Multi-Compartment SCFA Quantification in Human

Short-chain fatty acids (SCFA) play an important role in human biochemistry. They originate primarily from the digestive system through carbohydrates microbial fermentation. Most SCFA produced in the colon are absorbed by the intestinal wall and enter the bloodstream to be distributed throughout the body for multiple purposes. At the intestinal level, SCFA play a role in controlling fat storage and fatty acid metabolism. The effects of these beneficial compounds therefore concern overall health. They facilitate energy expenditure and are valuable allies in the fight against obesity and diabetes. SCFA are also involved in the regulation of the levels of several neurotransmitters such as GABA (γ-aminobutyric acid), glutamate, serotonin, dopamine, and norepinephrine. Their role is also highlighted in many inflammatory and neurodegenerative diseases such as Alzheimer’s disease (AD) or Parkinson’s disease (PD). To have a realistic picture of the distribution of SCFA in different biological compartments of the human body, we propose to study SCFA simultaneously in five human biological samples: feces, saliva, serum, cerebrospinal fluid (CSF), and urine, as well as in Dried Blood Spot (DBS). To evaluate their concentration and repeatability, we used 10 aliquots from pooled samples, analyzed by 3-nitrophenylhydrazine (3-NPH) derivation and liquid chromatography coupled with high sensitivity mass spectrometry (LC-QqQ-MS). We also evaluated the SCFA assay on Dried Blood Spot (DBS). In this work, we adapted the pre-analytical parts for each sample to be able to use a common calibration curve, thus facilitating multi-assay quantification studies and so being less time-consuming. Moreover, we proposed new daughter ions from the same neutral loss (43 Da) to quantify SCFAs, thus improving the sensitivity. In conclusion, our methodology, based on a unique calibration curve for all samples for each SCFA, is well-suited to quantified them in a clinical context.

Investigation on mechanical properties regulation of rock-like specimens based on 3D printing and similarity quantification

3D printing is widely adopted to quickly produce rock mass models with complex structures in batches,improving the consistency and repeatability of physical modeling.It is necessary to regulate the mechanical properties of 3D-printed specimens to make them proportionally similar to natural rocks.This study investigates mechanical properties of 3D-printed rock analogues prepared by furan resin-bonded silica sand particles.The mechanical property regulation of 3D-printed specimens is realized through quantifying its similarity to sandstone,so that analogous deformation characteristics and failure mode are acquired.Considering similarity conversion,uniaxial compressive strength,cohesion and stress–strain relationship curve of 3D-printed specimen are similar to those of sandstone.In the study ranges,the strength of 3D-printed specimen is positively correlated with the additive content,negatively correlated with the sand particle size,and first increases then decreases with the increase of curing temperature.The regulation scheme with optimal similarity quantification index,that is the sand type of 70/140,additive content of 2.5‰and curing temperature of 81.6℃,is determined for preparing 3D-printed sandstone analogues and models.The effectiveness of mechanical property regulation is proved through uniaxial compression contrast tests.This study provides a reference for preparing rock-like specimens and engineering models using 3D printing technology.

Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming

Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.

High-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer based on probability density evolution method

This paper proposed an efficient research method for high-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer.Firstly,the dynamic model of projectile motion is established considering the flexible deformation of the barrel and the interaction between the projectile and the barrel.Subsequently,the accuracy of the dynamic model is verified based on the external ballistic projectile attitude test platform.Furthermore,the probability density evolution method(PDEM)is developed to high-dimensional uncertainty quantification of projectile motion.The engineering example highlights the results of the proposed method are consistent with the results obtained by the Monte Carlo Simulation(MCS).Finally,the influence of parameter uncertainty on the projectile disturbance at muzzle under different working conditions is analyzed.The results show that the disturbance of the pitch angular,pitch angular velocity and pitch angular of velocity decreases with the increase of launching angle,and the random parameter ranges of both the projectile and coupling model have similar influence on the disturbance of projectile angular motion at muzzle.

