As a novel class of endogenous RNAs, circRNAs, have a covalently closed continuous loop, with neither a 5’to 3’polarity, nor a polyadenylated tail. Numerous circRNAs have been characterized by abundance, stabilization, conservation, and exhibit tissue/developmental stage-specific expression. Furthermore, circRNAs play vital roles in tumorigenesis and metastasis, such as functioning as a ceRNA or miRNA sponge, interacting with protein and encoding protein. Increasing evidence has revealed that it potentially serves as a required novel biomarker for cancer diagnosis. This review summarized the latest research on circRNAs, including its classification and biogenesis, mechanism and functions, as well as circRNAs in different cancers, as a potential biomarker.

Objective: To explore the effects of different lifestyle choices on mild cognitive impairment (MCI) and to establish a decision tree model to analyse their predictive significance on the incidence of MCI. Methods: Study participants were recruited from geriatric and physical examination centres from October 2015 to October 2019: 330 MCI patients and 295 normal cognitive (NC) patients. Cognitive function was evaluated by the Mini-Mental State Examination Scale (MMSE) and Clinical Dementia Scale (CDR), while the Barthel Index (BI) was used to evaluate life ability. Statistical analysis included the χ² test, logistic regression, and decision tree. The ROC curve was drawn to evaluate the predictive ability of the decision tree model. Results: Logistic regression analysis showed that low education, living alone, smoking, and a high-fat diet were risk factors for MCI, while young age, tea drinking, afternoon naps, social engagement, and hobbies were protective factors for MCI. Social engagement, a high-fat diet, hobbies, living condition, tea drinking, and smoking entered all nodes of the decision tree model, with social engagement as the root node variable. The importance of predictive variables in the decision tree model showed social engagement, a high-fat diet, tea drinking, hobbies, living condition, and smoking as 33.57%, 27.74%, 22.14%, 11.94%, 4.61%, and 0%, respectively. The area under the ROC curve predicted by the decision tree model was 0.827 (95% CI: 0.795∼ 0.856). Conclusion: The decision tree model has good predictive ability. MCI was closely related to lifestyle; social engagement was the most important factor in predicting the occurrence of MCI.

Immune dysregulation has been observed in the brain and blood of patients with Alzheimer’s disease (AD). However, a convenient assay to evaluate peripheral immune dysregulation in AD has not been developed, partly due to the inconsistent observations

Purpose: This study assessed the influences of different lifestyle on mild cognitive impairment (MCI) progression and established a decision tree prediction model to analyse their predictive significance on MCI progression incidence. Patients and Methods: From October 2015 to February 2020,330 patients with MCI were recruited, and demographic and lifestyle information collected. They were followed up for 19.04 ± 10.227 months. Cognitive function was assessed using the Mini-Mental State Examination Scale every 6 months, and they were divided into MCI stable group and MCI progression group. Results: The Kaplan Meier survival analysis showed an overall cohort survival rate of 33.2%; the annual conversion rate of MCI progression was 20%. Physical exercise, social engagement, high-fat diet, age, napping, and tea drinking were decision tree prediction model nodes. Hobbies were the most important factor for predicting MCI progression. The MCI progression probability rates were: with hobbies 26.829% (44 cases), without hobbies 57.831% (96 cases); for those withot hobbies, with physical exercise 43.077% (28 cases) without physical exercise 72.340% (68 cases); for those without hobbies with physical exercise and social engagement 20.000% (4 cases), without social engagement 53.333% (24 cases); for those without hobbies, physical exercises and social engagement and with nap habits 48.485% (16 cases), without nap habits 66.667% (8 cases). The decision tree prediction model AUC for predicting the MCI progression receiver operating characteristic curve was 0.737 (95% confidence interval: 0.685– 0.785) (75.71% sensitivity, 71.75% specificity, P < 0.001. Conclusion: Hobbies, physical exercise, social engagement, napping, and drinking tea can help prevent MCI progression, while a high-fat diet may exacerbate MCI progression. In this study the rule with the lowest MCI progress probability for those who had hobbies, high-fat diet, and social engagement. And the decision tree model had good prediction efficiency.

