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Gandhi M.,University of California at San Francisco | Gandhi R.T.,MGH | Gandhi R.T.,Massachusetts Institute of Technology
New England Journal of Medicine | Year: 2014

A 52-year-old man with a history of homelessness, depression, and polysubstance use received a diagnosis of human immunodeficiency virus type 1 (HIV-1) infection in 2005 but has declined antiretroviral therapy (ART) in the past. His CD4+ T-cell count is now 257 per cubic millimeter, and his plasma HIV-1 RNA level is 17,000 copies per milliliter. The patient was prescribed a multipill antiretroviral regimen 2 months ago but has not followed this regimen regularly because "taking out lots of pills in the shelter just announces to the world that I have AIDS [the acquired immunodeficiency syndrome]." The patient desires to keep his HIV status private and states that he would take medications regularly if he could take just "one pill once a day." The patient is not taking any other medications; his renal function is normal. How should he be evaluated and treated? Copyright © 2014 Massachusetts Medical Society.


News Article | March 1, 2017
Site: www.businesswire.com

SAN FRANCISCO--(BUSINESS WIRE)--Freenome, the health technology company reinventing disease management through systematized early detection and intervention, announced today that it has raised $65 million USD in Series A funding led by Andreessen Horowitz. Seed investors Andreessen Horowitz, Data Collective (DCVC), and Founders Fund also contributed to this Series A round, and are joined by GV (Google Ventures), Polaris Partners, Innovation Endeavors, Asset Management Ventures, Charles River Ventures and Spectrum 28. Andreessen Horowitz General Partner Vijay Pande will also be joining Freenome’s Board. Since 2014, Freenome has been using a combination of machine learning, biology and computer science to create simple and effective disease screenings. To date, the company has collaborated with 25 research partners around the world - including Moores Cancer Center at UC San Diego Health (UCSD), University of California San Francisco (UCSF) and Massachusetts General Hospital (MGH) - and have gathered and processed thousands of samples through their active clinical trials. Freenome has also partnered with five pharmaceutical companies to assess generalizability of their software to other questions in oncology such as pre-treatment drug response prediction. “Our goal is to bringing accurate, accessible and non-invasive disease screenings to doctors to proactively treat cancer and other diseases at their most manageable stages,“ said Freenome co-founder Gabe Otte. “This funding will allow us to increase the number of clinical trials in collaboration with top researchers and clinicians around the world, enabling us to bring our product to market more quickly and equip people with knowledge and tools to live healthier lives.” Freenome raised $5.5M USD in 2016 to prove the potential of its technology. Through machine learning, the team discovered signatures independent of traditional mutation calling, such as immunological and metabolic changes in cell-free DNA and other analytes, that are more robust for early cancer detection and allow for a cost-effective assay. Freenome is currently focused on scaling technology and accuracy of screenings for four types of cancer - lung, colorectal, breast and prostate - with plans to address other forms of cancer and diseases. This Series A funding will be used to expand clinical trials, accelerate research and bring their disease screenings to market. "There are many drugs and surgical techniques to cure patients of cancer –– if the cancer is caught early. While tests to detect cancer early exist, they are not sufficiently accurate, and are riddled with false positives and false negatives," said Andreessen Horowitz General Partner and Freenome Board member, Vijay Pande. “Freenome's machine learning-driven approach and impressively accurate results from blinded trials make them the right team to swing the pendulum toward a new era of prevention.” Headquartered in South San Francisco, Freenome is a health technology company bringing accurate, accessible and non-invasive disease screenings to you and your doctor to proactively treat cancer and other diseases at their most manageable stages. Freenome aims to reinvent disease management through systematized early detection and intervention. Freenome has raised $71.2M USD since launching in 2014, and is funded by Andreessen Horowitz, GV (Google Ventures), Polaris Partners, Innovation Endeavors, Spectrum 28, Asset Management Ventures, Charles River Ventures, Data Collective (DCVC), Third Kind Ventures, AME Cloud Ventures, Allen and Company and Founders Fund.


News Article | January 21, 2017
Site: www.techtimes.com

A new study shares that tracking immune cells could potentially be able to assist in the identification of inflammatory arthritis. According to the study conducted by the researchers of the Massachusetts General Hospital (MGH) Center for Immunology and Inflammatory Diseases (CIID), there appears to be a connection between immune cells and inflammatory arthritis. Senior author of the study, Andrew Luster noted that Inflammatory arthritis is the direct effect of immune cells that are "recruited from blood into the joint" by means of a highly controlled procedure that is monitored by adhesion receptors and chemoattractants. However, when the ailment becomes symptomatic, it becomes tough to ascertain what could have been the catalyst in the process of the immune cell recruitment in the joint, as well as what is the exact role of the various types of chemoattractants. The latest study is intended to gain a thorough understanding of this process notes Luster. By understating the process, scientists would be able to develop an effective treatment for inflammatory arthritis. For the unfamiliar, arthritis is a disease where one or more joints get inflamed, which leads to swelling, stiffness or soreness. The most common types of arthritis are Inflammatory and non-inflammatory. This disease is more frequently prevalent in women than men. Prior to the latest study, many experts opined that inflammatory arthritis may involve genetic or hormonal factors as well. Rheumatoid or inflammatory arthritis can also occur in children alongside adults. Apart from joints it can take a toll on other organs of a human body, such as eyes and lungs. Living with inflammatory arthritis is a big challenge, even if the condition is treatable and there is no cure. The researchers have zoned in on the importance of the complement C5a molecule for immune cells dubbed neutrophils.  This molecule gets the neutrophils to obey joint surfaces and travel to the joint. This process is the one that sets off the inflammatory cascade. "The control of immune cell entry into the joint represents a major point at which new therapies could be developed to reduce the symptoms of inflammatory arthritis," says Luster. The scientist also shared that the addition of imaging of the joints may aid in the understanding of the process of how a drug can have therapeutic effect. If it reveals that the joints have been affected by multiple chemoattractants, then the mechanism's understanding will enable the "rational design of combination therapies to completely shut down critical steps in the process." The study has been published in the journal Science Immunology. © 2017 Tech Times, All rights reserved. Do not reproduce without permission.


