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.

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 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.

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 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.

Objective:To investigate the diagnostic value of nucleic acid matrix-assisted laser desorption ionization-time of flight mass spectrometry（MALDI-TOF MS）in sputum smear-negative patients with nontuberculous Mycobacterial（NTM）pulmonary disease.Methods:Clinical data of 123 patients suspected of NTM pulmonary disease admitted in Public Health Clinical Center Affiliated to Shandong University between July 2022 and November 2023 were retrospectively analyzed. Bronchoalveolar lavage fluid（BALF）specimens were collected for MALDI-TOF MS assay and MGIT 960 culture. The diagnostic efficacy of MALDI-TOF MS for NTM pulmonary disease in patients with negative sputum smears for acid-fast bacilli was evaluated with receiver operating characteristic（ROC）curve. Statistical analysis was performed using SPSS 26.0 software and MedCalc statistical software.Results:Diagnosis of NTM pulmonary disease was finally confirmed in 66 out of the 123 suspected patients. It took 8 to 24 h for MALDI-TOF MS to identify NTM species and resistance. By MALDI-TOF MS，72 NTM strains were identified，with the Mycobacterium avium complex being the most prevalent（34 strains，47.22%），followed by the Mycobacterium abscessus complex（13 strains，18.06%）；resistance to macrolides was detected in 6 cases，while no resistance to aminoglycosides was found. It took 9 to 45 days for BALF MGIT 960 culture to identify NTM，and took 7 to 15 days for NTM typing and drug sensitivity testing. By BALF MGIT 960 culture，28 NTM strains were identified；and 1 case was found to be resistant to macrolides. Using confirmed diagnosis as the gold standard，MALDI-TOF MS demonstrated higher sensitivity，negative predictive value，and agreement rate compared to MGIT 960 culture（84.85% vs. 42.42%，81.13% vs. 56.32%，80.49% vs. 62.60%， χ2=25.667，8.998，9.664， P<0.05 or <0.01）. The area under ROC curve（AUC）for MALDI-TOF MS was significantly higher than that of MGIT 960 culture（0.801 vs. 0.642， Z=3.300， P=0.001）. Conclusion:Compared to MGIT 960 culture，MALDI-TOF MS exhibits superior diagnostic efficiency in detecting NTM pulmonary disease in patients with acid-fast bacilli smear-negative sputum，with advantage of rapidly identifying NTM species and resistance.