As new evidence emerges, treatment strategies toward the functional cure of chronic hepatitis B are evolving. In 2019, a panel of national hepatologists published a Consensus Statement on the functional cure of chronic hepatitis B. Currently, an international group of hepatologists has been assembled to evaluate research since the publication of the original consensus, and to collaboratively develop the updated statements. The 2.0 Consensus was aimed to update the original consensus with the latest available studies, and provide a comprehensive overview of the current relevant scientific literatures regarding functional cure of hepatitis B, with a particular focus on issues that are not yet fully clarified. These cover the definition of functional cure of hepatitis B, its mechanisms and barriers, the effective strategies and treatment roadmap to achieve this endpoint, in particular new surrogate biomarkers used to measure efficacy or to predict response, and the appropriate approach to pursuing a functional cure in special populations, the development of emerging antivirals and immunomodulators with potential for curing hepatitis B. The statements are primarily intended to offer international guidance for clinicians in their practice to enhance the functional cure rate of chronic hepatitis B.

As new evidence emerges, treatment strategies toward the functional cure of chronic hepatitis B are evolving. In 2019, a panel of national hepatologists published a Consensus Statement on the functional cure of chronic hepatitis B. Currently, an international group of hepatologists has been assembled to evaluate research since the publication of the original consensus, and to collaboratively develop the updated statements. The 2.0 Consensus was aimed to update the original consensus with the latest available studies, and provide a comprehensive overview of the current relevant scientific literatures regarding functional cure of hepatitis B, with a particular focus on issues that are not yet fully clarified. These cover the definition of functional cure of hepatitis B, its mechanisms and barriers, the effective strategies and treatment roadmap to achieve this endpoint, in particular new surrogate biomarkers used to measure efficacy or to predict response, and the appropriate approach to pursuing a functional cure in special populations, the development of emerging antivirals and immunomodulators with potential for curing hepatitis B. The statements are primarily intended to offer international guidance for clinicians in their practice to enhance the functional cure rate of chronic hepatitis B.

As new evidence emerges, treatment strategies toward the functional cure of chronic hepatitis B are evolving. In 2019, a panel of national hepatologists published a Consensus Statement on the functional cure of chronic hepatitis B. Currently, an international group of hepatologists has been assembled to evaluate research since the publication of the original consensus, and to collaboratively develop the updated statements. The 2.0 Consensus was aimed to update the original consensus with the latest available studies, and provide a comprehensive overview of the current relevant scientific literatures regarding functional cure of hepatitis B, with a particular focus on issues that are not yet fully clarified. These cover the definition of functional cure of hepatitis B, its mechanisms and barriers, the effective strategies and treatment roadmap to achieve this endpoint, in particular new surrogate biomarkers used to measure efficacy or to predict response, and the appropriate approach to pursuing a functional cure in special populations, the development of emerging antivirals and immunomodulators with potential for curing hepatitis B. The statements are primarily intended to offer international guidance for clinicians in their practice to enhance the functional cure rate of chronic hepatitis B.

