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#434648 The Pediatric AI That Outperformed ...

Training a doctor takes years of grueling work in universities and hospitals. Building a doctor may be as easy as teaching an AI how to read.

Artificial intelligence has taken another step towards becoming an integral part of 21st-century medicine. New research out of Guangzhou, China, published February 11th in Nature Medicine Letters, has demonstrated a natural-language processing AI that is capable of out-performing rookie pediatricians in diagnosing common childhood ailments.

The massive study examined the electronic health records (EHR) from nearly 600,000 patients over an 18-month period at the Guangzhou Women and Children’s Medical Center and then compared AI-generated diagnoses against new assessments from physicians with a range of experience.

The verdict? On average, the AI was noticeably more accurate than junior physicians and nearly as reliable as the more senior ones. These results are the latest demonstration that artificial intelligence is on the cusp of becoming a healthcare staple on a global scale.

Less Like a Computer, More Like a Person
To outshine human doctors, the AI first had to become more human. Like IBM’s Watson, the pediatric AI leverages natural language processing, in essence “reading” written notes from EHRs not unlike how a human doctor would review those same records. But the similarities to human doctors don’t end there. The AI is a machine learning classifier (MLC), capable of placing the information learned from the EHRs into categories to improve performance.

Like traditionally-trained pediatricians, the AI broke cases down into major organ groups and infection areas (upper/lower respiratory, gastrointestinal, etc.) before breaking them down even further into subcategories. It could then develop associations between various symptoms and organ groups and use those associations to improve its diagnoses. This hierarchical approach mimics the deductive reasoning human doctors employ.

Another key strength of the AI developed for this study was the enormous size of the dataset collected to teach it: 1,362,559 outpatient visits from 567,498 patients yielded some 101.6 million data points for the MLC to devour on its quest for pediatric dominance. This allowed the AI the depth of learning needed to distinguish and accurately select from the 55 different diagnosis codes across the various organ groups and subcategories.

When comparing against the human doctors, the study used 11,926 records from an unrelated group of children, giving both the MLC and the 20 humans it was compared against an even playing field. The results were clear: while cohorts of senior pediatricians performed better than the AI, junior pediatricians (those with 3-15 years of experience) were outclassed.

Helping, Not Replacing
While the research used a competitive analysis to measure the success of the AI, the results should be seen as anything but hostile to human doctors. The near future of artificial intelligence in medicine will see these machine learning programs augment, not replace, human physicians. The authors of the study specifically call out augmentation as the key short-term application of their work. Triaging incoming patients via intake forms, performing massive metastudies using EHRs, providing rapid ‘second opinions’—the applications for an AI doctor that is better-but-not-the-best are as varied as the healthcare industry itself.

That’s only considering how artificial intelligence could make a positive impact immediately upon implementation. It’s easy to see how long-term use of a diagnostic assistant could reshape the way modern medical institutions approach their work.

Look at how the MLC results fit snugly between the junior and senior physician groups. Essentially, it took nearly 15 years before a physician could consistently out-diagnose the machine. That’s a decade and a half wherein an AI diagnostic assistant would be an invaluable partner—both as a training tool and a safety measure. Likewise, on the other side of the experience curve you have physicians whose performance could be continuously leveraged to improve the AI’s effectiveness. This is a clear opportunity for a symbiotic relationship, with humans and machines each assisting the other as they mature.

Closer to Us, But Still Dependent on Us
No matter the ultimate application, the AI doctors of the future are drawing nearer to us step by step. This latest research is a demonstration that artificial intelligence can mimic the results of human deductive reasoning even in some of the most complex and important decision-making processes. True, the MLC required input from humans to function; both the initial data points and the cases used to evaluate the AI depended on EHRs written by physicians. While every effort was made to design a test schema that removed any indication of the eventual diagnosis, some “data leakage” is bound to occur.

In other words, when AIs use human-created data, they inherit human insight to some degree. Yet the progress made in machine imaging, chatbots, sensors, and other fields all suggest that this dependence on human input is more about where we are right now than where we could be in the near future.

