Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program.

BACKGROUND/AIMS: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1-3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. METHODS: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. RESULTS: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. CONCLUSION: Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Machine Learning-Assisted Large-Area Preparation of MoS2 Materials.

Molybdenum disulfide (MoS2) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physicochemical, biological and mechanical properties. MoS2 with high quality, large size and outstanding performance can be prepared via chemical vapor deposition (CVD). However, its preparation process is complex, and the area of MoS2 obtained is difficult to control. Machine learning (ML), as a powerful tool, has been widely applied in materials science. Based on this, in this paper, a ML Gaussian regression model was constructed to explore the growth mechanism of MoS2 material prepared with the CVD method. The parameters of the regression model were evaluated by combining the four indicators of goodness of fit (r2), mean squared error (MSE), Pearson correlation coefficient (p) and p-value (p_val) of Pearson's correlation coefficient. After comprehensive comparison, it was found that the performance of the model was optimal when the number of iterations was 15. Additionally, feature importance analysis was conducted on the growth parameters using the established model. The results showed that the carrier gas flow rate (Fr), molybdenum sulfur ratio (R) and reaction temperature (T) had a crucial impact on the CVD growth of MoS2 materials. The optimal model was used to predict the size of molybdenum disulfide synthesis under 185,900 experimental conditions in the simulation dataset so as to select the optimal range for the synthesis of large-size molybdenum disulfide. Furthermore, the model prediction results were verified through literature and experimental results. It was found that the relative error between the prediction results and the literature and experimental results was small. These findings provide an effective solution to the preparation of MoS2 materials with a reduction in the time and cost of trial and error.

Decision tree algorithm predicts hepatocellular carcinoma among chronic hepatitis C patients following viral eradication.

Successful eradication of the hepatitis C virus (HCV) cannot eliminate the risk of hepatocellular carcinoma (HCC). Next-generation RNA sequencing provides comprehensive genomic insights into the pathogenesis of HCC. Artificial intelligence has opened a new era in precision medicine. This study integrated clinical features and genetic biomarkers to establish a machine learning-based HCC model following viral eradication. A prospective cohort of 55 HCV patients with advanced fibrosis, who achieved a sustained virologic response after antiviral therapy, was enrolled. The primary outcome was the occurrence of HCC. The genomic signatures of peripheral blood mononuclear cells (PBMC) were determined by RNA sequencing at baseline and 24 weeks after end-of-treatment. Machine learning algorithms were implemented to extract the predictors of HCC. HCC occurred in 8 of the 55 patients, with an annual incidence of 2.7%. Pretreatment PBMC DEFA1B, HBG2, ADCY4, and posttreatment TAS1R3, ABCA3, and FOSL1 genes were significantly downregulated, while the pretreatment ANGPTL6 gene was significantly upregulated in the HCC group compared to that in the non-HCC group. A gene score derived from the result of the decision tree algorithm can identify HCC with an accuracy of 95.7%. Gene score = TAS1R3 (≥0.63 FPKM, yes/no = 0/1) + FOSL1 (≥0.27 FPKM, yes/no = 0/1) + ABCA3 (≥2.40 FPKM, yes/no = 0/1). Multivariate Cox regression analysis showed that this gene score was the most important predictor of HCC (hazard ratio = 2.38, 95% confidence interval [CI] = 1.06-5.36, P = 0.036). Combining the gene score and fibrosis-4 index, a nomogram was constructed to predict the probability of HCC with an area under the receiver operating characteristic curve up to 0.950 (95% CI = 0.888-1.000, P = 7.0 × 10-5). Decision curve analysis revealed that the nomogram had a net benefit in HCC detection. The calibration curve showed that the nomogram had optimal concordance between the predicted and actual HCC probabilities. In conclusion, down-regulated posttreatment PBMC TAS1R3, ABCA3, and FOSL1 expression were significantly correlated with HCC development after HCV eradication. Decision-tree-based algorithms can refine the assessment of HCC risk for personalized HCC surveillance.

Forecasting metro rail transit passenger flow with multiple-attention deep neural networks and surrounding vehicle detection devices.

