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1.
J Clin Microbiol ; 62(2): e0139623, 2024 02 14.
Article in English | MEDLINE | ID: mdl-38259071

ABSTRACT

Chemokine receptor 4 (CXCR4) plays a vital role in immunoregulation during hepatitis B virus (HBV) infection. This study aimed to screen single-nucleotide polymorphisms (SNPs) of CXCR4 for predicting pegylated interferon-alpha (PegIFNα) therapy response in chronic hepatitis B (CHB) patients. This retrospective cohort study enrolled a total of 945 CHB patients in two cohorts (Cohort 1, n = 238; Cohort 2, n = 707), and all the patients were hepatitis B e antigen (HBeAg)-positive and treated with PegIFNα for 48 weeks and followed up for 24 weeks. Twenty-two tag SNPs were selected in CXCR4 and its flanking region. A polygenic score (PGS) was utilized to evaluate the cumulative effect of multiple SNPs. The relationships between CXCR4 SNPs and PGS and PegIFNα treatment response were explored in the two cohorts. Among the 22 candidate SNPs of CXCR4, rs28367495 (T > C) was significantly linked to PegIFNα treatment response in both cohorts. In patients with more number of rs28367495 C allele, a higher rate of combined response (CR, defined as HBeAg seroconversion and HBV DNA level < 3.3 log10 IU/mL; P = 1.51 × 10-4), a lower mean hepatitis B surface antigen (HBsAg) level (P = 4.76 × 10-4), and a higher mean HBsAg decline (P = 3.88 × 10-4) at Week 72 were achieved. Moreover, a PGS integrating CXCR4_rs28367495 and five previously reported SNPs was strongly correlated with CR (P = 1.26 × 10-13), HBsAg level (P = 4.90 × 10-4), and HBsAg decline (P = 0.005) in all the patients of the two cohorts. CXCR4_rs28367495 is a promising indicator for predicting the responsiveness to PegIFNα treatment for HBeAg-positive CHB patients. The new PGS may further improve the prediction performance.


Subject(s)
Hepatitis B, Chronic , Hepatitis B , Humans , Antiviral Agents/therapeutic use , Antiviral Agents/pharmacology , DNA, Viral , Hepatitis B/drug therapy , Hepatitis B e Antigens , Hepatitis B Surface Antigens , Hepatitis B virus/genetics , Hepatitis B, Chronic/drug therapy , Interferon-alpha/therapeutic use , Interferon-alpha/pharmacology , Polyethylene Glycols/therapeutic use , Polyethylene Glycols/pharmacology , Receptors, CXCR4/genetics , Recombinant Proteins , Retrospective Studies , Treatment Outcome
2.
Sensors (Basel) ; 19(21)2019 Oct 25.
Article in English | MEDLINE | ID: mdl-31731564

ABSTRACT

In order to improve the flow measurement accuracy of oil-water two-phase flow at low flow rate, this paper presents a plug-in conductance sensor array (PICSA) for the measurement of water holdup and cross-correlation velocity. Due to the existence of the insert body in PICSA, the effect of slippage and the non-uniform distribution of dispersed phase on the measurement of oil-water two-phase flow at low flow rate can be reduced. The finite element method is used to analyze the electric field distribution characteristics of the plug-in conductance sensor, and the sensor geometry is optimized. The dynamic experiment of oil-water two-phase flow is carried out where water cut Kw and mixture velocity Um are set in the range of 10-98% and 0.0184-0.2580 m/s respectively. Experimental results show that the PICSA has good resolution in water holdup measurement for dispersed oil-in-water slug flow (D OS/W), transition flow (TF), dispersed oil-in-water bubble flow (D O/W) and very fine dispersed oil-in-water bubble flow (VFD O/W). In addition, the cross-correlation velocity of the oil-water two-phase flow is obtained by using the plug-in upstream and downstream conductance sensor arrays. The relationship between the cross-correlation velocity and mixture velocity is found to be sensitive to the change of flow pattern, but it has a good linear relationship under the same flow pattern. Based on the flow pattern identification, a good prediction result of the mixture velocity is obtained using kinematic wave theory. Finally, a high precision prediction of the individual phase volume fraction of oil-water two-phase flow at low flow rate is achieved by using the drift flux model.

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