The University of Texas Health Science Center at Houston is a comprehensive academic health center created in 1972 by The UT System Board of Regents. UTHealth is located in the Texas Medical Center, considered the largest medical center in the world. It is composed of six schools: UTHealth Medical School, The University of Texas Graduate School of Biomedical science, UTHealth School of Dentistry, UTHealth School of Nursing, UTHealth School of Biomedical Informatics, and UTHealth School of Public Health. UTHealth faculty have been instrumental in pioneering the use of Tissue plasminogen activator and the development of Life Flight. Wikipedia.
Cao B.,University of Texas Health Science Center at Houston
Neuropsychopharmacology | Year: 2017
Previous studies have found increased levels of choline-containing compounds (ie, glycerophosphocholine plus phosphocholine (GPC+PC)) in bipolar disorder using in vivo proton magnetic resonance spectroscopy (1H MRS), especially in bipolar I disorder (BD-I). Increased levels of GPC+PC suggest alterations in the membrane phospholipids metabolism in bipolar disorder. Rapid cycling (RC) bipolar disorder is considered as a severe course of bipolar disorder, but it is unclear whether rapid cycling bipolar disorder is linked to highly altered membrane phospholipid metabolism. The purpose of this study was to investigate whether the regional extent of elevated GPC+PC were greater in BD-I patients with rapid cycling compared to BD-I patients without rapid cycling and healthy controls. Using a multi-voxel 1H MRS approach at 3 Tesla with high spatial resolution and absolute quantification, GPC+PC levels from the anterior cingulate cortex (ACC), caudate and putamen of 16 RC BD-I, 34 non-RC BD-I and 44 healthy controls were assessed. We found significantly elevated GPC+PC levels in ACC, putamen and caudate of RC BD-I patients compared to healthy controls (P<0.005) and in ACC compared to non-RC BD-I patients (P<0.05). These results suggest greater alteration of membrane phospholipid metabolisms in rapid cycling BD-I compared to non-rapid-cycling BD-I.Neuropsychopharmacology advance online publication, 29 March 2017; doi:10.1038/npp.2017.39. © 2017 American College of Neuropsychopharmacology
News Article | April 17, 2017
Culminating a nearly 10 year effort, researchers have determined the atomic resolution structure of a key molecule that translates signals from a cell's local environment into a language that the cell can understand and use. The determination of the architecture of the Inositol Tris-Phosphate Receptor (IP3R) had long been considered a major goal in biomedical research because of its strategic role inside cells as a molecular train station for transferring signals that control many cell functions. The structure is expected to contribute to the development of better therapeutic approaches for many diseases. The work was conducted by a team at RIKEN Brain Science Institute under the direction of Professor Katsuhiko Mikoshiba, whose laboratory cloned the first IP3R gene in 1989. In all living cells, chemical signals are harnessed for intracellular communication. The inositol 1,4,5-trisphosphate (IP3) is one such signal that binds to the IP3 receptor (IP3R) to release calcium ions (Ca2+) from intracellular Ca2+ stores such as the endoplasmic reticulum. The IP3R-embedded Ca2+ stores are distributed in various microdomains within cells and have pivotal roles in processes as diverse as neural communication, differentiation, plasticity, and metabolism. Of the three genes identified, the brain dominant type 1 IP3R (IP3R1) is genetically causative for spinocerebellar ataxia 15/16/29 and Gillespie syndrome, and regulates cellular waste disposal processes implicated in the etiology of neurodegenerative diseases including Alzheimer's disease. Although the important roles of IP3R in normal and disease conditions are well known, understanding how IP3 signals trigger the opening of the Ca2+ channel was elusive. The new IP3R1 crystal structure reveals a rich cosmos of atomic scale details on its function. IP3R1 is a micromachine of 20 nm in diameter that contains two functional sub-structures, an IP3 binding site and a Ca2+ channel pore. The distance from the IP3-binding site to the channel pore is 7 nm, the longest among similar ion channels, and the fundamental question of how IP3-binding physically opens the channel from a long range has been unanswered in the decades since the gene was cloned. X-ray crystallography of the large cytosolic domain of a mouse IP3R1 in the absence and presence of IP3, at the RIKEN SPring-8 ion beam factory, pinpointed a long-range mechanism involving an IP3-dependent global movement of a part of the receptor called the curvature α-helical domain that serves as a bridge between the cytosolic and channel domains. Mutagenesis of this bridge revealed the essential role of a leaflet structure in the α-helical domain that relays IP3 signals to the channel, and may help to explain how long-range coupling from IP3 binding to the Ca2+ channel occurs. The findings reveal similarities and differences with a recently published report on the IP3R using a completely different method called