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  • richardmitnick 4:55 am on July 29, 2014 Permalink | Reply
    Tags: , , Medicine,   

    From PNNL Lab: “Mapping Molecules in the Human Lung” 


    PNNL Lab

    July 2014
    Molecular atlas could reduce mortality in premature infants caused by undeveloped lungs

    A team of investigators at Pacific Northwest National Laboratory (PNNL) will perform an unprecedented, systematic study, mapping the molecular components of normal lung development during late term and early childhood. They recently were awarded $4.5 million over 5 years by the National Heart Lung and Blood Institute (NHLBI) to develop a molecular atlas of the developing human lung (LungMAP).

    baby
    Expanding understanding of early human lung development is a critical step toward promoting proper lung formation in preterm infants.

    Why It Matters. Lung development from ~4 months before birth through ~24 months post-birth is crucial to the lifelong health of an individual and remains the critical factor in newborn viability. However, little is known of human lung development at this critical period. Thus, expanding understanding of early human lung development is a critical step toward promoting proper lung formation in preterm infants. This has significant implications toward reducing the high mortality rate in prematurely born infants in the United States.

    The multi-investigator PNNL LungMAP project is led by PNNL scientists Dr. Charles Ansong and Dr. Richard Corley and former PNNL scientist Dr. James Carson, now at Texas Advanced Computing Center. They will generate high-quality data using cutting-edge technologies for linking molecular information-including genes, proteins, lipids, and metabolites-to locations in the developing lung, as well as to specific cell types.

    two
    Mass spectrometry imaging showing spatial localization of lipids in lung tissue. Cell-type specific multi-omic profiling done at PNNL and EMSL will complement spatial imaging in research to better understand lung formation in preterm infants.

    The project leverages several PNNL signature strengths, in mass spectrometry-based imaging, proteomics, metabolomics and lipidomics technologies, image analysis and registration, organ/cellular atlas development, and multi-scale computational modeling. A portion of the work will be done at EMSL, a Department of Energy national user facility located at PNNL.

    “It’s an honor to be part of this consortium, which is important to NHLBI and will be highly visible,” said Corley. “Our involvement connects PNNL to some of the leading pulmonary developmental biologists in the field, which is also a significant honor.”

    The results of this new initiative will create the first spatial-temporal molecular atlas of the mammalian lung during alveologenesis-the ultimate phase of lung development.

    “Through our research over the next 5 years, we hope to fill the current knowledge gaps in lung development and set the foundation for answering a new generation of hypotheses in the context of prenatal and early childhood lung development,” said Ansong.

    Co-investigators and collaborators on the project include Dr. Cecilia Ljungberg (Baylor College of Medicine); Drs. Charles Frevert and Sina Gharib (University of Washington); and Drs. Julia Laskin, Richard Smith, Thomas Metz, Aaron Wright, and Wei-Jun Qian (all PNNL).

    About : LungMAP is a national consortium of four Research Centers including PNNL, Cincinnati Children’s Hospital Medical Center, University of Alabama at Birmingham, Children’s Hospital Los Angeles, a Data Coordination Center at Duke University, and a Human Tissue Core at University of Rochester, all working to produce information on human lung development that can be openly accessed and shared by the research community and the public.

    See the full article here.

    Pacific Northwest National Laboratory (PNNL) is one of the United States Department of Energy National Laboratories, managed by the Department of Energy’s Office of Science. The main campus of the laboratory is in Richland, Washington.

    PNNL scientists conduct basic and applied research and development to strengthen U.S. scientific foundations for fundamental research and innovation; prevent and counter acts of terrorism through applied research in information analysis, cyber security, and the nonproliferation of weapons of mass destruction; increase the U.S. energy capacity and reduce dependence on imported oil; and reduce the effects of human activity on the environment. PNNL has been operated by Battelle Memorial Institute since 1965.

    i1


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  • richardmitnick 6:08 pm on July 28, 2014 Permalink | Reply
    Tags: , , , , Medicine   

    From M.I.T.: “Forced mutations doom HIV” 


    MIT News

    July 28, 2014
    Anne Trafton

    Fifteen years ago, MIT professor John Essigmann and colleagues from the University of Washington had a novel idea for an HIV drug. They thought if they could induce the virus to mutate uncontrollably, they could force it to weaken and eventually die out — a strategy that our immune system uses against many viruses.

    The researchers developed such a drug, which caused HIV to mutate at an enhanced rate, as expected. But it did not eliminate the virus from patients in a small clinical trial reported in 2011. In a new study, however, Essigmann and colleagues have determined the mechanism behind the drug’s action, which they believe could help them develop better versions that would destroy the virus more quickly.

    This type of drug could, they say, help combat the residual virus that remains in the T cells of patients whose disease has been brought into long-term remission by the triple-drug combination typically used to treat HIV. These viruses re-emerge periodically, which is why patients must stay on the drug cocktail indefinitely and are not considered “cured.”

    “This has really been the biggest problem in HIV,” says Essigmann, the William R. and Betsy P. Leitch Professor of Chemistry, Toxicology, and Biological Engineering at MIT. “What we would hope is that over a long period of time on this type of therapy, a person would potentially have their latent pool mutated to the extent that it no longer causes active disease.”

