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.

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:This study aimed to construct a prediction model for diagnosis of proliferative lupus nephritis based on a machine learning algorithm. Additionally, a user-friendly platform was developed to propose a non-invasive method to assist the pathologic classification of lupus nephritis.Methods:A retrospective analysis was conducted on clinical and pathological data of lupus nephritis patients confirmed by renal biopsy at Zhengzhou University People′s Hospital from January 2017 to August 2023. The study population was randomly divided into training and testing sets in a 7∶3 ratio. Utilizing six machine learning algorithms, classification models were developed. The predictive performance of each model was assessed using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The optimal model, once identified, was deployed as a web-based calculator for convenient model application. SPSS 25.0 and R 4.2.2 were used to analyze the data.Results:The study included a total of 212 patients, with 138 cases with proliferative lupus nephritis and 74 cases with non-proliferative lupus nephritis. The AUC values for the six models, namely logistic regression, decision tree, random forest, support vector machine, extreme gradient boosting, and light gradient boosting machine, were 0.79, 0.62, 0.79, 0.88, 0.81, and 0.77, respectively; the accuracy rates were 82.54%, 65.08%, 74.60%, 85.71%, 69.84%, 71.43%, respectively. Among them, the support vector machine model demonstrated the optimal performance. This model had deployed as a web-based calculator. Based on feature importance scores, the top 10 influencing factors were identified, including anti URNP antibody, immunoglobulin G, serum globulin, estimated glomerular filtration rate, anti Smith antibody, BMI index, anti dsDNA antibody, uric acid, anti-Rib.p antibody, and gender.Conclusion:A prediction model based on machine learning algorithms was successfully established, and a web calculator was developed to offer a simple and non-invasive method for diagnosing proliferative lupus nephritis. This can assist clinicians in evaluating the risk-benefit ratio of kidney biopsy in patients with lupus nephritis.

Objective To analyze the deployment of policy instruments and the distribution of stakeholder engagement in provincial policies on nursing assistant training and management.Methods The relevant policy texts on nursing assistant training and management were systematically searched and collected from the official websites of provincial governments and their direct departments,CNKI,and the PKU Law Database.A two-dimensional framework of policy instruments-stakeholder was constructed,and the content analysis was used to classify,encode and quantify policy clauses.Results The study encompassed 20 provincial-level nursing assistant training and management policies,yielding a total of 359 codes.Within the policy instruments dimension,environmental,supply,and demand instruments constituted 66.30％,28.97％,and 4.74％,respectively.In terms of stakeholders,the management-side accounted for 56.55％,providers and trainers for 18.11％each,partners for 5.01％,and demand-side for 2.23％.Both management-side and trainers engaged with 3 policy instruments,providers with 2,and partners and demand-side with one each.Conclusion In the provincial nursing assistant training and management policies,there are differences in the deployment of policy instruments,and the distribution of stakeholders is uneven.Managers should pay attention to publicity guidance and platform construction,improve incentive mechanisms and training programs,and innovate cooperation models with stakeholders,as well as strengthen communication and exchange.

Objective To analyze the deployment of policy instruments and the distribution of stakeholder engagement in provincial policies on nursing assistant training and management.Methods The relevant policy texts on nursing assistant training and management were systematically searched and collected from the official websites of provincial governments and their direct departments,CNKI,and the PKU Law Database.A two-dimensional framework of policy instruments-stakeholder was constructed,and the content analysis was used to classify,encode and quantify policy clauses.Results The study encompassed 20 provincial-level nursing assistant training and management policies,yielding a total of 359 codes.Within the policy instruments dimension,environmental,supply,and demand instruments constituted 66.30％,28.97％,and 4.74％,respectively.In terms of stakeholders,the management-side accounted for 56.55％,providers and trainers for 18.11％each,partners for 5.01％,and demand-side for 2.23％.Both management-side and trainers engaged with 3 policy instruments,providers with 2,and partners and demand-side with one each.Conclusion In the provincial nursing assistant training and management policies,there are differences in the deployment of policy instruments,and the distribution of stakeholders is uneven.Managers should pay attention to publicity guidance and platform construction,improve incentive mechanisms and training programs,and innovate cooperation models with stakeholders,as well as strengthen communication and exchange.