cryo-electron microscopy. In the related study, a group led by Irina Serysheva from the University of Texas Health Science Center at Houston proposed that channel activation by IP3 may occur by direct binding of the C-terminus and IP3-binding domain and coupling from the IP3-binding domain to neighboring subunits. The current data disagree with these conclusions, instead suggesting that IP3-binding site to the leaflet region underlies the dynamic structural changes by IP3. A comparison of the two structures reveals agreement on an immobile part of the curvature helical domain and a variable arrangement of other helical domains. The authors hypothesize that the immobile section would act as a rigid-body conducting a torque from IP3-binding sites to the channel domain, whereas the flexible regions would contribute to the dynamic properties of IP3R function. Resolving the long-standing mystery of long-range communication that allows IP3 to open the channel will aid future rational drug design targeting the receptor that could allow a more diverse range of therapeutic avenues. The findings may also clarify IP3R roles in cellular senescence and tumor suppression linked to selective vulnerability of cancer cells. Surprisingly, the study also clarifies a role of IP3Rs in the function of pathogenic unicellular organisms like Trypanosoma cruzi, the parasite of Chagas disease, and brucei, that causes African trypanosomiasis or sleeping sickness. The team identified an amino acid sequence in the leaflet that is conserved in parasites, suggesting structural insights that may assist in drug discovery for these devastating conditions. Hamada K, Miyatake H, Terauchi A, Mikoshiba K (2017) IP3-mediated gating mechanism of the IP3 receptor revealed by mutagenesis and X-ray crystallography. Proceedings of the National Academy of Sciences USA doi: 10.1073/pnas1701420114
News Article | April 25, 2017
Engineering students have created a system designed to prevent seizures caused by epilepsy, a neurological disorder affecting millions. First, the team needed to develop a seizure-prediction algorithm. The students created a machine-learning algorithm that was “very good” at predicting seizures: It predicted all seizures in their data set at least two minutes before their onset with 3.9 false positives per hour. The team then transferred this prediction algorithm to custom hardware that runs on patient data. “What our system is trying to do is predict and prevent seizures in real time,” says Sarah Hooper, a senior electrical engineering major at Rice University. “The first stage of our system is to record neural activity from the brain. That activity is then sent to our piece of hardware, which has the algorithm that produces a seizure prediction. “Using the electrical signals that are produced in the brain, we can predict if a seizure is going to occur in the next five minutes or so.” Hooper says that if a seizure were about to occur, the hardware would then communicate back to electrodes implanted in the brain to apply electrical neurostimulation, which can actually stop the seizure before it occurs. The current hardware is a large computer board, but the team says the hardware could shrink to the size of a chip for implantation on the patient’s skull. This chip will communicate wirelessly with intracranial electrodes, which are also being designed. “Three years ago, the project was basically an idea,” says Erik Biegert, an electrical engineering student. “About one-third of the three million epilepsy patients in the United States don’t respond to anti-seizure medications. The only option left for those patients is to undergo surgery to remove the part of the brain that is the issue; we hope to replace that option with something a lot less invasive.” “In terms of next steps, I think that what mostly needs to be done is work on how the device is actually going to interact with the input electrodes and how it would pass on its output to actual neurostimulators,” says Randy Zhang, a senior electrical engineering major. Zhang says the team is preparing an academic paper on the project. “What we really focused on this year was to create the processing unit and all of the machine learning intelligence that can make this happen. On a higher level, the next steps could be to flesh out the design and move it onto a silicon chip so it can be created into an actual device,” he adds. “This is a work in progress, and we’re just scratching the surface,” says the team’s faculty adviser Behnaam Aazhang, a professor of electrical and computer engineering. “This is at least three to five to seven years away from a product that could begin the clinical trials process, and then there is forming a business partnership, along with the whole FDA approvals process.” The project is part of Rice’s Vertically Integrated Projects (VIP) program. The National Science Foundation is the sponsor of the broader research project that underlies VIP. Nitin Tandon, a neurosurgeon at University of Texas Health Science Center at Houston and co-principal investigator of the NSF project, provided real intracranial patient seizure data for the project as well as technical advice and specifications.
