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QuahogLife - named after the Quahog mollusc, a hard clam
with a lifespan of 400+ years, that symbolizes our mission.
Quahog Life Sciences
Our Goal @ Quahog
Our goal is to extend human lifespan.
Our mission is to come up with strategies/
solutions that can
(a) Delay natural aging processes by
optimizing cellular management of
production, digestion, replication and self
repair functions within the body.
(b) Find Preventive or Remedial solutions to
every disruption(diseases) that lead to
death
(c) Find solutions to monitor and avoid the
harm of pathogens.
(d) Find solutions to prevent death from forms
of physical damage.
(e) Create systems that help assure accurate
diagnosis and prevent the loss of lives due
to misdiagnosis.
General Views
# Preventing death through delayed
ageing would also mean extending
healthy energetic life too
# Death is not easy to solve . That's
true if we work in silos with
disconnected knowledge. The
approach needs a change to solve the
problem
# Too many variables. We cannot fix
this problem without a artificial
intelligent machine. We need the
capability to look at the integrated
picture by processing millions of
behavioral parameters. Human brain
cannot process and hence cannot see
the answer
Different Strategies
# We can resolve complexities of aging by
expanding the biotechnology of rejuvenation,
in order to understand how to restore
functional pathways and assist the body in
self repair.
# We can solve by replacing the core
functional components (artificial organs and
tissues) to restore body functions
# Another method is to restore youthful
blood factors; by way of replacing blood or
enhancing it periodically with personalized
compounds, in order to reduce organ
workload and boost immune system
functionality.
# Uploading memories and other system
parameters to an external new body or
hardware is being proposed for the future.
#Another way is to manage health by way of
constant monitoring and maintenance.
Evidence that it can be achieved
Evidence of other cellular forms living longer
(a) The Turritopsis dohrnii, commonly called the Immortal Jellyfish, species is shown to be
potentially immortal. This animal perpetually avoids deterioration by reverting to an immature
polyp stage and then maturing again.
(b) Ocean Quahogs, Greenland Sharks, Bowhead whales, Galapagos Giant tortoise can live for
200 years or longer.
Evidence that humans have lived well over 120
# The Hunza people are able to live up to 145 years. This tribe was first documented by Dr.
Robert McCarrison in the publication Studies in Deficiency Disease, followed in 1961 by an article
in JAMA documenting the remarkable lifespan of the Hunza.
https://sites.psu.edu/siowfa15/2015/09/15/the-hunza-people/
Saparman Sodimejo known more commonly as Mbah Gotho (died 30 April 2017) was an
Indonesian man who unverifiably claimed to be the oldest person ever recorded lived upto 142
years. https://en.wikipedia.org/wiki/Mbah_Gotho
However, the veritably oldest human lived for 122 years and 164 days, a female, Jeanne Louise
Calment from Arles, France.
https://www.demogr.mpg.de/books/odense/6/09.htm
The science of Abiogenesis presents a possible explanation
Abiogenesis, or informally the origin of life, is the natural process by which life arises from
non-living matter, such as simple compounds. The transition from non-living to living entities is
not thought to have taken place in a single event, but as a gradual process of increasing
complexity that involved molecular self-replication, self-assembly, autocatalysis and cell
membranes. Although the occurrence of abiogenesis is uncontroversial among scientists, there
is no single, generally accepted model for the origin of life.
https://en.wikipedia.org/wiki/Abiogenesis
Atomic Aggregates to
Cellular Living Forms
If we traced back how life was even formed,
we would start to see that they were formed
by non-living matter.
A certain characteristic of specific
compounds in an environment capable of
self replication and self assembly turned into
biological forms
In other words, if we carefully recreate this
entire process, we can probably create life
artificially
Understanding creation of life is key to
maintenance and restoration. Understanding
bottom-up technologies like molecular
manufacturing might throw more light on how
its done
Processes that keeps life going
The perfect orchestration of multiple pathways that account to energy continuum. Below is the
Unified map of global metabolic pathways mapped by Kyoto Encyclopedia of Genes and
Genomes.
https://www.genome.jp/kegg-bin/show_pathway?map01100
Bottom-up on
reasons that cause
death
Reverse engineering factors that lead
to cellular death, and learning how to
overcome them, can provide the
needed insights for avoiding this
problem.
