A sizeable part of the human genome is comprised of non-coding DNA that harbors ancient viruses. One such virus, LINE-1, remains active to this day. Activation of LINE-1 in cells triggers antiviral defense mechanisms that produce chronic inflammation, a hallmark of aging. Since LINE-1 activity irreversibly damages DNA, cells have developed several strategies to suppress it. However, innate suppression mechanisms weaken with age, so one of our goals is to create therapies to help our body keep retroelements in check.
By developing drugs against retroelements, we aim to effectively silence their activity, preventing the DNA damage and inflammation associated with cancer and age-related diseases.
What is hormesis?
And, does dopamine make you happy and what is the relationship between hormesis and d2 receptors?
First published in 2016, predictors of chronological and biological age developed using deep learning are rapidly gaining popularity in the aging research community. These deep aging clocks [that have been developed using artificial intelligence (AI)] can be used in a broad range of applications in the pharmaceutical industry, spanning target identification, drug discovery, data economics, and synthetic patient data generation.
Recent advances in machine learning, coupled with increases in computational power and availability of the large publicly available datasets, have led to a renaissance in AI. These advances have generated substantial investment and hype, and many data scientists and companies are exploiting the surge in AI hype for promotional purposes. This has sown confusion in the market and triggered criticism from scientists working in the pharmaceutical industry, where approval in clinical trials is the ultimate measure of success.
Most of the credible advances in the field have been in deep learning and reinforcement learning. Since 2013, deep learning systems have surpassed human performance in multiple applications, including strategy games as well as image and text recognition. In healthcare, deep learning systems outperformed human dermatologists, ophthalmologists, and radiologists in various tasks. Deep learning also demonstrated significant improvement over conventional machine learning methods in biomedical data analysis.
Biomarkers of aging
During this same period of deep learning progress, aging research has also experienced a renaissance, and new breakthroughs are rapidly emerging. Multiple data types can be used to predict age and associate the prediction with mortality, disease, general well-being, or other biological processes including methylation, gene expression, microbiome, and imaging data.
Since the publication of the first multitissue methylation aging clock by Steven Horvath in 2013, multiple methylation aging clocks and applications of these clocks in humans and mice were developed. Even though these clocks were developed using traditional machine learning approaches – notably linear regression with regularization and the use of a limited number of samples – the results suggest that gradual changes during aging can be tracked using various data types with reasonable accuracy.
The skin is the main barrier that protects us against environmental stressors (physical, chemical, and biological). These stressors, combined with internal factors, are responsible for cutaneous aging. Furthermore, they negatively affect the skin and increase the risk of cutaneous diseases, particularly skin cancer.
This review addresses the impact of environmental stressors on skin aging, especially those related to general and specific external factors (lifestyle, occupation, pollutants, and light exposure). More specifically, we evaluate ambient air pollution, household air pollutants from non-combustion sources, and exposure to light (ultraviolet radiation and blue and red light). We approach the molecular pathways involved in skin aging and pathology as a result of exposure to these external environmental stressors.
Finally, we reflect on how components of environmental stress can interact with ultraviolet radiation to cause cell damage and the critical importance of knowing the mechanisms to develop new therapies to maintain the skin without damage in old age and to repair its diseases.
Aging is arguably the leading risk factor for chronic diseases in the modern world. We have historically thought of aging as an inexorable decline of function, driven by the passage of time – something that we simply have to accept, and that cannot be changed.
But what if aging were actually a modifiable risk factor?
Your chronological age, meaning the length of time that you have been alive, obviously cannot be changed. But we know that biological aging can vary significantly, even among individuals who are of similar chronological age. If we can better understand the fundamental mechanisms that underlie biological aging, we might be able to devise interventions that could prevent or delay age-related diseases.
One of the relevant processes is cellular senescence. Cellular senescence is a phenomenon through which normal cells irreversibly cease to divide in response to genomic damage. Senescent cells accumulate in the body as we get older, and they actually do a lot of bad stuff in the body. Senescent cells secrete pro-inflammatory factors, like cytokines, which induces a state of chronic low-grade inflammation. But it gets even worse. These senescent cells can also drive other healthy neighboring cells into senescence. So senescent cells are basically microscopic zombies!
This has driven interest in identifying senolytics – compounds that can selectively kill senescent cells (while leaving normal cells alone).
In this episode of humanOS Radio, Dan talks to Paul Robbins. Paul is the principal investigator at the Robbins Lab at Scripps Research Institute. Notably, his lab has been screening for drugs that can safely and effectively clear out senescent cells.
This research has produced some remarkable results in animal models. For example, he and colleagues found that older mice that were given senolytics became faster and stronger, and experienced a 36% increased median post-treatment lifespan, compared to a control group.
What are the primary causes of aging?
In a previous video I introduced the different hallmarks of aging. These hallmarks can be split into three categories. We will look just at the primary causes of aging, the first category, otherwise referred to as the primary causes of damage in a cell.
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