Hepatocellular carcinoma （HCC） with biliary duct tumor thrombus （BDTT） is currently not common in clinical practice and is easily misdiagnosed， and previously， it was often considered an advanced stage of the disease with a poor prognosis， making its treatment challenging. However， in-depth studies in recent years have gradually deepened our understanding of this disease， leading to significant changes in diagnostic and treatment concepts. Currently， comprehensive treatment， mainly surgery， is used for treatment， but there is still controversy over the selection of clinical treatment strategies. This article provides a detailed discussion on surgical methods and prognosis， in order to provide a reference for clinical treatment options.

BACKGROUND:Poor prognosis of hip arthroplasty in patients with osteoporosis seriously affects the patients'quality of life.Accurately predicting the risk factors for poor prognosis of hip arthroplasty in patients with osteoporosis remains a major challenge for orthopedic surgeons.OBJECTIVE:To explore risk factors for poor prognosis after hip arthroplasty in patients with osteoporosis and construct a Nomogram prediction model.METHODS:A total of 192 patients with osteoporosis who underwent hip arthroplasty in Nanyang Second People's Hospital from July 2020 to June 2022 were selected as study subjects.Harris hip function scale was performed 6 months after operation.Patients with Harris score ≥ 80 were included in the good prognosis group(n=142),while patients with Harris score＜80 were included in the poor prognosis group(n=50).Clinical data of the two groups were collected and subjected to univariate analysis.Receiver operating characteristic curves were used to analyze the predictive value of the measures for poor prognosis after hip arthroplasty in patients with osteoporosis.Binary logistic regression was used to analyze the risk factors affecting poor prognosis after hip arthroplasty in patients with osteoporosis.The Nomogram prediction model for poor prognosis after hip arthroplasty in patients with osteoporosis was constructed.The calibration curve was internally validated and the concordance index was calculated,and the decision curve was evaluated for clinical predictive efficacy.RESULTS AND CONCLUSION:(1)The differences between the two groups were statistically significant in terms of age,body mass index,operative time,intraoperative bleeding,serum albumin,peripheral blood lymphocyte count,prognostic nutritional index,and complications(P＜0.05).(2)Area under the curve for age,body mass index,operative time,intraoperative bleeding,serum albumin,peripheral blood lymphocyte count,and prognostic nutritional index were 0.813,0.780,0.787,0.764,0.777,0.785,and 0.818.(3)Age,body mass index,intraoperative bleeding,and complications were risk factors for poor prognosis after hip arthroplasty in patients with osteoporosis.(4)The corrected,raw curve of the nomogram prediction model was close to the ideal curve with a concordance index of 0.851(0.815-0.886)and a good model fit,with a threshold of＞0.12 for the Nomogram prediction model to provide a net clinical benefit,and all net clinical benefits were higher than the independent predictors.(5)It is concluded that age,body mass index,intraoperative bleeding,and complications are risk factors affecting the poor prognosis of osteoporotic patients after hip arthroplasty.The Nomogram prediction model constructed based on this can help clinicians assess the prognosis of osteoporotic patients after hip arthroplasty,develop personalized interventions,improve prognosis,and enhance the quality of life.

