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Can AI and Biotech Conquer Death?

Posted on May 15, 2023May 15, 2023 by Jean Chalopin

Death is a certainty that all living beings must face, but what if we could beat it? The advances in biotechnology and artificial intelligence, or AI, have raised the question of whether death can be conquered.

This article explores the potential of biotech and AI to achieve immortality, farfetched as it seems, and the pros and cons associated with this idea.

Advancements in Biotech

Biotechnology is a rapidly evolving field that is focused on using biological processes, systems, and organisms to create new products, technologies, and solutions. Significant advancements in biotech have led to the development of treatments and therapies that can extend human life. One of the most promising areas of biotech research is the development of stem cell therapies.

Stem cells are undifferentiated cells that can differentiate into different types of cells and tissues in the body. Stem cell therapies involve transplanting stem cells into damaged or diseased tissues to regenerate and repair them. This can be used to treat various conditions, including spinal cord injuries, heart disease, and Parkinson’s disease.

Another area of biotech research that has the potential to extend human life is gene therapy. Gene therapy involves introducing genetic material into a patient’s cells to treat or prevent disease.

This can be used to treat genetic disorders, such as cystic fibrosis, and to prevent age-related diseases, such as Alzheimer’s disease.

Advancements in AI

AI focuses on developing machines that can perform tasks that typically require human intelligence, such as perception, learning, reasoning, and decision-making. AI can transform many industries, including healthcare, by providing new tools and solutions to improve patient outcomes and extend human life.

One of the most promising applications of AI in healthcare is precision medicine. Precision medicine involves genetic and other data to tailor medical treatments to individual patients. AI can be used to analyse vast amounts of data to identify patterns and insights that can be used to develop personalised treatment plans.

Another area of research that can potentially extend human life is the development of autonomous medical systems. Autonomous medical systems are machines that can perform medical tasks without human intervention. These systems can be used to monitor patient health, administer medications, and perform surgical procedures.

AI and Biotech

AI can help progress the biotech industry in fields such as stem cell treatment and gene therapy, which we mentioned above.

While stem cell treatment has shown promise in treating a range of diseases, it is still a relatively new field, and much is not yet understood about how stem cells work and how they can be effectively used in therapy.

AI can play a vital role in advancing stem cell treatment by helping to identify the best type of stem cell for a given condition and optimising the conditions under which the stem cells are grown and differentiated into specific cell types. It can also help to predict the likelihood of success for a given stem cell therapy and identify potential side effects or complications.

One company that is working on using AI to advance stem cell treatment is Insilico Medicine. The company uses AI to develop new drugs and therapies for various diseases, including cancer, fibrosis, and ageing. The company’s platform uses deep learning algorithms to analyse large amounts of data and identify potential drug targets and therapies. The video below is an explainer of one of their products.

Similarly, gene therapy is a new field that can benefit from AI; which can play a crucial role in advancing gene therapy by helping to identify the best targets for gene therapy and optimising the delivery of genes to the target cells. It can also help predict gene therapy’s potential outcomes and identify possible side effects or complications.

One company that is working on using AI to advance gene therapy is Homology Medicines. The company is developing gene therapies for various genetic diseases, including phenylketonuria (PKU) and sickle cell disease. The company’s platform uses AI to design and optimise the delivery of gene therapies to make gene therapy more effective and accessible.

The Issues With Conquering Death

The idea of conquering death with biotech and AI has several potential benefits and risks. Some of the most significant pros and cons are outlined below.

Pros

Improved quality of life: Conquering death could significantly improve the quality of life for older adults. We could eliminate many of the problems associated with ageing, such as chronic diseases and disability.
Advancements in science and technology: Immortality could lead to significant advances in science, technology, and culture by allowing our brightest minds to continue contributing to society.
Increased productivity: If people lived indefinitely, they would have more time to contribute to society, leading to increased productivity and economic growth.

Cons

Overpopulation: One of the most significant risks associated with conquering death is the potential for overpopulation. With people living indefinitely, the world’s population would continue to grow, straining resources and exacerbating environmental issues.
Unequal distribution of access: Ethical considerations are associated with unequal access to immortality technology. If only the wealthy and powerful could access these technologies, it could exacerbate existing inequalities.
Loss of cultural traditions: Immortality could lead to the loss of cultural traditions and the stagnation of cultural evolution.

