The purpose of this website is to discuss various theories of aging and evaluate several strategies for slowing down the aging processes.
The first strategy to be evaluated is the selective fortification of organic compounds with heavier isotopes of carbon (13C) and hydrogen (deuterium). The isotope effect will create more stable chemical bonds and reduce the effects of oxidative stress on amino acids, lipids and nucleic acids.
Aging.AI 2.0 is now available for testing. Please use your recent blood test to guess your age.
The system is available at http://www.Aging.AI
Citation (full text):
Putin, Evgeny, Polina Mamoshina, Alexander Aliper, Mikhail Korzinkin, Alexey Moskalev, Alexey Kolosov, Alexander Ostrovskiy, Charles Cantor, Jan Vijg, and Alex Zhavoronkov. "Deep biomarkers of human aging: Application of deep neural networks to biomarker development." Aging 8, no. 5 (2016): 1-021.
Collaboration of Insilico Inc. with Atlas Regeneration Inc, Vision Genomic Inc and Howard University has given two high impact papers to explain use of newly developed Regeneration Intelligence tool for the identification of perturbation in pathways of lung and liver fibrosis and glaucoma. It can be used as diagnosis tool for the fibrosis that is often mis-diagnosed.
A) Lung and Liver Fibrosis
Fibrosis is a age related condition that is marked by the accumulation of extracellular matrix that occurs in wide range organs. This leads to changes in the structural and functional properties of organ that leads to pathological conditions. Lungs and liver are the most commonly affected by fibrotic situation leading to development of idiopathic pulmonary fibrosis (IPF) and hepatic fibrosis.
Figure 1 a - c
The figure 1 a) & b) shows venn diagram of common pathway up-regulated and down regulated respectively for lung and liver fibrosis c) venn diagram d) PAS values for 20 common signaling pathway of lung and liver fibrosis
By their new Regeneration Intelligence algorithm they were able to pin down to TFG-beta, IL6 and ILK signaling pathways, which were found to be conserved in fibro-genesis. The software provides a validated mathematical frame work for assessment of signaling pathway alteration that drives the fibrosis in organs like lungs and liver.
Glaucoma is also kind of fibrotic condition. Primary open angle glaucoma (POAG) is most common form of glaucoma that is characterised by free aqueous humor (AH) outflow and logging between iris and cornea that do not get drained from trabecular meshwork (TM) due to clogging of the channels. This causes damaging effect on the mesh like Lamina Cribrosa (LC) through which optic nerve bypass. They have used a software suite, AMD medicine to understand the molecular pathway that causes the accumulation of AH and prevention of its outflow by carrying out intracellular signalling pathway activation (SAP). Accepted paper can be found here
Figure 2: It shows the pathology of formation of glaucoma due to increase intra ocular pressure due to blockage in the removal of AH. A) shows TM and optical nerve head containing TC. B) shows TM located between cornea and iris. It shows that AH produced by ciliary body is moved towards anterior end shown by arrows and expelled into schlemm's canal via TM. C) shows flow of AH into schlemm's canal through juxtacanaliular tissue. AH outflow is blocked due to clogging in TM leads to increase in intra ocular pressure. D) Optic nerve head containing axon of RGC and lamina cribrosa structure. E) shows morphology of collagen fibres of lamina cribrosa. F) SEM trabecular meshwork and G) SEM of lamina cribrosa.
They have found that TGF-beta causes activation of pro-fibrotic pathway in TM and LC. This activated pro-fibrotic pathway causes extracellular matrix re-modelling in TM and LC. This makes TM less efficient in draining the AH. This causes LC more susceptible to damage due to increase intra ocular pressure caused because of increased AH. They propose molecular pathways that could be used for developing therapeutic intervention against glaucoma. The significance of the finding was recently discussed Eureka Alert Science Magazine and Medical News. These two papers shows a proof principle that there are some common age related signalling pathways that are shared by organs. Regeneration Intelligence based mathematical frame work gives an opportunity to explore this tool towards study of common signatures for age related fibrosis in other organs of human body. This may give us an opportunity to design an effective therapeutic strategy to fight age related disease like fibrosis.
Life extension is currently a 35 billion dollar supplement producing company. The company has nearly 20 years history of developing innovative products to fight ageing and age related diseases. History of the company in antiaging research:
* 1983: Their research showed that low doses of aspirin may help in reducing the incidence of heart attack. They also recommended the use of co enzyme Q10 (CoQ10) as an anti ageing nutrient.
* 1992: Introduction of melatonin in America for anti-ageing therapy
* 1996: Published research to check the level of fibrinogen as risk factor for cardiovascular disease
*1997: Introduced s-adenosyl methionine (SAM) for alleviating depression, arthritis and liver diseases.
*2000: They found that taking hight level of antioxidants for extended period of time helps in reducing the atherosclerosis
*2006: A relatively better form of CoQ10 that is absorbed relatively better than parent product.
