Multivariate computational analysis of biosensor's data for improved CD64 quantification for sepsis diagnosis†,Lab on a Chip

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Multivariate computational analysis of biosensor's data for improved CD64 quantification for sepsis diagnosis†
Lab on a Chip ( IF 6.1 ) Pub Date : 2018-03-16 00:00:00 , DOI: 10.1039/c8lc00108a
U. Hassan _{1,

2,

3,

4,

5} , R. Zhu _{5,

6,

7,

8} , R. Bashir _{1,

2,

3,

4,

5}

Affiliation

Sepsis, as a leading cause of death worldwide, relies on systemic inflammatory response syndrome (SIRS) criteria for its diagnosis. SIRS is highly non-specific as it relies on monitoring of patients' vitals for sepsis diagnosis, which are known to change with many confounding factors. Changes in leukocyte counts and CD64 expression levels are known specific biomarkers of pro-inflammatory host response at the onset of sepsis. Recently, we have developed a biosensor chip that can enumerate the leukocyte counts and quantify the neutrophil CD64 expression levels from a drop of blood. We were able to show improved sepsis diagnosis and prognosis in clinical studies by measuring these parameters during different times of the patients' stay in hospital. In this paper, we investigated the rate of cell capture with CD64 expression levels and used this in a multivariate computational model using artificial neural networks (ANNs) and showed improved accuracy of quantifying CD64 expression levels from the biosensor (n = 106 whole blood experiments). We found a high coefficient of determination and low error between biosensor- and flow cytometry-based neutrophil CD64 expression levels using multiple ANN training methods in comparison to those of univariate regression commonly employed. This approach can find many applications in biosensor data analytics by utilizing multiple features of the biosensor's data for output determination.

中文翻译：

对生物传感器数据进行多变量计算分析，以改善CD64定量以进行败血症诊断†

脓毒症是全球主要的死亡原因，其诊断依赖于系统性炎症反应综合征（SIRS）标准。SIRS是高度非特异性的，因为它依赖于监测败血症诊断的生命力，已知败血症会随着许多混杂因素而变化。白细胞计数和CD64表达水平的变化是脓毒症发作前促炎性宿主反应的已知特定生物标志物。最近，我们开发了一种生物传感器芯片，该芯片可以枚举白细胞计数并从一滴血中量化中性粒细胞CD64表达水平。通过在患者住院期间的不同时间测量这些参数，我们能够在临床研究中显示出改善的败血症诊断和预后。在本文中，n = 106个全血实验）。我们发现，与常用的单变量回归方法相比，使用多种ANN训练方法在基于生物传感器和流式细胞仪的中性粒细胞CD64表达水平之间具有较高的确定系数和较低的误差。通过利用生物传感器数据的多个特征进行输出确定，该方法可以在生物传感器数据分析中找到许多应用。

更新日期：2018-03-16

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