The Challenges With Conquering Death

While AI and biotech hold significant promise for advancing medicine and extending human life, many challenges must be overcome before death can indeed be conquered.

One of the primary challenges is the ethical implications of using these technologies to extend life. While many people would welcome the opportunity to live longer, healthier lives, there are concerns about the potential consequences of such an advancement.

For example, there may be questions about who would have access to these technologies and how they would be distributed. There may also be concerns about the impact on the planet and the potential strain on resources if the population continues to grow as people live longer.

Another challenge is the complexity of the human body and the many factors that can impact health and longevity. While AI and biotech can help identify potential therapies and treatments, much is still not yet understood about how the body works and how it can be effectively treated.

For example, there are many different types of cancer, each with unique characteristics and challenges. Developing effective therapies for each type of cancer will require a deep understanding of the underlying biology and a willingness to experiment with new approaches.

There are also challenges related to developing and regulating new therapies and treatments. Developing new drugs and therapies is a long and expensive process. There is always a risk that a promising treatment will fail in clinical trials or have unforeseen side effects. In addition, there are regulatory challenges related to getting new therapies approved and ensuring they are safe and effective for humans.

Finally, there are challenges related to using AI and biotech in healthcare. For example, there may be concerns about the accuracy and reliability of AI algorithms, particularly when making decisions about human health. There may also be questions about how AI and biotech will impact the roles of healthcare providers and whether machines in the future will replace them.

Market Statistics and Use Cases

The biotech and AI industries are rapidly growing and have significant potential to transform healthcare and extend human life. According to a report by Grand View Research, the global biotech market size was valued at $1,023.92 billion in 2021 and is expected to grow at a compound annual growth rate (CAGR) of 13.9% from 2022 to 2030. The report cites the growing demand for biopharmaceuticals and increasing investment in biotech research as key market growth drivers.

Several companies are working in biotech and AI to develop new therapies and solutions that can extend human life. One such company is Unity Biotechnology, which focuses on developing therapies targeting the underlying causes of age-related diseases. The company’s lead program is a senolytic therapy that targets senescent cells, which are cells that have stopped dividing and contribute to age-related diseases.

Another company in the biotech space is Moderna, best known for developing one of the first COVID-19 vaccines. The company is also working on developing mRNA therapies that could be used to treat a range of diseases, including cancer and rare genetic disorders.

In AI, several companies are developing solutions to improve patient outcomes and extend human life. One such company is Deep Genomics, which uses AI to create new therapies for genetic diseases. The company’s platform combines genomics and machine learning to identify genetic mutations that cause disease and develop new therapies to treat them.

Closing Thoughts

The idea of conquering death with biotech and AI is a tantalising prospect but comes with significant challenges and risks. While biotech and AI have the potential to extend human life and improve the quality of life in old age, there are also substantial ethical considerations associated with immortality.

As these two industries continue to evolve, it is crucial to consider the challenges and risks and work towards developing solutions that can extend human life sustainably and ethically. The goal is to improve the quality of life for all, not just for some.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Longevity and the Future

Posted on May 5, 2023May 5, 2023 by Jean Chalopin

With continuous advancements in medical technology, the science of longevity has seen incredible progress in the past few decades. According to the World Health Organization, the global average life expectancy increased from 64.2 years in 1990 to 72.6 years in 2019.

The same report states that, in high-income countries, life expectancy at birth can reach up to 80 years. With ongoing research and advancements, there is a high probability that the average life expectancy will continue to rise in the future. In this article, we will explore the advances in the science of longevity, including the latest discoveries, potential future developments, and ethical considerations.

The Science of Longevity

The primary goal of longevity research is to improve the quality of life by extending the number of healthy years an individual can enjoy.

Several research areas contribute to the science of longevity, including genetics, epigenetics, stem cell research, and nutrition. Recent studies show that our lifestyle habits and environment also significantly determine our life span.

