Open-path Fourier transform infrared ( OP/FT-IR) spectra were analyzed with target factor analysis ( TFA) and band-target entropy minimization ( BTEM) respectively, with the purpose to reconstruct spectral characteristics of target molecules from matrices of spectra. Five sessions of continuous OP/FT-IR monitoring were carried out around farms. For each session, the spectra were arranged row-wisely in order of measuring time, which yielded five matrices for data analysis. Results showed that both BTEM and TFA could reconstruct spectral characteristics of target molecules from the spectral data matrix, but the performance of the two methods differed slightly. TFA can retrieve spectral features of target molecules in the presence of interferences, and the reconstructed spectra are similar to corresponding reference. BTEM can implement such spectral retrieval without the reference spectrum. This work presents not only the application of BTEM method to qualitative analysis for OP/FT-IR spectra, but also a thorough comparison between the two methods. The conclusion is helpful to qualitative analysis of complex multi-component systems.

Objective To investigate the mechanism of increasing of the level of kidney injury molecule-1(KIM-1)in culture supernatant of human kidney cells(HKC)induced by cyclosporine A(CsA),and to clarify the relationships between the expression levels of KIM-1 and p38 MAPK pathway and ERK1/2MAPK pathway in HKC. Methods The HKC at logarithmic growth phase were randomly divided into control group, CsA control group, CsA + p38 kinase inhibitor group, p38 kinase inhibitor group, CsA + ERK1/2 inhibitor group and ERK1/2 kinase inhibitor group.The inhibitory rates of proliferation of HKC in various groups were detected by MTT assay, and the expression levels of KIM-1 in HKC supernatant in various groups were detected by ELISA;the survival rates,apopototic rates and necrotic rates of the HKC in various groups were detected by flow cytometry. Results Compared with control group,the expression level of KIM-1 protein in the supernatant of HKC in CsA control group was significantly increased (P<0.05),and the survival rate was significantly decreased (P<0.05), while the apoptotic rate and the necrotic rate were significantly increased (P<0.05 ). Compared with control group,the survival rates, the apoptotic rates and the necrosis rates of cells in p38 kinase inhibitor group and ERK1/2 kinase inhibitor group had no significant differences(P>0.05).Compared with CsA control group,the expression levels of KIM-1 protein in CsA+ p38 kinase inhibitor group and CsA+ ERK1/2 kinase inhibitor group were significantly decreased (P<0.05),and the survival rate was significantly increased (P<0.05),while the apoptotic rate and the necrotic rate were significantly decreased (P<0.05).Conclusion p38 MAPK pathway and ERK1/2MAPK pathway are involved in the process of up-regulation of the KIM-1 level in HKC culture supernatant induced by CsA,and the expression of KIM-1 may become the biochemical marker of clinical monitoring of CsA nephrotoxicity.

Objective:To evaluate deep learning in improving the diagnostic rate of adenomatous and non-adenomatous polyps.Methods:Non-magnifying narrow band imaging (NBI) polyp images obtained from Endoscopy Center of Renmin Hospital, Wuhan University were divided into three datasets. Dataset 1 (2 699 adenomatous and 1 846 non-adenomatous non-magnifying NBI polyp images from January 2018 to October 2020) was used for model training and validation of the diagnosis system. Dataset 2 (288 adenomatous and 210 non-adenomatous non-magnifying NBI polyp images from January 2018 to October 2020) was used to compare the accuracy of polyp classification between the system and endoscopists. At the same time, the accuracy of 4 trainees in polyp classification with and without the assistance of this system was compared. Dataset 3 (203 adenomatous and 141 non-adenomatous non-magnifying NBI polyp images from November 2020 to January 2021) was used to prospectively test the system.Results:The accuracy of the system in polyp classification was 90.16% (449/498) in dataset 2, superior to that of endoscopists. With the assistance of the system, the accuracy of colorectal polyp diagnosis was significantly improved. In the prospective study, the accuracy of the system was 89.53% (308/344).Conclusion:The colorectal polyp classification system based on deep learning can significantly improve the accuracy of trainees in polyp classification.

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 blind area monitoring and independent image acquisition function of gastroscopic elves ( a real-time gastroscopic monitoring system) in gastroscopy. Methods A total of 38522 gastroscopic images from the database of Digestive Endoscopy Center of Renmin Hospital of Wuhan University were collected to train and validate the gastroscopic elves. Using computer to generate random numbers, 91 gastroscopic videos were selected to assess the position recognition accuracy of the gastroscopic elves, and 45 gastroscopic videos and matching gastroscopic images collected by endoscopists were selected to compare the coverage number and rate of gastroscopy sites between gastroscopic elves and endoscopists image acquisition. Two endoscopists entered the study to perform gastroscopies with or without gastroscopic elves. Forty-five gastroscopies respectively performed by the endoscopist A before and after usage of gastroscopic elves were collected, and 42 gastroscopies divided into 20 and 22 performed by the endoscopist B without use of gastroscopic elves in the same period were also collected. The coverage rate of gastroscopy sites was compared between the two endoscopists. Results The total position recognition accuracy of gastroscopic elves was 85. 125％ ( 1156/1358) . The coverage rate of gastroscopic sites for the endoscopist A was (76. 790±8. 848)％ and (87. 325±7. 065)％, respectively, before and after using gastroscopic elves, and the coverage rate in the same period for the endoscopist B was (75. 926 ±11. 565)％ and (75. 253 ± 14. 662)％, respectively. The coverage rate before using gastroscopic elves had no statistical difference between the two endoscopists (t=0. 324, P=0. 747). The coverage rate for the endoscopist A after using gastroscopic elves was higher than that before using gastroscopic elves ( t=6. 222, P=0. 001) , and that of the endoscopist B in the same period ( t'=3. 588, P=0. 002) . The coverage number and rate of gastroscopy sites for gastroscopic elves and endoscopists image acquisition were 20. 956 ± 3. 406 and ( 77. 613 ± 12. 613)％, and 15. 467 ± 2. 296 and ( 57. 284 ± 8. 503)％, respectively, with statistical differences ( t=11. 523, P=0. 000; t=11. 523, P=0. 000). Conclusion Gastroscopic elves can improve the coverage number and rate of gastroscopy sites, and is worthy of promotion in clinics.

