Nobel Prize Winner Archives - InnoHEALTH magazine

Molecular mechanics controlling the circadian rhtythm

InnoHEALTH Magazine — Wed, 07 Feb 2018 07:26:27 +0000

Jeffrey C. Hall, Michael W. Young and Michael Rosbash 2017 Nobel Prize winners in Physiology or Medicine. Honored for their discoveries of molecular mechanisms controlling the circadian rhythm.

Life on Earth is adapted to the rotation of our planet. For many years we have known that living organisms, including humans, have an internal, biological clock that helps them anticipate and adapt to the regular rhythm of the day. But how does this clock actually work? Jeffrey C. Hall, Michael Rosbash and Michael W. Young were able to peek inside our biological clock and elucidate its inner workings. Their discoveries explain how plants, animals and humans adapt their biological rhythm so that it is synchronized with the Earth’s revolutions.

Using fruit flies as a model Honored for their discoveries of molecular mechanisms controlling the circadian rhythm organism, this year’s Nobel laureates isolated a gene that controls the normal daily biological rhythm. They showed that this gene encodes a protein that accumulates in the cell during the night, and is then degraded during the day. Subsequently, they identified additional protein components of this machinery, exposing the mechanism governing the self-sustaining clockwork inside the cell. We now recognize that biological clocks function by the same principles in cells of other multi-cellular organisms, including humans.

With exquisite precision, our inner clock adapts our physiology to the dramatically different phases of the day. The clock regulates critical functions such as behavior, hormone levels, sleep, body temperature and metabolism. Our wellbeing is affected when there is a temporary mismatch between our external environment and this internal biological clock, for example when we travel across several time zones and experience “jet lag”. There are also indications that chronic misalignment between our lifestyle and the rhythm dictated by our inner timekeeper is associated with increased risk for various diseases.

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Our inner clock

Most living organisms anticipate and adapt to daily changes in the environment. During the 18th century, the astronomer Jean Jacques d’Ortous de Mairan studied mimosa plants, and found that the leaves opened towards the sun during daytime and closed at dusk. He wondered what would happen if the plant was placed in constant darkness. He found that independent of daily sunlight the leaves continued to follow their normal daily oscillation (See figure). Plants seemed to have their own biological clock.

Other researchers found that not only plants, but also animals and humans, have a biological clock that helps to prepare our physiology for the fluctuations of the day. This regular adaptation is referred to as the circadianrhythm, originating from the Latin words circa meaning “around” and diesmeaning “day”. But just how our internal circadian biological clock worked remained a mystery.

Identification of a clock gene

During the 1970’s, Seymour Benzer and his student Ronald Konopka asked whether it would be possible to identify genes that control the circadian rhythm in fruit flies. They demonstrated that mutations in an unknown gene disrupted the circadian clock of flies. They named this gene period. But how could this gene influence the circadian rhythm?

This year’s Nobel Laureates, who were also studying fruit flies, aimed to discover how the clock actually works. In 1984, Jeffrey Hall and Michael Rosbash, working in close collaboration at Brandeis University in Boston, and Michael Young at the Rockefeller University in New York, succeeded in isolating the period gene. Jeffrey Hall and Michael Rosbash then went on to discover that PER, the protein encoded by period, accumulated during the night and was degraded during the day. Thus, PER protein levels oscillate over a 24-hour cycle, in synchrony with the circadian rhythm.

A self-regulating clockwork mechanism

The next key goal was to understand how such circadian oscillations could be generated and sustained. Jeffrey Hall and Michael Rosbash hypothesized that the PER protein blocked the activity of the period gene. They reasoned that by an inhibitory feedback loop, PER protein could prevent its own synthesis and thereby regulate its own level in a continuous, cyclic rhythm.

The model was tantalizing, but a few pieces of the puzzle were missing. To block the activity of the period gene, PER protein, which is produced in the cytoplasm, would have to reach the cell nucleus, where the genetic material is located. Jeffrey Hall and Michael Rosbash had shown that PER protein builds up in the nucleus during night, but how did it get there? In 1994 Michael Young discovered a second clock gene, timeless, encoding the TIM protein that was required for a normal circadian rhythm. In elegant work, he showed that when TIM bound to PER, the two proteins were able to enter the cell nucleus where they blocked period gene activity to close the inhibitory feedback loop.

Such a regulatory feedback mechanism explained how this oscillation of cellular protein levels emerged, but questions lingered. What controlled the frequency of the oscillations? Michael Young identified yet another gene, double time, encoding the DBT protein that delayed the accumulation of the PER protein. This provided insight into how an oscillation is adjusted to more closely match a 24-hour cycle.

The paradigm-shifting discoveries by the laureates established key mechanistic principles for the biological clock. During the following years other molecular components of the clockwork mechanism were elucidated, explaining its stability and function. For example, this year’s laureates identified additional proteins required for the activation of the period gene, as well as for the mechanism by which light can synchronize the clock.

