脑出血患者炎性细胞外囊泡的特征

Harshal Sawant, Trevor J. Bihl, D. Nguyen, I. Iwuchukwu, J. Bihl
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引用次数: 0

摘要

脑出血是危及生命的主要脑卒中类型之一,死亡率高。脑出血的一个显著特征是涉及白细胞的神经炎症,如中性粒细胞和巨噬细胞。从各种细胞中释放的大细胞外囊泡(lEV)和小细胞外囊泡(sEV)被用作不同疾病的生物标志物。在这里,我们旨在测定脑出血患者不同白细胞的lEV和sEV的浓度/数量,并分析这些lEV/sEV与临床参数的相关性。方法采用串联离心分离法从39例脑出血患者血浆中分离lEV和sEV。采用纳米粒子跟踪分析(NTA, NS300)测定不同白细胞释放lEV/sEV的类型和浓度。特异性抗体CD66b、P2RY12和CD80用于不同的白细胞类型。结果住院时间(R2 = 0.83)与重症监护病房(ICU)住院时间(R2 = 0.88)与lEV、sEV及患者资料(包括低密度脂蛋白(LDL)、脑出血体积等)均存在预测关系。lEV和sEV浓度与MRSV3(90天修正Rankin量表)(R2 = 0.46)和MRSV5(180天修正Rankin量表)(R2 = 0.51)之间存在进一步的预测多元线性回归关系。此外,lEV和sEV浓度与ICU住院时间之间存在轻微的多元线性回归关系(R2 = 0.26),但具有统计学意义(p = 0.0151)。结论本研究发现患者预后与lEV和sEV之间存在预测关系。当与一般收集的患者数据(LDL等)相结合时,lEV和sEV的测量可以强烈预测患者的总体预后。进一步,更大规模的研究应该调查这些影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The profile of inflammatory extracellular vesicles in intracerebral hemorrhage patients
Background Intracerebral hemorrhage (ICH) is one of the leading life-threatening types of strokes with high mortality. A prominent feature of ICH is neuroinflammation involving leukocytes, such as neutrophils and macrophages. Large extracellular vesicles (lEV) and small extracellular vesicles (sEV) released from various cells are used as biomarkers for different diseases. Here, we aimed to determine the concentration/population of lEV and sEV from different leukocytes in ICH patients and analyze the correlation of these lEV/sEV with clinical parameters. Methods lEV and sEV were isolated from the plasma of ICH patients (n = 39) by using the serial centrifuge methods. Nanoparticle tracking analysis (NTA, NS300) was used to determine the type and concentration of different leukocytes-released lEV/sEV. Specific antibodies, CD66b, P2RY12, and CD80 were used for different leukocyte types. Results A predictive relationship between both hospital length of stay (R2 = 0.83) and Intensive care units (ICU) length of stay (R2 = 0.88) was found with lEV and sEV and patient data [including low-density lipoprotein (LDL), ICH volume, etc.]. Further predictive multiple linear regression relationship was seen between lEV and sEV concentrations and MRSV3 (Modified Rankin Scale at 90 days) (R2 = 0.46) and MRSV5 (modified Rankin Scale at 180 days) (R2 = 0.51). Additionally, a slight, but statistically significant (p = 0.0151), multiple linear regression relationship was seen between lEV and sEV concentrations and ICU length of stay (R2 = 0.26). Conclusion This study found predictive relationships between patient outcomes and lEV and sEV. When combined with generally collected patient data (LDL, etc.), measurements of lEV and sEV are strongly predictive of overall patient outcome. Further, larger studies should investigate these effects.
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