Vernonia amygdalina Delile Induces Apoptotic Effects of PC3 Cells: Implication in the Prevention of Prostate Cancer.

Background: Prostate cancer (PCa) is one of the common cancers in males and its incidence keeps increasing globally. Approximately 81% of PCa is diagnosed during the early stage of the disease. The treatment options for prostate care include surgery, radiotherapy, and chemotherapy, but these treatments often have side effects that may lead to issues such as impotence or decreased bowel function. Our central goal is to test the apoptotic effects of Vernonia amygdalina Delile (an edible medicinal plant that is relatively inexpensive, nontoxic, and virtually without side effects) for the prevention of PCa using human adenocarcinoma (PC-3) cells as a test model. Methods: To address our central goal, PC-3 cells were treated with Vernonia amygdalina Delile (VAD). Cell cycle arrest and cell apoptosis were evaluated by Flow Cytometry assessment. Nucleosomal DNA fragmentation was detected by agarose gel electrophoresis. Results: Flow cytometry data showed that VAD induced cell cycle arrest at the G0/G1 checkpoint and significantly upregulated caspase-3 in treated cells compared to the control cells. Agarose gel electrophoresis resulted in the formation of DNA ladders in VAD-treated cells. Conclusions: These results suggest that inhibition of cancer cell growth, induction of cell cycle arrest, and apoptosis through caspase-3 activation and nucleosomal DNA fragmentation are involved in the therapeutic mechanisms of VAD as a candidate drug towards the prevention and/or treatment of PCa.

Improving Invasive Breast Cancer Care Using Machine Learning Technology.

Breast cancer (BC) is the most common malignancy in women worldwide. In the United States, the lifetime risk of developing an invasive form of breast cancer is 12.5% among women. BC arises in the lining cells (epithelium) of the ducts or lobules in the glandular tissue of the breast. The goal of the present study was to use machine learning (ML) as a novel technology to assess and compare the invasive forms of BC including, infiltrating ductal carcinoma, infiltrating lobular carcinoma, and mucinous carcinoma. To achieve this goal, we used ML algorithms and collected a dataset of 334 BC patients available at https://www.kaggle.com/amandam1/breastcancerdataset and interpreted this dataset based on the form of BC, age, sex, tumor stages, surgery type, and survival rate. Among the 334 patients, 70% were diagnosed with infiltrating ductal carcinoma, 27% with infiltrating lobular carcinoma, and 3% with mucinous carcinoma. Overall, out of 334 BC patients: 64 (19.16%) were in stage I, 189 (56.59%) in stage II, and 81 (24.25%) in stage III. Sixty-six, 67, 96, and 105 patients underwent lumpectomy, simple mastectomy, modified radical mastectomy, and other types of surgery, respectively. The survival rates were 83.4% for stage I, 79.1% for stage II, and 77% for stage III. Findings from the present study demonstrated that ML provides an important tool to curate large amount of BC data, as well as a scientific means to improve BC outcomes.

Application of Machine Learning Algorithms in Breast Cancer Diagnosis and Classification.

Breast cancer continues to be the most frequent cancer in females, affecting about one in 8 women and causing the highest number of cancer-related deaths in females worldwide despite remarkable progress in early diagnosis, screening, and patient management. All breast lesions are not malignant, and all the benign lesions do not progress to cancer. However, the accuracy of diagnosis can be increased by a combination or preoperative tests such as physical examination, mammography, fine-needle aspiration cytology, and core needle biopsy. Despite some limitations, these procedures are more accurate, reliable, and acceptable, when compared with a single adopted diagnostic procedure. Recent studies have shown that breast cancer can be accurately predicted and diagnosed using machine learning (ML) technology. The objective of this study was to explore the application of ML approaches to classify breast cancer based on feature values generated from a digitized image of a fine-needle aspiration (FNA) of a breast mass. To achieve this objective, we used ML algorithms, collected a scientific dataset of 569 breast cancer patients from Kaggle (https://www.kaggle.com/uciml/breast-cancer-wisconsin-data), analyze and interpreted the data based on ten real-valued features of a breast mass FNA including the radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension. Among the 569 patients tested, 63% were diagnosed with benign breast cancer and 37% were diagnosed with malignant breast cancer. Benign tumors grow slowly and do not spread while malignant tumors grow rapidly and spread to other parts of the body.

Prostate Cancer Disparity, Chemoprevention, and Treatment by Specific Medicinal Plants.

Prostate cancer (PC) is one of the most common cancers in men. The global burden of this disease is rising. Its incidence and mortality rates are higher in African American (AA) men compared to white men and other ethnic groups. The treatment decisions for PC are based exclusively on histological architecture, prostate-specific antigen (PSA) levels, and local disease state. Despite advances in screening for and early detection of PC, a large percentage of men continue to be diagnosed with metastatic disease including about 20% of men affected with a high mortality rate within the African American population. As such, this population group may benefit from edible natural products that are safe with a low cost. Hence, the central goal of this article is to highlight PC disparity associated with nutritional factors and highlight chemo-preventive agents from medicinal plants that are more likely to reduce PC. To reach this central goal, we searched the PubMed Central database and the Google Scholar website for relevant papers. Our search results revealed that there are significant improvements in PC statistics among white men and other ethnic groups. However, its mortality rate remains significantly high among AA men. In addition, there are limited studies that have addressed the benefits of medicinal plants as chemo-preventive agents for PC treatment, especially among AA men. This review paper addresses this knowledge gap by discussing PC disparity associated with nutritional factors and highlighting the biomedical significance of three medicinal plants (curcumin, garlic, and Vernonia amygdalina) that show a great potential to prevent/treat PC, as well as to reduce its incidence/prevalence and mortality, improve survival rate, and reduce PC-related health disparity.

NOVEL CELLULAR STAINING PROTOCOL AND ANTIPROLIFERATIVE EFFECT OF VERNONIA AMYGDALINA DELILE ON LUNG AND PROSTATE CANCER CELLS.

There is a critical need for more effective therapeutic approaches for cancer. Vernonia amygdalina Delile (VAD) has been used in African traditional medicine for the prevention and/or treatment of several diseases including diarrhea, intestinal illnesses, and cancer. However, the effects of VAD on human lung cancer and human prostate cancer cells remain largely unknown. The aim of this study was to explore a novel cellular staining protocol using acridine orange/propidium iodide (AO/PI) and to test the antiproferative activity of VAD against human lung cancer (A-549) cells and human prostate cancer (PC-3) cells. Our studies demonstrate that VAD inhibits the proliferation of both A-549 and PC-3 cells in a dose-dependent manner. This finding suggests that VAD may be useful in lung and prostate cancer prevention. However, further research is needed to elucidate the chemopreventive effects of VAD against cancer.