News Article | April 29, 2017
Epilepsy is a neurological disorder that affects millions of people, regardless of age, but has yet to discover why it happens and how to effectively stop it. So far, most epilepsy patients manage their seizures with prescription drugs or by using cannabis. While scientists continue to study minibrains in order to understand the origins of epilepsy and other neurological disorders, however, a team of engineering students from Rice University developed an algorithm that could effectively predict an oncoming seizure and prevent it through neurostimulation. The Ictal Inhibitor project's goal is to replace invasive methods of treating epilepsy so they focused on developing a machine learning algorithm that could successfully predict oncoming seizures in real time. "About one-third of the 3 million epilepsy patients in the United States don't respond to anti-seizure medications. The only option left for those patients is to undergo surgery to remove the part of the brain that is the issue; we hope to replace that option with something a lot less invasive," Erik Biegert, a graduating member of Team Ictal Inhibitor, said. The team tested the algorithm afterwards using data sets from real intracranial patient seizure data supplied by renowned neurosurgeon and project co-investigator, Dr. Nitin Tandon. After running the algorithm and testing it on the data sets, the group found that the program was able to predict oncoming seizures at least two minutes prior, though it also produced 3.9 false positive results per hour. After getting favorable results from their algorithm, the team hooked up the program to custom-made hardware, which includes electrodes implanted in the brain. "Using the electrical signals that are produced in the brain, we can predict if a seizure is going to occur in the next five minutes or so," Sarah Hooper, a senior electrical engineering major explained. Hooper further explained that, using their group's program, the electrodes keep track of brain activity as the program runs and, if a seizure were about to occur, the hardware would communicate with the electrode to apply electrical neurostimulation and prevent the attack from happening. The project is still using a huge computer board as its hardware but the team claims the project will continue and may work on transforming the hardware into a small, wireless chip that can be implanted in the brain. "What we really focused on this year was to create the processing unit and all of the machine learning intelligence that can make this happen ... [The] next steps could be to flesh out the design and move it onto a silicon chip," Randy Zhang, also a senior electrical engineering major, explained. Professor Behnaam Aazhang, team Ictal Inhibitor's faculty adviser, said that the project is still a work in progress and is still five to seven years away from an actual product. The Ictal Inhibitor is a project borne from discussions between Professor Aazhang and Dr. Tandon from the University of Texas Health Science Center at Houston. It is a part of Rice University's Vertically Integrated Projects or VIP program, which brings together underclassmen, seniors, and graduate students to work on a project, and is funded by the National Science Foundation. © 2017 Tech Times, All rights reserved. Do not reproduce without permission.
