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Rui Duan,1,* Si-Yu Wang,1,* Bin Wei,1 Yang Deng,2 Xin-Xin Fu,2 Peng-Yu Gong,1 Yan E,1 Xiao-Jin Sun,2 Hai-Ming Cao,1 Jian-Quan Shi,1 Teng Jiang,1 Ying-Dong Zhang1,2 1Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, People’s Republic of China; 2School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, People’s Republic of China*These authors contributed equally to this workCorrespondence: Teng Jiang; Ying-Dong ZhangDepartment of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68, Changle Road, Nanjing, Jiangsu, 210006, People’s Republic of ChinaEmail jiang_teng@njmu.edu.cn; zhangyingdong@njmu.edu.cnObjective: Emerging evidence suggests that brain angiotensin-(1– 7) (Ang-(1– 7)) deficiency contributes to the pathogenesis of Alzheimer’s disease (AD). Meanwhile, our previous studies revealed that restoration of brain Ang-(1– 7) levels provided neuroprotection by inhibition of inflammatory responses during AD progress. However, the potential molecular mechanisms by which Ang-(1– 7) modulates neuroinflammation remain unclear.Materials and Methods: APP/PS1 mice were injected intraperitoneally with AVE0991 (a nonpeptide analogue of Ang-(1– 7)) once a day for 30 consecutive days. Cognitive functions, neuronal and synaptic integrity, and inflammation-related markers were assessed. Since astrocytes played a crucial role in AD-related neuroinflammation whilst long noncoding RNAs (lncRNAs) were reported to participate in modulating inflammatory responses, astrocytes of APP/PS1 mice were isolated for high-throughput lncRNA sequencing to identify the most differentially expressed lncRNA following AVE0991 treatment. Afterward, the downstream pathways of this lncRNA in the anti-inflammatory action of AVE0991 were investigated using primary astrocytes.Results: AVE0991 rescued spatial cognitive impairments and alleviated neuronal and synaptic damage in APP/PS1 mice. The levels of Aβ1-42 in the brain of APP/PS1 mice were not affected by AVE0991. By employing high-throughput lncRNA sequencing, our in vitro study demonstrated for the first time that AVE0991 suppressed astrocytic NLRP3 inflammasome-mediated neuroinflammation via a lncRNA SNHG14-dependent manner. SNHG14 acted as a sponge of miR-223-3p while NLRP3 represented a direct target of miR-223-3p in astrocytes. In addition, miR-223-3p participated in the AVE0991-induced suppression of astrocytic NLRP3 inflammasome.Conclusion: Our results suggest that Ang-(1– 7) analogue AVE0991 inhibits astrocyte-mediated neuroinflammation via SNHG14/miR-223-3p/NLRP3 pathway and offers neuroprotection in APP/PS1 mice. These findings reveal the underlying mechanisms by which Ang-(1– 7) inhibits neuroinflammation under AD condition and uncover the potential of its nonpeptide analogue AVE0991 in AD treatment.Keywords: Alzheimer’s disease, AVE0991, lncRNAs, SNHG14, miR-223-3p, astrocyte, neuroinflammation

Bian-Rong Wang,1 Hui-Fen Zheng,1 Chang Xu,1 Yi Sun,1 Ying-Dong Zhang,2 Jian-Quan Shi2 1Department of Neurology, Geriatric Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210024, People’s Republic of China; 2Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province 210006, People’s Republic of ChinaCorrespondence: Ying-Dong Zhang; Jian-Quan ShiDepartment of Neurology, Nanjing First Hospital, Nanjing Medical University, No. 68, Changle Road, Nanjing, Jiangsu Province 210006, People’s Republic of ChinaTel +86 0 258 772 6218; +86 0 258 772 6224Email zhangyingdong@aliyun.com; myxiyi_0717@163.comObjective: The aim of this study was to validate the reliability of the Chinese version of Addenbrooke’s Cognitive Examination III (ACE-III) for detecting mild cognitive impairment. Furthermore, the present study compares the diagnostic accuracy of ACE-III with that of Montreal Cognitive Assessment (MoCA).Methods: One hundred and twenty patients with MCI and 136 healthy controls were included in the study. All patients were evaluated by the Chinese version of ACE-III, MoCA and MMSE.Results: Subjects in the control group showed better performance in ACE-III total score and its subdomain scores than those in the MCI group. There was a significantly positive correlation between ACE-III total score and MoCA score. Meanwhile, there was also a significantly positive correlation between ACE-III total score and MMSE score. For ACE-III total score, a cut-off point of 85 yielded a sensitivity of 97.3% and a specificity of 90.7%. The AUC for ACE-III total score was 0.978. For MoCA, a cut-off point of 23 yielded a sensitivity of 86.5% and a specificity of 97.7%. The AUC for MoCA was 0.961. There were no significant differences in diagnostic accuracy between ACE-III and MoCA.Conclusion: The present findings support that both ACE-III and MoCA are useful for detecting MCI in early stages.Keywords: Addenbrooke’s Cognitive Examination III, Montreal Cognitive Assessment, mild cognitive impairment, cognitive screening, Chinese &nbsp

© 1989-2012 IEEE. Spectral analysis is critical in social network analysis. As a vital step of the spectral analysis, the graph construction in many existing works utilizes content data only. Unfortunately, the content data often consists of noisy, sparse, and redundant features, which makes the resulting graph unstable and unreliable. In practice, besides the content data, social network data also contain link information, which provides additional information for graph construction. Some of previous works utilize the link data. However, the link data is often incomplete, which makes the resulting graph incomplete. To address these issues, we propose a novel Distilled Graph Clustering (DGC) method. It pursuits a distilled graph based on both the content data and the link data. The proposed algorithm alternates between two steps: in the feature selection step, it finds the most representative feature subset w.r.t. an intermediate graph initialized with link data; in graph distillation step, the proposed method updates and refines the graph based on only the selected features. The final resulting graph, which is referred to as the distilled graph, is then utilized for spectral clustering on the large-scale social network data. Extensive experiments demonstrate the superiority of the proposed method.