Lifestyle Habits

Studies show that our lifestyle habits and environment can significantly impact our lifespan. For example, a study published in the American Journal of Clinical Nutrition found that eating a diet rich in fruits, vegetables, whole grains, nuts, and legumes reduces mortality risk from all causes, including cardiovascular disease and cancer.

Similarly, a study published in the British Medical Journal found that quitting smoking can add up to 10 years to a person’s life expectancy. The study also found that even those who quit smoking in their 60s can still add several years to their lifespan.

Other studies have looked at the impact of exercise on lifespan. A study published in the journal PLOS Medicine found that individuals who engaged in regular physical activity had a reduced risk of premature death from all causes, including cardiovascular disease and cancer.

Stress is also a factor that can impact lifespan. A study published in the journal ‘Science’ found that chronic stress can accelerate ageing at the cellular level by shortening telomeres. The study suggests that stress management techniques like mindfulness meditation and yoga may help slow ageing and extend lifespan.

These studies demonstrate that our lifestyle habits and environment can significantly impact our lifespan. Making healthy lifestyle choices, such as eating a nutritious diet, quitting smoking, engaging in regular physical activity, and managing stress, can help to extend our healthy years and improve our overall quality of life.

Genetic Research

Genetic research has made significant progress in identifying the genes contributing to ageing and age-related diseases. Studies have identified several genetic variants associated with an increased risk of Alzheimer’s, cancer, and heart disease.

Researchers are also exploring the potential of gene editing technologies, such as CRISPR, to modify genes associated with ageing and disease.

One study published in Nature Genetics found a genetic variant associated with an increased risk of Alzheimer’s disease that affects the immune system’s ability to clear beta-amyloid protein from the brain.

Beta-amyloid protein is a hallmark of Alzheimer’s disease. Another study published in the journal Nature Communications identified a genetic variant associated with an increased risk of heart disease that affects the metabolism of fats in the liver.

Epigenetics Research

Epigenetics is the study of changes in gene expression without altering the underlying DNA sequence. Recent research has shown that epigenetic changes can significantly impact ageing and age-related diseases.

For example, a study published in Aging Cell found that specific epigenetic changes in the brain are associated with cognitive decline in ageing adults. Another study published in Nature Communications found that DNA methylation changes in the blood are associated with ageing and age-related diseases, such as cancer and cardiovascular disease.

Stem Cell Research

Stem cell research focuses on developing therapies to regenerate damaged tissues and organs. Recent advancements in stem cell research have shown promising results in animal studies, including restoring damaged heart tissue and reversing age-related muscle loss.

A study published in the journal Cell Stem Cell found that injecting old mice with muscle stem cells from young mice improved muscle function and strength in the older mice. Another study published in the journal Nature found that transplanting neural stem cells into the brains of ageing mice improved cognitive function.

Nutrition Research

Nutrition research has shown that a healthy diet can significantly impact our lifespan. Studies have shown that diets high in fruits, vegetables, whole grains, and lean protein can reduce the risk of chronic diseases and improve overall health. Researchers are also exploring the potential of calorie restriction and intermittent fasting to extend lifespan.

Case Study in Okinawa

The Okinawan population in Japan is a fascinating case study in the science of longevity. Okinawa is known for having one of the highest percentages of centenarians in the world, with a significant number of individuals living beyond 100. Researchers have been studying the factors that contribute to the long lifespan of Okinawans for many years.

One of the critical factors that researchers have identified is the Okinawan diet, which is high in fruits, vegetables, and whole grains and low in calories and saturated fat. The traditional Okinawan diet consists of sweet potatoes, vegetables, tofu, seaweed, and fish. The diet is rich in antioxidants and anti-inflammatory compounds, which may help to reduce the risk of chronic diseases such as cardiovascular disease and cancer.

Regular physical activity is another factor that contributes to the longevity of Okinawans. Many Okinawans engage in physical activity, such as walking, gardening, and traditional martial arts practices. This physical activity may help to reduce the risk of age-related diseases and maintain physical function in old age.

Social connections are also a crucial factor in the longevity of Okinawans. Many Okinawans maintain strong social connections throughout their lives, which can provide emotional support and a sense of purpose. Studies have shown that social isolation is associated with increased mortality risk and poor health outcomes, emphasising the importance of social connections for overall health and longevity.