News Article | February 21, 2017
Site: www.eurekalert.org

Brain-computer interface advance allows fast, accurate typing by people with paralysis in Stanford-led study A clinical research publication led by Stanford University investigators has demonstrated that a brain-to-computer hookup can enable people with paralysis to type via direct brain control at the highest speeds and accuracy levels reported to date. The report involved three study participants with severe limb weakness -- two from amyotrophic lateral sclerosis, also called Lou Gehrig's disease, and one from a spinal cord injury. They each had one or two baby-aspirin-sized electrode arrays placed in their brains to record signals from the motor cortex, a region controlling muscle movement. These signals were transmitted to a computer via a cable and translated by algorithms into point-and-click commands guiding a cursor to characters on an onscreen keyboard. Each participant, after minimal training, mastered the technique sufficiently to outperform the results of any previous test of brain-computer interfaces, or BCIs, for enhancing communication by people with similarly impaired movement. Notably, the study participants achieved these typing rates without the use of automatic word-completion assistance common in electronic keyboarding applications nowadays, which likely would have boosted their performance. One participant, Dennis Degray of Menlo Park, California, was able to type 39 correct characters per minute, equivalent to about eight words per minute. This point-and-click approach could be applied to a variety of computing devices, including smartphones and tablets, without substantial modifications, the Stanford researchers said. "Our study's success marks a major milestone on the road to improving quality of life for people with paralysis," said Jaimie Henderson, MD, professor of neurosurgery, who performed two of the three device-implantation procedures. The third took place at Massachusetts General Hospital. Henderson and Krishna Shenoy, PhD, professor of electrical engineering, are co-senior authors of the study, which will be published online Feb. 21 in eLife. The lead authors are former postdoctoral scholar Chethan Pandarinath, PhD, and postdoctoral scholar Paul Nuyujukian, MD, PhD, both of whom spent well over two years working full time on the project at Stanford. "This study reports the highest speed and accuracy, by a factor of three, over what's been shown before," said Shenoy, a Howard Hughes Medical Institute investigator who's been pursuing BCI development for 15 years and working with Henderson since 2009. "We're approaching the speed at which you can type text on your cellphone." "The performance is really exciting," said Pandarinath, who now has a joint appointment at Emory University and the Georgia Institute of Technology as an assistant professor of biomedical engineering. "We're achieving communication rates that many people with arm and hand paralysis would find useful. That's a critical step for making devices that could be suitable for real-world use." Shenoy's lab pioneered the algorithms used to decode the complex volleys of electrical signals fired by nerve cells in the motor cortex, the brain's command center for movement, and convert them in real time into actions ordinarily executed by spinal cord and muscles. "These high-performing BCI algorithms' use in human clinical trials demonstrates the potential for this class of technology to restore communication to people with paralysis," said Nuyujukian. Millions of people with paralysis reside in the United States. Sometimes their paralysis comes gradually, as occurs in ALS. Sometimes it arrives suddenly, as in Degray's case. Now 64, Degray became quadriplegic on Oct. 10, 2007, when he fell and sustained a life-changing spinal-cord injury. "I was taking out the trash in the rain," he said. Holding the garbage in one hand and the recycling in the other, he slipped on the grass and landed on his chin. The impact spared his brain but severely injured his spine, cutting off all communication between his brain and musculature from the head down. "I've got nothing going on below the collarbones," he said. Degray received two device implants at Henderson's hands in August 2016. In several ensuing research sessions, he and the other two study participants, who underwent similar surgeries, were encouraged to attempt or visualize patterns of desired arm, hand and finger movements. Resulting neural signals from the motor cortex were electronically extracted by the embedded recording devices, transmitted to a computer and translated by Shenoy's algorithms into commands directing a cursor on an onscreen keyboard to participant-specified characters. The researchers gauged the speeds at which the patients were able to correctly copy phrases and sentences -- for example, "The quick brown fox jumped over the lazy dog." Average rates were 7.8 words per minute for Degray and 6.3 and 2.7 words per minute, respectively, for the other two participants. The investigational system used in the study, an intracortical brain-computer interface called the BrainGate Neural Interface System*, represents the newest generation of BCIs. Previous generations picked up signals first via electrical leads placed on the scalp, then by being surgically positioned at the brain's surface beneath the skull. An intracortical BCI uses a tiny silicon chip, just over one-sixth of an inch square, from which protrude 100 electrodes that penetrate the brain to about the thickness of a quarter and tap into the electrical activity of individual nerve cells in the motor cortex. Henderson likened the resulting improved resolution of neural sensing, compared with that of older-generation BCIs, to that of handing out applause meters to individual members of a studio audience rather than just stationing them on the ceiling, "so you can tell just how hard and how fast each person in the audience is clapping." Shenoy said the day will come -- closer to five than 10 years from now, he predicted --when a self-calibrating, fully implanted wireless system can be used without caregiver assistance, has no cosmetic impact and can be used around the clock. "I don't see any insurmountable challenges." he said. "We know the steps we have to take to get there." Degray, who continues to participate actively in the research, knew how to type before his accident but was no expert at it. He described his newly revealed prowess in the language of a video game aficionado. "This is like one of the coolest video games I've ever gotten to play with," he said. "And I don't even have to put a quarter in it." The study's results are the culmination of a long-running collaboration between Henderson and Shenoy and a multi-institutional consortium called BrainGate. Leigh Hochberg, MD, PhD, a neurologist and neuroscientist at Massachusetts General Hospital, Brown University and the VA Rehabilitation Research and Development Center for Neurorestoration and Neurotechnology in Providence, Rhode Island, directs the pilot clinical trial of the BrainGate system and is a study co-author. "This incredible collaboration continues to break new ground in developing powerful, intuitive, flexible neural interfaces that we all hope will one day restore communication, mobility and independence for people with neurologic disease or injury," said Hochberg. Stanford research assistant Christine Blabe was also a study co-author, as were BrainGate researchers from Massachusetts General Hospital and Case Western University. The study was funded by the National Institutes of Health (grants R01DC014034, R011NS066311, R01DC009899, N01HD53404 and N01HD10018), the Stanford Office of Postdoctoral Affairs, the Craig H. Neilsen Foundation, the Stanford Medical Scientist Training Program, Stanford BioX-NeuroVentures, the Stanford Institute for Neuro-Innovation and Translational Neuroscience, the Stanford Neuroscience Institute, Larry and Pamela Garlick, Samuel and Betsy Reeves, the Howard Hughes Medical Institute, the U.S. Department of Veterans Affairs, the MGH-Dean Institute for Integrated Research on Atrial Fibrillation and Stroke and Massachusetts General Hospital. Stanford's Office of Technology Licensing holds intellectual property on the intercortical BCI-related engineering advances made in Shenoy's lab. Stanford's departments of Neurosurgery and of Electrical Engineering also supported the work. *CAUTION: Investigational Device. Limited by Federal Law to Investigational Use. The Stanford University School of Medicine consistently ranks among the nation's top medical schools, integrating research, medical education, patient care and community service. For more news about the school, please visit http://med. . The medical school is part of Stanford Medicine, which includes Stanford Health Care and Stanford Children's Health. For information about all three, please visit http://med. .