    In the new study, which appears in the Proceedings of the National Academy of Sciences (PNAS) the week of July 28, the researchers discovered exactly how the drug, known as KP1212, induces the HIV genome to mutate. The paper’s lead authors are MIT postdocs Deyu Li, Bogdan Fedeles, and Vipender Singh, along with recent MIT PhD graduate Chunte Sam Peng. Essigmann and Andrei Tokmakoff, a former MIT professor who is now at the University of Chicago, are the paper’s senior authors.

    Too much mutation

    After HIV infects a cell, it rapidly begins making copies of its genetic material. This copying is very error-prone, so the virus mutates swiftly. This usually helps the virus survive by allowing it to evade both the immune system and human-made drugs. However, at a conference in the late 1990s, Essigmann learned from an evolutionary biologist that if the virus could be forced to double its mutation rate, it would no longer be able to produce functional proteins.

    Essigmann and Lawrence Loeb, a professor of biochemistry at the University of Washington, started working together to exploit this idea. Essigmann had been developing compounds that mimic natural nucleotides — the A, C, T, and G “letters” that form DNA base pairs — but that induce genetic mutations by binding with the wrong partner. Loeb is an expert on polymerases, the enzymes that string nucleotides together to form DNA or RNA.

    Together with James Mullins, an immunology professor and HIV expert at the University of Washington, Essigmann and Loeb designed a molecule called 5-hydroxycytosine, described in a 1999 PNAS paper. When given to HIV-infected cells grown in the lab, this molecule was incorporated into the viral genome in place of the natural form of cytosine. Within 25 viral replication cycles, HIV populations in those infected cells collapsed.

    The researchers then formed a company, Koronis Pharmaceuticals, which developed KP1212, a compound that is 100 times more mutagenic than 5-hydroxycytosine. In a four-month clinical trial of 32 patients, mutations accumulated in the patients’ viral DNA, but not enough to induce a population crash. The drug was also found to be safe: It did not mutate the patients’ own DNA, in part because the drug was designed so that human forms of DNA polymerase could not accept it.

    Shape-shifting molecules

    In the new PNAS paper, the researchers used advanced spectroscopy techniques to analyze KP1212’s ability to promote tautomerism, a chemical phenomenon that involves the migration of protons among the nitrogen and oxygen atoms on nucleic-acid bases. This allowed the researchers to see that once KP1212 inserts itself into the genome, it can switch among five different shapes, or tautomers. Some of these behave like cytosine, by pairing with guanine. However, some of the tautomers resemble thymine, so they will pair with adenine, introducing mutations.

    “The five molecules are changing shape on a nanosecond timescale, and each shape has a different base-pairing property, so you will see a promiscuity in terms of the bases with which KP1212 pairs,” Singh says.

    To see this shape-shifting, the researchers used NMR and a form of 2-D infrared spectroscopy developed by Tokmakoff. This technology allows scientists to determine the atomic composition and structure of nucleic-acid bases.

    Then, using a genetic tool developed in the Essigmann lab, the researchers determined that KP1212 induces a mutation rate of exactly 10 percent in the HIV genome. Based on these findings, Essigmann estimates that if KP1212 doubles the mutation rate of HIV, it could clear the virus from patients in one to two years.

    He says that Koronis hopes to run a longer trial of KP1212 and is also interested in developing drugs that would work faster, which could be accomplished by altering some of the chemical features of the molecule and testing whether they speed up the mutation rate.

    “This technology allows you to detect the quantitative contribution of different tautomers to the types and frequencies of mispairing by nucleoside analogs,” says Loeb, who was not involved in the new paper. “It would allow you to test ahead of time what is making the mispairing occur with the compound that you’re using.”

    The paper also identified other factors that scientists could manipulate to improve the drug’s performance.

    “There are other variables that are important to calculate the time it would take to eradicate a virus,” Fedeles says. “That includes the concentration that the drug needs to achieve inside the cell, and the ability of a cell to convert the nucleoside, the molecule without the phosphate, to the triphosphate version, which is the one incorporated by the polymerase.”

    “We’re building up a new strategy that can give us a lot of insights into how to design a new molecule,” Li says. “It’s a new toolset for developing future drugs. Those drugs are not limited to HIV. They could be candidates for dengue fever, or some other viruses such as yellow fever.”

    Ribavirin, a drug used to treat hepatitis C, and the influenza drug T-705 are also believed to provoke hypermutation in their target viruses. The MIT team also plans to work with Loeb to test the possibility of using similar compounds to force tumor cells to mutate themselves into extinction.

    The research was funded by the National Institutes of Health, the National Science Foundation, and the MIT Laser Biomedical Research Center.

    See the full article here.


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  • richardmitnick 8:09 am on July 24, 2014 Permalink | Reply
    Tags: , Medicine, ,   

    From physicsworld.com: “Plasmonic chip diagnoses diabetes” 

    physicsworld
    physicsworld.com
    Jul 23, 2014
    Belle Dumé

    A plasmonic chip that can diagnose type-1 diabetes (T1D) has been unveiled by researchers at Stanford University in the US. The chip is capable of detecting diabetes-related biomarkers such as insulin-specific autoantibodies and could be used in hospitals and doctors’ surgeries as a quick and simple way to detect early-stage T1D.