Data, and More Data
That near future may also have some clear winners and losers. For now, those winners seem to be the institutions that can capture and apply the largest sets of data. With a rapidly digitized society gathering incredible amounts of data, China has a clear advantage. Combined with their relatively relaxed approach to privacy, they are likely to continue as one of the driving forces behind machine learning and its applications. So too will Google/Alphabet with their massive medical studies. Data is the uranium in this AI arms race, and everyone seems to be scrambling to collect more.

In a global community that seems increasingly aware of the potential problems arising from this need for and reliance on data, it’s nice to know there’ll be an upside as well. The technology behind AI medical assistants is looking more and more mature—even if we are still struggling to find exactly where, when, and how that technology should first become universal.

Yet wherever we see the next push to make AI a standard tool in a real-world medical setting, I have little doubt it will greatly improve the lives of human patients. Today Doctor AI is performing as well as a human colleague with more than 10 years of experience. By next year or so, it may take twice as long for humans to be competitive. And in a decade, the combined medical knowledge of all human history may be a tool as common as a stethoscope in your doctor’s hands.

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Posted in Human Robots

#434643 Sensors and Machine Learning Are Giving ...

According to some scientists, humans really do have a sixth sense. There’s nothing supernatural about it: the sense of proprioception tells you about the relative positions of your limbs and the rest of your body. Close your eyes, block out all sound, and you can still use this internal “map” of your external body to locate your muscles and body parts – you have an innate sense of the distances between them, and the perception of how they’re moving, above and beyond your sense of touch.

This sense is invaluable for allowing us to coordinate our movements. In humans, the brain integrates senses including touch, heat, and the tension in muscle spindles to allow us to build up this map.

Replicating this complex sense has posed a great challenge for roboticists. We can imagine simulating the sense of sight with cameras, sound with microphones, or touch with pressure-pads. Robots with chemical sensors could be far more accurate than us in smell and taste, but building in proprioception, the robot’s sense of itself and its body, is far more difficult, and is a large part of why humanoid robots are so tricky to get right.

Simultaneous localization and mapping (SLAM) software allows robots to use their own senses to build up a picture of their surroundings and environment, but they’d need a keen sense of the position of their own bodies to interact with it. If something unexpected happens, or in dark environments where primary senses are not available, robots can struggle to keep track of their own position and orientation. For human-robot interaction, wearable robotics, and delicate applications like surgery, tiny differences can be extremely important.

Piecemeal Solutions
In the case of hard robotics, this is generally solved by using a series of strain and pressure sensors in each joint, which allow the robot to determine how its limbs are positioned. That works fine for rigid robots with a limited number of joints, but for softer, more flexible robots, this information is limited. Roboticists are faced with a dilemma: a vast, complex array of sensors for every degree of freedom in the robot’s movement, or limited skill in proprioception?

New techniques, often involving new arrays of sensory material and machine-learning algorithms to fill in the gaps, are starting to tackle this problem. Take the work of Thomas George Thuruthel and colleagues in Pisa and San Diego, who draw inspiration from the proprioception of humans. In a new paper in Science Robotics, they describe the use of soft sensors distributed through a robotic finger at random. This placement is much like the constant adaptation of sensors in humans and animals, rather than relying on feedback from a limited number of positions.

The sensors allow the soft robot to react to touch and pressure in many different locations, forming a map of itself as it contorts into complicated positions. The machine-learning algorithm serves to interpret the signals from the randomly-distributed sensors: as the finger moves around, it’s observed by a motion capture system. After training the robot’s neural network, it can associate the feedback from the sensors with the position of the finger detected in the motion-capture system, which can then be discarded. The robot observes its own motions to understand the shapes that its soft body can take, and translate them into the language of these soft sensors.

“The advantages of our approach are the ability to predict complex motions and forces that the soft robot experiences (which is difficult with traditional methods) and the fact that it can be applied to multiple types of actuators and sensors,” said Michael Tolley of the University of California San Diego. “Our method also includes redundant sensors, which improves the overall robustness of our predictions.”

