An on-column sample concentration method for capillary-based DNA sequencing was studied. This base-stacking method allows direct injection of unpurified products of dye-primer sequencing reactions onto capillaries without any pretreatment. On-column concentration of DNA fragments is achieved simply by electrokinetic injection of hydroxide ions. A neutralization reaction between these OH- ions and the cationic buffer component Tris+ results in a zone of lower conductivity, within which field focusing occurs. DNA fragments are concentrated at the front of this low-conductivity zone. With sample injection times as long as 360 s at 50 V/cm, resolution could still be restored by the stacking process. Using a 36/47-cm-long uncoated capillary, with poly(dimethylacrylamide) as the separation matrix, and electric field of 160 V/cm, a resolution of at least 0.5 could be generated for fragments up to 650 nucleotides long. Both resolution and signal strength are excellent relative to conventional injection of highly purified samples. No significant degradation of the capillary performance was observed over at least 20 sequencing runs using this new sample injection methods.

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Graphene oxide (GO) has gathered widespread interest within the scientific community as a result of its unique properties. In the present work, the interfacial behavior of the GO nanosheet at the crude oil/water interface was explored to investigate its demulsification mechanism for crude oil/water emulsions. The interfacial rheology properties and the interfacial tension were systematically discussed. The results revealed that GO was able to decrease the interfacial tension of the emulsion to a large extent, implying that the GO nanosheet was interfacially active. Unexpectedly, the dilational modulus monotonically increased with increasing the GO dosage. In addition, the coalescence kinetics and the interfacial assembly behaviors of GO were investigated. It was observed that the oil droplet became wrinkled once contacting with the crude oil/water interface, and a thin film was finally left at the interface. Therefore, the GO nanosheet was thought to be able to diffuse to the oil/water interface and self-assembled to jam into a new solid thin "GO film", leading to the increase of the determined dilational modulus of the interface. The morphology of the film was revealed by a confocal fluorescence microscope, and a wrinkled and continuous morphology was observed, implying that the GO nanosheet aligned parallel to the oil/water interface. The findings in the present study are crucial for fully understanding the demulsification mechanism of GO and might provide a facile way to prepare largearea GO thin films.

BackgroundSince 1980s the application of Prostate specific antigen (PSA) brought the revolution in prostate cancer diagnosis. However, it is important to underline that PSA is not the ideal screening tool due to its low specificity, which leads to the possible biopsy for the patient without High-grade prostate cancer (HGPCa). Therefore, the aim of this study was to establish a predictive nomogram for HGPCa in patients with PSA 4–10 ng/ml based on Prostate Imaging Reporting and Data System version 2 (PI-RADS v2), MRI-based prostate volume (PV), MRI-based PV-adjusted Prostate Specific Antigen Density (adjusted-PSAD) and other traditional classical parameters.MethodsBetween January 2014 and September 2015, Of 151 men who were eligible for analysis were formed the training cohort. A prediction model for HGPCa was built by using backward logistic regression and was presented on a nomogram. The prediction model was evaluated by a validation cohort between October 2015 and October 2016 (n = 74). The relationship between the nomogram-based risk-score as well as other parameters with Gleason score (GS) was evaluated. All patients underwent 12-core systematic biopsy and at least one core targeted biopsy with transrectal ultrasonographic guidance.ResultsThe multivariate analysis revealed that patient age, PI-RADS v2 score and adjusted-PSAD were independent predictors for HGPCa. Logistic regression (LR) model had a larger AUC as compared with other parameters alone. The most discriminative cutoff value for LR model was 0.36, the sensitivity, specificity, positive predictive value and negative predictive value were 87.3, 78.4, 76.3, and 90.4%, respectively and the diagnostic performance measures retained similar values in the validation cohort (AUC 0.82 [95% CI, 0.76–0.89]). For all patients with HGPCa (n = 50), adjusted-PSAD and nomogram-based risk-score were positively correlated with the GS of HGPCa in PSA gray zone (r = 0.455, P = 0.002 and r = 0.509, P = 0.001, respectively).ConclusionThe nomogram based on multiparametric magnetic resonance imaging (mp-MRI) for forecasting HGPCa is effective, which could reduce unnecessary prostate biopsies in patients with PSA 4–10 ng/ml and nomogram-based risk-score could provide a more robust parameter of assessing the aggressiveness of HGPCa in PSA gray zone.