    Diabetes could affect nearly 370 million people worldwide by 2030, according to the World Health Organization. More worrying still, diabetes is now the second most common chronic disease in children. For reasons that are still unclear, the rate of T1D (also known as autoimmune diabetes) in children is increasing by about 3% every year, with a projected increase of a staggering 70% between 2004 and 2020.

    Although T1D was once thought of as being exclusively a childhood disease, around a quarter of individuals now contract it as adults. The rate of type-2 diabetes (T2D) (also called metabolic or diet-induced diabetes), normally seen in overweight adults, has also alarmingly escalated in children since the early 1990s, in part because of the global obesity epidemic. Until quite recently, it was fairly simple to distinguish between T1D and T2D because the diseases had occurred in different groups of people. However, this is becoming more and more difficult because the groups are beginning to overlap. The main problem is that existing diagnostic tests are slow and expensive, and it would be better to detect diabetes as early as possible to ensure the best possible treatment.
    Higher concentration of autoantibodies

    T1D is different from T2D in that patients with the disorder have a much higher concentration of autoantibodies. These are produced by the body and work against one or more pancreatic islet antigens such as insulin, glutamic acid decarboxylase and/or tyrosine phosphatase. Detecting these autoantibodies, and especially those against insulin (which are the first to appear), is therefore a good way to detect T1D. Again, standard tests are not very efficient and even the most widely used technique, radioimmunoassay (RIA) with targeted antigens, is far from ideal because it is slow and relies on toxic radioisotopes.

    In an attempt to overcome these problems, the Stanford researchers have developed an autoantibody test that is more reliable, simple and faster than RIA and similar tests. It comprises a microarray of islet antigens arranged on a plasmonic gold (pGOLD) chip. It can be used to diagnose T1D by detecting the interaction of autoantibodies in a small blood sample with insulin, GAD65 and IA-2, and potentially new biomarkers of the disease. It works with just 2 µL of whole human blood (from a finger-prick sample, for example) and results can be obtained in the same day.

    chip
    Good as gold: detecting diabetes with plasmons

    Enhancing the fluorescence emission

    The team, led by Hongjie Dai, made its pGOLD chip by uniformly coating glass slides with gold nanoparticles that have a surface plasmon resonance in the near-infrared part of the electromagnetic spectrum. Plasmons are collective oscillations of the conduction electrons on the surfaces of the nanoparticles. They allow the nanoparticles to act like tiny antennas, absorbing light at certain resonant frequencies and transferring it efficiently to nearby molecules.

    The result can be a large boost in the fluorescence of the molecule, and the researchers have shown that the pGOLD chip is capable of enhancing the fluorescence emission of near-infrared tags of biological molecules by around 100 times. Together with Brian Feldman’s group, the researchers robotically printed the islet antigens in triplicate spots onto the plasmonic gold slide to create a chip containing a microarray of antigens.

    “We tested our device by applying 2 µL of human serum or blood (diluted by 10 or 100 times) to it,” explains Dai. “If the sample contains autoantibodies that match one or more of the islet antigens on the chip, those antibodies bind to the specific antigens, which are then tagged by a secondary antibody with a near-infrared dye to make the islet spots brightly fluoresce.”
    Antibody detected at much lower concentrations

    The samples came from Feldman’s patients who had new-onset diabetes. They were tested against non-diabetic controls at Stanford University Medical Center.

    The antigen spots fluoresce 100 times more brightly thanks to the plasmonic gold substrate, which allows the antibody to be detected at much lower concentrations (down to just 1 femtomolar) than if ordinary gold were to be employed in the microarray platform.

    “We believe that our technology will be able to address the current clinical need for improved diabetes diagnostics,” Dai says. “The pGOLD platform is also being commercialized by a new start-up company, Nirmidas Biotech, based in San Francisco, aimed at better detecting proteins for a range of research and diagnostic applications. It might even be able to detect biomarkers for other diseases such as heart disease with ultrahigh sensitivity.”

    The researchers describe their plasmonic chip in Nature Medicine.

    This article first appeared on nanotechweb.org

    See the full article here.

    PhysicsWorld is a publication of the Institute of Physics. The Institute of Physics is a leading scientific society. We are a charitable organisation with a worldwide membership of more than 50,000, working together to advance physics education, research and application.

    We engage with policymakers and the general public to develop awareness and understanding of the value of physics and, through IOP Publishing, we are world leaders in professional scientific communications.
    IOP Institute of Physics


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  • richardmitnick 6:57 pm on July 23, 2014 Permalink | Reply
    Tags: , , Medicine   

    From isgtw: “A case for computational mechanics in medicine” 

    international science grid this week

    July 23, 2014
    Monica Kortsha

    Members of the US National Committee on Theoretical and Applied Mechanics and collaborators, including Thomas Hughes, director of the computational mechanics group at the Institute for Computational Engineering and Sciences (ICES) at The University of Texas at Austin, US, and Shaolie Hossain, ICES research fellow and research scientist at the Texas Heart Institute, have published an article reviewing the new opportunities computational mechanics is creating in medicine.

    New treatments for tumor growth and heart disease are just two opportunities presenting themselves. The article is published in the Journal of the Royal Society Interface. “This journal truly serves as an interface between medicine and science,” Hossain says. “If physicians are looking for computational research advancements, the article is sure to grab their attention.”

