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- Volume 21, Issue 2, 2024
Current Alzheimer Research - Volume 21, Issue 2, 2024
Volume 21, Issue 2, 2024
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Research Progress of Eye Movement Analyses and its Detection Algorithms in Alzheimer's disease
Authors: Xueying He, Ivan Selesnick and Ming ZhuAlzheimer's disease (AD) has been considered one of the most challenging forms of dementia. The earlier the people are diagnosed with AD, the easier it is for doctors to find a treatment. Based on the previous literature summarizing the research results on the relationship between eye movement and AD before 2013, this paper reviewed 34 original eye movements research papers only closely related to AD published in the past ten years and pointed out that the prosaccade (4 papers) and antisaccade (5 papers) tasks, reading tasks (3 papers), visual search tasks (3 papers) are still the research objects of many researchers, Some researchers have looked at King-Devick tasks (2 papers), reading tasks (3 papers) and special tasks (8 papers), and began to use combinations of different saccade tasks to detect the relationship between eye movement and AD, which had not been done before. These reflect the diversity of eye movement tasks and the complexity and difficulty of the relationship between eye movement and AD. On this basis, the current processing and analysis methods of eye movement datasets are analyzed and discussed in detail, and we note that certain key data that may be especially important for the early diagnosis of AD by using eye movement studies cannot be miss-classified as noise and removed. Finally, we note that the development of methods that can accurately denoise and classify and quickly process massive eye movement data is quite significant for detecting eye movements in early diagnosis of AD.
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Prenatal Exposure to the 1944-45 Dutch Famine and Risk for Dementia up to Age 75: An Analysis of Primary Care Data
Background: A poor prenatal environment adversely affects brain development. Studies investigating long-term consequences of prenatal exposure to the 1944-45 Dutch famine have shown that those exposed to famine in early gestation had poorer selective attention, smaller brain volumes, poorer brain perfusion, older appearing brains, and increased reporting of cognitive problems, all indicative of increased dementia risk. Objective: In the current population-based study, we investigated whether dementia incidence up to age 75 was higher among individuals who had been prenatally exposed to famine. Methods: We included men (n=6,714) and women (n=7,051) from the Nivel Primary Care Database who had been born in seven cities affected by the Dutch famine. We used Cox regression to compare dementia incidence among individuals exposed to famine during late (1,231), mid (1,083), or early gestation (601) with those unexposed (born before or conceived after the famine). Results: We did not observe differences in dementia incidence for those exposed to famine in mid or early gestation compared to those unexposed. Men and women exposed to famine in late gestation had significantly lower dementia rates compared to unexposed individuals (HR 0.52 (95%CI 0.30-0.89)). Sex-specific analyses showed a lower dementia rate in women exposed to famine in late gestation (HR 0.39 (95%CI 0.17-0.86)) but not in men (HR 0.68 (95%CI 0.33-1.41)). Conclusion: Although prenatal exposure to the Dutch famine has previously been associated with measures of accelerated brain aging, the present population-based study did not show increased dementia incidence up to age 75 in those exposed to famine during gestation.
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Plasma Biomarkers in Neurodegenerative Dementias: Unrevealing the Potential of Serum Oxytocin, BDNF, NPTX1, TREM2, TNF-alpha, IL-1 and Prolactin
Authors: Yeşim Olun, Cana Aksoy Poyraz, Melda Bozluolçay, Dildar Konukoğlu and Burç Çağrı PoyrazBackground: Dementia encompasses a range of neurodegenerative disorders characterized by cognitive decline and functional impairment. The identification of reliable biomarkers is essential for accurate diagnosis and gaining insights into the mechanisms underlying diseases. Objective: This study aimed to investigate the plasma biomarker profiles associated with Brain- Derived Neurotrophic Factor (BDNF), Oxytocin, Neuronal Pentraxin-1 (NPTX1), Triggering Receptor Expressed on Myeloid Cells 2 (TREM2), Tumor Necrosis Factor-alpha (TNF-alpha), Interleukin- 1 (IL-1) and Prolactin in Alzheimer's disease (AD), dementia with Lewy bodies (DLB), frontotemporal dementias (FTD) and healthy controls. Methods: Serum levels of the aforementioned biomarkers were analyzed in 23 AD, 28 DLB, 15 FTD patients recruited from outpatient units and 22 healthy controls. Diagnostic evaluations followed established criteria and standardized clinical tests were conducted. Blood samples were collected and analyzed using ELISA and electrochemiluminescence immunoassay methods. Results: Serum BDNF and oxytocin levels did not significantly differ across groups. NPTX1, TREM2, TNF-alpha and IL-1 levels also did not show significant differences among dementia groups. However, prolactin levels exhibited distinct patterns, with lower levels in male DLB patients and higher levels in female AD patients compared to controls. Conclusion: The study findings suggest potential shared mechanisms in dementia pathophysiology and highlight the importance of exploring neuroendocrine responses, particularly in AD and DLB. However, further research is warranted to elucidate the role of these biomarkers in dementia diagnosis and disease progression.
