Enhancement of feline immunodeficiency virus infection after immunization with envelope glycoprotein subunit vaccines.

Cats were immunized three times with different recombinant feline immunodeficiency virus (FIV) candidate vaccines. Recombinant vaccinia virus (rVV)-expressed envelope glycoprotein with (vGR657) or without (vGR657 x 15) the cleavage site and an FIV envelope bacterial fusion protein (beta-Galactosidase-Env) were incorporated into immune-stimulating complexes or adjuvanted with Quil A. Although all immunized cats developed antibodies against the envelope protein, only the cats vaccinated with the rVV-expressed envelope glycoproteins developed antibodies which neutralized FIV infection of Crandell feline kidney cells. These antibodies failed to neutralize infection of thymocytes with a molecularly cloned homologous FIV. After the third immunization the cats were challenged with homologous FIV. Two weeks after challenge the cell-associated viral load proved to be significantly higher in the cats immunized with vGR657 and vGR657 x 15 than in the other cats. The cats immunized with vGR657 and vGR657 x 15 also developed antibodies against the Gag proteins more rapidly than the cats immunized with beta-Galactosidase-Env or the control cats. This suggested that immunization with rVV-expressed glycoprotein of FIV results in enhanced infectivity of FIV. It was shown that the observed enhancement could be transferred to naive cats with plasma collected at the day of challenge

An expandable approach for design and personalization of digital, just-in-time adaptive interventions

Objective: We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people's individual needs, momentary contexts, and psychosocial variables.Materials and Methods: We propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions.Results: We evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns.Conclusion: While the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.Diabetes mellitus: pathophysiological changes and therap

Segmentation of the heart muscle in 3-D pediatric echocardiographic images.

Contains fulltext : 52135.pdf (publisher's version ) (Closed access)This study aimed to show segmentation of the heart muscle in pediatric echocardiographic images as a preprocessing step for tissue analysis. Transthoracic image sequences (2-D and 3-D volume data, both derived in radiofrequency format, directly after beam forming) were registered in real time from four healthy children over three heart cycles. Three preprocessing methods, based on adaptive filtering, were used to reduce the speckle noise for optimizing the distinction between blood and myocardium, while preserving the sharpness of edges between anatomical structures. The filtering kernel size was linked to the local speckle size and the speckle noise characteristics were considered to define the optimal filter in one of the methods. The filtered 2-D images were thresholded automatically as a first step of segmentation of the endocardial wall. The final segmentation step was achieved by applying a deformable contour algorithm. This segmentation of each 2-D image of the 3-D+time (i.e., 4-D) datasets was related to that of the neighboring images in both time and space. By thus incorporating spatial and temporal information of 3-D ultrasound image sequences, an automated method using image statistics was developed to perform 3-D segmentation of the heart muscle

In vivo validation of cardiac output assessment in non-standard 3D echocardiographic images.

Contains fulltext : 80272.pdf (publisher's version ) (Closed access)Automatic segmentation of the endocardial surface in three-dimensional (3D) echocardiographic images is an important tool to assess left ventricular (LV) geometry and cardiac output (CO). The presence of speckle noise as well as the nonisotropic characteristics of the myocardium impose strong demands on the segmentation algorithm. In the analysis of normal heart geometries of standardized (apical) views, it is advantageous to incorporate a priori knowledge about the shape and appearance of the heart. In contrast, when analyzing abnormal heart geometries, for example in children with congenital malformations, this a priori knowledge about the shape and anatomy of the LV might induce erroneous segmentation results. This study describes a fully automated segmentation method for the analysis of non-standard echocardiographic images, without making strong assumptions on the shape and appearance of the heart. The method was validated in vivo in a piglet model. Real-time 3D echocardiographic image sequences of five piglets were acquired in radiofrequency (rf) format. These ECG-gated full volume images were acquired intra-operatively in a non-standard view. Cardiac blood flow was measured simultaneously by an ultrasound transit time flow probe positioned around the common pulmonary artery. Three-dimensional adaptive filtering using the characteristics of speckle was performed on the demodulated rf data to reduce the influence of speckle noise and to optimize the distinction between blood and myocardium. A gradient-based 3D deformable simplex mesh was then used to segment the endocardial surface. A gradient and a speed force were included as external forces of the model. To balance data fitting and mesh regularity, one fixed set of weighting parameters of internal, gradient and speed forces was used for all data sets. End-diastolic and end-systolic volumes were computed from the segmented endocardial surface. The cardiac output derived from this automatic segmentation was validated quantitatively by comparing it with the CO values measured from the volume flow in the pulmonary artery. Relative bias varied between 0 and -17%, where the nominal accuracy of the flow meter is in the order of 10%. Assuming the CO measurements from the flow probe as a gold standard, excellent correlation (r = 0.99) was observed with the CO estimates obtained from image segmentation

Automated quantification of the pulmonary vasculature in pulmonary embolism and chronic thromboembolic pulmonary hypertension

The shape and distribution of vascular lesions in pulmonary embolism (PE) and chronic thromboembolic pulmonary hypertension (CTEPH) are different. We investigated whether automated quantification of pulmonary vascular morphology and densitometry in arteries and veins imaged by computed tomographic pulmonary angiography (CTPA) could distinguish PE from CTEPH. We analyzed CTPA images from a cohort of 16 PE patients, 6 CTEPH patients, and 15 controls. Pulmonary vessels were extracted with a graph-cut method, and separated into arteries and veins using deep-learning classification. Vascular morphology was quantified by the slope (α) and intercept (β) of the vessel radii distribution. To quantify lung perfusion defects, the median pulmonary vascular density was calculated. By combining these measurements with densities measured in parenchymal areas, pulmonary trunk, and descending aorta, a static perfusion curve was constructed. All separate quantifications were compared between the three groups. No vascular morphology differences were detected in contrast to vascular density values. The median vascular density (interquartile range) was −567 (113), −452 (95), and −470 (323) HU, for the control, PE, and CTEPH group. The static perfusion curves showed different patterns between groups, with a statistically significant difference in aorta-pulmonary trunk gradient between the PE and CTEPH groups (p = 0.008). In this proof of concept study, not vasculature morphology but densities differentiated between patients of three groups. Further technical improvements are needed to allow for accurate differentiation between PE and CTEPH, which in this study was only possible statistically by measuring the density gradient between aorta and pulmonary trunk.</p