By reviewing modern research and integrating clinical practice， this paper elucidates the correlation between the traditional Chinese medicine theory of pathological accumulation from collateral obstruction and gap junction intercellular communication （GJIC）， as well as its theoretical connotation and clinical application in tumor prevention and treatment. Physiologically， gap junction and collateral channels share similarities in structural distribution， substance exchange and information transmission. Pathologically， metabolic coupling mediated by dysfunctional gap junction resembles collaterals stagnation， forming the basis of tumor pathogenesis. The establishment of heterotypic gap junction parallels collateral hyperactivity， contributing to tumor metastasis. The post-translational modifications （PTMs） disorder of connexins is similar to the deficiency of collaterals， serving as a driver of tumor progression. Clinically， tumor treatment should follow the pathomechanism of collateral obstruction leading to pathological accumulation. In the early stage， detoxifying and unblocking collaterals can restore intercellular communication and inhibit tumorigenesis； in the progressive stage， calming hyperactivity and suppressing aberrant collateral pathways can prevent metastasis by interrupting heterotypic gap junction formation； and in the terminal stage， supporting vital qi and modulating PTMs of connexins can help delay tumor progression.

Objective:To evaluate the effect of automated flexible endoscope channel brushing system (AFECBS) on endoscope reprocessing.Methods:A prospective randomized controlled study was conducted. The used endoscopes were divided into automatic group and manual group by random number table method, 200 in each group. In the automatic group, the AFECBS was used to scrub each tube 3 times during endoscope cleaning; and in the manual group, scrubbing and disinfection personnel routinely brushed each pipeline for 3 times. The primary end point was the qualified rate of endoscopic cleaning quality in the two groups, and the secondary end point was the time spent by the scrubbing and disinfection personnel on the two groups.Results:The qualified rate of overall cleaning in the automatic group was 90.0% (180/200), and in the manual group was 81.0% (162/200). The qualified rate of the automatic group was higher than that of the manual group ( χ2=6.534, P=0.011). The qualified rate of gastroscope cleaning in the automatic group was higher than that in the manual group [92.0% (127/138) VS 81.6% (120/147), χ2=6.658, P=0.010]. There was no significant difference in the qualified rate of colonoscope cleaning between the automatic group and the manual group [85.5% (53/62) VS 79.2% (42/53), χ2=0.774, P=0.379]. When the cleaning personnel scoured 5 endoscopes in each of the two groups, the time of the automatic group (5.17±0.42 min) was shorter than that of the manual group (9.60±0.53 min) ( t=92.644, P<0.001). Conclusion:Compared with manual scrubbing, AFECBS can improve the qualified rate of endoscope cleaning and the work efficiency of scrubbing and disinfection personnel, which is worthy of clinical application.

Objective To study the medication law of postoperative treatment of colorectal cancer by national TCM doctor Professor Piao Bingkui.Methods Professor Piao Bingkui's electronic medical records and paper medical records of colorectal cancer postoperative patients at Guang'anmen Hospital,China Academy of Chinese Medical Sciences and Beijing Yiqingtang Chinese Medicine Clinic were collected and organized from their outpatients from January 1st,2002 to February 28th,2022.R4.2.1 was used to study the prescriptions,including high-frequency drugs,drug types,properties of Chinese materia medica,yin yang and five elements and dosage of drugs,as well as the law of multi-drug association in postoperative patients with colorectal cancer.Results Totally 642 colorectal postoperative cancer patients were included,involving 2 226 prescriptions,180 kinds of Chinese materia medica,and a total frequency of 39 988 times.The high-frequency drugs were Astragali Radix,fried Aurantii Fructus with wheat bran,Dioscoreae Rhizoma,etc.The main drugs in terms of efficacy were tonics for tonifying deficiency,disinfectants,etc.;the main properties were warm and neutral,the main tastes were sweet,pungent and bitter,and the main meridians were spleen meridians and stomach meridians.Ascending medicines were often used,and the drug were basically non-toxic.The frequency of using yang tonifying medicine was high,and the five elements were commonly used as local medicines;the dosage was mostly 10,15 and 20 g.The complex network analysis and clustering analysis of the association between multiple drugs found that Professor Piao Bingkui's basic prescription for treating colorectal cancer included Astragali Radix,fried Aurantii Fructus with wheat bran,Dioscoreae Rhizoma,salt Alpiniae Oxyphyllae Fructus,Citri Reticulatae Pericarpium,Smilacis Glabrae Rhizoma,Pseudostellariae Radix,and fried Atractylodis Macrocephalae Rhizoma with wheat bran.Conclusion In the treatment of colorectal cancer,Professor Piao Bingkui focuses on reinforcing the healthy qi,nourishing spleen and stomach function,combined with detoxication method and clearing heat,expelling phlegm and dampness,guiding stagnation and dispelling stasis methods,making syndrome differentiation as well as tonifying and benefiting qi,regulating qi movement,so as to realize the"treating middle-energizer as balance"and achieve mild level when treating colorectal cancer.

