ObjectiveTo evaluate the application effect of morphology driven preparation technique in indirect restoration of posterior teeth. Methods84 patients with dental defects were selected and divided into the control group and the experimental group randomly， with 42 patients in each group （a total of 84 teeth）. Traditional preparation and morphology driven preparation techniques were used to complete dental preparation and adhesive glass ceramic restorations， respectively. The satisfaction， masticatory function， World Dental Federation （FDI） Scores for edge fracture and fixation，edge adaptability index，periodontal bleeding index （BI） and plaque index （PLI） of the two groups of patients were evaluated and various indicator data were recorded and statistically analyzed at 3 and 6 months after restoration completion. ResultsThere was no statistically significant difference in satisfaction between the two groups after 3 and 6 months of repair； Both groups of data showed a significant improvement in masticatory function. There was no statistical difference after 3 and 6 months of repair. However， a statistically significant difference in masticatory function was noted after 6 months of repair compared with before and after 3 months of repair. As the repair time increased， the masticatory effect improved significantly. There was no statistically significant difference in the edge fracture and fixation index between the two groups at 3 months after repair， but there was a statistically significant difference between the two groups at 6 months with the experimental group outperforming the control group（P<0.05）. The edge adaptability， BI， and PLI index were statistically significant between the two groups after 3 and 6 months of repair and the experimental group had a significant advantage （P<0.05）. ConclusionBoth types of tooth preparation techniques can improve patient masticatory function and reach high satisfaction after repair. However， morphology driven preparation technique has significant advantages in edge adaptability， edge fracture and fixation index scores and improve periodontal conditions. This technique is an effective method for improving dental preparation in adhesive indirect restoration of posterior teeth.

Objective:To evaluate the ability of ENDOANGEL artificial intelligence system to predict invasion depth and differentiation status of early gastric cancer using more diverse multi-center videos, and to test the performance of the new system upgraded from ENDOANGEL.Methods:Based on the completed 2020 man-machine competition for early gastric cancer diagnosis using single-center videos, the second man-machine competition was conducted in 2022, involving 30 endoscopists from 30 hospitals across 10 Chinese provinces. A multi-center video cohort was retrospectively collected from 12 institutions in 8 provinces/municipalities in China. The study proceeded in 3 stages. First, the ENDOANGEL was re-tested on multi-center videos, its performance on single and multi-center videos was compared, then the ENDOANGEL was upgraded to ENDOANGEL-2022. Second, the second man-machine competition was conducted between ENDOANGEL-2022 and 30 endoscopists using multi-center videos, and the performance between ENDOANGEL-2022, ENDOANGEL and endoscopists on multi-center videos were compared. Third, the ENDOANGEL-2022 was re-tested on the single-center videos previously collected in 2020, its performance on single and multi-center videos was also compared.Results:Compared with the performance on single-center videos, the sensitivity of ENDOANGEL for predicting submucosal invasion of early gastric cancer decreased significantly [18.18% (2/11) VS 70.00% (7/10), P=0.030], but demonstrated comparable ability to predict undifferentiated type of early gastric cancer ( P>0.05). On multi-center videos, in the respect of predicting submucosal invasion of early gastric cancer, the sensitivity of ENDOANGEL-2022 was higher than that of ENDOANGEL [40.00% (4/10) VS 18.18% (2/11), P=0.361], but inferior to that of 30 endoscopists [40.00% VS 52.04% (95% CI: 43.70%-60.38%), P<0.001]. The specificity of ENDOANGEL-2022 was lower than that of ENDOANGEL [82.86% (29/35) VS 100.00% (34/34), χ2=4.41, P=0.036] and higher than that of 30 endoscopists [82.86% VS 68.97% (95% CI: 60.83%-77.11%), P=0.018], the accuracy of ENDOANGEL-2022 was lower than that of ENDOANGEL [73.33% (33/45) VS 80.00% (36/45), χ2=0.56, P=0.455] and higher than that of 30 endoscopists [73.33% VS 65.30% (95% CI: 60.61%-69.99%), P=0.018]. In the respect of predicting undifferentiated type of early gastric cancer, the sensitivity of ENDOANGEL-2022 was higher than that of ENDOANGEL [71.43% (5/7) VS 57.14% (4/7), P>0.999] and 30 endoscopists [71.43% VS 63.11% (95% CI: 55.58%-70.64%), P=0.031], the specificity of ENDOANGEL-2022 was lower than that of ENDOANGEL [76.32% (29/38) VS 78.95% (30/38), χ2=0.08, P=0.783] and higher than that of 30 endoscopists [76.32% VS 65.27% (95% CI: 59.10%-71.44%), P=0.004],the accuracy of ENDOANGEL-2022 was similar to that of ENDOANGEL [75.56% (34/45) VS 75.56% (34/45), χ2=0.00, P>0.999] and higher than that of 30 endoscopists [75.56% VS 65.10% (95% CI: 59.96%- 70.24%), P<0.001]. Compared with performance in single center videos, the sensitivity [40.00% VS 60.00%(6/10), P=0.656], specificity [82.86% VS 93.75% (15/16), χ2=0.37, P=0.542] and accuracy [73.33% VS 80.77% (21/26), χ2=0.50, P=0.479] of ENDOANGEL-2022 for predicting submucosal invasion of early gastric cancer decreased; in predicting undifferentiated type of early gastric cancer, the sensitivity of ENDOANGEL-2022 increased [71.43% VS 37.50% (3/8), P=0.315], while the specificity [76.32% VS 100.00% (18/18), χ2=3.48, P=0.062] and accuracy [75.56% VS 80.77% (21/26), χ2=0.26, P=0.612] decreased. Conclusion:Multi-center cases introduce greater heterogeneity that may reduce artificial intelligence prediction accuracy, but the artificial intelligence system still outperforms endoscopists.

