Feeds:
Posts
Comments

Archive for the ‘Biomarkers & Medical Diagnostics’ Category


Predicting the Protein Structure of Coronavirus: Inhibition of Nsp15 can slow viral replication and Cryo-EM – Spike protein structure (experimentally verified) vs AI-predicted protein structures (not experimentally verified) of DeepMind (Parent: Google) aka AlphaFold

 

Curators: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN

This illustration, created at the Centers for Disease Control and Prevention (CDC), reveals ultrastructural morphology exhibited by coronaviruses. Note the spikes that adorn the outer surface of the virus, which impart the look of a corona surrounding the virion, when viewed electron microscopically. A novel coronavirus virus was identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China in 2019.

Image and Caption Credit: Alissa Eckert, MS; Dan Higgins, MAM available at https://phil.cdc.gov/Details.aspx?pid=23311

 

UPDATED on 3/11/2020

Coronaviruses

According to the World Health Organization, coronaviruses make up a large family of viruses named for the crown-like spikes found on their surface (Figure 1). They carry their genetic material in single strands of RNA and cause respiratory problems and fever. Like HIV, coronaviruses can be transmitted between animals and humans.  Coronaviruses have been responsible for the Severe Acute Respiratory Syndrome (SARS) pandemic in the early 2000s and the Middle East Respiratory Syndrome (MERS) outbreak in South Korea in 2015. While the most recent coronavirus, COVID-19, has caused international concern, accessible and inexpensive sequencing is helping us understand COVID-19 and respond to the outbreak quickly.

Figure 1. Coronaviruses with the characteristic spikes as seen under a microscope.

First studies that explore genetic susceptibility to COVID-19 are now being published. The first results indicate that COVID-19 infects cells using the ACE2 cell-surface receptor. Genetic variants in the ACE2 receptor gene are thus likely to influence how effectively COVID-19 can enter the cells in our bodies. Researchers hope to discover genetic variants that confer resistance to a COVID-19 infection, similar to how some variants in the CCR5 receptor gene make people immune to HIV. At Nebula Genomics, we are monitoring the latest COVID-19 research and will add any relevant discoveries to the Nebula Research Library in a timely manner.

The Role of Genomics in Responding to COVID-19

Scientists in China sequenced COVID-19’s genome just a few weeks after the first case was reported in Wuhan. This stands in contrast to SARS, which was discovered in late 2002 but was not sequenced until April of 2003. It is through inexpensive genome-sequencing that many scientists across the globe are learning and sharing information about COVID-19, allowing us to track the evolution of COVID-19 in real-time. Ultimately, sequencing can help remove the fear of the unknown and allow scientists and health professionals to prepare to combat the spread of COVID-19.

Next-generation DNA sequencing technology has enabled us to understand COVID-19 is ~30,000 bases long. Moreover, researchers in China determined that COVID-19 is also almost identical to a coronavirus found in bats and is very similar to SARS. These insights have been critical in aiding in the development of diagnostics and vaccines. For example, the Centers for Disease Control and Prevention developed a diagnostic test to detect COVID-19 RNA from nose or mouth swabs.

Moreover, a number of different government agencies and pharmaceutical companies are in the process of developing COVID-19 vaccines to stop the COVID-19 from infecting more people. To protect humans from infection inactivated virus particles or parts of the virus (e.g. viral proteins) can be injected into humans. The immune system will recognize the inactivated virus as foreign, priming the body to build immunity against possible future infection. Of note, Moderna Inc., the National Institute of Allergy and Infectious Diseases, and Coalition for Epidemic Preparedness Innovations identified a COVID-19 vaccine candidate in a record 42 days. This vaccine will be tested in human clinical trials starting in April.

For more information about COVID-19, please refer to the World Health Organization website.

