Objective:To analyze the result data of the first competition of medical-industrial integration innovation for transformation of the Pathological Equipment Branch of China Association of Medical Equipment,so as to explore the directions of medical-industrial integration and transformation innovation in the field of pathology.Methods:The data of the participated projects of the first competition of medical-industrial integration innovation for transformation in 2024,which was held by the Pathological Equipment Branch of China Association of Medical Equipment,were collected,and these data were divided into equipment category,and the category of reagents and consumables according to their declaration content.The data of the results of preliminary and final competition,award information,transformation number and other data of the two categories of participated projects were analyzed,and the scoring differences between the judges who were expert,and the judges from enterprise were compared.Results:This competition received 21 projects,which included 15 projects of equipment category,and 6 projects of the category of reagents and consumables.In 21 projects,there were 10 projects wined award,which included 7 projects of equipment category,and 3 projects of the category of reagents and consumables.There was not statistically significant difference in the results between equipment category and the category of reagents and consumables in preliminary and final competitions(P>0.05).There was not significant difference in the ratio between equipment category(46.7%)and the category of reagents and consumables(50%)(P>0.05).The results of preliminary and final competitions of the projects in equipment category were respectively(70.64±3.48)and(76.49±2.34),and the difference was significant(t=5.403,P<0.05).There was not significant difference in the category of reagents and consumables between preliminary and final competitions(P>0.05).In 15 projects of equipment category,6 projects obtained the intention of transformation from enterprises.In 6 projects of the category of reagents and consumables,3 projects obtained the intention of transformation from enterprises.The score of judges who were expert,and the score of judges from enterprise were respectively(68.90±4.76)and(71.90±4.38)for the results of preliminary competition,and the difference was significant(t=2.121,P<0.05).Conclusion:The competition of medical-industrial integration innovation for transformation has successfully built a bridge between pathological patent and enterprises,which can effectively promote the transformation of pathology-related patents,and promote a deep integration between medicine and engineering.It also can accelerate the application of innovational technique in pathological field,and the connection between product's development stage and related enterprises,and can construct a harmonious development environment for transformation and innovation of medical-industrial integration.

Objective:To analyze the result data of the first competition of medical-industrial integration innovation for transformation of the Pathological Equipment Branch of China Association of Medical Equipment,so as to explore the directions of medical-industrial integration and transformation innovation in the field of pathology.Methods:The data of the participated projects of the first competition of medical-industrial integration innovation for transformation in 2024,which was held by the Pathological Equipment Branch of China Association of Medical Equipment,were collected,and these data were divided into equipment category,and the category of reagents and consumables according to their declaration content.The data of the results of preliminary and final competition,award information,transformation number and other data of the two categories of participated projects were analyzed,and the scoring differences between the judges who were expert,and the judges from enterprise were compared.Results:This competition received 21 projects,which included 15 projects of equipment category,and 6 projects of the category of reagents and consumables.In 21 projects,there were 10 projects wined award,which included 7 projects of equipment category,and 3 projects of the category of reagents and consumables.There was not statistically significant difference in the results between equipment category and the category of reagents and consumables in preliminary and final competitions(P>0.05).There was not significant difference in the ratio between equipment category(46.7%)and the category of reagents and consumables(50%)(P>0.05).The results of preliminary and final competitions of the projects in equipment category were respectively(70.64±3.48)and(76.49±2.34),and the difference was significant(t=5.403,P<0.05).There was not significant difference in the category of reagents and consumables between preliminary and final competitions(P>0.05).In 15 projects of equipment category,6 projects obtained the intention of transformation from enterprises.In 6 projects of the category of reagents and consumables,3 projects obtained the intention of transformation from enterprises.The score of judges who were expert,and the score of judges from enterprise were respectively(68.90±4.76)and(71.90±4.38)for the results of preliminary competition,and the difference was significant(t=2.121,P<0.05).Conclusion:The competition of medical-industrial integration innovation for transformation has successfully built a bridge between pathological patent and enterprises,which can effectively promote the transformation of pathology-related patents,and promote a deep integration between medicine and engineering.It also can accelerate the application of innovational technique in pathological field,and the connection between product's development stage and related enterprises,and can construct a harmonious development environment for transformation and innovation of medical-industrial integration.

