Lab on a Chip · 2011 · 253 citations · 35 references
EngineeringBiomedical EngineeringOncologyBiosensorsEarly DetectionSensorsCancer BiomarkersCancer DetectionEarly Cancer DetectionPoint-of-care BiosensingPoint-of-care Diagnostic DevicesTumor MarkersBiosensing PlatformsCancer Biomarker BiosensorsCancer Biomarker DetectionOvarian Cancer DetectionOvarian Cancer Early DetectionOvarian Tumor BiomarkersPortable Diagnostic DevicesUrine-based Cancer Detection
Ovarian cancer is asymptomatic in the early stages and most patients present with advanced levels of disease. The lack of cost-effective methods that can achieve frequent, simple and non-invasive testing hinders early detection and causes high mortality in ovarian cancer patients. Here, we report a simple and inexpensive microchip ELISA-based detection module that employs a portable detection system, i.e., a cell phone/charge-coupled device (CCD) to quantify an ovarian cancer biomarker, HE4, in urine. Integration of a mobile application with a cell phone enabled immediate processing of microchip ELISA results, which eliminated the need for a bulky, expensive spectrophotometer. The HE4 level detected by a cell phone or a lensless CCD system was significantly elevated in urine samples from cancer patients (n = 19) than healthy controls (n = 20) (p < 0.001). Receiver operating characteristic (ROC) analyses showed that the microchip ELISA coupled with a cell phone running an automated analysis mobile application had a sensitivity of 89.5% at a specificity of 90%. Under the same specificity, the microchip ELISA coupled with a CCD had a sensitivity of 84.2%. In conclusion, integration of microchip ELISA with cell phone/CCD-based colorimetric measurement technology can be used to detect HE4 biomarker at the point-of-care (POC), paving the way to create bedside technologies for diagnostics and treatment monitoring.
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The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma.
Ingegerd Hellström, John Raycraft, Martha Hayden-Ledbetter et al. · PubMed · 2003 · 719 citations
Mobile Phone Based Clinical Microscopy for Global Health Applications
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