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Keeping time on our human physiology

The biological clock is involved in many aspects of our complex physiology. We now know that all multicellular organisms, including humans, utilize a similar mechanism to control circadian rhythms. A large proportion of our genes are regulated by the biological clock and, consequently, a carefully calibrated circadian rhythm circadian biology has developed into a vast and highly dynamic research field, with implications for our health and wellbeing.

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TIME TO TAKE SOME INTELLIGENT DECISIONS

InnoHEALTH Magazine — Tue, 28 Nov 2017 10:34:57 +0000

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The author Mikko Kotila has 12 years of continuous research and development in machine intelligence, and is the core developer of Autonomio, the first rapid machine intelligence prototyping platform for non-programmers. Mikko is the principal of Botlab, a nonprofit foundation focused on long-term thinking on machine intelligence, and decentralization, and a co-founder of Autom8, one of the world’s first deep learning focused startup foundries.

A new paradigm for use of machine intelligence in healthcare

Many national economies are on the brink of collapse under the burden of state-supported health insurance programs. For example in the US, a country that does not provide universal healthcare, the national debt is expected to double as a result of healthcare related liabilities over the next three decades. There is an even greater price to pay for the inefficiencies found in healthcare systems. When already overburdened systems are pushed to their limits, healthcare professionals are forced to make bad decisions.

In a startling example, during Hurricane Katrina, doctors and nurses at New Orleans’ Memorial Hospital found themselves incapable of making even simple decisions. In her Pulitzer Prize-winning coverage, Sheri Fink reported how patients deemed the least likely to survive, were injected with a lethal combination of drugs — even as the evacuation was already on its way.

This chilling anecdote sheds light on a greater problem underpinning many of the biggest threats hurting human society and the eco-system of our planet.

We humans are not equipped for making good decisions under stress. Positive outcomes across a multitude of fields, including healthcare, largely depend on the ability to make good decisions under unpredictable and stress inducing conditions. We, humans, are not only bad at making decisions under pressure but are poorly equipped for making any rational decisions at all.

Daniel Kahneman, in his Nobel Prize winning work on decision making, explains how after years of deliberation leading to a single decision, the person making the decision might still completely ignore the entire process of deliberation and instead make a decision driven by emotions in a whim of the moment.

An Age of Automated Decision Making

As the information age is about the focus on automating processes related to access to information, the next age, the age of “decisioning”, will focus on automation of processes related to decision making. Whereas humans are excellent in pattern detection and pattern making, an essential requirement for creating intelligent computer systems, computers are strong in making decisions where processing of facts is of vital importance. This almost magical ability of computing systems to process information includes the capability to identify and utilize extremely subtle connections between otherwise seemingly disconnected pieces of information, a feat us humans could never do on our own with the kind of precision and scale even a simple computer system can. In no other field, rational decisions are of such vital importance as in healthcare.

Not only healthcare is the most significant economic liability for many nations, but it is also the only field of practice affecting people’s everyday lives that can be considered truly as “life and death” matter. It is, therefore, the area where the human society most desperately needs help.

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Computer-aided decision systems can be categorized into three evolutionary stages, each with a corresponding quality of results, and the requirement for a level of human involvement.

Whereas up until a few years ago, most systems were still ‘descriptive’ with some examples of ‘predictive’ decision- making support systems, in the last two years the developments in the field of machine intelligence have for the first time made the dream of prescriptive expert systems realistic. Recent advancements in both open source software and commercial hardware have paved the way for rapid prototyping of ideas that promise to revolutionize the way decisions are made across a multitude of fields, including healthcare.

Examples of these advancements include deep learning platforms such as Google’s TensorFlow and Keras, unstructured data processing innovations such as word vectorization, and Nvidia’s data processing focused GPU product-line.

Regardless of the tremendous promise and the recent hype surrounding machine intelligence, some significant concerns remain without any serious attention. While machine intelligence innovators would like to focus on showcasing “what’s possible,” before introducing new ideas and processes into the healthcare apparatus, it is far more important to ask “what could go wrong”.

Morality and Healthcare Algorithms

Every machine intelligence solution can be reduced into two aspects; the data inputs available for the solution, and the means by which the solution process those inputs. These two act as the causes for the result the system provides. Algorithms underpin every decision a computer system makes, regardless of the kind of system it is. The unique feature in this regard, of modern machine intelligence systems, such as those based on neural networks and the deep learning method, is that humans cannot audit them. It is for this reason that we can get surprising results us humans could not arrive at without working with machine intelligence, and it is important to understand that surprising results may also be negative results. In many cases, adverse results in the healthcare context arise from causes that were set years or even decades before, and as of today, it is virtually impossible to establish true causality in such cases. This means that it would be almost impossible to know if a given machine intelligence solution is contributing positively in the long term or just driving shortterm efficiency. This will hold true at least for the next 100 years, or as long as it takes to understand causality in results that take decades to mature.

