Objective To compare the long-term survival of elderly patients with esophageal squamous cell carcinoma (ESCC) treated with surgical versus non-surgical treatment. Methods A retrospective analysis was conducted on the clinical data of elderly patients aged ≥70 years with ESCC who underwent esophagectomy or radiotherapy/chemotherapy at Sichuan Cancer Hospital from January 2009 to September 2017. Patients were divided into a surgical group (S group) and a non-surgical group (NS group) according to the treatment method. The propensity score matching method was used to match the two groups of patients at a ratio of 1∶1, and the survival of the two groups before and after matching was analyzed. Results A total of 726 elderly patients with ESCC were included, including 552 males and 174 females, with 651 patients aged ≥70-80 years and 75 patients aged ≥80-90 years. There were 515 patients in the S group and 211 patients in the NS group. The median follow-up time was 60.8 months, and the median overall survival of the S group was 41.9 months [95%CI (35.2, 48.5)], while that of the NS group was only 24.0 months [95%CI (19.8, 28.3)]. The 1-, 3-, and 5-year overall survival rates of the S group were 84%, 54%, and 40%, respectively, while those of the NS group were 72%, 40%, and 30%, respectively [HR=0.689, 95%CI (0.559, 0.849), P<0.001]. After matching, 138 patients were included in each group, and there was no statistical difference in the overall survival between the two groups [HR=0.871, 95%CI (0.649, 1.167), P=0.352]. Conclusion Compared with conservative treatment, there is no significant difference in the long-term survival of elderly patients aged ≥70 years who undergo esophagectomy for ESCC. Neoadjuvant therapy combined with surgery is still an important choice to potentially improve the survival of elderly patients with ESCC.

Long-term hypertension causes excessive vascular remodeling and leads to adverse cardiovascular events. Balance of ubiquitination and deubiquitination has been linked to several chronic conditions, including pathological vascular remodeling. In this study, we discovered that the expression of ubiquitin-specific protease 25 (USP25) is significantly up-regulated in angiotensin II (Ang II)-challenged mouse aorta. Knockout of Usp25 augments Ang II-induced vascular injury such as fibrosis and endothelial to mesenchymal transition (EndMT). Mechanistically, we found that USP25 interacts directly with Forkhead box O3 (FOXO3) and removes the K63-linked ubiquitin chain on the K258 site of FOXO3. We also showed that this USP25-mediated deubiquitination of FOXO3 increases its binding to light chain 3 beta isoform and autophagosomic-lysosomal degradation of FOXO3. In addition, we further validated the biological function of USP25 by overexpressing USP25 in the mouse aorta with AAV9 vectors. Our studies identified FOXO3 as a new substrate of USP25 and showed that USP25 may be a potential therapeutic target for excessive vascular remodeling-associated diseases.

Objective:To investigate the incidence of venous thromboembolism (VTE) in patients with esophageal cancer (EC).Methods:The retrospective cohort study was conducted. The clinicopathological data of 8 458 EC patients who were admitted to Sichuan Cancer Hospital from January 2017 to December 2021 were collected. There were 6 923 males and 1 535 females, aged (64±9)years. There were 3 187 patients undergoing surgical treatment, and 5 271 cases undergoing non-surgical treatment. Observation indicators: (1) incidence of VTE in EC patients; (2) treatment and outcomes of patients with VTE. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the t test. Measurement data with skewed distribution were represented as M(range), and comparison between groups was analyzed using the nonparameter rank sum test. Count data were expressed as absolute numbers or percentages, and comparison between groups was analyzed using the chi-square test or Fisher exact probability. Comparison of ordinal data was analyzed using the nonparameter rank sum test. Results:(1) Incidence of VTE in EC patients. Of 8 458 EC patients, 175 cases developed VTE, with an incidence rate of 2.069%(175/8 458). Among 175 VTE patients, there were 164 cases of deep venous thrombosis (DVT), 4 cases of pulmonary embolism (PE), 7 cases of DVT and PE. There were 59 surgical patients and 116 non-surgical patients. There was no significant difference in thrombus type between surgical and non-surgical EC patients with VTE ( χ2=1.95, P>0.05). Of 3 187 surgical patients, the incidence of VTE was 1.851%(59/3 187), including an incidence of 0.157%(5/3 187) of PE. PE accounted for 8.475%(5/59) of surgical patients with VTE. Of 5 271 non-surgical patients, the incidence of VTE was 2.201%(116/5 271), including an incidence of 0.114%(6/5 271) of PE. PE accounted for 5.172%(6/116) of non-surgical patients with VTE. There was no significant difference in the incidence of VTE or PE between surgical patients and non-surgical patients ( χ2=1.20, 0.05, P>0.05). (2) Treatment and outcomes of patients with VTE. Among 175 EC patients with VTE, 163 cases underwent drug treatment, and 12 cases did not receive treatment. Among 163 cases with drug therapy, 158 cases underwent anticoagulant therapy, 5 cases were treated with thrombolysis. All the 163 patients were improved and discharged from hospital. Conclusions:The incidence of VTE in patients with EC is relatively low, as 2.069%. There is no significant difference in the incidence of VTE or thrombus type between surgical EC patients and non-surgical EC patients.