News Article | February 15, 2017
Site: www.prnewswire.co.uk

Research and Markets has announced the addition of the "Viral Vectors and Plasmid DNA Manufacturing Market, 2016-2026" report to their offering. The "Viral Vectors and Plasmid DNA Manufacturing Market, 2016-2026" report provides an extensive study of the rapidly growing market of gene therapy vectors, with a special focus on lentivirus, AAV, adenovirus, retrovirus and plasmid DNA. Gene therapies require a viral or non-viral vector to efficiently transfer the therapeutic gene into targets cells. It is well known that the gene therapy market is characterized by a robust pipeline of drugs targeting several therapeutic indications. The pipeline is witnessing continuous progression that has further led to an upward surge in demand for gene delivery tools, including both viral and non-viral vectors. Several players, including pharmaceutical companies, research institutes, contract manufacturing organizations and non-profit organizations, are playing a critical role in the development and production of these vectors. Led by technological advancements, these organizations have developed and introduced proprietary platforms to overcome the challenges posed by conventional production technologies and have also made heavy investments in the expansion of their existing capabilities for vector production. During the course of our study, we identified over 140 organizations that are actively involved in the production of viral vectors and plasmid DNA. In addition to other elements, the study provides information on: - The current status of the market with respect to key players along with information on the location of their manufacturing facilities, scale of production, type of vectors manufactured, purpose of production (fulfilling in-house requirement/as a contract service provider) and the type of organization (industry/academia). - Most active regions in terms of vector manufacturing; the report contains schematic representations of world maps that clearly indicate the locations of global vector manufacturing hubs. - Elaborate profiles of key players that have commercial scale production capabilities for viral vector/plasmid DNA; each profile covers an overview of the company, its financial performance, information on its manufacturing facilities, vector manufacturing technology, recent investments, expansions and collaborations. - A discussion on the key enablers of the market and challenges associated with the vector production process. - Potential future growth of the vector manufacturing market segmented by the type of vector and phase of development. For the purposes of our analysis, we took into consideration several parameters that are likely to impact the growth of this market over the next decade; these include the likely increase in the number of clinical studies, increase in the patient population, existing price variations among different vector types, estimated dosage frequency and the anticipated success of commercial gene therapy products. The research, analysis and insights presented in this report are backed by a deep understanding of key insights gathered from both secondary and primary research. Actual figures have been sourced and analyzed from publicly available data. For the purpose of the study, we invited over 100 stakeholders to participate in a survey to solicit their opinions on upcoming opportunities and challenges that must be considered for a more inclusive growth.Our opinions and insights presented in this study were influenced by discussions conducted with several key players in this domain. The report features detailed transcripts of interviews held with Alain Lamproye (President of Biopharma Business Unit, Novasep), Bakhos A Tannous (Director, MGH Viral Vector Development Facility, Massachusetts General Hospital), Brain M Dattilo (Business Development Manager, Waisman Biomanufacturing), Joost van den Berg (Director, Amsterdam BioTherapeutics Unit), Nicole Faust (Chief Scientific Officer, Cevec) and Semyon Rubinchik (Scientific Director, ACGT). For more information about this report visit http://www.researchandmarkets.com/research/twvddp/viral_vectors_and


