Objective To study the application value of 3.0T MRI T2 mapping quantitative technology in the diagnosis of anterior cruciate ligament sprain and chronic injury of knee joint.Methods A total of 82 subjects were studied,and the experimental group 72 cases was divided into grade Ⅰ injury group(25 cases),grade Ⅱ injury group(25 cases),chronic injury group(22 cases),and control group 10 cases.The experimental group met the criteria of arthroscopy.The proximal,middle,and distal segments of the anterior cruciate ligament were selected as the region of interest(ROI),and T2 mapping values were measured.The differences in T2 mapping values of each area were compared between and within the groups,while compared with arthroscopy.Results The T2 mapping values in grade Ⅰ,Ⅱ,and chronic injury groups were higher than those in control group(P<0.05).Comparison within the experimental group:the T2 mapping values of each area in grade Ⅱ injury group were higher than those in grade Ⅰ injury group and chronic injury group(P<0.05).The T2 mapping values of each area in grade Ⅰ injury group were higher than those in chronic injury group(P<0.05).The specificity,sensitivity,positive predictive value,negative predictive value and accuracy of T2 mapping in diagnosing anterior cruciate ligament grade Ⅰ injury were 94.7%,95.5%,89.7%,96.6%,and 90.2%respectively.The specificity,sensitivity,positive predictive value,negative predictive value,and accuracy of grade Ⅱ injury were 89.4%,87.9%,92.1%,93.4%,and 93.8%respectively.The specificity,sensitivity,positive predictive value,negative predictive value,and accuracy of chronic injury were 92.2%,95.4%,90.3%,87.6%,and 91.5%respectively.Kappa test showed a good con-sistency between T2 mapping results and arthroscopic results,with a Kappa value of 0.763(P<0.01).Conclusion The value of MRI T2 mapping can provide a reference for the clinical diagnosis of anterior cruciate ligament sprain and chronic injury of knee joint,and the results are in good agreement with the control of arthroscopy.

In recent years, chimeric antigen receptor T-cell (CAR-T) therapy has shown a certain value in the treatment of malignant hematological tumors, especially for patients with relapsed/ refractory B-cell lymphoma. Currently, 18F-FDG PET-CT has proved its great clinical values in the diagnosis and treatment of lymphoma. There are relatively few studies using 18F-FDG PET-CT to evaluate B-cell lymphoma treated by CAR-T therapy, but some good results have also been achieved. This paper reviews the current research progress of 18F-FDG PET-CT in CAR-T therapy for B-cell lymphoma.

Objective To determine the effects of ciprofol and propofol on hemodynamics and cardiac work were determined by hemodynamic monitoring,so as to provide reference for anesthesia induction in car-diac surgery.Methods A total of 90 patients scheduled for mitral valve replacement under cardiopulmonary bypass from June 2022 to June 2023 were randomly divided into two groups:the ciprofol group (group P,n=45) and the propofol group (group B,n=45).The heart rate (HR),oxygen saturation (SpO2),index of con-sciousness (ICO1),mean arterial pressure (MAP),central venous pressure (CVP),stroke volume (SV),car-diac output (CO),systemic vascular resistance (SVR),cardiac cycle efficiency (CCE),stroke volume variation (SVV),maximum pressure gradient reflecting myocardial contractility (dp/dt) were recorded before adminis-tration (T0) and five min after administration (T1).Results In group P,ICO1,MAP,SV,CO,and SVR at T1 were lower than those at T0,and CCE was higher than that at T0 (P<0.01,P<0.05).In group B,ICO1, MAP,CVP,SV,CO,SVR,CCE,SVV,dp/dt at T1 were significantly lower than those at T0 (P<0.01,P<0.05).There was no statistically significant difference in HR,SpO2,ICO1,MAP,CVP and hemodynamic indi-cators between group B and group P at T0 (P>0.05).There was no statistically significant difference in HR, SpO2,ICO1,CVP and SVV between group B and group P at T1 (P>0.05).MAP,SV,CO,SVR,CCE and dp/dt at T1 in group P were higher than those in group B (P<0.05).Conclusion Ciprofol can better maintain hemodynamic stability and reduce cardiac impairment during the induction phase of mitral valve replacement surgery under cardiopulmonary bypass.

Objective:To explore the clinical effects of a 3D-printed intracranial pressure balancing device in preventing complications after suboccipital craniectomy (DC).Methods:This study is a retrospective cohort analysis. The clinical data of 35 patients who underwent DC at Department of Neurosurgery, Beijing Chaoyang Hospital, Capital Medical University, from September 2020 to September 2023 were reviewed. The cohort included 24 males and 11 females, with an age of (48.7±14.9) years (range:17 to 74 years). Nineteen patients (experimental group) received the intracranial pressure balancing device fixed to the bone defect site post-DC. This device was made using medical-grade dicyanamide resin and was three dimensional printed based on postoperative CT scans of the patients. The remaining 16 patients (control group) did not receive the intracranial pressure balancing device, while other treatments and procedures were consistent with the experimental group. Data were compared using the χ2 test or Fisher′s exact probability method. Results:Out of the 35 patients, 30 cases (85.7%) experienced complications following DC. Specific complications included cerebral infarction in 3 cases (8.6%), intracerebral hemorrhage in 1 case (2.9%), subdural effusion in 27 cases (77.1%) with a median onset of (8.8±6.5) days (range: 1 to 23 days), brain tissue protrusion in 15 cases (42.9%) with a median onset of ( M(IQR)) 7.0 (21.0) days (range:2 to 106 days), and hydrocephalus in 6 cases (17.14%) with a median onset of 34.5 (111.0) days (range: 22 to 136 days). There were no significant differences in the occurrence of complications(all P>0.05). However, there was a significant reduction in the incidence of subdural effusion in the experimental group prior to cranioplasty ( P=0.013). No significant differences were noted in mRS scores between the two groups after cranioplasty ( P>0.05). Conclusions:The intracranial pressure balancing device has the effect of prevention and treatment of subdural effusion. However, it did not significantly improve patient prognosis post-DC, warranting further investigation.

Objective To compare the efficacy of mediastinoscope-assisted transhiatal esophagectomy (MATHE) and functional minimally invasive esophagectomy (FMIE) for esophageal cancer. Methods Patients who underwent minimally invasive esophagectomy at Jining No.1 Hospital from March 2018 to September 2022 were retrospectively included. The patients were divided into a MATHE group and a FMIE group according to the procedures. The patients were matched via propensity score matching (PSM) with a ratio of 1 : 1 and a caliper value of 0.2. The clinical data of the patients were compared after the matching. Results A total of 73 patients were include in the study, including 54 males and 19 females, with an average age of (65.12±7.87) years. There were 37 patients in the MATHE group and 36 patients in the FMIE group. Thirty pairs were successfully matched. Compared with the FMIE group, MATHE group had shorter operation time (P=0.022), lower postoperative 24 h pain score (P=0.031), and less drainage on postoperative 1-3 days (P<0.001). FMIE group had more lymph node dissection (P<0.001), lower incidence of postoperative hoarseness (P=0.038), lower white blood cell and neutrophil counts on postoperative 1 day (P<0.001). There was no statistically significant difference in the bleeding volume, R0 resection, hospital mortality, postoperative hospital stay, anastomotic leak, chylothorax, or pulmonary infection between the two groups (P>0.05). Conclusion Compared with the FMIE, MATHE has shorter operation time, less postoperative pain and drainage, but removes less lymph nodes, which is deficient in oncology. For some special patients such as those with early cancer or extensive pleural adhesions, MATHE may be a suitable surgical method.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.