The use of machine learning lets the roboticists come up with a reliable model for this complex, non-linear system of motions for the actuators, something difficult to do by directly calculating the expected motion of the soft-bot. It also resembles the human system of proprioception, built on redundant sensors that change and shift in position as we age.

In Search of a Perfect Arm
Another approach to training robots in using their bodies comes from Robert Kwiatkowski and Hod Lipson of Columbia University in New York. In their paper “Task-agnostic self-modeling machines,” also recently published in Science Robotics, they describe a new type of robotic arm.

Robotic arms and hands are getting increasingly dexterous, but training them to grasp a large array of objects and perform many different tasks can be an arduous process. It’s also an extremely valuable skill to get right: Amazon is highly interested in the perfect robot arm. Google hooked together an array of over a dozen robot arms so that they could share information about grasping new objects, in part to cut down on training time.

Individually training a robot arm to perform every individual task takes time and reduces the adaptability of your robot: either you need an ML algorithm with a huge dataset of experiences, or, even worse, you need to hard-code thousands of different motions. Kwiatkowski and Lipson attempt to overcome this by developing a robotic system that has a “strong sense of self”: a model of its own size, shape, and motions.

They do this using deep machine learning. The robot begins with no prior knowledge of its own shape or the underlying physics of its motion. It then repeats a series of a thousand random trajectories, recording the motion of its arm. Kwiatkowski and Lipson compare this to a baby in the first year of life observing the motions of its own hands and limbs, fascinated by picking up and manipulating objects.

Again, once the robot has trained itself to interpret these signals and build up a robust model of its own body, it’s ready for the next stage. Using that deep-learning algorithm, the researchers then ask the robot to design strategies to accomplish simple pick-up and place and handwriting tasks. Rather than laboriously and narrowly training itself for each individual task, limiting its abilities to a very narrow set of circumstances, the robot can now strategize how to use its arm for a much wider range of situations, with no additional task-specific training.

Damage Control
In a further experiment, the researchers replaced part of the arm with a “deformed” component, intended to simulate what might happen if the robot was damaged. The robot can then detect that something’s up and “reconfigure” itself, reconstructing its self-model by going through the training exercises once again; it was then able to perform the same tasks with only a small reduction in accuracy.

Machine learning techniques are opening up the field of robotics in ways we’ve never seen before. Combining them with our understanding of how humans and other animals are able to sense and interact with the world around us is bringing robotics closer and closer to becoming truly flexible and adaptable, and, eventually, omnipresent.

But before they can get out and shape the world, as these studies show, they will need to understand themselves.

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Posted in Human Robots

#434637 AI Is Rapidly Augmenting Healthcare and ...

When it comes to the future of healthcare, perhaps the only technology more powerful than CRISPR is artificial intelligence.

Over the past five years, healthcare AI startups around the globe raised over $4.3 billion across 576 deals, topping all other industries in AI deal activity.

During this same period, the FDA has given 70 AI healthcare tools and devices ‘fast-tracked approval’ because of their ability to save both lives and money.

The pace of AI-augmented healthcare innovation is only accelerating.

In Part 3 of this blog series on longevity and vitality, I cover the different ways in which AI is augmenting our healthcare system, enabling us to live longer and healthier lives.

In this blog, I’ll expand on:

Machine learning and drug design
Artificial intelligence and big data in medicine
Healthcare, AI & China

Let’s dive in.

Machine Learning in Drug Design
What if AI systems, specifically neural networks, could predict the design of novel molecules (i.e. medicines) capable of targeting and curing any disease?

Imagine leveraging cutting-edge artificial intelligence to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.

And what if these molecules, accurately engineered by AIs, always worked? Such a feat would revolutionize our $1.3 trillion global pharmaceutical industry, which currently holds a dismal record of 1 in 10 target drugs ever reaching human trials.

It’s no wonder that drug development is massively expensive and slow. It takes over 10 years to bring a new drug to market, with costs ranging from $2.5 billion to $12 billion.

This inefficient, slow-to-innovate, and risk-averse industry is a sitting duck for disruption in the years ahead.