This paper discusses the necessity of artificial intelligence(AI)technology empowering the field of traditional Chinese medicine(TCM)in the context of the intelligent era,the connotation and tasks of smart TCM,and the progress of related research and transformation.It closely follows the national orientation,rigid needs and problems,conducts top-level design,and proposes popular AI technologies that can be used in the field of TCM in the future and the research directions that smart TCM will focus on in the fu-ture,in order to further promote the integration of multidisciplinary cross-innovation and help realize the modernization,inheritance and innovation of TCM and lay the foundation.

This paper discusses the necessity of artificial intelligence(AI)technology empowering the field of traditional Chinese medicine(TCM)in the context of the intelligent era,the connotation and tasks of smart TCM,and the progress of related research and transformation.It closely follows the national orientation,rigid needs and problems,conducts top-level design,and proposes popular AI technologies that can be used in the field of TCM in the future and the research directions that smart TCM will focus on in the fu-ture,in order to further promote the integration of multidisciplinary cross-innovation and help realize the modernization,inheritance and innovation of TCM and lay the foundation.

Cholangiocarcinoma (CCA) has emerged as an intractable cancer with scanty therapeutic regimens. The aberrant activation of Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ) are reported to be common in CCA patients. However, the underpinning mechanism remains poorly understood. Deubiquitinase (DUB) is regarded as a main orchestrator in maintaining protein homeostasis. Here, we identified Josephin domain-containing protein 2 (JOSD2) as an essential DUB of YAP/TAZ that sustained the protein level through cleavage of polyubiquitin chains in a deubiquitinase activity-dependent manner. The depletion of JOSD2 promoted YAP/TAZ proteasomal degradation and significantly impeded CCA proliferation

Objective:To investigate the clinicopathological features and immunohistochemical phenotypes of hybrid oncocytic/chromophobe tumor (HOCT) of the kidney and its associations with renal oncocytoma (RO) and eosinophilic chromophobe renal cell carcinoma (eChRCC).Methods:A total of 8 HOCT cases were collected from 2008 to 2019 at the Affiliated Hospital of Qingdao University (5 cases) and 971 Hospital of PLA Navy (3 cases), Qingdao, China for morphological studies, immunohistochemical staining and follow-up. The immunohistochemical results of HOCT were compared with those of 27 typical RO and 17 eChRCC.Results:Among the 8 patients, 3 were male and 5 were female. Their ages ranged from 39 to 75 years (median: 56 years). All cases were sporadic. Seven patients were asymptomatic and one suffered from lumbago. During a mean follow-up of 37 months in 7 patients, none of them developed tumor recurrence or metastasis. Seven cases were solitary and one was multiple. The tumor size ranged from 1.4 to 5.7 cm (mean, 3.6 cm). The cut surface of the tumors was dark red or yellowish. Histologically, the tumors were well-defined. Six cases were directly adjacent to the surrounding renal tissue, 2 cases had pseudocapsule, 3 cases showed entrapped renal tubules at the edge of tumor tissue, and one circumscribed with focal infiltrating borders. There were two types of histological morphology: one type (4 cases) was composed of mixed areas of otherwise typical RO and areas resembling chromophobe renal cell carcinoma; another type (4 cases) showed the morphological characteristics of both RO and eChRCC. Three second-type tumors showed nest-like, trabecular, and solid growth patterns with conspicuous edematous stroma. The cell border was conspicuous and the cytoplasm showed an eosinophilic appearance. The nuclei were small and round with clear perinuclear halo. One tumor showed a multi-nodular and solid growth pattern, and the cytoplasm was eosinophilic, hypochromatic or transparent. The nuclei were small and round, and some of them had obvious perinuclear halo. Immunohistochemically, the tumor cells in all 8 cases were positive for Ksp-cad but negative for vimentin. CD117 was diffusely positive in 6/8 cases. CK7 staining showed patchy positivity in 6/8 cases. S-100A1, cyclin D1 and claudin7 showed variable positivity in 4/8, 6/8 and 5/8 cases, respectively, but the range and intensity were narrower and weaker than those in RO and eChRCC.Conclusions:HOCT is a low-grade eosinophilic renal tumor with morphological characteristics resembling RO and eChRCC. The combined application of immunohistochemical stains of CK7, CD117, Ksp-cad, cyclin D1, claudin7 and S-100A1 may play an auxiliary role in the differentiation of the three tumors. HOCT has a good prognosis after surgical resection and can be regarded as a tumor with uncertain malignant potential.

