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Zhu XC, Tang BF, Zhu MZ, Lu J, Lin HX, Tang JM, Li R, Ma T. Analysis of complement system and its related factors in Alzheimer's disease. BMC Neurol 2023; 23:446. [PMID: 38114984 PMCID: PMC10729410 DOI: 10.1186/s12883-023-03503-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 12/09/2023] [Indexed: 12/21/2023] Open
Abstract
Alzheimer's disease (AD) is a primary cause of dementia. The complement system is closely related to AD pathology and may be a potential target for the prevention and treatment of AD. In our study, we conducted a bioinformatics analysis to analyze the role of the complement system and its related factors in AD using Gene Expression Omnibus (GEO) data. We also conducted a functional analysis. Our study verified that 23 genes were closely related to differentially expressed complement system genes in diseases after intersecting the disease-related complement system module genes and differentially expressed genes. The STRING database was used to predict the interactions between the modular gene proteins of the differential complement system. A total of 21 gene proteins and 44 interaction pairs showed close interactions. We screened key genes and created a diagnostic model. The predictive effect of the model was constructed using GSE5281 and our study indicated that the predictive effect of the model was good. Our study also showed enriched negative regulation of Notch signaling, cytokine secretion involved in the immune response pathway, and cytokine secretion involved in immune response hormone-mediated apoptotic signaling pathway. We hope that our study provides a promising target to prevent and delay the onset, diagnosis, and treatment of AD.
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Affiliation(s)
- Xi-Chen Zhu
- Department of Neurology, The Wuxi No. 2 People's Hospital, Jiangnan University Medical Center, Wuxi, Jiangsu Province, China.
- Brain Institue, Jiangnan University, Wuxi, Jiangsu Province, China.
- Department of Neurology, The Wuxi No. 2 People's Hospital, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu Province, China.
- Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, No. 68 Zhongshan Road, Wuxi, Jiangsu, 214000, China.
| | - Bin-Feng Tang
- Department of Neurology, The Wuxi No. 2 People's Hospital, Jiangnan University Medical Center, Wuxi, Jiangsu Province, China
| | - Meng-Zhuo Zhu
- Department of Neurology, The Wuxi No. 2 People's Hospital, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu Province, China
| | - Jing Lu
- Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, No. 68 Zhongshan Road, Wuxi, Jiangsu, 214000, China
| | - Han-Xiao Lin
- Department of Neurology, The Wuxi No. 2 People's Hospital, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu Province, China
| | - Jia-Ming Tang
- Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, No. 68 Zhongshan Road, Wuxi, Jiangsu, 214000, China
| | - Rong Li
- Department of Pharmacy, The Affiliated Wuxi No. 2 People's Hospital, Jiangnan University Medical Center, Wuxi, Jiangsu Province, China.
| | - Tao Ma
- Department of Neurology, The Wuxi No. 2 People's Hospital, Jiangnan University Medical Center, Wuxi, Jiangsu Province, China.
- Brain Institue, Jiangnan University, Wuxi, Jiangsu Province, China.
- Department of Neurology, The Wuxi No. 2 People's Hospital, Affiliated Wuxi Clinical College of Nantong University, Wuxi, Jiangsu Province, China.
- Department of Neurology, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, No. 68 Zhongshan Road, Wuxi, Jiangsu, 214000, China.
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Hayashi Y, Noguchi M, Oishi T, Ono T, Okada K, Onuki Y. Application of unsupervised and supervised learning to a material attribute database of tablets produced at two different granulation scales. Int J Pharm 2023; 641:123066. [PMID: 37217121 DOI: 10.1016/j.ijpharm.2023.123066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 05/04/2023] [Accepted: 05/17/2023] [Indexed: 05/24/2023]
Abstract
The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and data were collected according to the design of experiments at different scales. In total, 38 different tablets were prepared, and the tensile strength (TS) and dissolution rate after 10 min (DS10) were measured. In addition, 15 material attributes (MAs) related to particle size distribution, bulk density, elasticity, plasticity, surface properties, and moisture content of granules were evaluated. By using unsupervised learning including principal component analysis and hierarchical cluster analysis, the regions of tablets produced at each scale were visualized. Subsequently, supervised learning with feature selection including partial least squares regression with variable importance in projection and elastic net were applied. The constructed models could predict the TS and DS10 from the MAs and the compression force with high accuracy (R2= 0.777 and 0.748, respectively), independent of scale. In addition, important factors were successfully identified. ML can be used for better understanding of similarity/dissimilarity between scales, for constructing predictive models of critical quality attributes, and for determining critical factors.
