A Retrospective Study of Long-Term Clinical Outcomes in Patients with Chronic Hepatitis C Treated with Interferon and Ribavirin.

BACKGROUND: The key endpoint for treatment efficacy in chronic hepatitis C (CHC) is the absence of a detectable virus at 24 weeks after treatment. This study aims to determine the long-term clinical outcomes in patients with CHC after interferon and ribavirin treatment and the factors that influence them. METHODS: A retrospective study was conducted on 259 patients with CHC between 2003 and 2021, and the patients were divided into treated (n = 159) and untreated (n = 100) groups. The median observation duration was four years for the treated group (range: 1-15 years) and four years for untreated groups (range: 1-14 years). RESULTS: The mean ages of the treated and untreated groups were 47.38 ± 9.07 and 51.17 ± 8.38 years, respectively. Regardless of whether antiviral therapy had been administered, patients with undetectable hepatitis C virus (HCV) load had a lower risk of developing liver cirrhosis and hepatocellular carcinoma (HCC) than patients with detectable HCV load (p < 0.05). Furthermore, patients with HCV genotype 1b were more likely to develop cirrhosis and HCC than patients with HCV non-genotype 1b (p < 0.05). Based on the results of multivariate analysis, age of 50 years and above (hazard ratio [HR] = 6.74, 95% confidence interval [CI] = 2.79-16.28) and infection with HCV genotype 1b (HR = 2.43, 95% CI = 1.06-5.56) were significant predictors of liver cirrhosis and HCC development, whereas undetectable HCV RNA load (HR = 0.14, 95% CI = 0.43-0.46) was a protective factor. CONCLUSIONS: During the long-term follow-up, no cases of HCC were discovered in patients with undetectable HCV RNA load. Nevertheless, long-term monitoring is still required in patients with liver cirrhosis, since it increases the risk for developing liver cancer.

A pixel-wise framework based on convolutional neural network for surface defect detection.

The automatic surface defect detection system supports the real-time surface defect detection by reducing the information and high-lighting the critical defect regions for high level image under-standing. However, the defects exhibit low contrast, different textures and geometric structures, and several defects making the surface defect detection more difficult. In this paper, a pixel-wise detection framework based on convolutional neural network (CNN) for strip steel surface defect detection is proposed. First we extract the salient features by a pre-trained backbone network. Secondly, contextual weighting module, with different convolutional kernels, is used to extract multi-scale context features to achieve overall defect perception. Finally, the cross integrate is employed to make the full use of these context information and decoded the information to realize feature information complementation. The experimental results of this study demonstrate that the proposed method outperforms against the previous state-of-the-art methods on strip steel surface defect dataset (MAE: 0.0396; Fß: 0.8485).

A Cost-Effective Method for Preparing Robust and Conductive Superhydrophobic Coatings Based on Asphalt.

The wide application of superhydrophobic materials is mainly hindered by the poor mechanical robustness and complicated preparation method. To overcome these problems, we tried to make a combination of hierarchical and self-similar structure by the means of a simple spraying method. By adding nanofiller (carbon nanotube) and microfiller (graphite powder and expanded graphite), the hierarchical structure was constructed. By further doping the fillers in the commercial asphalt uniformly, the self-similar structure was prepared. Based on the aforementioned work, the as-prepared sample could withstand the sandpaper abrasion for 12.00 m under 4.90 kPa. Moreover, this superhydrophobic coating demonstrated good conductivity, superior self-cleaning property, and excellent corrosion resistance. The integration of conductivity with the superhydrophobicity might open new avenues for ground grid applications.