Objective:To construct an artificial intelligence-assisted diagnosis system to recognize the characteristics of Helicobacter pylori ( HP) infection under endoscopy, and evaluate its performance in real clinical cases. Methods:A total of 1 033 cases who underwent 13C-urea breath test and gastroscopy in the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from January 2020 to March 2021 were collected retrospectively. Patients with positive results of 13C-urea breath test (which were defined as HP infertion) were assigned to the case group ( n=485), and those with negative results to the control group ( n=548). Gastroscopic images of various mucosal features indicating HP positive and negative, as well as the gastroscopic images of HP positive and negative cases were randomly assigned to the training set, validation set and test set with at 8∶1∶1. An artificial intelligence-assisted diagnosis system for identifying HP infection was developed based on convolutional neural network (CNN) and long short-term memory network (LSTM). In the system, CNN can identify and extract mucosal features of endoscopic images of each patient, generate feature vectors, and then LSTM receives feature vectors to comprehensively judge HP infection status. The diagnostic performance of the system was evaluated by sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC). Results:The diagnostic accuracy of this system for nodularity, atrophy, intestinal metaplasia, xanthoma, diffuse redness + spotty redness, mucosal swelling + enlarged fold + sticky mucus and HP negative features was 87.5% (14/16), 74.1% (83/112), 90.0% (45/50), 88.0% (22/25), 63.3% (38/60), 80.1% (238/297) and 85.7% (36 /42), respectively. The sensitivity, specificity, accuracy and AUC of the system for predicting HP infection was 89.6% (43/48), 61.8% (34/55), 74.8% (77/103), and 0.757, respectively. The diagnostic accuracy of the system was equivalent to that of endoscopist in diagnosing HP infection under white light (74.8% VS 72.1%, χ2=0.246, P=0.620). Conclusion:The system developed in this study shows noteworthy ability in evaluating HP status, and can be used to assist endoscopists to diagnose HP infection.

Objective:To construct a real-time artificial intelligence (AI)-assisted endoscepic diagnosis system based on YOLO v3 algorithm, and to evaluate its ability of detecting focal gastric lesions in gastroscopy.Methods:A total of 5 488 white light gastroscopic images (2 733 images with gastric focal lesions and 2 755 images without gastric focal lesions) from June to November 2019 and videos of 92 cases (288 168 clear stomach frames) from May to June 2020 at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University were retrospectively collected for AI System test. A total of 3 997 prospective consecutive patients undergoing gastroscopy at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from July 6, 2020 to November 27, 2020 and May 6, 2021 to August 2, 2021 were enrolled to assess the clinical applicability of AI System. When AI System recognized an abnormal lesion, it marked the lesion with a blue box as a warning. The ability to identify focal gastric lesions and the frequency and causes of false positives and false negatives of AI System were statistically analyzed.Results:In the image test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 92.3% (5 064/5 488), 95.0% (2 597/2 733), 89.5% (2 467/ 2 755), 90.0% (2 597/2 885) and 94.8% (2 467/2 603), respectively. In the video test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 95.4% (274 792/288 168), 95.2% (109 727/115 287), 95.5% (165 065/172 881), 93.4% (109 727/117 543) and 96.7% (165 065/170 625), respectively. In clinical application, the detection rate of local gastric lesions by AI System was 93.0% (6 830/7 344). A total of 514 focal gastric lesions were missed by AI System. The main reasons were punctate erosions (48.8%, 251/514), diminutive xanthomas (22.8%, 117/514) and diminutive polyps (21.4%, 110/514). The mean number of false positives per gastroscopy was 2 (1, 4), most of which were due to normal mucosa folds (50.2%, 5 635/11 225), bubbles and mucus (35.0%, 3 928/11 225), and liquid deposited in the fundus (9.1%, 1 021/11 225).Conclusion:The application of AI System can increase the detection rate of focal gastric lesions.

