (MEPM) is another reasonable choice for cUTI empiric therapy.
Background
Breast cancer is the most common malignant disease in women. Metastasis is the most common cause of death from this cancer. Screening genes related to breast cancer metastasis may help elucidate the mechanisms governing metastasis and identify molecular targets for antimetastatic therapy. The development of advanced algorithms enables us to perform cross‐study analysis to improve the robustness of the results.
Materials and methods
Ten data sets meeting our criteria for differential expression analyses were obtained from the Gene Expression Omnibus (GEO) database. Among these data sets, five based on the same platform were formed into a large cohort using the XPN algorithm. Differentially expressed genes (DEGs) associated with breast cancer metastasis were identified using the differential expression via distance synthesis (DEDS) algorithm. A cross‐platform method was employed to verify these DEGs in all ten selected data sets. The top 50 validated DEGs are represented with heat maps. Based on the validated DEGs, Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Protein interaction (PPI) networks were constructed to further illustrate the direct and indirect associations among the DEGs. Survival analysis was performed to explore whether these genes can affect breast cancer patient prognosis.
Results
A total of 817 DEGs were identified using the DEDS algorithm. Of these DEGs, 450 genes were validated by the second algorithm. Enriched KEGG pathway terms demonstrated that these 450 DEGs may be involved in the cell cycle and oocyte meiosis in addition to their functions in ECM‐receptor interaction and protein digestion and absorption. PPI network analysis for the proteins encoded by the DEGs indicated that these genes may be primarily involved in the cell cycle and extracellular matrix. In particular, several genes played roles in multiple signalling pathways and were related to patient survival. These genes were also observed to be targetable in the CTD2 database.
Conclusions
Our study analysed multiple cross‐platform data sets using two different algorithms, helping elucidate the molecular mechanisms and identify several potential therapeutic targets of metastatic breast cancer. In addition, several genes exhibited promise for applications in targeted therapy against metastasis in future research.
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