Objective Based on the holistic view of traditional Chinese medicine,a more objective and comprehensive quality evaluation model for Huangjing Dami pills was established by combining external traits and internal qualities,and was applied to the optimization of the prescription process to verify its feasibility and applicability,so that it can provide reference for the optimization of the preparation process of other honey pills.Methods The texture characterization information of Huangjing Dami pills was collected based on the texture meter,and the water content and chemical composition information were collected based on the 2020 edition of the Chinese Pharmacopoeia.Given that the hardness and other texture characterization indexes are moderate indexes,the quality evaluation model established by the polar value method and principal component analysis(PCA)was used to quantify the values of the characterization evaluation indexes,which can be used to screen the significance of the influencing factors in the Plackett-Burman(PB)test;The quality evaluation model based on the optimal value method,the polar value method and the Analytic Hierarchy Process(AHP)-CRITIC method was used to characterize the quantification of the actual values of the evaluation indexes,and it can be used to optimize the process and derive the optimal process parameters for the steepest hill-climbing test,the response surface test and the validation test.Results The quality evaluation model based on the extreme value method-PCA was used in the PB test to screen out the three significant influencing factors of the ratio of honey to medicine,drying temperature and mesh number;the quality evaluation model based on the optimal value,extreme value method-AHP-CRITIC method was used in the steepest climb test,response surface test and validation test,which resulted in the optimal prescription process parameters of Huangjing Dami pills,namely,the ratio of medicine to honey was 1:0.64,the honey temperature was 60℃,the mesh number was 100 mesh,the drying temperature was 50.8℃,the drying time was 3 h,the sweating time was 1 day.Conclusion It was verified that the feasibility and applicability of the qualitative attributes guiding the optimization of prescriptions,and that the established quality evaluation model could guarantee the quality of the final product by taking into account the physical attributes and the chemical attributes during the optimization of the prescription,which showed a better prospect of application in the optimization of the process of honey pills and the method for the evaluation of the quality of honey pills preparation.

Objective To explore the impact of antimicrobial volume-based procurement(VBP)and classification management policy on the clinical use of carbapenem antibiotics.Methods Changing trend in defined daily doses(DDDs),procurement cost(Cost),defined daily dose cost(DDDc),and DDDs per 1 000 inhabitants daily(DID)of carbapenem antibiotics in all levels of medical institutions were analyzed by Mann-Kendall trend test.May 1,2020 was taken as the intervention cut-off point of VBP policy,September 2021 was as intervention cut-off point of cla-ssification management list.The impact of VBP and classification management policy on the clinical use of carbape-nem antibiotics were studied by interrupted time series analysis.Results After implementing VBP policy,the DDDs and DID of carbapenem antibiotics increased obviously,but the long-term trend didn't change significantly.Compared with before the implementation of the policy,the cost and DDDc of carbapenem antibiotics decreased im-mediately,the long-term trend of DDDc changed significantly,but the long-term trend of cost didn't change signifi-cantly.The DDDs and Cost of carbapenem antibiotics decreased immediately after the update of classification ma-nagement list,but the long-term downward trend was not significant,and DDDc presented a long-term upward trend.Conclusion VBP policy reduces the DDDc and short-term cost of carbapenem antibiotics,but its long-term impact on DDDs,cost and DID is limited.Classification management has limited impact on the use of carbapenem antibiotics in medical institutions.