Learning to manage cellular replication
errors and/or mutations causing DNA
damage, may allow for half of the battle
to be won.The other half may involve
replenishing lost functionality
(correcting telomere length, clearing
away cellular debris, etc.) and solutions
that negate larger forms of physical
damage.
Telomere Shortening
Many scientific studies have shown the strong connection between short telomeres and
cellular aging. The incorrect length of telomeres is involved in all aspects of the aging process
at the cellular level. That is, correct telomere length (not too long or too short) strongly
determines cellular health and biological age (as opposed to our chronological age). Without
the protection correct telomere length provides to DNA, our cells age, and the result is an
aging body. The enzyme telomerase rebuilds shortened telomeres, which otherwise shorten
during cell division without it.
In healthy cells, the telomerase gene is only expressed by reproductive cells. Hence, when
somatic cells divide via mitosis. because telomerase is absent (the gene is not expressed),
telomeres will gradually shorten due to replication errors. As cells recursively divide, at some
point each progeny reaches the Hayflick limit (believed to be between 50-70 cell divisions). At
this limit, cells become senescent and cell division stops. However, causing telomerase to
express in somatic cells allows for their telomeres to rebuild, creating healthy cell division. By
causing telomerase expression in somatic cells, the reversal of cellular and bodily aging was
achieved in test animals by Harvard Medical School in 2010.
https://www.tasciences.com/what-
is-a-telomere/
On Liz Parrish, CEO of BioViva
Liz Parrish, CEO of the Seattle-based company BioViva, reversed her biological age by 20
years by undergoing telomerase induction to lengthen her telomeres. She is the first human
to undergo telomerase induction therapy. Parrish learned that her telomeres were “unusually
short for her age” (she had the telomere shortening of someone in their mid-60s, perhaps due
to the lengthy childhood illness she suffered). She was treated at 44 years of age in
September of 2015, and has steadily regained telomere length.
The graph shows her status in 2018. Her telomeres increased in length from 7.33kb in 2016
to 8.12kb in 2018, amounting to two decades of cellular rejuvenation so far. Tests show that,
with a single telomerase induction treatment, her telomere length is now (in 2018) normal for
her biological age.
https://bioviva-science.com/blog/new-telomere-length-results-a-2018-update-by-liz-parrish/
Understanding the complex science of creation,
degradation, maintenance, and restoration requires much
more analytical power than the human brain is capable
of. What is required is a deep learning machine that can
learn across all variables to understand the intricate
balance of homeostasis.
Beyond Human Analytical Capability
Solving problems of ill health and aging are
With so many parameters influencing one another, it is extremely hard for humans to
manually analyze data in order to deduce accurate insights. We currently find numerous
analytical systems and machine learning platforms working to help healthcare workers
analyze and automate decision-making. Two shortcuts prevent the achievement of true
accuracy.
The need for Artificial Intelligence
1. The Data Structure:
Since healthcare data is stored in silos, users are developing heuristic data
models.This might temporarily solve a few sets of problems. Designing the data
structure requires thorough understanding of domain knowledge. The most efficient
approach is to create a bottom-up unified structure that can analyze all associated
dimensions, which would mean working on a designed experiment rather than relying
on random observation. In a designed experiment, we can ensure that every
confounding parameter is incorporated, thereby achieving accuracy
2. The Learning Network:
Popular machine learning (ML) models, which were primarily developed for image
recognition, are good for providing learning visuals, too. This helps with learning
cellular structure from visuals provided by scanned reports. It might be required that
the functional behavior of the cell or compounds be learned, which require us to
incorporate temporal models like recurrent neural network (RNN). We will require a
hybrid model to understand the structural as well as the functional changes in order to
analyze both spatial and temporal variables, in order to cause the machine to learn
from the accurate extraction of features.
The need for Artificial Intelligence
The Data Structure
The Quahog Platform proposes a
unified hierarchical data model
designed to encompass
underlying pathways, in order to
learn the outputs (diseases)
caused by pathway dysfunction.
By using a cell-centric model, we
have mapped the pathways
within the cell to understand any
and all disruptions that result in
the various functions of a
particular cell. Rolling up these
cellular functions help us in
analyzing the aggregated
disruption (diseases) and the
external effects of the disruption
(symptoms).
