Objective To construct a quality control model for cervical liquid-based thin cell smears using two different artificial intelligence(AI)techniques and to compare the total use of the two methods to improve the level of quality control of cervical liquid-based thin cell smears through the assistance of hybrid AI.Methods In this study,105 cervical liquid-based thin cell smear samples were used.Convolutional neural network(CNN)algorithm and Transformer network algorithm were used as specific AI algorithms in the AI model.The labeled features included the number of cells in the slice,excessive red blood cells,excessive inflammatory cells,and air bubbles.The smear samples were pre-processed and digitized by smear,followed by image segmentation and feature extraction.Using the labeled feature data,machine learning models were trained and optimized.Statistical AI and physician QC results were analyzed by calculating KAPPA index,sensitivity,specificity,area under the curve(AUC),and other indexes for AI QC results.Results CNN algorithm QC results in normal smear,inflammatory background and bloody background were significantly different from the expert review QC results(P＜0.001).Transformer algorithm QC results were similar to the expert review results,with no statistical difference(P＞0.05).General practitioner QC results were statistically different from the expert review QC results in normal smear detection rate and bloody background(P＜0.001).CNN algorithm Kappa value was 0.567,which had medium consistency with expert review results.Transformer algorithm Kappa value was 0.890,with the best consistency with expert review results.General practitioner Kappa value was 0.675,which had better consistency with expert review results.Using the expert review results as a reference standard,the predictive efficacy of the Transformer algorithm and the general practitioners' QC results was evaluated,and the predictive efficacy of the Transformer algorithm was higher than that of the general practitioners in detecting hemorrhagic backgrounds and normal smears(inflammatory backgrounds:AUC=1.000;normal smears:AUC=0.768)(hemorrhagic backgrounds:AUC=0.849;normal smears:AUC=0.849;normal smear:AUC=0.500).Conclusion In this study,we found that the Transformer algorithm was effective in improving the quality control of cervical liquid-based thin-layer cell smears by assisting doctors to perform smear quality control scoring and improving the efficiency and accuracy of smear sample quality control.It can be used as a new quality control method for cervical cancer cytological screening and has potential clinical applications.

Objective To explore the targets and mechanisms of Tongguanteng Injection in inhibiting the proliferation and migration of cervical cancer.Methods The biological activity of Tongguanteng Injection in inhibiting human cervical cancer SiHa cells was determined by MTT method.Detecting the effect of Tongguanteng Injection on SiHa cell migration through wound healing assay.Using network pharmacology to collect the key targets for treating cervical cancer,and perform molecular docking and enrichment analysis on the targets.Immunohistochemistry and Western blot were used to detect the key proteins to validate the network pharmacology predictions.Result Tongguanteng Injection significantly inhibited the proliferation and migration in a dose-dependent manner in human cervical cancer SiHa cells.Based on the main active ingredients of Marsdenia tenacissima,81 therapeutic targets for cervical cancer were obtained,which may treat cervical cancer by affecting key proteins such as FGF2,MAPK1,and MAPK3.Immunohistochemical results indicated that FGF2,MAPK1 and MAPK3 were expressed in cervical cancer tissues.The western bolt assays showed that Tongguanteng Injection could significantly reduce the FGF2 protein expression.Meanwhile,the MAPK1 and MAPK3 protein expressions were significantly increased.Conclusion Tongguanteng Injection may regulate the FGF2,MAPK1 and MAPK3,effectively impede the proliferation and migration of cervical cancer.

Objective:To evaluate the long-term effectiveness of lingual mucosal graft ureteroplasty (LMGU) for managing long-segment (≥5 cm) ureteral strictures in a multi-institutional cohort of patients.Methods:A multi-center retrospective case series study was conducted on clinical data from 42 patients undergoing LMGU for long-segment ureteral strictures (≥5 cm) across five institutions between February 2017 and June 2024. The cohort comprised 31 males and 11 females, with an age of (43.4±12.0) years (range: 15 to 64 years) and a body mass index of (24.6±2.6) kg/m2 (range: 16.0 to 30.0 kg/m2). Strictures involved the left ureter in 24 cases and right ureter in 18 cases, demonstrating a stricture length of (6.4±1.5) cm (range: 5.0 to 11.5 cm). Surgical interventions included either onlay ureteroplasty or augmented anastomotic ureteroplasty, selected according to intraoperative findings. Intraoperative parameters, postoperative complications, and follow-up outcomes were analyzed.Results:Laparoscopic surgery was performed in 22 cases and robot-assisted surgery in 20 cases. Among the 42 patients, 22 underwent onlay ureteroplasty while 20 received augmented anastomotic ureteroplasty. The graft length was (5.9±1.8) cm (range: 3.0 to 12.0 cm), operative time (191.5±55.6) minutes (range: 105.0 to 350.0 minutes), and intraoperative estimated blood loss (86.7±73.6) ml (range: 10.0 to 400.0 ml). All procedures were successfully completed without conversion to open surgery. The postoperative hospital stay was (7.6±2.0) days (range: 4.0 to 15.0 days), with double-J stent removal at 6 to 8 weeks postoperatively. During a follow-up of (49.1±25.0) months (range: 12.0 to 99.0 months), no stricture recurrence was observed in any patient.Conclusion:LMGU is a safe, feasible, and effective long-term technique for managing long-segment (≥5 cm) ureteral strictures.