Objective To establish an optimized diagnostic model for hepatocellular carcinoma(HCC),designated as G-GADA,in chronic hepatitis B(CHB)patients based on the parameters of age,sex,alpha-fetoprotein(AFP),des-γ-carboxy prothrombin(DCP),and Golgi protein 73(GP73),to address the problems of low sensitivity and specificity in the early diagnosis of hepatitis B virus(HBV)-related liver cancer,and to assess the value of this model in the diagnosis of HCC.Methods A retrospective analysis was performed for 201 CHB patients(CHB group),137 patients with HBV-related liver cirrhosis(LC group),and 111 treatment-na?ve patients with newly diagnosed HCC(HCC group)who were admitted to Mengchao Hepatobiliary Hospital of Fujian Medical University from June 2015 to June 2020.Serological markers(AFP,DCP,alpha-fetoprotein L3%[AFP-L3%],and GP73)were compared between groups and were analyzed in terms of their differences from the clinical and tumor characteristics of HCC patients,and the Spearman correlation analysis was used to assess the correlation between different markers.A Logistic regression analysis was used to establish a diagnostic model for liver cancer,and the receiver operating characteristic(ROC)curve was used to assess the diagnostic performance of each marker.Results Comparison of clinical features between CHB,LC,and HCC patients showed that HCC patients had significantly higher age,proportion of male patients,and serum levels of DCP,AFP,GP73,and AFP-L3%(all P<0.05).In HCC patients,DCP levels are associated with tumor size and microvascular invasion;AFP levels are related to patient age,tumor size,tumor number,distant metastasis,and microvascular invasion;AFP-L3%levels are associated with patient age,tumor size,tumor number,distant metastasis,Milan staging,and microvascular invasion;GP73 levels are linked to tumor number,distant metastasis,and microvascular invasion(all P<0.05).The correlation analysis of the serum markers showed a strong positive correlation between AFP and AFP-L3%(r=0.71,P<0.05)and a moderate positive correlation between AFP and GP73(r=0.33,P<0.05)and between AFP-L3%and GP73(r=0.41,P<0.05).Based on the features of age,sex,DCP,AFP,and GP73,the multivariate Logistic regression analysis was used to establish a G-GADA diagnostic model for HCC,and for all patients,the G-GADA model had an area under the ROC curve(AUC)of 0.915(95%confidence interval[CI]:0.875-0.945)in the derivation cohort and 0.913(95%CI:0.862-0.950)in the validation cohort for the diagnosis of HCC.In the AFP-negative patients,the G-GADA model achieved an AUC of 0.884(95%CI:0.833-0.924)in the derivation cohort and 0.851(95%CI:0.779-0.907)in the validation cohort,and in the patients with liver cirrhosis,the G-GADA model achieved an AUC of 0.901(95%CI:0.841-0.944)in the derivation cohort and 0.885(95%CI:0.806-0.940)in the validation cohort.Conclusion The G-GADA diagnostic model based on multiple variables significantly improves the detection rate of HCC,and demonstrates superior diagnostic performance in patients with low AFP expression and those with liver cirrhosis.The G-GADA model has a better clinical application value in the noninvasive diagnosis of HCC.

A 78-year-old male patient presented with significant dilation of the non-coronary sinus of the aorta,which compressed the right atrium and formed a local low-voltage area,resulting in frequent polymorphic atrial premature beats(24-hour load 40.9%).Through dual-chamber combined activation mapping,the target point(right atrial appendage-tricuspid valve annulus junction)was locked.After radiofrequency ablation,atrial premature beats disappeared,and there was no recurrence during the follow-up period of six months.Currently,there are many causes of atrial arrhythmias related to atrial compression,but it is extremely rare for the right atrium to be involved and cause isolated frequent atrial premature beats.Treatment should be individualized,choosing either interventional or surgical methods,and emphasizing the interpretation of anatomical-electrical correlations using multimodal imaging(ultrasound,CT angiography,three-dimensional electroanatomical system).

Objective:To develop a deep learning-based segmentation model MT-UNet to automatically segment bone metastases and benign bone lesions in bone scintigraphy with SPECT/CT.Methods:A total of 93 patients (48 males and 45 females, age 28-84 years) who underwent bone SPECT/CT in the Department of Nuclear Medicine, General Hospital of Northern Theater Command from June 2023 to December 2023 were enrolled retrospectively in this study, with a total of 184 bone lesions (94 benign lesions and 90 metastatic tumors). The MT-UNet was employed to segment bone lesions in SPECT, CT and SPECT/CT images respectively. Comparative analysis with 8 segmentation models was performed. The training set and validation set were divided by using 5-fold cross-validation and transfer learning was introduced to further enhance the robustness of the model. An additional cohort of 22 patients (15 males and 7 females, age 37-87 years) who received bone SPECT/CT in the Department of Nuclear Medicine, General Hospital of Northern Theater Command from April 2023 to May 2023 were included, comprising 40 bone lesions (22 benign lesions and 18 metastatic tumors) as the test set of MT-UNet. Segmentation performance of different models was assessed using accuracy, sensitivity, specificity, AUC, intersection over union and Dice similarity coefficient (DSC). Delong test was used to compare the segmentation efficacy among different models in the test set.Results:In the validation set, MT-UNet demonstrated DSC of 0.940, 0.962, and 0.963 for SPECT, CT, and SPECT/CT bone lesion segmentation, respectively, which were outperformed other models. Following transfer learning implementation, the SPECT/CT model′s DSC was improved to 0.984. In the test set, MT-UNet maintained comparable segmentation performance to the validation set, with significant AUC differences among the three models ( Z values: from -15.42 to -9.27, all P<0.01). Compared with conventional image interpretation, MT-UNet-based segmentation reduced physician interpretation time from 164min to 102min. Conclusion:MT-UNet has shown good performance in automatic segmentation of bone metastases and benign bone lesions, and is expected to become an important part of SPECT/CT image intelligent diagnosis system for bone metastases.

