Objective To investigate the causal association between bronchial asthma and bone mineral density at different sites using a two-sample Mendelian randomization (MR) approach. Methods Summary data for exposure factors and outcome were obtained from different genome-wide association studies.Single nucleotide polymorphisms strongly associated with bronchial asthma were selected as instrumental variables,and those in linkage disequilibrium were excluded.The inverse-variance weighted (IVW) method was used as the primary method for MR analysis,complemented by weighted median,simple mode,weighted mode,and MR-Egger regression methods.Sensitivity analyses were conducted to assess the stability of the results. Results The random-effects model of IVW analysis showed that heel bone mineral density (OR=0.986;95% CI,0.974 to 0.998;P=0.023) as the outcome dataset had a reverse causal effect with bronchial asthma,while lumbar spine bone mineral density (OR=1.031;95% CI,0.984 to 1.081;P=0.195),femoral neck bone mineral density (OR=1.014;95% CI,0.973 to 1.057;P=0.505),and forearm bone mineral density (OR=1.011;95% CI,0.935 to 1.094;P=0.775) as outcome datasets showed no causal effect with bronchial asthma.The MR-Egger intercept test results indicated that the P-values for the intercepts of lumbar bone mineral density,femoral neck bone mineral density,forearm bone mineral density,and calcaneal bone mineral density were all over 0.05,suggesting no horizontal pleiotropy and relatively stable results. Conclusion MR analysis reveals a reverse causal effect between bronchial asthma and heel bone mineral density,suggesting that clinicians should strengthen the monitoring of heel bone mineral density in patients with bronchial asthma to timely detect and intervene osteoporosis.

Objective:To summarize the hotspots and developmental status of non-suicidal self-injury research by clustering and co-occurrence to the literature on non-suicidal self-injury on the basis of Cite Space, and in order to provide references for future research and intervention.Methods:Non-suicidal self-injury literature included in the Web of science core collection from January 1975 to August 2020 was searched, and the included literature was visualized and analyzed using Cite Space 5.5.R2 knowledge mapping software.Results:A total of 974 articles were retrieved, and the number of articles published showed an increasing trend year by year, mostly in developed countries. The country with the highest cumulative number of articles was the United States, with a total of 412 articles, and the first organization was Katholieke Univ Leuven, with a total of 42 articles. Key words co-occurrence and clustering showed that the current research focus was on adolescents, suicidal behavior, dialectical behavior therapy, and borderline personality disorder. The most cited literature was by Muehlenkamp.Conclusions:Non-suicidal self-injury research has developed rapidly in recent years. At present, non-suicidal self-injury population, related intervention measures, screening and evaluation tools, Meta-analysis and risk factor analysis are its research frontiers and hot spots.

Objective:To study the predictive value of serum albumin (ALB) on the first day of life for early-onset sepsis (EOS) in very low birth weight infants (VLBWI).Methods:From January 2015 to December 2020, clinical data of VLBWI (gestational age < 34 weeks, birth weight < 1 500 g) born and hospitalized in our hospital were collected. Based on the serum ALB level at admission, the infants were assigned into high, moderate and low ALB groups. C-reactive protein (CRP) and procalcitonin (PCT) levels among different ALB groups were compared. The infants were also assigned into EOS and non-EOS groups according to the occurrence of EOS and perinatal complications were compared between the two groups. The relationship between EOS and ALB level was analyzed. The predictive value of serum ALB was studied using receiver operating characteristic (ROC) curve analysis.Results:A total of 183 infants were enrolled, including 62 in the high ALB group, 87 in the moderate ALB group and 34 in the low ALB group; and 36 in EOS group and 147 in non-EOS group. The incidence of maternal chorioamnionitis was significantly higher in EOS group than non-EOS group [33.3% (12/36) vs. 6.8% (10/147), P<0.001]. Serum CRP and PCT in the low and moderate ALB groups were significantly higher than the high ALB group ( P<0.05), and the low ALB group showed higher CRP and PCT than the moderate ALB group ( P<0.05). Compared with the non-EOS groups, ALB in the EOS group was significantly lower [24.9 (24.0, 28.5) g/L vs. 29.5 (27.4, 31.2) g/L, P<0.001] and the incidence of hypoproteinemia was significantly higher [52.8% vs.10.2%, P<0.001]. As ALB decreased, the incidence of EOS increased. The incidence of EOS was 55.9% in the low ALB group, 16.1% in the moderate ALB group and 4.8% in the high ALB group ( P<0.001). The sensitivity and specificity of ALB predicting EOS was 69.4% and 79.6%, respectively, with a cut-off value of 27.0 g/L. Conclusions:The VLBWI with maternal chorioamnionitis and serum albumin lower than 27.0 g/L on the first day of life have higher risk of EOS.