In the rapid development of public transportation led, the traffic flow prediction has become one of the most crucial issues, especially estimating the number of passengers using the Mass Rapid Transit (MRT) system. In general, predicting the passenger flow of traffic is a time-series problem that requires external information to improve accuracy. Because many MRT passengers take cars or buses to MRT stations, this study used external information from vehicle detection (VD) devices to improve the prediction of passenger flow. This study proposed a deep learning architecture, called a multiple-attention deep neural network (MADNN) model, based on historical MRT passenger flow and the flow from surrounding VD devices that estimates the weights of the vehicle detection devices. The model consists of (1) an MRT attention layer (MRT-AL) that generate hidden features for MRT stations, (2) a surrounding VD (SVD) attention layer (SVD-AL) that generate hidden features for SVD devices, and (3) an MRT-SVD attention layer (MRT-SVD-AL) that generate attention weights for each VD device in an MRT station. The results of the investigation indicated that the MADNN model outperformed the models without multiple-attention mechanisms in predicting the passenger flow of MRT traffic.

Machine Learning-Assisted Synthesis of Two-Dimensional Materials.

Two-dimensional (2D) materials have intriguing physical and chemical properties, which exhibit promising applications in the fields of electronics, optoelectronics, as well as energy storage. However, the controllable synthesis of 2D materials is highly desirable but remains challenging. Machine learning (ML) facilitates the development of insights and discoveries from a large amount of data in a short time for the materials synthesis, which can significantly reduce the computational costs and shorten the development cycles. Based on this, taking the 2D material MoS2 as an example, the parameters of successfully synthesized materials by chemical vapor deposition (CVD) were explored through four ML algorithms: XGBoost, Support Vector Machine (SVM), Naïve Bayes (NB), and Multilayer Perceptron (MLP). Recall, specificity, accuracy, and other metrics were used to assess the performance of these four models. By comparison, XGBoost was the best performing model among all the models, with an average prediction accuracy of over 88% and a high area under the receiver operating characteristic (AUROC) reaching 0.91. And these findings showed that the reaction temperature (T) had a crucial influence on the growth of MoS2. Furthermore, the importance of the features in the growth mechanism of MoS2 was optimized, such as the reaction temperature (T), Ar gas flow rate (Rf), reaction time (t), and so on. The results demonstrated that ML assisted materials preparation can significantly minimize the time spent on exploration and trial-and-error, which provided perspectives in the preparation of 2D materials.

Thermochromic Smart Windows Assisted by Photothermal Nanomaterials.

Thermochromic smart windows are optical devices that can regulate their optical properties actively in response to external temperature changes. Due to their simple structures and as they do not require other additional energy supply devices, they have great potential in building energy-saving. However, conventional thermochromic smart windows generally have problems with high response temperatures and low response rates. Owing to their great effect in photothermal conversion, photothermal materials are often used in smart windows to assist phase transition so that they can quickly achieve the dual regulation of light and heat at room temperature. Based on this, research progress on the phase transition of photothermal material-assisted thermochromic smart windows is summarized. In this paper, the phase transition mechanisms of several thermochromic materials (VO2, liquid crystals, and hydrogels) commonly used in the field of smart windows are introduced. Additionally, the applications of carbon-based nanomaterials, noble metal nanoparticles, and semiconductor (metal oxygen/sulfide) nanomaterials in thermochromic smart windows are summarized. The current challenges and solutions are further indicated and future research directions are also proposed.

[Analysis and Discussion of Problems Founded in Quality Control of Medical Device Clinical Trials].

The quality control problems of 49 institutional level medical device clinical trials in hospital from 2016 to 2021 were summarized, and the causes of the problems were analyzed from the perspective of all parties involved in the clinical trial. The improvement measures were discussed, which including improving the management system, strengthening the training of researchers and optimizing the selection method, strengthening the responsibility and regulation consciousness of applicants, and formulating the selection basis of applicants and so on. All the above aims to ensure the quality of clinical trials and provide reference for clinical trial managers and implementers.

Outreach onsite treatment with a simplified pangenotypic direct-acting anti-viral regimen for hepatitis C virus micro-elimination in a prison.

