Association between hospice care and psychological outcomes in Alzheimer's spousal caregivers.

CONTEXT Dementia care giving can lead to increased stress, physical and psychosocial morbidity, and mortality. Anecdotal evidence suggests that hospice care provided to people with dementia and their caregivers may buffer caregivers from some of the adverse outcomes associated with family caregiving in Alzheimer's Disease (AD). OBJECTIVES This pilot study examined psychological and physical outcomes among 32 spousal caregivers of patients with AD. It was hypothesized that caregivers who utilized hospice services would demonstrate better outcomes after the death of their spouse than caregivers who did not utilize hospice. METHODS The charts of all spousal caregivers enrolled in a larger longitudinal study from 2001 to 2006 (N=120) were reviewed, and participants whose spouse had died were identified. Of these, those who received hospice care (n=10) were compared to those who did not (n=22) for various physiological and psychological measures of stress, both before and after the death of the care recipient. An Analysis of Covariance (ANCOVA), with postdeath scores as the dependent variable and pre-death scores as covariates, was used for all variables. RESULTS Significant group differences were found in postdeath depressive symptoms (HAM-D; F(1,29)=6.10, p<0.05) and anxiety symptoms (HAM-A; F(1,29)=5.71, p<0.05). Most psychological outcome variables demonstrated moderate effect sizes with a Cohen's d of>0.5 between groups. CONCLUSIONS These data suggest that hospice enrollment may ameliorate the detrimental psychological effects in caregivers who have lost a spouse with Alzheimer's Disease. Based on these pilot data, further prospective investigation is warranted.

Estimation of aortic pressure waveforms from 4D phase-contrast MRI

Several approaches for the non-invasive MRI-based measurement of the aortic pressure waveform over the heart cycle have been proposed in the last years. These methods are normally based on time-resolved, two-dimensional phase-contrast sequences with uni-directionally encoded velocities (2D PC-MRI). In contrast, three-dimensional acquisitions with tridirectional velocity encoding (4D PC-MRI) have been shown to be a suitable data source for detailed investigations of blood flow and spatial blood pressure maps. In order to avoid additional MR acquisitions, it would be advantageous if the aortic pressure waveform could also be computed from this particular form of MRI. Therefore, we propose an approach for the computation of the aortic pressure waveform which can be completely performed using 4D PC-MRI. After the application of a segmentation algorithm, the approach automatically computes the aortic pressure waveform without any manual steps. We show that our method agrees well with catheter measurements in an experimental phantom setup and produces physiologically realistic results in three healthy volunteers.

Personalized Tuning of a Reinforcement Learning Control Algorithm for Glucose Regulation

Artificial pancreas is in the forefront of research towards the automatic insulin infusion for patients with type 1 diabetes. Due to the high inter- and intra-variability of the diabetic population, the need for personalized approaches has been raised. This study presents an adaptive, patient-specific control strategy for glucose regulation based on reinforcement learning and more specifically on the Actor-Critic (AC) learning approach. The control algorithm provides daily updates of the basal rate and insulin-to-carbohydrate (IC) ratio in order to optimize glucose regulation. A method for the automatic and personalized initialization of the control algorithm is designed based on the estimation of the transfer entropy (TE) between insulin and glucose signals. The algorithm has been evaluated in silico in adults, adolescents and children for 10 days. Three scenarios of initialization to i) zero values, ii) random values and iii) TE-based values have been comparatively assessed. The results have shown that when the TE-based initialization is used, the algorithm achieves faster learning with 98%, 90% and 73% in the A+B zones of the Control Variability Grid Analysis for adults, adolescents and children respectively after five days compared to 95%, 78%, 41% for random initialization and 93%, 88%, 41% for zero initial values. Furthermore, in the case of children, the daily Low Blood Glucose Index reduces much faster when the TE-based tuning is applied. The results imply that automatic and personalized tuning based on TE reduces the learning period and improves the overall performance of the AC algorithm.

