Tuesday, February 24, 2015

Why Do We Age?

By Angel Zhou, Branson School


Why do we age? It might seem like a silly question, but scientists have asked it in hopes that they might one day counteract the process.

Never before have so many people lived for so long. Life expectancy has nearly doubled over the last century, and today there are 36.8 million Americans age 65 and older. Longer life has obvious appeal, but it entails personal hardships and financial burdens. In addition to personal hardship, there is also a cost to society. The financial burden of treating the chronic diseases of aging is expected to rise steadily as Baby Boomers get older. Politics may come to be dominated by the old, who might vote themselves ever more generous benefits for which the young must pay. If longer life expectancy simply leads to more years in which pensioners are disabled and demand expensive services, health-care costs may balloon as never before, while other social needs go unmet.

Since 1999, scientists have studied ways to make organisms live much longer, and with better health than they naturally would.  Previous research assumed that chronic diseases arise and should be treated individually. What if, instead, aging is the root cause of many chronic diseases, and aging can be slowed?


The Buck Institute for Research on Aging (http://www.buckinstitute.org) is the nation’s first independent research facility focused solely on understanding the connection between aging and chronic disease. At the Buck Institute, world-class scientists work in a uniquely collaborative environment to understand how normal aging contributes to the development of conditions specifically associated with getting older such as Alzheimer’s and Parkinson’s diseases and cancer. Their interdisciplinary approach brings scientists from disparate fields together to develop diagnostic tests and treatments to prevent or delay these maladies and to ultimately increase the healthy years of life.

The aging of our population — in past decades and in the foreseeable future — presents both a challenge and an opportunity for all of us as we seek to stay healthy throughout our longer lives. If medical interventions to slow aging result in added years of reasonable fitness, life might extend in a sanguine manner, with most men and women living longer in good vigor, and also working longer, keeping pension and health-care subsidies under control. Indeed, the most exciting work being done in longevity science concerns making the later years vibrant, as opposed to simply adding time at the end.



In his Marin Science Seminar, Dr. Lithgow (http://buckinstitute.org/lithgowLab) of the Buck Institute will discuss the mechanisms of aging by identifying agents that extend lifespan or prevent age-related disease and solutions to eventually eradicate the chronic diseases of late life.

Join us this Wednesday, February 25 for this week's Marin Science Seminar "Do We Have to Grow Old? The New Science of Aging " with Gordon Lithgow, Ph.D. of the Buck Institute in Room 207 at Terra Linda High School in San Rafael. For more information, visit Marin Science Seminar's Facebook page: https://www.facebook.com/events/870825009620005/.

Monday, February 23, 2015

Teaser vid for "Do We Have to Grow Old: The New Science of Aging" Marin Science Seminar

Join us Wednesday, February 25th, 2015 at Terra Linda High School in San Rafael for 

Do We Have to Grow Old? The New Science of Aging 

with Gordon Lithgow PhD of the Buck Institute for Research on Aging, Novato

Aging remains one of the most mysterious processes in science. It is also the leading cause of chronic diseases such as cancer and Alzheimer's disease. Gordon Lithgow studies the basic science of aging at the Buck Institute in Novato. He will talk about what we know about the mechanisms of aging and what scientists are doing to slow aging and eventually eradicate the chronic diseases of late life.

Teaser video below by MSS Intern Talya Klinger, Homeschooler

Tuesday, February 10, 2015

Interview with Art Wallace, MD PhD on Big Data and Medical Innovation


By Angel Zhou, Branson School


Mobile technologies, sensors, genome sequencing, and advances in analytic software now make it possible to capture vast amounts of information that could transform medicine. The question is: can Big Data make health care better?

In the upcoming Marin Science Seminar, "Big Data and Medical Innovation," Dr. Art Wallace, Chief of Anesthesia Service at the San Francisco VA Medical Center and a Professor of Anesthesiology and Perioperative Care at UCSF Medical Center, will discuss applications of Big Data in medicine and how Big Data has changed epidemiology, quality improvement, and drug discovery. Read the following interview to learn more about Dr. 

Wallace’s thoughts on Big Data and its impact on medical innovation.

Art Wallace, MD PhD

What is Big Data and what is its significance to medicine?  What makes Big Data different from other data that people work with in the healthcare industry?

Big Data is data that is acquired for other purposes that can be analyzed to understand processes, people, and systems. Big Data includes many things: cell phone records, super market purchase card records, credit card records, medical records, internet search terms, medication usage, hospital admissions, social security records, etc. This data can be used for epidemiology to identify associations between factors and outcomes.

Big Data gives additional power to identify factors associated with rare outcomes. I can now easily do a study in 1 million people using data collected for administrative purposes. Doing a study in 1 million patients used to be enormously expensive, now it just requires computer programming and epidemiologic analysis. Before Big Data, the cost of collecting data was prohibitive, so many studies could not be done. With Big Data, there is little to no cost of collecting the data, making the analysis the entire cost for large studies. The profoundly lower costs with Big Data techniques make studies that were previously impossible, possible at minimal cost.

How does Big Data impact professionals in the medical field? Can Big Data be used to improve healthcare?

We have identified factors associated with adverse outcomes, identified medication practices that are associated with increased mortality, identified medications that can reduce morbidity and mortality, and we have identified possible therapies for diseases that have no current therapy. We can reduce morbidity, mortality, cost, and assist in the development of new therapies.
  
Big Data can be used to reduce morbidity, mortality, cost, and improve efficiency. Big Data can be used to ask questions that are morally, politically, technically, socially, ethically, or legally impossible to answer with randomized trials. Big Data is being used to improve quality of life while lowering costs.