Objective:To develop a deep learning-based segmentation model MT-UNet to automatically segment bone metastases and benign bone lesions in bone scintigraphy with SPECT/CT.Methods:A total of 93 patients (48 males and 45 females, age 28-84 years) who underwent bone SPECT/CT in the Department of Nuclear Medicine, General Hospital of Northern Theater Command from June 2023 to December 2023 were enrolled retrospectively in this study, with a total of 184 bone lesions (94 benign lesions and 90 metastatic tumors). The MT-UNet was employed to segment bone lesions in SPECT, CT and SPECT/CT images respectively. Comparative analysis with 8 segmentation models was performed. The training set and validation set were divided by using 5-fold cross-validation and transfer learning was introduced to further enhance the robustness of the model. An additional cohort of 22 patients (15 males and 7 females, age 37-87 years) who received bone SPECT/CT in the Department of Nuclear Medicine, General Hospital of Northern Theater Command from April 2023 to May 2023 were included, comprising 40 bone lesions (22 benign lesions and 18 metastatic tumors) as the test set of MT-UNet. Segmentation performance of different models was assessed using accuracy, sensitivity, specificity, AUC, intersection over union and Dice similarity coefficient (DSC). Delong test was used to compare the segmentation efficacy among different models in the test set.Results:In the validation set, MT-UNet demonstrated DSC of 0.940, 0.962, and 0.963 for SPECT, CT, and SPECT/CT bone lesion segmentation, respectively, which were outperformed other models. Following transfer learning implementation, the SPECT/CT model′s DSC was improved to 0.984. In the test set, MT-UNet maintained comparable segmentation performance to the validation set, with significant AUC differences among the three models ( Z values: from -15.42 to -9.27, all P<0.01). Compared with conventional image interpretation, MT-UNet-based segmentation reduced physician interpretation time from 164min to 102min. Conclusion:MT-UNet has shown good performance in automatic segmentation of bone metastases and benign bone lesions, and is expected to become an important part of SPECT/CT image intelligent diagnosis system for bone metastases.

A 78-year-old male patient presented with significant dilation of the non-coronary sinus of the aorta,which compressed the right atrium and formed a local low-voltage area,resulting in frequent polymorphic atrial premature beats(24-hour load 40.9%).Through dual-chamber combined activation mapping,the target point(right atrial appendage-tricuspid valve annulus junction)was locked.After radiofrequency ablation,atrial premature beats disappeared,and there was no recurrence during the follow-up period of six months.Currently,there are many causes of atrial arrhythmias related to atrial compression,but it is extremely rare for the right atrium to be involved and cause isolated frequent atrial premature beats.Treatment should be individualized,choosing either interventional or surgical methods,and emphasizing the interpretation of anatomical-electrical correlations using multimodal imaging(ultrasound,CT angiography,three-dimensional electroanatomical system).