BACKGROUND: Prisoners are at risk of hepatitis C virus (HCV) infection, especially among the people who inject drugs (PWID). We implemented an outreach strategy in combination with universal mass screening and immediate onsite treatment with a simplified pan-genotypic direct-acting antivirals (DAA) regimen, 12 wk of sofosbuvir/velpatasvir, in a PWID-dominant prison in Taiwan. AIM: To implement an outreach strategy in combination with universal mass screening and immediate onsite treatment with a simplified pan-genotypic DAA regimen in a PWID-dominant prison in Taiwan. METHODS: HCV-viremic patients were recruited for onsite treatment program for HCV micro-elimination with a pangenotypic DAA regimen, 12 wk of sofosbuvir/ velpatasvir, from two cohorts in Penghu Prison, either identified by mass screen or in outpatient clinics, in September 2019. Another group of HCV-viremic patients identified sporadically in outpatient clinics before mass screening were enrolled as a control group. The primary endpoint was sustained virological response (SVR12, defined as undetectable HCV ribonucleic acid (RNA) 12 wk after end-of-treatment). RESULTS: A total of 212 HCV-viremic subjects were recruited for HCV micro-elimination campaign; 91 patients treated with sofosbuvir/Ledipasvir or glecaprevir/ pibrentasvir before mass screening were enrolled as a control. The HCV micro-elimination group had significantly lower proportion of diabetes, hypertension, hyperlipidemia, advanced fibrosis and chronic kidney diseases, but higher levels of HCV RNA. The SVR12 rate was comparable between the HCV micro-elimination and control groups, 95.8% (203/212) vs 94.5% (86/91), respectively, in intent-to-treat analysis, and 100% (203/203) vs 98.9% (86/87), respectively, in per-protocol analysis. There was no virological failure, treatment discontinuation, and serious adverse event among sofosbuvir/velpatasvir-treated patients in the HCV micro-elimination group. CONCLUSION: Outreach mass screening followed by immediate onsite treatment with a simplified pangenotypic DAA regimen, sofosbuvir/velpatasvir, provides successful strategies toward HCV micro-elimination among prisoners.

Dynamics of cytokines predicts risk of hepatocellular carcinoma among chronic hepatitis C patients after viral eradication.

BACKGROUND: Chronic hepatitis C virus (HCV) infection induces profound alterations in the cytokine and chemokine signatures in peripheral blood. Clearance of HCV by antivirals results in host immune modification, which may interfere with immune-mediated cancer surveillance. Identifying HCV patients who remain at risk of hepatocellular carcinoma (HCC) following HCV eradication remains an unmet need. We hypothesized that antiviral therapy-induced immune reconstruction may be relevant to HCC development. AIM: To investigate the impact of differential dynamics of cytokine expression on the development of HCC following successful antiviral therapy. METHODS: One hundred treatment-naïve HCV patients with advanced fibrosis (F3/4) treated with direct-acting antivirals (DAAs) or peginterferon/ribavirin who achieved sustained virologic response [SVR, defined as undetectable HCV RNA throughout 12 wk (SVR12) for the DAA group or 24 wk (SVR24) for the interferon group after completion of antiviral therapy] were enrolled since 2003. The primary endpoint was the development of new-onset HCC. Standard HCC surveillance (abdominal ultrasound and α-fetoprotein) was performed every six months during the follow-up. Overall, 64 serum cytokines were detected by the multiplex immunoassay at baseline and 24 wk after end-of-treatment. RESULTS: HCC developed in 12 of the 97 patients over 459 person-years after HCV eradication. In univariate analysis, the Fibrosis-4 index (FIB-4), hemoglobin A1c (HbA1c), the dynamics of tumor necrosis factor-α (TNF-α), and TNF-like weak inducer of apoptosis (TWEAK) after antiviral therapy were significant HCC predictors. The multivariate Cox regression model showed that ΔTNF-α (≤ -5.7 pg/mL) was the most important risk factor for HCC (HR = 11.54, 95%CI: 2.27-58.72, P = 0.003 in overall cases; HR = 9.98, 95%CI: 1.88-52.87, P = 0.007 in the interferon group). An HCC predictive model comprising FIB-4, HbA1c, ΔTNF-α, and ΔTWEAK had excellent performance, with 3-, 5-, 10-, and 13-year areas under the curve of 0.882, 0.864, 0.903, and 1.000, respectively. The 5-year accumulative risks of HCC were 0%, 16.9%, and 40.0% in the low-, intermediate-, and high-risk groups, respectively. CONCLUSION: Downregulation of serum TNF-α significantly increases the risk of HCC after HCV eradication. A predictive model consisting of cytokine kinetics could ameliorate personalized HCC surveillance strategies for post-SVR HCV patients.

Artificial intelligence based on serum biomarkers predicts the efficacy of lenvatinib for unresectable hepatocellular carcinoma.

