Objective To investigate the serum levels of 25-hydroxyvitamin D3 (25 (OH)D3) and urine vitamin D binding protein(uVDBP) in patients with diabetic nephropathy (DN),and to determine the relationship between 25 (OH) D3,uVDBP and DN,in order to provide a new method for early diagnosis and treatment of DN.Methods From January 2015 to December 2015,85 DN patients admitted into Weihai Municipal Hospital were selected.According to the ratio of UALB to UCR(UACR),the patients were divided into three groups.Type 2 diabetes had 28 cases of normal albuminuria group,31 cases of microalbuminuria group,and 26 cases of clinical albuminuria group.We also enrolled 25 healthy people who received outpatient service as control group.Serum 25 (OH) D3 levels were measured by chemiluminescence method.Urine VDBP levels were assayed by ELISA.FPG,HbA1 c,UREA,SCr,TC,TG were measured by electrochemiluminescence.Results The results showed that serum 25 (OH)D3 was significantly lower in the normal albuminuria group,microalbuminuria group and clinical proteinuria group than that in the control group (P ＜ 0.05),and there was statistically significant difference among the four groups [(20.04 ± 7.52) ng/mL,(16.54 ± 6.51) ng/mL,(10.77 ± 4.63) ng/mL,(29.65 ± 5.47) ng/mL,F =86.294,P ＜ 0.001].The results showed that uVDBP was significantly higher in the DN group than that in the control group(all P ＜ 0.05),and there was statistically significant difference among the four groups [(8.44 ± 3.20) mg/L,(14.22 ± 3.26) mg/L,(2 1.77 ± 5.87) mg/L,(4.95 ± 1.34) mg/L,F =125.583,P ＜ 0.001].Correlation analysis showed that serum 25 (OH) D3 decreased gradually with the increase of DN and negatively correlated with UACR (r =-0.575,P ＜ 0.01),while uVDBP level was positively correlated with UACR (r =0.436,P =0.015).Conclusion With the progress of DN,serum 25 (OH) D3 levels gradually decreased,indicating that 25 (OH) D3 may play an important role in the pathogenesis of DN;uVDBP may be an early diagnostic method for DN.

Phage-microbe interactions are appealing systems to study coevolution,and have also been increasingly emphasized due to their roles in human health,disease,and the development of novel therapeutics.Phage-microbe interactions leave diverse signals in bacterial and phage geno-mic sequences,defined as phage-host interaction signals(PHISs),which include clustered regularly interspaced short palindromic repeats(CRISPR)targeting,prophage,and protein-protein interac-tion signals.In the present study,we developed a novel tool phage-host interaction signal detector(PHISDetector)to predict phage-host interactions by detecting and integrating diverse in silico PHISs,and scoring the probability of phage-host interactions using machine learning models based on PHIS features.We evaluated the performance of PHISDetector on multiple benchmark datasets and application cases.When tested on a dataset of 758 annotated phage-host pairs,PHISDetector yields the prediction accuracies of 0.51 and 0.73 at the species and genus levels,respectively,outper-forming other phage-host prediction tools.When applied to 125,842 metagenomic viral contigs(mVCs)derived from 3042 geographically diverse samples,a detection rate of 54.54％could be achieved.Furthermore,PHISDetector could predict infecting phages for 85.6％of 368 multidrug-resistant(MDR)bacteria and 30％of 454 human gut bacteria obtained from the National Institutes of Health(NIH)Human Microbiome Project(HMP).