在HIV感染患者中,BDNF和CD4与认知功能的相关性
Carina Shelia Puspitasari1, Fasihah Irfani Fitri1,2, Kiking Ritarwan1,2
1Department of Neurology, Faculty of Medicine, Universitas Sumatera Utara, Medan, Indonesia.
Current HIV research
|January 27, 2026
概括
大脑衍生神经营养因子 (BDNF) 水平与人类免疫缺陷病毒 (HIV) 患者的认知功能相关. 较高的BDNF表明更好的认知表现,表明其作为艾滋病毒相关神经认知障碍 (HAND) 生物标志物的潜力.
科学领域:
- 神经科学是一个神经科学.
- 免疫学 免疫学 免疫学
- 临床医学 临床医学
背景情况:
- 人类免疫缺陷病毒 (HIV) 感染与神经认知障碍有关,称为艾滋病毒相关的神经认知障碍 (HAND).
- 大脑衍生神经营养因子 (BDNF) 在神经可塑性,学习和记忆中起着至关重要的作用,可能会影响手的发育.
- 了解BDNF,CD4计数和认知功能之间的关系对于管理与艾滋病毒相关的神经并发症至关重要.
研究的目的:
- 研究血清BDNF水平,CD4细胞计数和HIV阳性个体的认知功能之间的相关性.
- 为了确定BDNF或CD4水平是否更多地表明抗逆转录病毒疗法患者的神经认知状态.
主要方法:
- 一项涉及58名HIV阳性患者 (18-60岁,CD4≥200细胞/毫米3) 接受抗逆转录病毒治疗的横截面研究.
- 用ELISA测量血清BDNF水平,确定CD4计数,并使用Stroop测试评估认知功能.
- 使用斯皮尔曼的相关性分析来检查变量之间的关系.
主要成果:
- 在BDNF水平和Stroop测试表现之间发现了显著的负相关性 (r = -0.288,p = 0.028),表明较高的BDNF与更好的认知功能有关.
- 没有观察到CD4计数和认知功能之间的显著相关性 (p = 0.336).
- 研究人口的平均年龄为38.77±9.28岁,男性为79.3%.
结论:
- 血清BDNF水平与HIV阳性患者的认知功能有显著的相关性.
- BDNF可以作为评估艾滋病毒中神经认知障碍的潜在生物标志物,特别是HAND.
- 在CD4计数≥200细胞/mm3.3的HIV患者中,CD4计数可能不是神经认知状态的可靠指标.
相关概念视频
02:20Correlations
35.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
35.8K
01:38Cognitive Dissonance
37.5K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
37.5K
01:27Correlation and Causation
42.4K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
42.4K
01:09Correlation
15.1K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
15.1K
00:53Correlation and Regression
3.4K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.4K
01:12Coefficient of Correlation
8.6K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.6K