In addition to these lifestyle factors, genetic and environmental factors may also contribute to the longevity of Okinawans. Researchers have identified several genetic variations that may play a role in the long lifespan of Okinawans, including variations in genes related to insulin sensitivity and inflammation. Environmental factors, such as low pollution levels and high exposure to natural light, may also contribute to the longevity of Okinawans.

Potential Future Developments

The future of longevity research looks promising, with ongoing advancements in medical technology and genetic analysis. Here are some potential future developments in the field of longevity.

Anti-Aging Drugs

Several drugs that can delay ageing and age-related diseases are currently in development. These drugs work by targeting specific genes and proteins that are associated with ageing and age-related diseases.

Gene Editing

Gene editing technologies such as CRISPR can potentially modify genes associated with ageing and disease. Researchers are exploring the potential of these technologies to extend lifespan and reduce the risk of age-related diseases.

Regenerative Therapies

Regenerative therapies such as stem cell treatments have shown promising results in animal studies. Researchers are exploring the potential of these therapies to regenerate damaged tissues and organs in humans.

Artificial Intelligence

Artificial intelligence (AI) can potentially revolutionise the field of longevity research. AI can analyse large datasets and identify patterns to help researchers develop new therapies and treatments.

Ethical Considerations

The potential to extend lifespan raises several ethical considerations that must be addressed. One concern is the unequal distribution of life-extending therapies.

If these therapies are only available to the wealthy, it could widen the gap between the rich and the poor. Another concern is the potential for overpopulation and strain on resources if the population continues to age and live longer. Researchers and policymakers must consider these ethical implications as they develop new therapies and treatments.

Closing Thoughts

In conclusion, the science of longevity has made significant progress in recent years, thanks to advancements in medical technology and research. Genetic, epigenetics, stem cell, and nutrition research have contributed to our understanding of ageing and age-related diseases.

Future developments in anti-ageing drugs, gene editing, regenerative therapies, and artificial intelligence promise to extend a healthy lifespan. However, researchers must also consider the ethical implications of extending lifespan, including unequal distribution of therapies and strain on resources. With ongoing research and advancements, the future looks bright for the science of longevity.

Ageing and AI

Posted on April 28, 2023April 28, 2023 by Jean Chalopin

The field of longevity medicine is reinvigorating. Until recently, the initiative of slowing and reversing ageing was not considered ‘proper science’ or a reasonable use of public funds.

Ageing was regarded as an unavoidable and permanent component of the human condition. It was not a disease but rather a susceptibility to disease that develops over time and cannot be reversed. The medical establishment viewed people who argued against it as entertaining mavericks at best and disreputable charlatans at worst.

The sneering has not gone away entirely, but there is now a significant investment in addressing ageing, rather than just the diseases that emerge with age, such as cancer, heart attacks, and Alzheimer’s.

Funding and approval are in place for the TAME (Targeting Aging With Metformin) project, showing that the medical profession is engaging in tackling the ageing process.

Billionaire cryptocurrency community members are emerging as the most prominent anti-ageing promoters, such as Richard Heart. The interest from influential figures could lead to sizable donations in the field and lead the fight against ageing.

Artificial Intelligence Addresses Longevity

Applying modern artificial intelligence techniques to healthcare, particularly deep neural nets and reinforcement learning, is one reason for the shift in attitudes toward ageing. Neural networks are data-processing algorithms that work in layers, with each layer taking data from the previous layer as input and passing an output up to the next layer.

The outcomes do not have to be binary, but they can be weighted. Reinforcement learning algorithms adapt their approach in response to feedback from their surroundings.

A significant contribution of AI towards anti-ageing is something known as ageing clocks.

Age is much more than how many birthdays you’ve had. Stress, sleep, and diet all have an impact on how our organs deal with the wear and tear of daily life, which may cause you to age faster or slower than people born on the same day. That means your biological age may differ significantly from your chronological age—the number of years you’ve lived.