Objective:To construct a nomogram model for predicting the incidence of hepatocellular carcinoma based on serum abnormal prothrombin and alpha-fetoprotein and evaluate the predictive effect.Methods:Retrospective analysis of data from 351 patients with liver disease who received treatment at the First Affiliated Hospital of Zhengzhou University from January 2021 to December 2023, including 285 males and 66 females, aged (52.9±11.9) years. Among the 351 patients, there were 229 cases (65.2%) of hepatocellular carcinoma, 87 cases (24.8%) of liver cirrhosis, and 35 cases (10.0%) of chronic hepatitis B. All patients were randomly divided into a training set ( n=245) and a testing set ( n=106) in a 7∶3 ratio without replacement sampling. The training set was used to construct the model, and the testing set was used to evaluate the model. At the same time, gender, age, disease type, and other indicators were compared between the two sets. The risk factors of hepatocellular carcinoma were analyzed by univariate and multivariate logistic regression based on the training set, and a nomogram was constructed to predict the incidence of hepatocellular carcinoma based on the multivariate results. Receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive performance of nomogram, and decision curve analysis was used to evaluate the clinical applicability of the model. Results:There was no statistically significant difference in age, gender, disease type, etc. between the training and testing sets of patients (all P>0.05). Univariate logistic regression analysis showed that age, abnormal prothrombin logarithm (LnPIVKA-Ⅱ), alpha-fetoprotein logarithm (LnAFP), and diabetes were associated with hepatocellular carcinoma (all P<0.05). Multivariate logistic regression analysis showed that older age ( OR=1.07, 95% CI: 1.03-1.12), higher LnPIVKA-Ⅱ ( OR=2.97, 95% CI: 1.97-4.46), higher LnAFP ( OR=1.43, 95% CI: 1.11-1.84) and diabetes ( OR=5.17, 95% CI: 1.02-26.17) were risk factors for hepatocellular carcinoma (all P<0.05). Based on the above variables, a nomogram model for predicting the incidence of hepatocellular carcinoma was constructed. The area under the ROC curve analysis of the nomogram for predicting the incidence of hepatocellular carcinoma was 0.920 (95% CI: 0.886-0.953) in the training set and 0.934 (95% CI: 0.891-0.977) in the testing set. The calibration curve fit well with the standard curve, and the prediction was basically consistent with the actual situation. The decision curve analysis showed that the net benefit of the model was greater than 0 under most thresholds (0.1-1.0). Conclusion:The nomogram constructed based on age, LnPIVKA-Ⅱ, LnAFP and diabetes can effectively predict the incidence of hepatocellular carcinoma and has clinical applicability.

By systematically summarizing the basic situation of the Arctium lappa L.industry in our country,the strengths,weaknesses,opportunities,and threats(SWOT)analysis method,and the multi-expert review method were used to analyze,screen,and test the elements obtained.It is determined that the development of Arctium lappa L.industry should adopt the S-O strategy of relying on internal advantages to use external opportunities and judging from the orientation,the external opportunities are more pronounced,to take the opportunity of pioneering strategy.Based on utilizing external opportunities and exerting our advantages,we should adopt the strategy of product diversification,the strategy of expanding the international market,and the strategy of promoting science and technology to promote the efficient development of Arctium lappa L.industry.

BACKGROUND:Poor prognosis of hip arthroplasty in patients with osteoporosis seriously affects the patients'quality of life.Accurately predicting the risk factors for poor prognosis of hip arthroplasty in patients with osteoporosis remains a major challenge for orthopedic surgeons.OBJECTIVE:To explore risk factors for poor prognosis after hip arthroplasty in patients with osteoporosis and construct a Nomogram prediction model.METHODS:A total of 192 patients with osteoporosis who underwent hip arthroplasty in Nanyang Second People's Hospital from July 2020 to June 2022 were selected as study subjects.Harris hip function scale was performed 6 months after operation.Patients with Harris score ≥ 80 were included in the good prognosis group(n=142),while patients with Harris score＜80 were included in the poor prognosis group(n=50).Clinical data of the two groups were collected and subjected to univariate analysis.Receiver operating characteristic curves were used to analyze the predictive value of the measures for poor prognosis after hip arthroplasty in patients with osteoporosis.Binary logistic regression was used to analyze the risk factors affecting poor prognosis after hip arthroplasty in patients with osteoporosis.The Nomogram prediction model for poor prognosis after hip arthroplasty in patients with osteoporosis was constructed.The calibration curve was internally validated and the concordance index was calculated,and the decision curve was evaluated for clinical predictive efficacy.RESULTS AND CONCLUSION:(1)The differences between the two groups were statistically significant in terms of age,body mass index,operative time,intraoperative bleeding,serum albumin,peripheral blood lymphocyte count,prognostic nutritional index,and complications(P＜0.05).(2)Area under the curve for age,body mass index,operative time,intraoperative bleeding,serum albumin,peripheral blood lymphocyte count,and prognostic nutritional index were 0.813,0.780,0.787,0.764,0.777,0.785,and 0.818.(3)Age,body mass index,intraoperative bleeding,and complications were risk factors for poor prognosis after hip arthroplasty in patients with osteoporosis.(4)The corrected,raw curve of the nomogram prediction model was close to the ideal curve with a concordance index of 0.851(0.815-0.886)and a good model fit,with a threshold of＞0.12 for the Nomogram prediction model to provide a net clinical benefit,and all net clinical benefits were higher than the independent predictors.(5)It is concluded that age,body mass index,intraoperative bleeding,and complications are risk factors affecting the poor prognosis of osteoporotic patients after hip arthroplasty.The Nomogram prediction model constructed based on this can help clinicians assess the prognosis of osteoporotic patients after hip arthroplasty,develop personalized interventions,improve prognosis,and enhance the quality of life.

