Activity

In the implementation of PSO, the use of a Gamma distribution to govern random movements was shown to improve the convergence rate and stability compared to a uniform distribution. Consequently, Gamma-based PSO with regularization was shown to outperform all other methods tested, including the conventional basis function method and Levenberg-Marquardt algorithm, in terms of its statistical properties. PURPOSE To characterize the dose distribution in water of a novel beta-emitting brachytherapy source for use in a Conformal Superficial Brachytherapy (CSBT) device. METHODS AND MATERIALS Yttrium-90 (90Y) sources were designed for use with a uniquely designed CSBT device. Depth dose and planar dose measurements were performed for bare sources and sources housed within a 3D printed source holder. Monte Carlo simulated dose rate distributions were compared to film-based measurements. Gamma analysis was performed to compare simulated and measured dose rates from seven 90Y sources placed simultaneously using the CSBT device. RESULTS The film-based maximum measured surface dose rate for a bare source in contact with the surface was 3.35 × 10-7 cGy s-1 Bq-1. When placed in the source holder, the maximum measured dose rate was 1.41 × 10-7 cGy s-1 Bq-1. The Monte Carlo simulated depth dose rates were within 10% or 0.02 cm of the measured dose rates for each depth of measurement. The maximum film surface dose rate measured using a seven-source configuration within the CSBT device was 1.78 × 10-7 cGy s-1 Bq-1. Measured and simulated dose rate distribution of the seven-source configuration were compared by gamma analysis and yielded a passing rate of 94.08%. GSK-3 activity The gamma criteria were 3% for dose-difference and 0.07056 cm for distance-to-agreement. The estimated measured dose rate uncertainty was 5.34%. CONCLUSIONS 90Y is a unique source that can be optimally designed for a customized CSBT device. The rapid dose falloff provided a high dose gradient, ideal for treatment of superficial lesions. The dose rate uncertainty of the 90Y-based CSBT device was within acceptable brachytherapy standards and warrants further investigation. Blood oxygen level-dependent (BOLD) MRI is a non-invasive diagnostic method for assessing tissue oxygenation level, by changes in the transverse relaxation time T2*. 3D BOLD imaging of lung tumours is challenging, because respiratory motion can lead to significant image quality degradation. The purpose of this work was to explore the feasibility of a three dimensional (3D) Cartesian multi gradient echo (MGRE) sequence for T2* measurements of non-small cell lung tumours during free-breathing. A non-uniform quasi-random reordering of the pahse encoding lines that allocates more sampling points near the k-space origin resulting in efficient undersampling pattern for parallel imaging was combined with multi echo acquisition and self-gating. In a series of three patients 3D T2* maps of lung carcinomas were generated with isotropic spatial resolution and full tumour coverage at air inhalation and after hyperoxic gas challenge in arbitrary respiratory phases using the proposed self-gated MGRE acquisition. The changes in T2* on the inhalation of hyperoxic gas relative to air were quantified. Significant changes in T2* were observed following oxygen inhalation in the tumour (p  less then  0.02). Thus, the self-gated MGRE sequence can be used for assessment of BOLD signal with isotropic resolution and arbitrary respiratory phases in non-small cell lung cancer. Sphagnum peatlands host a high abundance of protists, especially testate amoebae. Here, we designed a study to investigate the functional diversity of testate amoebae in relation to wetness and forest cover in Baltic bogs. We provided new data on the influence of openness/wetness gradient on testate amoebae communities, showing significant differences in selected testate amoebae (TA) traits. Three key messages emerged from our investigations 1) we recorded an effect of peatland surface openness on testate amoebae functional traits that led us to accept the hypothesis that TA traits differ according to light intensity and hydrology. Mixotrophic species were recorded in high relative abundance in open plots, whereas they were nearly absent in forested sites; 2) we revealed a hydrological threshold for the occurrence of mixotrophic testate amoebae that might be very important in terms of peatland functioning and carbon sink vs. source context; and 3) mixotrophic species with organic tests were nearly absent in forested sites that were dominated by heterotrophic species with agglutinated or idiosomic tests. An important message from this study is that taxonomy of TA rather indicates the hydrological gradient whereas traits of mixotrophs the openness gradient. BACKGROUND To explore attitudes about artificial intelligence (AI) among staff who utilized AI-based clinical decision support (CDS). METHODS A survey was designed to assess staff attitudes about AI-based CDS tools. The survey was anonymously and voluntarily completed by clinical staff in three primary care outpatient clinics before and after implementation of an AI-based CDS system aimed to improve glycemic control in patients with diabetes as part of a quality improvement project. The CDS identified patients at risk for poor glycemic control and generated intervention recommendations intended to reduce patients’ risk. RESULTS Staff completed 45 surveys pre-intervention and 38 post-intervention. Following implementation, staff felt that care was better coordinated (11 favorable responses, 14 unfavorable responses pre-intervention; 21 favorable responses, 3 unfavorable responses post-intervention; p less then 0.01). However, only 14 % of users would recommend the AI-based CDS. Staff feedback revealed that the most favorable aspect of the CDS was that it promoted team dialog about patient needs (N = 14, 52 %), and the least favorable aspect was inadequacy of the interventions recommended by the CDS. CONCLUSIONS AI-based CDS tools that are perceived negatively by staff may reduce staff excitement about AI technology, and hands-on experience with AI may lead to more realistic expectations about the technology’s capabilities. In our setting, although AI-based CDS prompted an interdisciplinary discussion about the needs of patients at high risk for poor glycemic control, the interventions recommended by the CDS were often perceived to be poorly tailored, inappropriate, or not useful. Developers should carefully consider tasks that are best performed by AI and those best performed by the patient’s care team. V.