Lenvatinib has been effective not only as a first-line but also as a later-line systemic therapy for unresectable hepatocellular carcinoma (uHCC) in real-world clinical practice. How to predict the efficacy of lenvatinib and guide appropriate therapy selection in patients with uHCC have become important issues. This study aimed to investigate the impact of serum biomarkers on the treatment outcomes of patients with uHCC treated with lenvatinib in a real-world setting using an artificial intelligence algorithm. We measured serum biomarkers, including alpha-fetoprotein (AFP), albumin-bilirubin (ALBI) grade, and circulating angiogenic factors (CAFs [i.e., vascular endothelial growth factor, angiopoietin-2, fibroblast growth factor-19 [FGF19], and FGF21]) and analyzed treatment outcomes, including objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) in patients with uHCC treated with lenvatinib. The results of this study demonstrated that an AFP reduction ≥ 40% from baseline within 8 weeks after lenvatinib induction was associated with a higher ORR. With baseline biomarkers using a decision tree-based model, we identified patients with high, intermediate, and low ORRs (84.6%, 21.7% and 0%, respectively; odds ratio, 53.04, P < 0.001, high versus intermediate/low groups). Based on the decision tree-based survival predictive model, baseline AFP was the most important factor for OS, followed by ALBI grade and FGF21.

Research Progress of Photo-/Electro-Driven Thermochromic Smart Windows.

Thermochromic smart windows can automatically control solar radiation according to the ambient temperature. Compared with photochromic and electrochromic smart windows, they have a stronger applicability and lower energy consumption, and have a wide range of application prospects in the field of building energy efficiency. At present, aiming at the challenge of the high transition temperature of thermochromic smart windows, a large amount of innovative research has been carried out via the principle that thermochromic materials can be driven to change their optical performance by photothermal or electrothermal effects at room temperature. Based on this, the research progress of photo- and electro-driven thermochromic smart windows is summarized from VO2-based composites, hydrogels and liquid crystals, and it is pointed out that there are two main development trends of photo-/electro-driven thermochromic smart windows. One is exploring the diversified combination methods of photothermal materials and thermochromic materials, and the other is developing low-cost large-area heating electrodes.

The Persistence of Hepatitis C Virus Infection in Hepatocytes Promotes Hepatocellular Carcinoma Progression by Pro-Inflammatory Interluekin-8 Expression.

BACKGROUND: A large amount of epidemiological evidence indicates that persistent HCV infection is the main risk factor for HCC. We aimed to study the effects of persistent HCV infection on the interaction of the virus and host cell to identify cancer gene profiles. METHODS: Next-generation sequencing (NGS) was used to identify differentially expressed genes between uninfected Huh7.5.1 control cells, short-term HCV (S-HCV), early long-term HCV (eL-HCV), and long-term HCV (L-HCV) infections, which were analyzed using different dynamic bioinformatics and analytic tools. mRNA expression was validated and quantified using q-PCR. One hundred ninety-six serum samples of HCV patients with IFN/RBV treatment were used to study chemokine levels. RESULTS: S-HCV activates an inflammatory response and drives cell death and apoptosis through cell cycle arrest via MAPK signaling. L-HCV promotes cell growth and alters cell adhesion and chemokine signaling via CXCL8-mediated-SRC regulation. A total of 196 serum samples from the HCV and HCV-HCC cohorts demonstrated significantly upregulated pro-inflammatory CXCL8 in non-SVR (persistent HCV infection) patients in the HCV-HCC group. CONCLUSIONS: Persistent infection with HCV induced pro-inflammatory CXCL8 and the oncogene SRC, thereby triggering and promoting hepatocarcinogenesis. CXCL8 may be a potential biomarker for monitoring HCV-related HCC progression.

2-Methoxyestradiol synergizes with Erlotinib to suppress hepatocellular carcinoma by disrupting the PLAGL2-EGFR-HIF-1/2α signaling loop.

Erlotinib, an EGFR tyrosine kinase inhibitor has been introduced into cancer chemotherapy. However, the therapeutic effects of erlotinib in hepatocellular carcinoma (HCC) remain vaguely understood. Our previous study found that a hypoxia-mediated PLAGL2-EGFR-HIF-1/2α signaling loop in HCC decreased response to erlotinib. The current study has demonstrated that the combination of erlotinib and 2ME2 exerted synergistic antitumor effects against HCC. Further investigation showed that erlotinib increased the expression level of EGFR, HIF-2α, and PLAGL2, which contributes to the insensitivity of hypoxic HCC cells to erlotinib. The simultaneous exposure to 2ME2 effectively inhibited the expression level of EGFR, HIF-2α, and PLAGL2 that was induced by erlotinib. This contributes to the synergistic effect of the two therapeutic agents. Furthermore, the combination of erlotinib and 2ME2 induced apoptosis and inhibited the stemness of hypoxic HCC cells. Our findings potentially explain the mechanism of HCC insensitivity to erlotinib and provide a new strategy of combining EGFR and HIF1/2α inhibitors for HCC treatment.

