BACKGROUND: Spicy food consumption has been reported to be inversely associated with mortality from multiple diseases. However, the effect of spicy food intake on the incidence of vascular diseases in the Chinese population remains unclear. This study was conducted to explore this association. METHODS: This study was performed using the large-scale China Kadoorie Biobank (CKB) prospective cohort of 486,335 participants. The primary outcomes were vascular disease, ischemic heart disease (IHD), major coronary events (MCEs), cerebrovascular disease, stroke, and non-stroke cerebrovascular disease. A Cox proportional hazards regression model was used to assess the association between spicy food consumption and incident vascular diseases. Subgroup analysis was also performed to evaluate the heterogeneity of the association between spicy food consumption and the risk of vascular disease stratified by several basic characteristics. In addition, the joint effects of spicy food consumption and the healthy lifestyle score on the risk of vascular disease were also evaluated, and sensitivity analyses were performed to assess the reliability of the association results. RESULTS: During a median follow-up time of 12.1 years, a total of 136,125 patients with vascular disease, 46,689 patients with IHD, 10,097 patients with MCEs, 80,114 patients with cerebrovascular disease, 56,726 patients with stroke, and 40,098 patients with non-stroke cerebrovascular disease were identified. Participants who consumed spicy food 1-2 days/week (hazard ratio [HR] = 0.95, 95% confidence interval [95% CI] = [0.93, 0.97], P <0.001), 3-5 days/week (HR = 0.96, 95% CI = [0.94, 0.99], P = 0.003), and 6-7 days/week (HR = 0.97, 95% CI = [0.95, 0.99], P = 0.002) had a significantly lower risk of vascular disease than those who consumed spicy food less than once a week ( Ptrend <0.001), especially in those who were younger and living in rural areas. Notably, the disease-based subgroup analysis indicated that the inverse associations remained in IHD ( Ptrend = 0.011) and MCEs ( Ptrend = 0.002) risk. Intriguingly, there was an interaction effect between spicy food consumption and the healthy lifestyle score on the risk of IHD ( Pinteraction = 0.037). CONCLUSIONS Our findings support an inverse association between spicy food consumption and vascular disease in the Chinese population, which may provide additional dietary guidance for the prevention of vascular diseases.
Humans ; Male ; Female ; Prospective Studies ; Middle Aged ; Aged ; Vascular Diseases/etiology* ; Risk Factors ; China/epidemiology* ; Adult ; Proportional Hazards Models ; Cerebrovascular Disorders/epidemiology* ; East Asian People

Objective:To evaluate deep learning for differentiating invasion depth of colorectal adenomas under image enhanced endoscopy (IEE).Methods:A total of 13 246 IEE images from 3 714 lesions acquired from November 2016 to June 2021 were retrospectively collected in Renmin Hospital of Wuhan University, Shenzhen Hospital of Southern Medical University and the First Hospital of Yichang to construct a deep learning model to differentiate submucosal deep invasion and non-submucosal deep invasion lesions of colorectal adenomas. The performance of the deep learning model was validated in an independent test and an external test. The full test was used to compare the diagnostic performance between 5 endoscopists and the deep learning model. A total of 35 videos were collected from January to June 2021 in Renmin Hospital of Wuhan University to validate the diagnostic performance of the endoscopists with the assistance of deep learning model.Results:The accuracy and Youden index of the deep learning model in image test set were 93.08% (821/882) and 0.86, which were better than those of endoscopists [the highest were 91.72% (809/882) and 0.78]. In video test set, the accuracy and Youden index of the model were 97.14% (34/35) and 0.94. With the assistance of the model, the accuracy of endoscopists was significantly improved [the highest was 97.14% (34/35)].Conclusion:The deep learning model obtained in this study could identify submucosal lesions with deep invasion accurately for colorectal adenomas, and could improve the diagnostic accuracy of endoscopists.

Autophagy is a cellular process in which proteins and organelles are engulfed in autophagosomal vesicles and transported to the lysosome/vacuole for degradation. Protein-protein interactions (PPIs) play a crucial role at many stages of autophagy, which present formidable but attainable targets for autophagy regulation. Moreover, selective regulation of PPIs tends to have a lower risk in causing undesired off-target effects in the context of a complicated biological network. Thus, small-molecule regulators, including peptides and peptidomimetics, targeting the critical PPIs involved in autophagy provide a new opportunity for innovative drug discovery. This article provides general background knowledge of the critical PPIs involved in autophagy and reviews a range of successful attempts on discovering regulators targeting those PPIs. Successful strategies and existing limitations in this field are also discussed.