News Article | February 23, 2017
Site: www.futurity.org

A brain-to-computer hookup recently allowed people with severe limb weakness to type via direct brain control at the highest speeds and accuracy levels reported to date. Two of the participants have amyotrophic lateral sclerosis, also called Lou Gehrig’s disease, and one has a spinal cord injury. They each had one or two baby-aspirin-sized electrode arrays placed in their brains to record signals from the motor cortex, a region controlling muscle movement. The signals were transmitted to a computer via a cable and translated by algorithms into point-and-click commands guiding a cursor to characters on an onscreen keyboard. Each participant, after minimal training, mastered the technique sufficiently to outperform the results of any previous test of brain-computer interfaces, or BCIs, for enhancing communication by people with similarly impaired movement. Notably, they achieved the typing rates without the use of automatic word-completion assistance common in electronic keyboarding applications nowadays, which likely would have boosted their performance. One participant, Dennis Degray of Menlo Park, California, was able to type 39 correct characters per minute, equivalent to about eight words per minute. This point-and-click approach could be applied to a variety of computing devices, including smartphones and tablets, without substantial modifications, the researchers say. Their findings appear in the journal eLife. “Our study’s success marks a major milestone on the road to improving quality of life for people with paralysis,” says Jaimie Henderson, professor of neurosurgery at Stanford University, who performed two of the three device-implantation procedures at Stanford Hospital. The third took place at Massachusetts General Hospital. “This study reports the highest speed and accuracy, by a factor of three, over what’s been shown before,” says co-senior author Krishna Shenoy, professor of electrical engineering. “We’re approaching the speed at which you can type text on your cellphone.” “The performance is really exciting,” says former postdoctoral scholar Chethan Pandarinath, who now has a joint appointment at Emory University and the Georgia Institute of Technology as an assistant professor of biomedical engineering. “We’re achieving communication rates that many people with arm and hand paralysis would find useful. That’s a critical step for making devices that could be suitable for real-world use.” Shenoy’s lab pioneered the algorithms used to decode the complex volleys of electrical signals fired by nerve cells in the motor cortex, the brain’s command center for movement, and convert them in real time into actions ordinarily executed by spinal cord and muscles. “These high-performing BCI algorithms’ use in human clinical trials demonstrates the potential for this class of technology to restore communication to people with paralysis,” says postdoctoral scholar Paul Nuyujukian. Millions of people with paralysis live in the United States. Sometimes their paralysis comes gradually, as occurs in ALS. Sometimes it arrives suddenly, as in Degray’s case. Now 64, Degray became quadriplegic on October 10, 2007, when he fell and sustained a life-changing spinal-cord injury. “I was taking out the trash in the rain,” he said. Holding the garbage in one hand and the recycling in the other, he slipped on the grass and landed on his chin. The impact spared his brain but severely injured his spine, cutting off all communication between his brain and musculature from the head down. “I’ve got nothing going on below the collarbones,” he says. Degray received two device implants at Henderson’s hands in August 2016. In several ensuing research sessions, he and the other two study participants, who underwent similar surgeries, were encouraged to attempt or visualize patterns of desired arm, hand, and finger movements. Resulting neural signals from the motor cortex were electronically extracted by the embedded recording devices, transmitted to a computer and translated by Shenoy’s algorithms into commands directing a cursor on an onscreen keyboard to participant-specified characters. The researchers gauged the speeds at which the patients were able to correctly copy phrases and sentences—for example, “The quick brown fox jumped over the lazy dog.” Average rates were 7.8 words per minute for Degray and 6.3 and 2.7 words per minute, respectively, for the other two participants. The investigational system used in the study, an intracortical brain-computer interface called the BrainGate Neural Interface System, represents the newest generation of BCIs. Previous generations picked up signals first via electrical leads placed on the scalp, then by being surgically positioned at the brain’s surface beneath the skull. An intracortical BCI uses a tiny silicon chip, just over one-sixth of an inch square, from which protrude 100 electrodes that penetrate the brain to about the thickness of a quarter and tap into the electrical activity of individual nerve cells in the motor cortex. Henderson likened the resulting improved resolution of neural sensing, compared with that of older-generation BCIs, to that of handing out applause meters to individual members of a studio audience rather than just stationing them on the ceiling, “so you can tell just how hard and how fast each person in the audience is clapping.” The day will come—closer to five than 10 years from now, Shenoy predicts—when a self-calibrating, fully implanted wireless system can be used without caregiver assistance, has no cosmetic impact. and can be used around the clock. “I don’t see any insurmountable challenges,” he says. “We know the steps we have to take to get there.” Degray, who continues to participate actively in the research, knew how to type before his accident but was no expert at it. He described his newly revealed prowess in the language of a video game aficionado. “This is like one of the coolest video games I’ve ever gotten to play with,” he says. “And I don’t even have to put a quarter in it.” Stanford research assistant Christine Blabe is also a study coauthor, as are BrainGate researchers from Massachusetts General Hospital and Case Western University. Funding came from the National Institutes of Health, the Stanford Office of Postdoctoral Affairs, the Craig H. Neilsen Foundation, the Stanford Medical Scientist Training Program, Stanford BioX-NeuroVentures, the Stanford Institute for Neuro-Innovation and Translational Neuroscience, the Stanford Neuroscience Institute, Larry and Pamela Garlick, Samuel and Betsy Reeves, the Howard Hughes Medical Institute, the US Department of Veterans Affairs, the MGH-Dean Institute for Integrated Research on Atrial Fibrillation and Stroke, and Massachusetts General Hospital. Stanford’s Office of Technology Licensing holds intellectual property on the intercortical BCI-related engineering advances made in Shenoy’s lab.


