International Journal of Molecular Medical Science, 2025, Vol.15, No.4, 175-184 http://medscipublisher.com/index.php/ijmms 177 Using standardized cognitive scores and scales (such as CDR, ADAS-cog), consistent classification and tracking of disease progression and changes can be achieved (Soldan et al., 2016; Jessen et al., 2018; Mukherjee et al., 2018). In addition to cognitive and functional criteria, genetic risks (such as the APOE ε4 genotype) and biomarker levels (such as whether amyloid or tau proteins are abnormal) are often considered to more precisely define the population and select participants who are more likely to have a worsening of the condition. Doing so makes it easier to identify meaningful changes and makes the results of early intervention or prevention trials more valuable (Soldan et al., 2016; Jessen et al., 2018; Insel et al., 2019). 3.2 Inclusion and matching principles for the healthy control group Recruiting healthy controls requires strict standards to ensure comparability and reduce interfering factors. The control group was usually people with normal cognition, who were confirmed by neuropsychological tests to have no obvious neurological or mental disorders (Mukherjee et al., 2018). Exclusion criteria typically include: history of drug abuse, severe physical illness, or biomarker evidence of neurodegeneration, to ensure that the control group is a truly healthy benchmark for comparison (Jessen et al., 2018; Insel et al., 2019). The principle of matching is to make the control group and the AD group as close as possible in important demographic characteristics (such as age, gender, and educational level), because these factors may affect cognition and biomarker levels (Soldan et al., 2016; Insel et al., 2019). Some studies also match genetic risk (such as APOE genotype) to further reduce bias and make comparisons between groups more reliable (Jessen et al., 2018; Mukherjee et al., 2018). When analyzing biomarkers and clinical outcomes, such a careful match is of great significance so as to accurately identify the impact of the disease. 3.3 Sample size calculation and statistical efficacy evaluation The number of participants (sample size) needed for AD research depends on the clinical significance of the cognitive or biomarker changes to be detected. The required number of people is usually estimated based on the expected effect size, the fluctuation of outcome indicators, and the speed of disease progression (statistical power calculation). For instance, in early AD trials, to have an 80% chance of detecting a 25% therapeutic effect, up to 2 000 participants may be needed in each group, especially when the disease progresses slowly and there are significant individual differences (Insel et al., 2019; Brookmeyer and Abdalla, 2019; Jutten et al., 2021). Statistical power is also influenced by the study design, such as the duration of follow-up, the number of examinations, and whether age, gender, APOE status, etc. are considered in the analysis model (Zhang, 2024). Nowadays, multi-state models and longitudinal mixed-effects models are often used to handle the complex progression process of AD and optimize the calculation of sample size. These methods can help ensure that the research can detect the true effect, while taking into account the issues of personnel withdrawal and individual differences in the research (Insel et al., 2019; Brookmeyer and Abdalla, 2019; Jutten et al., 2021). 4 Exosome Isolation, miRNA Analysis and Quality Control Methods 4.1 Blood collection and exosome isolation/identification Blood is usually collected using standard methods to reduce sample differences. Plasma or serum needs to be isolated in a timely manner to protect exosomes and mirnas within them (Sanz-Rubio et al., 2018; Malla et al., 2018). Common methods for isolating exosomes from blood include ultracentrifugation, commercial kit precipitation or chromatographic separation. Each method has its own advantages and disadvantages: ultracentrifugation has the highest purity (gold standard), while commercial kits are convenient to operate and have high yields, but they may be mixed with other proteins (Taylor et al., 2011; Cheng et al., 2014; Rekker et al., 2014; Ding et al., 2018; Bhadra and Sachan, 2024). After separation, exosomes need to be identified to confirm their type and purity. Transmission electron microscopy (TEM) is used to observe the unique "cup-shaped" morphology of exosomes. Nanoparticle tracking (NTA) measures particle size (generally between 40~150 nanometers) and quantity (Ding et al., 2018). Western
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