Uncertainty quantification of mechanism motion based on coupled mechanism—motor dynamic model for ammunition delivery system

In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to propose a novel mechanism-motor coupling dynamic modeling method,in which the relationship between mechanism motion and motor rotation is established according to the geometric coordination of the system.The advantages of this include establishing intuitive coupling between the mechanism and motor,facilitating the discussion for the influence of both mechanical and electrical parameters on the mechanism,and enabling dynamic simulation with controller to take the randomness of the electric load into account.Dynamic simulation considering feedback control of ammunition delivery system is carried out,and the feasibility of the model is verified experimentally.Based on probability density evolution theory,we comprehensively discuss the effects of system parameters on mechanism motion from the perspective of uncertainty quantization.Our work can not only provide guidance for engineering design of ammunition delivery mechanism,but also provide theoretical support for modeling and uncertainty quantification research of mechatronics system.

Quantification of the concrete freeze–thaw environment across the Qinghai–Tibet Plateau based on machine learning algorithms

The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze–thaw cycles(NFTCs) on the QTP. The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest(RF) model, generalized boosting method(GBM), generalized linear model(GLM), and generalized additive model(GAM), are used to fit the NFTCs. The root mean square error(RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R^(2) values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R^(2) values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP. Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.

Revolutionizing disease diagnosis and management:Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification

Artificial intelligence(AI),particularly machine learning(ML)and deep learning(DL)techniques,such as convolutional neural networks(CNNs),have emerged as transformative technologies with vast potential in healthcare.Body iron load is usually assessed using slightly invasive blood tests(serum ferritin,serum iron,and serum transferrin).Serum ferritin is widely used to assess body iron and drive medical management;however,it is an acute phase reactant protein offering wrong interpretation in the setting of inflammation and distressed patients.Magnetic resonance imaging is a non-invasive technique that can be used to assess liver iron.The ML and DL algorithms can be used to enhance the detection of minor changes.However,a lack of open-access datasets may delay the advancement of medical research in this field.In this letter,we highlight the importance of standardized datasets for advancing AI and CNNs in medical imaging.Despite the current limitations,embracing AI and CNNs holds promise in revolutionizing disease diagnosis and treatment.

不同病情乙型肝炎患者HBV-DNA定量、两对半检测结果差异

目的分析乙型肝炎患者乙型肝炎病毒(HBV)-DNA定量检测与两对半检测结果的相关性,并观察不同病情HBV-DNA阳性率和两对半检测结果差异。方法选取江苏省泰兴市人民医院2021年4月至2023年4月收治的375例乙型肝炎患者病例为研究资料。比较不同乙肝两对半检测[乙肝表面抗原(HBsAg)、乙肝表面抗体(HBsAb)、乙肝e抗原(HBeAg)、乙肝e抗体(HBeAb)、乙肝核心抗体(HBcAb)]结果患者HBV-DNA定量检测结果差异,将患者根据HBV-DNA定量检测结果分为阳性组和阴性组,比较乙肝两对半结果差异,使用Pearson相关性分析;将患者根据疾病类型分为肝炎组、肝硬化组和肝癌组,比较三组患者乙肝两对半和HBV-DNA定量检测结果差异。结果不同乙肝两对半类型患者HBV-DNA阳性率比较,差异有统计学意义(P<0.05);HBV-DNA阳性组患者HBsAg、HBeAg水平高于HBV-DNA阴性组,HBsAb、HBeAb、HBcAb水平低于HBV-DNA阴性组,差异有统计学意义(P<0.05);HBsAg、HBeAg水平与HBV-DNA呈正相关(P<0.05),HBsAb、HBeAb、HBcAb水平与HBV-DNA呈负相关(P<0.05);不同疾病类型患者乙肝两对半类型HBV-DNA阳性率比较,差异有统计学意义(P<0.05),不同疾病类型患者HBV-DNA定量检测结果比较,差异有统计学意义(P<0.05)。结论乙型肝炎患者HBV-DNA定量检测与两对半检测结果有相关性,不同乙肝两对半类型间及不同疾病类型间HBV-DNA均有差异,HBV-DNA定量检测联合两对半检测可较好地反映病毒复制和疾病进展情况,为临床治疗提供指导。

Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods

Complex systems exist widely,including medicines from natural products,functional foods,and biological samples.The biological activity of complex systems is often the result of the synergistic effect of multiple components.In the quality evaluation of complex samples,multicomponent quantitative analysis(MCQA)is usually needed.To overcome the difficulty in obtaining standard products,scholars have proposed achieving MCQA through the“single standard to determine multiple components(SSDMC)”approach.This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia.Depending on a convenient(ultra)high-performance liquid chromatography method,how can the repeatability and robustness of the MCQA method be improved?How can the chromatography conditions be optimized to improve the number of quantitative components?How can computer software technology be introduced to improve the efficiency of multicomponent analysis(MCA)?These are the key problems that remain to be solved in practical MCQA.First,this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years,as well as the method robustness and accuracy evaluation.Second,it also summarizes methods to improve peak capacity and quantitative accuracy in MCA,including column selection and twodimensional chromatographic analysis technology.Finally,computer software technologies for predicting chromatographic conditions and analytical parameters are introduced,which provides an idea for intelligent method development in MCA.This paper aims to provide methodological ideas for the improvement of complex system analysis,especially MCQA.

HBV-DNA病毒载量在不同HBV感染产妇产后血清、唾液、乳汁中的变化及与肝功能的相关性研究

目的 观察不同HBV感染产妇的HBV-DNA病毒载量及其与肝功能的相关性。方法 102例HBV产妇根据HBV血清标志物分为三组,比较各组的HBV-DNA载量以及ALT、 AST水平,分析HBV-DNA载量与肝功能的相关性。结果 产妇血清、唾液、乳汁中的HBV-DNA载量,以及血清中ALT、 AST水平由高到低依次为A组、 B组、 C组(P <0.05)。不同HBV感染产妇的血清、唾液、乳汁HBV-DNA载量与ALT、 AST水平均呈正相关(P <0.05)。结论 随着HBV感染程度的加深,产妇血清、唾液、乳汁中的HBV-DNA病毒载量呈上升趋势,产后血清、唾液及乳汁中HBV-DNA病毒载量均与肝功能密切相关。

Molecular diagnosis and direct quantification of cereal cyst nematode(Heterodera filipjevi) from field soil using TaqMan real-time PCR

Heterodera filipjevi continues to be a major threat to wheat production worldwide.Rapid detection and quantification of cyst nematodes are essential for more effective control against this nematode disease.In the present study,a TaqManminor groove binder(TaqMan-MGB)probe-based fluorescence quantitative real-time PCR(qPCR)was successfully developed and used for quantifying H.filipjevi from DNA extracts of soil.The primers and probe designed from the obtained RAPD-SCAR marker fragments of H.filipjevi showed high specificity to H.filipjevi using DNA from isolatesconfirmed species of 23 Heterodera spp.,1 Globodera spp.and 3 Pratylenchus spp.The qPCR assay is highly sensitive and provides improved H.filipjevi detection sensitivity of as low as 4^(-3) single second-stage juvenile(J2)DNAs,10^(-3) female DNAs,and 0.01μgμL^(-1) genomic DNAs.A standard curve relating to the threshold cycle and log values of nematode numbers was generated and validated from artificially infested soils and was used to quantify H.filipjevi in naturally infested field soils.There was a high correlation between the H.filipjevi numbers estimated from 32 naturally infested field soils by both conventional methods and the numbers quantified using the qPCR assay.qPCR potentially provides a useful platform for the efficient detection and quantification of H.filipjevi directly from field soils and to quantify this species directly from DNA extracts of field soils.

Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks

High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.

Host cell protein quantification workflow using optimized standards combined with data-independent acquisition mass spectrometry

Monitoring of host cell proteins(HCPs)during the manufacturing of monoclonal antibodies(mAb)has become a critical requirement to provide effective and safe drug products.Enzyme-linked immunosorbent assays are still the gold standard methods for the quantification of protein impurities.However,this technique has several limitations and does,among others,not enable the precise identification of proteins.In this context,mass spectrometry(MS)became an alternative and orthogonal method that delivers qualitative and quantitative information on all identified HCPs.However,in order to be routinely implemented in biopharmaceutical companies,liquid chromatography-MS based methods still need to be standardized to provide highest sensitivity and robust and accurate quantification.Here,we present a promising MS-based analytical workflow coupling the use of an innovative quantification standard,the HCP Profiler solution,with a spectral library-based data-independent acquisition(DIA)method and strict data validation criteria.The performances of the HCP Profiler solution were compared to more conventional standard protein spikes and the DIA approach was benchmarked against a classical datadependent acquisition on a series of samples produced at various stages of the manufacturing process.While we also explored spectral library-free DIA interpretation,the spectral library-based approach still showed highest accuracy and reproducibility(coefficients of variation<10%)with a sensitivity down to the sub-ng/mg mAb level.Thus,this workflow is today mature to be used as a robust and straightforward method to support mAb manufacturing process developments and drug products quality control.