    The article presents three research areas where computational medicine has already made important progress, and will likely continue to do so: nano and microdevices, biomedical devices — including diagnostic systems, and organ models — and cellular mechanics.

    “[Disease is a] multi-scale phenomena and investigators research diverse aspects of it,” says Hossain, explaining that although disease may be perceived at an organ level, treatments usually function at the molecular and cellular scales.

    Hughes and Hossain’s research on vulnerable plaques (VPs), a category of atherosclerosis responsible for 70% of all lethal heart attacks, is an example of applied research incorporating all three notable areas.

    two
    Hughes and Hossain pictured next to a simulation of a vulnerable plaque within an artery. Current medical techniques cannot effectively detect vulnerable plaques. However, Hughes and Hossain say that nano-particles and computational modeling technologies offer diagnostic and treatment solutions. Image courtesy the Institute for Computational Engineering and Sciences at The University of Texas at Austin, US.

    “The detection and treatment of VPs represents an enormous unmet clinical need,” says Hughes. “Progress on this has the potential to save innumerable lives. Computational mechanics combined with high-performance computing provides new and unique technologies for investigating disease, unlike anything that has been traditionally used in medical research.”

    heart
    HeartFlow uses anatomic data from coronary artery CT scans to create a 3D model of the coronary arteries. Coronary blood flow and pressure are computed by applying the principles of coronary physiology and computational fluid dynamics. Fractional flow reserve (FFRCT) is calculated as the ratio of distal coronary pressure to proximal aortic pressure, under conditions simulating maximal coronary hyperemia. The image demonstrates a stenosis (narrowing) of the left anterior descending coronary artery with an FFRCT of 0.58 distal to the stenosis (in red). FFR values ≤0.80 are hemodynamically significant (meaning they obstruct blood flow) and indicate that the patient may benefit from coronary revascularization (removing or bypassing blockages). Image courtesy HeartFlow.

    The high mortality rate attributed to VPs stems from their near clinical invisibility; conventional plaque detection techniques such as MRI and CT scanning do not register VPs because significant vascular narrowing is not present. Hughes and Hossain, however, have developed a computational toolset that can aid in making the plaques visible through targeted delivery of functionalized nanoparticles.

    Their computational models draw on patient-specific data to predict how well nanoparticles can adhere to a potential plaque, thus enabling researchers to test and refine site-specific treatments. If a VP is detected, the same techniques can be employed to send nanoparticles containing medicine directly to the VP.

    The models are being applied at the Texas Heart Institute, where Hossain is a research scientist and assistant professor. “Early intervention and prevention of heart attacks are where we certainly want to go and we are excited about the possibilities for computational mechanics being a vehicle to get us there safely and more rapidly,” says James Willerson, Texas Heart Institute president.

    Other computationally aided models are already being used to help physicians evaluate and treat patients. HeartFlow, a company founded by Charles Taylor, uses CT scan data to create patient-specific models of arteries, which can be used to diagnose coronary artery disease.

    Despite its success and demonstrated potential, computational mechanics in the medical field is still a new concept for scientists and physicians alike, says Hossain. “The potential that we have, in my opinion, hasn’t been tapped to the fullest because of the gap in knowledge.”

    To help integrate medicine into a field that has historically focused on more traditional engineering domains, the article advocates for incorporating biology and chemistry questions into computational mechanics classes, as well as offering classes that can benefit both medical and computational science students.

    See the full article here.

    iSGTW is an international weekly online publication that covers distributed computing and the research it enables.

    “We report on all aspects of distributed computing technology, such as grids and clouds. We also regularly feature articles on distributed computing-enabled research in a large variety of disciplines, including physics, biology, sociology, earth sciences, archaeology, medicine, disaster management, crime, and art. (Note that we do not cover stories that are purely about commercial technology.)

    In its current incarnation, iSGTW is also an online destination where you can host a profile and blog, and find and disseminate announcements and information about events, deadlines, and jobs. In the near future it will also be a place where you can network with colleagues.

    You can read iSGTW via our homepage, RSS, or email. For the complete iSGTW experience, sign up for an account or log in with OpenID and manage your email subscription from your account preferences. If you do not wish to access the website’s features, you can just subscribe to the weekly email.”


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  • richardmitnick 8:21 pm on July 22, 2014 Permalink | Reply
    Tags: , Imaging, Medicine, ,   

    From physicsworld.com: “New medical probe combines sound and electromagnetic induction” 

    physicsworld
    physicsworld.com

    Jul 22, 2014
    Tim Wogan

    The Lorentz force combined with acoustic shear waves could help doctors detect dangerous diseases, say researchers in France. The team has shown that the electromagnetic force could create oscillations in living tissue, producing shear waves that can be detected to reveal the tissue’s elasticity. The technique has shown promise in the laboratory and could now be developed as a clinical technique.

    shear
    shear wave

    An experienced doctor can determine a lot about the human body by simply pressing on it with their fingers, a process called palpation. Many serious medical conditions such as breast cancer can be diagnosed this way because they cause tissue to be firmer than normal. Some internal organ diseases such as liver fibrosis also cause the tissue to stiffen, but, in general, these organs are inaccessible to manual palpation. While the texture of internal tissue can be probed by medical imaging techniques such as ultrasound, these techniques measure a different quantity from palpation.

