Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits.Here,we developed a Multi-Dimensional Feature Fusion model named MDFF,which integrates one-dimensional simplified molec-ular input line entry system sequence features,two-dimensional molecular graph features,and three-dimensional geometric features to enhance drug representations for predicting DDIs.MDFF was trained and validated on two DDI datasets,evaluated across three distinct scenarios,and compared with advanced DDI prediction models using accuracy,precision,recall,area under the curve,and F1 score metrics.MDFF achieved state-of-the-art performance across all metrics.Ablation experiments showed that integrating multi-dimensional drug features yielded the best results.More importantly,we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs.Among 12 real-world adverse drug reaction reports,the predictions of 9 reports were supported by relevant evidence.Additionally,MDFF demon-strated the ability to explain adverse DDI mechanisms,providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

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.

Identifying drug-drug interactions (DDIs) is essential to prevent adverse effects from polypharmacy. Although deep learning has advanced DDI identification, the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits. Here, we developed a Multi-Dimensional Feature Fusion model named MDFF, which integrates one-dimensional simplified molecular input line entry system sequence features, two-dimensional molecular graph features, and three-dimensional geometric features to enhance drug representations for predicting DDIs. MDFF was trained and validated on two DDI datasets, evaluated across three distinct scenarios, and compared with advanced DDI prediction models using accuracy, precision, recall, area under the curve, and F1 score metrics. MDFF achieved state-of-the-art performance across all metrics. Ablation experiments showed that integrating multi-dimensional drug features yielded the best results. More importantly, we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs. Among 12 real-world adverse drug reaction reports, the predictions of 9 reports were supported by relevant evidence. Additionally, MDFF demonstrated the ability to explain adverse DDI mechanisms, providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

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 study the pharmacological substance basis of Shaoyao gancao decoction for relieving acute and chronic pain. METHODS The antispasmodic effect of Shaoyao gancao decoction， ethyl acetate extract of Shaoyao gancao decoction and its effluent part of macroporous resin and 90% ethanol elution part of macroporous resin （the concentration of 4 drugs was 13.44 g/mL according to crude drug） was observed by in vitro small intestine tension test in rats. The acetic acid writhing test was conducted in mice to evaluate the analgesic effects of macroporous resin efflux site and macroporous resin 90% ethanol elution site （the dosage of 2.4 g/kg according to crude drug）. The levels of tumor necrosis factor-α （TNF-α）， interleukin-1β （IL- 1β）， prostaglandin E2 （PGE2） and cyclooxygenase-2 （COX-2） in serum of mice were detected. The serum prototype and metabolites of mice after intragastric administration of macroporous resin 90% ethanol elution site were identified by high performance liquid chromatogre-time-of-flight mass spectrometry. RESULTS In vitro experiment showed that 90% ethanol eluting part of macroporous resin represented the best antispasmodic effect， and the inhibitory rate of small intestine tension was significantly higher than macroporous resin efflux site of Shaoyao gancao decoction （P＜0.05） without statistical significance， compared with Shaoyao gancao decoction （P＞0.05）. In the acetic acid writhing experiment， compared with model group， the writhing times of mice in the macroporous resin 90% ethanol elution part group were reduced significantly （P＜0.05）， the writhing latency was prolonged significantly （P＜0.05）， and the levels of COX-2， IL-1β， PGE2 and TNF-α in serum were decreased significantly （P＜0.05）. Ten kinds of protoproducts including paeoniflorin and glycyrrhizic acid were identified from serum of mice， and twenty-two kinds of metabolites including hydroxylated glycyrrhizin and glucosylated liquiritin were identified. CONCLUSIONS The effective part of Shaoyao gancao decoction for relieving acute and chronic pain is 90% ethanol elution part prepared by macroporous resin from the ethyl acetate extract. Ten components， including glycyrrhetinic acid and paeoniflorin， may be the basis of its pharmacological substances.

Objective To establish an indicator system for evaluation of entrustable professional activity(EPA)of PICC specialist nurses hence to provide a reference in evaluation of the post competency.Methods Through literature review and expert correspondence,two rounds of expert consultations were conducted with 18 PICC specialist nurses to determine the indicators for EPA evaluation.Results The effective recovery rate of the consultation questionnaires was 100.00%from the 2 rounds of expert consultations with the 18 PICC specialist nurses.In the second round of consultation,the expert authority coefficient was 0.94.The Kendall harmony coefficients for first-level secord-level and tertiary indicator were 0.388,0.257 and 0.159,respectively(all P<0.001).Four primary evaluative indicators were extracted,covering health assessment,professional skills operation,clinical nursing and teaching and research.In addition,4 first-level indicators(health assessment,professional skill operation,clinical nursing and teaching and scientific research),a total of 11 second-level indicators(mortal history collection,puncture site and catheter selection,occupational protection,specialist nursing operation technology,PICC venous safety assessment and management,first aid nursing skills,health guidance,educational consultation,communication and collaboration,teaching guidance,nursing scientific research application and professional development)and 47 tertiary indicators were included.All indicators scored between 4.56～5.00 for importance in the second round of consultation,between 4.50～4.94 for observability,and between 4.61～5.00 for reflecting the corresponding core competencies.Conclusion The EPA indicator system for evaluation of PICC specialist nurses is scientifically reliable and comprehensive.It can serve as an objective quantitative tool in evaluation of the post competency of PICC specialist nurses.

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 establish an indicator system for evaluation of entrustable professional activity(EPA)of PICC specialist nurses hence to provide a reference in evaluation of the post competency.Methods Through literature review and expert correspondence,two rounds of expert consultations were conducted with 18 PICC specialist nurses to determine the indicators for EPA evaluation.Results The effective recovery rate of the consultation questionnaires was 100.00%from the 2 rounds of expert consultations with the 18 PICC specialist nurses.In the second round of consultation,the expert authority coefficient was 0.94.The Kendall harmony coefficients for first-level secord-level and tertiary indicator were 0.388,0.257 and 0.159,respectively(all P<0.001).Four primary evaluative indicators were extracted,covering health assessment,professional skills operation,clinical nursing and teaching and research.In addition,4 first-level indicators(health assessment,professional skill operation,clinical nursing and teaching and scientific research),a total of 11 second-level indicators(mortal history collection,puncture site and catheter selection,occupational protection,specialist nursing operation technology,PICC venous safety assessment and management,first aid nursing skills,health guidance,educational consultation,communication and collaboration,teaching guidance,nursing scientific research application and professional development)and 47 tertiary indicators were included.All indicators scored between 4.56～5.00 for importance in the second round of consultation,between 4.50～4.94 for observability,and between 4.61～5.00 for reflecting the corresponding core competencies.Conclusion The EPA indicator system for evaluation of PICC specialist nurses is scientifically reliable and comprehensive.It can serve as an objective quantitative tool in evaluation of the post competency of PICC specialist nurses.

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.