Category: Cell Replacement

Nutrition, Diet, Health, Medicine

Continuing on the reference. building … interesting insights emerging.  Lots of fad diets, some kernels of truth, lots of confusion, lots of marketing. lots to sort out. Somewhere along the development,  the connection between inputs and outcomes will become much clearer. Especially if one really isn’t in the huckstering business.

Some sources to consider:

Agatston, A. (2005). The South Beach diet: The delicious, doctor-designed, foolproof plan for fast and healthy weight loss. New York: St. Martin’s Griffin.
Atkins, R. C. (2002). Dr. Atkins’ new diet revolution. New York: M. Evans.
Bijlefeld, M., & Zoumbaris, S. K. (2015). Encyclopedia of diet fads: Understanding science and society (Second edition). Santa Barbara, California: Greenwood, an imprint of ABC-CLIO, LLC.

Bullmore, E. T. (2019). The inflamed mind: A radical new approach to depression (First U.S. edition). New York: Picador.

CRUMPTON, M. J., & DEDMAN, J. R. (1990). Protein terminology tangle. Nature, 345(6272), 212–212. https://doi.org/10.1038/345212a0

Cummings, J. H., & Stephen, A. M. (2007). Carbohydrate terminology and classification. European Journal Of Clinical Nutrition, 61, S5.

DiNicolantonio, J., & Mercola, J. (2018). Super fuel: Ketogenic keys to unlock the secrets of good fats, bad fats, and great health (1st edition). Carlsbad, California: Hay House Inc.

Freeman, J. M., & Freeman, J. M. (Eds.). (2007). The ketogenic diet: A treatment for children and others with epilepsy (4th ed). New York: Demos : Distributed to the trade by Publishers Group West.

Gioffre, D., & Ripa, K. (2018). Get off your acid: 7 steps in 7 days to lose weight, fight inflammation and reclaim your health and energy (First edition). New York, NY: Da Capo.

Goff, S. L., Foody, J. M., Inzucchi, S., Katz, D., Mayne, S. T., & Krumholz, H. M. (2006). BRIEF REPORT: Nutrition and weight loss information in a popular diet book: is it fact, fiction, or something in between? Journal of General Internal Medicine, 21(7), 769–774. https://doi.org/10.1111/j.1525-1497.2006.00501.x

Gudzune, K. A., Doshi, R. S., Mehta, A. K., Chaudhry, Z. W., Jacobs, D. K., Vakil, R. M., … Clark, J. M. (2015). Efficacy of Commercial Weight-Loss Programs: An Updated Systematic Review. Annals of Internal Medicine, 162(7), 501. https://doi.org/10.7326/M14-2238

Ouzounis, C. A., Coulson, R. M. R., Enright, A. J., Kunin, V., & Pereira-Leal, J. B. (2003). Classification schemes for protein structure and function. Nature Reviews Genetics, 4(7), 508–519. https://doi.org/10.1038/nrg1113

Pritikin, N., & MacGrady, P. M. (1979). The Pritikin program for diet and exercise. New York: Grosset & Dunlap.

Rahfeld, P., Sim, L., Moon, H., Constantinescu, I., Morgan-Lang, C., Hallam, S. J., … Withers, S. G. (2019). An enzymatic pathway in the human gut microbiome that converts A to universal O type blood. Nature Microbiology. https://doi.org/10.1038/s41564-019-0469-7

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