## 4 Discussion
Thus, we anticipated high values of GLIE fluctuation parameters in these cells. This concept was experimentally tested in two models of lymphocytes. The first are malignant Jurkat cells. These cells are derived from a human cell line source, which originated from the peripheral blood of a T-cell leukemia patient. These cells were compared to primary T cells as a second model of benign human lymphocytes (see Material and Methods). Although the microscope illumination was the same during the experiment, the received light intensity and average gray level in these two cells types were different. This is probably due to different optical characteristics of these two types of cells. Since the parameters \(a\) and average GLIE were found to be sensitive to the illuminating light intensity, they had to be adjusted in order to neutralize the effect of the difference in light intensity (see Section 3.3 in Results). As expected, GLIE fluctuation parameters \(b\), \(SSE\) and \(SA\), as well as the adjusted parameter \(a\), were all found to be higher in Jurkat malignant cells in comparison to primary T cells (Figure 12a-d). Malignant cells, with their high intracellular dynamics and mechanical work, are expected to have more homogeneous content. Indeed, the average adjusted GLIE values were lower in malignant Jurkat cells relative to benign primary T cells (Figure 12e). Malignant and benign lymphocytes look generally similar (e.g., in Figure 9). Thus, a pathologist faces a substantial clinical challenge to accurately differentiate these two types of cells according to morphology-based criteria. For that, an accessory biophysical (rather than morphological-based) diagnostic tool may be beneficial. We present an initial evaluation of the accuracy of GLIE fluctuation parameters in differentiating malignant Jurkat cells from primary T cells, utilizing binary logistic regression with a ROC curve (Figure 13e). The percentage of correct classifications for single malignant cell detection (in a single cell examination) was 86% and AUC 0.92. This accuracy may seem relatively low, but it has to be considered in the appropriate clinical setup of malignancy investigation where usually hundreds of suspected cells are examined. Under such conditions, the basic accuracy level for a single cell examination will rise considerably. For instance, the modest increase in the number of the examined cells to 10 similar cells increases the percentage of correct classifications to 95% and AUC to 0.997. PMA is a Protein Kinas C (PKC) activator that induces cell proliferation and differentiation. Anti-CD3\(\varepsilon\) antibodies are known activators of T cells. Applying either of these conditions lowered GLIE fluctuation parameters in primary T cells. It is possible that the high dynamics in malignant cells that is reflected by the high GLIE fluctuations is related to the low differentiation state of these cells. PMA, which causes the primary T cells to differentiate, may have lowered GLIE fluctuations in these cells. This encouraging result indicates that GLIE fluctuation parameters could accurately diagnose malignancy across a wide range of physiological conditions that relate to cell activation, proliferation or differentiation. GLIE fluctuation analysis utilizing ASD and power-fit parameters requires a simple experimental setup of regular bright field microscope and label-free cell samples. The analysis could be easily automated. It further provides quick results that reflect biophysical cell parameters as intracellular motion and homogeneity. Thus, this analysis enables the study of cellular conditions of energetic imbalance as mitochondrial dysfunction and enables the accurate differentiation of malignant from benign lymphocytes. Further development of this tool may assist in research and clinical evaluation and diagnostics of malignancy and possibly other relevant clinical pathologies.