*2010: They stressed that pomogranate, resveratrol and quercetin may help in fighting ageing.
*2012: Introduced a product featuring oleuropein that helps in modulation of arterial resistance and stiffness.
*2013: Introduction of supplements that consists of polyphenols gastrodin that works as brain shield against antioxidants, inflammatory and excitatory damage.
*2014: Introduction of formulation that supports adenosine monophosphate activated protein kinase (AMPK)
*2015: Introduction of pollen extract that promote prostate function and healthy urination.
In silico Inc. has been a major point of attraction in my blogs about ageing. The company is being operated from John Hopkins University. They are a group of computational biogenrontologists who believe that they can cure ageing. The group is extensively working in the field of developing computational models for coming closer and closer to fight ageing.
BioTime Inc is a clinical stage regenerative medicine company that was focuses on the development of newer technologies fight ageing via. pluripotent cells. Unlike other pharmaceutical products that works on the basis of molecular drug targets, pathways and genetic expression profiles; BioTime's approach has been at the cellular level. Where the pluripotent cells can replace the damaged cells to provide therapeutic output. The simple it sounds the more complex it is. Regeneration of tissue from embryonic cells and formation of scar tissue for an adult for therapeutic purpose is extremely complex process. Hence, in June 2016 they collaborated with Insilico Inc. to study this complex process via machine learning method. The idea of this collaboration was combine the technological advances of AI by Insilico Inc. and pluripotent cells based therapy by BioTime to produce next generation therapeutic solutions for cancer and age related disease..
In this context, AI was developed by BioTime Inc called as Embryonic.AI. It is transcriptomic based classifier that can be used for searching the queries to check how much close is the sample to the embryonic state. Embryonic. AI is basically a deep learning machine that is validated based on the thousands of samples that representative of the human embryonic stem cells. This includes human embryonic stem cells (ESC), induced pluripotent stem cells (IPSC), embryonic progenitor cells (EPC), adult stem cells (ASC) and adult cells (AC). Once the query it provided to the AI it gives out score that acts as a measure of the development stage of cells. Where ES=1 represents embryonic state and ES=0 represents adult cellular states. Here is link to the FAQs that can be used for more details
Deep learning is an artificial intelligence (AI). It utilises higher level or multilayer of the neuron to model the high level of abstraction of data.
The paper published clearly shows that AI neural network was able to predict the therapeutic use of a large number of medicine depending on gene expression data that is obtained from high-throughput experiments on human cell lines.
The significance of the work got recently published in Eureka News Alert
Image from Paper that Shows Training of DNN for Drug Discovery
The study uses 678 drugs affecting A549, MCF-7 and PC-3 cell lines from LINCS library developed by NIH that is linked to 12 therapeutic categories established by MeSH (Medical Subject Heading). The library is maintained by NLM. So basically researchers trained DNN by utilising both transcriptomic data using a scoring algorithm for samples that are perturbed with different concentration of the drug after 6 or 24 hours. The cross-validation of the model showed that DNN achieved 54.6% accuracy in correctly predicting one out of 12 therapeutic categories for each drug. Interestingly, a large number of the drugs that were misclassified by DNN was found to have dual therapeutic utility. This suggests that may be the confusion matrix used for DNN can be used for drug repurposing
This is the first study where DNN based model was developed on the basis of transcriptomic data for predicting the therapeutic use of the drug. Hence, it was proof of concept study that DNNs can be used for annotation of drugs using transcriptomic signatures. Hence, they used this finding to progress towards the development of a pipeline program in order to accelerate the preclinical of drugs for most any therapeutic category. They believe that if this technique can be extrapolated to invite signatures then maybe this can double the number of molecules in drug discovery studies.
Aging is a major risk factor for a number of chronic diseases, including
cancer, type II diabetes, atherosclerosis, hypertension, myocardial
infarction, stroke, and neurodegenerative diseases. In animal models,
treatments that extend lifespan often protect against these chronic
diseases and there is a reason to believe that a similar approach might
work in humans. Therefore, the geroscience concept, which aims to
prolong the healthy state of the human body, is likely to become a key
paradigm of biomedicine in developed countries in coming decades
(Seals et al., 2015) .
Today, more than 200 substances belong to this
group, each reported to slow aging and/or increase lifespan in a variety
of organisms, including yeasts, nematodes, fruit flies, and rodents,
according to the Geroprotectors.org database. Despite such an impressive rate of discovery, not a single geropro-
tector has yet reached the pharmaceutical market as a recognized
intervention targeting aging.
This is due following reasons
a) There is
no unified mechanistic concept of aging, and primary triggers of aging
are still poorly understood.
b) There is no comprehensive system of objective human aging biomarkers.
c) Aging is
not recognized as a disease or a complex of syndromes
d) The scientific community has no consensus view on the concept of geroprotectors, on
selection criteria for potential geroprotectors, or on the development of
appropriate classification schemes, efficiency ratings, and approaches for
predicting and modeling geroprotective properties.