Incorporating capabilities to run spatiotemporal
analysis
The model defined helps extraction and organization as per the hierarchical structure, which
makes it ideal for back propagation through structure (Recursive Neural Network). The
relationship between nodes depicts the spatial arrangement, and the time series arrangement of
a particular node, with its associate (Veer, associate variable?) helps In analyzing the node
changes over a period of time. This is depicted through time association between the node and
the associated variable.
Creating a system for continuous learning
Using the network model, the platform has two networks that work like Generative and
Adversarial (GAN), wherein data collected from patients generates based on the new
parameters collected, which is compared with the Adversarial Network (the expert system) to
learn of deviations. This allows the machine is learn from every bit of new data collected, and
update the pattern database and confirm facts whenever a certain threshold is met.
Developing such a holistic decision-making system can have a wide range of applications,
spanning prevention, diagnosis, prediction, and remedial solutions that will help correct
health disruptions. Wellness contributes to longevity.
AI Decision System and Longevity
Delivering intelligence to users for better
preventive care
The Quahog Platform can help patients connect their device data, lab reports, prescription and
other data to a centralized module and then receive alerts and recommendations to prevent
early stage health disruptions. Preventive recommendations, in the form of diet, drug, exercise,
therapy, etc., can be delivered based on the combined data analysis.
Extending intelligence right up to automating
repair
The Quahog Platform can help patient care users monitor, diagnose and correct health
problems by delivering intelligence based on the integration of data from various devices, in
order to provide real-time monitoring of the effects of the treatment and recovery process.
Extending intelligence to all kind of medical quests
Comprehensively extending intelligence to a multiplicity of medical quests. N-dimensional
analysis allows various emerging health fields to determine the different probabilities
associated within their data-sets, including:
• Drug Personalization based on individual
patient variables
• Diet Personalization based on individual
patient variables
• Drug Personalization variables can be
further delivered by 3D printing and
pelletizing a wide range of drugs for oral
consumption
• Structural learning can assist when
surgeries are needed, and these trained
sets can be readily used to improve the
outcome of precision robotic surgeries
• Holistic Analysis can help cell researchers
machine learn both structural and
functional behavior
• Learning structural and functional behavior
can be extended to understand cellular
behavior in cryoenvironments, too
Extending intelligence to organ regeneration
efforts
Be it growing organs from stem
cells or creating artificial 3D printed
tissue, the knowledge of structural
dynamics and its chemical behavior
is critical for understanding
acceptance or rejection of
components placed within our body.
Better understanding cause and
effect requires 360 degree analysis,
to ensure that implants are risk free
and will survive without negative
side effects.
The behavior of the transplanted
organ can be predicted using the
learning model of the Platform.
Extending intelligence to better understand
cryopreservation
In addition, researchers are
working on ways to reduce the
toxicity of the cryoprotectants used
to vitrify water, allowing for organ
banking for later transplantation.
Alcor is optimistic that the toxicity
that occurs with vitrification will be
reversible with future molecular
repair technologies.
https://alcor.org/Library/html/vitrification.html
Sometimes, such as during sudden serious injuries, there is little time to act. Cascading effects
can quickly cause death. This is when cryogenics may present the best hope. The nonprofit
Alcor Life Extension Foundation works to preserve patients immediately upon death, with the
hope of repairing all damages in the future to bring them back to a healthy life.
A challenge lies in keeping the cellular structure and their functions intact at the low
temperatures used. It becomes extremely important to understand long-term cellular parameters
at this temperature, and understand the possible pathways that must be addressed in a
sequential manner to predict cellular survival and restoration potential.
Extending intelligence to understand revival
processes
http://www.bbc.com/future/story/20140704-i-bring-the-dead-back-to-life
Samuel A. Tisherman, of the University of Maryland, College Park, has demonstrated a method
that may be useful for preventing death in trauma patients. When perfected, the method can put
those who could not be saved and expired from trauma, such as heart attack or a severe wound,
into ‘suspended animation’ for hours at a time. The procedure, so far tested on animals, involves
draining the body of blood and cooling it more than 20° C below normal body temperature.