SOURCE

https://blog.nebula.org/role-of-genomics-coronavirus-covid-19/?utm_source=Nebula%20Genomics&utm_medium=email&utm_campaign=COVID-19

Aviva Lev-Ari
13.3K Tweets

Aviva Lev-Ari
@AVIVA1950

My BIO lnkd.in/eEyn69r MediaPharma ex-SRI ex-MITRE ex-McGraw-Hill Followed by

Aviva Lev-Ari
@AVIVA1950

Predicting the #ProteinStructure of #Coronavirus: #Inhibition of #Nsp15 #Cryo-EM – #spike #protein structure (#experimentally verified) vs #AI-predicted protein structures (not verified) of

(

) #AlphaFold

Quote Tweet
Eric Topol
@EricTopol
·
The problem w/ visionaries is that we don’t recognize them in a timely manner (too late) Ralph Baric @UNCpublichealth and Vineet Menachery deserve recognition for being 5 yrs ahead of #COVID19 nature.com/articles/nm.39 @NatureMedicine pnas.org/content/113/11 @PNASNews via @hoondy

Image

Image

Image

Image

 

 

Senior, A.W., Evans, R., Jumper, J. et al. Improved protein structure prediction using potentials from deep learningNature 577, 706–710 (2020)https://doi.org/10.1038/s41586-019-1923-7

Abstract

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7. https://doi.org/10.1038/s41586-019-1923-7

[ALA added bold face]

COVID-19 outbreak

The scientific community has galvanised in response to the recent COVID-19 outbreak, building on decades of basic research characterising this virus family. Labs at the forefront of the outbreak response shared genomes of the virus in open access databases, which enabled researchers to rapidly develop tests for this novel pathogen. Other labs have shared experimentally-determined and computationally-predicted structures of some of the viral proteins, and still others have shared epidemiological data. We hope to contribute to the scientific effort using the latest version of our AlphaFold system by releasing structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes COVID-19. We emphasise that these structure predictions have not been experimentally verified, but hope they may contribute to the scientific community’s interrogation of how the virus functions, and serve as a hypothesis generation platform for future experimental work in developing therapeutics. We’re indebted to the work of many other labs: this work wouldn’t be possible without the efforts of researchers across the globe who have responded to the COVID-19 outbreak with incredible agility.

Knowing a protein’s structure provides an important resource for understanding how it functions, but experiments to determine the structure can take months or longer, and some prove to be intractable. For this reason, researchers have been developing computational methods to predict protein structure from the amino acid sequence.  In cases where the structure of a similar protein has already been experimentally determined, algorithms based on “template modelling” are able to provide accurate predictions of the protein structure. AlphaFold, our recently published deep learning system, focuses on predicting protein structure accurately when no structures of similar proteins are available, called “free modelling”.  We’ve continued to improve these methods since that publication and want to provide the most useful predictions, so we’re sharing predicted structures for some of the proteins in SARS-CoV-2 generated using our newly-developed methods.

It’s important to note that our structure prediction system is still in development and we can’t be certain of the accuracy of the structures we are providing, although we are confident that the system is more accurate than our earlier CASP13 system. We confirmed that our system provided an accurate prediction for the experimentally determined SARS-CoV-2 spike protein structure shared in the Protein Data Bank, and this gave us confidence that our model predictions on other proteins may be useful. We recently shared our results with several colleagues at the Francis Crick Institute in the UK, including structural biologists and virologists, who encouraged us to release our structures to the general scientific community now. Our models include per-residue confidence scores to help indicate which parts of the structure are more likely to be correct. We have only provided predictions for proteins which lack suitable templates or are otherwise difficult for template modeling.  While these understudied proteins are not the main focus of current therapeutic efforts, they may add to researchers’ understanding of SARS-CoV-2.

Normally we’d wait to publish this work until it had been peer-reviewed for an academic journal. However, given the potential seriousness and time-sensitivity of the situation, we’re releasing the predicted structures as we have them now, under an open license so that anyone can make use of them.

Interested researchers can download the structures here, and can read more technical details about these predictions in a document included with the data. The protein structure predictions we’re releasing are for SARS-CoV-2 membrane protein, protein 3a, Nsp2, Nsp4, Nsp6, and Papain-like proteinase (C terminal domain). To emphasise, these are predicted structures which have not been experimentally verified. Work on the system continues for us, and we hope to share more about it in due course.

Citation:  John Jumper, Kathryn Tunyasuvunakool, Pushmeet Kohli, Demis Hassabis, and the AlphaFold Team, “Computational predictions of protein structures associated with COVID-19”, DeepMind website, 5 March 2020, https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19

SARS-COV-2 MEMBRANE PROTEIN: A RENDERING OF ONE OF OUR PROTEIN STRUCTURE PREDICTIONS

SOURCES

Computational predictions of protein structures associated with COVID-19

https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19

AlphaFold: Using AI for scientific discovery 

https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery

 

DeepMind has shared its results with researchers at the Francis Crick Institute, a biomedical research lab in the UK, as well as offering it for download from its website.

