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Fentanyl seized by law enforcement doubled in 2016, DEA says

(CNN)The United States is seeing a dramatic increase in drugs containing fentanyl, newly released data from the Drug Enforcement Administration shows.

From 2015 to 2016, more than twice as many drugs seized by law enforcement agencies and submitted to labs have tested positive for fentanyl, in what appears to be an escalating trend.

The National Forensic Laboratory Information System (NFLIS), a program of the DEA, points to a drastic surge of lab submissions that tested positive for fentanyl — going from 15,209 in 2015 to 31,700 in 2016.



In addition, lab testing of fentanyl analogues — drugs with close structural resemblance and similar effects to fentanyl — went from 2,230 in 2015 to 4,782 in 2016.

“Drug use today has become a game of Russian roulette. There’s no such thing as a safe batch, this is the opioid crisis at its worst,” DEA spokesman Rusty Payne told CNN.

Fentanyl, a synthetic opioid typically prescribed to treat patients with severe pain, is approximately 50 times more potent than heroin and 100 times more powerful than morphine, according to the NFLIS.

Last year, the country lost more than 52,000 Americans to drug overdose — more than 33,000 of those from opioids, according to the Centers of Disease Control and Prevention. It means more people die from opioid-related causes than from gun homicides and traffic fatalities — combined, the DEA states.

Fentanyl reports remained fairly steady between 2003-2013, until sharp increases occurred beginning in 2014 through 2016, particularly noticeable in the Northeast and Midwest.

“Drug addicts know they are taking fentanyl at times, and know it can kill them, but are willing to take the risk,” Payne said, adding that Chinese labs that manufacture the substances are trying to stay ahead of law enforcement using chemistry advances, tweaking the chemical structure to create a slightly different analogue.

Payne also pointed at “a tremendous opioid demand in this country” that pushes Mexican drug cartels to add pure fentanyl to heroin batches, creating hundreds of thousands of dosage units. According to DEA reports, Mexico continues to supply up to 85-90% of the domestic heroin market.

The DEA is working hard on education, enforcement and prevention to battle the crisis, Payne said, but added it is a vicious cycle that is eventually up to the community to end. “The next generation has to be better. We need to make sure people don’t ever start,” he said.

Read more: http://www.cnn.com/2017/05/19/health/fentanyl-surge/index.html

Advances in AI and ML are reshaping healthcare

The healthcare technology sector has given rise to some of the most innovative startups in the world, which are poised to help people live longer, better lives. The innovations have primarily been driven by the advent of software and mobility, allowing the health sector to digitize many of the pen and paper-based operations and processes that currently slow down service delivery.

More recently, we’re seeing software become far more intelligent and independent. These new capabilities studied under the banner of artificial intelligence and machine learning are accelerating the pace of innovation in healthcare. Thus far, the applications of AI and ML in healthcare have enabled the industry to take on some of its biggest challenges in these areas:

  • Personal genetics
  • Drug discovery
  • Disease identification and management

Upon close evaluation of the opportunities that exist within each area, it becomes obvious that the stakes are high. As such, those that are first to market with a sustainable product differentiation and value-add will benefit tremendously.

Ushering in a new era of personal genetics

The most significant application of AI and ML in genetics is understanding how DNA impacts life. Although the last several years saw the complete sequencing of the human genome and a mastery of the ability to read and edit it, we still dont know what most of the genome is actually telling us. Genes are constantly acting out of place in combination with other variables such as food, environment and body types.

If we are to understand what influences life and biology, we must first understand the language that is DNA. This is where ML algorithms come in and the advent of systems such as Googles Deep Mind and IBMs Watson. Now, more than ever, it has become possible to digest immense amounts of data (e.g. patient records, clinical notes, diagnostic images, treatment plans) and perform pattern recognition in a short period of time which otherwise would have taken a lifetime to complete.

Businesses such as Deep Genomics are making meaningful progress in this realm. The company is developing the capability to interpret DNAby creating a system that predicts the molecular effects of genetic variation. Their database is able to explain how hundreds of millions of genetic variations can impact a genetic code.

Once a better understanding of human DNA is established, there is an opportunity to go one step further and provide personalized insights to individuals based on their idiosyncratic biological dispositions. This trend is indicative of a new era of personalized genetics, whereby individuals are able to take full control of their health through access to unprecedented information about their own bodies.

The technology must have access to vast amounts of data in order to better curate lifestyle changes for individuals.

