Natural YMDD-motif mutants affect clinical course of lamivudine in chronic hepatitis B

AIM:To investigate the prevalence of nature tyrosinemethionine-aspartic acid-aspartic acid motif mutations in chronic hepatitis B(CHB)patients and to evaluate the efficacy of lamivudine.METHODS:A total of 1268 CHB patients were recruited from the outpatient and inpatient departments of six centers.Tyrosine-methionine-aspartic acid-aspartic acid(YMDD)mutations were analyzed using the hepatitis B virus(HBV)drug resistance line probe assay.Forty voluntary patients were selected from those with positive or negative natural YMDD mutations to undergo treatment with lamivudine.RESULTS:YMDD mutations were detected in 288(22.71%)of the 1268 CHB patients.Multivariate analysis revealed that the patients’HBV DNA level(P=0.0282)and hepatitis B e antigen status(P=0.0133)were also associated with natural YMDD mutations.The rates of normalization of alanine aminotransferase levels and HBV DNA nondetection at 6,24,36,and 48 wk were compared between the patients with natural YMDD mutations and those without,and the differences were not significant.However,there was a significant difference in the cumulative emergence rates of virological breakthrough at 48 wk in the patients with natural YMDD mutations and those without(32.5%vs 12.5%,P=0.032).CONCLUSION:Naturally occurring YMDD mutationsare detectable in a large proportion of CHB patients;breakthrough hepatitis tended to occur in patients with natural YMDD mutations.

Highly efficient nanocatalyst Ni1Co9@graphene for hydrolytic dehydrogenation of sodium borohydride

Bimetal materials derived from transition metals can be good catalyst candidates towards some specific reactions.When loaded on graphene(GP),these catalysts exhibit remarkable performance in the hydrolysis of sodium borohydride.To obtain such catalysts easily and efficiently,a simple thermal reduction strategy was used in this study,and Ni_(x)Co_(10-x) series bimetal catalysts were prepared.Among all the catalysts,Ni_(1)Co_(9) exhibited the best catalytic performance.The turnover frequency(TOF)related to the total number of atoms within the bimetallic nanoparticles reached 603.82 m L·mmol^(-1)·min^(-1) at 303 K.Furthermore,graphene was introduced as a supporting frame.The Ni1Co9@Graphene(Ni1Co9@GP)had a large surface area and high TOF,25534 mL·mmol^(-1)·min^(-1),at 303 K.The Ni_(1)Co_(9)@GP exhibited efficient catalytic properties for H2 generation in alkaline solution because of its high specific surface area.Moreover,the high kinetic isotope effect observed in the kinetic studies suggests that using D2O led to the oxidative addition of an O-H bond of water in the rate-determining step.

Pascal Realization by Comb-Spectral-Interferometry Based Refractometer

To break through the limitations of existing pressure standards, which rely on the gravity and toxic mercury,the national metrological institutes prefer a quantum-based pressure standard. Combining the ideal gas law with helium refractivity measurement, we demonstrate a scheme for the realization of the pressure unit. The refractometer is based on a spectral interferometry with an optical frequency comb and a double-spaced vacuum cell. Through fast Fourier transform of the spectral interferograms of the two beams propagating inside and outside the vacuum cell, the helium refractivity can be obtained with a combined standard uncertainty u（n） of2.9 × 10-9. Moreover, the final u（n） is -8.7 × 10-6 in a measurement range of several megapascals（MPa）. Our apparatus is compact, fast（15 ms for one single measurement） and easy to handle. Furthermore, the measurement uncertainty will be improved to-1 × 10-9 or lower if a VIPA-based spectrometer is used. The value of u（p） will thus increase to 3 × 10-6 or better in several MPa.

Layer-Wise Pre-Training Low-Rank NMF Model for Mammogram-Based Breast Tumor Classification

Image-based breast tumor classification is an active and challenging problem.In this paper,a robust breast tumor classification framework is presented based on deep feature representation learning and exploiting available information in existing samples.Feature representation learning of mammograms is fulfilled by a modified nonnegative matrix factorization model called LPML-LRNMF,which is motivated by hierarchical learning and layer-wise pre-training(LP)strategy in deep learning.Low-rank(LR)constraint is integrated into the feature representation learning model by considering the intrinsic characteristics of mammograms.Moreover,the proposed LPML-LRNMF model is optimized via alternating direction method of multipliers and the corresponding convergence is analyzed.For completing classification,an inverse projection sparse representation model is introduced to exploit information embedded in existing samples,especially in test ones.Experiments on the public dataset and actual clinical dataset show that the classification accuracy,specificity and sensitivity achieve the clinical acceptance level.