Cerebral small vessel disease (CSVD) is a prevalent cerebrovascular disease in clinical practice that is often associated with macrovascular disease. A clear understanding of the underlying causes of CSVD remains elusive.

To explore the association between intercellular adhesion molecule-1 (ICAM-1) and blood-brain barrier (BBB) penetration in CSVD.

This study included patients admitted to Fuyang People's Hospital and Fuyang Community (Anhui, China) between December 2021 and March 2022. The study population comprised 142 patients, including 80 in the CSVD group and 62 in the control group. Depression was present in 53 out of 80 patients with CSVD. Multisequence magnetic resonance imaging (MRI) and dynamic contrast-enhanced MRI were applied in patients to determine the brain volume, cortical thickness, and cortical area of each brain region. Moreover, neuropsychological tests including the Hamilton depression scale, mini-mental state examination, and Montreal cognitive assessment basic scores were performed.

The multivariable analysis showed that age [P = 0.011; odds ratio (OR) = 0.930, 95% confidence interval (CI): 0.880-0.983] and ICAM-1 levels (P = 0.023; OR = 1.007, 95%CI: 1.001-1.013) were associated with CSVD. Two regions of interest (ROIs; ROI3 and ROI4) in the white matter showed significant (both P < 0.001; 95%CI: 0.419-0.837 and 0.366-0.878) differences between the two groups, whereas only ROI1 in the gray matter showed significant difference (P = 0.046; 95%CI: 0.007-0.680) between the two groups. ICAM-1 was significantly correlated (all P < 0.05) with cortical thickness in multiple brain regions in the CSVD group.

This study revealed that ICAM-1 levels were independently associated with CSVD. ICAM-1 may be associated with cortical thickness in the brain, predominantly in the white matter, and a significant increase in BBB permeability, proposing the involvement of ICAM-1 in BBB destruction.

The purposes of this pilot randomized controlled trial were to establish feasibility of participant recruitment and engagement in the exercise intervention.

A total of 50 participants with type 2 diabetes were recruited from the study and randomly assigned to an aerobic exercise group or an attention control group.

Our study demonstrated the feasibility of the exercise program, achieving a recruitment rate of 2.94 persons per month, a 76% retention rate, and an 80.56% attendance rate. The adherence rates were 79.54% and 76.48% for the aerobic exercise and attention control groups, respectively. However, we were not able to detect any statistically significant difference between the two groups.

This pilot study established the feasibility of recruiting and engaging middle-aged older adults with type 2 diabetes in aerobic exercise. Based on these findings, a large-scale study assessing the effects of aerobic exercise on cognitive function in this population is needed.

Cerebral small vessel disease (CSVD) is a syndrome of pathology, imaging, and clinical manifestations caused primarily by a variety of functional or structural lesions in the small blood vessels of the brain. CSVD contributes to approximately 45% of dementia and 25% of ischemic strokes worldwide and is one of the most important causes of disability. The disease progresses insidiously, and patients often have no typical symptoms in the early stages, but have an increased risk of stroke, death, and poor long-term prognosis. Therefore, early diagnosis of CSVD is particularly important. Neuroimaging is the most important diagnostic tool used for CSVD. Therefore, it is important to explore the imaging mechanisms of CSVD for its early diagnosis and precise treatment. In this article, we review the principles and analysis methods of DTI, analyze the latest DTI studies on CSVD, clarify the disease-lesion mapping relationships between cerebral white matter (WM) microstructural damage and CSVD, explore the pathogenic mechanisms and preclinical imaging features of CSVD, and summarize the latest research directions of CSVD and research methods to provide a comprehensive and objective imaging basis for the diagnosis and treatment of CSVD.

There are currently no effective prediction methods for evaluating the occurrence of cognitive impairment in patients with cerebral small vessel disease (CSVD).

To investigate the risk factors for cognitive dysfunction in patients with CSVD and to construct a risk prediction model.

A retrospective study was conducted on 227 patients with CSVD. All patients were assessed by brain magnetic resonance imaging (MRI), and the Montreal Cognitive Assessment (MoCA) was used to assess cognitive status. In addition, the patient's medical records were also recorded. The clinical data were divided into a normal cognitive function group and a cognitive impairment group. A MoCA score < 26 (an additional 1 point for education < 12 years) is defined as cognitive dysfunction.

A total of 227 patients (mean age 66.7 ± 6.99 years) with CSVD were included in this study, of whom 68.7% were male and 100 patients (44.1%) developed cognitive impairment. Age (OR = 1.070; 95% CI = 1.015 ~ 1.128, p < 0.05), hypertension (OR = 2.863; 95% CI = 1.438 ~ 5.699, p < 0.05), homocysteine(HCY) (OR = 1.065; 95% CI = 1.005 ~ 1.127, p < 0.05), lacunar infarct score(Lac_score) (OR = 2.732; 95% CI = 1.094 ~ 6.825, P < 0.05), and CSVD total burden (CSVD_score) (OR = 3.823; 95% CI = 1.496 ~ 9.768, P < 0.05) were found to be independent risk factors for cognitive decline in the present study. The above 5 variables were used to construct a nomogram, and the model was internally validated by using bootstrapping with a C-index of 0.839. The external model validation C-index was 0.867.

The nomogram model based on brain MR images and clinical data helps in individualizing the probability of cognitive impairment progression in patients with CSVD.

This study aimed to investigate a variety of machine learning (ML) methods to predict the association between cardiovascular risk factors and coronary artery disease-reporting and data system (CAD-RADS) scores.

This is a retrospective cohort study. Demographical, cardiovascular risk factors and coronary CT angiography (CCTA) characteristics of the patients were obtained. Coronary artery disease (CAD) was evaluated using CAD-RADS score. The stenosis severity component of the CAD-RADS was stratified into two groups: CAD-RADS score 0-2 group and CAD-RADS score 3-5 group. CAD-RADS scores were predicted with random forest (RF), k-nearest neighbors (KNN), support vector machines (SVM), neural network (NN), decision tree classification (DTC) and linear discriminant analysis (LDA). Prediction sensitivity, specificity, accuracy and area under the curve (AUC) were calculated. Feature importance analysis was utilized to find the most important predictors.

A total of 442 CAD patients with CCTA examinations were included in this study. 234 (52.9%) subjects were CAD-RADS score 0-2 group and 208 (47.1%) were CAD-RADS score 3-5 group. CAD-RADS score 3-5 group had a high prevalence of hypertension (66.8%), hyperlipidemia (50%) and diabetes mellitus (DM) (35.1%). Age, systolic blood pressure (SBP), mean arterial pressure, pulse pressure, pulse pressure index, plasma fibrinogen, uric acid and blood urea nitrogen were significantly higher (p < 0.001), and high-density lipoprotein (HDL-C) lower (p < 0.001) in CAD-RADS score 3-5 group compared to the CAD-RADS score 0-2 group. Nineteen features were chosen to train the models. RF (AUC = 0.832) and LDA (AUC = 0.81) outperformed SVM (AUC = 0.772), NN (AUC = 0.773), DTC (AUC = 0.682), KNN (AUC = 0.707). Feature importance analysis indicated that plasma fibrinogen, age and DM contributed most to CAD-RADS scores.

ML algorithms are capable of predicting the correlation between cardiovascular risk factors and CAD-RADS scores with high accuracy.