Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

In the multi-campus development and construction of public hospitals,hospital-associated infection man-agement has consistently faced escalating difficulties and challenges.Throughout the long-term exploration and practice,the Second Affiliated Hospital Zhejiang University School of Medicine has adhered to the principles of"integrated planning and deployment,homogeneous and homologous training,information and data sharing,and a focus on professional features".By continuously addressing emerging infection control risk points and weak points,overcoming barriers posed by dispersed human resources,and balancing homogeneity with individualization,the hospital has actively promoted integration and collaborative development across its campuses.This approach has significantly contributed to the sustained improvement of healthcare service capacity and quality across multiple campuses.From the novel perspective of symbiosis theory,this paper analyzes the symbiotic dilemmas in multi-campus hospital-associated infection management,summarizes practical experiences,and explores future direc-tions,aiming to provide references for other public hospitals in multi-campus hospital-associated infection manage-ment.

In the multi-campus development and construction of public hospitals,hospital-associated infection man-agement has consistently faced escalating difficulties and challenges.Throughout the long-term exploration and practice,the Second Affiliated Hospital Zhejiang University School of Medicine has adhered to the principles of"integrated planning and deployment,homogeneous and homologous training,information and data sharing,and a focus on professional features".By continuously addressing emerging infection control risk points and weak points,overcoming barriers posed by dispersed human resources,and balancing homogeneity with individualization,the hospital has actively promoted integration and collaborative development across its campuses.This approach has significantly contributed to the sustained improvement of healthcare service capacity and quality across multiple campuses.From the novel perspective of symbiosis theory,this paper analyzes the symbiotic dilemmas in multi-campus hospital-associated infection management,summarizes practical experiences,and explores future direc-tions,aiming to provide references for other public hospitals in multi-campus hospital-associated infection manage-ment.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Objective To observe the impact of ultrasonic image quality on the consistency of artificial intelligence(Al)assisted diagnosis system and manual measurements of biological indicators of developmental dysplasia of hip(DDH).Methods Hip ultrasonic data of 75 DDH and 345 non-DDH children were retrospectively analyzed,and the quality of ultrasonic images were subjectively scored.An evaluation model of ultrasonic image quality was constructed based on 140 ultrasonic images acquired from 140 cases(group A,containing 25 DDH and 115 non-DDH)using entropy weighting method,the weight of anatomic structures and impact factors related to DDH were obtained.The comprehensive image quality scores of other ultrasonic images acquired from 280 cases(group B,including 50 DDH and 230 non-DDH)were calculated,and the images in group B were classified into grade A,B and C in descending order.The consistency of AI and manual measurements of DDH biological indicators in group B was assessed.Results The weight of each anatomic structure and impact factors of DDH obtained with the model were as follows:The lower edge of iliac branch＞ilium＞glenoid labrum＞bony margin＞femoral head＞motion artifacts.In group B,grade A was observed in 67(9 DDH and 58 non-DDH),grade B was found in 160(26 DDH and 134 non-DDH),while grade C was noticed in 53(15 DDH and 38 non DDH)images.Except for β,femoral head coverage(FHC)and femoral head length diameter,the consistencies between AI and manual measurements of other indicators of DDH were grade A＞B＞C.In group B,AI and manual measurements were more consistent in DDH than in non-DDH cases.Conclusion Ultrasonic image quality affected the consistency between AI and manual measurements of biological indicators of DDH.When image quality was not good enough,further attention should be paid to measurement of FHC and sizes of femoral head.

Lung cancers are the leading cause of cancer deaths worldwide and pose a grave threat to human life and health. Non-small cell lung cancer (NSCLC) is the most frequent malignancy occupying 80% of all lung cancer subtypes. Except for other mutations (

Objective To identify the best cell line for Pneumocystis carinii(Pc) proliferation Methods Specimens containing 5?10 5 Pc cysts were inoculated onto 8 kinds of cell lines separately. At the same time, Pc were inoculated in different amounts on HepG-2 cell line to identify the most suitable number of Pc needed for Pc proliferation in vitro within seven days. Results On HepG-2 cell line, Pc increased by 7-fold in trophozoite forms with a peak increasing rate between 4～5 days with the optimal growth at the cyst number of 5?10 5 .Conclusions HepG-2 cell line is found to be the most suitable cell line for Pc culture and the best inoculation number of Pc is 5?10 5.