Your biological age is more likely to reflect your physical health and even mortality than your chronological age. However, calculating it is far from simple. Scientists have spent the last decade developing ageing clocks, which analyse markers in your body to determine your biological age.

The basic idea behind ageing clocks is that they will tell you how much your organs have degraded and, thus, how many healthy years you have left. The accuracy of hundreds of ageing clocks developed in the last decade, on the other hand, varies greatly. And scientists are still debating an important question: What does it mean to be biologically young?

When an AI is trained to predict age using specific types of biological data, it learns biology. The hope is that these AIs will eventually help us understand how ageing works.

How Does AI in Ageing Work?

AI will analyse the vast amounts of health data we collect, including time series (longitudinal) data and comparative data across social and national groups. The patterns it uncovers in the data will lead to the generation of hypotheses to test. The ageing clocks will reveal treatments’ effectiveness – or lack thereof.

AI can identify trends and determine casualties by analysing patterns in medical data, images, and other sources.

AI algorithms analysed daily photographs of mice in a project to extract potential ageing markers and develop lifespan control solutions. The same learning process for visual biomarkers can then be applied to other species, including humans.

In addition to image analysis algorithms, AI can detect recurring patterns in human ageing to identify biomarkers. AI can identify ageing trends in different populations by sifting through mass data on various blood tests, retinal scans, muscle analyses, and more, or by comparing human data to other species.

Longevity and Psychological Age

Some academics believe that psychological age defines us much more than biological age.

People age at different rates, and their ageing dynamics are shaped and defined by their mindset and environment. Our psychological state, in turn, influences our perception of time and health status.

An individual’s psychological age, also known as subjective age, is based on how young or old they perceive themselves to be compared to their chronological age, depending on an individual’s self-assessment of the degree of ageing and how this perception influences their overall well-being.

Exciting research on AI-powered engines reveals novel tools that can estimate one’s psychological age and future well-being based on a psychological survey. Essentially, these AI tools can be used to determine ways to improve and maximise long-term well-being based on information inferred from available datasets.

There are two models that exist as predictors of chronological and subjective age, considering around 50 psychological features. Both clocks had modifiable features that could be altered through social and behavioural interventions. More importantly, it may be useful in shifting personal perceptions of ageing toward a mindset that promotes productive and healthy behaviours.

AI has made it possible to find the best path to emotional stability. As previously stated, this new deep learning model can predict a person’s current psychological age and guide future well-being by providing personalised recommendations for improving mental resilience.

The model’s SOM offers a set of non-trivial, personalised paths to improved well-being that can be used as a reference for the cognitive behavioural therapy and online mental health approaches. Perhaps this tool can be used as a supplement or stand-alone approach to adjusting one’s psychological age and providing emotional and mental stability.

Closing Thoughts

AI can recommend health solutions and identify potential issues by analysing the effects of therapeutic treatments, preventive measures, different lifestyles, and other factors.

On a more fundamental level, understanding how proteins and cells respond positively or negatively to treatments allows AI to contribute to the efficient development of medicine.

Deep learning’s power may soon help us alleviate the discomforts of biological ageing.

AI in Healthcare

Posted on April 5, 2023April 5, 2023 by Jean Chalopin

When you think about technological breakthroughs from history, the full promise is never what it initially does but what it eventually enables. If you go as far back as the steam engine, it cost far more than other power sources when first commercialised. However, as soon as it enabled faster transportation and cheaper product shipping, suddenly, it did not seem so expensive.

AI in healthcare is the modern-day steam engine. Although applications are still relatively sparse, the fourth industrial revolution of data and digital is starting to enable the new future.

The market for artificial intelligence in healthcare, estimated to be worth USD 10.4 billion in 2021, is anticipated to increase at a CAGR of 38.4% from 2022 to 2030. Key factors propelling the market’s expansion are the expanding datasets of digital patient health information, the desire for individualized treatment, and the rising demand for lowering healthcare costs.

The Current State of AI in Healthcare

Despite having the highest healthcare spending in the world, the United States now has inferior individual health outcomes than most other industrialised countries.

People of all generations need healthcare that is tailored to their requirements. Millennials want to be able to order their meals and receive medical advice from the same place—their sofa. In contrast, groups like the baby boomer generation take a totally different tack.