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:This study aimed to construct a prediction model for diagnosis of proliferative lupus nephritis based on a machine learning algorithm. Additionally, a user-friendly platform was developed to propose a non-invasive method to assist the pathologic classification of lupus nephritis.Methods:A retrospective analysis was conducted on clinical and pathological data of lupus nephritis patients confirmed by renal biopsy at Zhengzhou University People′s Hospital from January 2017 to August 2023. The study population was randomly divided into training and testing sets in a 7∶3 ratio. Utilizing six machine learning algorithms, classification models were developed. The predictive performance of each model was assessed using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The optimal model, once identified, was deployed as a web-based calculator for convenient model application. SPSS 25.0 and R 4.2.2 were used to analyze the data.Results:The study included a total of 212 patients, with 138 cases with proliferative lupus nephritis and 74 cases with non-proliferative lupus nephritis. The AUC values for the six models, namely logistic regression, decision tree, random forest, support vector machine, extreme gradient boosting, and light gradient boosting machine, were 0.79, 0.62, 0.79, 0.88, 0.81, and 0.77, respectively; the accuracy rates were 82.54%, 65.08%, 74.60%, 85.71%, 69.84%, 71.43%, respectively. Among them, the support vector machine model demonstrated the optimal performance. This model had deployed as a web-based calculator. Based on feature importance scores, the top 10 influencing factors were identified, including anti URNP antibody, immunoglobulin G, serum globulin, estimated glomerular filtration rate, anti Smith antibody, BMI index, anti dsDNA antibody, uric acid, anti-Rib.p antibody, and gender.Conclusion:A prediction model based on machine learning algorithms was successfully established, and a web calculator was developed to offer a simple and non-invasive method for diagnosing proliferative lupus nephritis. This can assist clinicians in evaluating the risk-benefit ratio of kidney biopsy in patients with lupus nephritis.

ObjectiveThe natural history of chronic HBV infection often involves a histologically defined immune tolerance state， and once such immune tolerance state is broken， antiviral therapy should be initiated immediately. This study aims to investigate the correlation between immune-mediated liver injury and virological indicators for HBV and precisely identify the patients with a histologically defined immune tolerance state. MethodsThis study was conducted among 577 HBeAg-positive chronic hepatitis B （CHB） patients with HBV DNA >2×10⁶ IU/mL who did not receive antiviral therapy in The Fifth Medical Center of PLA General Hospital， Tianjin Second People’s Hospital， Shanghai Ruijin Hospital， and Taizhou Hospital of Zhejiang Province from January 2010 to December 2022. Liver biopsy was performed to determine the extent of liver injury， and the serum levels of alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， and virological indicators were measured. The proportion of patients with a histologically defined immune tolerance state was analyzed based on the cut-off values of noninvasive indicators recommended in various guidelines， especially HBV load. In addition， a diagnostic model was established for the histologically defined immune tolerance state based on serum HBV DNA at the time when its correlation with liver immunopathological injury disappeared as the new threshold in combination with multiple indicators. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. The Spearman method was used for correlation analysis. The binary Logistic regression analysis was used to establish a multivariate diagnostic model； the area under the receiver operating characteristic curve （AUC） was used to investigate the diagnostic efficiency of different models， and the Z test was used for comparison of AUC. ResultsAmong the patients with an immune tolerance state defined by the noninvasive indicators in the Chinese guidelines （2022 edition）， the EASL guidelines （2017 edition）， the AASLD guidelines （2018 edition）， and the APASL guidelines （2015 edition） for the prevention and treatment of CHB， the patients with a histologically defined immune tolerance state who met the definition in this article （HBV DNA>2×10⁶ IU/mL） accounted for 47.0%， 38.5%， 36.0%， and 44.6%， respectively， which did not exceed 50%. When the threshold of serum HBV DNA increased to >2×10⁸ IU/mL， although the correlation between immune-mediated liver injury and HBV DNA disappeared （r=-0.029， P=0.704）， the patients with a histologically defined immune tolerance state reached only 52.0%. In the cohort of 251 HBeAg-positive patients with serum HBV DNA >1×10⁸ IU/mL， there were significant differences in the levels of HBsAg， HBeAg， HBV DNA， ALT， and AST between the significant liver injury group with 140 children and the non-significant liver injury group with 111 patients （all P<0.05）， and the multivariate binary Logistic regression analysis showed that AST， HBV DNA， and HBeAg were influencing factors for histologically defined immune tolerance state in patients （all P<0.05）. Based on the above indicators and related clinical data， a predictive model was established as logit（P）=1.424－0.028×AST， with an AUC of 0.730， an optimal cut-off value of 30.5 U/L， a sensitivity of 52.8%， and a specificity of 84.1%. A total of 238 adult patients with chronic HBV infection who underwent liver biopsy in Taizhou Hospital of Zhejiang Province were enrolled as the validation cohort， and the analysis showed that the predictive model established in this study had a better efficiency than AST/ALT， FIB-4， and APRI， with an AUC of 0.698， 0.555， 0.518， and 0.373， respectively （all P<0.05）. ConclusionFor HBeAg-positive patients with chronic HBV infection and HBV DNA>2×10⁸ IU/mL， an AST level of >30.5 U/L might indicate the “breakdown” of histologically defined immune tolerance state.