Objective:To explore the effectiveness of the artificial intelligence-endoscopic ultrasound (AI-EUS) biliary and pancreatic recognition system in assisting the recognition of EUS images.Methods:Subjects who received EUS due to suspicious biliary and pancreatic diseases from December 2019 to August 2020 were prospectively collected from the database of Department of Gastroenterology, Renmin Hospital of Wuhan University. Pancreatic EUS images of 28 subjects were included for recognition of pancreas standard station. EUS images of bile duct of 29 subjects were included for recognition of bile duct standard station. Eight new endoscopists from the Gastroenterology Department of Renmin Hospital of Wuhan University read the 57 EUS videos with and without the assistance of AI-EUS biliary and pancreatic recognition system. Accuracy of endoscopists' identification of biliary and pancreatic standard sites with and without the assistance of AI-EUS was compared.Results:The accuracy of pancreas standard station identification of the new endoscopists increased from 67.2% (903/1 344) to 78.4% (1 054/1 344) with the assistance of AI-EUS. The accuracy of bile duct standard station identification increased from 56.4% (523/928) to 73.8% (685/928).Conclusion:AI-EUS biliary and pancreatic recognition system can improve the accuracy of EUS images recognition of biliary and pancreatic system, which can assist diagnosis in clinical work.

ObjectiveTo explore the histological characteristics of the kidney in Mongolian gerbils of different ages. MethodsTen Mongolian gerbils of 2, 6, and 12 months old (half male and half female) were selected. After euthanasia, the kidneys were taken and paraffin sections were made. After HE, MASSON and PAS staining, the structural differences of different parts of the kidney tissue in gerbils of different ages were observed by digital scanning, and the relevant data of the kidney tissue were measured by image analysis software. ResultsThe number of proximal convoluted tubules was more than that of distal convoluted tubules in the renal cortex and outer medulla of gerbils. With age, the glomerular density decreased, the glomerular diameter increased, the basement membrane of renal tubules thickened, and the fibrous components between renal tubules increased. ConclusionThe histological structure of Mongolian gerbil's kidney varies with age, which may be related to glomerulosclerosis and parenchymal cell reduction. The specific mechanism needs further study.

The anterior cruciate ligament (ACL) injury is a common sports injury that has a significant impact on knee function and patients′ mobility. With the popularity of national fitness campaign in China, the incidence of ACL injury is increasing year by year. Currently, there still lacks clinical standards or guidelines on how to choose appropriate treatment methods, surgical plans and rehabilitation protocols for ACL injury. In order to timely reflect the new treatment concept of ACL injury, standardize its diagnosis and treatment and improve the curative effect, the Sports Medicine Society of Chinese Research Hospital Association and the Editorial Board of Chinese Journal of Trauma organized domestic orthopedic and sports medicine experts to formulate the "clinical evidence-based guideline for the diagnosis and treatment of anterior cruciate ligament injury (2022 version)" based on the level of evidence-based medicine and in compliance with the principle of scientificity, practicability and advancement. The present guideline includes 12 recommendations for the diagnosis, treatment and rehabilitation of ACL injury in order to provide guidance and assistance for the clinical diagnosis and treatment of ACL injury in China.

Objective:To construct a deep learning-based diagnostic system for gastrointestinal submucosal tumor (SMT) under endoscopic ultrasonography (EUS), so as to help endoscopists diagnose SMT.Methods:From January 1, 2019 to December 15, 2021, at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University, 245 patients with SMT confirmed by pathological diagnosis who underwent EUS and endoscopic submucosal dissection were enrolled. A total of 3 400 EUS images were collected. Among the images, 2 722 EUS images were used for training of lesion segmentation model, while 2 209 EUS images were used for training of stromal tumor and leiomyoma classification model; 283 and 191 images were selected as independent test sets to evaluate lesion segmentation model and classification model, respectively. Thirty images were selected as an independent data set for human-machine competition to compare the lesion classification accuracy between lesion classification models and 6 endoscopists. The performance of the segmentation model was evaluated by indexes such as Intersection-over-Union and Dice coefficient. The performance of the classification model was evaluated by accuracy. Chi-square test was used for statistical analysis.Results:The average Intersection-over-Union and Dice coefficient of lesion segmentation model were 0.754 and 0.835, respectively, and the accuracy, recall and F1 score were 95.2%, 98.9% and 97.0%, respectively. Based on the lesion segmentation, the accuracy of classification model increased from 70.2% to 92.1%. The results of human-machine competition showed that the accuracy of classification model in differential diagnosis of stromal tumor and leiomyoma was 86.7% (26/30), which was superior to that of 4 out of the 6 endoscopists(56.7%, 17/30; 56.7%, 17/30; 53.3%, 16/30; 60.0%, 18/30; respectively), and the differences were statistically significant( χ2=7.11, 7.36, 8.10, 6.13; all P<0.05). There was no significant difference between the accuracy of the other 2 endoscopists(76.7%, 23/30; 73.3%, 22/30; respectively) and model(both P<0.05). Conclusion:This system could be used for the auxiliary diagnosis of SMT under ultrasonic endoscope in the future, and to provide a powerful evidence for the selection of subsequent treatment decisions.