They are far more likely to want a primary care physician, so we can move away from these systems’ one-size-fits-all approach to actual care delivery–toward leveraging data and AI to genuine care.

For AI to be successful in the 21^st century, there are three vital components.

Responsibility

Sometimes, problems are unsuitable for AI; deciphering intent is paramount. Similarly, poor data and algorithm management might unintentionally introduce biases into analyses, with negative consequences for people.

Competence

Innovations must function, and the health ecosystem must agree on what constitutes an acceptable margin of error. The same forgiveness that is extended to a human physician who makes a single error is not extended to computer systems that prescribe cancer therapies.

Transparency

Being open about the limits of data and AI in healthcare can aid in the maintenance of confidence in the face of imperfect performance.

Early adopters of AI in healthcare have already enabled breakthroughs paving the way for a shift from scepticism to a beginning of trust, as well as a jump from efficiency to better efficacy.

Use Cases for AI in Healthcare

There are several ways in which AI is influencing the healthcare sector.

Medical Diagnoses

Misdiagnosis is a significant problem in the healthcare industry. According to recent research, around 12 million people in the United States are misdiagnosed yearly, with cancer patients accounting for 44% of them. AI is assisting in overcoming this problem by increasing diagnostic accuracy and efficiency.

AI-enabled digital medical solutions, such as computer vision, provide accurate analysis of medical imaging, such as patient reports, CT scans, MRI reports, X-rays, mammograms, and so on, to extract data that is not apparent to human eyes.

While AI can analyse most medical data quicker and more accurately than radiologists, it is still not sophisticated enough to replace radiologists.

Automation in Patient Care

Poor communication is seen as the worst aspect of the patient experience by 83% of patients. AI can assist in overcoming this obstacle.

AI can automate reminders, payment issues and appointment management. Clinicians can spend more time caring for patients than doing administrative work. AI can also do a lot of the background work of analysing data and ensuring patients are assigned to the correct doctor or department.

AI in Surgery

Healthcare robot AI is making procedures safer and smarter. In complex surgical operations, robotic-assisted surgery allows doctors to attain more precision, safety, flexibility, and control.

It also allows for remote surgery to be conducted from anywhere in the world in locations where surgeons are not available. This is especially true during worldwide pandemics when social distance is required.

The primary benefits of robotic surgery include the following:

Reduction in hospital stay time after a procedure
Reduced pain relative to human-operated surgery
Decreased chance of post-surgery complications

Sharing Medical Data

Another advantage of using AI in healthcare is its capacity to handle enormous volumes of patient data.

Diabetes, for example, affects more than 10% of the US population. Patients may watch their glucose levels in real-time and get data to manage their progress with doctors and support personnel using tools like the FreeStyle Libre glucose monitoring device driven by AI.

Research and Development

AI has a wide range of applications in medical research. It can help to find new drugs or repurpose existing ones. In this example, AI was used to analyse cell images and understand which were most effective for patients with specific diseases. A conventional computer is slow to spot the differences that AI can find in seconds.

Staff Training

AI tutors can provide instant feedback to students, allowing them to learn skills safely and effectively. In the example, students could learn skills 2.6 times faster and 36% better than those who are not taught with AI.

Virtual patients can help with remote training. During the pandemic, AI supported skill development remotely when group gatherings were impossible.

AI-based apps are being created to aid nurses in various ways, including decision support, sensors to alert them of patient requirements, and robotic assistance in difficult or dangerous circumstances.

Overcoming Challenges with Healthcare AI

There are some best practices to follow for healthcare sector incumbents to overcome the barriers associated with AI and seize the opportunities.

First, systems must be explainable. You don’t want to be in a position where an AI system detects cancer, and the radiologist cannot explain the decision. Prioritise building hybrid explainable AI.

AI-powered medical diagnoses are accurate but not flawless. AI systems can make mistakes that have profound implications. More testing of your AI models is a smart strategy to improve accuracy and reduce false positives.

Due to privacy and ethical limitations in the healthcare industry, gathering training medical data might be complex. Even when automated, this procedure can be costly and time-consuming. Investing in privacy-enhancing technology can help reassure users that their data is safe when acquiring and processing sensitive medical data.