    Shear propagation

    When tissue oscillates, it supports both pressure wave (back-and-forth motion) and shear waves (side-to-side movement). Traditional ultrasound techniques operate in the megahertz range and at these frequencies shear waves propagate just a few microns in tissue. As a result, most ultrasound techniques rely on using pressure waves to determine the compression modulus of the tissue.

    sw
    Shear waves propagating through a tissue dummy

    However, tissue is mainly water – an incompressible fluid – so its firmness to the touch depends on how easily it moves aside to allow a doctor’s fingers to sink in. This is defined by the shear modulus, which can be calculated from the speed of the shear waves in the tissue. Therefore, measuring the sheer modulus can give doctors a map of the inside of the human body as if they could “touch organs and evaluate their stiffness”, says team member Stephan Catheline of the University of Lyon.
    Frequency drop

    In the past few years, researchers have developed ways of measuring the shear modulus by using shear waves with a much lower frequency, which propagate further in soft tissue. These waves are created inside the body by firing focused ultrasound through the skin, but this has its drawbacks. The brain, for example, is protected against shock and vibration by both the skull and the thin layer of cerebrospinal fluid lining it, which makes inducing shear waves difficult.

    Now Catheline and colleagues have adapted an idea called magneto-acoustical electrical tomography to create the shear waves. This involves passing an alternating electric current through tissue in an applied magnetic field. The resulting electromagnetic Lorentz force induces shear-wave oscillations in the tissue. While other researchers had used a high-frequency alternating current, the team used a frequency of only 10–1000 Hz. Using a synthetic tissue substitute called a phantom, and then a sample of pig liver, the researchers tested out their idea, showing that they could induce low-frequency waves with an electric current and detect them using ultrasound transducers. Their results for the pig liver agreed with accepted values for the shear elasticity of healthy liver tissue.
    High electric fields

    Before the research can be used in medicine, there are some difficulties to address. First, the researchers needed high electric fields to generate a large enough Lorentz force. They estimate that the electrical current passing through the tissue was 100 times higher than accepted safety limits, albeit only momentarily. However, modern magnetic resonance imaging (MRI) scanners can generate magnetic fields many times higher than the 100 mT available from the permanent magnets in the team’s laboratory: using these, one could generate the same Lorentz force with a lower electric field. Second, the cerebrospinal fluid that prevents ultrasound from getting into the brain would also stop it getting out, so one would need another way to detect cerebral shear waves. Here too, MRI might provide the answer, as it has been used in the clinic to detect tissue oscillations.

    Kathy Nightingale, an elastography expert at Duke University in North Carolina, says that “so far, what’s exciting about this research is that it’s the first demonstration that I’m aware of of the generation of shear waves using this Lorentz force approach”. There are clear challenges in liver elastography, her own specialism, on patients with livers further below the skin, such as obese patients, she explains. “If this were to be successful in that population, that could be significant,” she says, but stresses that we will have to “wait and see”.

    The research will be published in Physical Review Letters.

    See the full article here.

    PhysicsWorld is a publication of the Institute of Physics. The Institute of Physics is a leading scientific society. We are a charitable organisation with a worldwide membership of more than 50,000, working together to advance physics education, research and application.

    We engage with policymakers and the general public to develop awareness and understanding of the value of physics and, through IOP Publishing, we are world leaders in professional scientific communications.
    IOP Institute of Physics


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  • richardmitnick 10:16 pm on July 21, 2014 Permalink | Reply
    Tags: , , , , Medicine, ,   

    From WCG: “Pioneering a Molecular Approach to Fighting AIDS” 

    World Community Grid

    Dr. Arthur Olson
    Professor, The Scripps Research Institute
    21 Jul 2014

    Summary
    World Community Grid is being featured at the 20th International AIDS Conference which begins today in Melbourne, Australia. Dr. Arthur Olson, FightAIDS@Home principal investigator, shares his perspective on how World Community Grid is helping his team develop therapies and a potential cure for AIDS.

    The Scripps Research Institute’s FightAIDS@Home initiative is a large-scale computational research project whose goal is to use our knowledge of the molecular biology of the AIDS virus HIV to help defeat the AIDS epidemic. We rely on World Community Grid to provide massive computational power donated by people around the world to speed our research. The “virtual supercomputer” of World Community Grid enables us to model the known atomic structures of HIV molecules to help us design new drugs that could disrupt the function of these molecules. World Community Grid is an essential tool in our quest to understand and subvert the HIV virus’s ability to infect, spread and develop resistance to drug therapies.

    FightAidsOlsonLab@home

    Since the early 1980s – when AIDS was first recognized as a new epidemic and a serious threat to human health – our ability to combat the HIV virus has evolved. Using what we call “structure-based drug discovery,” researchers have been able to use information about HIV’s molecular component to design drugs to defeat it. Critical to this process has been our ability to develop and deploy advanced computational models to help us predict how certain chemical compounds could affect the HIV virus. The development of our AutoDock modelling application – combined with the computational power of World Community Grid – represents a significant breakthrough in our ability to fight HIV.