Objective Based on the holistic view of traditional Chinese medicine,a more objective and comprehensive quality evaluation model for Huangjing Dami pills was established by combining external traits and internal qualities,and was applied to the optimization of the prescription process to verify its feasibility and applicability,so that it can provide reference for the optimization of the preparation process of other honey pills.Methods The texture characterization information of Huangjing Dami pills was collected based on the texture meter,and the water content and chemical composition information were collected based on the 2020 edition of the Chinese Pharmacopoeia.Given that the hardness and other texture characterization indexes are moderate indexes,the quality evaluation model established by the polar value method and principal component analysis(PCA)was used to quantify the values of the characterization evaluation indexes,which can be used to screen the significance of the influencing factors in the Plackett-Burman(PB)test;The quality evaluation model based on the optimal value method,the polar value method and the Analytic Hierarchy Process(AHP)-CRITIC method was used to characterize the quantification of the actual values of the evaluation indexes,and it can be used to optimize the process and derive the optimal process parameters for the steepest hill-climbing test,the response surface test and the validation test.Results The quality evaluation model based on the extreme value method-PCA was used in the PB test to screen out the three significant influencing factors of the ratio of honey to medicine,drying temperature and mesh number;the quality evaluation model based on the optimal value,extreme value method-AHP-CRITIC method was used in the steepest climb test,response surface test and validation test,which resulted in the optimal prescription process parameters of Huangjing Dami pills,namely,the ratio of medicine to honey was 1:0.64,the honey temperature was 60℃,the mesh number was 100 mesh,the drying temperature was 50.8℃,the drying time was 3 h,the sweating time was 1 day.Conclusion It was verified that the feasibility and applicability of the qualitative attributes guiding the optimization of prescriptions,and that the established quality evaluation model could guarantee the quality of the final product by taking into account the physical attributes and the chemical attributes during the optimization of the prescription,which showed a better prospect of application in the optimization of the process of honey pills and the method for the evaluation of the quality of honey pills preparation.

Objective:To evaluate the ability of ENDOANGEL artificial intelligence system to predict invasion depth and differentiation status of early gastric cancer using more diverse multi-center videos, and to test the performance of the new system upgraded from ENDOANGEL.Methods:Based on the completed 2020 man-machine competition for early gastric cancer diagnosis using single-center videos, the second man-machine competition was conducted in 2022, involving 30 endoscopists from 30 hospitals across 10 Chinese provinces. A multi-center video cohort was retrospectively collected from 12 institutions in 8 provinces/municipalities in China. The study proceeded in 3 stages. First, the ENDOANGEL was re-tested on multi-center videos, its performance on single and multi-center videos was compared, then the ENDOANGEL was upgraded to ENDOANGEL-2022. Second, the second man-machine competition was conducted between ENDOANGEL-2022 and 30 endoscopists using multi-center videos, and the performance between ENDOANGEL-2022, ENDOANGEL and endoscopists on multi-center videos were compared. Third, the ENDOANGEL-2022 was re-tested on the single-center videos previously collected in 2020, its performance on single and multi-center videos was also compared.Results:Compared with the performance on single-center videos, the sensitivity of ENDOANGEL for predicting submucosal invasion of early gastric cancer decreased significantly [18.18% (2/11) VS 70.00% (7/10), P=0.030], but demonstrated comparable ability to predict undifferentiated type of early gastric cancer ( P>0.05). On multi-center videos, in the respect of predicting submucosal invasion of early gastric cancer, the sensitivity of ENDOANGEL-2022 was higher than that of ENDOANGEL [40.00% (4/10) VS 18.18% (2/11), P=0.361], but inferior to that of 30 endoscopists [40.00% VS 52.04% (95% CI: 43.70%-60.38%), P<0.001]. The specificity of ENDOANGEL-2022 was lower than that of ENDOANGEL [82.86% (29/35) VS 100.00% (34/34), χ2=4.41, P=0.036] and higher than that of 30 endoscopists [82.86% VS 68.97% (95% CI: 60.83%-77.11%), P=0.018], the accuracy of ENDOANGEL-2022 was lower than that of ENDOANGEL [73.33% (33/45) VS 80.00% (36/45), χ2=0.56, P=0.455] and higher than that of 30 endoscopists [73.33% VS 65.30% (95% CI: 60.61%-69.99%), P=0.018]. In the respect of predicting undifferentiated type of early gastric cancer, the sensitivity of ENDOANGEL-2022 was higher than that of ENDOANGEL [71.43% (5/7) VS 57.14% (4/7), P>0.999] and 30 endoscopists [71.43% VS 63.11% (95% CI: 55.58%-70.64%), P=0.031], the specificity of ENDOANGEL-2022 was lower than that of ENDOANGEL [76.32% (29/38) VS 78.95% (30/38), χ2=0.08, P=0.783] and higher than that of 30 endoscopists [76.32% VS 65.27% (95% CI: 59.10%-71.44%), P=0.004],the accuracy of ENDOANGEL-2022 was similar to that of ENDOANGEL [75.56% (34/45) VS 75.56% (34/45), χ2=0.00, P>0.999] and higher than that of 30 endoscopists [75.56% VS 65.10% (95% CI: 59.96%- 70.24%), P<0.001]. Compared with performance in single center videos, the sensitivity [40.00% VS 60.00%(6/10), P=0.656], specificity [82.86% VS 93.75% (15/16), χ2=0.37, P=0.542] and accuracy [73.33% VS 80.77% (21/26), χ2=0.50, P=0.479] of ENDOANGEL-2022 for predicting submucosal invasion of early gastric cancer decreased; in predicting undifferentiated type of early gastric cancer, the sensitivity of ENDOANGEL-2022 increased [71.43% VS 37.50% (3/8), P=0.315], while the specificity [76.32% VS 100.00% (18/18), χ2=3.48, P=0.062] and accuracy [75.56% VS 80.77% (21/26), χ2=0.26, P=0.612] decreased. Conclusion:Multi-center cases introduce greater heterogeneity that may reduce artificial intelligence prediction accuracy, but the artificial intelligence system still outperforms endoscopists.

