Predicting Breast Cancer Risk for Women Veterans of African Ancestry in the Million Veteran Program.

Breast cancer is a leading cause of cancer and, therefore, a major health threat for women in the United States and worldwide. We have seen over the years major advances in breast cancer prevention and care. Breast cancer screening with mammography leads to reduction in breast cancer mortality, and breast cancer prevention treatment with antiestrogens results in reduction in breast cancer incidence. More progress, however, is urgently needed for this common cancer that affects 1 in 11 American women in their lifetime. Not all women have the same breast cancer risk. A personalized approach is highly desirable as women with higher breast cancer risk may benefit from more intense breast cancer screening and/or prevention intervention while lower risk women may be spared with the cost, inconvenience, and emotional burden of these procedures. In addition to age, demographics, family history, lifestyle, and personal health, genetics is an important determinant of an individual's risk for breast cancer. Over the past 10 years, advances in cancer genomics identified multiple common genetic variants from population studies that collectively can contribute significantly to an individual's breast cancer risk. The effects of these genetic variants can be summarized as a "polygenic risk score" (PRS). We are among the first groups to prospectively evaluate the performance of these risk prediction instruments among women veterans of the Million Veteran Program (MVP). A 313-variant PRS (PRS313) predicted incident breast cancer for a prospective cohort of European (EUR) ancestry women veterans with an area under the receiver operating characteristic curve (AUC) of 0.622. The PRS313 performed less well for AFR ancestry however, with an AUC of 0.579. This is not surprising as most genome-wide association studies were conducted in people of European ancestry. This is an important area of health disparity and unmet need. The large population size and diversity of the MVP provide a unique and important opportunity to explore novel approaches to produce accurate and clinically useful genetic risk prediction instruments for minority populations.

Primary spinal peripheral primitive neuroectodermal tumour: a report of 3 cases and review of the literature.

Peripheral primitive neuroectodermal tumours (PNETs) are rare and highly malignant tumours in the spine, with a predilection for young adults. There are no standard guidelines for treating these tumours. Surgical resection combined with postoperative radiotherapy and chemotherapy is a common and effective treatment at present. Even so, survival time of patients with these tumours is still very short. In this study, we present three rare cases of thoracic epidural PNETs and review the literature.

Role of a mixed probiotic product, VSL#3, in the prevention and treatment of colorectal cancer.

Colorectal cancer (CRC) is a multifactorial disease. The incidence of this type of cancer in younger patients has increased in recent years, and more strategies are needed to prevent and delay the progression of CRC. Probiotics play an adjunctive role in the prevention and treatment of CRC and can not only prevent the onset and delay the progression of disease but also reduce the side effects after the application of anti-cancer drugs. The anti-cancer effect of individual probiotics has been extensively studied, and the exact curative effect of various probiotics has been found, but the anti-cancer effect of mixed probiotics is still not well summarized. In this review, we discuss the positive effects of mixed probiotics on CRC and the related mechanisms of action, especially VSL#3 (VSL Pharmaceuticals, Inc., Gaithersburg, MD, USA), thus providing new ideas for the treatment of CRC. Moreover, we suggest the need to search for more therapeutic possibilities, especially via the research and application of synbiotics and postbiotics.

Machine Learning Based Healthcare System for Investigating the Association Between Depression and Quality of Life.

New technological innovations are changing the future of healthcare system. Identification of factors that are responsible for causing depression may lead to new experiments and treatments. Because depression as a disease is becoming a leading community health concern worldwide. Using machine learning techniques this article presents a complete methodological framework to process and explore the heterogenous data and to better understand the association between factors related to quality of life and depression. Subsequently, the experimental study is mainly divided into two parts. In the first part, a data consolidation process is presented. The relationship of data is formed and to uniquely identify each relation in data the concept of the Secure Hash Algorithm is adopted. Hashing is used to locate and index the actual items in the data. The second part proposed a model using both unsupervised and supervised machine learning techniques. The consolidation approach helped in providing a base for formulation and validation of the research hypothesis. The Self organizing map provided 08 cluster solution and the classification problems were taken from the clustered data to further validate the performance of the posterior probability multi-class Support Vector Machine. The expectations of the importance sampling resulted in factors responsible for causing depression. The proposed model was adopted to improve the classification performance, and the result showed classification accuracy of 91.16%.