【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 develop an artificial intelligence-based system for measuring the size of gastrointestinal lesions under white light endoscopy in real time.Methods:The system consisted of 3 models. Model 1 was used to identify the biopsy forceps and mark the contour of the forceps in continuous pictures of the video. The results of model 1 were submitted to model 2 and classified into open and closed forceps. And model 3 was used to identify the lesions and mark the boundary of lesions in real time. Then the length of the lesions was compared with the contour of the forceps to calculate the size of lesions. Dataset 1 consisted of 4 835 images collected retrospectively from January 1, 2017 to November 30, 2019 in Renmin Hospital of Wuhan University, which were used for model training and validation. Dataset 2 consisted of images collected prospectively from December 1, 2019 to June 4, 2020 at the Endoscopy Center of Renmin Hospital of Wuhan University, which were used to test the ability of the model to segment the boundary of the biopsy forceps and lesions. Dataset 3 consisted of 302 images of 151 simulated lesions, each of which included one image of a larger tilt angle (45° from the vertical line of the lesion) and one image of a smaller tilt angle (10° from the vertical line of the lesion) to test the ability of the model to measure the lesion size with the biopsy forceps in different states. Dataset 4 was a video test set, which consisted of prospectively collected videos taken from the Endoscopy Center of Renmin Hospital of Wuhan University from August 5, 2019 to September 4, 2020. The accuracy of model 1 in identifying the presence or absence of biopsy forceps, model 2 in classifying the status of biopsy forceps (open or closed) and model 3 in identifying the presence or absence of lesions were observed with the results of endoscopist review or endoscopic surgery pathology as the gold standard. Intersection over union (IoU) was used to evaluate the segmentation effect of biopsy forceps in model 1 and lesion segmentation effect in model 3, and the absolute error and relative error were used to evaluate the ability of the system to measure lesion size.Results:(1)A total of 1 252 images were included in dataset 2, including 821 images of forceps (401 images of open forceps and 420 images of closed forceps), 431 images of non-forceps, 640 images of lesions and 612 images of non-lesions. Model 1 judged 433 images of non-forceps (430 images were accurate) and 819 images of forceps (818 images were accurate), and the accuracy was 99.68% (1 248/1 252). Based on the data of 818 images of forceps to evaluate the accuracy of model 1 on judging the segmentation effect of biopsy forceps lobe, the mean IoU was 0.91 (95% CI: 0.90-0.92). The classification accuracy of model 2 was evaluated by using 818 forceps pictures accurately judged by model 1. Model 2 judged 384 open forceps pictures (382 accurate) and 434 closed forceps pictures (416 accurate), and the classification accuracy of model 2 was 97.56% (798/818). Model 3 judged 654 images containing lesions (626 images were accurate) and 598 images of non-lesions (584 images were accurate), and the accuracy was 96.65% (1 210/1 252). Based on 626 images of lesions accurately judged by model 3, the mean IoU was 0.86 (95% CI: 0.85-0.87). (2) In dataset 3, the mean absolute error of systematic lesion size measurement was 0.17 mm (95% CI: 0.08-0.28 mm) and the mean relative error was 3.77% (95% CI: 0.00%-10.85%) when the tilt angle of biopsy forceps was small. The mean absolute error of systematic lesion size measurement was 0.17 mm (95% CI: 0.09-0.26 mm) and the mean relative error was 4.02% (95% CI: 2.90%-5.14%) when the biopsy forceps was tilted at a large angle. (3) In dataset 4, a total of 780 images of 59 endoscopic examination videos of 59 patients were included. The mean absolute error of systematic lesion size measurement was 0.24 mm (95% CI: 0.00-0.67 mm), and the mean relative error was 9.74% (95% CI: 0.00%-29.83%). Conclusion:The system could measure the size of endoscopic gastrointestinal lesions accurately and may improve the accuracy of endoscopists.

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.