    By the mid 1990s, the first structure-based HIV protease inhibitors were approved for the treatment of AIDS. These inhibitors enabled the development of highly active antiretroviral therapy (HAART), which in turn resulted in a rapid decline of AIDS deaths where such treatment was available. In the intervening years, thanks in part to the U.S. National Institute of General Medical Sciences AIDS-related Structural Biology Program, we have learned a lot about the molecular structure of HIV. But the more we understand the structure of the virus, the more complex our computational models need to be to unlock the secrets of HIV.

    World Community Grid has enabled our research to progress well beyond what we could have dreamed of when we started our HIV research in the early 1990s. Through our FightAIDS@Home project, we can screen millions of chemical compounds to evaluate their effectiveness against HIV target proteins – including those known to be drug-resistant. By deploying these and other methods, we have significantly increased our understanding of HIV and its ability to evolve to resist treatment. Using these computational capabilities, we have just begun working with an HIV Cure researcher to help us move beyond treatment in search of a cure.

    See the full article here.

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    Computing for Sustainable Water

    Mapping Cancer Markers
    Mapping Cancer Markers Banner

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp


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  • richardmitnick 8:57 pm on July 20, 2014 Permalink | Reply
    Tags: , , , , Medicine, ,   

    From Mapping Cancer Markers at WCG: “Project roadmap and first phase results from the Mapping Cancer Markers team” 

    Mapping Cancer Markers

    Mapping Cancer Markers Banner

    Mapping Cancer Markers

    By: The Mapping Cancer Markers research team
    10 Jul 2014

    Summary
    The lead researcher for Mapping Cancer Markers presents a roadmap for the project to analyze signatures for 4 types of cancer: lung, ovarian, prostate and sarcoma; an update on his team’s progress thus far, and an invitation to join the research team in an August cancer fundraiser.

    On behalf of the Mapping Cancer Markers team, we want to start by saying thank you! In just 7 months, World Community Grid members have donated over 60,000 years of processing time to support our research. As a result, we are nearly done with the “benchmarking” portion of the project, which determines the characteristics of our search space. Over the coming months and years, we will pursue more targeted approaches to discover relevant gene signatures. Today we want to give you both a high-level roadmap and some further detail about what is happening with the project.

    Project roadmap

    The project is anticipated to run for two years, and we plan to analyze signatures for 4 different types of cancer. At the moment, we’re enlisting your help to process research tasks for lung cancer, and will move on to ovarian cancer, prostate cancer and sarcoma.

    Currently, the Mapping Cancer Markers project has two phases:

    In the first phase we have been attempting to set a benchmark for further experiments.
    The second phase will be geared towards finding clinically useful molecular signatures, initially focusing on gene signatures that can predict the occurrence of various types of cancer.

    We expect a smooth transition between the two phases, with no interruption in work. The “benchmarking” phase of our project is important not only for our own research, but for other researchers around the world. Every year, numerous groups worldwide develop and publish interesting molecular signatures for various diseases, including multiple cancers. One of the challenges of interpreting these findings is that many of the reports are not directly comparable to each other. The benchmarking phase of our project is designed to set a standard benchmark so that we and other groups can estimate how well individual signatures perform.

    You can think of this benchmarking phase as a bit like designing an IQ test. By establishing a standard test and scoring system, we can evaluate any person’s intelligence. The results from the first phase of Mapping Cancer Markers will allow us to create such a test for existing and future gene signatures, so that we can tell which ones have the best predictive ability.

    Benchmarking

    Our preliminary analysis of the work units processed so far (roughly 26 billion gene signatures) is focused on the nature of genes in the signatures, measuring their quality by assessing how accurately they contribute to identifying patients with poor prognosis. On the analytics side, we have also been evaluating the use of a software package to aid with post-processing our results.

    One of the goals of the first project phase is to understand if some genes might have better predictive ability than others. To do this, we took the top 0.1% of the gene signatures and identified the individual genes that make up each signature. For each gene, we looked at how many times it occurred within top scoring signatures and plotted the scores of those signatures (see figure below). The blue line shows the average of all of the genes together. The red line highlights the worst-performing single gene while the green line indicates our best-performing gene. The average of all the genes is very similar to the worst single gene. This is not surprising, because most genes are likely to have poor predictive ability. However, we are looking for the few genes that stand out from the field. In other words, if we have 1 million potential gene signatures, and we look at the top 1,000 scoring signatures, we can find groups of genes such as the one shown in green, which have better predictive ability.

    This information is important because if we know which genes have the best predictive ability, it may help us and other researchers to evaluate the value of other signatures: if an unknown signature has one of the top genes in it, it is likely to be a useful signature for identifying, assessing, predicting or treating a disease.

    As a side note, this benchmarking process is why members may have experienced shorter or longer than usual runtimes over the past several months. The core algorithm of the Mapping Cancer Markers engine, used to evaluate each potential gene signature, has a processing time that is highly dependent on the statistical characteristics of each signature. The search space targeted by a single work unit can sometimes contain time-consuming signatures, which together lead to a longer total runtime. This also means variability with the size of Mapping Cancer Markers results. A typical work unit will evaluate tens of thousands of potential gene signatures, many of which are of low quality. Signatures below a certain quality threshold are removed from the returned results. However, the search space targeted by a single work unit can sometimes contain a high proportion of high-quality gene signatures. If this happens, the result file is larger than usual.