Objective:To investigate the clinical features and therapeutic efficacy of patients with hereditary leiomyomatosis and renal cell carcinoma(RCC) syndrome-associated RCC (HLRCC-RCC) in China.Methods:The clinical data of 119 HLRCC-RCC patients with fumarate hydratase (FH) germline mutation confirmed by genetic diagnosis from 15 medical centers nationwide from January 2008 to December 2021 were retrospectively analyzed. Among them, 73 were male and 46 were female. The median age was 38(13, 74) years. The median tumor diameter was 6.5 (1.0, 20.5) cm. There were 38 cases (31.9%) in stage Ⅰ-Ⅱand 81 cases (68.1%) in stage Ⅲ-Ⅳ. In this group, only 11 of 119 HLRCC-RCC patients presented with skin smooth muscle tumors, and 44 of 46 female HLRCC-RCC patients had a history of uterine fibroids. The pathological characteristics, treatment methods, prognosis and survival of the patients were summarized.Results:A total of 86 patients underwent surgical treatment, including 70 cases of radical nephrectomy, 5 cases of partial nephrectomy, and 11 cases of reductive nephrectomy. The other 33 patients with newly diagnosed metastasis underwent renal puncture biopsy. The results of genetic testing showed that 94 patients had FH gene point mutation, 18 had FH gene insertion/deletion mutation, 4 had FH gene splicing mutation, 2 had FH gene large fragment deletion and 1 had FH gene copy number mutation. Immunohistochemical staining showed strong 2-succinocysteine (2-SC) positive and FH negative in 113 patients. A total of 102 patients received systematic treatment, including 44 newly diagnosed patients with metastasis and 58 patients with postoperative metastasis. Among them, 33 patients were treated with tyrosine kinase inhibitor (TKI) combined with immune checkpoint inhibitor (ICI), 8 patients were treated with bevacizumab combined with erlotinib, and 61 patients were treated with TKI monotherapy. Survival analysis showed that the median progression-free survival (PFS) of TKI combined with ICI was 18 (5, 38) months, and the median overall survival (OS) was not reached. The median PFS and OS were 12 (5, 14) months and 30 (10, 32) months in the bevacizumab combined with erlotinib treatment group, respectively. The median PFS and OS were 10 (3, 64) months and 44 (10, 74) months in the TKI monotherapy group, respectively. PFS ( P=0.009) and OS ( P=0.006) in TKI combined with ICI group were better than those in bevacizumab combined with erlotinib group. The median PFS ( P=0.003) and median OS ( P=0.028) in TKI combined with ICI group were better than those in TKI monotherapy group. Conclusions:HLRCC-RCC is rare but has a high degree of malignancy, poor prognosis and familial genetic characteristics. Immunohistochemical staining with strong positive 2-SC and negative FH can provide an important basis for clinical diagnosis. Genetic detection of FH gene germ line mutation can confirm the diagnosis. The preliminary study results confirmed that TKI combined with ICI had a good clinical effect, but it needs to be confirmed by the results of a large sample multi-center randomized controlled clinical study.