Objective:To develop early gastric cancer (EGC) detection system of magnifying blue laser imaging (ME-BLI) model and magnifying narrow-band imaging (ME-NBI) model based on deep convolutional neural network, to compare the performance differences of the two models and to explore the effects of training methods on the accuracy.Methods:The images of benign gastric lesions and EGC under ME-BLI and ME-NBI were respectively collected. A total of five data sets and three test sets were collected. Data set 1 included 2 024 noncancerous lesions and 452 EGC images under ME-BLI. Data set 2 included 2 024 noncancerous lesions and 452 EGC images under ME-NBI. Data set 3 was the combination of data set 1 and 2 (a total of 4 048 noncancerous lesions and 904 EGC images under ME-BLI and ME-NBI). Data set 4: on the basis of data set 2, another 62 noncancerous lesions and 2 305 EGC images under ME-NBI were added (2 086 noncancerous lesions and 2 757 EGC images under ME-NBI). Data set 5: on the basis of data set 3, another 62 noncancerous lesions and 2 305 EGC images under ME-NBI were added(4 110 noncancerous lesions and 3 209 EGC images under ME-NBI and ME-BLI). Test set A included 422 noncancerous lesions and 197 EGC images under ME-BLI. Test set B included 422 noncancerous lesions and 197 EGC images under ME-NBI. Test set C was the combination of test set A and B (844 noncancerous and 394 EGC images under ME-BLI and ME-NBI). Five models were constructed according to these five data sets respectively and their performance was evaluated in the three test sets. Per-lesion video was collected and used to compare the performance of deep convolutional neural network models under ME-BLI and ME-NBI for the detection of EGC in clinical environment, and compared with four senior endoscopy doctors. The primary endpoint was the diagnostic accuracy of EGG, sensitivity and specificity. Chi-square test was used for statistical analysis.Results:The performance of model 1 was the best in test set A with the accuracy, sensitivity and specificity of 76.90% (476/619), 63.96% (126/197) and 82.94% (350/422), respectively. The performance of model 2 was the best in test set B with the accuracy, sensitivity and specificity of 86.75% (537/619), 92.89% (183/197) and 83.89% (354/422), respectively. The performance of model 3 was the best in test set B with the accuracy, sensitivity and specificity of 86.91% (538/619), 84.26% (166/197) and 88.15% (372/422), respectively. The performance of model 4 was the best in test set B with the accuracy, sensitivity and specificity of 85.46% (529/619), 95.43% (188/197) and 80.81% (341/422), respectively. The performance of model 5 was the best in test set B, with the accuracy, sensitivity and specificity of 83.52% (517/619), 96.95% (191/197) and 77.25% (326/422), respectively. In terms of image recognition of EGC, the accuracy of models 2 to 5 was higher than that of model 1, and the differences were statistically significant ( χ2=147.90, 149.67, 134.20 and 115.30, all P<0.01). The sensitivity and specificity of models 2 and 3 were higher than those of model 1, the specificity of model 2 was lower than that of model 3, and the differences were statistically significant ( χ2=131.65, 64.15, 207.60, 262.03 and 96.73, all P < 0.01). The sensitivity of models 4 and 5 was higher than those of models 1 to 3, and the specificity of models 4 and 5 was lower than those of models 1 to 3, and the differences were statistically significant ( χ2=151.16, 165.49, 71.35, 112.47, 132.62, 153.14, 176.93, 74.62, 14.09, 15.47, 6.02 and 5.80, all P<0.05). The results of video test based on lesion showed that the average accuracy of doctors 1 to 4 was 68.16%. And the accuracy of models 1 to 5 was 69.47% (66/95), 69.47% (66/95), 70.53% (67/95), 76.84% (73/95) and 80.00% (76/95), respectively. There were no significant differences in the accuracy among models 1 to 5 and between models 1 to 5 and doctors 1 to 4 (all P>0.05). Conclusions:ME-BLI EGC recognition model based on deep learning has good accuracy, but the diagnostic effecacy is sligntly worse than that of ME-NBI model. The effects of EGC recognition model of ME-NBI combined with ME-BLI is better than that of a single model. A more sensitive ME-NBI model can be obtained by increasing the number of ME-NBI images, especially the images of EGG, but the specificity is worse.

