24 research outputs found

    Characterization of pre-transplant psychosocial burden in an integrated national islet transplant programme

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    The psychological burden experienced by people with diabetes prior to islet transplantation is recognized but has not been studied comprehensively, especially in relation to glycemia. Therefore, we conducted a rigorous pre-operative psychosocial profile of UK islet transplant recipients, and compared groups with higher/lower HbA1 c to test the null hypothesis that pre-transplant hypoglycemia awareness and psychosocial burden would not be related to baseline HbA1 c in this high-risk cohort. Pre-transplant, recipients (n = 44) completed validated hypoglycemia awareness questionnaires and generic/diabetes-specific measures of psychological traits and states. Scores were compared in groups, dichotomized by HbA1 c (≤8% versus >8%). Participants were aged (mean±SD) 53 ± 10 years; 64% were women; with HbA1 c 8.3 ± 1.7%. Median rate of severe hypoglycemia over the preceding 12 months was 13 events/person-year and 90% had impaired awareness of hypoglycemia (Gold/Clarke score ≥4). Participants had elevated fear of hypoglycemia (HFS-II Worry), impaired diabetes-specific quality of life (DQoL) and low generic health status (SF-36; EQ-5D). One quarter reported scores indicating likely anxiety/depression (HAD). Dispositional optimism (LOT-R) and generalized self-efficacy (GSE) were within published ‘norms.’ Despite negative perceptions of diabetes (including low personal control), participants were confident that islet transplantation would help (BIPQ). Hypoglycemia awareness and psychosocial profile were comparable in lower (n = 24) and higher (n = 20) HbA1 c groups. Islet transplant candidates report sub-optimal generic psychological states (anxiety/depressive symptoms), health status and diabetes-specific psychological states (fear of hypoglycemia, diabetes-specific quality of life). While their generic psychological traits (optimism, self-efficacy) are comparable with the general population, they are highly optimistic about forthcoming transplant. HbA1 c is not a proxy measure of psychosocial burden, which requires the use of validated questionnaires to systematically identify those who may benefit most from psychological assessment and support

    The impact of islet mass, number of transplants, and time between transplants on graft function in a national islet transplant program

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    The UK islet allotransplant program is nationally funded to deliver one or two transplants over 12 months to individuals with type 1 diabetes and recurrent severe hypoglycemia. Analyses were undertaken 10 years after program inception to evaluate associations between transplanted mass; single versus two transplants; time between two transplants and graft survival (stimulated C-peptide >50 pmol/L) and function. In total, 84 islet transplant recipients were studied. Uninterrupted graft survival over 12 months was attained in 23 (68%) single and 47 (94%) (p =.002) two transplant recipients (separated by [median (IQR)] 6 (3–8) months). 64% recipients of one or two transplants with uninterrupted function at 12 months sustained graft function at 6 years. Total transplanted mass was associated with Mixed Meal Tolerance Test stimulated C-peptide at 12 months (p <.01). Despite 1.9-fold greater transplanted mass in recipients of two versus one islet infusion (12 218 [9291–15 417] vs. 6442 [5156–7639] IEQ/kg; p <.0001), stimulated C-peptide was not significantly higher. Shorter time between transplants was associated with greater insulin dose reduction at 12 months (beta −0.35; p =.02). Graft survival over the first 12 months was greater in recipients of two versus one islet transplant in the UK program, although function at 1 and 6 years was comparable. Minimizing the interval between 2 islet infusions may maximize cumulative impact on graft function

    Agronomic and Economic Performance Characteristics of Conventional and Low-External-Input Cropping Systems in the Central Corn Belt

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    We conducted a 9-ha field experiment near Boone, IA, to test the hypothesis that yield, weed suppression, and profit characteristics of low-external-input (LEI) cropping systems can match or exceed those of conventional systems. Over a 4-yr period, we compared a conventionally managed 2-yr rotation system {corn (Zea mays L.)/soybean [Glycine max (L.) Merr.]} with two LEI systems: a 3-yr corn/soybean/small grain + red clover (Trifolium pratense L.) rotation, and a 4-yr corn/soybean/small grain + alfalfa (Medicago sativa L.)/alfalfa rotation. Synthetic N fertilizer use was 59 and 74% lower in the 3- and 4-yr systems, respectively, than in the 2-yr system; similarly, herbicide use was reduced 76 and 82% in the 3- and 4-yr systems. Corn and soybean yields were as high or higher in the LEI systems as in the conventional system, and weed biomass in corn and soybean was low (≤4.2 g m−2) in all systems. Experimentally supplemented giant foxtail (Setaria faberi Herrm.) seed densities in the surface 20 cm of soil declined in all systems; supplemented velvetleaf (Abutilon theophrasti Medik.) seed densities declined in the 2- and 4-yr systems and remained unchanged in the 3-yr system. Without subsidy payments, net returns were highest for the 4-yr system (540ha−1yr−1),lowestforthe3−yrsystem(540 ha−1 yr−1), lowest for the 3-yr system (475 ha−1 yr−1), and intermediate for the 2-yr system ($504 ha−1 yr−1). With subsidies, differences among systems in net returns were smaller, as subsidies favored the 2-yr system, but rank order of the systems was maintained

    A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA

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    This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R ^2 = 0.8, RMSE = 115 Mg ha ^−1 , Bias = 2 Mg ha ^−1 ). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R ^2 = 0.8, RMSE = 152 Mg ha ^−1 , Bias = 9 Mg ha ^−1 ), including higher AGB values (>400 Mg ha ^−1 ) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders. Social media abstract Lidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps
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