Investigating the impact of a clinical pharmacist on the health outcomes of a paediatric pharmacists

Background: Recent fiscal scrutiny and changes in health care financing have necessitated that health care providers justify a clinical and economical basis for their involvement in patient care. Although clinical pharmacists have been shown to enhance patient health outcomes and reduce costs among adult patients, the impact of a pharmacist in paediatric patient care has not been extensively documented. Method: A team of pharmacists was established to conduct a systematic review of the literature. A title scan of papers in 5 databases was performed by 14 pharmacists using the MeSH terms Pharmacists, Medical Intervention, Paediatrics and Cost-Benefit Analysis. The underpinning research question was: "How do the professional activities of a clinical pharmacist impact the health outcomes of paediatric in-patients?" The abstracts of suitable titles were scanned and articles were read to assess relevance. Relevant articles were then evaluated independently by at least two members of the team, using critical appraisal tools suitable for quantitative, qualitative or systematic review studies. Results: The initial search identified 327 citations which after full text review and application of the scoring tool, resulted in 12 studies included in the systematic review. The average number of interventions reported varied from study to study. Dosing recommendations, pharmacokinetics and drug allergy alerts were the most commonly recorded interventions by pharmacists for a paediatric population. Evidence from this review will be used to formulate improvements to in-patient paediatric care. Conclusion: Clinical pharmacists have a positive impact on inpatient paediatric care

Communication Biophysics

Contains reports on six research projects.National Institutes of Health (Grant 5 PO1 NS13126)National Institutes of Health (Grant 5 RO1 NS18682)National Institutes of Health (Grant 5 RO1 NS20322)National Institutes of Health (Grant 5 R01 NS20269)National Institutes of Health (Grant 5 T32NS 07047)Symbion, Inc.National Science Foundation (Grant BNS 83-19874)National Science Foundation (Grant BNS 83-19887)National Institutes of Health (Grant 6 RO1 NS 12846)National Institutes of Health (Grant 1 RO1 NS 21322

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

A cross-ancestry genome-wide association meta-analysis of amyotrophic lateral sclerosis (ALS) including 29,612 patients with ALS and 122,656 controls identifies 15 risk loci with distinct genetic architectures and neuron-specific biology. Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons

Analysis of shared common genetic risk between amyotrophic lateral sclerosis and epilepsy

Because hyper-excitability has been shown to be a shared pathophysiological mechanism, we used the latest and largest genome-wide studies in amyotrophic lateral sclerosis (n = 36,052) and epilepsy (n = 38,349) to determine genetic overlap between these conditions. First, we showed no significant genetic correlation, also when binned on minor allele frequency. Second, we confirmed the absence of polygenic overlap using genomic risk score analysis. Finally, we did not identify pleiotropic variants in meta-analyses of the 2 diseases. Our findings indicate that amyotrophic lateral sclerosis and epilepsy do not share common genetic risk, showing that hyper-excitability in both disorders has distinct origins

Generic, deformable models for 3-d vehicle surveillance

Vehicle surveillance is the task of measuring moving road vehicles to automatically obtain information about vehicle shape, appearance, identity, path of motion, and, ultimately, driver behavior. While various vehicle sensors exist, none are as versatile as the surveillance camera. Computer vision algorithms can interpret digital images to make a wide variety of vehicle measurements using a single sensor. An ideal algorithm would reconstruct a detailed three-dimensional (3-d) representation of the dynamic traffic scene complete with 3-d vehicle surfaces, trajectories of motion, and identities. Unfortunately, much of the 3-d information is lost during the projection of the world into a 2-d image. As a result, the reconstruction problem is ill-posed. Several researchers have addressed this problem by incorporating prior knowledge about the world to rule out implausible reconstructions. Specifically, in the case of vehicle surveillance, a prior model of 3-d vehicle shape is often used. A constrained alignment of the model to images allows for 3-d shape recovery, tracking, and recognition. Previous 3-d vehicle models are either generic but overly simple or rigid and overly complex. Rigid models represent exactly one vehicle design, so a large collection is needed. A single generic model can deform to a wide variety of shapes, but those shapes have been far too primitive. This thesis presents a new generic 3-d vehicle model that deforms to match a wide variety of passenger vehicles. It is adjustable in complexity between the two extremes. The model is aligned to images by predicting and matching image intensity edges. Novel algorithms are presented for fitting models to images, tracking in video, and learning shape deformation from a collection of detailed rigid models. Experiments compare the proposed model to simple generic models in accuracy and reliability of 3-d shape recovery from images and tracking in video. Standard techniques for recognition are also used to compare the models. The proposed model out performs the existing simple models at each task. Yet, there is still much room for improvement, especially since training data is limited

Learning background and shadow appearance with 3-D vehicle models

This paper presents a novel algorithm for simultaneous background appearance modeling and coarse-scale vehicle recognition in traffic surveillance applications. 3-d mesh models representing a small set of vehicle classes are used to the hypothesize image segmentations into background, shadow, and vehicle regions. The algorithm optimizes vehicle class and motion parameters to best agree with a Hidden Markov Model for the image appearance. The best hypothesis, combined with image data, is used to adapt the parameters of the appearance model. Experiments on real video show that an appearance model trained in this way performs almost as well as one trained using manually segmented images.

Learning Background and Shadow Appearance with 3-D Vehicle Models ∗

This paper presents a novel algorithm for simultaneous background appearance modeling and coarse-scale vehicle recognition in traffic surveillance applications. 3-d mesh models representing a small set of vehicle classes are used to the hypothesize image segmentations into background, shadow, and vehicle regions. The algorithm optimizes vehicle class and motion parameters to best agree with a Hidden Markov Model for the image appearance. The best hypothesis, combined with image data, is used to adapt the parameters of the appearance model. Experiments on real video show that an appearance model trained in this way performs almost as well as one trained using manually segmented images.

Ref # TITB-00244-2006.R2 Automated Retinal Image Analysis over the Internet

Abstract—Retinal clinicians and researchers make extensive use of images, and the current emphasis is on digital imaging of the retinal fundus. The goal of this paper is to introduce a system, known as RIVERS (Retinal Image Vessel Extraction and Registration System), which provides the community of retinal clinicians, researchers, and study directors an integrated suite of advanced digital retinal image analysis tools over the Internet. The capabilities include vasculature tracing and morphometry, joint (simultaneous) montaging of multiple retinal fields, cross-modality registration (color/red-free fundus photographs, and fluorescein angiograms), and generation of flicker animations for visualization of changes from longitudinal image sequences. Each capability has been carefully-validated in our previous research work. The integrated internet-based system can enable significant advances in retina-related clinical diagnosis, visualization of the complete fundus at full resolution from multiple low-angle views, analysis of longitudinal changes, research on the retinal vasculature, and objective, quantitative computer-assisted scoring of clinical trials imagery. It could pave the way for future screening services from optometry facilities. Index Terms—internet-based, registration, alignment, retinal image analysis, vasculature tracing

Analysis of shared common genetic risk between amyotrophic lateral sclerosis and epilepsy

Because hyper-excitability has been shown to be a shared pathophysiological mechanism, we used the latest and largest genome-wide studies in amyotrophic lateral sclerosis (n = 36,052) and epilepsy (n = 38,349) to determine genetic overlap between these conditions. First, we showed no significant genetic correlation, also when binned on minor allele frequency. Second, we confirmed the absence of polygenic overlap using genomic risk score analysis. Finally, we did not identify pleiotropic variants in meta-analyses of the 2 diseases. Our findings indicate that amyotrophic lateral sclerosis and epilepsy do not share common genetic risk, showing that hyper-excitability in both disorders has distinct origins