Detection and quantification of Pb and Cr in oysters using laser-induced breakdown spectroscopy

The quantitative determination of heavy metals in aquatic products is of great importance for food security issues.Laser-induced breakdown spectroscopy(LIBS)has been used in a variety of foodstuff analysis,but is still limited by its low sensitivity when targeting trace heavy metals.In this work,we compare three sample enrichment methods,namely drying,carbonization,and ashing,for increasing detection sensitivity by LIBS analysis for Pb and Cr in oyster samples.The results demonstrate that carbonization can remove a significant amount of the contributions of organic elements C,H,N and O;meanwhile,the signals of the metallic elements such as Cu,Pb,Sr,Ca,Cr and Mg are enhanced by3–6 times after carbonization,and further enhanced by 5–9 times after ashing.Such enhancement is not only due to the more concentrated metallic elements in the sample compared to the dried ones,but also the unifying of the matter in carbonized and ashed samples from which higher plasma temperature and electron density are observed.This condition favors the detection of trace elements.According to the calibration curves with univariate and multivariate analysis,the ashing method is considered to be the best choice.The limits of detection of the ashing method are 0.52 mg kg-1 for Pb and0.08 mg kg-1 for Cr,which can detect the presence of heavy metals in the oysters exceeding the maximum limits of Pb and Cr required by the Chinese national standard.This method provides a promising application for the heavy metal contamination monitoring in the aquatic product industry.

CHB血清核心抗体与HBV-DNA、HBsAg、肝脏组织纤维化分期相关性及临床意义

目的探讨慢性乙型肝炎(CHB)患者血清核心抗体(HBcAb)与乙肝病毒脱氧核糖核酸(HBV-DNA)、乙肝表面抗原(HBsAg)、肝脏组织纤维化分期相关性及临床意义。方法选取我院2018年1月—2022年12月收治的CHB患者作为研究对象,根据纤维化严重程度分为轻度组(S0~S1期,50例)、中重度组(S2~S4期,150例),比较两组患者临床资料和实验室指标,应用Spearman/Pearson分析HBcAb与HBV-DNA、HBsAg、肝脏组织纤维化分期相关性;运用LASSO回归筛选变量;诊断效能分析采用受试者工作特征曲线(ROC)。结果单因素分析显示,中重度组病程长于轻度组,肝脏炎症分级分布较轻度组严重,ALT、AST、总胆红素(TBIL)、碱性磷酸酶(ALP)、层粘连蛋白、Ⅲ型前胶原N端肽、透明质酸、Ⅳ型胶原、HBcAb高于轻度组,HBV-DNA、HBsAg低于轻度组(P<0.05);HBCAb与肝纤维化呈正相关,HBVDNA、HBsAg与肝脏组织纤维化分期呈负相关(P<0.05);将单因素分析P<0.05的13个变量进行LASSO回归分析筛选出4个变量肝脏炎症分级、HBcAb、HBV-DNA、HBsAg,其是中重度肝纤维化相关影响因素(P<0.05);肝脏炎症分级、HBcAb、HBV-DNA、HBsAg联合诊断中重度肝脏组织纤维化的AUC优于任一单一项目。结论HBCAb与肝纤维化呈正相关,HBV-DNA和HBSAg与肝纤维化负相关,以上指标均是中重度肝纤维化相关影响因素,具有一定诊断价值,为临床预测中重度肝纤维化提供一定参考。