Objective To construct a quality control model for cervical liquid-based thin cell smears using two different artificial intelligence(AI)techniques and to compare the total use of the two methods to improve the level of quality control of cervical liquid-based thin cell smears through the assistance of hybrid AI.Methods In this study,105 cervical liquid-based thin cell smear samples were used.Convolutional neural network(CNN)algorithm and Transformer network algorithm were used as specific AI algorithms in the AI model.The labeled features included the number of cells in the slice,excessive red blood cells,excessive inflammatory cells,and air bubbles.The smear samples were pre-processed and digitized by smear,followed by image segmentation and feature extraction.Using the labeled feature data,machine learning models were trained and optimized.Statistical AI and physician QC results were analyzed by calculating KAPPA index,sensitivity,specificity,area under the curve(AUC),and other indexes for AI QC results.Results CNN algorithm QC results in normal smear,inflammatory background and bloody background were significantly different from the expert review QC results(P＜0.001).Transformer algorithm QC results were similar to the expert review results,with no statistical difference(P＞0.05).General practitioner QC results were statistically different from the expert review QC results in normal smear detection rate and bloody background(P＜0.001).CNN algorithm Kappa value was 0.567,which had medium consistency with expert review results.Transformer algorithm Kappa value was 0.890,with the best consistency with expert review results.General practitioner Kappa value was 0.675,which had better consistency with expert review results.Using the expert review results as a reference standard,the predictive efficacy of the Transformer algorithm and the general practitioners' QC results was evaluated,and the predictive efficacy of the Transformer algorithm was higher than that of the general practitioners in detecting hemorrhagic backgrounds and normal smears(inflammatory backgrounds:AUC=1.000;normal smears:AUC=0.768)(hemorrhagic backgrounds:AUC=0.849;normal smears:AUC=0.849;normal smear:AUC=0.500).Conclusion In this study,we found that the Transformer algorithm was effective in improving the quality control of cervical liquid-based thin-layer cell smears by assisting doctors to perform smear quality control scoring and improving the efficiency and accuracy of smear sample quality control.It can be used as a new quality control method for cervical cancer cytological screening and has potential clinical applications.

Objective To explore the targets and mechanisms of Tongguanteng Injection in inhibiting the proliferation and migration of cervical cancer.Methods The biological activity of Tongguanteng Injection in inhibiting human cervical cancer SiHa cells was determined by MTT method.Detecting the effect of Tongguanteng Injection on SiHa cell migration through wound healing assay.Using network pharmacology to collect the key targets for treating cervical cancer,and perform molecular docking and enrichment analysis on the targets.Immunohistochemistry and Western blot were used to detect the key proteins to validate the network pharmacology predictions.Result Tongguanteng Injection significantly inhibited the proliferation and migration in a dose-dependent manner in human cervical cancer SiHa cells.Based on the main active ingredients of Marsdenia tenacissima,81 therapeutic targets for cervical cancer were obtained,which may treat cervical cancer by affecting key proteins such as FGF2,MAPK1,and MAPK3.Immunohistochemical results indicated that FGF2,MAPK1 and MAPK3 were expressed in cervical cancer tissues.The western bolt assays showed that Tongguanteng Injection could significantly reduce the FGF2 protein expression.Meanwhile,the MAPK1 and MAPK3 protein expressions were significantly increased.Conclusion Tongguanteng Injection may regulate the FGF2,MAPK1 and MAPK3,effectively impede the proliferation and migration of cervical cancer.

Objective:To evaluate the long-term effectiveness of lingual mucosal graft ureteroplasty (LMGU) for managing long-segment (≥5 cm) ureteral strictures in a multi-institutional cohort of patients.Methods:A multi-center retrospective case series study was conducted on clinical data from 42 patients undergoing LMGU for long-segment ureteral strictures (≥5 cm) across five institutions between February 2017 and June 2024. The cohort comprised 31 males and 11 females, with an age of (43.4±12.0) years (range: 15 to 64 years) and a body mass index of (24.6±2.6) kg/m2 (range: 16.0 to 30.0 kg/m2). Strictures involved the left ureter in 24 cases and right ureter in 18 cases, demonstrating a stricture length of (6.4±1.5) cm (range: 5.0 to 11.5 cm). Surgical interventions included either onlay ureteroplasty or augmented anastomotic ureteroplasty, selected according to intraoperative findings. Intraoperative parameters, postoperative complications, and follow-up outcomes were analyzed.Results:Laparoscopic surgery was performed in 22 cases and robot-assisted surgery in 20 cases. Among the 42 patients, 22 underwent onlay ureteroplasty while 20 received augmented anastomotic ureteroplasty. The graft length was (5.9±1.8) cm (range: 3.0 to 12.0 cm), operative time (191.5±55.6) minutes (range: 105.0 to 350.0 minutes), and intraoperative estimated blood loss (86.7±73.6) ml (range: 10.0 to 400.0 ml). All procedures were successfully completed without conversion to open surgery. The postoperative hospital stay was (7.6±2.0) days (range: 4.0 to 15.0 days), with double-J stent removal at 6 to 8 weeks postoperatively. During a follow-up of (49.1±25.0) months (range: 12.0 to 99.0 months), no stricture recurrence was observed in any patient.Conclusion:LMGU is a safe, feasible, and effective long-term technique for managing long-segment (≥5 cm) ureteral strictures.

