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.

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.

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.

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.

Objective To explore the effect of 12-week simplified Tai Chi training on rehabilitation and neuromuscular control in individuals with chronic ankle instability(CAI).Methods Thirty-four partic-ipants with CAI were randomly divided into an experimental group and a control group,each of 17.The control group received 12-week health education,while the experimental group underwent simpli-fied Tai Chi training for the same length.Before and after the intervention,both groups were evaluat-ed their self-reported instability feeling,ankle muscle strength,proprioception and dynamic postural stability.Results After intervention,a significant increase was observed in the average Cumberland An-kle Instability Tool(CAIT),the maximum extension distance in the posteromedial and posterolateral di-rections of the modified Star Excursion Balance Test(mSEBT)in,as well as the ankle peak torque in plantarflexion,inversion and eversion(P<0.05)in the experimental.Moreover,the ankle passive motion proprioceptive threshold in plantarflexion and inversion decreased significantly in the experimental group after intervention(P<0.05).Meanwhile,after intervention,the average CAIT score,maximum ex-tension distances in the posteromedial and posterolateral directions in the mSEBT,and the ankle peak torques in plantarflexion,inversion and eversion of the experimental group were significantly higher than the control group(P<0.05),while the ankle passive motion proprioceptive threshold in plantarflex-ion was significantly lower than the latter(P<0.05).Conclusion Twelve-week simplified Tai Chi train-ing can improve the clinical subjective instability of CAI participants,maybe related to the improve-ment of ankle muscle strength,proprioception and dynamic postural stability.Therefore,it is suggest-ed that simplified Tai Chi should be one of the rehabilitation methods for CAI patients.

Concurrent chemoradiotherapy (CCRT) refers to the simultaneous treatment of chemotherapy and radiotherapy, and the effect of radiotherapy is enhanced with low-dose chemotherapy, which can reduce tumor recurrence and metastasis and improve clinical prognosis of patients. At present, the main factors for the increase of radiosensitivity of concurrent chemotherapy is that concurrent chemotherapy prevents the repair of tumor cells, and chemotherapy and radiotherapy act on different cell cycles and have synergistic effects. However, even for patients with locally advanced cervical cancer (LACC) who have undergone CCRT, the 5-year survival rate is only 60%, which is still not ideal. In order to improve the efficacy, researchers have conducted a series of exploratory studies, which consist of the combination of targeted drugs and immunodrugs, and neoadjuvant regimens before CCRT, etc. Although targeted or immunologic drugs are effective treatment of LACC, in view of the lack of large-scale evidence-based medical evidence, multi-center prospective and randomized phase III clinical trials and high-level articles are needed to improve the level of evidence-based medicine. This consensus summarizes several key evidence-based medical studies published recently, especially the clinical research progress in targeted and immunological therapies, providing reference for domestic peers.

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.