Objective:To study the characteristics of vaginal microbiota in pregnant women with premature rupture of membranes (PROM) and to establish prediction models for PROM.Methods:This study involved 35 women with preterm premature rupture of membranes (PPROM), 180 with term premature rupture of membranes (TPROM) and 255 term birth cases without premature rupture of membranes (TBWPROM, control group). The V3-V4 hypervariable region sequences in the vaginal samples collected at 16-28 weeks of gestation were detected by 16S rRNA gene next-generation sequencing. The differences in Alpha and Beta diversity, and the attributes and metabolic function prediction of each recognized species among the three groups were analyzed. Subsequently, a random forest model was used to establish the prediction models for PROM using vaginal microbiota species and environmental risk factors.Results:Compared with the control group, the Alpha diversity of the PPROM group was higher (Observed features, P=0.022; Faith_pd index, P=0.024) and Beta diversity was also significantly different (Unweighted UniFrac, P=0.010; Jaccard index, P=0.008). In PPROM cases, Megasphaera genomosp. typeⅠ was significantly increased ( P=0.017) and Lactobacillus mulieris was significantly decreased ( P=0.003). In the patients with TPROM, Megasphaera was significantly increased ( P=0.009) and Lactobacillus mulieris was significantly decreased ( P=0.002). In terms of functional pathways, sulfur oxidation ( P=0.021), methanogenesis from acetate ( P=0.036), L-histidine biosynthesis ( P=0.009), adenosylcobalamin biosynthesis ( P=0.041) and fucose degradation ( P=0.001) were significantly increased in patients with PPROM; L-histidine biosynthesis ( P<0.001) and fucose degradation ( P=0.030) were significantly increased in patients with TPROM. The prediction models were established using the random forest model with vaginal microbiota species and environmental risk factors and the prediction model for PPROM performed well [AUC: 0.739 (95%CI: 0.609-0.869), sensitivity: 0.928, specificity: 0.659, positive predictive value: 0.750, negative predictive value: 0.906], which had a certain reference value. Conclusions:Vaginal microbiota might be related to the development and progression of PROM. Studying the differences in vaginal microbiota might provide a new idea for the prevention and treatment of PROM. Functional prediction provided a direction for further research on the mechanism of PROM. The established prediction model could prevent the occurrence of PPROM and promote maternal and infant health.

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 evaluate the value of Sonazoid contrast enhanced ultrasound (CEUS) in preoperative prediction of proliferating cell nuclear antigen 67 (Ki-67) level of hepatocellular carcinoma (HCC) by establishing predictive model based on radiomics features of Kupffer phase.Methods:From October 2020 to August 2021, patients with histologically confirmed HCC lesion and who underwent Sonazoid CEUS examination 1 week before surgery were prospectively enrolled. The radiomics signatures were extracted from the whole tumor region on gray scale images and Kupffer phase images. Two predictive radiomics models were constructed using radiomic method. The predictive performance of 2 models was compared.Results:A total of 50 patients with histologically confirmed single HCC lesions were prospectively enrolled in this study. Among them, histological results revealed 24 HCC lesions with high level representation of Ki-67 (>20%) and 26 HCC lesions with low level representation of Ki-67 (≤20%). Two radiomics predictive models were established based on gray scale images and Kupffer phase images respectively. While compared with model based on B-mode ultrasound images, model based on Kupffer phase images showed significantly higher area under receiver operating characteristic curve (0.753 vs 0.535, P=0.017), accuracy (0.720 vs 0.580, P=0.023) and sensitivity (0.458 vs 0.250, P=0.043). Calibration plot indicated that Kupffer phase model showed better consistency with the actual Ki-67 level than gray scale model. Conclusions:The radiomics model based on Kupffer phase features of Sonazoid CEUS is a preoperative and noninvasive prediction the presentation level of Ki-67 in HCC lesions.

Objective:To quantitatively evaluate the stiffness of pancreatic parenchyma and lesions by virtual touch tissue imaging and quantification (VTIQ) technique, and to investigate the potential usefulness of ultrasound shear wave elastography (SWE) in the prediction of clinically relevant post-operative pancreatic fistula (CR-POPF) after pancreatectomy.Methods:Patients who scheduled to receive pancreatectomy were prospectively enrolled in Zhongshan Hospital, Fudan University from March 2021 to December 2021. VTIQ assessment was applied to patients within one week before the scheduled surgery to make quantitative SWE evaluation of target tissue. The SWV values of body part pancreatic parenchyma and lesions were measured and recorded. The palpation stiffness of pancreas was qualitatively evaluated during the operation by the surgeon.CR-POPF was diagnosed according to 2016 International Study Group of Pancreatic Fistula (ISGPF) standard.Grade B/C pancreatic fistula was defined as CR-POPF positive. Recognized peri-operative risk factors of CR-POPF were analyzed. ROC curve analysis was used to evaluate the diagnostic efficacy of SWV value in predicting CR-POPF.Results:A total of 72 patients were finally enrolled in this study, including 47 (65.3%, 47/72) patients who received pancreaticoduodenectomy (PD) and 25 (34.7%, 25/72) patients who underwent distal pancreatectomy. CR-POPF occurred in 22 (30.6%, 22/72) patients after pancreatectomy. The SWV value of body part pancreatic parenchyma was significant lower in CR-POPF positive group than in CR-POPF negative group ( P<0.001). There was no significant difference in lesion SWV value between CR-POPF positive and negative groups ( P=0.664). Besides, the palpation stiffness was no difference between the two groups ( P=0.689). Taking SWV value of pancreatic parenchyma >1.16 m/s as a cut-off value for predicting CR-POPF, the area under the ROC curve (AUROC) was 0.816 with 0.760 of sensitivity, 0.634 of specificity, 67.5% of positive predictive value and 72.5% of negative predictive value, respectively. Conclusions:The VTIQ method may improve the objectivity and accuracy of CR-POPF prediction via pre-operative, non-invasive and quantitative evaluation of pancreatic stiffness, which has potential value in clinical applications.

