Science and technology ethics are important measures to strengthen science and technology governance and promote responsible innovation.China's science and technology ethics construction started relatively late.To adapt to China's science and technology management system and alleviate the contradiction between the constructions of science and technology ethics and the explosive growth of future scientific and technological activities,this paper constructed a"one-top-three-layers"hierarchical and classified science and technology ethics governance framework,as well as analyzed the current situation.It was found that China's science and technology ethics governance system has shortcomings in top-level systems,hierarchical management,ethics governance of emerging technologies,policy implementation in provinces and cities,and the responsibility of innovation subjects.Subsequently,in response to the shortcomings of China's hierarchical and classified science and technology ethics governance system,international advanced experiences were summarized and supplemented by specific cases.Finally,based on actual conditions and international experiences,recommendations for improving China's hierarchical and classified science and technology ethics governance system were proposed from seven aspects,including improving the science and technology ethics provisions in China's legal documents,clarifying the ethical risk levels of various scientific and technological activities in life sciences,medicine,and artificial intelligence,strengthening the ethical governance of emerging technologies,urging provincial government departments to improve their science and technology ethics governance policies,reinforcing the main responsibility of innovation subjects in the hierarchical and classified science and technology ethics governance system,encouraging third-party to take part in science and technology ethics governance work,as well as building a large language model of science and technology ethics governance.

Intrahepatic cholangiocarcinoma (ICC) is the second most common primary malignant tumor of the liver, characterized by high lethality and poor prognosis. Among the three subtypes of ICC, the intrahepatic mass-forming cholangiocarcinoma (IMCC) is the most prevalent. In recent years, the incidence of IMCC has been continuously rising, and its differential diagnosis and prognostic prediction have received widespread attention. Multimodal functional magnetic resonance imaging (MRI) integrates the advantages of various imaging modalities, capable of monitoring tumor hemodynamic changes, cellular metabolism, and other factors. Radiomics, with MRI as its basis, utilizes high-throughput extraction of imaging features to non-invasively acquire information on intra-tumor heterogeneity, subsequently assisting in the diagnosis of liver tumors. This article mainly summarizes the advancements in the application of multimodal functional MRI and MRI-based radiomics in the differential diagnosis and prognostic prediction of IMCC.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Science and technology ethics are important measures to strengthen science and technology governance and promote responsible innovation.China's science and technology ethics construction started relatively late.To adapt to China's science and technology management system and alleviate the contradiction between the constructions of science and technology ethics and the explosive growth of future scientific and technological activities,this paper constructed a"one-top-three-layers"hierarchical and classified science and technology ethics governance framework,as well as analyzed the current situation.It was found that China's science and technology ethics governance system has shortcomings in top-level systems,hierarchical management,ethics governance of emerging technologies,policy implementation in provinces and cities,and the responsibility of innovation subjects.Subsequently,in response to the shortcomings of China's hierarchical and classified science and technology ethics governance system,international advanced experiences were summarized and supplemented by specific cases.Finally,based on actual conditions and international experiences,recommendations for improving China's hierarchical and classified science and technology ethics governance system were proposed from seven aspects,including improving the science and technology ethics provisions in China's legal documents,clarifying the ethical risk levels of various scientific and technological activities in life sciences,medicine,and artificial intelligence,strengthening the ethical governance of emerging technologies,urging provincial government departments to improve their science and technology ethics governance policies,reinforcing the main responsibility of innovation subjects in the hierarchical and classified science and technology ethics governance system,encouraging third-party to take part in science and technology ethics governance work,as well as building a large language model of science and technology ethics governance.

Intrahepatic cholangiocarcinoma (ICC) is the second most common primary malignant tumor of the liver, characterized by high lethality and poor prognosis. Among the three subtypes of ICC, the intrahepatic mass-forming cholangiocarcinoma (IMCC) is the most prevalent. In recent years, the incidence of IMCC has been continuously rising, and its differential diagnosis and prognostic prediction have received widespread attention. Multimodal functional magnetic resonance imaging (MRI) integrates the advantages of various imaging modalities, capable of monitoring tumor hemodynamic changes, cellular metabolism, and other factors. Radiomics, with MRI as its basis, utilizes high-throughput extraction of imaging features to non-invasively acquire information on intra-tumor heterogeneity, subsequently assisting in the diagnosis of liver tumors. This article mainly summarizes the advancements in the application of multimodal functional MRI and MRI-based radiomics in the differential diagnosis and prognostic prediction of IMCC.

Atrial fibrillation (AF) is one of the most common arrhythmias, associated with high morbidity, mortality, and healthcare costs, and it places a significant burden on both individuals and society. Anti-arrhythmic drugs are the most commonly used strategy for treating AF. However, drug therapy faces challenges because of its limited efficacy and potential side effects. Catheter ablation is widely used as an alternative treatment for AF. Nevertheless, because the mechanism of AF is not fully understood, the recurrence rate after ablation remains high. In addition, the outcomes of ablation can vary significantly between medical institutions and patients, especially for persistent AF. Therefore, the issue of which ablation strategy is optimal is still far from settled. Computational modeling has the advantages of repeatable operation, low cost, freedom from risk, and complete control, and is a useful tool for not only predicting the results of different ablation strategies on the same model but also finding optimal personalized ablation targets for clinical reference and even guidance. This review summarizes three-dimensional computational modeling simulations of catheter ablation for AF, from the early-stage attempts such as Maze III or circumferential pulmonary vein isolation to the latest advances based on personalized substrate-guided ablation. Finally, we summarize current developments and challenges and provide our perspectives and suggestions for future directions.