News Article | February 17, 2017
Site: www.biosciencetechnology.com

Regina Barzilay is working with MIT students and medical doctors in an ambitious bid to revolutionize cancer care. She is relying on a tool largely unrecognized in the oncology world but deeply familiar to hers: machine learning. Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science, was diagnosed with breast cancer in 2014. She soon learned that good data about the disease is hard to find. “You are desperate for information — for data,” she says now. “Should I use this drug or that? Is that treatment best? What are the odds of recurrence? Without reliable empirical evidence, your treatment choices become your own best guesses.” Across different areas of cancer care — be it diagnosis, treatment, or prevention — the data protocol is similar. Doctors start the process by mapping patient information into structured data by hand, and then run basic statistical analyses to identify correlations. The approach is primitive compared with what is possible in computer science today, Barzilay says. These kinds of delays and lapses (which are not limited to cancer treatment), can really hamper scientific advances, Barzilay says. For example, 1.7 million people are diagnosed with cancer in the U.S. every year, but only about 3 percent enroll in clinical trials, according to the American Society of Clinical Oncology. Current research practice relies exclusively on data drawn from this tiny fraction of patients. “We need treatment insights from the other 97 percent receiving cancer care,” she says. To be clear: Barzilay isn’t looking to up-end the way current clinical research is conducted. She just believes that doctors and biologists — and patients — could benefit if she and other data scientists lent them a helping hand. Innovation is needed and the tools are there to be used. Barzilay has struck up new research collaborations, drawn in MIT students, launched projects with doctors at Massachusetts General Hospital, and begun empowering cancer treatment with the machine learning insight that has already transformed so many areas of modern life. At the MIT Stata Center, Barzilay, a lively presence, interrupts herself mid-sentence, leaps up from her office couch, and runs off to check on her students. She returns with a laugh. An undergraduate group is assisting Barzilay with a federal grant application, and they’re down to the wire on the submission deadline. The funds, she says, would enable her to pay the students for their time. Like Barzilay, they are doing much of this research for free, because they believe in its power to do good. “In all my years at MIT I have never seen students get so excited about the research and volunteer so much of their time,” Barzilay says. At the center of Barzilay’s project is machine learning, or algorithms that learn from data and find insights without being explicitly programmed where to look for them. This tool, just like the ones Amazon, Netflix, and other sites use to track and predict your preferences as a consumer, can make short work of gaining insight into massive quantities of data. Applying it to patient data can offer tremendous assistance to people who, as Barzilay knows well, really need the help. Today, she says, a woman cannot retrieve answers to simple questions such as: What was the disease progression for women in my age range with the same tumor characteristics? What a machine can see Working closely with collaborators Taghian Alphonse, chief of breast radiation oncology at Massachusetts General Hospital (MGH); Kevin Hughes, co-director of the Avon Comprehensive Breast Evaluation Center at MGH; and Constance Lehman, the chief of the breast imaging division at MGH, Barzilay intends to bring data science into clinical research nationwide. But first, she’s content with connecting her world with theirs. Barzilay’s work in natural language processing (NLP) enables machines to search, summarize, and interpret textual documents, such as those about cancer patients in pathology reports. Using NLP tools, she and her students extracted clinical information from 108,000 reports provided by area hospitals. The database they've created has an accuracy rate of 98 percent. Next she wants to incorporate treatment outcomes into it. For another study, Barzilay has developed a database that Hughes and his team can use to monitor the development of atypias, which help identify which patients are at risk of developing cancer later in life. Machines are good at making predictions — “Why not throw all the information you have about a breast cancer patient into a model?” she says — but Barzilay is wary of having the recommendations arrive as highly complex, computational recommendations without explanation. Jointly with Tommi Jaakkola, a professor of electrical engineering and computer science at MIT, and graduate student Tao Lei, she is also developing interpretable neural models that can justify and explain the machine-based predictive reasoning. Barzilay is also looking at how new tools can help do preventive work. Mammograms contain lots of information that may be hard for a human eye to decipher. Machines can detect subtle changes and are more capable of detecting low-level patterns. Jointly with Lehman and graduate student Nicolas Locascio, Barzilay is applying deep learning for automating analysis of mammogram data. As the first step, they are aiming to compute density and other scores currently derived by radiologists who manually analyze these images. Their ultimate goal is to identify patients who are likely to develop a tumor before it’s even visible on a mammogram, and also to predict which patients are heading toward recurrence after their initial treatment. Ultimate success, Barzilay says, will involve drawing on computer science in unexpected ways, and pushing it in a variety of new health-related directions. Outside her door, several of Barzilay’s students are talking ideas, hunching over laptops, and drinking coffee. An object set against the back wall resembles an odd coatrack. Guided by an idea from Taghian, six undergraduate students, led by graduate student Julian Straub, built a device that uses machine-learning to detect lymphedema, a swelling of the extremities that can be caused by the removal of or damage to lymph nodes as part of cancer treatment. It can be disabling and incurable unless detected early. Because of their high cost, these machines — lymphometers — are rare in the U.S.; very few hospitals have them. Students have created an affordable version. And they hope to start testing this device at MGH in a couple of months. “These students are doing amazing work,” says Barzilay. “These innovations will make a really big difference. It is an entry point. There is so much to do. We are just getting started.”