Objective: To understand the association of maternal emotional symptoms and parent-child relationship with emotional and behavioral problems (EBPs) in primary school students, so as to provide a reference for the prevention of EBPs in primary school students. Methods: In May 2023, a convenient cluster sampling method was used to select 1 324 students from two primary schools in Hefei City, Anhui Province as the study subjects. The Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire (PHQ-9), Family Adaptation and Cohesion Evaluation ScalesⅡ (FACES Ⅱ), and Strengths and Difficulties Questionnaire (SDQ) of parental version were used to investigate maternal emotional symptoms, parent-child relationship, and EBPs of primary school students, respectively. Multivariate Logistic regression analysis model was used to analyze the association of different maternal emotional symptoms and parent-child relationship with EBPs of primary school students. Results: The abnormal detection rates of emotional symptoms, conduct problems, hyperactivity disorder, peer problems, prosocial behavior and total difficulties in primary school students were 13.6%, 19.4%, 20.2%, 36.5%, 20.9% and 51.9%, respectively. Multivariate Logistic regression analysis showed that the detection rates of EBPs in primary school students whose mothers had anxiety and depression symptoms with low level of parent-child relationship increased ( OR=1.52-6.97, P <0.01). The combined effect of mothers who had anxiety and depression symptoms with low level of parent-child relationship increased the risk of EBPs in all dimensions of primary school students ( OR=4.07-10.14, P <0.01). After gender stratification, the combined effect of mothers who had anxiety and depression symptoms with low level of parent-child relationship increased the risk of EBPs in all dimensions of boys and girls ( OR=3.71- 10.95 , P <0.01). Conclusions Maternal emotional symptoms and parent-child relationship are associated with EBPs in primary school students. Reducing maternal anxiety and depression symptoms, and improving parent-child relationship are beneficial to reduce the incidence of EBPs problems in primary school students.

Objective To explore and validate the application value of a deep learning model based on the Huawei Cloud ModelArts platform in the diagnosis of atypical cervical cells in liquid-based cytology(LBC)and to evaluate its assistive effect for pathologists with different diagnostic experiences.Methods We retrospectively analyzed 1 044 cervical cytology specimens from Dongguan People's Hospital in 2020.The artifical intelligence(AI)-assisted diagnostic system developed on the Huawei Cloud ModelArts platform was compared with junior,intermediate,and senior pathologists for diagnosis.Sensitivity,specificity,precision,recall,and area under the receiver operating characteristic curve(AUC)were calculated to assess the diagnostic performance of the Al system and its assistive effect for pathologists with different levels of experience.The McNemar test was used to compare the differences between the Al system and manual diagnosis.P＜0.05 was considered statistically significant.Results For the 1 044 cervical exfoliated cytology specimens,the sensitivity and specificity of the AI system in detecting atypical cells was 98.96％and 89.15％,both of which were higher than those of junior doctors(81.95％and 91.81％,respectively).The overall diagnostic accuracy of the Al system was 93.68％,which was significantly higher than that of junior doctors(87.26％,P＜0.001).Al assistance could significantly improve junior doctors'ability to detect atypical cells,with the sensitivity and specificity increasing from 80.1％to 96.5％and from 85.6％to 92.3％,respectively.Conclusion The AI-assisted cervical cytology diagnostic system developed in this study demonstrated superior performance,particularly in significantly improving the diagnostic level of junior pathologists,showing promising clinical application prospects.

The dengue virus,a member of the Flavivirus genus within the Flaviviridae family,is responsible for dengue fever,an acute mosquito-borne infectious disease that can progress to severe forms such as dengue hemorrhagic fever and dengue shock syndrome,taking a heavy toll on human health.Despite its impact on global public health,there are currently no specific drugs available that are both safe and effective for the treatment of dengue fever so that the research and development of related drugs are facing huge challenges.This paper not only pointed to the critical importance and urgency of developing antiviral medications,but also reviewed recent advancements in the development of dengue virus inhibitors of two categories:candidate drugs that directly target viral structural and non-structural proteins,and antiviral candidate agents that focus on host receptors.Additionally,this paper provided an overview of the progress in and the mechanisms of various inhibitors in the hope of providing a reference for antiviral drug development and the treatment of dengue fever.