In recent review article published by Dr. Alex Zhavoronkov discusses about the primary selection criteria for the geroprotectors..........................................................Click here for downloading the article Most commonly used geroprotectors Acarbose Dephernyl D-glucosamine Dihydroergocristine methanesulfonate Ellagic acid Glutathione Metformin Vinpocetine All these molecules are et In addition the there is a conference on longevity in Russia............click here for registration
Recent studies shows that these molecules may be anticancer drugs but may also be useful in attaining longevity. People interested in longevity conference and to learn more about the recent advancement in cancer therapy can visit the above page.
Anti-ageing and reversing it seems to be a science fiction. However, technological innovations are being developed to achieve this goal. Longevity is also sited in ancient hindu literature (click here). Attaining longer life has been a major quest for many eminent researchers and pharmaceutical companies (some of them are listed in table 1).
I have often seen following FAQs on ageing problem in variety of discussion forums:
a) Is it possible to slow down ageing?
b) Is it possible to live longer than 100 years?
c) Is reversing of ageing possible?
Figure 1: Top 10 countries with increased life expectancy
In light of the above mentioned questions it is certainly possible to slow, reverse and live longer. Evidences of long living naked mole rats, longest living lady Jeanne Louise Calmest and based on the life expectancy data of WHO (figure 1) and "Our World in Data" shows (figure 2) increase in average life expectancy in last 160 years.
Figure 2: Increase In Life Expectancy over 160 Years
These results indicates that there is a chance of increasing life expectancy.
Molecules For Anti-ageing
Ironically, drinking wine can help in anti-ageing. It doesn't mean that people should be drunk to achieve a long life (seems to be impractical). A Harvard Professor, Dr. David Sinclair discovered that the active ingredient of red wine (called as resveratrol)obtained from black grapes may be responsible for anti-ageing by activating SIRT1 de-actylase mediated mechanism at cellular level (Science Magazine). They found that natural (resveratrol) and synthetic sirtuin activating compounds (STACs) may help in anti-ageing by targeting SIRT1. Resveratrol supplements are available by pharmaceutical companies for consumption on amazon. Following TEDMED video of Prof. Sinclair explains the importance of anti-ageing research and how to achieve it at cellular level can be seen here.
Figure 3: Resveratrol and Wine In Anti-aging
Although, this discovery is an important scientific finding towards attaining longevity. However, it is difficult to say with certainty, if resveratrol will work effectively in human conditions because of its complexity.
Another commonly used molecule for anti-ageing application is Curcumin. Similar to resveratrol this molecule is also available from food material (figure 4) and it forms one the most common ingredient of Indian Spices. Curcumin is a phytochemical molecule and it may help slowing ageing process by inhibiting mTOR pathway.
Figure 4: Curcumin
Molecules like resveratrol and curcumin have shown to be the proof of principle that there is a possibility of attaining long life by slowing the ageing process. Eminent researchers are still struggling to find newer and better molecule which may help in anti-ageing by targeting telomeres, m-TOR or SIRT1. Further, following documented video by Dr. Alex Zhavoronkov showcase Insilco Inc. finding about healthy ageing in an international symposium as shown below:
Insilco Inc under leadership of Dr. Alex is not only trying to study ageing at molecular level but also trying to fight against age related disease by developing personalised medicine segment. In addition to the use of these molecular findings a new mobile app is developed by RYNLK to detect the effects of these anti-aging molecule by taking selfies. App can be downloaded from the following link. More technical details about the use of this app can also be available from here
Use of artificial intelligence (AI) had been increasing tremendously. Use of AI in air planes and other automobiles are most commonly used and being evolved continuously. But it is interesting even to think that an AI would judge the beauty context.
Parameters for Judging AI beauty Contest?
Wrinkles
Face symmetry
Skin colour
Gender
Age group
Ethnicity and others
Unlike other beauty contests this one is backed by scientists who are working to fight against ageing. It is a normal psychology that people don't worry about how to fight ageing. However, they worry about how they look. It is noted that more than 1 million people take selfies and post them on social networking site. Idea is what if this selfie can be used for winning a first international AI beauty contest.
People who are interested to provide their entries mark deadlines are, Human entries: 15 Jan where as for codes for AI robots: 20 Jan --------------------------Log on this website for registration
Recently article published in TechCrunch, organisers/ scientists discussed about their long term plans with their idea to host this beauty contest.
Understanding of faces through this contest will reveal about the youth and age related aspects. The topic is extensively discussed at TechCrunch. This is not only an opportunity for people to test their beauty features but also for computational chemists to upload their algorithms for AI which can help in judging human attractiveness.