"I have never experienced the circadian clock. I have never heard the ticking of the circadian clock." - Jane Lynch, a well-known American film and television comedy star
Comedian Jane Lynch can laugh at the circadian clock, but for most of us, our circadian clock is constantly ticking. We can even say that the circadian clock is making a rumbling sound.
The biological clock of each of us is constantly running. Our biological clocks are actually made up of many clocks that tickle at different rates at the molecular, cellular, tissue, organ, systemic, physical, physiological, and social levels. But a US company wants its new technology to help health-conscious people around the world find their weakest clock and repair it or reverse it.
American scientists who study artificial intelligence (AI) say they have developed methods to measure their physiological age, and can predict whether a person will live longer than their peers or die prematurely, and the method also proposes What should be done to increase the chances of longevity.
They call it the Aging Clock, the aging clock is in the blood of our body, predicting the maximum possible time of death for our cells and body. The aging clock can also predict whether a person will age compared to his peers. Faster.
These artificial intelligence scientists have put forward the concept of aging clocks after analyzing big data and artificial intelligence on blood tests of 130,000 patients from Korea, Canada and Eastern Europe. Scientists at Insilico Medicine say the computer algorithms used in their research can accurately measure a person's physiological age. They said that the algorithm and its website www. Young. AI can provide website visitors with real-time predictive life information, and they hope that such information can help them extend their life expectancy.
Dr. Alex Zhavoronkov, CEO and founder of Insilico Medicine, explained: "Our tests can make people see so clearly that their biological clocks go faster or slower. Some people's bodies age too fast to affect health. They knew it after the test, and it was a reminder for them. This can convince them to take measures now and take measures to extend their lives. All this useful information comes from blood tests."
According to Zhavoronkov, most people tend to think that age is the number of birthdays, and age is also known as the age of time. Scientists believe that age indicators are not very accurate in predicting our death limit, or predicting the time of life. Moreover, the deviation between them can exceed 30 years.
“Physiological age is a more accurate predictor. Physiological age is a measure of the rate of deterioration of cells in our body compared to the general population. Everyone's genetics and the lifestyle we choose (eg diet, exercise, Weight, stress, and habits such as smoking or drinking are different, and the difference between physiological age and time age can reach 30 years."
He said that people we meet sometimes have white hair and wrinkles, which look much older than the driver's license, which explains. Sometimes, a healthy person who is 60 years old is only 40 years old, the truth is the same. He said that his technology can help each of us eventually be like the latter. Zhavoronkov said, “Blood testing can be used to diagnose diseases and monitor our health, which is well known. Blood tests can now be used to predict future conditions.â€
According to Zhavoronkov, “Physiological age is the true aging clock because of the physiological age in predicting death. Scientists have been trying for years to find accurate formulas for measuring physiological age. Precise formulas will help scientists better understand aging. Under what circumstances the process ran past the age of time or fell behind the age of time, the precise formula also helps scientists understand how to effectively slow down our clock and medical intervention to achieve longevity."
Zhavoronkov said Insilico Medicine analyzed blood tests for 130,000 Koreans, Canadians and Eastern Europeans and published the results in the Journal of Gerontology, which used the largest sample pool in longevity research.
Polina Mamoshina, a senior research scientist at Insilico Medicine, said, “In recent years, it has cost a lot of money to find a precise Biomark of aging. These attempts have largely failed. Now, artificial intelligence Coupled with our tremendous use of deep learning and neural networks for rapid calculations, we can find patterns and formulas in a large number of blood test samples that were not available a few years ago."
According to Zhavoronkov, the study analyzed 130,000 blood test samples, with 21 parameters and 17 chemical variants from each sample. 21 parameters are common measurement parameters, including cholesterol, inflammatory markers, hemoglobin concentration, white. Protein concentration and other parameters. He said, "Researchers used artificial intelligence to analyze and compare the blood composition, age, ethnicity, and other data of more than a million people in a single study to create a computer algorithm that scientists believe is the first truly reliable human. Aging clock. The algorithm formula only needs a drop of data in the blood to reliably predict how long we can live and predict whether we are older or older."
He said that the results obtained are consistent with the following hypothesis: "The ethnic diversity aging clock is likely to accurately predict the age of time and quantify the physiological age more effectively than the normal aging clock", and the ethnic diversity aging clock is explaining some specific factors. The effects of time-age prediction and biological age measurement are greater, such as the impact on factors such as race, geography, behavior, and environment. These factors have a significant impact on time-age prediction and biological age measurement. be surprised.