By systematically summarizing the basic situation of the Arctium lappa L.industry in our country,the strengths,weaknesses,opportunities,and threats(SWOT)analysis method,and the multi-expert review method were used to analyze,screen,and test the elements obtained.It is determined that the development of Arctium lappa L.industry should adopt the S-O strategy of relying on internal advantages to use external opportunities and judging from the orientation,the external opportunities are more pronounced,to take the opportunity of pioneering strategy.Based on utilizing external opportunities and exerting our advantages,we should adopt the strategy of product diversification,the strategy of expanding the international market,and the strategy of promoting science and technology to promote the efficient development of Arctium lappa L.industry.

Objective:To construct a nomogram model for predicting the incidence of hepatocellular carcinoma based on serum abnormal prothrombin and alpha-fetoprotein and evaluate the predictive effect.Methods:Retrospective analysis of data from 351 patients with liver disease who received treatment at the First Affiliated Hospital of Zhengzhou University from January 2021 to December 2023, including 285 males and 66 females, aged (52.9±11.9) years. Among the 351 patients, there were 229 cases (65.2%) of hepatocellular carcinoma, 87 cases (24.8%) of liver cirrhosis, and 35 cases (10.0%) of chronic hepatitis B. All patients were randomly divided into a training set ( n=245) and a testing set ( n=106) in a 7∶3 ratio without replacement sampling. The training set was used to construct the model, and the testing set was used to evaluate the model. At the same time, gender, age, disease type, and other indicators were compared between the two sets. The risk factors of hepatocellular carcinoma were analyzed by univariate and multivariate logistic regression based on the training set, and a nomogram was constructed to predict the incidence of hepatocellular carcinoma based on the multivariate results. Receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive performance of nomogram, and decision curve analysis was used to evaluate the clinical applicability of the model. Results:There was no statistically significant difference in age, gender, disease type, etc. between the training and testing sets of patients (all P>0.05). Univariate logistic regression analysis showed that age, abnormal prothrombin logarithm (LnPIVKA-Ⅱ), alpha-fetoprotein logarithm (LnAFP), and diabetes were associated with hepatocellular carcinoma (all P<0.05). Multivariate logistic regression analysis showed that older age ( OR=1.07, 95% CI: 1.03-1.12), higher LnPIVKA-Ⅱ ( OR=2.97, 95% CI: 1.97-4.46), higher LnAFP ( OR=1.43, 95% CI: 1.11-1.84) and diabetes ( OR=5.17, 95% CI: 1.02-26.17) were risk factors for hepatocellular carcinoma (all P<0.05). Based on the above variables, a nomogram model for predicting the incidence of hepatocellular carcinoma was constructed. The area under the ROC curve analysis of the nomogram for predicting the incidence of hepatocellular carcinoma was 0.920 (95% CI: 0.886-0.953) in the training set and 0.934 (95% CI: 0.891-0.977) in the testing set. The calibration curve fit well with the standard curve, and the prediction was basically consistent with the actual situation. The decision curve analysis showed that the net benefit of the model was greater than 0 under most thresholds (0.1-1.0). Conclusion:The nomogram constructed based on age, LnPIVKA-Ⅱ, LnAFP and diabetes can effectively predict the incidence of hepatocellular carcinoma and has clinical applicability.