Objective To establish an optimized diagnostic model for hepatocellular carcinoma(HCC),designated as G-GADA,in chronic hepatitis B(CHB)patients based on the parameters of age,sex,alpha-fetoprotein(AFP),des-γ-carboxy prothrombin(DCP),and Golgi protein 73(GP73),to address the problems of low sensitivity and specificity in the early diagnosis of hepatitis B virus(HBV)-related liver cancer,and to assess the value of this model in the diagnosis of HCC.Methods A retrospective analysis was performed for 201 CHB patients(CHB group),137 patients with HBV-related liver cirrhosis(LC group),and 111 treatment-na?ve patients with newly diagnosed HCC(HCC group)who were admitted to Mengchao Hepatobiliary Hospital of Fujian Medical University from June 2015 to June 2020.Serological markers(AFP,DCP,alpha-fetoprotein L3%[AFP-L3%],and GP73)were compared between groups and were analyzed in terms of their differences from the clinical and tumor characteristics of HCC patients,and the Spearman correlation analysis was used to assess the correlation between different markers.A Logistic regression analysis was used to establish a diagnostic model for liver cancer,and the receiver operating characteristic(ROC)curve was used to assess the diagnostic performance of each marker.Results Comparison of clinical features between CHB,LC,and HCC patients showed that HCC patients had significantly higher age,proportion of male patients,and serum levels of DCP,AFP,GP73,and AFP-L3%(all P<0.05).In HCC patients,DCP levels are associated with tumor size and microvascular invasion;AFP levels are related to patient age,tumor size,tumor number,distant metastasis,and microvascular invasion;AFP-L3%levels are associated with patient age,tumor size,tumor number,distant metastasis,Milan staging,and microvascular invasion;GP73 levels are linked to tumor number,distant metastasis,and microvascular invasion(all P<0.05).The correlation analysis of the serum markers showed a strong positive correlation between AFP and AFP-L3%(r=0.71,P<0.05)and a moderate positive correlation between AFP and GP73(r=0.33,P<0.05)and between AFP-L3%and GP73(r=0.41,P<0.05).Based on the features of age,sex,DCP,AFP,and GP73,the multivariate Logistic regression analysis was used to establish a G-GADA diagnostic model for HCC,and for all patients,the G-GADA model had an area under the ROC curve(AUC)of 0.915(95%confidence interval[CI]:0.875-0.945)in the derivation cohort and 0.913(95%CI:0.862-0.950)in the validation cohort for the diagnosis of HCC.In the AFP-negative patients,the G-GADA model achieved an AUC of 0.884(95%CI:0.833-0.924)in the derivation cohort and 0.851(95%CI:0.779-0.907)in the validation cohort,and in the patients with liver cirrhosis,the G-GADA model achieved an AUC of 0.901(95%CI:0.841-0.944)in the derivation cohort and 0.885(95%CI:0.806-0.940)in the validation cohort.Conclusion The G-GADA diagnostic model based on multiple variables significantly improves the detection rate of HCC,and demonstrates superior diagnostic performance in patients with low AFP expression and those with liver cirrhosis.The G-GADA model has a better clinical application value in the noninvasive diagnosis of HCC.

Chronic hepatitis B virus (HBV) infection is one of the major public health issues of ongoing global concern. Due to inadequate understanding of the HBV life cycle, there is a lack of effective drugs to cure chronic hepatitis B. During HBV replication, covalently closed circular DNA (cccDNA) serves as the template for viral replication and can be transcribed to produce five viral RNAs of 3.5, 2.4, 2.1 kb and 0.7 kb in length, which are translated to produce HBeAg, core protein, polymerase (P) protein, HBsAg and HBx proteins, respectively. Among them, the 3.5 kb pregenomic RNA (pgRNA) is also the template for viral reverse transcription. Polymerase protein recognizes and binds to the capsid assembly signal on the pgRNA to initiate capsid assembly and reverse transcription. Recent studies have revealed that the processes of splicing, nuclear export, stability, translation, and pgRNA encapsidation of HBV RNAs are regulated by a post-transcriptional regulatory network within the host cell and depend on unique post-transcriptional regulatory elements in the HBV RNA structure. The aim of this review is to overview the post-transcriptional regulatory mechanisms of HBV RNA and their applications in the study of HBV antiviral therapeutics, with the aim of providing new ideas for the development of new drugs targeting HBV RNA.