Another critical obstacle to adopting AI in healthcare is patient resistance. At first sight, robotic surgery may frighten patients, but their reservations may dissipate when they learn about the benefits. To solve this dilemma, patients must be appropriately educated.

Closing Thoughts

Clinicians need to become aware of the potential of this new technology and grasp that the world is changing. It is readily adapting AI to improve the patient experience, to eliminate errors, and to ultimately save more lives.

In a human-centric field such as medicine, AI can never fully replace doctors–their care, empathy, touch, and years of experience. What AI can do, today, is eliminate the barriers to delivering care in a globalising, rapidly growing world that is falling behind with its healthcare.

Next Generation DNA Sequencing

Posted on January 3, 2023January 3, 2023 by Jean Chalopin

A sequence tells a scientist the genetic information carried on a particular DNA or RNA segment. For example, the sequence can be used to determine where genes are located on a DNA strand and where regulatory instructions turn those genes on or off.

In the mid-90s, colleges started teaching their undergraduates about DNA sequencing, with DNA sample amplification tech as the new kid on the block. The Human Genome project was ongoing, and the first human sequence had yet to be completed.

Twenty-five years later, DNA sequencing is done regularly for many and has helped dramatically with medical and forensic needs. We are now entering a whole new era of sequencing that is the “next generation.” Let’s look at this change and how this generation alters science and medicine.

What Is Next Generation DNA Sequencing?

Next generation DNA sequencing (NGS) started gaining notoriety in the early 2010s and is a term that describes DNA sequencing technologies that have revolutionized genomic research.

Original DNA Sequencing

To understand NGS, we need to understand the original type of DNA sequencing.

First, a DNA strand was copied to create enough material. Then one by one, the base pairs were determined using gels with capillaries that pulled them through using electricity, the chain-termination method, or as it is commonly known, Sanger sequencing.

The Human Genome Project used Sanger sequencing, which multiple international teams utilized to decipher the human genome, taking 13 years and $3 billion to produce the final draft released in 2003. By 2008, using several NGS techniques, the famous discoverer of DNA, James Watson’s genome sequence, was provided to him on a hard drive at an estimated cost of $1 million.

In 2011, Apple co-founder and billionaire Steve Jobs had his DNA sequence done to help in his cancer fight for $100,000. Using NGS, a lab can now sequence an entire human genome in only one day at the cost of $100 (Ultima Genomics).

Source: National Human Genome Research Institute

How NGS Works

NGS involves a stepwise process (four total steps) that breaks up the sample (DNA or RNA) and sequences the parts simultaneously to get faster results.

The process is generally as follows:

1. Sample preparation involves fragmenting DNA/RNA into multiple pieces (millions for the human genome) and then adding “adapters” to the ends of the DNA fragments.

2. Cluster generation is where the separated strands are copied millions of times to produce a larger sample.

3. Sequencing the libraries: each of the strands is sequenced with unique fluorescent markers.

4. A genomic sequence is formed by reassembling the strands using data analysis techniques.

In principle, the NGS concept is similar to capillary electrophoresis (gels used to sequence DNA in Sanger sequencing). The critical difference is that with NGS, because the fragments are broken up, the sequences of millions of fragments are obtained in a massively parallel fashion, improving accuracy and speed while reducing the cost of sequencing.

NGS’ Impact

Compared to the conventional Sanger sequencing method’s capillary electrophoresis, NGS’ short-read massively parallel sequencing technique is a fundamentally different approach that revolutionizes our sequencing capabilities, launching the second generation of sequencing methods.

NGS allows for the sequencing of both DNA and RNA at a drastically cheaper cost than Sanger sequencing, and it, therefore, has revolutionized the studies of genomics and molecular biology.

NGS’ Advantages

Because NGS can analyze both DNA and RNA samples, it’s a popular tool for functional genomics. In addition, NGS has several advantages over microarray methods.

· A priori knowledge of the genome or of any genomic features is not a requirement.

· NGS offers single nucleotide resolution, which detects related genes and features, genetic variations, and even single base pair differences. In short, it can spot slight differences in code between two samples.