Objective:To construct an intelligent endoscopic image analysis system that could monitor the blind spot of the upper gastrointestinal tract, and to test its performance.Methods:A total of 87 167 upper gastrointestinal endoscopy images (dataset 1) including 75 551 for training and 11 616 for testing, and a total of 2 414 pharyngeal images (dataset 2) including 2 233 for training and 181 for testing were retrospectively collected from the Digestive Endoscopy Center of Renmin Hospital of Wuhan University between 2016 to 2020. A 27-category-classification model for blind spot monitoring in the upper gastrointestinal tract (model 1, which distinguished 27 anatomical sites such as the pharynx, esophagus, and stomach) and a 5-category-classification model for blind spot monitoring in the pharynx (model 2, which distinguished palate, posterior pharyngeal wall, larynx, left and right pyriform sinuses) were constructed. The above models were trained and tested based on dataset 1 and 2, respectively, and trained based on the EfficientNet-B4, ResNet50 and VGG16 models of the keras framework. Thirty complete upper gastrointestinal endoscopy videos were retrospectively collected from the Digestive Endoscopy Center of Renmin Hospital of Wuhan University in 2021 to test model 2 blind spot monitoring performance.Results:The cross-sectional comparison results of the accuracy of model 1 in identifying 27 anatomical sites of the upper gastrointestinal tract in images showed that the mean accuracy of EfficientNet-B4, ResNet50, and VGG16 were 90.90%, 90.24%, and 89.22%, respectively, with the EfficientNet-B4 model performance the best, and the accuracy of EfficientNet-B4 model for each site ranged from 80.49% to 97.80%. The cross-sectional comparison results of the accuracy of model 2 in identifying the 5 anatomical sites of the pharynx in the images showed that the mean accuracy of EfficientNet-B4, ResNet50, and VGG16 were 99.40%, 98.56%, and 97.01%, respectively, in which the EfficientNet-B4 model had the best performance, and the accuracy of EfficientNet-B4 model for each site ranged from 96.15% to 100.00%. The overall accuracy of model 2 in identifying the 5 anatomical sites of the pharynx in the video was 97.33% (146/150).Conclusion:The intelligent endoscopic image analysis system based on deep learning can monitor blind spots in the upper gastrointestinal tract, coupled with pharyngeal blind spot monitoring and esophagogastroduodenal blind spot monitoring functions. The system shows high accuracy in both images and videos, which is expected to have a potential role in clinical practice and assisting endoscopists to achieve full observation of the upper gastrointestinal tract.

Objective:To develop an artificial intelligence model based on multi-modal endoscopic images for identifying gastric neoplasms and to compare its diagnostic efficacy with traditional models and endoscopists.Methods:A total of 3 267 images of gastric neoplasms and non-neoplastic lesions under white light (WL) endoscopy and weak magnification (WM) endoscopy from 463 patients at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from March 2018 to December 2019 were utilized. Two single-modal models (WL model and WM model) were constructed based on WL and WM images separately. WL and WM images of corresponding lesions were combined into image pairs for creating a multi-modal (MM) characteristics integration model. A test set consisting of 696 images of 102 lesions from 97 patients from March 2020 to March 2021 was used to compare the diagnostic efficacy of the single-modal models and a multi-modal model for gastric neoplastic lesions at both the image and the lesion levels. Additionally, video clips of 80 lesions from 80 patients from January 2022 to June 2022 were employed to compare diagnostic efficacy of the WM model, the MM model and 7 endoscopists at the lesion level for gastric neoplasms.Results:In the image test set, the sensitivity and accuracy of MM model were 84.96% (576/678), and 86.89% (1 220/1 289), respectively, for diagnosing gastric neoplasms at the image level, which were superior to 63.13% (113/179) and 80.59% (353/438) of WM model ( χ2=42.81, P<0.001; χ2=10.33, P=0.001), and also better than those of WL model [70.47% (74/105), χ2=13.52, P<0.001; 67.82% (175/258), χ2=57.27, P<0.001]. The MM model showed a sensitivity of 87.50% (28/32), a specificity of 88.57% (62/70), and an accuracy of 88.24% (90/102) at the lesion level. The specificity ( χ2=22.99, P<0.001) and accuracy ( χ2=19.06, P<0.001) were significantly higher than those of WL model; however, there was no significant difference compared with those of the WM model ( P>0.05). In the video test, the sensitivity, specificity and accuracy of the MM model at the lesion level were 95.00% (19/20), 93.33% (56/60) and 93.75% (75/80). These results were significantly better than those of endoscopists, who had a sensitivity of 77.14% (108/140), a specificity of 79.29% (333/420), and an accuracy of 78.75% (441/560), with significant differences ( χ2=18.62, P<0.001; χ2=35.07, P<0.001; χ2=53.12, P<0.001), and was higher than the sensitivity of advanced endoscopists [83.33% (50/60)] with significant difference ( χ2=4.23, P=0.040). Conclusion:The artificial intelligence model based on multi-modal endoscopic images for the diagnosis of gastric neoplasms shows high efficacy in both image and video test sets, outperforming the average diagnostic performance of endoscopists in the video test.

