Objective To investigate the role of subtype 1 autoantibody against angiotensin Ⅱ receptor in the pathogenesis of preeclampsia by detecting its expression in the peripheral blood of preeclampsia patients,Methods Thirty patients with preeclampsia were assigned to preeclampsia group. Twenty normal pregnant women at the late stage and twenty non-pregnant healthy women as controls were investigated. The level of type 1 antoantibody against angiotensin Ⅱ in the peripheral blood was detected by indirect SA-ELISA assay with the produced ATR-1 as the antigen. Results The level of subtype 1 antoantibody against angiotensin Ⅱ (65 ±4. 7) % in the peripheral blood of preeclampsia patients is significantly higher than that of normal late pregnant (26 ±2. 8)% and non-pregnant women(7.8 ±2. 2)% groups (t1 =24. 97 ;t2 =38.56;P ＜0. 01 for both) ;The angiotensin Ⅱ receptor subtype 1 autoantibody in the group of normal late pregnancy (26 ± 2. 8 )% was significantly higher than that of healthy non-pregnant women group ( 7. 8 ± 2. 2 ) % ( t = 4. 58, P ＜ 0. 05 ).Conclusion Compared with the normal pregnant women and the healthy non-pregant women, the autoantibody against AT1 receptor in sera of preeclamptic patients is elevated ata high frequency. These results suggest that overproduction of AT1-AA may play an important role during the development of preeclamptic patients. AT1-AA is a novel risk factor in pregnant women. Immune mechanisms may be involved in the process of pregnancy.

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 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.

The article takes the experiment teaching combining Chaoxing Network Teaching Platform with BOPPPS model of obstetrics and gynecology in Chongqing Medical University as an example, and introduces the six teaching modules in detail that are followed in the mixed teaching mode: bridge in, objective, pre-assessment, participatory learning, post-assessment, and summary. Using the three-in-one assessment method of "process evaluation + incentive evaluation + summative evaluation", the learning effect of students was comprehensively evaluated. The practice proved that this mode can improve students' learning autonomy, exercise communication skills, cultivate teamwork spirit, promote the construction of clinical thinking, and improve teaching effect and classroom teaching quality.

Amyotrophic lateral sclerosis (ALS) is an irreversible, fatal neurodegenerative disorder whose incidence is positively correlated with the aging population. ALS is characterized by the progressive loss of motor neurons, leading to muscle weakness, atrophy, and ultimately respiratory failure. The pathogenesis of ALS involves multiple factors, including genetic and environmental influences, with genetic factors playing a particularly significant role. To date, several causative genes have been identified in ALS, such as the Cu/Zn superoxide dismutase 1 (Cu/Zn SOD1, also known as SOD1) gene, transactive response DNA-binding protein 43 (TDP-43) gene, fused in sarcoma (FUS) gene, and chromosome open reading frame 72 (C9orf72). Mutations in these genes have been found not only in familial ALS but also in sporadic ALS. Based on the identified ALS risk genes, various ALS animal models have been established through multiple approaches, including transgenic models, gene knockout/knock-in models, and adeno-associated virus-mediated overexpression models. These models simulate some typical pathological features of human ALS, such as motor neuron loss, ubiquitinated inclusions, and neuromuscular junction degeneration. However, these models still have limitations: (1) single-gene mutation models are insufficient to fully replicate the complex multi-factorial pathogenesis of sporadic ALS; (2) significant differences in microenvironmental regulation mechanisms and the rate of neurodegeneration between model organisms and humans may affect the accurate reproduction of disease phenotypes and the reliable evaluation of drug efficacy. To better understand the pathogenesis of ALS and promote the development of effective therapies, constructing and optimizing ALS animal models is crucial. This review aims to summarize commonly used ALS gene mutation mouse models, analyze their phenotypes and pathological characteristics, including transgenic mouse models, gene knockout/knock-in mouse models, and adeno-associated virus-mediated overexpression mouse models, and further discuss their specific applications in ALS pathogenesis research and drug development by comparing the advantages and limitations of each model.

Amyotrophic lateral sclerosis (ALS) is an irreversible, fatal neurodegenerative disorder whose incidence is positively correlated with the aging population. ALS is characterized by the progressive loss of motor neurons, leading to muscle weakness, atrophy, and ultimately respiratory failure. The pathogenesis of ALS involves multiple factors, including genetic and environmental influences, with genetic factors playing a particularly significant role. To date, several causative genes have been identified in ALS, such as the Cu/Zn superoxide dismutase 1 (Cu/Zn SOD1, also known as SOD1) gene, transactive response DNA-binding protein 43 (TDP-43) gene, fused in sarcoma (FUS) gene, and chromosome open reading frame 72 (C9orf72). Mutations in these genes have been found not only in familial ALS but also in sporadic ALS. Based on the identified ALS risk genes, various ALS animal models have been established through multiple approaches, including transgenic models, gene knockout/knock-in models, and adeno-associated virus-mediated overexpression models. These models simulate some typical pathological features of human ALS, such as motor neuron loss, ubiquitinated inclusions, and neuromuscular junction degeneration. However, these models still have limitations: (1) single-gene mutation models are insufficient to fully replicate the complex multi-factorial pathogenesis of sporadic ALS; (2) significant differences in microenvironmental regulation mechanisms and the rate of neurodegeneration between model organisms and humans may affect the accurate reproduction of disease phenotypes and the reliable evaluation of drug efficacy. To better understand the pathogenesis of ALS and promote the development of effective therapies, constructing and optimizing ALS animal models is crucial. This review aims to summarize commonly used ALS gene mutation mouse models, analyze their phenotypes and pathological characteristics, including transgenic mouse models, gene knockout/knock-in mouse models, and adeno-associated virus-mediated overexpression mouse models, and further discuss their specific applications in ALS pathogenesis research and drug development by comparing the advantages and limitations of each model.

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.