An Alternative Approach to Detecting Cancer Cells by Multi-Directional Fluorescence Detection System Using Cost-Effective LED and Photodiode.

The enumeration of cellular proliferation by covering from hemocytometer to flow cytometer is an important procedure in the study of cancer development. For example, hemocytometer has been popularly employed to perform manual cell counting. It is easily achieved at a low-cost, however, manual cell counting is labor-intensive and prone to error for a large number of cells. On the other hand, flow cytometer is a highly sophisticated instrument in biomedical and clinical research fields. It provides detailed physical parameters of fluorescently labeled single cells or micro-sized particles depending on the fluorescence characteristics of the target sample. Generally, optical setup to detect fluorescence uses a laser, dichroic filter, and photomultiplier tube as a light source, optical filter, and photodetector, respectively. These components are assembled to set up an instrument to measure the amount of scattering light from the target particle; however, these components are costly, bulky, and have limitations in selecting diverse fluorescence dyes. Moreover, they require multiple refined and expensive modules such as cooling or pumping systems. Thus, alternative cost-effective components have been intensively developed. In this study, a low-cost and miniaturized fluorescence detection system is proposed, i.e., costing less than 100 US dollars, which is customizable by a 3D printer and light source/filter/sensor operating at a specific wavelength using a light-emitting diode with a photodiode, which can be freely replaceable. The fluorescence detection system can quantify multi-directional scattering lights simultaneously from the fluorescently labeled cervical cancer cells. Linear regression was applied to the acquired fluorescence intensities, and excellent linear correlations (R2 > 0.9) were observed. In addition, the enumeration of the cells using hemocytometer to determine its performance accuracy was analyzed by Student's t-test, and no statistically significant difference was found. Therefore, different cell concentrations are reversely calculated, and the system can provide a rapid and cost-effective alternative to commercial hemocytometer for live cell or microparticle counting.

RKF-PCA: robust kernel fuzzy PCA.

Principal component analysis (PCA) is a mathematical method that reduces the dimensionality of the data while retaining most of the variation in the data. Although PCA has been applied in many areas successfully, it suffers from sensitivity to noise and is limited to linear principal components. The noise sensitivity problem comes from the least-squares measure used in PCA and the limitation to linear components originates from the fact that PCA uses an affine transform defined by eigenvectors of the covariance matrix and the mean of the data. In this paper, a robust kernel PCA method that extends the kernel PCA and uses fuzzy memberships is introduced to tackle the two problems simultaneously. We first introduce an iterative method to find robust principal components, called Robust Fuzzy PCA (RF-PCA), which has a connection with robust statistics and entropy regularization. The RF-PCA method is then extended to a non-linear one, Robust Kernel Fuzzy PCA (RKF-PCA), using kernels. The modified kernel used in the RKF-PCA satisfies the Mercer's condition, which means that the derivation of the K-PCA is also valid for the RKF-PCA. Formal analyses and experimental results suggest that the RKF-PCA is an efficient non-linear dimension reduction method and is more noise-robust than the original kernel PCA.