Changing epidemiology and viral interplay of hepatitis B, C and D among injecting drug user-dominant prisoners in Taiwan.

The spreading of viral hepatitis among injecting drug users (IDU) is an emerging public health concern. This study explored the prevalence and the risks of hepatitis B virus (HBV), hepatitis C virus (HCV) and hepatitis D virus (HDV) among IDU-dominant prisoners in Taiwan. HBV surface antigen (HBsAg), antibodies to HCV (anti-HCV) and HDV (anti-HDV), viral load and HCV genotypes were measured in 1137(67.0%) of 1697 prisoners. 89.2% of participants were IDUs and none had HIV infection. The prevalence of HBsAg, anti-HCV, dual HBsAg/anti-HCV, HBsAg/anti-HDV, and triple HBsAg/anti-HCV/anti-HDV was 13.6%, 34.8%, 4.9%, 3.4%, and 2.8%, respectively. HBV viremia rate was significantly lower in HBV/HCV-coinfected than HBV mono-infected subjects (66.1% versus 89.9%, adjusted odds ratio/95% confidence intervals [aOR/CI] = 0.27/0.10-0.73). 47.5% anti-HCV-seropositive subjects (n = 396) were non-viremic, including 23.2% subjects were antivirals-induced. The predominant HCV genotypes were genotype 6(40.9%), 1a(24.0%) and 3(11.1%). HBsAg seropositivity was negatively correlated with HCV viremia among the treatment naïve HCV subjects (44.7% versus 72.4%, aOR/CI = 0.27/0.13-0.58). Anti-HCV seropositivity significantly increased the risk of anti-HDV-seropositivity among HBsAg carriers (57.1% versus 7.1%, aOR/CI = 15.73/6.04-40.96). In conclusion, IUDs remain as reservoirs for multiple hepatitis viruses infection among HIV-uninfected prisoners in Taiwan. HCV infection increased the risk of HDV infection but suppressed HBV replication in HBsAg carriers. An effective strategy is mandatory to control the epidemic in this high-risk group.

Elevated interleukin-4 levels predicted advanced fibrosis in chronic hepatitis C.

BACKGROUND: Cytokine imbalance has been associated with chronic hepatitis C virus (HCV) infection. We hypothesized that cytokines have an important role in fibrosis development in HCV infection. METHODS: Data of 92 patients were analyzed retrospectively. Fluorescent Bead immunoassay was used to measure the following serum cytokine levels: Interferon γ, tumor necrosis factor α, interleukin (IL)-1ß, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, and IL-12. Various statistical analyses were used as appropriate. RESULTS: Of the 92 HCV-infected patients, 49 (53.3%) were male, 23 (25%) patients had advanced (fibrosis grades 3-4) fibrosis, and the mean age of the study population was 51.9 ± 9.4 years. Elevation of baseline IL-4 level (>490 pg/mL) was associated with liver fibrosis grade by χ test (odds ratio [OR] = 2.99; 95%, CI = 1.02-8.78; p = 0.042) and multivariate logistic regression (OR = 4.26; 95% CI = 1.13-16.02; p = 0.032). Also, IL-4 had strong diagnostic value in advanced liver fibrosis by using area under receiver operating characteristics curve analysis. Assessment of fibrosis score was consequently developed from our findings and compared with other noninvasive serum markers to assess liver fibrosis. CONCLUSION: This study provides evidence that increased IL-4 expression predicted advanced liver fibrosis in treatment of naive HCV-infected patients. The newly developed "FIL4" score had good predictive value for advanced fibrosis before treatment and this value was even strong in HCV-genotype 1b patients.

Dynamics of PBMC gene expression in hepatitis C virus genotype 1-infected patients during combined peginterferon/ribavirin therapy.

