Quantitative analysis of plasma HBV DNA for early evaluation of the response to transcatheter arterial embolization for HBV-related hepatocellular carcinoma

AIM： To assesse changes in plasma HBV DNA after TAE in HBV-related HCC and correlate the levels with the pattern of lipiodol accumulation on CT. METHODS： Between April and June 2001, 14 patients with HBV-associated HCC who underwent TAE for inoperable or recurrent tumor were studied. Levels of plasma HBV DNA were measured by real-time quantitative PCR daily for five consecutive days after TAE. More than twofold elevation of circulating HBV DNA was considered as a definite elevation. Abdominal CT was performed 1-2 mo after TAE for the measurement of lipiodol retention. RESULTS： Circulating HBV DNA in 10 out of 13 patients was elevated after TAE, except for one patient whose plasma HBV DNA was undetectable before and after TAE. In group Ⅰ patients （n = 6）, the HBV DNA elevation persisted for more than 2 d, while in group Ⅱ （n = 7）, the HBV DNA elevation only appeared for i d or did not reach a definite elevation. There were no significant differences in age or tumor size between the two groups. Patients in group Ⅰ had significantly better lipiodol retention （79.31±28.79%） on subsequent abdominal CT than group Ⅱ （18.43±10.61%） （P = 0.02）. CONCLUSION： Patients with durable HBV DNA elevation for more than 2 d correlated with good lipiodol retention measured 1 mo later, while others associated with poor lipiodol retention. Thus, circulating HBV DNA may be an early indicator of the success or failure of TAE.

Role of epithelial-to-mesenchymal transition in the pulmonary fi brosis induced by paraquat in rats

BACKGROUND:This study aims to explore the characteristics of the epithelial-to-mesenchymal transition(EMT)process and its underlying molecular mechanisms in the period of paraquat(PQ)-induced pulmonary fi brosis(PF).METHODS:Picrosirius red staining and collagen volume fraction were utilized to evaluate the pathological changes of PQ-induced PF in rats.Immunohistochemistry,Western blot,and real-time reverse transcriptase-polymerase chain reaction(RT-PCR)were used to measure the protein and gene expression of EMT markers,EMT-associated transcription factors,and regulators of EMT-related pathways,respectively.RESULTS:The collagen deposition in the alveolar septum and increased PF markers were characteristics of pathological changes in PQ-induced PF,reached a peak on day 14 after PQ poisoning,and then decreased on day 21.The protein and gene expression of the fibrosis marker,EMT markers,transcription factors,and regulators of EMT-related signaling pathways signifi cantly increased at diff erent time points after PQ poisoning compared with corresponding controls(P<0.05),and most of them reached a peak on day 14,followed by a decrease on day 21.The gene expression of EMT markers was significantly correlated with PF markers,transcription factors,and regulators of EMT-related signaling pathways(P<0.05).The mRNA expression of transcription factors was signifi cantly correlated with that of TGF-β1 and Smad2(P<0.05 or P<0.01),instead of Wnt2 andβ-catenin(P>0.05).CONCLUSIONS:EMT process plays a role in the PQ-induced PF,in which most PF and EMT markers have a peak phenomenon,and its underlying molecular mechanisms might be determined by further studies.

Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition

Photoplethysmography(PPG)biometrics have received considerable attention.Although deep learning has achieved good performance for PPG biometrics,several challenges remain open:1)How to effectively extract the feature fusion representation from time and frequency PPG signals.2)How to effectively capture a series of PPG signal transition information.3)How to extract timevarying information from one-dimensional time-frequency sequential data.To address these challenges,we propose a dual-domain and multiscale fusion deep neural network(DMFDNN)for PPG biometric recognition.The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics,which can learn the time-varying and multiscale discriminative features from the time and frequency domains.Meanwhile,we design a multiscale extraction module to capture transition information,which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information.In addition,the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics.Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.

ECG Biometrics via Enhanced Correlation and Semantic-rich Embedding

Electrocardiogram(ECG)biometric recognition has gained considerable attention,and various methods have been proposed to facilitate its development.However,one limitation is that the diversity of ECG signals affects the recognition performance.To address this issue,in this paper,we propose a novel ECG biometrics framework based on enhanced correlation and semantic-rich embedding.Firstly,we construct an enhanced correlation between the base feature and latent representation by using only one projection.Secondly,to fully exploit the semantic information,we take both the label and pairwise similarity into consideration to reduce the influence of ECG sample diversity.Furthermore,to solve the objective function,we propose an effective and efficient algorithm for optimization.Finally,extensive experiments are conducted on two benchmark datasets,and the experimental results show the effectiveness of our framework.

Multi-Scale Deep Cascade Bi-Forest for Electrocardiogram Biometric Recognition

Electrocardiogram(ECG)biometric recognition has emerged as a hot research topic in the past decade.Although some promising results have been reported,especially using sparse representation learning(SRL)and deep neural network,robust identification for small-scale data is still a challenge.To address this issue,we integrate SRL into a deep cascade model,and propose a multi-scale deep cascade bi-forest(MDCBF)model for ECG biometric recognition.We design the bi-forest based feature generator by fusing L1-norm sparsity and L2-norm collaborative representation to efficiently deal with noise.Then we propose a deep cascade framework,which includes multi-scale signal coding and deep cascade coding.In the former,we design an adaptive weighted pooling operation,which can fully explore the discriminative information of segments with low noise.In deep cascade coding,we propose level-wise class coding without backpropagation to mine more discriminative features.Extensive experiments are conducted on four small-scale ECG databases,and the results demonstrate that the proposed method performs competitively with state-of-the-art methods.