“Normally we’d wait to publish this work until it had been peer-reviewed for an academic journal. However, given the potential seriousness and time-sensitivity of the situation, we’re releasing the predicted structures as we have them now, under an open license so that anyone can make use of them,” it said. [ALA added bold face]

There are 93,090 cases of COVID-19, and 3,198 deaths, spread across 76 countries, according to the latest report from the World Health Organization at time of writing. ®

SOURCE

https://www.theregister.co.uk/2020/03/06/deepmind_covid19_outbreak/

 

  • MHC content – The spike protein is thought to be the key to binding to cells via the angiotensin II receptor, the major mechanism the immune system uses to distinguish self from non-self

Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies

Syed Faraz Ahmed 1,† , Ahmed A. Quadeer 1, *,† and Matthew R. McKay 1,2, *

1 Department of Electronic and Computer Engineering, The Hong Kong University of Science and

Technology, Hong Kong, China; sfahmed@connect.ust.hk

2 Department of Chemical and Biological Engineering, The Hong Kong University of Science and

Technology, Hong Kong, China

* Correspondence: eeaaquadeer@ust.hk.com (A.A.Q.); m.mckay@ust.hk (M.R.M.)

These authors contributed equally to this work.

Received: 9 February 2020; Accepted: 24 February 2020; Published: 25 February 2020

Abstract:

The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the 120 available SARS-CoV-2 sequences (as of 21 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.

Keywords: Coronavirus; 2019-nCoV; 2019 novel coronavirus; SARS-CoV-2; COVID-19; SARS-CoV; MERS-CoV; T cell epitopes; B cell epitopes; vaccine [ALA added bold face]

SOURCE

https://www.mdpi.com/1999-4915/12/3/254/pdf

 

Selected Online COMMENTS to

https://forums.theregister.co.uk/forum/all/2020/03/06/deepmind_covid19_outbreak/

MuscleguySilver badge

Re: Protein structure prediction has been done for ages…

Not quite, Natural Selection does not measure methods, it measures outputs, usually at the organism level.

Sure correct folding is necessary for much protein function and we have prions and chaperone proteins to get it wrong and right.

The only way NS measures methods and mechanisms is if they are very energetically wasteful. But there are some very wasteful ones out there. Beta-Catenin at the end of point of Wnt signalling comes particularly to mind.

Chemist

Re: Does not matter at all

“Determining the structure of the virus proteins might also help in developing a molecule that disrupts the operation of just those proteins, and not anything else in the human body.”

Well it might, but predicting whether a ‘drug’ will NOT interact with any other of the 20000+ protein in complex organisms is well beyond current science. If we could do that we could predict/avoid toxicity and other non-mechanism related side-effects & mostly we can’t.

rob miller

Title

There are 480 structures on PDBe resulting from a search on ‘coronavirus,’ the top hits from MERS and SARS. PR stunt or not, they did win the most recent CASP ‘competition’, so arguably it’s probably our best shot right now – and I am certainly not satisfied that they have been sufficiently open in explaining their algorithms though I have not checked in the last few months. No one is betting anyone’s health on this, and it is not like making one wrong turn in a series of car directions. Latest prediction algorithms incorporate contact map predictions, so it’s not like a wrong dihedral angle sends the chain off in the wrong direction. A decent model would give something to run docking algorithms against with a series of already approved drugs, then we take that shortlist into the lab. A confirmed hit could be an instantly available treatment, no two year wait as currently estimated. [ALA added bold face]

jelabarre59Silver badge

Re: these structure predictions have not been experimentally verified

Naaaah. Can’t possibly be a stupid marketing stunt.

Well yes, a good possibility. But it can also be trying to build on the open-source model of putting it out there for others to build and improve upon. Essentially opening that “peer review” to a larger audience quicker. [ALA added bold face]

We shall see.

