Nanotechnology for colorectal cancer detection and treatment

Colorectal cancer (CRC) is the third most diagnosed cancer and the second leading cause of cancer-related mortality in the United States. Across the globe, people in the age group older than 50 are at a higher risk of CRC. Genetic and environmental risk factors play a significant role in the development of CRC. If detected early, CRC is preventable and treatable. Currently, available screening methods and therapies for CRC treatment reduce the incidence rate among the population, but the micrometastasis of cancer may lead to recurrence. Therefore, the challenge is to develop an alternative therapy to overcome this complication. Nanotechnology plays a vital role in cancer treatment and offers targeted chemotherapies directly and selectively to cancer cells, with enhanced therapeutic efficacy. Additionally, nanotechnology elevates the chances of patient survival in comparison to traditional chemotherapies. The potential of nanoparticles includes that they may be used simultaneously for diagnosis and treatment. These exciting properties of nanoparticles have enticed researchers worldwide to unveil their use in early CRC detection and as effective treatment. This review discusses contemporary methods of CRC screening and therapies for CRC treatment, while the primary focus is on the theranostic approach of nanotechnology in CRC treatment and its prospects. In addition, this review aims to provide knowledge on the advancement of nanotechnology in CRC and as a starting point for researchers to think about new therapeutic approaches using nanotechnology.     

Education Outcomes of Children Born Late Preterm: A Retrospective Whole-Population Cohort Study

Maternal and Child Health Journal - Early life exposures can have an impact on a child’s developmental trajectory and children born late preterm (34–36 weeks gestational age)...     

Bone Microarchitecture Phenotypes Identified in Older Adults Are Associated With Different Levels of Osteoporotic Fracture Risk

Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).