阿拉尔市某医院HBsAg阳性孕妇血清标志物模式分布与HBV-DNA载量分析

目的分析阿拉尔市某医院的乙型肝炎病毒表面抗原(HBsAg)阳性孕妇乙型肝炎病毒(HBV)感染血清标志物模式与HBV-DNA载量之间的关系,为监控孕期HBV感染和预防阻断HBV母婴传播提供理论依据。方法选取2021年10月至2023年10月期间在新疆生产建设兵团第一师阿拉尔医院产科建档及生产的HBsAg阳性孕妇为研究对象,通过磁微粒化学发光法检测孕妇五项HBV血清标志物包括测HBsAg和乙肝病毒表面抗体(HBsAb)、乙肝病毒e抗原(HBeAg)、乙肝病毒e抗体(HBeAg)以及乙肝病毒核心抗体(HBcAb),并利用荧光定量PCR检测HBV-DNA载量,分析HBV-DNA载量以及孕妇HBV感染模式对新生儿的影响。结果共纳入145例HBsAg阳性孕妇,HBV感染模式以感染模式Ⅱ[HbsAg(+)、HBeAb(+)和HBcAb(+)]为主,占62.76%。HBV-DNA阳性(>0.5×10^(3)IU/mL)阳性率为63.45%(92/145),不同HBV感染模式的HBsAg阳性孕妇HBV-DNA阳性率及载量分布情况比较差异有统计学意义(χ^(2)=16.532、64.367,P<0.05),主要表现为感染模式Ⅰ比例随HBV-DNA载量升高而增高。同时,HBV-DNA阳性组随HBeAg浓度升高而比例增高,而HBV-DNA阴性组正好相反,Spearman相关分析表明HBsAg阳性孕妇的HBeAg浓度与HBV DNA载量呈现明显正相关性(r=0.327,P<0.05)。不同HBV感染模式的HBsAg阳性孕妇的新生儿脐带血HBV阳性率比较差异有统计学意义(χ^(2)=16.851,P=0.002),同时HBsAg阳性孕妇的新生儿脐带血HBV阳性率随着HBV-DNA载量增高而明显增高,差异有统计学意义(χ^(2)=15.062,P=0.005)。结论HBsAg阳性孕妇血清标志物模式与HBV-DNA病毒载量密切相关,产前筛查对孕产妇及新生儿的乙肝预防及干预有重要意义,是母婴传播防控的重点。

乙肝患者HBV-DNA载量与血清标志物水平的相关性及其对抗病毒疗效的预测价值

目的:观察乙型肝炎(简称乙肝)患者HBV-DNA载量与血清标志物[乙肝表面抗原(HBsAg)、乙肝病毒e抗原(HBeAg)、乙肝病毒核心抗体(HBcAb)、HBcAb-免疫球蛋白M(IgM)]水平的相关性及其对抗病毒疗效的预测价值。方法:选取2020年7月至2022年7月该院收治的154例乙肝患者进行前瞻性研究,测定其入院时、抗病毒治疗1个月后HBV-DNA载量与血清标志物水平。采用Pearson相关性分析,分析乙肝患者入院时HBV-DNA载量与血清标志物水平的相关性;比较抗病毒治疗6个月不同疗效患者抗病毒治疗1个月时HBV-DNA载量与血清标志物水平;采用受试者工作特征(ROC)曲线分析抗病毒治疗1个月HBV-DNA载量与血清标志物水平单项及联合检测对抗病毒治疗6个月后疗效的预测价值。结果:Pearson相关性分析结果显示,入院时,乙肝患者HBVDNA载量与血清HBsAg、HBeAg、HBcAb、HBcAb-IgM水平均呈正相关(r>0,P<0.05);抗病毒治疗6个月后完全应答患者治疗1个月时HBV-DNA载量和血清HBsAg、HBeAg、HBcAb、HBcAb-IgM水平均低于未完全应答患者,差异有统计学意义(P<0.05);ROC曲线分析结果显示,乙肝患者抗病毒治疗1个月后HBV-DNA载量与血清HBsAg、HBeAg、HBcAb、HBcAb-IgM水平联合检测对抗病毒治疗6个月后的疗效预测价值[曲线下面积(AUC)=0.905]高于五者单项检测(AUC=0.779、0.802、0.792、0.739、0.831)。结论:乙肝患者HBV-DNA载量与血清HBsAg、HBeAg、HBcAb、HBcAb-IgM水平均呈正相关,且乙肝患者抗病毒治疗1个月后HBV-DNA载量与血清HBsAg、HBeAg、HBcAb、HBcAb-IgM水平联合检测对抗病毒治疗6个月后的疗效预测价值高于五者单项检测。

