Acute Myeloid Leukemia Affects Mouse Sperm Parameters, Spontaneous Acrosome Reaction, and Fertility Capacity.

Leukemia is one of the most common cancers in patients of reproductive age. It is well known that chemotherapy, used as anti-cancer therapy, adversely affects male fertility. Moreover, the negative effect of leukemia on sperm quality, even before chemotherapy treatment, has been reported. However, the mechanisms behind this disease's effect on sperm quality remains unknown. In this study, we examine the direct effect of leukemia and chemotherapy alone and in combination on sperm parameters and male fertility. For this, we developed an acute myeloid leukemia (AML) mouse model (mice were treated with AML cells C1498 and developed leukemia); these mice then received cytarabine chemotherapy. Our findings reveal a significant reduction in sperm concentration and motility and a significant increase in abnormal morphology and spontaneous acrosome reaction of the sperm following AML and chemotherapy treatment, alone and in combination. We also found a reduction in male fertility and the number of delivered offspring. Our results support previous findings that AML impairs sperm parameters and show for the first time that AML increases spontaneous acrosome reaction and decreases male fertility capacity and number of offspring.

Detection of cancer using advanced computerized analysis of infrared spectra of peripheral blood.

We have developed a novel approach for detection of cancer based on biochemical analysis of peripheral blood plasma using Fourier transform infrared spectroscopy. This approach has proven to be quick, safe, minimal invasive, and effective. Our approach recognizes any signs of solid tumor presence, regardless of location in the body or cancer type by measuring a spectrum that gives information regarding the total molecular composition and structure of the peripheral blood samples. The analysis includes clinically relevant preprocessing and feature extraction with principal component analysis, and uses Fisher's linear discriminant analysis to classify between cancer patients and healthy controls. We evaluated our method with leave-one-out cross validation and were able to establish sensitivity of 93.33%, specificity of 87.8%, and overall accuracy of 90.7%. Using our method for cancer detection should result in fewer unnecessary invasive procedures and yield fast detection of solid tumors.