Anonymous Coward

Anonymous CowardWhat bothers me, besides the obvious PR stunt, is that they say this prediction is licensed. How can a prediction from software be protected by, I presume, patents? And if this can be protected without even verifying which predictions actually work, what’s to stop someone spitting out millions of random, untested predictions just in case they can claim ownership later when one of them is proven to work? [ALA added bold face]

 

 

SOURCES

 

  • AI-predicted protein structures could unlock vaccine for Wuhan coronavirus… if correct… after clinical trials It’s not quite DeepMind’s ‘Come with me if you want to live’ moment, but it’s close, maybe

Experimentally derived by a group of scientists at the University of Texas at Austin and the National Institute of Allergy and Infectious Diseases, an agency under the US National Institute of Health. They both feature a “Spike protein structure.”

  • Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation

See all authors and affiliations

Science  19 Feb 2020:
eabb2507
DOI: 10.1126/science.abb2507

 

  • Israeli scientists: We have developed a coronavirus vaccine

https://www.fromthegrapevine.com/health/coronavirus-vaccine-israel-migal-research-institute-david-zigdon

Other related articles published in this Open Access Online Scientific Journal include the following:

 

  • Group of Researchers @ University of California, Riverside, the University of Chicago, the U.S. Department of Energy’s Argonne National Laboratory, and Northwestern University solve COVID-19 Structure and Map Potential Therapeutics

Reporters: Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/03/06/group-of-researchers-solve-covid-19-structure-and-map-potential-therapeutic/

 

  • Is It Time for the Virtual Scientific Conference?: Coronavirus, Travel Restrictions, Conferences Cancelled Curator:

Stephen J. Williams, PhD

https://pharmaceuticalintelligence.com/2020/03/06/is-it-time-for-the-virtual-scientific-conference-coronavirus-travel-restrictions-conferences-cancelled/

Read Full Post »


Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Parkinson’s Disease (PD), characterized by both motor and non-motor system pathology, is a common neurodegenerative disorder affecting about 1% of the population over age 60. Its prevalence presents an increasing social burden as the population ages. Since its introduction in the 1960’s, dopamine (DA)-replacement therapy (e.g., L-DOPA) has remained the gold standard treatment. While improving PD patients’ quality of life, the effects of treatment fade with disease progression and prolonged usage of these medications often (>80%) results in side effects including dyskinesias and motor fluctuations. Since the selective degeneration of A9 mDA neurons (mDANs) in the substantia nigra (SN) is a key pathological feature of the disease and is directly associated with the cardinal motor symptoms, dopaminergic cell transplantation has been proposed as a therapeutic strategy.

 

Researchers showed that mammalian fibroblasts can be converted into embryonic stem cell (ESC)-like induced pluripotent stem cells (iPSCs) by introducing four transcription factors i.e., Oct4, Sox2, Klf4, and c-Myc. This was then accomplished with human somatic cells, reprogramming them into human iPSCs (hiPSCs), offering the possibility of generating patient-specific stem cells. There are several major barriers to implementation of hiPSC-based cell therapy for PD. First, probably due to the limited understanding of the reprogramming process, wide variability exists between the differentiation potential of individual hiPSC lines. Second, the safety of hiPSC-based cell therapy has yet to be fully established. In particular, since any hiPSCs that remain undifferentiated or bear sub-clonal tumorigenic mutations have neoplastic potential, it is critical to eliminate completely such cells from a therapeutic product.

 

In the present study the researchers established human induced pluripotent stem cell (hiPSC)-based autologous cell therapy. Researchers reported a platform of core techniques for the production of mDA progenitors as a safe and effective therapeutic product. First, by combining metabolism-regulating microRNAs with reprogramming factors, a method was developed to more efficiently generate clinical grade iPSCs, as evidenced by genomic integrity and unbiased pluripotent potential. Second, a “spotting”-based in vitro differentiation methodology was established to generate functional and healthy mDA cells in a scalable manner. Third, a chemical method was developed that safely eliminates undifferentiated cells from the final product. Dopaminergic cells thus produced can express high levels of characteristic mDA markers, produce and secrete dopamine, and exhibit electrophysiological features typical of mDA cells. Transplantation of these cells into rodent models of PD robustly restored motor dysfunction and reinnervated host brain, while showing no evidence of tumor formation or redistribution of the implanted cells.

