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Epidemiology and anatomic distribution of colorectal cancer in South Africa.

S Afr J Surg

December 2024

Centre for Global Surgery, Department of Global Health, Stellenbosch University, South Africa.

Background: Colorectal cancer (CRC) is the fifth most common cancer in sub-Saharan Africa (SSA) and the third most common in South Africa (SA). CRC characteristics in SSA are not well described. The aim is to describe patient characteristics and anatomic location of colorectal adenocarcinoma (CRC-AC) in SA.

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Thanks to the plummeting costs of continuously evolving omics analytical platforms, research centers collect multiomics data more routinely. They are, however, confronted with the lack of a versatile software solution to harmoniously analyze single-omics and interpret multiomics data. We have developed iSODA, a web-based application for the analysis of single- and multiomics data.

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Background: Patients with acute myocardial infarction and angiographically obstructive non-culprit lesions are at high risk for recurrent major adverse cardiac events (MACEs). However, it remains largely unknown whether events are due to stenosis severity or due to the underlying high-risk lesion morphology.

Methods: Between January 2017 and December 2021, 1312 patients with acute myocardial infarction underwent optical coherence tomography of all the 3 main epicardial arteries after successful percutaneous coronary intervention.

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Objective: To understand how breast cancer patients experience the surgical decision process and identify strategies surgeons can employ to empower patients to engage in decision-making.

Background: Patient engagement in decision-making is associated with improved patient outcomes. Although, some patients prefer that their healthcare provider drive the decision, the benefits of engaging in decision-making hold true even for patients who prefer to defer to their provider.

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X-ray diffraction is ideal for probing the sub-surface state during complex or rapid thermomechanical loading of crystalline materials. However, challenges arise as the size of diffraction volumes increases due to spatial broadening and because of the inability to deconvolute the effects of different lattice deformation mechanisms. Here, we present a novel approach that uses combinations of physics-based modeling and machine learning to deconvolve thermal and mechanical elastic strains for diffraction data analysis.

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