Want to avoid lung cancer?

Listen. … Just don’t smoke to start with … No one can guarantee what happens … but, if you smoke you triple your risk over people who never smoked …

Framingham Heart Study researchers find that former smokers who quit smoking 25 or more years ago still have three times as much risk of developing lung cancer compared to people who have never smoked.

from the Framingham Heart Study highlights

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Artificial Intelligence for Regenerative Medicine

Next on the research reading queue, pointers to applications of AI in Regenerative Medicine. We’ll be including this in discussions.

 

Principle texts:

Wikipedia: Regenerative medicine is a branch of translational research ] in tissue engineering and molecular biology which deals with the “process of replacing, engineering or regenerating human cells, tissues or organs to restore or establish normal function”.  This field holds the promise of engineering damaged tissues and organs by stimulating the body’s own repair mechanisms to functionally heal previously irreparable tissues or organs.
Regenerative medicine also includes the possibility of growing tissues and organs in the laboratory and implanting them when the body cannot heal itself. If a regenerated organ’s cells would be derived from the patient’s own tissue or cells, this would potentially solve the problem of the shortage of organs available for donation, and the problem of organ transplant rejection.
Some of the biomedical approaches within the field of regenerative medicine may involve the use of stem cells.[8] Examples include the injection of stem cells or progenitor cells obtained through directed differentiation (cell therapies); the induction of regeneration by biologically active molecules administered alone or as a secretion by infused cells (immunomodulation therapy); and transplantation of in vitro grown organs and tissues (tissue engineering). ]

 

along these lines, I encountered this interesting title:

 

Zhavoronkova, Anna A., Polina Mamoshinaa, Quentin Vanhaelena, Morten Scheibye-Knudsene, Alexey Moskalevf and Alex Alipera. “Artificial intelligence for aging and longevity research.” (2018).

Abstract: The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tre- mendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models—extracting the most important features and identifying biological targets and mechanisms. Recent developments in generative adversarial networks (GANs) and re- inforcement learning (RL) permit the generation of diverse synthetic molecular and patient data, identification of novel biological targets, and generation of novel molecular compounds with desired properties and ger- oprotectors. These novel techniques can be combined into a unified, seamless end-to-end biomarker develop- ment, target identification, drug discovery and real world evidence pipeline that may help accelerate and im- prove pharmaceutical research and development practices

Experiment 2 -The Search for Deep Learning Impact

Now added a recent search that looked for recent USPTO grants that specified Deep Learning and Medicine …

filed under

PATEX 2: Patent Exploration for Artificial Intelligence in Medicine (AIM)

Experiment 2 -The Search for Deep Learning Impact

As an amusing element, it picked up this:

US-10040551-B2. – Drone delivery of coffee based on a cognitive state of an individual.

enjoy!