One of the hottest startups in digital drug discovery today is Insilico Medicine. Leveraging AI in its end-to-end drug discovery pipeline, Insilico Medicine aims to extend healthy longevity through drug discovery and aging research.

Their comprehensive drug discovery engine uses millions of samples and multiple data types to discover signatures of disease, identify the most promising protein targets, and generate perfect molecules for these targets. These molecules either already exist or can be generated de novo with the desired set of parameters.

In late 2018, Insilico’s CEO Dr. Alex Zhavoronkov announced the groundbreaking result of generating novel molecules for a challenging protein target with an unprecedented hit rate in under 46 days. This included both synthesis of the molecules and experimental validation in a biological test system—an impressive feat made possible by converging exponential technologies.

Underpinning Insilico’s drug discovery pipeline is a novel machine learning technique called Generative Adversarial Networks (GANs), used in combination with deep reinforcement learning.

Generating novel molecular structures for diseases both with and without known targets, Insilico is now pursuing drug discovery in aging, cancer, fibrosis, Parkinson’s disease, Alzheimer’s disease, ALS, diabetes, and many others. Once rolled out, the implications will be profound.

Dr. Zhavoronkov’s ultimate goal is to develop a fully-automated Health-as-a-Service (HaaS) and Longevity-as-a-Service (LaaS) engine.

Once plugged into the services of companies from Alibaba to Alphabet, such an engine would enable personalized solutions for online users, helping them prevent diseases and maintain optimal health.

Insilico, alongside other companies tackling AI-powered drug discovery, truly represents the application of the 6 D’s. What was once a prohibitively expensive and human-intensive process is now rapidly becoming digitized, dematerialized, demonetized and, perhaps most importantly, democratized.

Companies like Insilico can now do with a fraction of the cost and personnel what the pharmaceutical industry can barely accomplish with thousands of employees and a hefty bill to foot.

As I discussed in my blog on ‘The Next Hundred-Billion-Dollar Opportunity,’ Google’s DeepMind has now turned its neural networks to healthcare, entering the digitized drug discovery arena.

In 2017, DeepMind achieved a phenomenal feat by matching the fidelity of medical experts in correctly diagnosing over 50 eye disorders.

And just a year later, DeepMind announced a new deep learning tool called AlphaFold. By predicting the elusive ways in which various proteins fold on the basis of their amino acid sequences, AlphaFold may soon have a tremendous impact in aiding drug discovery and fighting some of today’s most intractable diseases.

Artificial Intelligence and Data Crunching
AI is especially powerful in analyzing massive quantities of data to uncover patterns and insights that can save lives. Take WAVE, for instance. Every year, over 400,000 patients die prematurely in US hospitals as a result of heart attack or respiratory failure.

Yet these patients don’t die without leaving plenty of clues. Given information overload, however, human physicians and nurses alone have no way of processing and analyzing all necessary data in time to save these patients’ lives.

Enter WAVE, an algorithm that can process enough data to offer a six-hour early warning of patient deterioration.

Just last year, the FDA approved WAVE as an AI-based predictive patient surveillance system to predict and thereby prevent sudden death.

Another highly valuable yet difficult-to-parse mountain of medical data comprises the 2.5 million medical papers published each year.

For some time, it has become physically impossible for a human physician to read—let alone remember—all of the relevant published data.

To counter this compounding conundrum, Johnson & Johnson is teaching IBM Watson to read and understand scientific papers that detail clinical trial outcomes.

Enriching Watson’s data sources, Apple is also partnering with IBM to provide access to health data from mobile apps.

One such Watson system contains 40 million documents, ingesting an average of 27,000 new documents per day, and providing insights for thousands of users.

After only one year, Watson’s successful diagnosis rate of lung cancer has reached 90 percent, compared to the 50 percent success rate of human doctors.

But what about the vast amount of unstructured medical patient data that populates today’s ancient medical system? This includes medical notes, prescriptions, audio interview transcripts, and pathology and radiology reports.

In late 2018, Amazon announced a new HIPAA-eligible machine learning service that digests and parses unstructured data into categories, such as patient diagnoses, treatments, dosages, symptoms and signs.

