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ObjectiveTo explore the elements， distribution and characteristics of traditional Chinese medicine （TCM） syndromes in depressive episodes of bipolar disorder （BD）. MethodsBasic information， along with the four examination information， the Hamilton Depression Scale and Young Mania Rating Scale scores， were collected from 293 outpatients with BD at Beijing Anding Hospital， Capital Medical University. The four examination information with an occurrence rate greater than 12% were retained. The R language “dist” function was used to calculate the distances between samples using the Euclidean distance method. The hierarchical clustering of the four examination information was performed using the “hclust” function and the squared Euclidean distance method. A team of five researchers was formed to determine the nature and location of the essential elements of TCM syndrome in BD based on the clustering results. The PC algorithm was used to construct a Bayesian network model of the essential elements. The working group combined the essential elements of TCM syndromes in the Bayesian network according to the reference model results， and then extracted common TCM syndromes. The score of each patient based on the essential elements was matched with the common TCM syndromes to determine the syndrome type of each patient. The working group then performs conformity and revision based on this， obtaining the final distribution of TCM syndromes for the patients. ResultsThere were 77 common TCM symptoms in BD with a frequency greater than 12%. The top 15 symptoms with higher frequencies were slippery pulse， mental fatigue and lack of strength， wiry pulse， excessive rumination， preference for solitude， vexation， agitation and irritability， dry mouth， palpitations， profuse dreaming， unwarranted worries， chest oppression， thin white coating， amnesia， frequent sighing， and poor appetite. TCM syndrome elements of BD can be grouped into 11 categories. The nature of disease-related essential elements included fire， qi deficiency， blood deficiency， qi counterflow， yin deficiency， dampness， heat， fire from constraint， and phlegm. The location of disease-related essential elements included heart， liver， spleen， stomach， kidney， bladder channel， and gallbladder. By constructing a Bayesian network model and considering the opinions from the experts， six common syndromes of BD were identified， among which the highest proportion was heart-stomach heat accumulation， accounting for 27.99% （82 cases）， followed by heart-spleen deficiency （55 cases， 18.77%）， non-interaction between the heart and the kidney （49 cases， 16.72%）， liver constraint and blood deficiency （42 cases， 14.33%）， heart qi deficiency （37 cases， 12.63%）， and damp-heat in the liver and gallbladder （28 cases， 9.56%）. ConclusionsThe nature of disease-related elements of BD are predominantly fire and heat， while the location of disease-related essential elements are primarily associated with the heart， liver， and spleen. The most common TCM syndromes are heart-stomach heat accumulation and heart-spleen deficiency.
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OBJECTIVE@#To develop a noninvasive method for prediction of 1p/19q codeletion in diffuse lower-grade glioma (DLGG) based on multimodal magnetic resonance imaging (MRI) radiomics.@*METHODS@#We collected MRI data from 104 patients with pathologically confirmed DLGG between October, 2015 and September, 2022. A total of 535 radiomics features were extracted from T2WI, T1WI, FLAIR, CE-T1WI and DWI, including 70 morphological features, 90 first order features, and 375 texture features. We constructed logistic regression (LR), logistic regression least absolute shrinkage and selection operator (LRlasso), support vector machine (SVM) and Linear Discriminant Analysis (LDA) radiomics models and compared their predictive performance after 10-fold cross validation. The MRI images were reviewed by two radiologists independently for predicting the 1p/19q status. Receiver operating characteristic curves were used to evaluate classification performance of the radiomics models and the radiologists.@*RESULTS@#The 4 radiomics models (LR, LRlasso, SVM and LDA) achieved similar area under the curve (AUC) in the validation dataset (0.833, 0.819, 0.824 and 0.819, respectively; P>0.1), and their predictive performance was all superior to that of resident physicians of radiology (AUC=0.645, P=0.011, 0.022, 0.016, 0.030, respectively) and similar to that of attending physicians of radiology (AUC=0.838, P>0.05).@*CONCLUSION@#Multiparametric MRI radiomics models show good performance for noninvasive prediction of 1p/19q codeletion status in patients with in diffuse lower-grade glioma.