Much pre-clinical work must be
done to show the efficacy of this
method, and machine learning
can afford a better understanding
of the various chemical reactions
that take place in the body, and
help resolve the various risks and
other problems involved with the
procedure.
Delivering intelligence to extend life span
Quahog Life Sciences

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Role of AI in Longevity

  • 1. QuahogLife - named after the Quahog mollusc, a hard clam with a lifespan of 400+ years, that symbolizes our mission. Quahog Life Sciences
  • 2. Our Goal @ Quahog Our goal is to extend human lifespan. Our mission is to come up with strategies/ solutions that can (a) Delay natural aging processes by optimizing cellular management of production, digestion, replication and self repair functions within the body. (b) Find Preventive or Remedial solutions to every disruption(diseases) that lead to death (c) Find solutions to monitor and avoid the harm of pathogens. (d) Find solutions to prevent death from forms of physical damage. (e) Create systems that help assure accurate diagnosis and prevent the loss of lives due to misdiagnosis.
  • 3. General Views # Preventing death through delayed ageing would also mean extending healthy energetic life too # Death is not easy to solve . That's true if we work in silos with disconnected knowledge. The approach needs a change to solve the problem # Too many variables. We cannot fix this problem without a artificial intelligent machine. We need the capability to look at the integrated picture by processing millions of behavioral parameters. Human brain cannot process and hence cannot see the answer Different Strategies # We can resolve complexities of aging by expanding the biotechnology of rejuvenation, in order to understand how to restore functional pathways and assist the body in self repair. # We can solve by replacing the core functional components (artificial organs and tissues) to restore body functions # Another method is to restore youthful blood factors; by way of replacing blood or enhancing it periodically with personalized compounds, in order to reduce organ workload and boost immune system functionality. # Uploading memories and other system parameters to an external new body or hardware is being proposed for the future. #Another way is to manage health by way of constant monitoring and maintenance.
  • 4. Evidence that it can be achieved Evidence of other cellular forms living longer (a) The Turritopsis dohrnii, commonly called the Immortal Jellyfish, species is shown to be potentially immortal. This animal perpetually avoids deterioration by reverting to an immature polyp stage and then maturing again. (b) Ocean Quahogs, Greenland Sharks, Bowhead whales, Galapagos Giant tortoise can live for 200 years or longer. Evidence that humans have lived well over 120 # The Hunza people are able to live up to 145 years. This tribe was first documented by Dr. Robert McCarrison in the publication Studies in Deficiency Disease, followed in 1961 by an article in JAMA documenting the remarkable lifespan of the Hunza. https://sites.psu.edu/siowfa15/2015/09/15/the-hunza-people/ Saparman Sodimejo known more commonly as Mbah Gotho (died 30 April 2017) was an Indonesian man who unverifiably claimed to be the oldest person ever recorded lived upto 142 years. https://en.wikipedia.org/wiki/Mbah_Gotho However, the veritably oldest human lived for 122 years and 164 days, a female, Jeanne Louise Calment from Arles, France. https://www.demogr.mpg.de/books/odense/6/09.htm
  • 5. The science of Abiogenesis presents a possible explanation Abiogenesis, or informally the origin of life, is the natural process by which life arises from non-living matter, such as simple compounds. The transition from non-living to living entities is not thought to have taken place in a single event, but as a gradual process of increasing complexity that involved molecular self-replication, self-assembly, autocatalysis and cell membranes. Although the occurrence of abiogenesis is uncontroversial among scientists, there is no single, generally accepted model for the origin of life. https://en.wikipedia.org/wiki/Abiogenesis
  • 6. Atomic Aggregates to Cellular Living Forms If we traced back how life was even formed, we would start to see that they were formed by non-living matter. A certain characteristic of specific compounds in an environment capable of self replication and self assembly turned into biological forms In other words, if we carefully recreate this entire process, we can probably create life artificially Understanding creation of life is key to maintenance and restoration. Understanding bottom-up technologies like molecular manufacturing might throw more light on how its done
  • 7. Processes that keeps life going The perfect orchestration of multiple pathways that account to energy continuum. Below is the Unified map of global metabolic pathways mapped by Kyoto Encyclopedia of Genes and Genomes. https://www.genome.jp/kegg-bin/show_pathway?map01100
  • 8. Bottom-up on reasons that cause death Reverse engineering factors that lead to cellular death, and learning how to overcome them, can provide the needed insights for avoiding this problem. Learning to manage cellular replication errors and/or mutations causing DNA damage, may allow for half of the battle to be won.The other half may involve replenishing lost functionality (correcting telomere length, clearing away cellular debris, etc.) and solutions that negate larger forms of physical damage.