· NGS has a higher dynamic signal range, making it easier to read.

· NGS requires less DNA or RNA as an input (nanograms of material are sufficient).

· NGS has higher reproducibility. Because of its other advantages, the chance of an error between repeated tests is reduced.

Most Common NGS Technologies

Three sequencing methods are and were widely used that fall under the NGS umbrella:

· Roche 454 sequencing (discontinued in 2016). This method uses a pyrosequencing technique that detects a pyrophosphate release. It uses bioluminescence (a natural light signal). Broken-up DNA stands had unique markers attached.

· Illumina (Solexa) sequencing. The Illumina process simultaneously identifies DNA base pairs. This is done as each base emits a different and unique fluorescent signal, continuously added to the nucleic acid chain.

· Ion Torrent (Proton/PGM) sequencing. This kind of sequencing measures the direct release of positive Hydrogen protons when incorporating individual base pairs. They are released when added by a DNA polymerase. The Ion Torrent method differs from the previous two methods because it is not using a light measurement to do the sequencing.

How Is NGS Being Used?

The advent of NGS has changed the biotechnology industry. There are now new questions that scientists can ask and get the answers to that were either cost-prohibitive or the samples needed were more significant than the available material. The main applications possible with NGS include:

· Rapidly sequencing the whole genome of any life form, from prions and RNA viruses to individual humans and other mammals.

· Utilize RNA sequencing to discover novel RNA variants and splice sites.

· Quantify mRNAs for gene expression.

· Sequence cancer samples to study rare variants, specific tumor subtypes, and more.

· Identify novel pathogens (such as viruses in bats).

What Can NGS Do?

Notable organizations, such as Illumina, 454 Life Sciences, Pacific Biosciences, and Oxford Technologies Nanopore, are working on getting prices down so nearly anyone can get sequencing done. For example, Ultima Genomics has claimed a cost of $100 for its sequencing. Now, companies are marketing benchtop sequencing platforms that will bring these advances to as many labs as possible.

The Illumina NextSeq Sequencer (above) is a benchtop system that can do nearly any task except “Large Whole-Genome Sequencing.” However, there is a cost of $210,000-335,000.

We expect NGS to become more efficient and affordable over time, and these cost reductions will revolutionize several genomics-related fields. Currently, all NGS approaches demand “library preparation” after the DNA fragmentation step, where adapters are attached to the ends of the various fragments. That is generally followed by a DNA amplification step to create a library that can be sequenced with the NGS device.

As we know more about different DNA molecules, we can develop ways to fight disease through gene therapy or particular drugs. This knowledge will help change our way of thinking about medicine.

Third Generation Sequencing

A new class of sequencing tech, called third-generation sequencing or TGS, is being developed. These technologies can sequence single DNA molecules without the amplification step, producing longer reads than NGS.

Single-molecule sequencing was started in 2009 by Helicos Biosciences. Unfortunately, it was slow and expensive, and the company went out of business in 2012. Nonetheless, other companies saw the benefit and took over the third-gen space.

Pacific Bioscience has its “Single-Molecule Sequencing in Real Time (SMRT),” and Oxford Nanopore has nanopore sequencing. Each can produce long reads of 15,000 bases from a single DNA or RNA molecule. This evolution means smaller genomes can be produced without the biases or errors inherent to amplification.

Closing Thoughts

The DNA sequence is a simple format in which a broad range of biological marvels can be projected for high-value data collection. Over the past decade, NGS platforms have become widely available, with the costs of services lowering by orders of magnitude, much faster than Moore’s law, democratizing genomics, and putting the tech into the hands of more scientists.

Third generation sequencing will require robust protocols and practical data approaches. The coming expanse of DNA sequencing will require a complete rethinking of experimental design. Still, it will accelerate biological and biomedical research, enabling the analysis of complex systems inexpensively and at scale. We can then fight and prevent genetic diseases before they become realized issues.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. Using this, you agree that the information does not constitute investment or financial instructions. Do research and reach out to financial advisors before making any investment decisions.

The views, thoughts, and opinions expressed in this text are solely the authors’ views, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.