Insilico scientists described their research work and aging clock in a research paper published last month by the University of Oxford, published by The Gerontological Society of America, which Insilico scientists point out, based on deep learning. The blood aging clock "can only show a high degree of accuracy in predicting the age of time even if it is trained in a limited feature space... Our next step is definitely to include more population-specific blood biochemical data sets, To further improve the predictive power and general utility of blood aging clocks based on deep learning..."
According to Insilico's scientists, the algorithm is also useful in clinical trials of anti-aging drugs because it allows researchers to measure the effectiveness of a drug by simply observing whether the patient has transitioned from a highly aging, high-risk state to the drug. A healthy, low-risk state.
Dr. Alex Zhavoronkov, Founder and CEO of Insilico Medicine
Zhavoronkov said, “Every creature has an age. This is a universal feature that we all have, but we are different in many ways, such as specific age, cancer or diabetes, male or female. When we train deep neural networks (DNNs) based on age, these neural networks learn a lot about biology. We try to train DNN with as many samples as possible, such as race, ethnicity, diet. We are from millions By processing the clinical blood test data, we can train AI to predict the age of the patient. We train DNN with healthy people when training DNN, so these predictors are not only the predictors of age, but also the best predictors of health. We then use these predictors for those with health problems and try to predict that these people are older or younger than they are."
Think of it as an imaginary electronic mirror. The electronic mirror said today that you are 60 years old. The electronic mirror can see wrinkles, dark spots, and the like. Tomorrow, you remove the features that electronic mirrors can see and add to your age. Then go to the electronic mirror. The electronic mirror will now say that you are five years younger. If you do the same thing through blood tests, the working principle is the same. Lifestyle affects the age at which DNN perceives, perhaps the way diet or exercise can predict this effect.
Zhavoronkov said that there are already several applications that can mimic the function of the aging clock. Zhavoronkov said that although the application is interesting, it is actually a waste of valuable resources for aging and disease research. He said, "These applications are already there. Many cool apps will show you what you would look like if you were a woman or a man. These are deep learning. Some can be implemented using the Generated Confrontation Network (GAN). They will create a look or imagine a look to show you. Basically it is a waste of everyone's time and computing resources. We hope to create an accurate physiological age and find predictive factors in the future, so that everyone looks younger. There is also the use to prevent disease."
Deep learning is part of a broader family of machine learning methods based on the representation of learning data rather than on specific task algorithms. In recent years, deep learning has progressed in many disciplines. We see machine learning in computer science projects, industry conferences, and various news. Now some algorithms can even teach themselves to play games.
According to SingularityHub. According to com site Jason Dorrier, medical deep learning algorithms are trained in medical image databases to identify life-threatening diseases with the same or higher accuracy than human professionals. He even said, "There is even speculation that if we learn to trust AI, AI may be invaluable in diagnosing disease."
Zhavoronkov believes that this trust will come as long as there are more applications and long-term performance. It is estimated that by 2029, the number of people aged 65 or over in the United States will reach 71.44 million, accounting for 20% of the US population. In this case, the trust in AI naturally comes as soon as possible.
He said, but everyone must work together. Zhavoronkov said, "The war against aging is not a single individual, a single institution, a single organization or even a single country. This war requires a lot of collaboration because the process is very complicated."
Those who are interested in knowing their physical age can visit www. Young. AI. After the user has subscribed to the free age analysis, they must upload at least 18 parameters appearing in the latest blood test report, including albumin concentration, glucose and 16 other data points. In addition, users must upload facial photos and must allow the operation of another Insilico AI-driven algorithm that recognizes signs of aging in the photo and can make the user's biometric estimation more accurate. After the user uploads the blood test data and photos, a report will be sent within a few seconds and the report is free.
Zhavoronkov said that you don't have to worry about uploading to Young. AI information. “The value of this information is very low and the information is safe. We don’t want sensitive private information. The information we actually want is actually less than what we put on Facebook. We can’t enter it through the user,†he said. Information to identify someone." However, if a user uploads an image, he suggests using a nickname instead of a real name.
<Source: Forbes; Compilation: Technology Walker>
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