News Article | February 16, 2017
Site: phys.org

Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science, was diagnosed with breast cancer in 2014. She soon learned that good data about the disease is hard to find. "You are desperate for information—for data," she says now. "Should I use this drug or that? Is that treatment best? What are the odds of recurrence? Without reliable empirical evidence, your treatment choices become your own best guesses." Across different areas of cancer care—be it diagnosis, treatment, or prevention—the data protocol is similar. Doctors start the process by mapping patient information into structured data by hand, and then run basic statistical analyses to identify correlations. The approach is primitive compared with what is possible in computer science today, Barzilay says. These kinds of delays and lapses (which are not limited to cancer treatment), can really hamper scientific advances, Barzilay says. For example, 1.7 million people are diagnosed with cancer in the U.S. every year, but only about 3 percent enroll in clinical trials, according to the American Society of Clinical Oncology. Current research practice relies exclusively on data drawn from this tiny fraction of patients. "We need treatment insights from the other 97 percent receiving cancer care," she says. To be clear: Barzilay isn't looking to up-end the way current clinical research is conducted. She just believes that doctors and biologists—and patients—could benefit if she and other data scientists lent them a helping hand. Innovation is needed and the tools are there to be used. Barzilay has struck up new research collaborations, drawn in MIT students, launched projects with doctors at Massachusetts General Hospital, and begun empowering cancer treatment with the machine learning insight that has already transformed so many areas of modern life. At the MIT Stata Center, Barzilay, a lively presence, interrupts herself mid-sentence, leaps up from her office couch, and runs off to check on her students. She returns with a laugh. An undergraduate group is assisting Barzilay with a federal grant application, and they're down to the wire on the submission deadline. The funds, she says, would enable her to pay the students for their time. Like Barzilay, they are doing much of this research for free, because they believe in its power to do good. "In all my years at MIT I have never seen students get so excited about the research and volunteer so much of their time," Barzilay says. At the center of Barzilay's project is machine learning, or algorithms that learn from data and find insights without being explicitly programmed where to look for them. This tool, just like the ones Amazon, Netflix, and other sites use to track and predict your preferences as a consumer, can make short work of gaining insight into massive quantities of data. Applying it to patient data can offer tremendous assistance to people who, as Barzilay knows well, really need the help. Today, she says, a woman cannot retrieve answers to simple questions such as: What was the disease progression for women in my age range with the same tumor characteristics? What a machine can see Working closely with collaborators Taghian Alphonse, chief of breast radiation oncology at Massachusetts General Hospital (MGH); Kevin Hughes, co-director of the Avon Comprehensive Breast Evaluation Center at MGH; and Constance Lehman, the chief of the breast imaging division at MGH, Barzilay intends to bring data science into clinical research nationwide. But first, she's content with connecting her world with theirs. Barzilay's work in natural language processing (NLP) enables machines to search, summarize, and interpret textual documents, such as those about cancer patients in pathology reports. Using NLP tools, she and her students extracted clinical information from 108,000 reports provided by area hospitals. The database they've created has an accuracy rate of 98 percent. Next she wants to incorporate treatment outcomes into it. For another study, Barzilay has developed a database that Hughes and his team can use to monitor the development of atypias, which help identify which patients are at risk of developing cancer later in life. Machines are good at making predictions—"Why not throw all the information you have about a breast cancer patient into a model?" she says—but Barzilay is wary of having the recommendations arrive as highly complex, computational recommendations without explanation. Jointly with Tommi Jaakkola, a professor of electrical engineering and computer science at MIT, and graduate student Tao Lei, she is also developing interpretable neural models that can justify and explain the machine-based predictive reasoning. Barzilay is also looking at how new tools can help do preventive work. Mammograms contain lots of information that may be hard for a human eye to decipher. Machines can detect subtle changes and are more capable of detecting low-level patterns. Jointly with Lehman and graduate student Nicolas Locascio, Barzilay is applying deep learning for automating analysis of mammogram data. As the first step, they are aiming to compute density and other scores currently derived by radiologists who manually analyze these images. Their ultimate goal is to identify patients who are likely to develop a tumor before it's even visible on a mammogram, and also to predict which patients are heading toward recurrence after their initial treatment. Ultimate success, Barzilay says, will involve drawing on computer science in unexpected ways, and pushing it in a variety of new health-related directions. Outside her door, several of Barzilay's students are talking ideas, hunching over laptops, and drinking coffee. An object set against the back wall resembles an odd coatrack. Guided by an idea from Taghian, six undergraduate students, led by graduate student Julian Straub, built a device that uses machine-learning to detect lymphedema, a swelling of the extremities that can be caused by the removal of or damage to lymph nodes as part of cancer treatment. It can be disabling and incurable unless detected early. Because of their high cost, these machines—lymphometers—are rare in the U.S.; very few hospitals have them. Students have created an affordable version. And they hope to start testing this device at MGH in a couple of months. "These students are doing amazing work," says Barzilay. "These innovations will make a really big difference. It is an entry point. There is so much to do. We are just getting started." Explore further: Meeting of the minds for machine intelligence