Okusaga O.O.,University of Texas Health Science Center at Houston
Aging and Disease | Year: 2014
Several lines of evidence suggest that schizophrenia, a severe mental illness characterized by delusions, hallucinations and thought disorder is associated with accelerated aging. The free radical (oxidative stress) theory of aging assumes that aging occurs as a result of damage to cell constituents and connective tissues by free radicals arising from oxygen-associated reactions. Schizophrenia has been associated with oxidative stress and chronic inflammation, both of which also appear to reciprocally induce each other in a positive feedback manner. The buildup of damaged macromolecules due to increased oxidative stress and failure of protein repair and maintenance systems is an indicator of aging both at the cellular and organismal level. When compared with age-matched healthy controls, schizophrenia patients have higher levels of markers of oxidative cellular damage such as protein carbonyls, products of lipid peroxidation and DNA hydroxylation. Potential confounders such as antipsychotic medication, smoking, socio-economic status and unhealthy lifestyle make it impossible to solely attribute the earlier onset of aging-related changes or oxidative stress to having a diagnosis of schizophrenia. Regardless of whether oxidative stress can be attributed solely to a diagnosis of schizophrenia or whether it is due to other factors associated with schizophrenia, the available evidence is in support of increased oxidative stress-induced cellular damage of macromolecules which may play a role in the phenomenon of accelerated aging presumed to be associated with schizophrenia.
Sevick-Muraca E.M.,University of Texas Health Science Center at Houston
Annual Review of Medicine | Year: 2012
Technical developments in near-infrared fluorescence (NIRF) imaging and tomography have enabled recent translation into investigational human studies. Noninvasive imaging of the lymphatic vasculature for diagnosis and assessment of function has been uniquely accomplished with NIR using indocyanine green (ICG), a nonspecific dye that has comparatively poor fluorescent properties compared to emerging dyes. Adjunct use of NIRF-ICG for (a) intraoperative sentinel lymph node mapping for cancer staging, (b) video-angiography during surgery, and (c) discrimination of malignant from benign breast lesions detected by mammography and ultrasongraphy also evidences the clinical utility of NIRF. Future NIRF imaging agents that consist of bright fluorescent dyes conjugated to disease-targeting moieties promise molecular imaging and image-guided surgery. In this review, emerging NIRF imaging is described within the context of nuclear imaging technologies that remain the "gold standard" of molecular imaging. © 2012 by Annual Reviews. All rights reserved.
Reveille J.D.,University of Texas Health Science Center at Houston
Nature Reviews Rheumatology | Year: 2012
Ankylosing spondylitis (AS), psoriasis and inflammatory bowel disease (IBD) often coexist in the same patient and in their families. In AS, genes within the MHC region, in particular HLA-B27, account for nearly 25% of disease hereditability, with additional small contributions from genes outside of the MHC locus, including those involved in intracellular antigen processing (that is, ERAP1, which interacts with HLA-B27) and cytokine genes such as those involved in the IL-17-IL-23 pathway. Similar to AS, the strongest genetic signal of susceptibility to psoriasis and psoriatic arthritis also emanates from the MHC region (attributable mostly to HLA-C * 06:02 although other genes have been implicated), and gene-gene interaction of HLA-C with ERAP1. The remaining hereditary load is from genes involved in cytokine production, specifically genes in the IL-17-IL-23 pathway, the NFκB pathway and the type 2 T-helper pathway. In IBD, similar genetic influences are operative. Indeed, genes important in the regulation of the IL-17-IL-23 pathway and, in Crohn's disease, genes important for autophagy (that is, NOD2 and ATG16L1 and IRGM) have a role in conferring susceptibility of individuals to these diseases. Thus, AS, psoriasis and IBD seem to share similar pathogenic mechanisms of aberrant intracellular antigen processing or elimination of intracellular bacteria and cytokine production, especially in the IL-17-IL-23 pathway. © 2012 Macmillan Publishers Limited All rights reserved.
Wu T.-C.,University of Texas Health Science Center at Houston |
Grotta J.C.,University of Texas Health Science Center at Houston
The Lancet Neurology | Year: 2013
Ischaemic stroke is one of the leading causes of death and disability worldwide, and intravenous alteplase is the only proven effective treatment in the acute setting. Hypothermia has been shown to improve neurological outcomes after global ischaemia-hypoxia in comatose patients who have had cardiac arrest, and is one of the most extensively studied and powerful therapeutic strategies in acute ischaemic stroke. The protective mechanisms of therapeutic hypothermia affect the ischaemic cascade across several parallel pathways and, when coupled with reperfusion strategies, might yield synergistic benefits for patients who have had a stroke. Technological advances have allowed hypothermia to be induced rapidly, and the treatment has been used safely in acute stroke patients. Conclusive efficacy trials assessing therapeutic hypothermia combined with reperfusion therapies in acute ischaemic stroke are ongoing. © 2013 Elsevier Ltd.