Hepatitis C virus (HCV) can replicate in peripheral blood mononuclear cells (PBMCs), which can produce interferon to defend against virus infection. We hypothesized that dynamic gene expression in PBMCs might impact the treatment efficacy of peginterferon/ribavirin in HCV patients. PBMCs were collected at baseline, 1st week and 4th week of treatment from 27 chronic HCV-1 patients with 48-week peginterferon/ribavirin therapy (screening dataset n = 7; validation dataset n = 20). A sustained virologic response (SVR) was defined as undetectable HCV RNA throughout the 24 weeks after end-of-treatment. A complete early virologic response (cEVR) was defined as negative HCV RNA at treatment week 12. Forty-three differentially expressed genes identified by Affymetrix microarray were validated by quantitative polymerase chain reaction. Thirteen genes at week 1 and 24 genes at week 4 were upregulated in the SVR group compared with the non-SVR group. We selected 8 target genes (RSAD2, LOC26010, HERC5, HERC6, IFI44, SERPING1, IFITM3, and DDX60) at week 1 as the major components of the predictive model. This predictive model reliably stratified the responders and non-responders at week 1 (AUC = 0.89, p = 0.007 for SVR; AUC = 0.95, p = 0.003 for cEVR), especially among patients carrying the IL28B rs8099917 TT genotype (AUC = 0.89, p = 0.02 for SVR; AUC = 1.0, p = 0.008 for cEVR). The performance of this predictive model was superior to traditional predictors, including the rapid virologic response, viral load and IL28B genotype.

Elevated on-treatment levels of serum IFN-gamma is associated with treatment failure of peginterferon plus ribavirin therapy for chronic hepatitis C.

Chronic hepatitis C virus (HCV) infection had been associated with cytokine imbalance. Cytokine dynamics in response to peginterferon/ribavirin therapy have an impact on the treatment efficacy for HCV patients. Ninety-two treatment-naive chronic hepatitis C patients were treated with 24 or 48 weeks of peginterferon/ribavirin therapy according to their viral genotypes. Sustained virologic response (SVR) is defined as undetectable HCV RNA throughout a 24-week post-treatment follow-up period. Dynamic serum levels of the following cytokines: (1) Th1-mediated cytokines: IFN-γ, interleukin-2, and TNF-alpha; (2)Th2-mediated cytokines: interleukin-4, interleukin-5, interleukin-6, and interleukin-10 and (3)immuno-modulatory cytokines: interleukin-1ß, interleukin-8, and interleukin-12 were determined by Fluorescent Bead immunoassay. Serial dynamic cytokine expression demonstrated that not only elevated IFN-γ concentrations at specific time points but also the total IFN-γ amount was strongly linked to non-response in peginterferon/ribavirin therapy. IFN-γ levels could serve as an independent predictor for SVR analyzed by multivariate logistic regression test. The accuracy of discriminating responders from non-responders was acceptable when IFN-γ cut-off levels were set at 180, 120, and 40 pg/ml at the 4th week, 12th week, and end-of-treatment of therapy, respectively. Elevated on-treatment IFN-γ concentration was significantly associated with treatment failure among interleukin-28B rs8099917TT carriers and those patients failed to achieve rapid virologic response.

Lack of association between the IL-10 gene polymorphisms and features of the metabolic syndrome.

BACKGROUNDS: Insulin resistance plays a major role in the pathogenesis of the metabolic syndrome. Inflammation is the leading cause of insulin resistance, and interleukin 10 (IL-10) is one of the anti-inflammatory cytokines. We conducted a case-control study to investigate the association between the IL-10 polymorphisms and the metabolic syndrome. METHODS: One thousand two hundred two unrelated subjects residing in southern Taiwan were retrospectively recruited from a community-based health screening program. Two hundred sixty subjects were defined as the metabolic syndrome (3-5 risk components) and 549 subjects as controls (0-1 risk component) on the basis of the Asian version of the Adult Treatment Panel III criteria. A functional IL-10 single nucleotide polymorphism (SNP; rs1800871) and 2 tagging SNPs (rs3790622, rs3021094) were genotyped by TaqMan method. RESULTS: We analyzed the association between the genotypes and the presence of the metabolic syndrome or metabolic traits by χ² test and multivariant logistic regression. None of the IL-10 SNPs were found to be significantly related with the metabolic syndrome or its risk components. All the 3 SNPs were in single linkage disequilibrium block. Haplotype analysis did not yield significant association between IL-10 gene and the metabolic syndrome (global P = 0.88). CONCLUSIONS: Because we used tagging SNPs and a modest clinical cohort, we concluded that the IL-10 gene polymorphisms may be unlikely to play an important role for the metabolic syndrome.