Stapled peptides with significantly enhanced pharmacological profiles have emerged as promising therapeutic molecules due to their remarkable resistance to proteolysis and performance to penetrate cells. The all-hydrocarbon peptide stapling technique has already widely adopted with great success, yielding numerous potent peptide-based molecules. Based on our prior efforts, we conceived and prepared a double-stapled peptide in this study, termed FRNC-1, which effectively attenuated the bone resorption capacity of mature osteoclasts in vitro through specific inhibition of phosphorylated GSK-3β. The double-stapled peptide FRNC-1 displayed notably improved helical contents and resistance to proteolysis than its linear form. Additionally, FRNC-1 effectively prevented osteoclast activation and improved bone density for ovariectomized (OVX) mice after intravenous injection and importantly, after oral (intragastric) administration. The double-stapled peptide FRNC-1 is the first orally effective peptide that has been validated to date as a therapeutic candidate for postmenopausal osteoporosis (PMOP).

ObjectiveTo compare the regulatory effects of five plant polysaccharides on immune function of cyclophosphamide （CTX）-induced immunosuppressed mice by network Meta-analysis， to provide evidence for the clinical application of polysaccharides and the development of effective polysaccharides and oligosaccharides. MethodSeven databases including PubMed， Embase and Web of Science were searched， and studies that met the inclusion criteria were selected. The methodological quality of the included studies was evaluated using the risk of bias tool of Systematic Review Centre for Laboratory Animal Experimentation （SYRCLE）， and the data were analyzed using RStudio and StataSE 17. ResultA total of 62 randomized controlled trials （RCTs） were included， involving 1 512 mice and five plant polysaccharides： Astragalus polysaccharide （APS）， lentinan （LNT）， Ganoderma lucidum polysaccharide （GLP）， Poria cocos polysaccharide （PCC）， and Codonopsis pilosula polysaccharide （CPP）. The network Meta-analysis showed that APS ranked first in increasing spleen index （mean deviation （MD）=3.54， 95% confidence interval （CI） ［2.10， 5.96］）， thymus index （MD=1.98， 95%CI ［1.55， 2.54］） and T helper cells （CD4⁺）/T suppressor cells （CD8⁺） （MD=1.63， 95%CI ［1.13， 2.37］）， while CPP ranked first in up-regulating the number of peripheral blood leukocytes （MD=24.16， 95%CI ［8.21， 71.12］）， macrophage phagocytosis index （MD=2.52， 95%CI ［1.32， 4.82］） and immunoglobulin M （IgM） content （MD=1.79， 95%CI ［1.12， 2.85］）. ConclusionAll the five plant polysaccharides can regulate the immune function of immunosuppressed mice. Among them， APS has advantages in elevating spleen index， thymus index and CD4⁺/CD8⁺， while CPP focuses on increasing the number of peripheral blood leukocytes， macrophage phagocytosis index and IgM content. Due to limited number and quality of included studies， the conclusions needs to be further verified with large samples and high-quality studies.

Objective:To investigate the effect of albumin to fibrinogen ratio on the prognosis of patients undergoing radical resection for colorectal cancer.Methods:Clinical and pathological data of 216 patients who underwent laparoscopic radical resection of colorectal cancer at the General Surgery Department of Taizhou People's Hospital from Aug 2015 to Jul 2017 were retrospectively analyzed. Albumin and fibrinogen results within 7 days before surgery was collected. The optimal cut-off point of AFR was determined by Youden index of ROC curve. Kaplan-Meier analysis, univariate and multivariate COX regression models were used to analyze the prognostic factors of OS and DFS.Results:The best postoperative OS threshold of AFR for patients undergoing laparoscopic radical resection of colorectal cancer was 9.43. Univariate analysis and multivariate COX regression analysis showed that age ≤65 years, TNM stage Ⅰ-Ⅱ, and AFR≥9.43 had better OS and DFS (all P<0.05). Conclusions:Preoperative AFR level had a good predictive value on postoperative survival of patients undergoing laparoscopic radical resection of colorectal cancer, and AFR<9.43 was an independent risk factor for postoperative OS and DFS.

AIM: To evaluate the bioequivalence of two candesartan cilexetil tablet formulations in healthy Chinese subjects after administration of a single dose, and an artificial neural network model was established to predict the candesartan plasma concentration, and provide a basis for clinical rational use of drugs. METHODS: Thirty-two healthy Chinese subjects were enrolled for oral administration of a single 8 mg dose of candesartan cilexetil tablet (test or reference product) under fasting or fed conditions to conduct a bioequivalence study. The bioequivalence results were used to build a back-propagation artificial neural network model by MATLAB software, and the model was internally and externally verified to predict the plasma concentration. RESULTS: Under both fasting and fed conditions, the C

