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Background: Young breast cancer (YBC) patients are more prone to lymph node metastasis than other age groups. Our study aimed to investigate the predictive value of lymph node ratio (LNR) in YBC patients and create a nomogram to predict overall survival (OS), thus helping clinical diagnosis and treatment. Methods: Patients diagnosed with YBC between January 2010 and December 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled and randomly divided into a training set and an internal validation set with a ratio of 7:3. An independent cohort from our hospital was used for external validation. Univariate and least absolute shrinkage and selection operator (LASSO) regression were used to identify the significant factors associated with prognosis, which were used to create a nomogram for predicting 3- and 5-year OS. Results: We selected seven survival predictors (tumor grade, T-stage, N-stage, LNR, ER status, PR status, HER2 status) for nomogram construction. The C-indexes in the training set, the internal validation set, and the external validation set were 0.775, 0.778 and 0.817, respectively. The nomogram model was well calibrated, and the time-dependent ROC curves verified the superiority of our model for clinical usefulness. In addition, the nomogram classification could more precisely differentiate risk subgroups and improve the discrimination of YBC prognosis. Conclusions: LNR is a strong predictor of OS in YBC patients. The novel nomogram based on LNR is a reliable tool to predict survival, which may assist clinicians in identifying high-risk patients and devising individual treatments. [ABSTRACT FROM AUTHOR]

Biofilters have been broadly applied to degrade the odorous gases from industrial emissions. A industrial scale biofilter was set up to treat the odorous gases. To explore biofilter potentials, the microbial community structure and function must be well defined. Using of improved biofilter, the differences in microbial community structures and functions in biofilters before and after treatment were investigated by metagenomic analysis. Odorous gases have the potential to alter the microbial community structure in the sludge of biofilter. A total of 90,016 genes assigned into various functional metabolic pathways were identified. In the improved biofilter, the dominant phyla were Proteobacteria, Planctomycetes, and Chloroflexi, and the dominant genera were Thioalkalivibrio, Thauera, and Pseudomonas. Several xenobiotic biodegradation-related pathways showed significant changes during the treatment process. Compared with the original biofilter, Thermotogae and Crenarchaeota phyla were significantly enriched in the improved biofilter, suggesting their important role in nitrogen-fixing. Furthermore, several nitrogen metabolic pathway-related genes, such as nirA and nifA, and sulfur metabolic pathway-related genes, such as fccB and phsA, were considered to be efficient genes that were involved in removing odorous gases. Our findings can be used for improving the efficiency of biofilter and helping the industrial enterprises to reduce the emission of waste gases. [ABSTRACT FROM AUTHOR]