    Funding & Fundraising

    We’re happy to report that there are several potential sources for further funding. Applications are in progress with the Ontario Research Fund, the Canada Foundation for Innovation, and the US Department of Defense. Of course, the free computing power provided by World Community Grid volunteers is absolutely essential to our research. However, additional funding will help us to both leverage contributions from volunteers, and fully utilize findings of the Mapping Cancer Markers computations, with a primary focus on lung and ovarian cancer.

    Finally, if you will be in Ontario between 15-17 August, please consider donating to, or cheering on the Team Ian Ride from Kingston to Montreal, which raises money for the Ian Lawson Van Toch Cancer Informatics Fund at the Princess Margaret Cancer Centre (if you are interested, please contact us about joining the Team Ian ride this or next year). If you can join us, it will give you the chance to meet some of the research team, as well as raise money for a worthy cause and participate in an outstanding event. For more details visit:http://www.team-ian.org/

    Cancers, one of the leading causes of death worldwide, come in many different types and forms in which uncontrolled cell growth can spread to other parts of the body. Unchecked and untreated, cancer can spread from an initial site to other parts of the body and ultimately lead to death. The disease is caused by genetic or environmental changes that interfere with biological mechanisms that control cell growth. These changes, as well as normal cell activities, can be detected in tissue samples through the presence of their unique chemical indicators, such as DNA and proteins, which together are known as “markers.” Specific combinations of these markers may be associated with a given type of cancer.

    The pattern of markers can determine whether an individual is susceptible to developing a specific form of cancer, and may also predict the progression of the disease, helping to suggest the best treatment for a given individual. For example, two patients with the same form of cancer may have different outcomes and react differently to the same treatment due to a different genetic profile. While several markers are already known to be associated with certain cancers, there are many more to be discovered, as cancer is highly heterogeneous.

    Mapping Cancer Markers on World Community Grid aims to identify the markers associated with various types of cancer. The project is analyzing millions of data points collected from thousands of healthy and cancerous patient tissue samples. These include tissues with lung, ovarian, prostate, pancreatic and breast cancers. By comparing these different data points, researchers aim to identify patterns of markers for different cancers and correlate them with different outcomes, including responsiveness to various treatment options.

    This project runs on BOINC software. Visit BOINC or WCG, download and install the software and attach to the project. While you are at BOINC and WCG, look over the other projects for some that you might find of interest.

    WCG

    BOINC


    ScienceSprings is powered by MAINGEAR computers

     
  • richardmitnick 11:18 am on July 19, 2014 Permalink | Reply
    Tags: , , , , , , Medicine, ,   

    Mapping Cancer Markers From WCG 

    Mapping Cancer Markers

    Mapping Cancer Markers Banner

    Mapping Cancer Markers

    Cancers, one of the leading causes of death worldwide, come in many different types and forms in which uncontrolled cell growth can spread to other parts of the body. Unchecked and untreated, cancer can spread from an initial site to other parts of the body and ultimately lead to death. The disease is caused by genetic or environmental changes that interfere with biological mechanisms that control cell growth. These changes, as well as normal cell activities, can be detected in tissue samples through the presence of their unique chemical indicators, such as DNA and proteins, which together are known as “markers.” Specific combinations of these markers may be associated with a given type of cancer.

    The pattern of markers can determine whether an individual is susceptible to developing a specific form of cancer, and may also predict the progression of the disease, helping to suggest the best treatment for a given individual. For example, two patients with the same form of cancer may have different outcomes and react differently to the same treatment due to a different genetic profile. While several markers are already known to be associated with certain cancers, there are many more to be discovered, as cancer is highly heterogeneous.

    Mapping Cancer Markers on World Community Grid aims to identify the markers associated with various types of cancer. The project is analyzing millions of data points collected from thousands of healthy and cancerous patient tissue samples. These include tissues with lung, ovarian, prostate, pancreatic and breast cancers. By comparing these different data points, researchers aim to identify patterns of markers for different cancers and correlate them with different outcomes, including responsiveness to various treatment options.

    This project runs on BOINC software. Visit BOINC or WCG, download and install the software and attach to the project. While you are at BOINC and WCG, look over the other projects for some that you might find of interest.

    WCG

    BOINC


    ScienceSprings is powered by MAINGEAR computers

     
  • richardmitnick 11:20 am on July 18, 2014 Permalink | Reply
    Tags: , , , Medicine,   

    From Rosetta@home 

    Rosetta@home

    Rosetta@home

    Rosetta@home needs your help to determine the 3-dimensional shapes of proteins in research that may ultimately lead to finding cures for some major human diseases. By running the Rosetta program on your computer while you don’t need it you will help us speed up and extend our research in ways we couldn’t possibly attempt without your help. You will also be helping our efforts at designing new proteins to fight diseases such as HIV, Malaria, Cancer, and Alzheimer’s (See our Disease Related Research for more information). Please join us in our efforts! Rosetta@home is not for profit.

