Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

This study aims to prepare nanobodies for the epitopes of spike protein(S)of porcine ep-idemic diarrhea virus(PEDV),and verify its reactivity.The expression vector pCZN1-SN was con-structed by the prokaryotic expression system,and SN fragment was expressed in prokaryotic ex-pression and purified by Ni-NTA chromatography column.The SN nanobodies were displayed u-sing phage display technology and screened from the natural nanobody library.The results showed that SN fragments with a size of 26 kDa were obtained by prokaryotic induction,and the specific nanobodies were obtained through three rounds screening by phage display technology.One strain with the best reactivity was selected for prokaryotic expression and purified.The nanobodies were obtained with a size of 14 kDa by Western blot and demonstrated to have a good binding ability to PEDV SN protein.In summary,nanobodies with epitope fragments of PEDV S protein were suc-cessfully screened and prepared based on the phage display technology,which provided a new way for the in-depth application of nanobodies.

This study aims to prepare nanobodies for the epitopes of spike protein(S)of porcine ep-idemic diarrhea virus(PEDV),and verify its reactivity.The expression vector pCZN1-SN was con-structed by the prokaryotic expression system,and SN fragment was expressed in prokaryotic ex-pression and purified by Ni-NTA chromatography column.The SN nanobodies were displayed u-sing phage display technology and screened from the natural nanobody library.The results showed that SN fragments with a size of 26 kDa were obtained by prokaryotic induction,and the specific nanobodies were obtained through three rounds screening by phage display technology.One strain with the best reactivity was selected for prokaryotic expression and purified.The nanobodies were obtained with a size of 14 kDa by Western blot and demonstrated to have a good binding ability to PEDV SN protein.In summary,nanobodies with epitope fragments of PEDV S protein were suc-cessfully screened and prepared based on the phage display technology,which provided a new way for the in-depth application of nanobodies.

Objective To investigate the biological effects of Pasteurella multocida(Pm)culture supernatant and Pm-derived outer membrane vesicle(OMV)on bladder cancer cells.Methods Pm was cultured and its supernatant was collected.The effects of the supernatant on proliferation,migration and invasion of bladder cancer cell lines(T24 and 5637)were assessed by cell counting kit 8(CCK-8),wound healing assay,and Transwell migration and invasion assays with phosphate-buffered saline(PBS)and brain heart infusion(BHI)broth as controls.Pm-OMV were isolated from the supernatant via ultracentrifugation,and the remaining components of the supernatant served as control.The effects of Pm-OMV on proliferation,migration and invasion of T24 and 5637 cells were assessed by CCK-8 and Transwell migration and invasion assays.Apoptosis was analyzed by flow cytometry.A nude mouse xenograft tumor model was established.After intratumoral multi-point injections of Pm-OMV or PBS,the tumor growth was evaluated and the effects of Pm-OMV on proliferation and apoptosis of bladder cancer cells in vivo were verified by Ki67(a proliferation marker)immunohistochemical staining and TUNEL assay.Results Pm culture supernatant significantly inhibited the proliferation,invasion,and migration of T24 and 5637 cells in vitro compared with PBS and BHI controls(all P＜0.01).Pm-OMV not only inhibited the proliferation,invasion,and migration of T24 and 5637 cells,but also induced the apoptosis,and the differences were significant compared with the remaining components of the supernatant(all P＜0.05).The nude mouse subcutaneous tumor transplantation experiment further confirmed that Pm-OMV inhibited the proliferation of bladder cancer cells and promoted apoptosis in vivo,and the differences were significant compared with the PBS control(all P＜0.05).Conclusion Pm-OMV can inhibit the proliferation,invasion,and migration of bladder cancer cells and promote the apoptosis.It provides an experimental basis for studying the mechanism of microbial regulation of tumor progression and for developing new treatment strategies for bladder cancer.

Schizophrenia is a serious mental disorder that is often associated with profound impairment in patients′ daily functioning, and its etiology and pathophysiology are still to be fully elucidated. There is a pathological correlation between inflammation, brain injuries, and the pathogenesis of schizophrenia, with microglia actively participating in these processes. This review provides a comprehensive overview of the impact of microglial cells on neurodevelopment and neuroplasticity, and microglia abnormalities mediating the onset of schizophrenia by contributing to damage in different brain regions.

Schizophrenia is a serious mental disorder that is often associated with profound impairment in patients′ daily functioning, and its etiology and pathophysiology are still to be fully elucidated. There is a pathological correlation between inflammation, brain injuries, and the pathogenesis of schizophrenia, with microglia actively participating in these processes. This review provides a comprehensive overview of the impact of microglial cells on neurodevelopment and neuroplasticity, and microglia abnormalities mediating the onset of schizophrenia by contributing to damage in different brain regions.

The Asian Society of Gynecologic Oncology International Workshop 2014 on gynecologic oncology was held in Asan Medical Center, Seoul, Korea on the 23rd to 24th August 2014. A total of 179 participants from 17 countries participated in the workshop, and the up-to-date findings on the management of gynecologic cancers were presented and discussed. This meeting focused on the new trends in the management of cervical cancer, fertility-sparing management of gynecologic cancers, surgical management of gynecologic cancers, and recent advances in translational research on gynecologic cancers.
Female ; Fertility Preservation/methods ; Genital Neoplasms, Female/*therapy ; Humans ; Ovarian Neoplasms/therapy ; Translational Medical Research/methods ; Uterine Cervical Neoplasms/therapy

Objective To investigate the prevention and management of severe bleeding during gynecological operations. Methods A retrospective study of 85 505 gynecological operations from 21 hospitals in China during the period of 1990 1999 was analyzed. Results There were 683 cases with bleeding more than 1 000 ml during surgery, an incidence of 0 80% (range 0 07%～6 98%). Operation for removal of malignant ovarian tumor was the commonest cause of severe bleeding (42 31%); followed by cervical carcinoma (28 71%); endometrial carcinoma (16 11%). Only 6 transvaginal surgeries (0 88%) had severe bleeding. The most common site of bleeding was massive oozing from the raw wound surface, then the paracervical area (15 7%), around sacral ligament (12 14%). Conclusions Advanced malignant tumors, tumors located at retroperitoneal or with extensive adhesion were the main causes of profuse bleeding during operation. Good surgical skill and well understanding of the pelvic anatomy are the basic key points for surgeons, and a supportive anesthesia is also important in reducing hemorrhage during operations. Once bleeding occurs, to stop the bleeding accurately and promptly by pressing, clamping, and suturing, and internal iliac artery ligation may be needed occasionally. Special attention should be paid to the hemostasis of the venous plexus of pelvic floor.