Objective:To investigate the value of digital medical 3D technology versus traditional 2D technology in the diagnosis and treatment of solid abdominal tumors in children. Methods:A total of 80 children with solid abdominal tumors who received surgical treatment guided by digital medical 3D technology at Guigang People's Hospital from January 2018 to January 2022 were included in the observation group. An additional 80 children with solid abdominal tumors who received surgical treatment guided by traditional 2D technology at the same hospital from January 2014 to December 2017 were included in the control group. Clinical efficacy was compared between the two groups.Results:The surgical time, intraoperative blood loss, postoperative exhaust time, postoperative hospital stay in the observation group were (111.8 ± 28.9) minutes, (26.8 ± 25.2) mL, (2.2 ± 1.2) days, (7.5 ± 1.4) days, respectively, which were significantly shorter or less than those in the control group [(193.1 ± 66.0) minutes, (86.2 ± 47.0) mL, (3.7 ± 0.9) days, (12.2 ± 3.5) days, t = 7.00, 6.88, 5.87, 7.53, all P < 0.05]. The complete surgical resection rate in the observation group was significantly higher than that in the control group [92.5% (74/80) vs. 81.3% (65/80), χ2 = 4.44, P < 0.05]. The incidence of complications in the observation group was significantly lower than that in the control group [6.3% (5/80) vs. 16.3% (13/80), χ2 = 4.00, P < 0.05]. Conclusion:The utilization of digital medical 3D technology in the surgical treatment of solid abdominal tumors in children can markedly decrease surgical time, reduce intraoperative blood loss, promote postoperative recovery, achieve a high surgical resection rate, and minimize postoperative complications.

In order to standardize the clinical diagnosis and treatment of upper airway cough syndrome (UACS) for children in China, Dongzhimen Hospital of Beijing University of Chinese Medicine and Affiliated Hospital of Liaoning University of Traditional Chinese Medicine initiated the development of this Traditional Chinese Medicine Diagnosis and Treatment Guidelines for Children with Upper Airway cough Syndrome based on evidence-based medical evidence. This guideline will process registration, write a plan, and develop relevant processes and writing norms, develop and publish official documents. This plan mainly introduces the scope of the guidelines, the purpose and significance, the composition of the guidelines working group, the management of conflicts of interest, the collection, selection and determination of clinical problems, the retrieval, screening and rating of evidence, and the consensus of recommendations. Registration information: This study has been registered in the international practice guidelines registry platform with the registration code of PREPARE-2023CN087.

Objective To study the effect of nano-ceria on doxorubicin-induced cardiotoxic injury and its effect on P53 gene expression,and to explore the mechanism of nano-ceria on doxorubicin-induced cardiotoxic injury.Methods H9C2 myocardial cells were cultured and randomly divided into five groups:control group,model group(1μmol/L adriamycin),nano-cerium oxide group(10μg/ml nano-cerium oxide),experimental group(1μmol/L adriamycin +10μg/ml nano-cerium oxide),and positive control group(1μmol/L adriamycin+10μmol/L dexperimine).The adriamycin induced cardiotoxicity model was established,and the viability of myocardial cells was measured by CCK-8 method.The contents of lactate dehydrogenase(LDH)and malondialdehyde(MDA)in myocardial cells were detected by biochemical method.The levels of reactive oxygen(ROS)and the apoptosis rate in myocardial cells were detected by flow cytometry.The expressions of Bax,Bcl-2 and P53 proteins in myocardial cells were detected by Western blot.Results Compared with the control group,the cell viability was decreased in the model group,the cell LDH and MDA contents were increased,the intracellular ROS level and apoptosis rate were increased,the expressions of Bax and P53 proteins were increased,and the expression of Bcl-2 protein was decreased,and the ratio of Bcl-2/Bax was decreased(all P<0.001).Compared with the model group,the experimental group showed increased cell viability,decreased cell LDH and MDA contents,decreased cell ROS content and apoptosis rate,decreased Bax and P53 protein expressions,and increased Bcl-2 protein expression,and the Bcl-2/Bax ratio was increased(all P<0.001).Conclusion Ceria nanoparticles can effectively prevent adriamycin-induced cardiotoxic injury,and its effect may be related to the down-regulation of P53 gene to inhibit cardiomyocyte apoptosis.

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.