Taha Kass-Hout, Amazon’s senior leader in health care and artificial intelligence, told the Wall Street Journal that internal tests demonstrated that the software even performs as well as or better than other published efforts.

On the heels of this announcement, Amazon confirmed it was teaming up with the Fred Hutchinson Cancer Research Center to evaluate “millions of clinical notes to extract and index medical conditions.”

Having already driven extraordinary algorithmic success rates in other fields, data is the healthcare industry’s goldmine for future innovation.

Healthcare, AI & China
In 2017, the Chinese government published its ambitious national plan to become a global leader in AI research by 2030, with healthcare listed as one of four core research areas during the first wave of the plan.

Just a year earlier, China began centralizing healthcare data, tackling a major roadblock to developing longevity and healthcare technologies (particularly AI systems): scattered, dispersed, and unlabeled patient data.

Backed by the Chinese government, China’s largest tech companies—particularly Tencent—have now made strong entrances into healthcare.

Just recently, Tencent participated in a $154 million megaround for China-based healthcare AI unicorn iCarbonX.

Hoping to develop a complete digital representation of your biological self, iCarbonX has acquired numerous US personalized medicine startups.

Considering Tencent’s own Miying healthcare AI platform—aimed at assisting healthcare institutions in AI-driven cancer diagnostics—Tencent is quickly expanding into the drug discovery space, participating in two multimillion-dollar, US-based AI drug discovery deals just this year.

China’s biggest, second-order move into the healthtech space comes through Tencent’s WeChat. In the course of a mere few years, already 60 percent of the 38,000 medical institutions registered on WeChat allow patients to digitally book appointments through Tencent’s mobile platform. At the same time, 2,000 Chinese hospitals accept WeChat payments.

Tencent has additionally partnered with the U.K.’s Babylon Health, a virtual healthcare assistant startup whose app now allows Chinese WeChat users to message their symptoms and receive immediate medical feedback.

Similarly, Alibaba’s healthtech focus started in 2016 when it released its cloud-based AI medical platform, ET Medical Brain, to augment healthcare processes through everything from diagnostics to intelligent scheduling.

Conclusion
As Nvidia CEO Jensen Huang has stated, “Software ate the world, but AI is going to eat software.” Extrapolating this statement to a more immediate implication, AI will first eat healthcare, resulting in dramatic acceleration of longevity research and an amplification of the human healthspan.

Next week, I’ll continue to explore this concept of AI systems in healthcare.

Particularly, I’ll expand on how we’re acquiring and using the data for these doctor-augmenting AI systems: from ubiquitous biosensors, to the mobile healthcare revolution, and finally, to the transformative power of the health nucleus.

As AI and other exponential technologies increase our healthspan by 30 to 40 years, how will you leverage these same exponential technologies to take on your moonshots and live out your massively transformative purpose?

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Posted in Human Robots

#434580 How Genome Sequencing and Senolytics Can ...

The causes of aging are extremely complex and unclear. With the dramatic demonetization of genome reading and editing over the past decade, and Big Pharma, startups, and the FDA starting to face aging as a disease, we are starting to find practical ways to extend our healthspan.

Here, in Part 2 of a series of blogs on longevity and vitality, I explore how genome sequencing and editing, along with new classes of anti-aging drugs, are augmenting our biology to further extend our healthy lives.

In this blog I’ll cover two classes of emerging technologies:

Genome Sequencing and Editing;
Senolytics, Nutraceuticals & Pharmaceuticals.

Let’s dive in.

Genome Sequencing & Editing
Your genome is the software that runs your body.

A sequence of 3.2 billion letters makes you “you.” These base pairs of A’s, T’s, C’s, and G’s determine your hair color, your height, your personality, your propensity to disease, your lifespan, and so on.

Until recently, it’s been very difficult to rapidly and cheaply “read” these letters—and even more difficult to understand what they mean.

Since 2001, the cost to sequence a whole human genome has plummeted exponentially, outpacing Moore’s Law threefold. From an initial cost of $3.7 billion, it dropped to $10 million in 2006, and to $5,000 in 2012.

