Objective: To investigate the incidence, characteristics, and influencing factors of resolution discrepancies within the minipool (MP) testing model across Chinese blood station laboratories in 2024. Methods: A nationwide, multicenter, cross-sectional study was conducted, including 334 blood station laboratories that reported nucleic acid reactive data among enzyme immunoassay non-reactive samples. Of these, 296 laboratories adopted the pool resolution model, with a total of 12 536 273 samples tested. Systematic analysis was performed on resolution data, focusing on the MP-NAT reactivity rate, the pool resolution concordance rate, and the resolution discrepancy rate. Subgroup analyses were conducted based on reagent types, viral targets, and Ct values. Potential causes were further explored through laboratory surveys and re-examination of raw amplification curves. Results: In 2024, the national average MP-NAT reactivity rate was 0.15%. The overall pool resolution concordance rate was 57.86%, which showed a gradual decline as Ct values increased across all reagents. The national average resolution discrepancy rate was 0.081‱(102/12 536 273), with 17.91%(53/296) of laboratories reporting at least one discrepancy. Nine reagent types were associated with these events, exhibiting reagent-specific patterns. For Reagent A2, the predominant discrepancy was HBV reactive pools resolving as HIV (36.36%); for Reagent D1, HBV pools frequently resolved as HCV (38.89%); and for Reagent E, the most common pattern was HIV pools resolving as HBV (48.00%). These resolution discrepancies were strongly associated with high Ct values: the median pool Ct for HBV exceeded 38, while those for HCV and HIV both exceeded 40. Investigations across 16 laboratories revealed that most discrepant samples exhibited “tailing” amplification curves, with some cases linked to cross-contamination or reagent batch-specific issues. Conclusion: While the incidence of resolution discrepancies in the MP-NAT model remains low in China, variations exist across different reagents and laboratories. These discrepancies are closely associated with low viral load, reagent performance, and laboratory operational practices.

Objective To explore the clinical values of non-invasive myocardial work (MW) and tissue motion annular displacement (TMAD) in evaluation of anthracycline therapy-related cardiac dysfunction in patients with non-Hodgkin lymphoma. Methods A total of 62 patients with non-Hodgkin lymphoma who received standardized chemotherapy based on doxorubicin. Two and three dimensional transthoracic echocardiography, along with two dimensional speckle tracking echocardiography, were performed one day before chemotherapy and at 3, 6, and 9 months after chemotherapy to assess left ventricular ejection fraction, global longitudinal strain (GLS), MW parameters, and TMAD. Logistic regression analysis was used to evaluate the risk factors for cancer therapy-related cardiac dysfunction (CTRCD). The receiver operating characteristic curve was used to assess the diagnostic values of MW- and TMAD-related parameters for CTRCD. Results Compared to baseline, GLS, global work index (GWI), global constructive work (GCW), global work efficiency (GWE), TMAD at midpoint (TMADmid), and TMADmid percentage of left ventricular long-axis diameter (TMADmid%) decreased at 3 months after chemotherapy, while global wasted work (GWW) increased at 6 months after chemotherapy (P＜0.05). Logistic regression analysis showed that the relative reduction in GLS and TMADmid% at 3 months after chemotherapy were independent predictors for CTRCD （P＜0.05), while MW parameters were not independent predictors for CTRCD. GLS reduction≥10.3% and TMADmid% reduction≥15.8% at 3 months after chemotherapy predicted CTRCD with 0.866 and 0.824 of area under the curve (AUC), 92% and 75% of sensitivity, and 74% and 80% of specificity, respectively. AUC of combination of two indexes improved to 0.905, with 75% of sensitivity and 90% of specificity. Conclusions In non-Hodgkin lymphoma patients, the combination of GLS and TMADmid% is helpful of predicting CTRCD early, TMAD may be a novel diagnostic index for CTRCD, and GLS has superior predictive performance than MW for CTRCD.

To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Objective: To establish a prediction model for high-risk cardiovascular disease (CVD) among residents aged 35 to 75 years, so as to provide the basis for improving CVD prevention and control measures. Methods: Permanent residents aged 35 to 75 years were selected from Dongcheng District, Beijing Municipality using the stratified random sampling method from 2018 to 2023. Demographic information, lifestyle, waist circumference and blood biochemical indicators were collected through questionnaire surveys, physical examinations and laboratory tests. Influencing factors for high-risk CVD among residents aged 35 to 75 years were identified using a multivariable logistic regression model, and a prediction model for high-risk CVD was established. The predictive effect was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 6 968 individuals were surveyed, including 2 821 males (40.49%) and 4 147 females (59.51%), and had a mean age of (59.92±9.33) years. There were 1 155 high-risk CVD population, with a detection rate of 16.58%. Multivariable logistic regression analysis showed that gender, age, smoking, central obesity, systolic blood pressure, fasting blood glucose, triglyceride and low-density lipoprotein cholesterol were influencing factors for high-risk CVD among residents aged 35 to 75 years (all P<0.05). The area under the ROC curve of the established prediction model was 0.849 (95%CI: 0.834-0.863), with a sensitivity of 0.693 and a specificity of 0.863, indicating good discrimination. Conclusion The model constructed by eight factors including demographic characteristics, lifestyle and blood biochemical indicators has good predictive value for high-risk CVD among residents aged 35 to 75 years.