Objective:Peripheral nerve invasion (PNI) is associated with local recurrence and poor prognosis in patients with advanced gastric cancer. A risk-assessment model based on preoperative indicators for predicting PNI of gastric cancer may help to formulate a more reasonable and accurate individualized diagnosis and treatment plan.Methods:Inclusion criteria: (1) electronic gastroscopy and enhanced CT examination of the upper abdomen were performed before surgery; (2) radical gastric cancer surgery (D2 lymph node dissection, R0 resection) was performed; (3) no distant metastasis was confirmed before and during operation; (4) postoperative pathology showed an advanced gastric cancer (T2-4aN0-3M0), and the clinical data was complete. Those who had other malignant tumors at the same time or in the past, and received neoadjuvant radiochemotherapy or immunotherapy before surgery were excluded. In this retrospective case-control study, 550 patients with advanced gastric cancer who underwent curative gastrectomy between September 2017 and June 2019 were selected from the Affiliated Hospital of Qingdao University for modeling and internal verification, including 262 (47.6%) PNI positive and 288 (52.4%) PNI negative patients. According to the same standard, clinical data of 50 patients with advanced gastric cancer who underwent radical surgery from July to November 2019 in Qingdao Municipal Hospital were selected for external verification of the model. There were no statistically significant differences between the clinical data of internal verification and external verification (all P>0.05). Univariate analysis and multivariate logistic regression analysis were used to determine the independent risk factors for PNI in advanced gastric cancer, and the clinical indicators with statistically significant difference were used to establish a preoperative nomogram model through R software. The Bootstrap method was applied as internal verification to show the robustness of the model. The discrimination of the nomogram was determined by calculating the average consistency index (C-index). The calibration curve was used to evaluate the consistency of the predicted results with the actual results. The Hosmer-Lemeshow test was used to examine the goodness of fit of the discriminant model. During external verification, the corresponding C-index index was also calculated. The area under ROC curve (AUC) was used to evaluate the predictive ability of the nomogram in the internal verification and external verification groups. Results:A total of 550 patients were identified in this study, 262 (47.6%) of which had PNI. Multivariate logistic regression analysis revealed that carcinoembryonic antigen level ≥ 5 μg/L (OR=5.870, 95% CI: 3.281-10.502, P<0.001), tumor length ≥5 cm (OR=5.539,95% CI: 3.165-9.694, P<0.001), mixed Lauren classification (OR=2.611, 95%CI: 1.272-5.360, P=0.009), cT3 stage (OR=13.053, 95% CI: 5.612-30.361, P<0.001) and the presence of lymph node metastasis (OR=4.826, 95% CI: 2.729-8.533, P<0.001) were significant independent risk factors of PNI in advanced gastric cancer (all P<0.05). Based on these results, diffused Lauren classification and cT4 stage were included to establish a predictive nomogram model. CEA ≥ 5 μg/L was for 68 points, tumor length ≥ 5 cm was for 67 points, mixed Lauren classification was for 21 points, diffused Lauren classification was for 38 points, cT3 stage was for 75 points, cT4 stage was for 100 points, and lymph node metastasis was for 62 points. Adding the scores of all risk factors was total score, and the probability corresponding to the total score was the probability that the model predicted PNI in advanced gastric cancer before surgery. The internal verification result revealed that the AUC of nomogram was 0.935, which was superior than that of any single variable, such as CEA, Lauren classification, cT stage, tumor length and lymph node metastasis (AUC: 0.731, 0.595, 0.838, 0.757 and 0.802, respectively). The external verification result revealed the AUC of nomogram was 0.828. The C-ndex was 0.931 after internal verification. External verification showed a C-index of 0.828 from the model. The calibration curve showed that the predictive results were good in accordance with the actual results ( P=0.415). Conclusion:A nomogram model constructed by CEA, tumor length, Lauren classification (mixed, diffuse), cT stage, and lymph node metastasis can predict the PNI of advanced gastric cancer before surgery.