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

Objective    To compare the clinical effects of segmentectomy and lobectomy for ≤2 cm lung adenocarcinoma with micropapillary and solid subtype negative by intraoperative frozen sections. Methods    The patients with adenocarcinoma who received segmentectomy or lobectomy in multicenter from June 2020 to March 2021 were included. They were divided into two groups according to a random number table, including a segmentectomy group (n=119, 44 males and 75 females with an average age of 56.6±8.9 years) and a lobectomy group (n=115, 43 males and 72 females with an average of 56.2±9.5 years). The clinical data of the patients were analyzed. Results    There was no significant difference in the baseline data between the two groups (P>0.05). No perioperative death was found. There was no statistical difference in the operation time (111.2±30.0 min vs. 107.3±34.3 min), blood loss (54.2±83.5 mL vs. 40.0±16.4 mL), drainage duration (2.8±0.6 d vs. 2.6±0.6 d), hospital stay time (3.9±2.3 d vs. 3.7±1.1 d) or pathology staging (P>0.05) between the two groups. The postoperative pulmonary function analysis revealed that the mean decreased values of forced vital capacity and forced expiratory volume in one second percent predicted in the segmentectomy group were significantly better than those in the lobectomy group (0.2±0.3 L vs. 0.4±0.3 L, P=0.005; 0.3%±8.1% vs. 2.9%±7.4%, P=0.041). Conclusion    Segmentectomy is effective in protecting lungs function, which is expected to improve life quality of patients.

Objective:To evaluate deep learning in improving the diagnostic rate of adenomatous and non-adenomatous polyps.Methods:Non-magnifying narrow band imaging (NBI) polyp images obtained from Endoscopy Center of Renmin Hospital, Wuhan University were divided into three datasets. Dataset 1 (2 699 adenomatous and 1 846 non-adenomatous non-magnifying NBI polyp images from January 2018 to October 2020) was used for model training and validation of the diagnosis system. Dataset 2 (288 adenomatous and 210 non-adenomatous non-magnifying NBI polyp images from January 2018 to October 2020) was used to compare the accuracy of polyp classification between the system and endoscopists. At the same time, the accuracy of 4 trainees in polyp classification with and without the assistance of this system was compared. Dataset 3 (203 adenomatous and 141 non-adenomatous non-magnifying NBI polyp images from November 2020 to January 2021) was used to prospectively test the system.Results:The accuracy of the system in polyp classification was 90.16% (449/498) in dataset 2, superior to that of endoscopists. With the assistance of the system, the accuracy of colorectal polyp diagnosis was significantly improved. In the prospective study, the accuracy of the system was 89.53% (308/344).Conclusion:The colorectal polyp classification system based on deep learning can significantly improve the accuracy of trainees in polyp classification.

Objective:To construct an intelligent performance measurement system of gastrointestinal endoscopy and to analyze its value for endoscopic quality improvement.Methods:The intelligent gastrointestinal endoscopy performance measurement system was developed by using the deep convolutional neural network (DCNN) and deep reinforcement learning, based on the Digital Imaging and Communications in Medicine. Images were acquired of patients undergoing gastrointestinal endoscopy at Digestive Endoscopy Center of Renmin Hospital of Wuhan University from December 2016 to October 2018. The system applied cecum recognition model (DCNN1), images in vitro and in vivo recognition model (DCNN2), and identification model at 26 gastric sites (DCNN3) to monitor indices such as cecal intubation rate, colonoscopic withdrawal time, gastroscopic inspection time, and gastroscopic coverage. Images of 83 gastroscopies and 205 colonoscopies acquired at Digestive Endoscopy Center of Renmin Hospital of Wuhan University from March to November 2019 were randomly selected to examine the effectiveness of the system. Results:The intelligent gastrointestinal endoscopy performance measurement system consisted of quality analysis of both gastroscopy and colonoscopy, including all indices, and could be generated automatically at any time. The accuracy for cecal intubation rate, colonoscopic withdrawal time, gastroscopic inspection time, and gastroscopic coverage were 92.5% (172/186), 91.7% (188/205), 100.0% (83/83), 89.3% (1 928/2 158), respectively.Conclusion:The intelligent performance measurement system for gastrointestinal endoscopy can be recommended for the quality control of gastrointestinal endoscopy, from which endoscopists can get feedback and improve the quality of gastrointestinal endoscopy.