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hdWGCNA and Cellular Communication Identify Active NK Cell Subtypes in Alzheimer's Disease and Screen for Diagnostic Markers through Machine Learning
Authors: Guobin Song, Haoyang Wu, Haiqing Chen, Shengke Zhang, Qingwen Hu, Haotian Lai, Claire Fuller, Guanhu Yang and Hao ChiBackground: Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global health. Its hallmark pathological features include the deposition of β-amyloid plaques and the formation of neurofibrillary tangles. Given this context, it becomes imperative to develop an early and accurate biomarker model for AD diagnosis, employing machine learning and bioinformatics analysis. Methods: In this study, single-cell data analysis was employed to identify cellular subtypes that exhibited significant differences between the diseased and control groups. Following the identification of NK cells, hdWGCNA analysis and cellular communication analysis were conducted to pinpoint NK cell subset with the most robust communication effects. Subsequently, three machine learning algorithms-LASSO, Random Forest, and SVM-RFE-were employed to jointly screen for NK cell subset modular genes highly associated with AD. A logistic regression diagnostic model was then designed based on these characterized genes. Additionally, a protein-protein interaction (PPI) networks of model genes was established. Furthermore, unsupervised cluster analysis was conducted to classify AD subtypes based on the model genes, followed by the analysis of immune infiltration in the different subtypes. Finally, Spearman correlation coefficient analysis was utilized to explore the correlation between model genes and immune cells, as well as inflammatory factors. Results: We have successfully identified three genes (RPLP2, RPSA, and RPL18A) that exhibit a high association with AD. The nomogram based on these genes provides practical assistance in diagnosing and predicting patients' outcomes. The interconnected genes screened through PPI are intricately linked to ribosome metabolism and the COVID-19 pathway. Utilizing the expression of modular genes, unsupervised cluster analysis unveiled three distinct AD subtypes. Particularly noteworthy is subtype C3, characterized by high expression, which correlates with immune cell infiltration and elevated levels of inflammatory factors. Hence, it can be inferred that the establishment of an immune environment in AD patients is closely intertwined with the heightened expression of model genes. Conclusion: This study has not only established a valuable diagnostic model for AD patients but has also delved deeply into the pivotal role of model genes in shaping the immune environment of individuals with AD. These findings offer crucial insights into early AD diagnosis and patient management strategies.
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Effects of Cycloastragenol on Alzheimer's Disease in Rats by Reducing Oxidative Stress, Inflammation, and Apoptosis
Background: As individuals age, they may develop Alzheimer's disease (AD), which is characterized by difficulties in speech, memory loss, and other issues related to neural function. Cycloastragenol is an active ingredient of Astragalus trojanus and has been used to treat inflammation, aging, heart disease, and cancer. Objectives: This study aimed to explore the potential therapeutic benefits of cycloastragenol in rats with experimentally induced AD. Moreover, the underlying molecular mechanisms were also evaluated by measuring Nrf2 and HO-1, which are involved in oxidative stress, NFΚB and TNF-α, which are involved in inflammation, and BCL2, BAX, and caspase-3, which are involved in apoptosis. Methods: Sprague-Dawley rats were given 70 mg/kg of aluminum chloride intraperitoneally daily for six weeks to induce AD. Following AD induction, the rats were given 25 mg/kg of cycloastragenol daily by oral gavage for three weeks. Hippocampal sections were stained with hematoxylin/ eosin and with anti-caspase-3 antibodies. The Nrf2, HO-1, NFΚB, TNF-α, BCL2, BAX, and caspase-3 gene expressions and protein levels in the samples were analyzed. Results: Cycloastragenol significantly improved rats' behavioral test performance. It also strengthened the organization of the hippocampus. Cycloastragenol significantly improved behavioral performance and improved hippocampal structure in rats. It caused a marked decrease in the expression of NFΚB, TNF-α, BAX, and caspase-3, which was associated with an increase in the expression of BCL2, Nrf2, and HO-1. Conclusion: Cycloastragenol improved the structure of the hippocampus in rats with AD. It enhanced the outcomes of behavioral tests, decreased the concentration of AChE in the brain, and exerted antioxidant and anti-inflammatory effects. Antiapoptotic effects were also noted, leading to significant improvements in cognitive function, memory, and behavior in treated rats.
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Volumes & issues
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Volume 21 (2024)
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Volume 20 (2023)
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Volume 19 (2022)
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Volume 18 (2021)
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Volume 17 (2020)
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Volume 16 (2019)
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Volume 15 (2018)
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Volume 14 (2017)
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Volume 13 (2016)
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Volume 12 (2015)
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Volume 11 (2014)
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Volume 10 (2013)
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Volume 9 (2012)
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Volume 8 (2011)
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Volume 7 (2010)
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Volume 6 (2009)
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Volume 5 (2008)
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Volume 4 (2007)
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Volume 3 (2006)
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Volume 2 (2005)
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Volume 1 (2004)
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Cognitive Reserve in Aging
Authors: A. M. Tucker and Y. Stern
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