Breast cancer classification in point-of-care ultrasound imaging-the impact of training data.

Purpose: The survival rate of breast cancer for women in low- and middle-income countries is poor compared with that in high-income countries. Point-of-care ultrasound (POCUS) combined with deep learning could potentially be a suitable solution enabling early detection of breast cancer. We aim to improve a classification network dedicated to classifying POCUS images by comparing different techniques for increasing the amount of training data.

Methacrylate-based copolymers as tunable hosts for triplet-triplet annihilation upconversion.

The ability to convert light to higher energies through triplet-triplet annihilation upconversion (TTA-UC) is attractive for a range of applications including solar energy harvesting, bioimaging and anti-counterfeiting. Practical applications require integration of the TTA-UC chromophores within a suitable host, which leads to a compromise between the high upconversion efficiencies achievable in liquids and the durability of solids. Herein, we present a series of methacrylate copolymers as TTA-UC hosts, in which the glass transition temperature ( ), and hence upconversion efficiency can be tuned by varying the co-monomer ratios (-hexyl methacrylate (HMA) and 2,2,2-trifluoroethyl methacrylate (TFEMA)).

Capnodynamic determination of end-expiratory lung volume in a porcine model of hypoxic pulmonary vasoconstriction.

Purpose: The capnodynamic method, End Expiratory Lung Volume CO (EELV-CO), utilizes exhaled carbon dioxide analysis to estimate End-Expiratory Lung Volume (EELV) and has been validated in both normal lungs and lung injury models. Its performance under systemic hypoxia and variations in CO elimination is not examined. This study aims to validate EELV-CO against inert gas wash in/wash out (EELV- SF6, sulfur hexafluoride) in a porcine model of stable hemodynamic conditions followed by hypoxic pulmonary vasoconstriction and inhaled nitric oxide (iNO).

Validation of the Assessment of Rehabilitation Needs Checklist in a Swedish cancer population.

Background: Assessment of Rehabilitation Needs Checklist (ARNC), has been developed to assess rehabilitation need in cancer patients and is recommended by the Confederation of Regional Cancer Centres in Sweden, known as Hälsoskattningen. The aim of the study was to test the reliability and validity of the ARNC, mainly by comparing it with the Distress thermometer and EORTC QLQ-C30.

Methodology: A sample of 993 persons identified in the Swedish cancer register.