  • 9. Telomere Shortening Many scientific studies have shown the strong connection between short telomeres and cellular aging. The incorrect length of telomeres is involved in all aspects of the aging process at the cellular level. That is, correct telomere length (not too long or too short) strongly determines cellular health and biological age (as opposed to our chronological age). Without the protection correct telomere length provides to DNA, our cells age, and the result is an aging body. The enzyme telomerase rebuilds shortened telomeres, which otherwise shorten during cell division without it. In healthy cells, the telomerase gene is only expressed by reproductive cells. Hence, when somatic cells divide via mitosis. because telomerase is absent (the gene is not expressed), telomeres will gradually shorten due to replication errors. As cells recursively divide, at some point each progeny reaches the Hayflick limit (believed to be between 50-70 cell divisions). At this limit, cells become senescent and cell division stops. However, causing telomerase to express in somatic cells allows for their telomeres to rebuild, creating healthy cell division. By causing telomerase expression in somatic cells, the reversal of cellular and bodily aging was achieved in test animals by Harvard Medical School in 2010. https://www.tasciences.com/what- is-a-telomere/
  • 10. On Liz Parrish, CEO of BioViva Liz Parrish, CEO of the Seattle-based company BioViva, reversed her biological age by 20 years by undergoing telomerase induction to lengthen her telomeres. She is the first human to undergo telomerase induction therapy. Parrish learned that her telomeres were “unusually short for her age” (she had the telomere shortening of someone in their mid-60s, perhaps due to the lengthy childhood illness she suffered). She was treated at 44 years of age in September of 2015, and has steadily regained telomere length. The graph shows her status in 2018. Her telomeres increased in length from 7.33kb in 2016 to 8.12kb in 2018, amounting to two decades of cellular rejuvenation so far. Tests show that, with a single telomerase induction treatment, her telomere length is now (in 2018) normal for her biological age. https://bioviva-science.com/blog/new-telomere-length-results-a-2018-update-by-liz-parrish/
  • 11. Understanding the complex science of creation, degradation, maintenance, and restoration requires much more analytical power than the human brain is capable of. What is required is a deep learning machine that can learn across all variables to understand the intricate balance of homeostasis. Beyond Human Analytical Capability Solving problems of ill health and aging are
  • 12. With so many parameters influencing one another, it is extremely hard for humans to manually analyze data in order to deduce accurate insights. We currently find numerous analytical systems and machine learning platforms working to help healthcare workers analyze and automate decision-making. Two shortcuts prevent the achievement of true accuracy. The need for Artificial Intelligence
  • 13. 1. The Data Structure: Since healthcare data is stored in silos, users are developing heuristic data models.This might temporarily solve a few sets of problems. Designing the data structure requires thorough understanding of domain knowledge. The most efficient approach is to create a bottom-up unified structure that can analyze all associated dimensions, which would mean working on a designed experiment rather than relying on random observation. In a designed experiment, we can ensure that every confounding parameter is incorporated, thereby achieving accuracy 2. The Learning Network: Popular machine learning (ML) models, which were primarily developed for image recognition, are good for providing learning visuals, too. This helps with learning cellular structure from visuals provided by scanned reports. It might be required that the functional behavior of the cell or compounds be learned, which require us to incorporate temporal models like recurrent neural network (RNN). We will require a hybrid model to understand the structural as well as the functional changes in order to analyze both spatial and temporal variables, in order to cause the machine to learn from the accurate extraction of features. The need for Artificial Intelligence
  • 14. The Data Structure The Quahog Platform proposes a unified hierarchical data model designed to encompass underlying pathways, in order to learn the outputs (diseases) caused by pathway dysfunction. By using a cell-centric model, we have mapped the pathways within the cell to understand any and all disruptions that result in the various functions of a particular cell. Rolling up these cellular functions help us in analyzing the aggregated disruption (diseases) and the external effects of the disruption (symptoms).
