Association Study of Thyroid Papillary Carcinoma with Depression and BDNF Expression

Objective: To analyze the correlation between Val66Met gene of brain-derived neurotrophic factor (BDNF) and papillary thyroid carcinoma (PTC) complicated with depression. To evaluate the clinical value of papillary thyroid carcinoma. Methods: Self-rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS) were used to assess the correlation of BDNF in the blood of patients with depressive disorder of thyroid papillary carcinoma using polymerase chain reaction PCR. The relationship between BDNF gene polymorphism and the incidence of thyroid papillary carcinoma was analyzed, and the susceptibility factors of thyroid papillary carcinoma complicated with depression were explored. Results: Compared with normal control group, T3 and T4 of PTC in non-depressed group were decreased, while TSH and TPOAb were increased (P < 0.05). Compared with normal control group, T3, T4 and TPOAb were increased and TSH was decreased in PTC depression group (P < 0.05). PTC depression score was higher than that of healthy control group (P < 0.05), and PTC combined depression score was higher than that of normal control group (P < 0.05). The depression rate of PTC combined with depression was 86.7%, which was higher than that of other groups (P < 0.05). The distribution of RS6265 locus genotype of BDNF gene was significantly different between PTC with depression group and PTC without depression group (P Conclusion: There is a significant difference between PTC with depression and PTC without depression, which is related to SNP rs6265 of BDNF gene. AG and GG are both risk sites of PTC with depression, and GG type is more prone to PTC with depression.

Advances in Depression and Brain-Derived Neurotrophic Factor

In 2006, Duman et al. proposed “Neurotrophic Theory of Depression” [1]. According to the hypothesis, stress leads to a decrease in the expression of neurotrophic factors such as Brain-derived neurotrophic factor (BDNF) in the limbic structure, and antidepressant therapy can partially reverse the effect caused by stress. The reduction of BDNF and other neurotrophic factors promotes the atrophy of certain brain structures, especially the hippocampus and prefrontal cortex, while antidepressant treatment increases the level of BDNF in the brain, and improves synaptic plasticity and neuronal survival in related brain regions. Neurotrophic factors are a class of molecules that act on the nervous system and play an important role in maintaining cell function. They can regulate the growth, survival, differentiation and cell cycle of nerve cells. There is a hypothesis of neuroendocrine dysfunction in the neurobiochemical mechanism of depression, which is mainly the abnormal activity of the hypothalamic-pituitary-adrenal axis (HPA) and hypothalamic-pituitary-thyroid axis (HPT). This article reviews the related research on depression and brain-derived neurotrophic factors in order to guide clinical research and treatment.

Support Vector Machine Prediction Modeling for Automobile Ownership

To address some inherent defects of artificial neural networks, such as insufficient generalization performance, local extremum problem, and dimensional catastrophe problem, a support vector machine was proposed and applied to the modeling of automobile ownership prediction. By analyzing the data on automobile ownership and its influencing factors, the learning sample couples for automobile ownership prediction modeling were constructed, and support vector machine (SVM) was used to regression the nonlinear function relation of automobile ownership prediction model, and the established automobile ownership prediction model was used to predict the automobile ownership in different years. To reduce the impact on the accuracy of automobile ownership prediction caused by the large order of magnitude difference between the data of automobile ownership and its influence factors, the normalization method was used to pre-process the automobile ownership and its influence factors, and the inverse normalization was used to process the automobile ownership prediction results. The comparison between the automobile ownership prediction results and the statistical results shows that the automobile ownership prediction model has good generalization performance, and the support vector machine is an effective method to model the automobile ownership prediction.