Investigation of Antiproliferative Effects of Home-Made and Commercial Apple Vinegars on Myeloma Cells

Vinegar is an aqueous food product made by a succession of yeast and acetic acid bacteria activities from fruits that contain high carbohydrates such as apples and grapes. Vinegar has been used as a dietary spice and natural remedy since ancient times due to its therapeutic properties including antimicrobial, antidiabetic, and anticancer activities. It has been shown that some bioactive compounds exhibiting antioxidant activity in vinegars lead to anticancer activity. The aim of the present study was to investigate antiproliferative effect of commercial and home-made apple vinegars in native and neutralized form on myeloma cells. In order to neutralize the vinegars, sodium hydroxide (NaOH) was used. A serial two-fold dilutions of the vinegars (50%, 25%, 12.5%, 6.25%, 3.12%, 1.56%, 0.78%, 0.39%) prepared with cell medium were treated to the cells. The MTT (3-(4.5-Dimethylthiazol-2-yl)-2.5-Diphenyltetrazolium Bromide) assay was used to determine the cellular viability in the cells treated with the vinegars. In this study, while commercial vinegar possessed a stronger antiproliferative activity than home-made vinegar, all native vinegars possessed stronger antiproliferative effect than neutralized vinegars. Interestingly, when home-made vinegar (both native and neutralized) concentrations were from 6.25 to 1.56%, the cell viability increased. Apple vinegar exhibited antiproliferative activity on myeloma cells; however, further studies are required to clarify the mechanisms underlying this activity.

Targeting B-cell maturation antigen increases sensitivity of multiple myeloma cells to MCL-1 inhibition

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Inhibition of Cytosolic Phospholipase A2α Induces Apoptosis in Multiple Myeloma Cells

Cytosolic phospholipase A2α (cPLA2α) is the rate-limiting enzyme in releasing arachidonic acid and biosynthesis of its derivative eicosanoids. Thus, the catalytic activity of cPLA2α plays an important role in cellular metabolism in healthy as well as cancer cells. There is mounting evidence suggesting that cPLA2α is an interesting target for cancer treatment; however, it is unclear which cancers are most relevant for further investigation. Here we report the relative expression of cPLA2α in a variety of cancers and cancer cell lines using publicly available datasets. The profiling of a panel of cancer cell lines representing different tissue origins suggests that hematological malignancies are particularly sensitive to the growth inhibitory effect of cPLA2α inhibition. Several hematological cancers and cancer cell lines overexpressed cPLA2α, including multiple myeloma. Multiple myeloma is an incurable hematological cancer of plasma cells in the bone marrow with an emerging requirement of therapeutic approaches. We show here that two cPLA2α inhibitors AVX420 and AVX002, significantly and dose-dependently reduced the viability of multiple myeloma cells and induced apoptosis in vitro. Our findings implicate cPLA2α activity in the survival of multiple myeloma cells and support further studies into cPLA2α as a potential target for treating hematological cancers, including multiple myeloma.

Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning

Inside the bone marrow, plasma cells are created, and they are a type of white blood cells. They are made from B lymphocytes. Antigens are produced by plasma cells to combat bacteria and viruses and prevent inflammation and illness. Multiple myeloma is a plasma cell cancer that starts in the bone marrow and causes the formation of abnormal plasma cells. Multiple myeloma is firmly identified by examining bone marrow samples under a microscope for myeloma cells. To diagnose myeloma cells, pathologists have to be very selective. Furthermore, because the ultimate decision is based on human sight and opinion, there is a possibility of error in the result. The nobility of this research is that it provides a computer-assisted technique for recognizing and detecting myeloma cells in bone marrow smears. For recognizing purposes, we have used Mask-Recurrent Convolutional Neural Network, and for detection purposes, Efficient Net B3 has been used. There are already many studies on white blood cell cancer, but very few with both segmentation and classification. We have designed two models. One is for recognizing myeloma cells, and the other is for differentiating them from nonmyeloma cells. Also, a new data set has been made from the multiple myeloma data sets, which has been used in our classification model. This research focuses on hybrid segmentation models and increases the accuracy level of the classification model. Both of our models are trained pretty well, where the Mask-RCNN model gives a mean average precision (mAP) of 93% and the Efficient Net B3 model gives 94.68% accuracy. The result of this research indicates that the Mask-RCNN model can recognize multiple myeloma and Efficient Net B3 can distinguish between myeloma and nonmyeloma cells and beats most of the state of the art in myeloma recognition and detection.