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Clinical records of traditional Chinese medicine (TCM) are documented by TCM doctors during their routine diagnostic work. These records contain abundant knowledge and reflect the clinical experience of TCM doctors. In recent years, with the modernization of TCM clinical practice, these clinical records have begun to be digitized. Data mining (DM) and machine learning (ML) methods provide an opportunity for researchers to discover TCM regularities buried in the large volume of clinical records. There has been some work on this problem. Existing methods have been validated on a limited amount of manually well-structured data. However, the contents of most fields in the clinical records are unstructured. As a result, the previous methods verified on the well-structured data will not work effectively on the free-text clinical records (FCRs), and the FCRs are, consequently, required to be structured in advance. Manually structuring the large volume of TCM FCRs is time-consuming and labor-intensive, but the development of automatic methods for the structuring task is at an early stage. Therefore, in this paper, symptom name recognition (SNR) in the chief complaints, which is one of the important tasks to structure the FCRs of TCM, is carefully studied. The SNR task is reasonably treated as a sequence labeling problem, and several fundamental and practical problems in the SNR task are studied, such as how to adapt a general sequence labeling strategy for the SNR task according to the domain-specific characteristics of the chief complaints and which sequence classifier is more appropriate to solve the SNR task. To answer these questions, a series of elaborate experiments were performed, and the results are explained in detail.

Obsessive-compulsive disorder (OCD) patients have difficulty in switching between obsessive thought and compulsive behavior, which may be related to the dysfunction of the salience network (SN). However, little is known about the changes in intra- and inter- intrinsic functional connectivity (iFC) of the SN in patients with OCD. In this study, we parceled the SN into 19 subregions and investigated iFC changes for each of these subregions in 40 drug-naïve patients with OCD and 40 healthy controls (HCs) using seed-based functional connectivity resting-state functional magnetic resonance imaging (rs-fMRI). We found that patients with OCD exhibited decreased iFC strength between subregions of the SN, as well as decreased inter-network connectivity between SN and DMN, and ECN. These findings highlight a specific alteration in iFC patterns associated with SN in patients with OCD and provide new insights into the dysfunctional brain organization of the SN in patients with OCD.

Background and aims: Cholesterol crystals have been shown to cause inflammation. As a response to cholesterol crystal accumulation, the NLRP3 inflammasome is activated to produce IL-1β which eventually leads to atherosclerotic lesions. As a part of innate immunity, CARD8 is involved in the modulation of above mentioned inflammatory activities. The primary objective of this study was to investigate the association between polymorphism of CARD8 rs2043211 and susceptibility to arteriosclerosis obliterans (ASO) in Chinese Han male population. Methods: 758 male arteriosclerosis obliterans patients and 793 male controls were genotyped for rs2043211 with the TaqMan allele assays. Fasting blood-glucose (FBG), total cholesterol (TC), triglycerides (TG), urea nitrogen, creatinine, Serum uric acid, high density lipoprotein, low density lipoprotein, ALT, AST, and IL-1β in the blood were detected for all subjects. Clinical data were recorded to analyze the genotype-phenotype. Independent samples t-test was used to perform the comparisons between two groups. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to measure the strength of relationship in the genotype distribution and allele frequencies between patients and controls. The analysis of variance was used for a genotype-phenotype analysis of the ASO patients. Results: The genotypic and allelic frequencies in the ASO group were significantly different from that in the control group (P = 0.014 by genotype, P = 0.003 by allele). Those carrying the genotype TT had a higher risk for ASO than those carrying the genotype AA (OR = 1.494, 95%CI1.131-1.974, P = 0.005).The difference was also significant after the adjustment for the history of smoking, TC, LDL, fasting blood glucose, systolic blood pressure and BMI(OR = 1.525, 95%CI1.158-2.009, P = 0.003). Conclusion: Our finding suggests that the polymorphism of CARD8 rs2043211 is probably associated with the development of ASO in Chinese Han male population.