Today, the cost of genome sequencing has dropped below $500, and according to Illumina, the world’s leading sequencing company, the process will soon cost about $100 and take about an hour to complete.

This represents one of the most powerful and transformative technology revolutions in healthcare.

When we understand your genome, we’ll be able to understand how to optimize “you.”

We’ll know the perfect foods, the perfect drugs, the perfect exercise regimen, and the perfect supplements, just for you.
We’ll understand what microbiome types, or gut flora, are ideal for you (more on this in a later blog).
We’ll accurately predict how specific sedatives and medicines will impact you.
We’ll learn which diseases and illnesses you’re most likely to develop and, more importantly, how to best prevent them from developing in the first place (rather than trying to cure them after the fact).

CRISPR Gene Editing
In addition to reading the human genome, scientists can now edit a genome using a naturally-occurring biological system discovered in 1987 called CRISPR/Cas9.

Short for Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9, the editing system was adapted from a naturally-occurring defense system found in bacteria.

Here’s how it works:

The bacteria capture snippets of DNA from invading viruses (or bacteriophage) and use them to create DNA segments known as CRISPR arrays.
The CRISPR arrays allow the bacteria to “remember” the viruses (or closely related ones), and defend against future invasions.
If the viruses attack again, the bacteria produce RNA segments from the CRISPR arrays to target the viruses’ DNA. The bacteria then use Cas9 to cut the DNA apart, which disables the virus.

Most importantly, CRISPR is cheap, quick, easy to use, and more accurate than all previous gene editing methods. As a result, CRISPR/Cas9 has swept through labs around the world as the way to edit a genome.

A short search in the literature will show an exponential rise in the number of CRISPR-related publications and patents.

2018: Filled With CRISPR Breakthroughs
Early results are impressive. Researchers from the University of Chicago recently used CRISPR to genetically engineer cocaine resistance into mice.

Researchers at the University of Texas Southwestern Medical Center used CRISPR to reverse the gene defect causing Duchenne muscular dystrophy (DMD) in dogs (DMD is the most common fatal genetic disease in children).

With great power comes great responsibility, and moral and ethical dilemmas.

In 2015, Chinese scientists sparked global controversy when they first edited human embryo cells in the lab with the goal of modifying genes that would make the child resistant to smallpox, HIV, and cholera.

Three years later, in November 2018, researcher He Jiankui informed the world that the first set of CRISPR-engineered female twins had been delivered.

To accomplish his goal, Jiankui deleted a region of a receptor on the surface of white blood cells known as CCR5, introducing a rare, natural genetic variation that makes it more difficult for HIV to infect its favorite target, white blood cells.

Setting aside the significant ethical conversations, CRISPR will soon provide us the tools to eliminate diseases, create hardier offspring, produce new environmentally resistant crops, and even wipe out pathogens.

Senolytics, Nutraceuticals & Pharmaceuticals
Over the arc of your life, the cells in your body divide until they reach what is known as the Hayflick limit, or the number of times a normal human cell population will divide before cell division stops, which is typically about 50 divisions.

What normally follows next is programmed cell death or destruction by the immune system. A very small fraction of cells, however, become senescent cells and evade this fate to linger indefinitely.

These lingering cells secrete a potent mix of molecules that triggers chronic inflammation, damages the surrounding tissue structures, and changes the behavior of nearby cells for the worse.

Senescent cells appear to be one of the root causes of aging, causing everything from fibrosis and blood vessel calcification, to localized inflammatory conditions such as osteoarthritis, to diminished lung function.

Fortunately, both the scientific and entrepreneurial communities have begun to work on senolytic therapies, moving the technology for selectively destroying senescent cells out of the laboratory and into a half-dozen startup companies.

Prominent companies in the field include the following:

Unity Biotechnology is developing senolytic medicines to selectively eliminate senescent cells with an initial focus on delivering localized therapy in osteoarthritis, ophthalmology and pulmonary disease.
Oisin Biotechnologiesis pioneering a programmable gene therapy that can destroy cells based on their internal biochemistry.
SIWA Therapeuticsis working on an immunotherapy approach to the problem of senescent cells.

