Paper
19 February 2010 Rapid optical heating of blood for clinical point-of-care diagnostics
Brian E. Catanzaro, Ted Hill, Steve Hankins, Kent Gandola
Author Affiliations +
Abstract
Clinical testing of human blood requires adherence to a number of regulatory standards, including maintaining a temperature that is representative of the human body (e.g. 37 C). The economics of private and public healthcare drives blood assays to be conducted using low cost, disposable assay devices that also eliminate the possibility of cross contamination. Unfortunately, the materials that meet the economic and disposable constraints of the marketplace are thermal insulators, not ideal for rapid heating. We present a novel means of optically heating blood samples in plastic assay devices within a time period suitable for point-of-care use. The novel approach uses LED's in the red portion of the visible spectrum. The lower absorption of optical radiation in the visible spectrum enables the absorption of energy deep into the assay device. This produces even heating, avoiding the gradients that can occur by surface heating (conduction) or surface absorption (highly absorbing wavelengths). Analytical and computational models will be discussed. A specific application to a point-of-care blood assay instrument will be reviewed. In this application, optical heating was achieved using a small array of high brightness LED's. Experimental results will be discussed. The experimental results with this instrument validated the predictions.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Brian E. Catanzaro, Ted Hill, Steve Hankins, and Kent Gandola "Rapid optical heating of blood for clinical point-of-care diagnostics", Proc. SPIE 7555, Advanced Biomedical and Clinical Diagnostic Systems VIII, 75550S (19 February 2010); https://doi.org/10.1117/12.842881
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KEYWORDS
Blood

Light emitting diodes

Absorption

Point-of-care devices

Visible radiation

Diagnostics

Statistical modeling

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