Abstract. Radical resection is the first choice for hepatic alveolar echinococcosis (HAE). However, many patients with advanced HAE have no chance to be treated with curative resection owing to the long clinical latency. This study aimed to evaluate the necessity of aggressive operations, like palliative resection and orthotopic liver transplantation (OLT), in the management of advanced HAE. A retrospective study analyzed 42 patients with advanced HAE treated with palliative resection (N = 15), palliative nonresective procedures (N = 13), OLT (N = 3), or albendazole therapy alone (N = 11). The patients' condition before treatments was comparable among all the four groups. The overall 1-year, 3-year, and 5-year survival rates of the 42 cases were 81.0%, 45.2%, and 23.8%, respectively. No event occurred to end the follow-up during the 5-year observation period except death. The survival time (median ± standard error) of the palliative resection group (3.6 ± 1.4 years) was longer than that of the palliative nonresective procedures group (1.5 ± 0.2) and the albendazole therapy-alone group (1.0 ± 0.4). The 5-year survival rates after palliative resection and liver transplantation were 40.0% and 66.7%, compared with only 7.7% and 9.1% after palliative nonresective procedures or albendazole therapy alone. Therefore, we concluded that aggressive treatment with a multimodality strategy could contribute to prolonged survival in patients with advanced HAE. Moreover, the prognosis of the patients who received albendazole therapy alone or palliative nonresective procedures is poor.
Migration-inducing gene 7 (MIG7) is highly expressed and is implicated in multiple malignant tumors with vasculogenic mimicry (VM) which renders possible routes without the endothelium for invasion and metastasis. However, there are few reports in the literature describing the relationship between MIG7 expression and VM formation in hepatocellular carcinoma (HCC). In the present study, we found a significantly positive correlation between MIG7 expression and VM in 40 HCC specimens. Three-dimensional (3D) culture showed that VM formation in the HCC cell line MHCC-97H with high metastatic potential was enhanced to a greater extent than that of MHCC-97L and Huh-7 with low and non-metastatic potential. There was no VM formation in human normal hepatocyte line L-02. Moreover, MIG7 expression was higher in MHCC-97H than in MHCC-97L and Huh-7 cells and non-detectable in L-02 cells. MIG7 knockdown in MHCC-97H cells reduced VM formation, and weakened the invasive properties accompanying the enhanced cellular adhesion. Notably, there was no significant effect of endostatin (ES), a broad-spectrum angiogenesis inhibitor applied to clinical treatment, on both MIG7 expression and VM formation. Thus, the present study presents a causal link between MIG7 expression and VM formation in HCC, suggesting a potential treatment target for invasion and metastasis.
Blended learning is a learning approach that combines face-to-face classroom lectures and e-learning. It has grown rapidly to be commonly used in medical institutions, especially in the local medical universities where there is lack of qualified teachers and instructional materials. Massive open online courses (MOOCs) are the latest revolution in e-learning and provides learners with access to quality educational resources. Nevertheless, there is seldom reports concerning how to effectively integrate MOOCs into blended learning in local universities, as well as the evaluation of knowledge outcomes. In order to achieve this aim, a blended learning approach was carried out in teaching pathophysiology in Guilin Medical University. This blended learning model was based on combination of Chinese University MOOC with case based learning (CBL), as an alternative to conventional learning. The medical students in the 2017 and 2018 classes received the blended learning method, while the medical students in the 2015 class received the traditional classroom instruction. The results showed that students in the 2017 and 2018 performed significantly better than students in the 2015 class at mid-term exam and the final exam. Perception surveys also revealed that both students and teachers had positive attitude toward blended learning, and they shared similar viewpoints of blended learning. A large proportion of students and teachers believed that the blended learning enhanced students' motivation to learn independently, improved their time management skills, and allowed them to experience personalized learning. Also, most students and teachers recognized that Chinese University MOOC provided substantial educational resources suitable for their need. In addition, teachers indicated that the blended learning improved student learning quality, facilitated interaction between teachers and students, and helped them to establish a student-centered model in teaching pathophysiology. Overall, the blended learning method that combines Chinese University MOOC with CBL is effective in enhancing students' achievement and motivation in pathophysiology than the traditional learning method, and helps to strengthen the cultivation of talent in local medical universities.
Background: Colorectal cancer (CRC) is one of the most common tumors in the digestive system, and all its risk factors are not yet known. It is important to identify valuable clinical indicators to predict the risk of CRC.Methods: A total of 227 participants, comprising 162 healthy adults and 65 patients diagnosed with CRC at Tianjin Hospital from January 2017 to March 2022, were included in this study. Electrochemiluminescence was adopted to test the expression levels of carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA199). Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for CRC, and a joint prediction model was then constructed. A nomogram was prepared, and the model was later assessed using the receiver operating characteristic curve and calibration curve. Results:The univariate analysis showed that there were statistically significant differences between the two groups in terms of smoking (χ 2 =8.67), fecal occult blood (χ 2 =119.41), Helicobacter pylori (H. pylori) infection (χ 2 =30.87), a history of appendectomy (χ 2 =5.47), serum total bile acid levels (t=19.80), serum CEA levels (t=37.82), serum CA199 levels (t=6.82), and serum ferritin levels (t=54.31) (all P<0.05). The multiple logistic regression analysis showed that smoking, fecal occult blood, H. pylori infection, a history of appendectomy, serum CEA levels, and serum CA199 levels were independent risk factors for CRC (all P<0.05). Based on the above findings, a joint prediction model was constructed, and the area under the receiver operator characteristic (ROC) curve of the model was 0.842. A nomogram and calibration curve was drawn, and the internal validation results indicated that the model had good diagnostic value.Conclusions: Smoking, fecal occult blood, H. pylori infection, a history of appendectomy, serum CEA levels, and serum CA199 levels are independent risk factors for CRC, and the prediction model based on these factors had good predictive ability.
With the enhancement of data mining technology, competitive sports informatization has become an inevitable development trend. It has become a common phenomenon to use data mining technology to help athletes train scientifically, assist coaches in rational decision-making, and improve team competitiveness. In competitive sports, cyclists' adaptation to training has a complex relationship with their physical performance. In order to explore the correlation between data and provide better training data for athletes, this study proposes a load prediction model based on BP neural network (Back propagation, BP). Considering the local convergence and random assignment of traditional BP model, an adaptive genetic algorithm with improved selection operator is used to determine the initial weights and thresholds of BP neural network to improve the accuracy of the prediction model. The experimental results show that the improved adaptive genetic algorithm improves the overall optimization ability of the BP neural network, the improved BP neural network model has good stability in the convergence process, and the algorithm can search for better weight thresholds. Compared with the basic BP neural network prediction model, the accuracy of the optimized prediction model is increased by 11.86%, and the average error value is reduced by 26.21%, which is a guide to improve the training effect of the cycling team's competitive sports.
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