 

Together these results supported the promise of these techniques to provide clinically applicable personalized autologous cell therapy for PD. It was recognized by researchers that this methodology is likely to be more costly in dollars and manpower than techniques using off-the-shelf methods and allogenic cell lines. Nevertheless, the cost for autologous cell therapy may be expected to decrease steadily with technological refinement and automation. Given the significant advantages inherent in a cell source free of ethical concerns and with the potential to obviate the need for immunosuppression, with its attendant costs and dangers, it was proposed that this platform is suitable for the successful implementation of human personalized autologous cell therapy for PD.

 

References:

 

https://www.jci.org/articles/view/130767/pdf?elqTrackId=2fd7d0edee744f9cb6d70a686d7b273b

 

https://www.ncbi.nlm.nih.gov/pubmed/31714896

 

https://www.ncbi.nlm.nih.gov/pubmed/23666606

 

https://www.ncbi.nlm.nih.gov/pubmed/27343168

 

https://www.ncbi.nlm.nih.gov/pubmed/21495962

 

https://www.ncbi.nlm.nih.gov/pubmed/28083784

 

https://www.ncbi.nlm.nih.gov/pubmed/20336395

 

https://www.ncbi.nlm.nih.gov/pubmed/28585381

 

Read Full Post »


Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

RNA plays various roles in determining how the information in our genes drives cell behavior. One of its roles is to carry information encoded by our genes from the cell nucleus to the rest of the cell where it can be acted on by other cell components. Rresearchers have now defined how RNA also participates in transmitting information outside cells, known as extracellular RNA or exRNA. This new role of RNA in cell-to-cell communication has led to new discoveries of potential disease biomarkers and therapeutic targets. Cells using RNA to talk to each other is a significant shift in the general thought process about RNA biology.

 

Researchers explored basic exRNA biology, including how exRNA molecules and their transport packages (or carriers) were made, how they were expelled by producer cells and taken up by target cells, and what the exRNA molecules did when they got to their destination. They encountered surprising complexity both in the types of carriers that transport exRNA molecules between cells and in the different types of exRNA molecules associated with the carriers. The researchers had to be exceptionally creative in developing molecular and data-centric tools to begin making sense of the complexity, and found that the type of carrier affected how exRNA messages were sent and received.

 

As couriers of information between cells, exRNA molecules and their carriers give researchers an opportunity to intercept exRNA messages to see if they are associated with disease. If scientists could change or engineer designer exRNA messages, it may be a new way to treat disease. The researchers identified potential exRNA biomarkers for nearly 30 diseases including cardiovascular disease, diseases of the brain and central nervous system, pregnancy complications, glaucoma, diabetes, autoimmune diseases and multiple types of cancer.

 

As for example some researchers found that exRNA in urine showed promise as a biomarker of muscular dystrophy where current studies rely on markers obtained through painful muscle biopsies. Some other researchers laid the groundwork for exRNA as therapeutics with preliminary studies demonstrating how researchers might load exRNA molecules into suitable carriers and target carriers to intended recipient cells, and determining whether engineered carriers could have adverse side effects. Scientists engineered carriers with designer RNA messages to target lab-grown breast cancer cells displaying a certain protein on their surface. In an animal model of breast cancer with the cell surface protein, the researchers showed a reduction in tumor growth after engineered carriers deposited their RNA cargo.

 

Other than the above research work the scientists also created a catalog of exRNA molecules found in human biofluids like plasma, saliva and urine. They analyzed over 50,000 samples from over 2000 donors, generating exRNA profiles for 13 biofluids. This included over 1000 exRNA profiles from healthy volunteers. The researchers found that exRNA profiles varied greatly among healthy individuals depending on characteristics like age and environmental factors like exercise. This means that exRNA profiles can give important and detailed information about health and disease, but careful comparisons need to be made with exRNA data generated from people with similar characteristics.

 

Next the researchers will develop tools to efficiently and reproducibly isolate, identify and analyze different carrier types and their exRNA cargos and allow analysis of one carrier and its cargo at a time. These tools will be shared with the research community to fill gaps in knowledge generated till now and to continue to move this field forward.