Objective To explore the impact of antimicrobial volume-based procurement(VBP)and classification management policy on the clinical use of carbapenem antibiotics.Methods Changing trend in defined daily doses(DDDs),procurement cost(Cost),defined daily dose cost(DDDc),and DDDs per 1 000 inhabitants daily(DID)of carbapenem antibiotics in all levels of medical institutions were analyzed by Mann-Kendall trend test.May 1,2020 was taken as the intervention cut-off point of VBP policy,September 2021 was as intervention cut-off point of cla-ssification management list.The impact of VBP and classification management policy on the clinical use of carbape-nem antibiotics were studied by interrupted time series analysis.Results After implementing VBP policy,the DDDs and DID of carbapenem antibiotics increased obviously,but the long-term trend didn't change significantly.Compared with before the implementation of the policy,the cost and DDDc of carbapenem antibiotics decreased im-mediately,the long-term trend of DDDc changed significantly,but the long-term trend of cost didn't change signifi-cantly.The DDDs and Cost of carbapenem antibiotics decreased immediately after the update of classification ma-nagement list,but the long-term downward trend was not significant,and DDDc presented a long-term upward trend.Conclusion VBP policy reduces the DDDc and short-term cost of carbapenem antibiotics,but its long-term impact on DDDs,cost and DID is limited.Classification management has limited impact on the use of carbapenem antibiotics in medical institutions.

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.

Neoadjuvant therapy for locally advanced colorectal cancer has made great progress in the past 20 years, but there are still limitations such as side effects, organ dysfunction and unsatisfactory control of metastasis. In recent years, with the improvement of surgical techniques and further development of molecular research, how to further improve local control, reduce distant metastasis, and even avoid surgery according to clinical remission to achieve organ preservation, is the current demand and research goal. With the advancement of molecular research, colorectal cancer has different treatment strategies based on microsatellite status. For patients with microsatellite instability locally advanced colorectal cancer, immune checkpoint inhibitor therapy significantly increased the pathologic complete response rate, reduced the incidence of adverse events and improved organ function compared with conventional chemoradiotherapy. For patients with microsatellite stable locally advanced colon cancer, neoadjuvant therapy is still in the exploratory stage. The standard of care is surgery combined with perioperative chemotherapy. For microsatellite stable locally advanced rectal cancer, the complete response rate is improved by enhancing neoadjuvant therapy, which helps to preserve organs. On the other hand, selective radiotherapy preserves organ function and improves quality of life. This article reviews the neoadjuvant treatment strategies for locally advanced colorectal cancer based on organ-sparing strategies.