In recent years, researchers have identified or designed a handful of senolytic compounds that can curb aging by regulating senescent cells. Two of these drugs that have gained mainstay research traction are rapamycin and metformin.

Rapamycin
Originally extracted from bacteria found on Easter Island, Rapamycin acts on the m-TOR (mechanistic target of rapamycin) pathway to selectively block a key protein that facilitates cell division.

Currently, rapamycin derivatives are widely used as immunosuppression in organ and bone marrow transplants. Research now suggests that use results in prolonged lifespan and enhanced cognitive and immune function.

PureTech Health subsidiary resTORbio (which started 2018 by going public) is working on a rapamycin-based drug intended to enhance immunity and reduce infection. Their clinical-stage RTB101 drug works by inhibiting part of the mTOR pathway.

Results of the drug’s recent clinical trial include:

Decreased incidence of infection
Improved influenza vaccination response
A 30.6 percent decrease in respiratory tract infections

Impressive, to say the least.

Metformin
Metformin is a widely-used generic drug for mitigating liver sugar production in Type 2 diabetes patients.

Researchers have found that Metformin also reduces oxidative stress and inflammation, which otherwise increase as we age.

There is strong evidence that Metformin can augment cellular regeneration and dramatically mitigate cellular senescence by reducing both oxidative stress and inflammation.

Over 100 studies registered on ClinicalTrials.gov are currently following up on strong evidence of Metformin’s protective effect against cancer.

Nutraceuticals and NAD+
Beyond cellular senescence, certain critical nutrients and proteins tend to decline as a function of age. Nutraceuticals combat aging by supplementing and replenishing these declining nutrient levels.

NAD+ exists in every cell, participating in every process from DNA repair to creating the energy vital for cellular processes. It’s been shown that NAD+ levels decline as we age.

The Elysium Health Basis supplement aims to elevate NAD+ levels in the body to extend one’s lifespan. Elysium’s clinical study reports that Basis increases NAD+ levels consistently by a sustained 40 percent.

Conclusion
These are just a taste of the tremendous momentum that longevity and aging technology has right now. As artificial intelligence and quantum computing transform how we decode our DNA and how we discover drugs, genetics and pharmaceuticals will become truly personalized.

The next blog in this series will demonstrate how artificial intelligence is converging with genetics and pharmaceuticals to transform how we approach longevity, aging, and vitality.

We are edging closer to a dramatically extended healthspan—where 100 is the new 60. What will you create, where will you explore, and how will you spend your time if you are able to add an additional 40 healthy years to your life?

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Posted in Human Robots

#434534 To Extend Our Longevity, First We Must ...

Healthcare today is reactive, retrospective, bureaucratic, and expensive. It’s sick care, not healthcare.

But that is radically changing at an exponential rate.

Through this multi-part blog series on longevity, I’ll take a deep dive into aging, longevity, and healthcare technologies that are working together to dramatically extend the human lifespan, disrupting the $3 trillion healthcare system in the process.

I’ll begin the series by explaining the nine hallmarks of aging, as explained in this journal article. Next, I’ll break down the emerging technologies and initiatives working to combat these nine hallmarks. Finally, I’ll explore the transformative implications of dramatically extending the human health span.

In this blog I’ll cover:

Why the healthcare system is broken
Why, despite this, we live in the healthiest time in human history
The nine mechanisms of aging

Let’s dive in.

The System is Broken—Here’s the Data:

Doctors spend $210 billion per year on procedures that aren’t based on patient need, but fear of liability.
Americans spend, on average, $8,915 per person on healthcare—more than any other country on Earth.
Prescription drugs cost around 50 percent more in the US than in other industrialized countries.
At current rates, by 2025, nearly 25 percent of the US GDP will be spent on healthcare.
It takes 12 years and $359 million, on average, to take a new drug from the lab to a patient.
Only 5 in 5,000 of these new drugs proceed to human testing. From there, only 1 of those 5 is actually approved for human use.

