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The article aimed to explore the implementation of systemic integrated new clinical medical teaching mode and the functions of basic medicine specialty module teaching on innovative talents training.It showed that the implementation of systemic integrated new medical teaching mode was facilitated to build up a platform for the training of innovative talents.And the application of the integrated method to teaching was beneficial to develop the creative thinking of medical students.

Objective To explore the characteristic of fractional amplitude of low frequency fluctuation (fALFF) and the relationship with the severity of depression, suicidal ideation and suicide risk in depression patients with suicidal ideation with resting-state functional magnetic resonance imaging (rs-fMRI). Methods Resting state functional magnetic resonance imaging maps were conducted using fractional amplitude of low frequency fluctuation (fALFF) in 52 depression patients (30 with suicidal ideation and 22 without) and 21 healthy controls (HCs). The severity of depression was evaluat-ed by using Hamilton Depression scale(HAMD). The suicidal ideation, the suicide risk in depression patients with sui-cidal ideation were both assessed by the Beck Scale for Suicide Ideation. The correlation between the fALFF value and the score of HAMD and the Beck Scale for Suicide Ideation was analyzed. Results MRI revealed significant differences in fALFF in the left superior/middle occipital gyrus and the right middle/inferior occipital gyrus (P<0.05, AlphaSim cor-rected)between depression patients with suicidal ideation and the HCs. Compared to the HCs, depression patients with-out suicidal ideation showed a higher fALFF in the left middle occipital gyrus (P<0.05, AlphaSim corrected). MRI re-vealed significant differences in fALFF in the left middle occipital gyrus (P<0.01, AlphaSim corrected)and the right mid-dle occipital gyrus (P<0.01, AlphaSim corrected) between depression patients with suicidal ideation and without. The fALFF of left middle occipital gyrus (r=0.366, P=0.046) and right middle occipital gyrus (r=0.513, P=0.004) were posi-tively correlated with the scores of HAMD, respectively whereas were not correlated with suicidal ideation and suicide risk. Conclusions Depression patients with suicidal ideation have an abnormal spontaneous activity in their left and right middle occipital gyrus. The increased activity in these brain areas are probably associated with the severity of de-pression whereas are not associated with suicidal ideation or suicide risk.

The clinical manifestations of patients with schizophrenia and patients with depression not only have a certain similarity, but also change with the patient's mood, and thus lead to misdiagnosis in clinical diagnosis. Electroencephalogram (EEG) analysis provides an important reference and objective basis for accurate differentiation and diagnosis between patients with schizophrenia and patients with depression. In order to solve the problem of misdiagnosis between patients with schizophrenia and patients with depression, and to improve the accuracy of the classification and diagnosis of these two diseases, in this study we extracted the resting-state EEG features from 100 patients with depression and 100 patients with schizophrenia, including information entropy, sample entropy and approximate entropy, statistical properties feature and relative power spectral density (rPSD) of each EEG rhythm (δ, θ, α, β). Then feature vectors were formed to classify these two types of patients using the support vector machine (SVM) and the naive Bayes (NB) classifier. Experimental results indicate that: ① The rPSD feature vector performs the best in classification, achieving an average accuracy of 84.2% and a highest accuracy of 86.3%; ② The accuracy of SVM is obviously better than that of NB; ③ For the rPSD of each rhythm, the β rhythm performs the best with the highest accuracy of 76%; ④ Electrodes with large feature weight are mainly concentrated in the frontal lobe and parietal lobe. The results of this study indicate that the rPSD feature vector in conjunction with SVM can effectively distinguish depression and schizophrenia, and can also play an auxiliary role in the relevant clinical diagnosis.
Bayes Theorem ; Depression ; Electroencephalography ; Humans ; Schizophrenia ; Signal Processing, Computer-Assisted ; Support Vector Machine