The efficacy of traditional Chinese medicine (TCM) treatments for Western medicine (WM) diseases relies heavily on the proper classification of patients into TCM syndrome types. The authors developed a data-driven method for solving the classification problem, where syndrome types were identified and quantified based on statistical patterns detected in unlabeled symptom survey data. The new method is a generalization of latent class analysis (LCA), which has been widely applied in WM research to solve a similar problem, i.e., to identify subtypes of a patient population in the absence of a gold standard. A well-known weakness of LCA is that it makes an unrealistically strong independence assumption. The authors relaxed the assumption by first detecting symptom co-occurrence patterns from survey data and used those statistical patterns instead of the symptoms as features for LCA. This new method consists of six steps: data collection, symptom co-occurrence pattern discovery, statistical pattern interpretation, syndrome identification, syndrome type identification and syndrome type classification. A software package called Lantern has been developed to support the application of the method. The method was illustrated using a data set on vascular mild cognitive impairment.
The MoCA executive tasks are more sensitive in detecting executive dysfunction compared with the MMSE executive tasks. Geriatr Gerontol Int 2017; 17: 2329-2335.
Background
Autophagy is characterized by the degradation of cellular components in autophagosomes. It plays a significant role in cerebral ischemic injury and has a complex functional connection with apoptosis. The neurovascular unit (NVU) is a structural and functional unit of the nervous system presented as a therapeutic target of stroke. This study aimed to investigate the effect of autophagy induced by ischemic damage on NVUs.
Material/Methods
SH-SY5Y cells, C6 cells, and rat brain microvascular endothelial cells were cultured with oxygen-glucose deprivation (OGD) exposure for different time durations, and 3-methyladenine (3-MA) was added as an autophagy inhibitor. In all 3 cell lines, lactate dehydrogenase (LDH) release was measured. Furthermore, apoptosis was detected using Annexin V-fluorescein isothiocyanate/propidium iodide labeling and immunofluorescence staining. Autophagosomes were observed through AO/MDC (acridine orange/monodansycadaverine) double staining. LC3-II expression levels were evaluated by western blot analysis.
Results
In the OGD groups of 3 cell lines, LDH leakage, and apoptotic rates were obviously increased. Remarkable increase in LC3-II expression was found in the OGD groups of SH-SY5Y cells and C6 cells. However, 3-MA decreased the LC3-II expression to varying degrees.
Conclusions
OGD could induce the over-activation of autophagy and augment the apoptotic activity in neurons and glial cells of NVUs.
Background
Apathy is a neuropsychiatric symptom frequently observed in patients with cognitive impairment. It has been found to be a predictor of conversion from mild cognitive impairment (MCI) to dementia of Alzheimer disease type. However, this association between apathy and dementia conversion has not yet been confirmed in vascular MCI, especially post‐stroke MCI. The aim of this study was to evaluate whether apathy would increase the risk of dementia conversion in patients with post‐stroke MCI after 6 months.
Method
A prospective multi‐centre cohort study was performed in 14 clinics in seven provinces and cities of China. A total of 989 subjects were included 2 weeks to 6 months after stroke, and met the diagnostic criteria of International Working Group for MCI. Symptoms of apathy were assessed using the apathy subscale of Geriatric Depression Scale. Subjects were divided into an apathy group (n = 128) and a non‐apathy group (n = 861). The primary outcome was the dementia conversion after 6 months. To eliminate potential biases, subjects were chosen from 861 non‐apathy patients with similarity in seven potential predictors of cognitive impairment to match with the apathy group (n = 128) at a 1:1 ratio, as a matched non‐apathy group (n = 128). The dementia conversion rate was compared between the apathy group (n = 128) and its correspondingly matched non‐apathy group (n = 128), and the relative risk (RR) was calculated.
Results
The prevalence of apathy in post‐stroke MCI was 12.9%. After 6 months, 5.2% of patients with post‐stroke MCI converted to dementia. The dementia conversion rate of the apathy group was significantly higher than that of the non‐apathy group before case‐matching (17.2% vs 3.4%, P < 0.001), and also after case‐matching (17.2% vs 6.3%, P < 0.001). Symptoms of apathy increased the risk of conversion from MCI to dementia (RR 2.75, 95% CI 1.272–5.947, P < 0.001).
Conclusions
For patients with post‐stroke MCI, apathy symptoms increase the risk of conversion from MCI to dementia.
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