OBJECTIVE To study the pharmacological substance basis of Shaoyao gancao decoction for relieving acute and chronic pain. METHODS The antispasmodic effect of Shaoyao gancao decoction， ethyl acetate extract of Shaoyao gancao decoction and its effluent part of macroporous resin and 90% ethanol elution part of macroporous resin （the concentration of 4 drugs was 13.44 g/mL according to crude drug） was observed by in vitro small intestine tension test in rats. The acetic acid writhing test was conducted in mice to evaluate the analgesic effects of macroporous resin efflux site and macroporous resin 90% ethanol elution site （the dosage of 2.4 g/kg according to crude drug）. The levels of tumor necrosis factor-α （TNF-α）， interleukin-1β （IL- 1β）， prostaglandin E2 （PGE2） and cyclooxygenase-2 （COX-2） in serum of mice were detected. The serum prototype and metabolites of mice after intragastric administration of macroporous resin 90% ethanol elution site were identified by high performance liquid chromatogre-time-of-flight mass spectrometry. RESULTS In vitro experiment showed that 90% ethanol eluting part of macroporous resin represented the best antispasmodic effect， and the inhibitory rate of small intestine tension was significantly higher than macroporous resin efflux site of Shaoyao gancao decoction （P＜0.05） without statistical significance， compared with Shaoyao gancao decoction （P＞0.05）. In the acetic acid writhing experiment， compared with model group， the writhing times of mice in the macroporous resin 90% ethanol elution part group were reduced significantly （P＜0.05）， the writhing latency was prolonged significantly （P＜0.05）， and the levels of COX-2， IL-1β， PGE2 and TNF-α in serum were decreased significantly （P＜0.05）. Ten kinds of protoproducts including paeoniflorin and glycyrrhizic acid were identified from serum of mice， and twenty-two kinds of metabolites including hydroxylated glycyrrhizin and glucosylated liquiritin were identified. CONCLUSIONS The effective part of Shaoyao gancao decoction for relieving acute and chronic pain is 90% ethanol elution part prepared by macroporous resin from the ethyl acetate extract. Ten components， including glycyrrhetinic acid and paeoniflorin， may be the basis of its pharmacological substances.

Remodeling of the mitochondrial network is an important process in the maintenance of cellular homeostasis and is closely related to mitochondrial function. Interactions between the biogenesis of new mitochondria and the clearance of damaged mitochondria (mitophagy) is an important manifestation of mitochondrial network remodeling. Mitochondrial fission and fusion act as a bridge between biogenesis and mitophagy. In recent years, the importance of these processes has been described in a variety of tissues and cell types and under a variety of conditions. For example, robust remodeling of the mitochondrial network has been reported during the polarization and effector function of macrophages. Previous studies have also revealed the important role of mitochondrial morphological structure and metabolic changes in regulating the function of macrophages. Therefore, the processes that regulate remodeling of the mitochondrial network also play a crucial role in the immune response of macrophages. In this paper, we focus on the molecular mechanisms of mitochondrial regeneration, fission, fusion, and mitophagy in the process of mitochondrial network remodeling, and integrate these mechanisms to investigate their biological roles in macrophage polarization, inflammasome activation, and efferocytosis.
Mitochondria ; Mitophagy ; Homeostasis/physiology* ; Phagocytosis ; Macrophages/metabolism*

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 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.

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.

Objective:To compare the cost-effectiveness before and after using an artificial intelligence gastroscopy-assisted system for early gastric cancer screening.Methods:The gastroscopy cases before (non-AI group) and after (AI group) the use of artificial intelligence gastroscopy-assisted system were retrospectively collected in Renmin Hospital of Wuhan University from January 1, 2017 to February 28, 2022. The proportion of early gastric cancer among all gastric cancer was analyzed. Costs were estimated based on the standards of Renmin Hospital of Wuhan University and the 2021 edition of Wuhan Disease Diagnosis-related Group Payment Standards. Cost-effectiveness analysis was conducted per 100 thousand cases with and without the system. And the incremental cost-effectiveness ratio was calculated.Results:For the non-AI group, the proportion of early gastric cancer among all gastric cancer was 28.81% (70/243). The cost of gastroscopy screening per 100 thousand was 54 598.0 thousand yuan, early gastric treatment cost was 221.8 thousand yuan, and a total cost was 54 819.8 thousand yuan. The direct effectiveness was 894.2 thousand yuan, the indirect effectiveness was 1 828.2 thousand yuan and the total effectiveness was 2 722.4 thousand yuan per 100 thousand cases. For the AI group, the early gastric cancer diagnositic rate was 36.56%(366/1 001), where gastroscopy cost was 53 440.0 thousand yuan, early gastric treatment cost 315.8 thousand yuan, the total cost 53 755.8 thousand yuan. The direct effectiveness was 1 273.5 thousand yuan, indirect effectiveness 2 603.1 thousand yuan and the total effectiveness 3 876.6 thousand yuan per 100 thousand cases. The use of the system reduced the cost of early gastric cancer screening by 1 064.0 thousand yuan, and increased the benefit by 1 154.2 thousand yuan per 100 thousand cases. The incremental cost-effectiveness ratio was -0.92.Conclusion:The use of artificial intelligence gastroscopy-assisted system for gastric early cancer screening can reduce medical costs as well as improve the efficiency of screening, and it is recommended for gastroscopy screening .

