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 study the effect of silica in soil on the extraction of biological evidence DNA at the crime scene using the silica bead method.Methods Mud suspension and diluted blood were mixed to prepare biological samples mixed with dust and soil,which is to simulate biological evidence at the crime scene.Cell lysis was performed using heating lysis and guanidine salt chemical lysis,respectively.DNA was extracted using the silica bead method,amplified by PCR using Identifiler Plus kit and detected by capillary electrophoresis.The electrophoresis results were compared.Using mud suspension instead of silica beads to extract diluted blood DNA to validate the effect of silica in soil on the extraction of biological evidence DNA at crime scene using silica beads method.Results The complete STR loci were obtained after the extraction and amplification of 4 μL,20 μL dilute blood mixed with mud and lysed with heating cracking,whoes average peak heights arel 969.7±376.9 RFU and 9 706.7±349.8 RFU.For the 4 μL dilute blood mixed with mud guanidine salt chemical lysis,it cannot obtain complete STR loci after extraction and amplification.20 μL dilute blood mixed with mud guanidine salt was chemically cleaved and amplified to obtain complete STR loci with an average peak height of 1 899.8±801.3 RFU.After extraction and amplification by mud suspension instead of silica beads to extract 20 μL diluted blood DNA,complete STR loci were obtained.Conclusion Silicon dioxide in soil can bind to DNA in the presence of guanidine salts,leading to a decrease in the efficiency of recovering on-site biological evidence DNA using the silicon bead method.

Objective:To explore the role of serum cholinesterase (CHE) levels in the prognosis of patients with acute decompensated heart failure (ADHF).Methods:Total of 244 consecutive patients with ADHF who were admitted to the emergency department and were successfully discharged were prospectively enrolled from January 2018 to June 2020. Patients were divided into groups according to the first and third quartile of CHE level and the clinical data, laboratory tests and other nutritional indices were recorded after discharge, and then were followed up. The primary end points were the composites of cardiovascular death and hospitalization for worsening HF (composite end points). The secondary end points were all-cause mortality and cardiovascular death. Cox proportional risk analysis, time-dependent Cox regression model or stratified cox regression were used to identify the risk of primary and secondary endpoints. Clinical, biomarker and the compound models of clinical and biomarker were constructed. Kaplan-Meier method was used to plot the survival curves of different groups and compare their differences. Receiver Operating characteristics (ROC) curves were used to compare the area under the curve for CHE levels and other nutritional or prognostic indicators to identify composite end-point events.Results:During a follow-up period of 350(100,683) days, 158 patients reached the composite end points. In the multivariable Cox analysis, cholinesterase level was significantly associated with the composite end points after adjustment for major confounders. Cox proportional risk analysis or time-dependent Cox regression model showed that CHE level was significantly associated with the composite end points, all-cause mortality and cardiovascular mortality in both clinical, biomarker and composite models (all P< 0.05). A Kaplan–Meier analysis revealed that patients with low cholinesterase levels had significantly greater risk of reaching the composite end points than those with middle or high cholinesterase levels (78.1% vs 66.7% vs. 46.7%, P<0.001); Cholinesterase level showed the largest area under the receiver operating characteristic curve (AUROC) of 0.736 (95% CI, 0.664-0.888) for prediction of the composite end points among other nutritional indices. The AUROC of the Global Meta-Analysis Group Chronic Heart Failure (MAGGIC) Risk Score for prediction of the composite end points was increased from 0.704 to 0.762 ( P=0.038), when cholinesterase level was added. Conclusions:Cholinesterase may serve as a simple and effective prognostic marker for predicting adverse outcomes in ADHF patients.