Nerve Stimulator–Guided Sciatic-Femoral Block in Pet Rabbits (Oryctolagus cuniculus) Undergoing Hind Limb Surgery: A Case Series

This article describes the clinical applicability of a nerve stimulator–guided technique, previously described in dogs, to block the sciatic and the femoral nerves in 4 pet rabbits (Oryctolagus cuniculus) undergoing hind limb surgeries. Preanesthetic intramuscular doses of medetomidine (0.08 mg/kg), ketamine (15 mg/kg), and buprenorphine (0.03 mg/kg) were administered to the rabbit patients. The rabbits were intubated and general anesthesia was maintained using isoflurane in oxygen. The sciatic-femoral nerve block was performed with 2% lidocaine at a volume of 0.05 mL/kg/nerve. Sciatic-femoral block was feasible in rabbits, and the motoric responses following electrical stimulation of both nerves were consistent with those reported in dogs after successful nerve location. Iatrogenic complications, namely nerve damage and local anesthetic toxicity, did not occur. Based on these results, the authors conclude that the sciatic-femoral nerve block described in dogs can be safely performed in rabbits. Clinical trials are required to assess the analgesic efficacy of the combined sciatic-femoral nerve block in rabbits as a part of multimodal pain management.

Prima pars Ebreae grammatices pro indipientibus ...

Johanne Auenario Egrano. Cum praef. Philippi Melanthonis

Development of a specific tracer for metabolic imaging of alveolar echinococcosis: A preclinical study

Positron emission tomography (PET)-computed tomography (CT) using [18F]-fluorodeoxyglucose (FDG) (FDG-PET/CT) is a valuable method for initial staging and follow up of patients with alveolar echinococcosis (AE). However, the cells responsible for FDG uptake have not been clearly identified. The main goal of our study was to evaluate the uptake of PET tracers by the cells involved in the host-parasite reaction around AE lesions as the first step to develop a specific PET tracer that would allow direct assessment of parasite viability in AE. Candidate molecules ([18F]-fluorotyrosine (FET), [18F]-fluorothymidine (FLT), and [18F]-fluorometylcholine (FMC), were compared to FDG by in vitro studies on human leukocytes and parasite vesicles. Our results confirmed that FDG was mainly consumed by immune cells and showed that FLT was the best candidate tracer for parasite metabolism. Indeed, parasite cells exhibited high uptake of FLT. We also performed PET/CT scans in mice infected intraperitoneally with E. multilocularis metacestodes. PET images showed no FDG or FLT uptake in parasitic lesions. This preliminary study assessed the metabolic activity of human leukocytes and AE cells using radiolabeling. Future studies could develop a specific PET tracer for AE lesions to improve lesion detection and echinococcosis treatment in patients. Our results demonstrated that a new animal model is needed for preclinical PET imaging to better mimic human hepatic and/or periparasitic metabolism.

Classification of interstitial lung disease patterns using local DCT features and random forest.

Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.

Targum Onkelos

hrsg. und erläutert von A. Berliner

Multi-Model Data Fusion to Improve an Early Warning System for Hypo-/Hyperglycemic Events

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.

Hemodynamic Performance of Edwards Intuity Valve in a Compliant Aortic Root Model

Numerous designs of bioprosthetic valves exist. The sutureless surgical valve is a newer design concept which combines elements of the transcatheter valve technology with surgical valves. This design aims at shorter and easier implantation. It was the aim of this study to perform hemodynamic and kinematic measurements for this type of valves to serve as a baseline for following studies which investigate the effect of the aortic root on the valve performance. To this end, the Edwards Intuity aortic valve was investigated in a new in vitro flow loop mimicking the left heart. The valve was implanted in a transparent, compliant aortic root model, and the valve kinematics was investigated using a high speed camera together with synchronized hemodynamic measurements of pressures and flows. The valve closure was asynchronous (one by one leaflet), and the valve started to close before the deceleration of the fluid. The aortic root model showed a dilation of the sinuses which was different to the ascending aorta, and the annulus was found to move towards the left ventricle during diastole and towards the aorta during systole.

Liber S. Athanasii De Variis Qvaestionibvs

Nvper È Graeco In Latinvm Tradvctvs, Iohanne Revchlin Interprete