Introduction: We studied the regulatory mechanism of the habitual brain network in tobacco dependence to provide a theoretical basis for the regulation and cessation of tobacco dependence. Methods: We used resting-state functional magnetic resonance imaging (rs-fMRI) to explore the Fractional amplitude of low-frequency fluctuations (fALFF) and functional connectivity (FC) of the habitual brain network in tobacco-dependent subjects and to evaluate the relationship between the FC level and tobacco selection preference behavior. In total, 29 male tobacco-dependent participants and 28 male nonsmoking participants were recruited. rs-fMRI was used to collect blood oxygen level-dependent signals of the participants in the resting and awake states. After rs-fMRI, all subjects completed cigarette/coin selection tasks (task 1 and task 2). Results: Compared with the control group, the tobacco dependence group showed increased fractional amplitude values of fALFF in the left posterior cingulate cortex and right parahippocampus. FC in the tobacco-dependent group was increased in the right inferior temporal gyrus, left middle frontal gyrus, left cingulated gyrus, and bilateral superior frontal gyrus, compared with that in the control group. Moreover, the preference selection behavior was associated with the enhancement of FC about parts of the brain regions in the habitual brain network of the tobacco-dependent participants. Thus, habitual network activity was significantly enhanced in tobacco-dependent participants in the resting state. Moreover, a positive correlation was found between the cigarette selection preference of the smokers and certain brain regions related to the habitual network. Discussion: This suggests that increased activity of the habitual brain network may be essential in the development of tobacco-dependent behavior.

Background and purpose: According to the classic cognitive behavioral theory proposes, dysfunctional goal‐directed and habit control systems are considered central to the pathogenesis of dependent behavior and impair recovery from addictions. The functional connectivity (FC) of the brain circuits for goal‐directed or habitual behavior has not been clearly reported in tobacco‐dependent groups. Smoking is one of the factors in the formation of atherosclerosis. Studies have shown that the thickness of carotid intima‐media (cIMT) is associated with attention‐executive‐psychomotor functioning. Therefore, we hypothesized whether cIMT in tobacco‐dependent individuals is associated with changes in the FC of the dual‐system network.Methods: A total of 29 male tobacco‐dependent subjects (tobacco‐dependent group) (mean age: 64.20 years, standard deviation [SD]: 4.81 years) underwent resting‐state functional magnetic resonance imaging (rs‐fMRI). Exactly 28 male nonsmokers (control group) (mean age: 61.95 years, SD: 5.52 years) were also recruited to undergo rs‐fMRI. We used the dorsolateral striatum (putamen) and dorsomedial striatum (caudate) as regions of interest for whole‐brain resting‐state connectivity to construct habitual and goal‐directed brain networks, respectively. In addition, all participants were evaluated by carotid artery ultrasound to obtain the cIMT values. Then, we compared the dual‐system brain networks between the tobacco dependence and control groups and the relationship between cIMT and imbalance of dual‐system brain networks in tobacco dependence.Results: The results showed a reduction in the connection between the caudate and precuneus and an increased connection between the putamen and prefrontal cortex; and supplementary motor area. The bilateral connectivity between the caudate and inferior frontal gyrus showed a significant negative correlation with the cIMT, and no positive correlation was observed with cIMT in the brain region that connects to the caudate. However, for the putamen, increased connectivity with the inferior temporal and medial frontal gyri was strongly associated with a high cIMT.Conclusions: The results indicate that the formation of tobacco dependence behavior is related to changes in the dual‐system brain network. Carotid sclerosis is associated with the weakening of the goal‐directed network and enhancement of the habit network in tobacco dependence. This finding suggests that tobacco dependence behavior and clinical vascular diseases are related to changes in brain functional networks.