Consumer genetics companies such as 23andMe and Rthm represent a few of the first movers in this domain. They have developed consumerized genetic diagnostic tools to help individuals understand their genetic makeup. With Rthm, users are able to go one step further and leverage the insights produced from their genetic test to implement changes to their everyday routine through a mobile application, all in real time.

As is the case with any application of AI/ML, the technology must have access to vast amounts of data in order to better curate lifestyle changes for individuals. Startups that are focused on mastering the delivery of personal genetics are doing so by considering the following key activities, as highlighted by Japan-based researcher Takashi Kido:

  • Acquiring reliable personal genome data and genetic risk prediction
  • Conducting behavior pattern analyses on peoples attitude to the personal genome to determine what kind of information is valuable/helpful and what type of information is damaging
  • Data mining for scientific discovery

The second point is interesting in that not all genetic information about a patients biological predispositions is productive. Being able to control the information in a manner that is conducive to psychological well-being is critical.

Hyper targeted drugs are the future

Another exciting application of AI/ML in healthcare is the reduction of both cost and time in drug discovery. New drugs typically take 12 to 14 years to make it to market, with the average cost hovering around $2.6 billion. During the process of drug discovery, chemical compounds are tested against every possible combination of different cell type, genetic mutation and other conditions relating to a particular ailment.

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As the task of doing this is time-consuming, this limits the number of experiments or diseases that scientists can look to attack. ML algorithms can allow computers to learn how to make predictions based on the data they have previously processed or choose (and in some cases, even conduct) what experiments need to be done. Similar types of algorithms also can be used to predict the side effects of specific chemical compounds on humans, speeding up approvals.

San Francisco-based startup Atomwise is looking to replace test tubes with supercomputers during the drug development process. The company uses ML and 3D neural networks that sift through a database of molecular structures to uncover therapies, helping to discover the effectiveness of new chemical compounds on diseases and identifying what existing medications can be repurposed to cure another ailment.

In 2015, the company applied its solution and uncovered two new drugs which may significantly reduce Ebola infectivity. The analysis was completed in one day as opposed to years, which is common using traditional methods of drug development. A recent study by Insilico Medicine solidified the approach Atomwise is taking, showing that deep neural networks can be used to predict pharmacologic properties of drugs and drug repurposing.

The application of AI/ML in healthcare is reshaping the industry and making what was once impossible into a tangible reality.

Berg Health, a Boston-based biopharma company, attacks drug discovery from a different angle. Berg mines patient biological data using AI to determine why some people survive diseases, and then applies this insight to improve current therapies or create new ones.

BenevolentAI, a London-based startup, aims to expedite the drug discovery process by harnessing AI to look for patterns in scientific literature. Only a small portion of globally generated scientific information is actually used or usable by scientists, as new healthcare-related studies are published every 30 seconds. BenevolentAI enables analysis on vast amounts of data to provide experts with insights they need to dramatically expedite drug discovery and research. Recently, the company identified two potential chemical compounds that may work on Alzheimers, attracting the attention of pharmaceutical companies.

As advances in ML and AI continue, the future of drug discovery looks promising.A recent Google Research paper notes that using data from various sources can better determine which chemical compounds will serve as effective drug treatments for a variety of diseases, and how ML can save a lot of time by testing millions of compounds at scale.

Discovering and managing new diseases

Most diseases are far more than just a simple gene mutation. Despite the healthcare system generating copious amounts of (unstructured) data which is progressively improving in quality we have previously not had the necessary hardware and software in place to analyze it and produce meaningful insights.

Disease diagnosis is a complicated process that involves a variety of factors, from the texture of a patients skin to the amount of sugar that he or she consumes in a day. For the past 2,000 years, medicine has been governed by symptomatic detection, where a patients ailment is diagnosed based on the symptoms they are displaying (e.g. if you have a fever and stuffy nose, you most likely have the flu).

But often the arrival of detectable symptoms is too late, especially when dealing with diseases such as cancer and Alzheimers. With ML, the hope is that faint signatures of diseases can be discovered well in advance of detectable symptoms, increasing the probability of survival (sometimes by up to 90 percent) and/or treatment options.

The opportunities continue to grow and inspire healthcare practitioners to find new ways to enhance our health and well-being.

Freenome, a San Francisco-based startup, has created an Adaptive Genomics Engine that helps dynamically detect disease signatures in your blood. To make this possible, the company uses your freenome the dynamic collection of genetic material floating in your blood that is constantly changing over time and provides a genomic thermometer of who you are as you grow, live and age.