Objective: To improve physical activity by assessing pedometer-based physical activity during specific intervals over a week, among junior high school students,and to provide a reference for improving the level of students’ physical activity. Methods: Students (n=675) of grades 6-8 from six public junior high schools in Shanghai were recruited and instructed to wear a pedometer for a week, step counts and contents were recorded over a specific period. This period mainly included steps taken on the way to and from school, at school, at home in the evening on weekdays, and in the morning, afternoon, and evening on weekends. Results: The daily step counts recorded were 8 332 steps, with those on weekdays showing significantly higher values than step counts on weekends (9 065 steps vs. 6 392 steps)(t=22.9, P<0.01). Proportionately, the physical activity level at school contributed more to daily step counts (61.3%), followed by those on the commute to and from school(25.5%). Boys were more active than girls. For all intervals on weekdays, the step counts of students in rural districts, with overweight or obese, in grades 6 and 7, were higher than those in urban districts, with normal status, in grade 8 respectively (P<0.05). Low-activity students with physical education were more active than one without physical education(t boy=1.99,t girl=2.45,P<0.05). Conclusion These findings facilitate the implementation of effective, feasible interventions to enhance physical activity over a series of intervals during the day.

Objective:To evaluate the impact of artificial intelligence (AI) system on the diagnosis rate of precancerous state of gastric cancer.Methods:A single center self-controlled study was conducted under the premise that such factors were controlled as mainframe and model of the endoscope, operating doctor, season and climate, and pathology was taken as the gold standard. The diagnosis rate of precancerous state of gastric cancer, including atrophic gastritis (AG) and intestinal metaplasia (IM) in traditional gastroscopy (from September 1, 2019 to November 30, 2019) and AI assisted endoscopy (from September 1, 2020 to November 15, 2020) in the Eighth Hospital of Wuhan was statistically analyzed and compared, and the subgroup analysis was conducted according to the seniority of doctors.Results:Compared with traditional gastroscopy, AI system could significantly improve the diagnosis rate of AG [13.3% (38/286) VS 7.4% (24/323), χ2=5.689, P=0.017] and IM [33.9% (97/286) VS 26.0% (84/323), χ2=4.544, P=0.033]. For the junior doctors (less than 5 years of endoscopic experience), AI system had a more significant effect on the diagnosis rate of AG [11.9% (22/185) VS 5.8% (11/189), χ2=4.284, P=0.038] and IM [30.3% (56/185) VS 20.6% (39/189), χ2=4.580, P=0.032]. For the senior doctors (more than 10 years of endoscopic experience), although the diagnosis rate of AG and IM increased slightly, the difference was not statistically significant. Conclusion:AI system shows the potential to improve the diagnosis rate of precancerous state of gastric cancer, especially for junior endoscopists, and to reduce missed diagnosis of early gastric cancer.

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 .

Objective To investigate the safety and efficacy of endoscopic submucosal dissection ( ESD) for early stage colorectal cancer and precancerous lesions. Methods Clinical data of 108 patients who received ESD for early stage colorectal cancer and precancerous lesions from December 2016 to June 2017 in Renmin Hospital of Wuhan University were analyzed. The lesion characteristics, postoperative pathological features, intraoperative and postoperative complications and postoperative follow-up outcomes were analyzed. Results The 108 patients all underwent ESD successfully with median operation time of 45 min. The rate of intraoperative perforation and postoperative delayed bleeding was 2. 8％ ( 3/108) and 2. 8％ (3/108), respectively. No postoperative delayed perforation occurred. Postoperative pathology showed that there were 41 cases ( 38. 0％) of tubular adenoma, 4 ( 3. 7％) villous adenoma, 39 ( 36. 1％) villous tubular adenoma [ including 41 ( 38. 0％) low-grade intraepithelial neoplasia and 16 ( 14. 8％) high-grade intraepithelial neoplasia] , 19 ( 17. 6％) adenocarcinoma, and 5 ( 4. 6％) other types. Among the 19 cases of adenocarcinoma, there were 11 cases of well-differentiated, 5 median-differentiated and 3 low-differentiated. The complete resection rate was 100. 0％ and the en bloc resection rate was 92. 3％ ( 100/108) . The mean follow-up time was 8. 1 months, and no recurrence was found during this period. Conclusion ESD is safe and effective in the treatment of early stage colorectal lesions. It is important to improve preoperative assessment, strengthen surgical skills, analyze postoperative pathological features and regularly follow up to guarantee the treatment quality of ESD.

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.