News Article | February 24, 2017
Site: www.eurekalert.org

Massachusetts General Hospital (MGH) researchers have identified a mechanism that controls the expression of genes regulating the growth of the most aggressive form of medulloblastoma, the most common pediatric brain tumor. In their report published online in Cancer Discovery, the team also identifies potential targets for future treatments. "We set out to find the most important regulators of gene expression programs in medulloblastoma," says senior author Miguel Rivera, MD, of theMGH Department of Pathology / and the Center for Cancer Research. "To do that we used a powerful genomic technology called chromatin profiling to map all the genomic elements contributing to transcription regulation in Group 3 medulloblastoma - the most aggressive subtype. This goes beyond measuring gene expression because it tells you how genes are turned on and off." Medulloblastoma is a fast-growing tumor that arises in the developing brain and most commonly affects children under the age of 10. Four molecular variants, each with different patterns of DNA alteration and gene expression, have been identified. Subtypes WNT and SSH are the best understood; the other two - Group 3 and Group 4 - are poorly understood and account for 60 percent of tumors. Cells regulate whether specific genes are transcribed into RNA through the action of transcription factors, proteins that bind to DNA and either stimulate or suppress the expression of their target genes. Rivera's team used advanced genomic technologies to identify key DNA elements called enhancers that were active in primary Group 3 medulloblastoma samples and cell lines. The transcription factor OTX2, which plays a role in normal brain development and is known to be highly expressed in Group 3 medulloblastomas, was present at the majority of active enhancer sites in tumors, suggesting it may have a role in promoting the expression of tumor-associated genes. Subsequent experiments revealed that OTX2 can function as a "pioneer factor," opening up chromatin - which consists of DNA wound around proteins called histones - to activate enhancers and that its function is amplified by a second transcription factor called NEUROD1. The investigators then identified a set of genes the expression of which was significantly reduced when OTX2 was suppressed. Among these genes, they found that expression of the kinase NEK2 responded to OTX2 levels and that its depletion or pharmacologic inhibition strongly reduced the growth and survival of medulloblastoma cells. "Overall, our findings show that OTX2 is a critical factor in regulating gene expression programs in Group 3 medulloblastoma and possibly in the WNT and Group 4 subtypes, where it is also expressed," says Rivera, who is an assistant professor of Pathology at Harvard Medical School. "This work points to OTX2 itself and its target genes - including NEK2 - as potential therapeutic targets. Disruption of the relationship between OTX2 and NEUROD1 may also be a potential treatment strategy. We now need to get a more a detailed picture of the mechanisms OTX2 uses to activate enhancers and improve our understanding of the function of NEK2 and other target genes regulated by OTX2." Gaylor Boulay, PhD, of the MGH Center for Cancer Research is lead author of the Cancer Discovery paper. Additional co-authors are Mary Awad, Sowmya Iyer, Wannaporn Boonseng, Nikki Rossetti, Beverly Naigles, Shruthi Rengarajan, Angela Volorio, James Kim, and Martin Aryee, PhD, MGH Center for Cancer Research; Nicolo Riggi, MD, PhD, University of Lausanne, Switzerland; Tenley Archer, PhD, and Scott Pomeroy, MD, PhD, Boston Children's Hospital; and Jill Mesirov, PhD, and Pablo Tamayo, PhD, University of California, San Diego. The research team also worked in close contact with clinical colleagues at MGH - including Nancy Tarbell, MD, David Ebb, MD, Torunn Yock, MD, MCh, and Howard Weinstein, MD. The study was supported by grants from A Kids' Brain Tumor Cure Foundation/The PLGA Foundation. Massachusetts General Hospital, founded in 1811, is the original and largest teaching hospital of Harvard Medical School. The MGH Research Institute conducts the largest hospital-based research program in the nation, with an annual research budget of more than $800 million and major research centers in HIV/AIDS, cardiovascular research, cancer, computational and integrative biology, cutaneous biology, human genetics, medical imaging, neurodegenerative disorders, regenerative medicine, reproductive biology, systems biology, photomedicine and transplantation biology. The MGH topped the 2015 Nature Index list of health care organizations publishing in leading scientific journals and earned the prestigious 2015 Foster G. McGaw Prize for Excellence in Community Service. In August 2016 the MGH was once again named to the Honor Roll in the U.S. News & World Report list of "America's Best Hospitals."