As people’s attention to health continues to increase, the market demand for traditional Chinese medicine (TCM) is growing steadily. The quality and safety of Chinese medicinal materials have attracted unprecedented social attention. In particular, the issue of exogenous harmful residue pollution in TCM has become a hot topic of concern for both regulatory authorities and society. The Chinese Pharmacopoeia 2025 Edition further refines the detection methods and limit standards for exogenous harmful residues in TCM. This not only reflects China’s high-level emphasis on the quality and safety of TCM but also demonstrates the continuous progress made by China in the field of TCM safety supervision. Basis on this study, by systematically reviewing the development history of the detection standards for exogenous harmful residues in TCM and analyzing the revisions and updates of these detection standards in the Chinese Pharmacopoeia 2025 Edition, deeply explores the key points of the changes in the monitoring standards for exogenous harmful residues in TCM in the Chinese Pharmacopoeia 2025 Edition. Moreover, it interprets the future development directions of the detection of exogenous residues in TCM, aiming to provide a reference for the formulation of TCM safety supervision policies.

With the development of science and technology worldwide, the blooming of artificial intelligence (AI) and big data has brought new opportunities and challenges to the promotion of the research capacity of professional master's students in oncology. The construction of the new medical education in China aims to cultivate high-level medical talents with comprehensive multidisciplinary skills and innovative abilities to flexibly solve complex problems at the frontier of medicine. In this context, professional master's students in oncology, who are facing problems such as low scientific research output and uneven quality and needing improving scientific research literacy, have been required to develop into compound talents with both clinical and research prowess. To cultivate and promote the research capacity of professional master's students in oncology, the key steps include accelerating the construction of AI education and databases, highlighting the cultivation of their scientific research capacity, implementing and fostering the cultivation of innovative ability and scientific research thinking, piloting joint cultivation models by engineering universities and medical universities, emphasizing the construction of the curriculum and teacher team for oncology, piloting the multidisciplinary mode and COME mode, and establishing a multidisciplinary cooperation network.

As people's attention to health continues to increase,the market demand for traditional Chinese medi-cine(TCM)is growing steadily.The quality and safety of Chinese medicinal materials have attracted unprecedent-ed social attention.In particular,the issue of exogenous harmful residue pollution in TCM has become a hot topic of concern for both regulatory authorities and society.The Chinese Pharmacopoeia 2025 Edition further refines the detection methods and limit standards for exogenous harmful residues in TCM .This not only reflects China's high-level emphasis on the quality and safety of TCM but also demonstrates the continuous progress made by China in the field of TCM safety supervision.Basis on this study,by systematically reviewing the development history of the detection standards for exogenous harmful residues in TCM and analyzing the revisions and updates of these detec-tion standards in the Chinese Pharmacopoeia 2025 Edition,deeply explores the key points of the changes in the monitoring standards for exogenous harmful residues in TCM in the Chinese Pharmacopoeia 2025 Edition.Moreo-ver,it interprets the future development directions of the detection of exogenous residues in TCM ,aiming to provide a reference for the formulation of TCM safety supervision policies.

Objective To review the application of sexual dysfunction intervention in cervical cancer patients,and refine the intervention elements,so as to provide ideas and methods for the development of personalized intervention measures.Methods Guided by the 2021 updated methodological guidelines for scoping reviews from the Joanna Briggs Institute in Australia,CINAHL,PubMed,Web of Science,Embase,Cochrane Library,CNKI,Wanfang and VIP databases were systematically searched from inception to February 29,2024,and the included literature were analyzed and summarized.Results A total of 17 studies were included,involving 9 randomized controlled trials,7 quasi-experimental studies and 1 pilot study.The theoretical basis of the intervention includes PLISSIT model,BETTER model,FOCUS program,PERMA flourish theory,empowerment theory,the trans-theoretical model and narrative therapy.The intervention content involves 4 themes:sexual problem disclosing,sexual science education,sexual physiological rehabilitation and sexual psychological rehabilitation.The main forms are offline and online independent or combined intervention.The outcomes include sexual health function,social function,psychological status and self-management.Conclusion Sexual dysfunction interventions have a positive impact on cervical cancer patients.Future research should develop sexual dysfunction interventions with more comprehensive content,richer forms and more accurate evaluation based on the in-depth understanding of the theoretical connotation,and play the role of nurse-led multidisciplinary teams to maintain the long-term sexual and reproductive health of cervical cancer patients.