Type 2 Diabetes Mellitus(T2DM)is a chronic metabolic disease characterized by disorders of insulin and glucose.As a disease closely related to our daily diet,the molecular mechanism of its pathogenesis is still unclear.Sweet taste receptors are a kind of G protein-coupled receptors with 7 times transmembrane.Although the research on the pathogenesis of T2DM has been gradually deepened,there are few studies on the mechanism of sweet taste damage in the state of disease.Therefore,in-depth study of the pathological mechanism of T2DM sweet taste receptor damage is of great significance to further understand the molecular characteristics of sweet taste receptors and explore new targets of traditional Chinese medicine in the treatment of T2DM.In this paper,the biological characteristics of sweet taste receptors,signal transduction mechanism of sweet taste receptors,sweet taste receptors and T2DM,sweet taste receptors and"spleen is sweet"are reviewed in order to provide the latest opinions for the targeted prevention and treatment of this kind of diseases by traditional Chinese medicine and the elucidation of related pathological mechanism.

Objective:To compare the ability of deep convolutional neural network-crop (DCNN-C) and deep convolutional neural network-whole (DCNN-W), 2 artificial intelligence systems based on different training methods to dignose early gastric cancer (EGC) diagnosis under magnifying image-enhanced endoscopy (M-IEE).Methods:The images and video clips of EGC and non-cancerous lesions under M-IEE under narrow band imaging or blue laser imaging mode were retrospectively collected in the Endoscopy Center of Renmin Hospital of Wuhan University, for the training set and test set for DCNN-C and DCNN-W. The ability of DCNN-C and DCNN-W in EGC identity in image test set were compared. The ability of DCNN-C, DCNN-W and 3 senior endoscopists (average performance) in EGC identity in video test set were also compared. Paired Chi-squared test and Chi-squared test were used for statistical analysis. Inter-observer agreement was expressed as Cohen′s Kappa statistical coefficient (Kappa value).Results:In the image test set, the accuracy, sensitivity, specificity and positive predictive value of DCNN-C in EGC diagnosis were 94.97%(1 133/1 193), 97.12% (202/208), 94.52% (931/985), and 78.91%(202/256), respectively, which were higher than those of DCNN-W(86.84%, 1 036/1 193; 92.79%, 193/208; 85.58%, 843/985 and 57.61%, 193/335), and the differences were statistically significant ( χ2=4.82, 4.63, 61.04 and 29.69, P=0.028, =0.035, <0.001 and <0.001). In the video test set, the accuracy, specificity and positive predictive value of senior endoscopists in EGC diagnosis were 67.67%, 60.42%, and 53.37%, respectively, which were lower than those of DCNN-C (93.00%, 92.19% and 87.18%), and the differences were statistically significant ( χ2=20.83, 16.41 and 11.61, P<0.001, <0.001 and =0.001). The accuracy, specificity and positive predictive value of DCNN-C in EGC diagnosis were higher than those of DCNN-W (79.00%, 70.31% and 64.15%, respectively), and the differences were statistically significant ( χ2=7.04, 8.45 and 6.18, P=0.007, 0.003 and 0.013). There were no significant differences in accuracy, specificity and positive predictive value between senior endoscopists and DCNN-W in EGC diagnosis (all P>0.05). The sensitivity of senior endoscopists, DCNN-W and DCNN-C in EGC diagnosis were 80.56%, 94.44%, and 94.44%, respectively, and the differences were not statistically significant (all P>0.05). The results of the agreement analysis showed that the agreement between senior endoscopists and the gold standard was fair to moderate (Kappa=0.259, 0.532, 0.329), the agreement between DCNN-W and the gold standard was moderate (Kappa=0.587), and the agreement between DCNN-C and the gold standard was very high (Kappa=0.851). Conclusion:When the training set is the same, the ability of DCNN-C in EGC diagnosis is better than that of DCNN-W and senior endoscopists, and the diagnostic level of DCNN-W is equivalent to that of senior endoscopists.