Though the practice of acupuncture predates current understanding of physiology by several millennia, it often provides measureable improvements in health outcomes, particularly in the area of chronic pain. Now, in a study reported in the journal Brain, a team of investigators based at the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH) sheds new light on the question of how. "Acupuncture is a medical therapy that originated in China several thousand years ago," said Vitaly Napadow, PhD, director of the Center for Integrative Pain Neuroimaging at the Martinos Center and senior author of the Brain paper. "But despite its long history, the intervention itself - particularly when coupled with electrical stimulation - has significant similarities to many conventional therapies, such as transcutaneous electrical nerve stimulation (TENS). A large body of clinical research exploring acupuncture for chronic pain disorders has demonstrated that it may be marginally better than a placebo procedure in reducing pain ratings. But questions still remain: How exactly does acupuncture work? Is it any better at improving objective outcomes for chronic pain?" To get to the root of these questions, Napadow and colleagues performed a sham-controlled acupuncture neuroimaging study of carpal tunnel syndrome (CTS), a neuropathic pain disorder. Few chronic pain disorders have established biomarkers or measureable treatment outcomes. However, in CTS measurements taken at the wrist of the speed at which signals are transmitted along the median nerve are a well known and accepted biomarker. In addition, studies by Napadow and others have shown that the brain - particularly the primary somatosensory cortex, which receives signals related to the sense of touch - is remapped in CTS. Specifically, brain cells that usually respond to touch signals from specific fingers start to respond to signals from multiple fingers, which provides another measureable outcome. Using functional magnetic resonance imaging (fMRI) taken before and after several months of therapy in three different groups of CTS patients - one receiving electro-acupuncture at the affected hand, one receiving electro-acupuncture at the ankle opposite the affected hand, and the other receiving sham electro-acupuncture with placebo needles near the affected hand - the researchers found that both real and sham acupuncture improved patient-reported CTS symptoms. However, there were notable differences in physiologic measures. Real acupuncture at the affected hand led to measurable improvements in outcomes both at the affected wrist and in the brain, while acupuncture at the opposite ankle produced improvement at the wrist only. Brain remapping immediately after real acupuncture was linked to long-term improvement in CTS symptoms. No physiologic improvements resulted from sham acupuncture. Even after years of clinical research, controversy continues as to whether acupuncture works primarily by the placebo effect, especially given the slight differences between the efficacy of real and sham acupuncture. The findings of the Brain study help to address this question. Sham acupuncture may produce a stronger placebo effect than a pill because it sends inputs to the brain via skin receptors and is coupled with a specific ritual. But the symptom improvement produced by sham treatment for conditions like CTS also might derive from entirely different mechanisms than those elicited by real acupuncture, the mechanisms of which may more specifically target CTS pathophysiology. "Sham acupuncture may 'work' by modulating known placebo circuitry in the brain," Napadow said. "In contrast, real acupuncture may improve CTS symptoms by rewiring the primary somatosensory cortex, in addition to modulating local blood flow to the peripheral nerve in the wrist. In other words, both peripheral and central neurophysiological changes in CTS may be halted or even reversed by electro-acupuncture interventions that provide more prolonged and regulated input to the brain - something that future, longer-term neuroimaging studies should explore." Napadow and colleagues plan to follow up the Brain study with further research linking objective/physiological and subjective/psychological outcomes for acupuncture-produced pain relief. Better understanding of how acupuncture works to relieve pain ultimately will enable them and others to optimize the therapy to provide effective, non-pharmacological care for chronic pain patients. Yumi Maeda and Hyungjun Kim of the Martinos Center are co-lead authors of the Brain paper. Additional co-authors are Jieun Kim, Stephen Cina, Jessica Gerber, Pia Mezzacappa, Alexandra Libby and Ishtiaq Mawla, Martinos Center; Norman Kettner, Logan University, Chesterfield, Mo.; Cristina Malatesta, Clair McManus, Rebecca Ong-Sutherland and Leslie Morse, Spaulding Rehabilitation Hospital; Ted Kaptchuk, Beth Israel Deaconess Medical Center, and Joseph Audette, Harvard Vanguard Medical Associates. The study was supported by National Center for Complementary and Integrative Health grants R01 AT004714, R01 AT004714-02S1, P01 AT002048 and K24 AT004095; Korea Institute of Oriental Medicine grant C16210; and National Center for Research Resources grants P41 RR14075, S10 RR021110 and S10 RR023043. Massachusetts General Hospital, founded in 1811, is the original and largest teaching hospital of Harvard Medical School. The MGH Research Institute conducts the largest hospital-based research program in the nation, with an annual research budget of more than $800 million and major research centers in HIV/AIDS, cardiovascular research, cancer, computational and integrative biology, cutaneous biology, human genetics, medical imaging, neurodegenerative disorders, regenerative medicine, reproductive biology, systems biology, photomedicine and transplantation biology. The MGH topped the 2015 Nature Index list of health care organizations publishing in leading scientific journals and earned the prestigious 2015 Foster G. McGaw Prize for Excellence in Community Service. In August 2016 the MGH was once again named to the Honor Roll in the U.S. News & World Report list of "America's Best Hospitals."

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