Objective To establish bovine corneal opacity and permeability （BCOP） test， and determine its predictive ability for the eye irritation evaluation of cosmetics. Methods A total of ten reference chemicals were selected to establish the BCOP test. Then eye irritation of 16 routinely collected cosmetics in our laboratory was predicted. In vitro scores were calculated by the change in the opacity and sodium fluorescein permeability after exposure to the testing cosmetics， and subsequently compared with the historical data by Draize test. Results Reference chemicals with known irritation classification were correctly classified by the BCOP test， which was consistent with the classification of UN globally harmonized system of classification and labeling of chemicals. Moreover， the specificity of the BCOP test for the classification of non-irritating cosmetics samples was 80.0% （8/10）， and the sensitivity for weak to mild irritating cosmetics samples was 83.3% （5/6）. The BCOP test demonstrated an overall classification consistency of 81.3% （13/16） with in vivo test. Conclusion BCOP test may be independently used to identify chemicals with potential eye irritation and serious eye damage， suggesting it is significant for in vitro integrated test strategy for predicting eye irritation due to cosmetics.

Objective:To study the characteristics and influencing factors of vaginal microbiota in normal pregnant women.Methods:This study was based on a cohort of pregnant women established in Anqing Municipal Hospital Affiliated to Anhui Medical University from February 2018 to February 2020. Vaginal samples of normal pregnant women who met the inclusion and exclusion criteria were ordered by the gestational weeks at sampling. Five samples were randomly selected from each gestational week group and if the samples were less than five, all samples were included. Sequencing of the V3-V4 region of the 16S rRNA gene was performed. Dominant species were analyzed by MicrobiomeAnalyst. Alpha diversity was measured with Chao1, Observed Features, Shannon diversity, Simpson diversity, Faith_pd and Pielou′s Evenness. The dominant status of Lactobacillus was also described and compared. Multiple linear regression and logistic regression were used to analyze the factors influencing vaginal microbiota. Analysis of variance and Kruskal Wallis test were used for statistical analysis of continuous variables, and Chi-square test and Fisher′s exact test were used for categorical data. The differences were considered statistically significant when the P value was less than 0.05. Results:This study enrolled 91 pregnant women (91 vaginal samples) with an average age of (27.37±3.60) years. There were 18, 56 and 17 vaginal samples collected at the median gestational age of 11.93 weeks (the first trimester), 19.43 weeks (the second trimester) and 38.29 weeks (the third trimester), respectively. The relative abundance of Firmicutes and Lactobacillus was 91.30% and 87.67%, respectively. Lactobacillus iners and Lactobacillus crispatus had a relative abundance of 43.95% and 36.33%, respectively. Moreover, Lactobacillus iners-dominated vaginal microbiota was detected in all trimesters. The number of samples with high relative abundance of Lactobacillus iners gradually decreased with gestational age. Lactobacillus crispatus-dominated vaginal microbiota was found in the second and third trimesters and the number of samples with high relative abundance gradually increased during pregnancy. The Alpha diversity of vaginal microbiota had a decreasing trend during the gestation. There were significant differences in Pielou′s Evenness diversity index of vaginal microbiota between different smoking groups ( P<0.05) and in Shannon diversity index between different drinking groups ( P<0.05). There were significant differences in Chao1, Observed Features and Faith_pd diversity index of vaginal microbiota between pregnant women with different education ( P<0.05) and in Shannon and Simpson diversity index between different income groups ( P<0.05). Conclusions:Vaginal microbiota was dominated by Lactobacillus in normal pregnant women. The dominance of Lactobacillus iners gradually decreased, while that of Lactobacillus crispatus increased during gestation. In normal pregnant women, the Alpha diversity of vaginal microbiota was correlated with smoking, drinking, education and family annual income. Smoking cessation and drinking before pregnancy were related to lower Alpha diversity of vaginal microbiota in pregnant women, while lower education and higher family income were associated with higher Alpha diversity.

Varicose veins of the lower extremities are a common disease in vascular surgery and are the result of multiple factors. The clinical manifestations are mainly thickening, tortuosity and dilation of the superficial veins of the lower extremities, which may be accompanied by discomforts such as lower extremity pain and swelling of the lower legs. In severe cases, skin pigmentation of lower extremities, venous ulcers, etc. The current common treatment options include high saphenous vein ligation and stripping, endogenous laser treatment, radiofrequency ablation and foam sclerotherapy, etc. This article reviews some parameter settings and equipment selection that may affect the therapeutic effect and complications of endogenous laser treatment. Hope to help clinicians better choose the device of intracavity laser closure and find the best possible treatment plan for patients.

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.