【Objective】 To confirm the role of Wnt signaling pathway in the occurrence and development of gastric cancer （GC）， establish a prognostic model composed of Wnt pathway related genes， and then evaluate the predictive value of the model. 【Methods】 We downloaded the gene expression data and survival data of GC in TCGA database， and used GSEA enrichment analysis to verify the enrichment of Wnt pathway in GC and para-cancer samples. In this study， univariable COX regression analysis and survival curve analysis were used to select the prognosis-related genes of GC. Then the multivariate COX proportional hazard regression model was used to obtain the prognostic model of Wnt signaling pathway related genes. Then， receiver operating characteristic （ROC） curve and forest plot were used to verify the clinical predictive value of the model. The model was then validated in GEO external database. Finally， by utilizing quantitative real-time PCR （qPCR）， we detected the expressions of Wnt signaling pathway related genes in 8 pairs of clinical GC and para-cancer samples. 【Results】 We downloaded 32 samples of normal para-cancer samples and 375 cancer samples and their corresponding clinical data. GSEA enrichment showed that compared with normal samples， Wnt pathway was significantly enriched in GC samples （P<0.05）. The results of univariate COX analysis showed that 13 Wnt pathway genes were closely related to the prognosis of GC patients. Multivariate COX determined that the model was multiplied and accumulated by ETV2， SERPINE1， CPZ， VPS35 and IGFBP1 and their corresponding coefficient β. The survival curve and ROC curve showed that the model could accurately predict the prognosis of GC patients， and the 1-year， 3-year， and 5-year areas under the curve （AUC） were 68.0%， 69.4% and 78.5%， respectively. Clinical univariate and multivariate COX analyses showed that the model could become an independent prognostic factor other than TNM system of GC. The external data set （GSE84437） validation results of GC showed that the model could better predict the prognosis of GC patients. qPCR results indicated that ETV2， SERPINE1， CPZ， VPS35 and IGFBP1 expressions were upregulated in GC samples compared with para-cancer samples. 【Conclusion】 This study further confirmed that Wnt pathway plays an important role in the progress of GC from the perspective of bioinformatics， and we have established a prognosis-related risk model， providing a new perspective for clinical genetic testing， targeted therapy and individualized therapy.

Objective:To assess the influence of an artificial intelligence (AI) -assisted diagnosis system on the performance of endoscopists in diagnosing gastric cancer by magnifying narrow banding imaging (M-NBI).Methods:M-NBI images of early gastric cancer (EGC) and non-gastric cancer from Renmin Hospital of Wuhan University from March 2017 to January 2020 and public datasets were collected, among which 4 667 images (1 950 images of EGC and 2 717 of non-gastric cancer)were included in the training set and 1 539 images (483 images of EGC and 1 056 of non-gastric cancer) composed a test set. The model was trained using deep learning technique. One hundred M-NBI videos from Beijing Cancer Hospital and Renmin Hospital of Wuhan University between 9 June 2020 and 17 November 2020 were prospectively collected as a video test set, 38 of gastric cancer and 62 of non-gastric cancer. Four endoscopists from four other hospitals participated in the study, diagnosing the video test twice, with and without AI. The influence of the system on endoscopists′ performance was assessed.Results:Without AI assistance, accuracy, sensitivity, and specificity of endoscopists′ diagnosis of gastric cancer were 81.00%±4.30%, 71.05%±9.67%, and 87.10%±10.88%, respectively. With AI assistance, accuracy, sensitivity and specificity of diagnosis were 86.50%±2.06%, 84.87%±11.07%, and 87.50%±4.47%, respectively. Diagnostic accuracy ( P=0.302) and sensitivity ( P=0.180) of endoscopists with AI assistance were improved compared with those without. Accuracy, sensitivity and specificity of AI in identifying gastric cancer in the video test set were 88.00% (88/100), 97.37% (37/38), and 82.26% (51/62), respectively. Sensitivity of AI was higher than that of the average of endoscopists ( P=0.002). Conclusion:AI-assisted diagnosis system is an effective tool to assist diagnosis of gastric cancer in M-NBI, which can improve the diagnostic ability of endoscopists. It can also remind endoscopists of high-risk areas in real time to reduce the probability of missed diagnosis.