Objective:To construct a feature visualization system utilizing deep learning for superficial esophageal squamous cell carcinoma (SESCC) under magnifying endoscopy with narrow band imaging (ME-NBI) to predict the infiltration depth of SESCC.Methods:The feature visualization system consisted of four models: two for segmenting the intrapapillary capillary loops (IPCL) area and avascular area (AVA) in ME-NBI images of SESCC lesions (models 1 and 2, respectively), one for obtaining the principal component of color (PCC) in ME-NBI images of SESCC lesions (model 3), and another for automatically predicting the depth of SESCC infiltration based on the features extracted from the first three models (model 4). A total of 2 341 ME-NBI images of SESCC lesions from April 2016 to October 2021 were used to develop the feature visualization system, which was divided into 3 datasets: dataset 1 (1 077 ME-NBI images) was used to train and test models 1-3, dataset 2 (1 069 ME-NBI images) was expanded by 20 times through feature combination to generate 21 380 feature synthetic images to train and test model 4, and dataset 3 (195 ME-NBI images), containing 146 ME-NBI images with lesion invasion depth from the epithelium to the upper 1/3 of the submucosa (EP-SM1), and 49 ME-NBI images with lesion invasion depth from the middle 1/3 to the lower 1/3 of the submucosa (SM2-SM3), was used to validate the diagnostic performance of the feature visualization system in predicting the invasion depth of SESCC (EP-SM1/SM2-SM3). In order to evaluate the superiority of the feature visualization system, the prediction results of dataset 3 of the traditional deep learning system (trained directly with ME-NBI images), single-item feature models (single-item IPCL feature model, single-item AVA feature model and single-item PCC feature model) were compared with the prediction results of the feature visualization system. In order to evaluate the clinical utility of the feature visualization system, 4 expert physicians (with more than 10 years of endoscopic operation, expert physician group) and 5 senior physicians (with more than 5 years of endoscopic operation, senior physician group) were invited to participate in the human-computer competition to diagnose dataset 3, and the results were compared with the feature visualization system.Results:The accuracy, sensitivity and specificity of the feature visualization system in predicting the invasion depth of SESCC (EP-SM1/SM2-SM3) were 83.08% (162/195), 82.88% (121/146) and 83.67% (41/49), respectively. The above indicators were 60.00% (117/195), 52.05% (76/146) and 83.67% (41/49) for the traditional deep learning system, 74.87% (146/195), 75.34% (110/146) and 73.47% (36/49) for the single IPCL feature model, 58.97% (115/195), 60.27% (88/146) and 55.10% (27/49) for single AVA feature model, 71.28% (139/195), 71.23% (104/146) and 71.43% (35/49) for single PCC feature model, respectively. The results were 66.67%, 78.22% and 32.24% in senior physician group, and 72.31%, 85.96% and 31.63% in expert physician group, respectively. The accuracy of the feature visualization system in predicting the invasion depth of SESCC was significantly higher than that of the other 6 groups ( P<0.05). The sensitivity of feature visualization system was slightly higher than that of senior physician group ( χ2=1.59, P=0.21) and single-item IPCL feature model ( χ2=2.51, P=0.11), slightly lower than that of expert physician group ( χ2=0.89, P=0.35), and significantly higher than that of three other groups ( P<0.05). The specificity of the feature visualization system was similar to the traditional deep learning system ( χ2=0.00, P=1.00), slightly higher than that of single-item IPCL feature model ( χ2=1.52, P=0.22) and single-item PCC feature model (χ2=2.11, P=0.15), and significantly higher than that of the single AVA feature model ( χ2=9.42, P<0.01), senior physician group ( χ2=44.71, P<0.01) and expert physician group ( χ2=43.57, P<0.01). Conclusion:The developed deep learning-based feature visualization system using ME-NBI shows excellent diagnostic performance in predicting the infiltration depth of SESCC (EP-SM1/SM2-SM3), surpassing the accuracy levels of experienced endoscopists with over 10 years of experience.