Objective To investigate the diagnostic value of Bcl-2, miR-451 and Th17 cells in esophageal cancer and their relation with recurrence. Methods We selected 101 patients with esophageal cancer as the experimental group and 95 healthy patients as the control group. The correlation between the clinicopathological characteristics and the level of each peripheral blood index was analyzed. The ROC curve was used to analyze the diagnostic value of each peripheral blood index. Multiple linear regression analysis was used to analyze the relation between each index and tumor recurrence. Results Peripheral blood Bcl-2, miR-451 and Th17 cells in the experimental group were higher than those in the control group (all P < 0.05); differentiation degree, clinical stage, tumor diameter and lymph node metastasis were positively correlated with the levels of Bcl-2, miR-451, and Th17 cells (all P < 0.05). Bcl-2, miR-451 and Th17 cells in relapsed patients were higher than those in non-relapsed patients (all P < 0.05); after controlling other factors such as differentiation degree, clinical stage, tumor size and lymph node metastasis, the levels of Bcl-2, miR-451 and Th17 cells were significantly correlated with recurrence (all P < 0.05). Conclusion Bcl-2, miR-451 and Th17 cells in peripheral blood of esophageal cancer patients are abnormally expressed and their expression are closely related to differentiation degree, clinical stage and recurrence. The combined detection could provide an objective basis for clinical diagnosis and prognosis assessment.

Objective:To explore the clinical application of a new type of medical drainage device designed to measure the intra-abdominal pressure of ICU patients.Methods:The 65 patients with severe acute pancreatitis treated in our hospital from April to September 2018 were selected as the experimental group; the 54 patients with severe acute pancreatitis treated from October 2017 to March 2018 were selected as the control group. Patients in the control group used traditional drainage bags to measure intra-abdominal pressure, while patients in the experimental group used a new-designed medical drainage device to measure intra-abdominal pressure. Compare the cost of consumables used for the first time in the experimental group with the control group, the incidence of acupuncture injuries, the incidence of urethral leakage, the incidence of rupture of the catheter balloon, and the satisfaction of the nurses. The 32 patients admitted from January to March 2018 in the control group were measured again using a new drainage device after the abdominal pressure measurement operation was stopped in order to compare the accuracy of the two methods.Results:The per capita consumable cost of the experimental group was 5.71 yuan, 0 cases of needle stick injury, 0 case of catheter leakage, which were lower than the control group (22.36 yuan, 1 case, 7 cases), the difference was significant ( P<0.05). The nurse operation satisfaction score was 13.85±0.93, which was higher than the control group (10.00±1.05). The difference was statistically significant ( t value was -20.323, P<0.05). Conclusion:In the operation of intra-abdominal pressure measurement, the use of a new type of medical drainage device can ensure the accuracy of the measurement, reduce the cost of consumables, needle stick injuries and the incidence of urinary catheter leakage, and improve nurse operation satisfaction.

Objective:To construct a real-time monitoring system based on computer vision for monitoring withdrawal speed of colonoscopy and to validate its feasibility and performance.Methods:A total of 35 938 images and 63 videos of colonoscopy were collected in endoscopic database of Renmin Hospital of Wuhan University from May to October 2018. The images were divided into two datasets, one dataset included in vitro, in vivo and unqualified colonoscopy images, and another dataset included ileocecal and non-cecal area images. And then 3 594 and 2 000 images were selected respectively from the two datasets for testing the deep learning model, and the remaining images were used to train the model. Three colonoscopy videos were selected to evaluate the feasibility of real-time monitoring system, and 60 colonoscopy videos were used to evaluate its performance.Results:The accuracy rate of the deep learning model for classification for in vitro, in vivo, and unqualified colonoscopy images was 90.79% (897/988), 99.92% (1 300/1 301), and 99.08% (1 293/1 305), respectively, and the overall accuracy rate was 97.11% (3 490/3 594). The accuracy rate of identifying ileocecal and non-cecal area was 96.70% (967/1 000) and 94.90% (949/1 000), respectively, and the overall accuracy rate was 95.80% (1 916/2 000). In terms of feasibility evaluation, 3 colonoscopy videos data showed a linear relationship between the retraction speed and the image processing interval, which indicated that the real-time monitoring system automatically monitored the retraction speed during the colonoscopy withdrawal process. In terms of performance evaluation, the real-time monitoring system correctly predicted entry time and withdrawal time of all 60 examinations, and the results showed that the withdrawal speed and withdrawal time was significantly negative-related ( R=-0.661, P<0.001). The 95% confidence interval of withdrawal speed for the colonoscopy with withdrawal time of less than 5 min, 5-6 min, and more than 6 min was 43.90-49.74, 40.19-45.43, and 34.89-39.11 respectively. Therefore, 39.11 was set as the safe withdrawal speed and 45.43 as the alarm withdrawal speed. Conclusion:The real-time monitoring system we constructed can be used to monitor real-time withdrawal speed of colonoscopy and improve the quality of endoscopy.