 

References:

 

https://www.nih.gov/news-events/news-releases/scientists-explore-new-roles-rna

 

https://www.cell.com/consortium/exRNA

 

https://www.sciencedaily.com/releases/2016/06/160606120230.htm

 

https://www.pasteur.fr/en/multiple-roles-rnas

 

https://www.nature.com/scitable/topicpage/rna-functions-352

 

https://www.umassmed.edu/rti/biology/role-of-rna-in-biology/

 

Read Full Post »


Hypertriglyceridemia: Evaluation and Treatment Guideline

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Severe and very severe hypertriglyceridemia increase the risk for pancreatitis, whereas mild or moderate hypertriglyceridemia may be a risk factor for cardiovascular disease. Individuals found to have any elevation of fasting triglycerides should be evaluated for secondary causes of hyperlipidemia including endocrine conditions and medications. Patients with primary hypertriglyceridemia must be assessed for other cardiovascular risk factors, such as central obesity, hypertension, abnormalities of glucose metabolism, and liver dysfunction. The aim of this study was to develop clinical practice guidelines on hypertriglyceridemia.

The diagnosis of hypertriglyceridemia should be based on fasting levels, that mild and moderate hypertriglyceridemia (triglycerides of 150–999 mg/dl) be diagnosed to aid in the evaluation of cardiovascular risk, and that severe and very severe hypertriglyceridemia (triglycerides of >1000 mg/dl) be considered a risk for pancreatitis. The patients with hypertriglyceridemia must be evaluated for secondary causes of hyperlipidemia and that subjects with primary hypertriglyceridemia be evaluated for family history of dyslipidemia and cardiovascular disease.

The treatment goal in patients with moderate hypertriglyceridemia should be a non-high-density lipoprotein cholesterol level in agreement with National Cholesterol Education Program Adult Treatment Panel guidelines. The initial treatment should be lifestyle therapy; a combination of diet modification, physical activity and drug therapy may also be considered. In patients with severe or very severe hypertriglyceridemia, a fibrate can be used as a first-line agent for reduction of triglycerides in patients at risk for triglyceride-induced pancreatitis.

Three drug classes (fibrates, niacin, n-3 fatty acids) alone or in combination with statins may be considered as treatment options in patients with moderate to severe triglyceride levels. Statins are not be used as monotherapy for severe or very severe hypertriglyceridemia. However, statins may be useful for the treatment of moderate hypertriglyceridemia when indicated to modify cardiovascular risk.

 

References:

 

https://www.medpagetoday.com/clinical-connection/cardio-endo/77242?xid=NL_CardioEndoConnection_2019-01-21

https://www.ncbi.nlm.nih.gov/pubmed/19307519

https://www.ncbi.nlm.nih.gov/pubmed/23009776

https://www.ncbi.nlm.nih.gov/pubmed/6827992

https://www.ncbi.nlm.nih.gov/pubmed/22463676

https://www.ncbi.nlm.nih.gov/pubmed/17635890

 

Read Full Post »


Changes in Levels of Sex Hormones and N-Terminal Pro–B-Type Natriuretic Peptide as Biomarker for Cardiovascular Diseases

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Considerable differences exist in the prevalence and manifestation of atherosclerotic cardiovascular disease (CVD) and heart failure (HF) between men and women. Premenopausal women have a lower risk of CVD and HF compared with men; however, this risk increases after menopause. Sex hormones, particularly androgens, are associated with CVD risk factors and events and have been postulated to mediate the observed sex differences in CVD.

 

B-type natriuretic peptides (BNPs) are secreted from cardiomyocytes in response to myocardial wall stress. BNP plays an important role in cardiovascular remodelling and volume homeostasis. It exerts numerous cardioprotective effects by promoting vasodilation, natriuresis, and ventricular relaxation and by antagonizing fibrosis and the effects of the renin-angiotensin-aldosterone system. Although the physiological role of BNP is cardioprotective, pathologically elevated N-terminal pro–BNP (NT-proBNP) levels are used clinically to indicate left ventricular hypertrophy, dysfunction, and myocardial ischemia. Higher NT-proBNP levels among individuals free of clinical CVD are associated with an increased risk of incident CVD, HF, and cardiovascular mortality.

 

BNP and NT-proBNP levels are higher in women than men in the general population. Several studies have proposed the use of sex- and age-specific reference ranges for BNP and NT-proBNP levels, in which reference limits are higher for women and older individuals. The etiology behind this sex difference has not been fully elucidated, but prior studies have demonstrated an association between sex hormones and NT-proBNP levels. Recent studies measuring endogenous sex hormones have suggested that androgens may play a larger role in BNP regulation by inhibiting its production.

