Wenyan Liu

Lesion location is an important determinant for post-stroke cognitive impairment. Although several 'strategic' brain regions have previously been identified, a comprehensive map of strategic brain regions for post-stroke cognitive impairment is lacking due to limitations in sample size and methodology. We aimed to determine strategic brain regions for post-stroke cognitive impairment by applying multivariate lesion-symptom mapping in a large cohort of 410 acute ischemic stroke patients. Montreal Cognitive Assessment at three to six months after stroke was used to assess global cognitive functioning and cognitive domains (memory, language, attention, executive and visuospatial function). The relation between infarct location and cognition was assessed in multivariate analyses at the voxel-level and the level of regions of interest using support vector regression. These two assumption-free analyses consistently identified the left angular gyrus, left basal ganglia structures and the white matter around the left basal ganglia as strategic structures for global cognitive impairment after stroke. A strategic network involving several overlapping and domain-specific cortical and subcortical structures was identified for each of the cognitive domains. Future studies should aim to develop even more comprehensive infarct location-based models for post-stroke cognitive impairment through multicenter studies including thousands of patients.

BACKGROUND: Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children. METHODS AND FINDINGS: Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ -6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered. CONCLUSIONS: To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia.

BACKGROUND: We hypothesized that chronic brain changes are important substrates for incident dementia after stroke and transient ischemic attack (TIA). METHODS: We compared clinical and imaging features between patients with consecutive stroke/TIA with (n = 88) and without (n = 925) incident dementia at 3 to 6 months after a stroke/TIA. Pittsburg compound B (PiB) positron emission tomography was performed in 50 patients, including those with (n = 37) and without (n = 13) incident dementia. RESULTS: Age, history of diabetes mellitus, severity of white matter changes (WMCs), and medial temporal lobe atrophy (MTLA) were associated with incident dementia. Alzheimer's disease (AD)--like PiB retention was found in 29.7% and 7.7% (P = .032) of patients with and without incident dementia, respectively. CONCLUSIONS: Chronic brain changes including WMCs, MTLA, and AD pathology are associated with incident dementia after stroke/TIA. Interventions targeting these chronic brain changes may reduce burden of vascular cognitive impairment.

BACKGROUND AND PURPOSE: It was recently demonstrated that cerebral microinfarcts (CMIs) can be detected in vivo using 3.0 tesla (T) magnetic resonance imaging. We investigated the prevalence, risk factors, and the longitudinal cognitive consequence of cortical CMIs on 3.0T magnetic resonance imaging, in patients with ischemic stroke or transient ischemic attack. METHODS: A total of 231 patients undergoing 3.0T magnetic resonance imaging were included. Montreal Cognitive Assessment was used to evaluate global cognitive functions and cognitive domains (memory, language, and attention visuospatial and executive functions). Cognitive changes were represented by the difference in Montreal Cognitive Assessment score between baseline and 28-month after stroke/transient ischemic attack. The cross-sectional and longitudinal associations between cortical CMIs and cognitive functions were explored using ANCOVA and regression models. RESULTS: Cortical CMIs were observed in 34 patients (14.7%), including 13 patients with acute (hyperintense on diffusion-weighted imaging) and 21 with chronic CMIs (isointense on diffusion-weighted imaging). Atrial fibrillation was a risk factor for all cortical CMIs (odds ratio, 4.8; 95% confidence interval, 1.5-14.9; P=0.007). Confluent white matter hyperintensities was associated with chronic CMIs (odds ratio, 2.8; 95% confidence interval, 1.0-7.8; P=0.047). The presence of cortical CMIs at baseline was associated with worse visuospatial functions at baseline and decline over 28-month follow-up (β=0.5; 95% confidence interval, 0.1-1.0; P=0.008, adjusting for brain atrophy, white matter hyperintensities, lacunes, and microbleeds). CONCLUSIONS: Cortical CMIs are a common finding in patients with stroke/transient ischemic attack. Associations between CMI with atrial fibrillation and white matter hyperintensities suggest that these lesions have a heterogeneous cause, involving microembolism and cerebral small vessel disease. CMI seemed to preferentially impact visuospatial functions as assessed by a cognitive screening test.

The digital economy has been a great impetus to the sustainable development of enterprises. This study aims to analyze the impact and mechanism of the digital economy on the sustainable development of enterprises of the digital economy on the sustainable development of enterprises and its mechanism. Therefore, on the basis of measuring the level of urban digital economy and the level of sustainable development of enterprises, this study empirically analyzed the impact of the digital economy on enterprise sustainable development and its mechanism by using panel data of 280 A-share listed companies in cities from 2011 to 2019. The research shows that, first, the digital economy and sustainable development of enterprises have obvious spatial differentiation characteristics. Second, the digital economy can significantly promote the sustainable development of enterprises and play a role through regional innovation and entrepreneurship. In addition, compared with the midwest, the promotion effect of the east is more significant. At the provincial level, the promotion effect is better in the developed eastern provinces, such as Jiangsu, Zhejiang and Guangdong. The digital economy promotion effect is more obvious in the Yangtze River Delta, Beijing-Tianjin-Hebei, and other developed urban agglomerations in the east. Therefore, the government can accelerate the development of the digital economy, active regional innovation, and entrepreneurship activities so as to find a way to promote the sustainable development of enterprises.