    About Rosetta

    One of the major goals of Rosetta is to predict the shapes that proteins fold up into in nature. Proteins are linear polymer molecules made up of amino acid monomers and are often refered to as “chains.” Amino acids can be considered as the “links” in a protein “chain”. Here is a simple analogy. When considering a metal chain, it can have many different shapes depending on the forces exerted upon it. For example, if you pull its ends, the chain will extend to a straight line and if you drop it on the floor, it will take on a unique shape. Unlike metal chains that are made of identical links, proteins are made of 20 different amino acids that each have their own unique properties (different shapes, and attractive and repulsive forces, for example), and in combination, the amino acids exert forces on the chain to make it take on a specific shape, which we call a “fold.” The order in which the amino acids are linked determines the protein’s fold. There are many kinds of proteins that vary in the number and order of their amino acids.

    To predict the shape that a particular protein adopts in nature, what we are really trying to do is find the fold with the lowest energy. The energy is determined by a number of factors. For example, some amino acids are attracted to each other so when they are close in space, their interaction provides a favorable contribution to the energy. Rosetta’s strategy for finding low energy shapes looks like this:

    Start with a fully unfolded chain (like a metal chain with its ends pulled).
    Move a part of the chain to create a new shape.
    Calculate the energy of the new shape.
    Accept or reject the move depending on the change in energy.
    Repeat 2 through 4 until every part of the chain has been moved a lot of times.

    We call this a trajectory. The end result of a trajectory is a predicted structure. Rosetta keeps track of the lowest energy shape found in each trajectory. Each trajectory is unique, because the attempted moves are determined by a random number. They do not always find the same low energy shape because there are so many possibilities.

    A trajectory may consist of two stages. The first stage uses a simplified representation of amino acids which allows us to try many different possible shapes rapidly. This stage is regarded as a low resolution search and on the screen saver you will see the protein chain jumping around a lot. In the second stage, Rosetta uses a full representation of amino acids. This stage is refered to as “relaxation.” Instead of moving around a lot, the protein tries smaller changes in an attempt to move the amino acids to their correct arrangment. This stage is regarded as a high resolution search and on the screen saver, you will see the protein chain jiggle around a little. Rosetta can do the first stage in a few minutes on a modern computer. The second stage takes longer because of the increased complexity when considering the full representation (all atoms) of amino acids.

    Your computer typically generates 5-20 of these trajectories (per work unit) and then sends us back the lowest energy shape seen in each one. We then look at all of the low energy shapes, generated by all of your computers, to find the very lowest ones. This becomes our prediction for the fold of that protein.

    To join this project, download and install the BOINC software on which it runs. Then attach to the project. While you are at BOINC, look at some of the other projects to see what else might be of interest to you.

    Rosetta screensaver

    BOINC


    ScienceSprings is powered by MAINGEAR computers

     
  • richardmitnick 9:00 am on July 18, 2014 Permalink | Reply
    Tags: , , , Malariacontrol.net, Medicine,   

    From Malariacontrol.net 

    Malariaconrtrol

    Malariacontrol.net

    What is malariacontrol.net?

    The malariacontrol.net project is an application that makes use of network computing for stochastic modelling of the clinical epidemiology and natural history of Plasmodium falciparum malaria.

    Simulation modeling of malaria

    The fight against malaria was given a new impetus by the call for eradication at the Gates Malaria Forum in October 2007, making more but still limited resources available for research, development, and combating malaria. To inform decisions on which new or existing tools to prioritize, we have developed a general platform for comparing, fitting, and evaluating stochastic simulation models of Plasmodium falciparum malaria, programmed in C++ (openmalaria).

    We use this to inform the target product profiles for novel interventions like vaccines, addressing questions such as minimal efficacy and duration of effects needed for a vaccine to be worthwhile, and also to optimize deployment of established interventions and integrated strategies. Field trials of interventions consider effects over 1-2 years at most, but the dynamics of immunity and human demography also lead to longer term effects. We consider many different outcomes, including transmission reduction or interruption, illness, hospitalization, or death, as well as economic aspects.

    Malaria occurs in an enormous variety of ecological settings, and interventions are not always universally applicable. For instance, indoor residual spraying works only with indoor-resting mosquitoes, and insecticide treated mosquito nets only with nocturnal vectors. The best combinations of interventions vary, as do optimal delivery approaches and their health system implications. There are trade-offs between high coverage and costs or feasibility of deployment. Indiscriminate deployment may lead to evolution of drug resistance or insensitivity to other interventions. To support the analysis of these elements we are assembling databases of health system descriptions, intervention costing, and vector bionomics across different malaria ecotypes.

    Uncertainties inherent in simulations of complex systems are addressed using probabilistic sensitivity analyses, fitting multiple different models, and basing predictions on model ensembles not single simulations. This requires super-computing, both for statistical fitting (which must simultaneously reproduce a wide range of outcomes across different settings), and for exploring predictions. We obtain this computing power over the internet from spare capacity on the computers of volunteers (malariacontrol.net).

    Meetings with potential users of these predicitons are used to promote the models and their predictions to wider communities of malariologists, planners, and policy specialists. We are also developing web-based job submission and analysis systems to increase internet access to models.

    To help in this fight, download and install BOINC software, attach to the project. While you are at BOINC, look over the other projects to see where else you might find interest and be of service.

    BOINC


    ScienceSprings is powered by MAINGEAR computers

     
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