Describe how Big Data is reshaping the drug industry?

Big Data can be used to identify medications that reduce or increase risks. Post marketing testing can identify medications that have significant associated morbidity and mortality. For example, we identified a drug that increased mortality risk 5 fold (increased from 3 to 15% with drug use). This use of Big Data led to a medication being taken off the market. It had been used in Europe for 30 years, in the U.S. for 10 years, and it increased the risk of death from 3 to 15%. Big Data was used to identify a very serious risk to patients and led to the medication being taken off the market.

How will Big Data accelerate innovation in medicine?

Big Data will be used to identify new uses of medications. It will identify risk factors for morbidity and mortality. It will lead to further randomized trials.

What are the benefits and dangers of providing Big Data online as the "ever expanding cloud of information" becomes more accessible?

It is easy to identify people from their digital detritus. It is easy to identify very personal things about people from their data trails. Factors such as financial status, interests, sexual orientation, political beliefs, religious beliefs, health status, pre-existing medical conditions, drug and alcohol use, pregnancy status, and proclivities can all be assessed via Big Data. Big Data can be used to manipulate, track, and market to people. At the same time, Big Data can identify very serious risks to patients’ health. Scientific method is an approach; Big Data is a tool. Both can be used for good or bad purposes. Big Data is simply a new and extremely powerful scientific tool.   


Join us Wednesday, February 11th, 2015 to learn more about "Big Data and Medical Innovation" with Dr. Art Wallace from 7:30 - 8:30 PM Terra Linda High School, San Rafael in Room 207.

Sunday, February 8, 2015

Big Data Is a Big Deal

by Talya Klinger, Homeschooler  

Ever wonder how IBM’s supercomputer Watson won Jeopardy or how global epidemics are tracked? It's all in the numbers. In the upcoming Marin Science Seminar, "Big Data and Medical Innovation," Dr. Art Wallace, a Professor of Anesthesiology and Perioperative Care at UCSF Medical Center and Chief of Anesthesia Service at the San Francisco VA Medical Center, will discuss the many applications of big data in medicine and healthcare: http://www.marinscienceseminar.com/speakers/awallace.html.


Big data is used to find trends in everything from consumers’ shopping preferences to the transmission of contagious diseases. These days, data is often described in terabytes and petabytes -- trillions and quadrillions of bytes -- but even these units of measurement might not be large enough to size up big data for much longer. Doug Laney's 2001 definition of big data has become more and more relevant with every passing year. He characterizes big data by the "three V's:" volume, velocity, and variety (http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf, http://www.forbes.com/sites/gartnergroup/2013/03/27/gartners-big-data-definition-consists-of-three-parts-not-to-be-confused-with-three-vs/). Big data not only comes in larger and larger volumes, but also streams at higher and higher speeds. While it must be processed and analyzed with increasing rapidity, one of the largest challenges big data poses is that it comes in a wide variety of forms from organized to unorganized, including text, images, audio, and video. Thus, big data is defined less by any specific dimensions than by the technological challenges and the ethical questions it poses.


The fields of science, industry, and commerce are deeply invested in sorting through big data to find underlying correlations and patterns. The traditional database and software techniques used for analytics just weren't up to the task of extracting useful information from the billions of records generated with each click, swipe, and tap. For example, Microsoft’s early approaches to spell checking and autocorrecting software for its word processing programs anticipated which ways people were most likely to misspell words and built in corrections for them. Google's spell checking algorithms simply analyze a sizeable database of real people's typos and misspellings to create better spell checkers. In this way, big data does not rely only on prediction to correct people’s mistakes but captures and catalogues people’s actual keystrokes. Similarly, I.B.M.'s Watson relied on big data to amass its collection of factoids and trivia, as well as its understanding of the intricacies of the English language.


With its innovation and insights, however, big data also raises ethical concerns and controversies. Case in point: every Google search or online purchase you make is tracked, codified, and analyzed. Whether or not this infringes on your privacy is hotly debated. Corporate entities with the massive resources to store and interpret big data also have a significant advantage over those that cannot. And even the most statistically valid big data analysis techniques can make incorrect assumptions about a patient's needs or a consumer's wants -- much as large sample sizes can produce significant p-values for statisticians even when effect sizes are small. After all, big data is a tool for tracking trends and finding correlations, not for magically solving problems with complete accuracy. However controversial some of the uses of big data may be, its analytics have been used to discover the Higgs boson and they could even help contain such epidemics as the Ebola virus. (https://blogs.datadirect.com/2012/07/trends-in-big-connectivity-higgs-boson-the-science-of-big-data.html; http://www.economist.com/news/science-and-technology/21627557-mobile-phone-records-would-help-combat-ebola-epidemic-getting-look; http://www.cnbc.com/id/102049616; http://smartdatacollective.com/gilallouche/286011/what-ebola-crisis-has-taught-us-about-big-data)


In his Marin Science Seminar, Dr. Wallace will detail how big data, and our real-time access to it, has become a force for medical innovation and quality improvement, when researchers, doctors, epidemiologists, public health officials, and pharmaceutical companies collaborate to prevent diseases, develop new drugs, create treatment protocols, anticipate outbreaks, and respond to emergencies. Dr. Wallace will explain why increasingly sophisticated algorithms and predictive analytics make big data a big deal, not just for selling shoes and correcting typos, but also for much needed medical and healthcare breakthroughs.


Marin Science Seminar takes place on Wednesday, Feb. 11, 2015, from 7:30-8:30pm, at Terra Linda High School, Room 207, 320 Nova Albion Way, San Rafael, CA 94903: https://www.facebook.com/events/1374238652888346/.