When looking at disease diagnosis and treatment plans, companies such as Enlitic are focused on improving patient outcomes by coupling deep learning with medical data to distill actionable insights from billions of clinical cases. IBMs Watson is working with Memorial Sloan Kettering in New York to digest reams of data on cancer patients and treatments used over decades to present and suggest treatment options to doctors in dealing with unique cancer cases.

In London, Googles Deep Mind is mining through medical records of Moorfields Eye Hospital to analyze digital scans of the eye to help doctors better understand and diagnose eye disease. In parallel, Deep Mind also has a project running to help with radiation therapy mapping for patients suffering from neck and head cancer, freeing up hours of planning for oncologists to allow them to focus on more patient care-oriented tasks.

What does all of this mean?

The application of AI/ML in healthcare is reshaping the industry and making what was once impossible into a tangible reality.

For AI/ML to become pervasive in healthcare, continued access to relevant data is essential to success. The more proprietary data a system can ingest, the smarter it will become. As a result, companies are going to great lengths to acquire data (which resides in an anonymized format). For example, IBM bought out healthcare analytics company Truven Health for $2.6 billion in February 2016 primarily to gain access to their repository of data and insights. In addition, they recently partnered with Medtronic to further Watsons ability to make sense of diabetes through gaining access to real-time insulin data.

As the data becomes richer and the technology keeps advancing, the opportunities continue to grow and inspire healthcare practitioners to find new ways to enhance our health and well-being.

Read more: https://techcrunch.com/2017/03/16/advances-in-ai-and-ml-are-reshaping-healthcare/

Know when it’s time to fire your doctor

(CNN)Dr. Jerome Groopman knew that he needed to break up with his doctor.

Five years ago, when he started seeing his internist, everything was fine. But Groopman says that in time, the internist became more popular — and hence more busy and harried — right when Groopman needed him most.
“I have a strong family history of high cholesterol and heart disease. Every male in my family has had a [heart attack] in his 50s and 60s,” he said. “I was moving into middle age, and I just didn’t feel that my doctor was looking at me as an individual and taking those factors into account.”
But Groopman — a physician and author of four books about doctors and patients — found it difficult to leave his internist of five years. “It sounds strange, but I didn’t want to insult him.”

Debra Roter, a behavioral scientist at Johns Hopkins and co-author of “Doctors Talking with Patients,” says it’s a red flag when your doctor doesn’t pay attention to what you have to say. “A doctor suggested my friend take a certain drug, but she’d taken it before, and she told him it hadn’t worked for her,” she said. “But her doctor wanted her to try it anyway. He didn’t give her any credibility.”

3. If your doctor can’t explain your illness to you in terms you understand

“It’s really important that a physician be able to communicate in plain speak and plain language,” Roter said. “A doctor has to be able to explain things so you can put the information to use to take good care of yourself.”

4. If you feel bad when you leave your doctor’s office

DiMatteo says sometimes you just have to go with your gut. “For example, if a patient says, ‘My pain is still there,’ and the doctor says, ‘It shouldn’t be; this treatment works for other people,’ and you walk out of the office feeling badly, I don’t think you should stay.”

5. If you feel your doctor just doesn’t like you — or if you don’t like him or her

“Sometimes there’s chemistry ,and people click right away, and there are some people you don’t click with,” Roter said. “If your gut says you’re not crazy about your doctor, they probably aren’t crazy about you, and that’s not good.”
Groopman agrees. He says a doctor who doesn’t like a patient often stereotypes him or her. “I was terribly guilty of this as a young doctor. One of my patients said she had indigestion, and I got very irritated with her and thought she was a whiner and a complainer,” he said. “It was catastrophic, because she actually had a torn aorta.”
The woman died. “I have never forgiven myself for failing to diagnose it,” he writes in “How Doctors Think.” “There was a chance she could have been saved.”

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So once you’ve decided it might be time to divorce your doctor, how do you do it? First of all, make sure whatever’s bothering you isn’t just a one-time thing. “Make sure it’s not just a quirk of the doctor’s day,” Groopman said. “Maybe they’re just having a bad day.”
If the problems continue, Groopman, Roter and DiMatteo agree it’s best to try to express your dissatisfaction instead of just bolting. “Use the first person plural, such as ‘We’re not communicating well’ as opposed to ‘You seem distracted or irritable with me,’ ” suggested Groopman. “That may cause cause the physician to stop and reflect and shift gears.”
When it doesn’t, you can be sure it’s time to get another doctor, Roter says. She described two friends who wrote letters to their doctor saying they were unhappy with some of the treatments they’d received. “The both got back letters saying, ‘Good luck with your new doctor.’ “

Read more: http://www.cnn.com/2016/10/07/health/fire-your-doctor/index.html

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