1.Relationship between duodenogastric reflux and chronic inflammation of gastric mucosa
Jinkun LIN ; Zhirong ZENG ; Xiuren GAO ; Wei CHEN ; Pinjin HU ;
Chinese Journal of Digestion 2001;0(02):-
0.05) for penetrating blood vessel, 46% and 18%( P
2.Functions of New Medical Teaching Mode on Innovative Talents Training
Weiying CHEN ; Danian QIN ; Shaoxian CAI ; Jinkun ZHANG ; Yingming ZENG ; Yong WANG
Chinese Journal of Medical Education Research 2006;0(11):-
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.
3.The characteristic of fractional amplitude of low frequency fluctuation in depression patients with suicidal ideation:a resting-state functional magnetic resonance imaging study
Jinkun ZENG ; Dejian TANG ; Huan LIU ; Dan LIU ; Lei HAO ; Qinghua LUO ; Lian DU ; Haitang QIU ; Tian QIU ; Huaqing MENG ; Yixiao FU
Chinese Journal of Nervous and Mental Diseases 2017;43(1):19-25
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.
4.Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression
Jinkun ZENG ; Minne CAO ; Dandan LIU ; Wei DENG
Chinese Journal of Nervous and Mental Diseases 2024;50(3):129-134
Objective This study aims to build a differential diagnosis model for unipolar and bipolar depression based on clinical features and blood indicators.Methods According to inclusion and exclusion criteria,participants with unipolar and bipolar depression were included,and clinical data and blood test indicators of the participants were extracted.The data were randomly divided into a training set and a testing set.Classification models were trained on the training set using extreme gradient boosting based on different feature combinations,and the performance of the models was validated on the testing set.Receiver operating characteristic(ROC),area under curve(AUC),sensitivity,specificity and accuracy were used to evaluate model performance.The SHapley additive explanations(SHAP)method was used to calculate the importance of selected features for the differential diagnosis of unipolar and bipolar depression.Results In the unipolar and bipolar depression classification model,the XGBoost model performs the best,with an AUC of 0.889,sensitivity of 0.831,specificity of 0.839,and accuracy of 0.863.The main features in this model include duration of illness,age of onset,albumin,low-density lipoprotein,blood potassium concentration,white blood cell count,platelet/lymphocyte ratio,and monocytes.Conclusion Duration of illness and hematological biomarkers,which are easily obtainable in clinical settings,can provide important support for the differential diagnosis of unipolar and bipolar depression.
5.Resting-state electroencephalogram classification of patients with schizophrenia or depression.
Hongyu LAI ; Jingwen FENG ; Yi WANG ; Wei DENG ; Jinkun ZENG ; Tao LI ; Junpeng ZHANG ; Kai LIU
Journal of Biomedical Engineering 2019;36(6):916-923
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
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Depression
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Electroencephalography
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Humans
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Schizophrenia
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Signal Processing, Computer-Assisted
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Support Vector Machine