High Resolution Vascular Imaging of the Rat Spine using Liposomal Blood Pool MR Agent

High resolution, vascular magnetic resonance imaging of the spine region in small animals poses several challenges. The small anatomical features, extravascular diffusion, and the low signal-to-noise ratio limit the use of conventional contrast agents. We hypothesize that a long circulating, intravascular liposomal-encapsulated MR contrast agent (liposomal-Gd) would facilitate visualization of small anatomical features of the perispinal vasculature not visible with conventional contrast agent (Gd-DTPA)

Development of a Clinical Functional Magnetic Resonance Imaging Service

One of the limitations of anatomical based imaging approaches is its relative inability to identify whether specific brain functions may be compromised by the location of brain lesions or contemplated brain surgeries. For this reason, methods for identifying the regions of eloquent brain that should not be disturbed are absolutely critical to the surgeon. By accurately identifying these regions preoperatively, virtually every pre-surgical decision from the surgical approach, operative goals (biopsy, sub-total vs. gross-total resection), and the potential need for awake craniotomy with intraoperative cortical-mapping is affected. Of the many techniques available to the surgeon, functional magnetic resonance imaging (fMRI) has become the primary modality of choice due to the ability of MRI to serve as a “one-stop shop” for assessing both anatomy and functionality of the brain. Given their prevalence, brain tumors serve as the model pathology for the included discussion; however, a similar case can be made for the use of fMRI in other neurological conditions, most notably epilepsy. The value of fMRI was validated in 2007 when the Centers for Medicare and Medicaid Services (CMS) established three new current procedural terminology (CPT) codes for clinical fMRI based upon its use for pre-therapeutic planning. In this article we will discuss the specific requirements for establishing an fMRI program, including specific software and hardware requirements. In addition, the nature of the fMRI CPT codes will be discussed

Defining language networks from resting-state fMRI for surgical planning-a feasibility study

Presurgical language mapping for patients with lesions close to language areas is critical to neurosurgical decision-making for preservation of language function. As a clinical noninvasive imaging technique, functional MRI (fMRI) is used to identify language areas by measuring blood-oxygen-level dependent (BOLD) signal change while patients perform carefully timed language vs. control tasks. This task-based fMRI critically depends on task performance, excluding many patients who have difficulty performing language tasks due to neurologic deficits. On the basis of recent discovery of resting-state fMRI (rs-fMRI), we propose a “task-free” paradigm acquiring fMRI data when patients simply are at rest. This paradigm is less demanding for patients to perform and easier for technologists to administer. We investigated the feasibility of this approach in right-handed healthy control subjects. First, group independent component analysis (ICA) was applied on the training group (14 subjects) to identify group level language components based on expert rating results. Then, four empirically and structurally defined language network templates were assessed for their ability to identify language components from individuals' ICA output of the testing group (18 subjects) based on spatial similarity analysis. Results suggest that it is feasible to extract language activations from rs-fMRI at the individual subject level, and two empirically defined templates (that focuses on frontal language areas and that incorporates both frontal and temporal language areas) demonstrated the best performance. We propose a semi-automated language component identification procedure and discuss the practical concerns and suggestions for this approach to be used in clinical fMRI language mapping

Relationship of cognitive function in patients with schizophrenia in remission to disability: a cross-sectional study in an Indian sample