Background::Due to the wide variety of morphology, size, and dynamics, selecting an optimal valve size and location poses great difficulty in percutaneous pulmonary valve implantation (PPVI). This study aimed to report our experience with in vitro bench testing using patient-specific three-dimensional (3D)-printed models for planning PPVI with the Venus P-valve. Methods::Patient-specific 3D soft models were generated using PolyJet printing with a compliant synthetic material in 15 patients scheduled to undergo PPVI between July 2018 and July 2020 in Central China Fuwai Hospital of Zhengzhou University.Results::3D model bench testing altered treatment strategy in all patients (100%). One patient was referred for surgery because testing revealed that even the largest Venus P-valve would not anchor properly. In the remaining 14 patients, valve size and/or implantation location was altered to avoid valve migration and/or compression coronary artery. In four patients, it was decided to change the point anchoring because of inverted cone-shaped right ventricular outflow tract (RVOT) ( n = 2) or risk of compression coronary artery ( n = 2). Concerning sizing, we found that an oversize of 2-5 mm suffices. Anchoring of the valve was dictated by the flaring of the in- and outflow portion in the pulmonary artery. PPVI was successful in all 14 patients (absence of valve migration, no coronary compression, and none-to-mild residual pulmonary regurgitation [PR]). The diameter of the Venus P-valve in the 3D simulation group was significantly smaller than that of the conventional planning group (36 [2] vs. 32 [4], Z = -3.77, P <0.001). Conclusions::In vitro testing indicated no need to oversize the Venus P-valve to the degree recommended by the balloon-sizing technique, as 2-5 mm sufficed.

Bone defects caused by different causes such as trauma, severe bone infection and other factors are common in clinic and difficult to treat. Usually, bone substitutes are required for repair. Current bone grafting materials used clinically include autologous bones, allogeneic bones, xenografts, and synthetic materials, etc. Other than autologous bones, the major hurdles of rest bone grafts have various degrees of poor biological activity and lack of active ingredients to provide osteogenic impetus. Bone marrow contains various components such as stem cells and bioactive factors, which are contributive to osteogenesis. In response, the technique of bone marrow enrichment, based on the efficient utilization of components within bone marrow, has been risen, aiming to extract osteogenic cells and factors from bone marrow of patients and incorporate them into 3D scaffolds for fabricating bone grafts with high osteoinductivity. However, the scientific guidance and application specification are lacked with regard to the clinical scope, approach, safety and effectiveness. In this context, under the organization of Chinese Orthopedic Association, the Expert consensus for the clinical application of autologous bone marrow enrichment technique for bone repair ( version 2023) is formulated based on the evidence-based medicine. The consensus covers the topics of the characteristics, range of application, safety and application notes of the technique of autologous bone marrow enrichment and proposes corresponding recommendations, hoping to provide better guidance for clinical practice of the technique.

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 observe the effect of methylprednisolone on elderly patients undergoing percutaneous balloon kyphoplasty(PKP).Methods A total of 180 elderly patients with PKP due to thoracolumbar fractures under general anesthesia were selected,and randomly divided into methylprednisolone 1mg/kg group(group M)and normal saline control group(group C),with 90 cases in each group.The pain visual analogue scale(VAS),blood glucose,C-reactive protein(CRP),white blood cell(WBC),percentage of neu-trophils,nausea and vomiting scores were recorded before operation,Postanesthesia Care Unit(PACU),2h and 1 day after operation.confusion assessment method(CAM)was used to evaluate the occurrence of postoperative delirium(POD)within 3days after operation,and the quality of life and mortality were assessed at 3months after follow-up.Results The blood glucose level of patients in group M in PACU was significantly higher than that in group C(P＜0.05),and the degree of nausea and the incidence of nausea and vomiting in PACU and 2h after operation(8.86％vs 23.38％)were significantly lower than those in group C(P＜0.05),and the degree of vomi-ting at 2h after operation was also lower than that in group C(P＜0.05).There was no significant difference in the incidence of POD be-tween the two groups within 3days after surgery(11.7％vs 12.7％)(P＞0.05).There was no significant difference in CRP,WBC,and neutrophil percentage between the two groups before operation and 1day after operation(P＞0.05).There was no significant difference in hospital stay,anesthesia cost and total medical cost between the two groups(P＞0.05).There was no significant difference in the 3-month mortality and quality of life score(SF-36)between the two groups(P＞0.05).Conclusion The intraoperative application of methylprednisolone can reduce the degree and incidence of postoperative nausea and vomiting in elderly patients with PKP,but it does not reduce the incidence of POD,and can lead to a transient increase in blood glucose level.