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 analyze the cost-effectiveness of a relatively mature artificial intelligence (AI)-assisted diagnosis and treatment system (ENDOANGEL) for gastrointestinal endoscopy in China, and to provide objective and effective data support for hospital acquisition decision.Methods:The number of gastrointestinal endoscopy procedures at the Endoscopy Center of Renmin Hospital of Wuhan University from January 2017 to December 2019 were collected to predict the procedures of gastrointestinal endoscopy during the service life (10 years) of ENDOANGEL. The net present value, payback period and average rate of return were used to analyze the cost-effectiveness of ENDOANGEL.Results:The net present value of an ENDOANGEL in the expected service life (10 years) was 6 724 100 yuan, the payback period was 1.10 years, and the average rate of return reached 147.84%.Conclusion:ENDOANGEL shows significant economic benefits, and it is reasonable for hospitals to acquire mature AI-assisted diagnosis and treatment system for gastrointestinal endoscopy.

Objective:To assess the diagnostic efficacy of upper gastrointestinal endoscopic image assisted diagnosis system (ENDOANGEL-LD) based on artificial intelligence (AI) for detecting gastric lesions and neoplastic lesions under white light endoscopy.Methods:The diagnostic efficacy of ENDOANGEL-LD was tested using image testing dataset and video testing dataset, respectively. The image testing dataset included 300 images of gastric neoplastic lesions, 505 images of non-neoplastic lesions and 990 images of normal stomach of 191 patients in Renmin Hospital of Wuhan University from June 2019 to September 2019. Video testing dataset was from 83 videos (38 gastric neoplastic lesions and 45 non-neoplastic lesions) of 78 patients in Renmin Hospital of Wuhan University from November 2020 to April 2021. The accuracy, the sensitivity and the specificity of ENDOANGEL-LD for image testing dataset were calculated. The accuracy, the sensitivity and the specificity of ENDOANGEL-LD in video testing dataset for gastric neoplastic lesions were compared with those of four senior endoscopists.Results:In the image testing dataset, the accuracy, the sensitivity, the specificity of ENDOANGEL-LD for gastric lesions were 93.9% (1 685/1 795), 98.0% (789/805) and 90.5% (896/990) respectively; while the accuracy, the sensitivity and the specificity of ENDOANGEL-LD for gastric neoplastic lesions were 88.7% (714/805), 91.0% (273/300) and 87.3% (441/505) respectively. In the video testing dataset, the sensitivity [100.0% (38/38) VS 85.5% (130/152), χ2=6.220, P=0.013] of ENDOANGEL-LD was higher than that of four senior endoscopists. The accuracy [81.9% (68/83) VS 72.0% (239/332), χ2=3.408, P=0.065] and the specificity [ 66.7% (30/45) VS 60.6% (109/180), χ2=0.569, P=0.451] of ENDOANGEL-LD were comparable with those of four senior endoscopists. Conclusion:The ENDOANGEL-LD can accurately detect gastric lesions and further diagnose neoplastic lesions to help endoscopists in clinical work.