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.

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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.