Objective To explore the in vitro cytotoxicity of sunitinib in highly metastatic hepatocellular carcinoma cell line MHCC97-H, and the effect of it on the expression level of focal adhesion kinase (FAK). Methods MHCC97-H hepatoma cells were cultured and divided into control group and experimental (sunitinib) group. Experimental groups were added 2.5, 5,10 and 20μmol/L of sunitinib for 24, 48 and 72 hours respectively. The morphological changes were observed before and after sunitinib treatment in MHCC97-H with Giemsa stain. The inhibitory rate of proliferation in MHCC97-H was detected by MTT assay. The expressions of FAK protein before and after sunitinib treatment were detected by Western blot assay. Results Sunitinib showed the inhibitory effect on hepatoma cell line MHCC97-H. Giemsa staining showed that chromatin condensation, nuclear fragmentation, apoptotic bodies and other typical morphological features. The inhibitory rate was the most obvious in 48-h treatment group. The inhibitory rates were (0.433 ± 0.115)%, (32.863 ± 1.471)%, (49.240 ± 2.256)%, (63.797±2.707)%and (58.887±3.409)%for 2.5, 5, 10 and 20μmol/L concentration groups, and there were signifi-cant differences between groups (P<0.05). Results of Western blot assay showed that the expression levels of FAK protein were significantly reduced after different concentrations of sunitinib treatment for 48 h (P<0.05). Conclusion Sunitinib has inhibitory effect on hepatoma cell line MHCC97-H, enhances the apoptosis and decreases the the expression of FAK pro-tein.

Objective:To construct a non-invasive pre-hospital screening model and early based on artificial intelligence algorithms to provide the severity of stroke in patients, provide screening, guidance and early warning for stroke patients and their families, and provide data support for clinical decision-making.Methods:A retrospective study was conducted. The clinical information of stroke patients ( n = 53?793) were extracted from the Yidu cloud big data server system of the Second Affiliated Hospital of Dalian Medical University from January 1, 2001 to July 31, 2023. Combined with the results of single factor screening and the opinions of experts with senior professional titles in neurology, the input variable was determined, and the output variable was the National Institutes of Health Stroke Scale (NIHSS) representing the severity of the disease at admission. Python 3.7 was used to build DeepFM algorithm model, and five data mining models including Logistic regression, CART decision tree, C5.0 decision tree, Bayesian network and deep neural network (DNN) were built at the same time. The original data were randomly divided into 80% training set and 20% test set, which were used to train and test the models, adjust the parameters of each model, respectively calculate the accuracy, sensitivity and F-index of the six models, carry out the comprehensive comparison and evaluation of the model. The receiver operator characteristic curve (ROC curve) and calibration curve were drawn, compared the prediction performance of DeepFM model and the other five algorithms. In addition, the data of stroke patients ( n = 1?028) were extracted from Dalian Central Hospital for external verification of the model. Results:A total of 14?015 stroke patients with complete information were selected, including 11?212 in the training set and 2?803 in the testing set. After univariate screening, 14 indicators were included to construct the model, including gender, age, recurrence, physical impairment, facial problems, speech disorders, head reactions, disturbance of consciousness, visual disorders, abnormal cough and swallowing, high risk factor, family history, smoking history and drinking history. DeepFM model adopted the two-order crossover feature. The number of hidden layers in DNN layer was 3. Dropout was used to discard the neurons in the neural network. Rule was used as the activation function. Each layer used Dense full connection. The objective function was random gradient descent. The number of iterations was 15. There were 133?922 training parameters in total. Comparing the predictive value of the six models showed that the accuracy of DeepFM model was 0.951, the sensitivity was 0.992, the specificity was 0.814, the F-index was 0.950, and the area under the curve (AUC) was 0.916. The accuracy of the other five data mining models were between 0.771-0.780, the sensitivity were between 0.978-0.987, the F-index were between 0.690-0.707, and the AUC were between 0.568-0.639. The calibration curve of the DeepFM model was more aligned with the ideal curve than the other five data mining models. Suggesting that the prediction performance of DeepFM model was the best. External validation was conducted on the DeepFM model, and its accuracy was 0.891, indicating good generalization performance of the model.Conclusion:The pre-hospital non-invasive screening prediction model based on DeepFM can accurately predict the severity grading of stroke patients, and has potential application value in rapid screening and early clinical decision-making of stroke.

In response to the Opinions of the CPC Central Committee and the State Council on Promoting the Inheritance， Innovation， and Development of traditional Chinese medicine（TCM） and the spirit of the National Conference on TCM， Chinese Association of Chinese Medicine organized experts in Otorhinolaryngology Head and Neck Surgery of traditional Chinese and western medicine to discuss the clinical advantages of TCM and integrated traditional Chinese and western medicine in the treatment of allergic rhinitis （AR） and they reached a basic consensus. In recent years， the prevalence of AR has been on the rise， threatening the quality of life of patients and giving rise to a heavy burden to both the patients and the society. AR is resulted from immune imbalance rather than reduced immunity or hyperimmunity， and the imbalance is similar to the Yin-yang disharmony in TCM. In the treatment of this disease， western medicine features rapid onset. However， it is cost-intensive and causes severe surgical trauma， and the recurrence is common. TCM boasts diverse methods for AR， which can be used in all stages of this disease. It has advantages in controlling symptoms such as nasal congestion， runny nose， or dysosmia in the attack stage， preventing recurrence in the remission stage， and treating refractory AR or steroid-resistant AR. In particular， acupuncture enjoys a reputation in treatment of AR， which has been supported by evidence-based medicine and recommended by guidelines. While treating local symptoms of AR， TCM regulates the psychosomatic conditions， which facilitates chronic disease management and long-term follow-up. We should integrate the advantages of TCM and western medicine， give full play to the unique nonnegligible and irreplaceable advantages of TCM， formulate a comprehensive diagnosis and treatment scheme for learning and promotion， and summarize the research outcomes to promote the theoretical innovation of TCM on AR from the perspective of integrated traditional Chinese and western medicine.