Criterion for Assessing Accumulated Neurotoxicity of Alpha-Synuclein Oligomers in Parkinson's Disease

IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Andrey V. Kuznetsov
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Abstract

The paper introduces a parameter called “accumulated neurotoxicity” of α-syn oligomers, which measures the cumulative damage these toxic species inflict on neurons over time, given the years it typically takes for such damage to manifest. A threshold value for accumulated neurotoxicity is estimated, beyond which neuron death is likely. Numerical results suggest that rapid deposition of α-syn oligomers into fibrils minimizes neurotoxicity, indicating that the formation of Lewy bodies might play a neuroprotective role. Strategies such as reducing α-syn monomer production or enhancing degradation can decrease accumulated neurotoxicity. In contrast, slower degradation (reflected by longer half-lives of monomers and free aggregates) increases neurotoxicity, supporting the idea that impaired protein degradation may contribute to Parkinson's disease progression. Accumulated neurotoxicity is highly sensitive to the half-deposition time of free α-syn aggregates into fibrils, exhibiting a sharp increase as it transitions from negligible to elevated levels, indicative of neural damage.

Abstract Image

帕金森病α -突触核蛋白寡聚物累积神经毒性评估标准
这篇论文引入了α-syn低聚物的“累积神经毒性”参数,该参数测量了这些有毒物质对神经元造成的累积损害,考虑到这种损害通常需要数年才能显现出来。估计累积神经毒性的阈值,超过该阈值可能导致神经元死亡。数值结果表明,α-syn低聚物快速沉积到原纤维中可以最大限度地减少神经毒性,这表明路易小体的形成可能具有神经保护作用。减少α-syn单体产生或增强降解等策略可以减少累积的神经毒性。相反,较慢的降解(反映在单体和自由聚集体的半衰期较长)会增加神经毒性,这支持了蛋白质降解受损可能导致帕金森病进展的观点。累积的神经毒性对游离α-syn聚集体进入原纤维的半沉积时间高度敏感,当其从可忽略到升高时表现出急剧增加,表明神经损伤。
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来源期刊
International Journal for Numerical Methods in Biomedical Engineering
International Journal for Numerical Methods in Biomedical Engineering ENGINEERING, BIOMEDICAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
4.50
自引率
9.50%
发文量
103
审稿时长
3 months
期刊介绍: All differential equation based models for biomedical applications and their novel solutions (using either established numerical methods such as finite difference, finite element and finite volume methods or new numerical methods) are within the scope of this journal. Manuscripts with experimental and analytical themes are also welcome if a component of the paper deals with numerical methods. Special cases that may not involve differential equations such as image processing, meshing and artificial intelligence are within the scope. Any research that is broadly linked to the wellbeing of the human body, either directly or indirectly, is also within the scope of this journal.
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