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


The Mediterranean Diet and Nutritional Adequacy: A Review.

Castro-Quezada, I., Román-Viñas, B., & Serra-Majem, L. (2014). The Mediterranean Diet and Nutritional Adequacy: A Review. Nutrients.

Interesting … the claims

The Mediterranean dietary pattern, through a healthy profile of fat intake, low proportion of carbohydrate, low glycemic index, high content of dietary fiber, antioxidant compounds, and anti-inflammatory effects, reduces the risk of certain pathologies, such as cancer or Cardiovascular Disease (CVD).

The inclusion of foods typical of the Mediterranean diet and greater adherence to this healthy pattern was related to a better nutrient profile, both in children and adults, with a lower prevalence of individuals showing inadequate intakes of micronutrients.

We’ll be reviewing this and related studies in the overall evaluation …

later …


Mediterranean diet and telomere length

Looking at


Conclusion In this large study, greater adherence to the Mediterranean diet was associated with longer telomeres. These results further support the benefits of adherence to the Mediterranean diet for promoting health and longevity.”

So,  that seems promising … the question….. can you ‘stick with the diet’?  Of course, there are other studies that provide other results.  How does one resolve the discrepancies?

Stat tuned … Ideas coming soon 🙂


in the meanwhile some people like these dish ideas


Carotenoids Database provides information on 1182 natural carotenoids 

The Carotenoids Database looks pretty cool!

According to the site it “currently provides information on 1182 natural carotenoids  in 700 source organisms..

Check it out here 

A recent review paper,  [Rodriguez-Concepcion, M., Avalos, J., Bonet, M. L., Boronat, A., Gomez-Gomez, L., Hornero-Mendez, D., … & Ribot, J. (2018). A global perspective on carotenoids: Metabolism, biotechnology, and benefits for nutrition and health. Progress in lipid research, 70, 62-93.]


Carotenoids are isoprenoid metabolites synthesized by all photosynthetic organisms (including plants, algae and cyanobacteria) and some non-photosynthetic archaea, bacteria, fungi and animals. In photosynthetic systems, carotenoids participate in light harvesting and they are essential for photoprotection



In addition, carotenoids can be cleaved to produce compounds with roles as growth regulators, such as abscisic acid (ABA) and strigolactones, as well as bioactive molecules. Most animals (including humans) do not synthesize carotenoids de novo but take them in the diet and use them as essential precursors for the production of retinoids such as vitamin A. Additionally; carotenoids have been proposed to confer other health benefits whose discovery is spurring their use in functional food products.


This needs to be included in the Artificial Intelligence for Medicine Initiative

Bats are the longest-lived mammals for their size

I didn’t know that 🙂 apparently others do … cool!

Only 19 species of mammal are longer-lived than humans given their body size, and 18 of these species are bats

check this paper out.  [Foley, Nicole M., Graham M. Hughes, Zixia Huang, Michael Clarke, David Jebb, Conor V. Whelan, Eric J. Petit et al. “Growing old, yet staying young: The role of telomeres in bats’ exceptional longevity.” Science advances 4, no. 2 (2018): eaao0926.]



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

AI in medicine – not ready for prime time?

Am exploring what really can be said for AI in medicine.  There are lots of good things going on … but some reality seems to have set in.

I ran into this conclusion in the paper  Deep Learning for Genomics: A Concise Overview by Yue and Wang at Carnegie Mellon. [Yue, Tianwei and Haohan Wang. “Deep Learning for Genomics: A Concise Overview.” CoRR abs/1802.00810 (2018):]

Current applications, however, have not brought about a watershed revolution in genomic research. The predictive performances in most problems have not reach the expec- tation for real-world applications, neither have the interpretations of these abstruse models elucidate insightful knowledge. A plethora of new deep learning methods is constantly being proposed but awaits artful applications in genomics.

I was really hoping we were farther along. Maybe there’s hope … there’s always hope        [Elvis: Farther along we’ll know more about it. Farther along we’ll understand why. Cheer up my brother live in the sunshine].  Right now, what I am seeing with Watson for Genomics, and other ‘production systems ‘ suggest lots of work ahead.