Agency: NSF | Branch: Standard Grant | Program: | Phase: CDS&E-MSS | Award Amount: 138.00K | Year: 2016
This project aims to develop a system of statistical analysis tools to tackle several important challenges in analysis of complex bioinformatics data, which involves a variety of response variables and tens of thousands independent variables. The interest often lies in identifying the key independent variables associated with the response variables, and understanding such associations as well as the interactions among the independent variables.
The extreme magnitude and complexity of bioinformatics data have posed serious challenges for data analysis. To overcome these challenges, we propose (i) to systematically and properly integrate multi-scale data before we can apply our novel modeling and analysis methods since the data we explore are collected by numerous independent studies at phenotypic, cellular, protein, and genetic levels with information from very different time and dimension scales; (ii) to develop feature screening criteria for a mixed type of longitudinal data using the combination of correlation tests in bivariate longitudinal regression models and the Benjamini-Hochberg-Yekutieli procedure, (iii) to develop graphical models that allow the variables being a mix of continuous and discrete longitudinal variables, with the nodes representing variables and each edge indicating the dependence of the two relevant variables conditional on the other variables; and (iv) to investigate the functioning form of each predictor by resorting to the data themselves under the framework of a mixed effects regression model with a continuous or discrete response and a high dimensional vector of predictors, with the resulting procedure allowing a user to simultaneously determine the form of each predictor effect to be zero, linear or nonlinear.
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 249.29K | Year: 2016
IIS-1637347 Increasing Healthcare Access to At-Risk Populations:Research-based Policies for Mobile Health Clinics Project Description
Mobile clinics play an important role in providing healthcare to at-risk populations in both urban and rural areas. Currently, over 1500 mobile clinics operate in the US and handle over 5 million visits per year providing essential healthcare services. These programs, however, have grown organically over time and it is important to establish evidence-based approaches to encourage a systematic expansion of this essential healthcare delivery model. This proposal addresses this issue through the use of epidemiological and economic GIS data, combined with proprietary routing software, to implement a program for optimal delivery of mobile clinic healthcare services in the Houston, Texas, region. The project involves eight different providers and includes designing systematic strategies for meeting future healthcare needs of low-income communities. The aim is also to develop models and techniques that can be implemented by mobile clinic programs throughout the country. Early estimates indicate that this project can result in 20% increase in mobile health clinic capacity, which could translate into significant savings in healthcare costs, and considerable improvements in quality of life for the poor. These results are in line with the NSF mission goal of promoting the advancement of health.
The overall goal of this project is to optimize and implement a data-based program for coordinated deployment of mobile clinic programs. We will initially identify optimal expansion strategies for the eight current mobile clinics programs to meet the fast-growing demand for healthcare services in underserved communities. The project will then measure the potential of the developed models and techniques and apply them to mobile clinic programs in other regions of Texas and other states in the nation. To achieve the above goals, the team will first apply data mining and forecasting techniques to estimate present and future demand of healthcare services in selected communities. We will combine these approaches with advance GIS mapping tools and stochastic measures to identify target population clusters. We will also conduct survey and economic analysis to measure the operational cost and identify operational constraints in the present mobile clinic programs, and develop new optimization models for the deployment of the mobile clinic service. Then, we will develop new effective techniques to solve the developed optimization models to estimate the maximum capacity of the current mobile health clinic programs in the Houston region, and identify expansion strategies to meet the future service demand while minimizing cost. Lastly, we will estimate overall healthcare cost savings and quality of life impact at the community level at baseline and post-intervention.