  • 15. Incorporating capabilities to run spatiotemporal analysis The model defined helps extraction and organization as per the hierarchical structure, which makes it ideal for back propagation through structure (Recursive Neural Network). The relationship between nodes depicts the spatial arrangement, and the time series arrangement of a particular node, with its associate (Veer, associate variable?) helps In analyzing the node changes over a period of time. This is depicted through time association between the node and the associated variable.
  • 16. Creating a system for continuous learning Using the network model, the platform has two networks that work like Generative and Adversarial (GAN), wherein data collected from patients generates based on the new parameters collected, which is compared with the Adversarial Network (the expert system) to learn of deviations. This allows the machine is learn from every bit of new data collected, and update the pattern database and confirm facts whenever a certain threshold is met.
  • 17. Developing such a holistic decision-making system can have a wide range of applications, spanning prevention, diagnosis, prediction, and remedial solutions that will help correct health disruptions. Wellness contributes to longevity. AI Decision System and Longevity
  • 18. Delivering intelligence to users for better preventive care The Quahog Platform can help patients connect their device data, lab reports, prescription and other data to a centralized module and then receive alerts and recommendations to prevent early stage health disruptions. Preventive recommendations, in the form of diet, drug, exercise, therapy, etc., can be delivered based on the combined data analysis.
  • 19. Extending intelligence right up to automating repair The Quahog Platform can help patient care users monitor, diagnose and correct health problems by delivering intelligence based on the integration of data from various devices, in order to provide real-time monitoring of the effects of the treatment and recovery process.
  • 20. Extending intelligence to all kind of medical quests Comprehensively extending intelligence to a multiplicity of medical quests. N-dimensional analysis allows various emerging health fields to determine the different probabilities associated within their data-sets, including: • Drug Personalization based on individual patient variables • Diet Personalization based on individual patient variables • Drug Personalization variables can be further delivered by 3D printing and pelletizing a wide range of drugs for oral consumption • Structural learning can assist when surgeries are needed, and these trained sets can be readily used to improve the outcome of precision robotic surgeries • Holistic Analysis can help cell researchers machine learn both structural and functional behavior • Learning structural and functional behavior can be extended to understand cellular behavior in cryoenvironments, too
  • 21. Extending intelligence to organ regeneration efforts Be it growing organs from stem cells or creating artificial 3D printed tissue, the knowledge of structural dynamics and its chemical behavior is critical for understanding acceptance or rejection of components placed within our body. Better understanding cause and effect requires 360 degree analysis, to ensure that implants are risk free and will survive without negative side effects. The behavior of the transplanted organ can be predicted using the learning model of the Platform.
  • 22. Extending intelligence to better understand cryopreservation In addition, researchers are working on ways to reduce the toxicity of the cryoprotectants used to vitrify water, allowing for organ banking for later transplantation. Alcor is optimistic that the toxicity that occurs with vitrification will be reversible with future molecular repair technologies. https://alcor.org/Library/html/vitrification.html Sometimes, such as during sudden serious injuries, there is little time to act. Cascading effects can quickly cause death. This is when cryogenics may present the best hope. The nonprofit Alcor Life Extension Foundation works to preserve patients immediately upon death, with the hope of repairing all damages in the future to bring them back to a healthy life. A challenge lies in keeping the cellular structure and their functions intact at the low temperatures used. It becomes extremely important to understand long-term cellular parameters at this temperature, and understand the possible pathways that must be addressed in a sequential manner to predict cellular survival and restoration potential.
  • 23. Extending intelligence to understand revival processes http://www.bbc.com/future/story/20140704-i-bring-the-dead-back-to-life Samuel A. Tisherman, of the University of Maryland, College Park, has demonstrated a method that may be useful for preventing death in trauma patients. When perfected, the method can put those who could not be saved and expired from trauma, such as heart attack or a severe wound, into ‘suspended animation’ for hours at a time. The procedure, so far tested on animals, involves draining the body of blood and cooling it more than 20° C below normal body temperature. Much pre-clinical work must be done to show the efficacy of this method, and machine learning can afford a better understanding of the various chemical reactions that take place in the body, and help resolve the various risks and other problems involved with the procedure.
  • 24. Delivering intelligence to extend life span Quahog Life Sciences