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Affiliation(s)
- Yoshihiro Hayashi
- Pharmaceutical Technology Management Department, Production Division, Nichi-Iko Pharmaceutical Co., Ltd, 205-1 Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan; Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani Toyama-shi, Toyama 930-0194, Japan.
| | - Miho Noguchi
- Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani Toyama-shi, Toyama 930-0194, Japan
| | - Takuya Oishi
- Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani Toyama-shi, Toyama 930-0194, Japan
| | - Takashi Ono
- Toyama Pharmaceutical Technology Department, Pharmaceutical Technology, 15 Management Department, Production Division, Nichi-Iko Pharmaceutical Co. Ltd, 205-1, Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan
| | - Kotaro Okada
- Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani Toyama-shi, Toyama 930-0194, Japan
| | - Yoshinori Onuki
- Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research, University of Toyama, 2630 Sugitani Toyama-shi, Toyama 930-0194, Japan
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Xu C, Zhao L, Dong C. A Review of Application of Aβ42/40 Ratio in Diagnosis and Prognosis of Alzheimer’s Disease. J Alzheimers Dis 2022; 90:495-512. [DOI: 10.3233/jad-220673] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The number of patients with Alzheimer’s disease (AD) and non-Alzheimer’s disease (non-AD) has drastically increased over recent decades. The amyloid cascade hypothesis attributes a vital role to amyloid-β protein (Aβ) in the pathogenesis of AD. As the main pathological hallmark of AD, amyloid plaques consist of merely the 42 and 40 amino acid variants of Aβ (Aβ 42 and Aβ 40). The cerebrospinal fluid (CSF) biomarker Aβ 42/40 has been extensively investigated and eventually integrated into important diagnostic tools to support the clinical diagnosis of AD. With the development of highly sensitive assays and technologies, blood-based Aβ 42/40, which was obtained using a minimally invasive and cost-effective method, has been proven to be abnormal in synchrony with CSF biomarker values. This paper presents the recent progress of the CSF Aβ 42/40 ratio and plasma Aβ 42/40 for AD as well as their potential clinical application as diagnostic markers or screening tools for dementia.
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Affiliation(s)
- Chang Xu
- Department of Neurology, the First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Li Zhao
- Department of Neurology, the First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Chunbo Dong
- Department of Neurology, the First Affiliated Hospital, Dalian Medical University, Dalian, China
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Hansen N, Rauter C, Wiltfang J. [Blood Based Biomarker for Optimization of Early and Differential Diagnosis of Alzheimer's Dementia]. FORTSCHRITTE DER NEUROLOGIE-PSYCHIATRIE 2022; 90:326-335. [PMID: 35858611 DOI: 10.1055/a-1839-6237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
AIM Dementia in Alzheimer´s disease is a global challenge. There is growing evidence that investigating blood biomarkers to diagnose Alzheimer´s disease is a promising fast, minimally invasive, and less costly method. The aim of this study was to review available studies on promising biomarkers for Alzheimer´s disease. METHOD The latest studies were collated for this review. RESULTS Immunoassays followed by mass spectrometry and immunomagnetic reduction were reported to be highly relevant methods for detecting amyloid-ß 42 (Aß42) and amyloid-ß 40 (Aß40) to calculate the Aß42/Aß40 ratio, thereby improving the early diagnosis of Alzheimer´s disease. Amyloid-ß (Aß) peptides in blood plasma were considered as potential markers, as they correlated with the brain's Aß pathology. Phosphorylated tau protein 181 (p-tau181), phosphorylated tau protein 217 (p-tau217) and phosphorylated tau protein 231 (p-tau231) in blood samples assessed via Simoa technology served as parameters for the early and differential diagnosis of AD, and were markers of tau pathology in the brain. Neurofilament light chain (Nfl) and glial fibrillary acid protein (GFAP) were additional markers possibly facilitating the assessment of axonal and astroglial brain damage in Alzheimer´s disease. GFAP in blood was useful as an additional marker to detect early and to predict the time course of Alzheimer´s disease. CONCLUSIONS Determining blood biomarkers represents less invasive and less costly diagnostics for Alzheimer´s disease. The investigation of blood biomarkers such as the Aß42/Aß40 ratio, p-tau217, p-tau231, Nfl and GFAP have been promising in establishing the AT(N) classification for Alzheimer´s disease. High-throughput methods should be evaluated in large patient cohort studies and via meta-analyses of studies. Consensus criteria with standard protocols for measuring these biomarkers while considering ethical issues and Alzheimer´s phenotype should unify normative values from different laboratories. The AT(N) classification of Alzheimer´s disease in blood would be a key element towards the implementation of minimally-invasive precision medicine.
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Affiliation(s)
- Niels Hansen
- Klinik für Psychiatrie und Psychotherapie, Universitätsmedizin, Göttingen, Deutschland
| | - Carolin Rauter
- Klinik für Psychiatrie und Psychotherapie, Universitätsmedizin, Göttingen, Deutschland
| | - Jens Wiltfang
- Klinik für Psychiatrie und Psychotherapie, Universitätsmedizin, Göttingen, Deutschland.,Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Göttingen, Deutschland.,Neurosciences and Signaling Group, Biomedizinisches Institut (iBiMED), Abteilung für medizinische Wissenschaft, Universität Aveiro, Aveiro, Portugal
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