And Yet, We Live in the Healthiest Time in Human History
Consider these insights, which I adapted from Max Roser’s excellent database Our World in Data:

Right now, the countries with the lowest life expectancy in the world still have higher life expectancies than the countries with the highest life expectancy did in 1800.
In 1841, a 5-year-old had a life expectancy of 55 years. Today, a 5-year-old can expect to live 82 years—an increase of 27 years.
We’re seeing a dramatic increase in healthspan. In 1845, a newborn would expect to live to 40 years old. For a 70-year-old, that number became 79. Now, people of all ages can expect to live to be 81 to 86 years old.
100 years ago, 1 of 3 children would die before the age of 5. As of 2015, the child mortality rate fell to just 4.3 percent.
The cancer mortality rate has declined 27 percent over the past 25 years.

Figure: Around the globe, life expectancy has doubled since the 1800s. | Image from Life Expectancy by Max Roser – Our World in Data / CC BY SA
Figure: A dramatic reduction in child mortality in 1800 vs. in 2015. | Image from Child Mortality by Max Roser – Our World in Data / CC BY SA
The 9 Mechanisms of Aging
*This section was adapted from CB INSIGHTS: The Future Of Aging.

Longevity, healthcare, and aging are intimately linked.

With better healthcare, we can better treat some of the leading causes of death, impacting how long we live.

By investigating how to treat diseases, we’ll inevitably better understand what causes these diseases in the first place, which directly correlates to why we age.

Following are the nine hallmarks of aging. I’ll share examples of health and longevity technologies addressing each of these later in this blog series.

Genomic instability: As we age, the environment and normal cellular processes cause damage to our genes. Activities like flying at high altitude, for example, expose us to increased radiation or free radicals. This damage compounds over the course of life and is known to accelerate aging.
Telomere attrition: Each strand of DNA in the body (known as chromosomes) is capped by telomeres. These short snippets of DNA repeated thousands of times are designed to protect the bulk of the chromosome. Telomeres shorten as our DNA replicates; if a telomere reaches a certain critical shortness, a cell will stop dividing, resulting in increased incidence of disease.
Epigenetic alterations: Over time, environmental factors will change how genes are expressed, i.e., how certain sequences of DNA are read and the instruction set implemented.
Loss of proteostasis: Over time, different proteins in our body will no longer fold and function as they are supposed to, resulting in diseases ranging from cancer to neurological disorders.
Deregulated nutrient-sensing: Nutrient levels in the body can influence various metabolic pathways. Among the affected parts of these pathways are proteins like IGF-1, mTOR, sirtuins, and AMPK. Changing levels of these proteins’ pathways has implications on longevity.
Mitochondrial dysfunction: Mitochondria (our cellular power plants) begin to decline in performance as we age. Decreased performance results in excess fatigue and other symptoms of chronic illnesses associated with aging.
Cellular senescence: As cells age, they stop dividing and cannot be removed from the body. They build up and typically cause increased inflammation.
Stem cell exhaustion: As we age, our supply of stem cells begins to diminish as much as 100 to 10,000-fold in different tissues and organs. In addition, stem cells undergo genetic mutations, which reduce their quality and effectiveness at renovating and repairing the body.
Altered intercellular communication: The communication mechanisms that cells use are disrupted as cells age, resulting in decreased ability to transmit information between cells.

Conclusion
Over the past 200 years, we have seen an abundance of healthcare technologies enable a massive lifespan boom.

Now, exponential technologies like artificial intelligence, 3D printing and sensors, as well as tremendous advancements in genomics, stem cell research, chemistry, and many other fields, are beginning to tackle the fundamental issues of why we age.

In the next blog in this series, we will dive into how genome sequencing and editing, along with new classes of drugs, are augmenting our biology to further extend our healthy lives.

What will you be able to achieve with an extra 30 to 50 healthy years (or longer) in your lifespan? Personally, I’m excited for a near-infinite lifespan to take on moonshots.

Join Me
Abundance-Digital Online Community: I’ve created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is my ‘onramp’ for exponential entrepreneurs – those who want to get involved and play at a higher level. Click here to learn more.

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