Background: Cognitive deficits in various domains have been consistently replicated in patients with schizophrenia. Most studies looking at the relationship between cognitive dysfunction and functional disability are from developed countries. Studies from developing countries are few. The purpose of the present study was to compare the neurocognitive function in patients with schizophrenia who were in remission with that of normal controls and to determine if there is a relationship between measures of cognition and functional disability. <p/>Methods: This study was conducted in the Psychiatric Unit of a General Hospital in Mumbai, India. Cognitive function in 25 patients with schizophrenia in remission was compared to 25 normal controls. Remission was confirmed using the brief psychiatric rating scale (BPRS) and scale for the assessment of negative symptoms (SANS). Subjects were administered a battery of cognitive tests covering aspects of memory, executive function and attention. The results obtained were compared between the groups. Correlation analysis was used to look for relationship between illness factors, cognitive function and disability measured using the Indian disability evaluation and assessment scale. <p/>Results: Patients with schizophrenia showed significant deficits on tests of attention, concentration, verbal and visual memory and tests of frontal lobe/executive function. They fared worse on almost all the tests administered compared to normal controls. No relationship was found between age, duration of illness, number of years of education and cognitive function. In addition, we did not find a statistically significant relationship between cognitive function and scores on the disability scale. <p/>Conclusion: The data suggests that persistent cognitive deficits are seen in patients with schizophrenia under remission. The cognitive deficits were not associated with symptomatology and functional disability. It is possible that various factors such as employment and family support reduce disability due to schizophrenia in developing countries like India. Further studies from developing countries are required to explore the relationship between cognitive deficits, functional outcome and the role of socio-cultural variables as protective factors

Tiny Medicine: Nanomaterial-Based Biosensors

Tiny medicine refers to the development of small easy to use devices that can help in the early diagnosis and treatment of disease. Early diagnosis is the key to successfully treating many diseases. Nanomaterial-based biosensors utilize the unique properties of biological and physical nanomaterials to recognize a target molecule and effect transduction of an electronic signal. In general, the advantages of nanomaterial-based biosensors are fast response, small size, high sensitivity, and portability compared to existing large electrodes and sensors. Systems integration is the core technology that enables tiny medicine. Integration of nanomaterials, microfluidics, automatic samplers, and transduction devices on a single chip provides many advantages for point of care devices such as biosensors. Biosensors are also being used as new analytical tools to study medicine. Thus this paper reviews how nanomaterials can be used to build biosensors and how these biosensors can help now and in the future to detect disease and monitor therapies

Design and analysis of supply chain networks using genetic algorithms and numerical clustering

In an increasingly competitive world, a major problem confronted by management is the design of their supply chain networks so as to minimize costs and time to market while meeting stringent customer requirements. Once a design or a set of alternative designs is in place, management is still faced with the task of detailed analysis of their supply chains in a variety of scenarios, involving hundreds or thousands of items. The first portion of this thesis presents a methodology for supply chain design to generate viable network alternatives which can then be subject to further analysis. In the second part of the research, a clustering methodology to identify groups of similar items which can be used to support efficient inventory analysis of supply chains is provided. The approach used for the design of supply chain networks is based on genetic algorithms (GA) where the goal is to identify the set of locations and the flow of material in the network. The objectives included in this model are the minimization of cost and cycle time. Extensive experiments to demonstrate the effectiveness of the approach and the ability to generate diverse design alternatives are presented. The main contributions of this methodology are (i) the ability to consider multiple objectives explicitly, and (ii) the ability to incorporate the stochastic elements inherent in supply chains. The clustering methodology identifies groups of similar items which can then be used to determine very good approximations of inventory levels required to support a given service level. Two distinct features of this approach are the ability to consider items spread across multiple locations and the ability to capture the relationships between the items using a set of heuristics. Examples are provided to demonstrate the effectiveness of the methodology and the performance of the heuristics, by comparing the results obtained with the optimal solution. Applications of this methodology presented in this research include inventory-service level tradeoff analysis, forecast variability analysis, and commonality analysis. This thesis also includes a case study using data drawn from the computer industry to demonstrate the usefulness of the methodology and its advantages over the commonly used ABC classification method

Molecular Imaging of Brain Tumors Using Liposomal Contrast Agents and Nanoparticles

The advent of genomic, proteomic, and high-throughput screening technologies has made available many new targets for brain tumor imaging; however, target availability and accessibility need to be carefully considered when designing imaging probes. Nanoparticles, although largely still only used in preclinical studies, are a versatile tool for targeted imaging of physiologic and molecular aspects of brain tumors through many clinically used modalities. Liposomes can be used to transport diverse payloads in vivo, including contrast agents and drugs, and may be functionalized to increase circulation half-life and achieve targeting specificity. Polymeric, gold, and iron oxide nanoparticles have been used for diverse applications in preclinical studies; however, the utility of other methods, such as quantum dots and self-assembling DNA molecules, is yet to be established