Peptide inhibition of the interactions of the tumor suppressor protein P53 with its negative regulators MDM2 and MDMX activates P53

Objective:To develop an endoscopic ultrasonography (EUS) station recognition and pancreatic segmentation system based on deep learning and to validate its efficacy.Methods:Data of 269 EUS procedures were retrospectively collected from Renmin Hospital of Wuhan University between December 2016 and December 2019, and were divided into 3 datasets: (1)Dataset A of 205 procedures for model training containing 16 305 images for classification training and 1 953 images for segmentation training; (2)Dataset B of 44 procedures for model testing containing 1 606 images for classification testing and 480 images for segmentation testing; (3) Dataset C of 20 procedures with 150 images for comparing the performance between models and endoscopists. EUS experts (with more than 10 years of experience) A and B classified and labeled all images of dataset A, B and C through discussion, and the results were used as the gold standard. EUS expert C and senior EUS endoscopists (with more than 5 years of experience) D and E classified and labeled the images in dataset C, and the results were used for comparison with model. The main outcomes included accuracy of classification, Dice (F1 score) of segmentation and Cohen Kappa coefficient of consistency analysis.Results:In test dataset B, the model achieved a mean accuracy of 94.1% in classification. The mean Dice of pancreatic and vascular segmentation were 0.826 and 0.841 respectively. In dataset C, the classification accuracy of the model reached 90.0%. The classification accuracy of expert C, senior endoscopist D and E were 89.3%, 88.7% and 87.3%, respectively. The Dice of pancreatic and vascular segmentation in the model were 0.740 and 0.859, 0.708 and 0.778 for expert C, 0.747 and 0.875 for senior endoscopist D, and 0.774 and 0.789 for senior endoscopist E. The model was comparable to the expert level.Consistency analysis showed that there was high consistency between the model and endoscopists (the Kappa coefficient was 0.823 between model and expert C, 0.840 between model and senior endoscopist D, and 0.799 between model and senior endoscopist E).Conclusion:EUS station classification and pancreatic segmentation system based on deep learning can be used for quality control of pancreatic EUS, with a comparable performance of classification and segmentation to that of EUS experts.

Objective To investigate the safety and efficacy of endoscopic submucosal dissection ( ESD) for early stage colorectal cancer and precancerous lesions. Methods Clinical data of 108 patients who received ESD for early stage colorectal cancer and precancerous lesions from December 2016 to June 2017 in Renmin Hospital of Wuhan University were analyzed. The lesion characteristics, postoperative pathological features, intraoperative and postoperative complications and postoperative follow-up outcomes were analyzed. Results The 108 patients all underwent ESD successfully with median operation time of 45 min. The rate of intraoperative perforation and postoperative delayed bleeding was 2. 8％ ( 3/108) and 2. 8％ (3/108), respectively. No postoperative delayed perforation occurred. Postoperative pathology showed that there were 41 cases ( 38. 0％) of tubular adenoma, 4 ( 3. 7％) villous adenoma, 39 ( 36. 1％) villous tubular adenoma [ including 41 ( 38. 0％) low-grade intraepithelial neoplasia and 16 ( 14. 8％) high-grade intraepithelial neoplasia] , 19 ( 17. 6％) adenocarcinoma, and 5 ( 4. 6％) other types. Among the 19 cases of adenocarcinoma, there were 11 cases of well-differentiated, 5 median-differentiated and 3 low-differentiated. The complete resection rate was 100. 0％ and the en bloc resection rate was 92. 3％ ( 100/108) . The mean follow-up time was 8. 1 months, and no recurrence was found during this period. Conclusion ESD is safe and effective in the treatment of early stage colorectal lesions. It is important to improve preoperative assessment, strengthen surgical skills, analyze postoperative pathological features and regularly follow up to guarantee the treatment quality of ESD.