血清肝纤维化指标、HBV-DNA病毒载量联合检验在乙型肝炎肝纤维化中的评估价值

目的:探究血清肝纤维化指标、HBV-DNA病毒载量联合检验在乙型肝炎肝纤维化中的评估价值。方法:选取2022年1月至2023年5月本院收治的100例乙型肝炎肝纤维化患者作为观察组,并选择同期100例慢性乙型肝炎患者作为对照组。分别采用化学发光法和荧光定量PCR法检测两组患者肝纤维化指标及HBV-DNA水平,并比较分析其与乙型肝炎肝纤维化的关系。结果:观察组透明质酸(HA)、层粘连蛋白(LN)、Ⅲ型前胶原(PCⅢ)、Ⅳ型胶原(Ⅳ-C)水平均显著高于对照组(P<0.05);HBV-DNA水平显著低于对照组(P<0.05)。结论:HBV在慢性乙型肝炎患者具有高度复制性,在肝纤维化阶段复制水平下降。随着乙肝的进一步发展,肝纤维化指标水平逐渐升高。联合检测肝纤维化指标和HBV-DNA病毒载量对临床诊断乙型肝炎肝纤维化有一定的参考价值。

Two-point Dixon and six-point Dixon magnetic resonance techniques in the detection,quantification and grading of hepatic steatosis

BACKGROUND Hepatic steatosis is a very common problem worldwide.AIM To assess the performance of two-and six-point Dixon magnetic resonance(MR)techniques in the detection,quantification and grading of hepatic steatosis.METHODS A single-center retrospective study was performed in 62 patients with suspected parenchymal liver disease.MR sequences included two-point Dixon,six-point Dixon,MR spectroscopy(MRS)and MR elastography.Fat fraction(FF)estimates on the Dixon techniques were compared to the MRS-proton density FF(PDFF).Statistical tests used included Pearson’s correlation and receiver operating characteristic.RESULTS FF estimates on the Dixon techniques showed excellent correlation(≥0.95)with MRS-PDFF,and excellent accuracy[area under the receiver operating characteristic(AUROC)≥0.95]in:(1)Detecting steatosis;and(2)Grading severe steatosis,(P<0.001).In iron overload,two-point Dixon was not evaluable due to confounding T2*effects.FF estimates on six-point Dixon vs MRS-PDFF showed a moderate correlation(0.82)in iron overload vs an excellent correlation(0.97)without iron overload,(P<0.03).The accuracy of six-point Dixon in grading mild steatosis improved(AUROC:0.59 to 0.99)when iron overload cases were excluded.The excellent correlation(>0.9)between the Dixon techniques vs MRSPDFF did not change in the presence of liver fibrosis(P<0.01).CONCLUSION Dixon techniques performed satisfactorily for the evaluation of hepatic steatosis but with exceptions.

Hepatic MR imaging using IDEAL-IQ sequence:Will Gd-EOB-DTPA interfere with reproductivity of fat fraction quantification?

BACKGROUND Iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification sequence(IDEAL-IQ)is based on chemical shift-based water and fat separation technique to get proton density fat fraction.Multiple studies have shown that using IDEAL-IQ to test the stability and repeatability of liver fat is acceptable and has high accuracy.AIM To explore whether Gadoxetate Disodium(Gd-EOB-DTPA)interferes with the measurement of the hepatic fat content quantified with the IDEAL-IQ and to evaluate the robustness of this technique.METHODS IDEAL-IQ was used to quantify the liver fat content at 3.0T in 65 patients injected with Gd-EOB-DTPA contrast.After injection,IDEAL-IQ was estimated four times,and the fat fraction(FF)and R2* were measured at the following time points:Precontrast,between the portal phase(70 s)and the late phase(180 s),the delayed phase(5 min)and the hepatobiliary phase(20 min).One-way repeated-measures analysis was conducted to evaluate the difference in the FFs between the four time points.Bland-Altman plots were adopted to assess the FF changes before and after injection of the contrast agent.P<0.05 was considered statistically significant.RESULTS The assessment of the FF at the four time points in the liver,spleen and spine showed no significant differences,and the measurements of hepatic FF yielded good consistency between T1 and T2[95%confidence interval:-0.6768%,0.6658%],T1 and T3(-0.3900%,0.3178%),and T1 and T4(-0.3750%,0.2825%).R2* of the liver,spleen and spine increased significantly after injection(P<0.0001).CONCLUSION Using the IDEAL-IQ sequence to measure the FF,we can obtain results that will not be affected by Gd-EOB-DTPA.The high reproducibility of the IDEAL-IQ sequence makes it available in the scanning interval to save time during multiphase examinations.