Objective:To investigate the effectiveness of an enhanced CT automatic recognition system based on Faster R-CNN for pancreatic cancer and its clinical value.Methods:In this study, 4 024 enhanced CT imaging sequences of 315 patients with pancreatic cancer from January 2013 to May 2016 at the Affiliated Hospital of Qingdao University were collected retrospectively, and 2 614 imaging sequences were input into the faster R-CNN system as training dataset to create an automatic image recognition model, which was then validated by reading 1 410 enhanced CT images of 135 cases of pancreatic cancer.In order to identify its effectiveness, 3 750 CT images of 150 patients with pancreatic lesions were read and a followed-up was carried out.The accuracy and recall rate in detecting nodules were recorded and regression curves were generated.In addition, the accuracy, sensitivity and specificity of Faster R-CNN diagnosis were analyzed, the ROC curves were generated and the area under the curves were calculated.Results:Based on the enhanced CT images of 135 cases, the area under the ROC curve was 0.927 calculated by Faster R-CNN. The accuracy, specificity and sensitivity were 0.902, 0.913 and 0.801 respectively.After the data of 150 patients with pancreatic cancer were verified, 893 CT images showed positive and 2 857 negative.Ninety-eight patients with pancreatic cancer were diagnosed by Faster R-CNN.After the follow-up, it was found that 53 cases were post-operatively proved to be pancreatic ductal carcinoma, 21 cases of pancreatic cystadenocarcinoma, 12 cases of pancreatic cystadenoma, 5 cases of pancreatic cyst, and 7 cases were untreated.During 5 to 17 months after operation, 6 patients died of abdominal tumor infiltration, liver and lung metastasis.Of the 52 patients who were diagnosed negative by Faster R-CNN, 9 were post-operatively proved to be pancreatic ductal carcinoma.Conclusion:Faster R-CNN system has clinical value in helping imaging physicians to diagnose pancreatic cancer.

