A novel blood based triage test for colorectal cancer in primary care: a pilot study.

Background: The majority of colorectal cancers (CRCs) are detected after symptomatic presentation to primary care. Given the shared symptoms of CRC and benign disorders, it is challenging to manage the risk of missed diagnosis. Colonoscopy resources cannot keep pace with increasing demand.

Optimised Pre-Processing of Raman Spectra for Colorectal Cancer Detection Using High-Performance Computing.

Spectral pre-processing is an essential step in data analysis for biomedical diagnostic applications of Raman spectroscopy, allowing the removal of undesirable spectral contributions that could mask biological information used for diagnosis. However, due to the specificity of pre-processing for a given sample type and the vast number of potential pre-processing combinations, optimisation of pre-processing via a manual "trial and error" format is often time intensive with no guarantee that the chosen method is optimal for the sample type. Here we present the use of high-performance computing (HPC) to trial over 2.

Vibrational Spectroscopy: A Valuable Screening and Diagnostic Tool for Obstetric Disorders?

Preeclampsia (PE) is a common obstetric disorder typically affecting 2-8% of all pregnancies and can lead to several adverse obstetric outcomes for both mother and fetus with the greatest burden of severe outcomes in low middle-income countries (LMICs), therefore, screening for PE is vital. Globally, screening is based on maternal characteristics and medical history which are nonspecific for the disorder. In 2004, the World Health Organization acknowledged that no clinically useful test was able to predict the onset of PE, which prompted a universal search for alternative means of screening.

Integrated assessment of the spatial distribution and structural dynamics of deep benthic marine communities.

Characterizing the spatial distribution and variation of species communities and validating these characteristics with data from the field are key elements for an ecosystem-based approach to management. However, models of species distributions that yield community structure are usually not linked to models of community dynamics, constraining understanding and management of the ecosystem, particularly in data-poor regions. Here we use a qualitative network model to predict changes in Antarctic benthic community structure between major marine habitats characterized largely by seafloor depth and slope, and use multivariate mixture models of species distributions to validate the community dynamics.