When in the 1980’s game theory-based principles were widely introduced in the western healthcare context, nobody predicted the consequences. For example, while nurses and doctors were incentivized to meet certain productivity quotas such as the number of days spent in the ICU, mortality rates of patients skyrocketed as a result of ICU beds being more available. In effect, a quota scheme is an example of a simple algorithm and can be used to highlight the danger that comes with introducing machine intelligence into healthcare.

This applies in particular in countries with universal healthcare and poorly performing national economy. Under such conditions, humans that make the decisions about the use of machine intelligence in the national healthcare system, are under tremendous stress. Not only their decisions affect individual patients’ lives but also have the potential for changing the destiny of an entire nation. It is very hard to see how under such conditions, non-experts, being bombarded with endless hype by self-proclaimed machine intelligence experts, would be able to make the right decisions.

In fact, government officials, healthcare professionals, nor computer and data scientists, are formally trained in morality and often lack even the most basic understanding of ontology, epistemology, and formal logic. The three legs of the stool on which rational decisions sit. As a result, as we have seen through examples in financial markets, online advertising, and other early embracers of algorithmic decision making, we end up with so-called greedy algorithms that optimize towards a given end ruthlessly without caring about anything else.

Unlike healthcare professionals, these algorithms are not afraid of losing their livelihood and reputation as a result of making the wrong decision that ends up hurting people.

While a healthcare facility or a professional working in one, could be sued for damages, in the case of machine intelligence systems, liabilities in the healthcare context have so far not been defined.

Security in Healthcare Systems

In the light of the recent events regarding ransomware, and the rapid growth in its popularity as a cybercrime tool, it does not seem too far-fetched that in the near-future entire hospitals will be targeted and held for ransom. Indeed many hospitals had already become victims of ransomware as a consequence of passive global or national attacks. In the recent WannaCrypt ransomware attack individual medical devices were rendered temporarily useless after being infected.

Siemens released multiple warnings about its healthcare devices being possibly vulnerable to WannaCrypt. Beau Woods, deputy director of Cyber Statecraft Initiative at the Atlantic Council said that it was likely that many important medical devices such as MRIs and other crucial computer-aided systems were rendered temporarily useless by the attack.

These examples show how healthcare organizations and their technology partners are currently incapable of securing important systems. Whereas in the human operated healthcare apparatus, the devastation is so easy to create, in a highly automated machine intelligence based healthcare apparatus, the problem would be significantly amplified.

Here too, regarding security and healthcare systems, we have to seriously consider the implications of the machine intelligence ideas that are adopted today, regarding the threat landscape over the next few decades.

For example, current cryptographic methods are all based on so-called key exchange cryptography. Any sudden improvement in computation power, for instance in the form of quantum computing becoming practical, will lead to an immediate collapse of the key exchange based security paradigm. In a machine intelligence dominated healthcare apparatus, collapse of the key exchange based security paradigm has the potential for leading to the greatest human travesty in the history of the world. In the absence of serious discussion about such longer-term threats, it will not be possible to make the right decisions regarding decision-making automation and wider adoption of machine intelligence based expert systems in healthcare.

The Importance of Human Touch

Sometime in the distant future, we may be able to completely automate certain key aspects of healthcare, such as triage management. Even then it is of significant importance to not lose sight of the essence of healthcare; taking care of people. Taking care of people is one part taking care of their physical body, and one part taking care of their feelings.

In a completely machine intelligence and robotics based triage management approach it would be hard for patients to feel that they are being taken care of in the way they feel when an actual doctor is treating them.

On the other hand, in a very short period of time, the doctors would lose their ability to deal with the basic day-to-day taking care of patients that still today keep them overworked.

Because algorithms don’t feel anything, for example, empathy, it is also likely that as specialist doctors increasingly get their inputs from those algorithms, they become further distanced from the human aspect that some argue is necessary for healing practice. In this light, perhaps it’s more reasonable for the allopathic medicine to seek intelligence from its eastern counterparts such as TCM, TTM, and Ayurveda, as opposed to seeking it from machines. Machines that ultimate base their decisions on the combination of the information they are receiving and the algorithms that process the information. Both the information and the algorithms being a product of, and therefore limited by, the people who produce them.

Combining the instruments and other marvels of the western symptomatic healthcare approach with the more holistic but in some cases inferior Eastern practices, have the potential for driving significant change within the healthcare system as we know it today.

In terms of specific machine intelligence implementations, as the point-of-view presented in this article clearly shows, the focus should be on long-term macro effects of such implementations, as opposed to focusing on short-term and micro context.

Perhaps in a future world where the western and eastern medicinal practices are better integrated and institutionalized into a new era of taking care of patients, we will be better equipped to handle the challenges that come with machine intelligence focused healthcare.

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