Objective:Peripheral nerve invasion (PNI) is associated with local recurrence and poor prognosis in patients with advanced gastric cancer. A risk-assessment model based on preoperative indicators for predicting PNI of gastric cancer may help to formulate a more reasonable and accurate individualized diagnosis and treatment plan.Methods:Inclusion criteria: (1) electronic gastroscopy and enhanced CT examination of the upper abdomen were performed before surgery; (2) radical gastric cancer surgery (D2 lymph node dissection, R0 resection) was performed; (3) no distant metastasis was confirmed before and during operation; (4) postoperative pathology showed an advanced gastric cancer (T2-4aN0-3M0), and the clinical data was complete. Those who had other malignant tumors at the same time or in the past, and received neoadjuvant radiochemotherapy or immunotherapy before surgery were excluded. In this retrospective case-control study, 550 patients with advanced gastric cancer who underwent curative gastrectomy between September 2017 and June 2019 were selected from the Affiliated Hospital of Qingdao University for modeling and internal verification, including 262 (47.6%) PNI positive and 288 (52.4%) PNI negative patients. According to the same standard, clinical data of 50 patients with advanced gastric cancer who underwent radical surgery from July to November 2019 in Qingdao Municipal Hospital were selected for external verification of the model. There were no statistically significant differences between the clinical data of internal verification and external verification (all P>0.05). Univariate analysis and multivariate logistic regression analysis were used to determine the independent risk factors for PNI in advanced gastric cancer, and the clinical indicators with statistically significant difference were used to establish a preoperative nomogram model through R software. The Bootstrap method was applied as internal verification to show the robustness of the model. The discrimination of the nomogram was determined by calculating the average consistency index (C-index). The calibration curve was used to evaluate the consistency of the predicted results with the actual results. The Hosmer-Lemeshow test was used to examine the goodness of fit of the discriminant model. During external verification, the corresponding C-index index was also calculated. The area under ROC curve (AUC) was used to evaluate the predictive ability of the nomogram in the internal verification and external verification groups. Results:A total of 550 patients were identified in this study, 262 (47.6%) of which had PNI. Multivariate logistic regression analysis revealed that carcinoembryonic antigen level ≥ 5 μg/L (OR=5.870, 95% CI: 3.281-10.502, P<0.001), tumor length ≥5 cm (OR=5.539,95% CI: 3.165-9.694, P<0.001), mixed Lauren classification (OR=2.611, 95%CI: 1.272-5.360, P=0.009), cT3 stage (OR=13.053, 95% CI: 5.612-30.361, P<0.001) and the presence of lymph node metastasis (OR=4.826, 95% CI: 2.729-8.533, P<0.001) were significant independent risk factors of PNI in advanced gastric cancer (all P<0.05). Based on these results, diffused Lauren classification and cT4 stage were included to establish a predictive nomogram model. CEA ≥ 5 μg/L was for 68 points, tumor length ≥ 5 cm was for 67 points, mixed Lauren classification was for 21 points, diffused Lauren classification was for 38 points, cT3 stage was for 75 points, cT4 stage was for 100 points, and lymph node metastasis was for 62 points. Adding the scores of all risk factors was total score, and the probability corresponding to the total score was the probability that the model predicted PNI in advanced gastric cancer before surgery. The internal verification result revealed that the AUC of nomogram was 0.935, which was superior than that of any single variable, such as CEA, Lauren classification, cT stage, tumor length and lymph node metastasis (AUC: 0.731, 0.595, 0.838, 0.757 and 0.802, respectively). The external verification result revealed the AUC of nomogram was 0.828. The C-ndex was 0.931 after internal verification. External verification showed a C-index of 0.828 from the model. The calibration curve showed that the predictive results were good in accordance with the actual results ( P=0.415). Conclusion:A nomogram model constructed by CEA, tumor length, Lauren classification (mixed, diffuse), cT stage, and lymph node metastasis can predict the PNI of advanced gastric cancer before surgery.

Objective:To investigate the effectiveness of an enhanced CT automatic recognition system based on Faster R-CNN for pancreatic cancer and its clinical value.Methods:In this study, 4 024 enhanced CT imaging sequences of 315 patients with pancreatic cancer from January 2013 to May 2016 at the Affiliated Hospital of Qingdao University were collected retrospectively, and 2 614 imaging sequences were input into the faster R-CNN system as training dataset to create an automatic image recognition model, which was then validated by reading 1 410 enhanced CT images of 135 cases of pancreatic cancer.In order to identify its effectiveness, 3 750 CT images of 150 patients with pancreatic lesions were read and a followed-up was carried out.The accuracy and recall rate in detecting nodules were recorded and regression curves were generated.In addition, the accuracy, sensitivity and specificity of Faster R-CNN diagnosis were analyzed, the ROC curves were generated and the area under the curves were calculated.Results:Based on the enhanced CT images of 135 cases, the area under the ROC curve was 0.927 calculated by Faster R-CNN. The accuracy, specificity and sensitivity were 0.902, 0.913 and 0.801 respectively.After the data of 150 patients with pancreatic cancer were verified, 893 CT images showed positive and 2 857 negative.Ninety-eight patients with pancreatic cancer were diagnosed by Faster R-CNN.After the follow-up, it was found that 53 cases were post-operatively proved to be pancreatic ductal carcinoma, 21 cases of pancreatic cystadenocarcinoma, 12 cases of pancreatic cystadenoma, 5 cases of pancreatic cyst, and 7 cases were untreated.During 5 to 17 months after operation, 6 patients died of abdominal tumor infiltration, liver and lung metastasis.Of the 52 patients who were diagnosed negative by Faster R-CNN, 9 were post-operatively proved to be pancreatic ductal carcinoma.Conclusion:Faster R-CNN system has clinical value in helping imaging physicians to diagnose pancreatic cancer.