Borregaardkirkeby4916

The most common metastatic site of breast cancer is the bone. Metastatic bone disease can alter the integrity of the bone and cause serious complications, thereby greatly reducing health-related quality of life and leading to high medical costs. Although diagnostic methods and treatments for bone metastases (BM) are improving, some patients with early breast cancer who are at high risk of BM are not diagnosed early enough, leading to delayed intervention. Moreover, whole-body scintigraphy cannot easily distinguish BM from non-malignant bone diseases. To circumvent these issues, specific gene and protein biomarkers are being investigated for their potential to predict, diagnose, and evaluate breast cancer prognosis. In this review, we summarized the current biomarkers associated with BM in breast cancer and their role in clinical applications to assist in the diagnosis and treatment of BM in the future.Breast cancer affects 1 in 8 women globally, and is the leading cause of cancer-related deaths in female patients. The majority of breast cancer cases are of unknown cause; few are linked to genetic predisposition, and some arise sporadically. Finding the cause of these sporadic cases is an important area in cancer research. Investigations into the microbiome show links between microbiome dysbiosis and breast cancer, with possible mechanisms in the association of the microbiome and breast cancer, including estrogen metabolism and the 'oestrobolome,' immune regulation, propensity for obesity, and the regulation of the tumor microenvironment. This paper reviews the literature and discusses the potential implications of links between the microbiome and breast cancer, and concludes that the microbiome may have significant applications as a biomarker for breast cancer diagnosis, prognosis, and management. Further investigation is crucial, since modification of the microbiome can, at the most basic level, be achieved via dietary modification.Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain between 2008-2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. PU-H71 The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5.A growing number of international standards promote Healthy Built Environment (HBE) principles which aim to enhance occupant and user health and wellbeing. Few studies examine the implementation of these standards; whether and how they affect health through changes to built-environment design, construction, and operations. This study reviews a set of sustainability and HBE standards, based on a qualitative analysis of standard documents, standard and socio-technical literature on normalization and negotiation, and interviews with 31 practitioners from four geographical regions. The analysis indicates that standards can impact individual, organizational, and market-scale definitions of an HBE. Some changes to practice are identified, such as procurement and internal layout decisions. There is more limited evidence of changes to dominant, short-term decision-making practices related to cost control and user engagement in operational decisions. HBE standards risk establishing narrow definitions of health and wellbeing focused on building occupants rather than promoting broader, contextually situated, principles of equity, inclusion, and ecosystem functioning crucial for health. There is a need to improve sustainability and HBE standards to take better account of local contexts and promote systems thinking. Further examination of dominant collective negotiation processes is required to identify opportunities to better embed standards within organizational practice.The fundamental basis of how arboviruses evolve in nature and what regulates the adaptive process remain unclear. To address this problem, we established a Zika virus (ZIKV) vector-borne transmission system in immunocompromised mice to study the evolutionary characteristics of ZIKV infection. Using this system, we defined factors that influence the evolutionary landscape of ZIKV infection and show that transmission route and specific organ microenvironments impact viral diversity and defective viral genome production. In addition, we identified in mice the emergence of ZIKV mutants previously seen in natural infections, including variants present in currently circulating Asian and American strains, as well as mutations unique to the mouse infections. With these studies, we have established an insect-to-mouse transmission model to study ZIKV evolution in vivo. We also defined how organ microenvironments and infection route impact the ZIKV evolutionary landscape, providing a deeper understanding of the factors that regulate arbovirus evolution and emergence.