Publications by authors named "E M Murage"
Background: Accessible prebiotic foods hold strong potential to jointly target gut health and metabolic health in high-risk patients. The BE GONE trial targeted the gut microbiota of obese surveillance patients with a history of colorectal neoplasia through a straightforward bean intervention.
Methods: This low-risk, non-invasive dietary intervention trial was conducted at MD Anderson Cancer Center (Houston, TX, USA).
Article Synopsis
- Emerging research suggests that gut bacteria (microbiome) may play a role in pancreatic cancer (PaCa) development.
- The study analyzed blood samples from 172 individuals diagnosed with PaCa and 863 matched control samples to explore the relationship between microbial-related metabolites and PaCa risk.
- A panel of microbial and non-microbial metabolites was created to enhance risk prediction for PaCa, identifying individuals at high risk who could benefit from closer monitoring and potential preventive strategies.
Article Synopsis
- Lynch syndrome (LS) is a hereditary condition that increases the risk of colorectal and endometrial cancers, and this study explores the effects of a 12-month aerobic exercise program on LS patients' immune systems.
- The trial involved 21 LS patients who participated in cycling classes three times a week for a year, and the results showed significant improvements in cardiorespiratory fitness and reduced inflammation markers compared to a control group.
- The exercise group experienced changes in immune cell profiles in their colon, suggesting that regular exercise may help lower cancer risk in LS patients by affecting their immune system.
Purpose: To assess the contributions of circulating metabolites for improving upon the performance of the risk of ovarian malignancy algorithm (ROMA) for risk prediction of ovarian cancer among women with ovarian cysts.
Experimental Design: Metabolomic profiling was performed on an initial set of sera from 101 serous and nonserous ovarian cancer cases and 134 individuals with benign pelvic masses (BPM). Using a deep learning model, a panel consisting of seven cancer-related metabolites [diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminate, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid] was developed for distinguishing early-stage ovarian cancer from BPM.
Article Synopsis
- - The study aims to find biomarkers that can predict how triple-negative breast cancer (TNBC) patients will respond to neoadjuvant chemotherapy (NACT), focusing on blood polyamine levels.
- - Researchers discovered that high levels of acetylated polyamines in pre-treatment plasma were linked to TNBC patients with a worse response to NACT, indicating a moderate to extensive tumor burden.
- - By using artificial intelligence, a deep learning model was created to identify a panel of metabolites, including polyamines, which can help predict which TNBC patients are less likely to benefit from NACT and may require alternative treatments.