At present,precise radiotherapy has been widely used through the development with many years,but the existing technique still is limited by the limitation of tolerance dose of normal tissues,which cannot achieve the optimal goal of treating tumor.Flash radiotherapy(Flash-RT)is one kind of radiotherapy technique that uses the beam with ultra-high dose rate(UHDR)to conduct irradiation,which can furthest treat tumors while significantly reduce radiation injury of normal tissues.But until now,the biological mechanism,key physical parameters and triggering mechanism of Flash-RT are still unclear,and its principle and clinical translational application are still in the stage of research.This review clarified the technological advance and clinical translational application of Flash-RT research through summarized the relevant research of Flash-RT.

Radiotherapy is an important means to treat lung cancer,but it is easy to cause lung injury and reduce the quality of life of patients.Flash radiotherapy(FLASH-RT)has attracted attention due to its extremely short radiation duration and high dose rate,which can reduce toxicity of normal tissue while ensures treatment intensity of tumor.Whether Flash-RT can reduce radiation-induced lung injury has become an important research topic in recent years.Based on the literature analysis method,this review systematically assessed the effects and mechanisms of Flash-RT and radiotherapy with conventional dose rate on lung injury through searching relevant literatures at home and abroad,so as to provide scientific basis for the treatment of patients with lung cancer by reviewing the comparisons about the effects and mechanisms between Flash-RT and radiotherapy with conventional dose rate on lung injury.Compared with radiotherapy with conventional radiation rate,Flash-RT can significantly reduce lung injury and improve quality of life of patients.It is still demanded to explore the Flash-RT mechanism in future,so as to develop the Flash-RT instrument that is suitable for different tumors and to conduct larger-scale clinical researches.

Objective To evaluate the application of three deep learning algorithms in automatic segmentation of clinical target volumes (CTVs) in high-dose-rate brachytherapy after surgery for endometrial carcinoma. Methods A dataset comprising computed tomography scans from 306 post-surgery patients with endometrial carcinoma was divided into three subsets: 246 cases for training, 30 cases for validation, and 30 cases for testing. Three deep convolutional neural network models, 3D U-Net, 3D Res U-Net, and V-Net, were compared for CTV segmentation. Several commonly used quantitative metrics were employed, i.e., Dice similarity coefficient, Hausdorff distance, 95th percentile of Hausdorff distance, and Intersection over Union. Results During the testing phase, CTV segmentation with 3D U-Net, 3D Res U-Net, and V-Net showed a mean Dice similarity coefficient of 0.90 ± 0.07, 0.95 ± 0.06, and 0.95 ± 0.06, a mean Hausdorff distance of 2.51 ± 1.70, 0.96 ± 1.01, and 0.98 ± 0.95 mm, a mean 95th percentile of Hausdorff distance of 1.33 ± 1.02, 0.65 ± 0.91, and 0.40 ± 0.72 mm, and a mean Intersection over Union of 0.85 ± 0.11, 0.91 ± 0.09, and 0.92 ± 0.09, respectively. Segmentation based on V-Net was similarly to that performed by experienced radiation oncologists. The CTV segmentation time was < 3.2 s, which could save the work time of clinicians. Conclusion V-Net is better than other models in CTV segmentation as indicated by quantitative metrics and clinician assessment. Additionally, the method is highly consistent with the ground truth, reducing inter-doctor variability and treatment time.