 

Data were collected from a large, multiethnic community-based cohort of individuals free of CVD and HF at baseline to analyze both the cross-sectional and longitudinal associations between sex hormones [total testosterone (T), bioavailable T, freeT, dehydroepiandrosterone (DHEA), SHBG, and estradiol] and NT-proBNP, separately for women and men. It was found that a more androgenic pattern of sex hormones was independently associated with lower NT-proBNP levels in cross-sectional analyses in men and postmenopausal women.

 

This association may help explain sex differences in the distribution of NT-proBNP and may contribute to the NP deficiency in men relative to women. In longitudinal analyses, a more androgenic pattern of sex hormones was associated with a greater increase in NT-proBNP levels in both sexes, with a more robust association among women. This relationship may reflect a mechanism for the increased risk of CVD and HF seen in women after menopause.

 

Additional research is needed to further explore whether longitudinal changes in NT-proBNP levels seen in our study are correlated with longitudinal changes in sex hormones. The impact of menopause on changes in NT-proBNP levels over time should also be explored. Furthermore, future studies should aim to determine whether sex hormones directly play a role in biological pathways of BNP synthesis and clearance in a causal fashion. Lastly, the dual role of NTproBNP as both

  • a cardioprotective hormone and
  • a biomarker of CVD and HF, as well as
  • the role of sex hormones in delineating these processes,

should be further explored. This would provide a step toward improved clinical CVD risk stratification and prognostication based on

  • sex hormone and
  • NT-proBNP levels.

 

References:

 

https://www.medpagetoday.com/clinical-connection/cardio-endo/76480?xid=NL_CardioEndoConnection_2018-12-27

 

https://www.ncbi.nlm.nih.gov/pubmed/30137406

 

https://www.ncbi.nlm.nih.gov/pubmed/22064958

 

https://www.ncbi.nlm.nih.gov/pubmed/24036936

 

https://www.ncbi.nlm.nih.gov/pubmed/19854731

 

Read Full Post »


Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Over the past 20 years, studies have shown that girls and possibly boys have been experiencing puberty at progressively younger ages. This is troubling news, as earlier age at puberty has been linked with increased risk of mental illness, breast and ovarian cancer in girls and testicular cancer in boys. Researchers found that daughters of mothers who had higher levels of diethyl phthalate and triclosan in their bodies during pregnancy experienced puberty at younger ages. The same trend was not observed in boys. So, researchers suspected that girls exposed to chemicals commonly found in toothpaste, makeup, soap and other personal care products before birth may hit puberty earlier.

 

Diethyl phthalate is often used as a stabilizer in fragrances and cosmetics. The antimicrobial agent triclosan — which the FDA banned from use in hand soap in 2017 because it was shown to be ineffective — is still used in some toothpastes. Researchers suspected that many chemicals in personal care products can interfere with natural hormones in human bodies, and studies have shown that exposure to these chemicals can alter reproductive development in rats. Chemicals that have been implicated include phthalates, which are often found in scented products like perfumes, soaps and shampoos; parabens, which are used as preservatives in cosmetics; and phenols, which include triclosan.

 

However, few studies have looked at how these chemicals might affect the growth of human children. This present study at UC Berkeley, USA recruited pregnant women living in the farm-working, primarily Latino communities of Central California’s Salinas Valley between 1999 and 2000. While the primary aim of the study was to examine the impact of pesticide exposure on childhood development, the researchers used the opportunity to examine the effects of other chemicals as well. The scientists measured concentrations of phthalates, parabens and phenols in urine samples taken from mothers twice during pregnancy, and from children at the age of 9. They then followed the growth of the children — 159 boys and 179 girls — between the ages of 9 and 13 to track the timing of developmental milestones marking different stages of puberty.

 

The vast majority — more than 90 percent — of urine samples of both mothers and children showed detectable concentrations of all three classes of chemicals, with the exception of triclosan which was present in approximately 70 percent of samples. The researchers found that every time the concentrations of diethyl phthalate and triclosan in the mother’s urine doubled, the timing of developmental milestones in girls shifted approximately one month earlier. Girls who had higher concentrations of parabens in their urine at age 9 also experienced puberty at younger ages. However, it is unclear if the chemicals were causing the shift, or if girls who reached puberty earlier were more likely to start using personal care products at younger ages.

 

The limitations are that these chemicals are quickly metabolized and one to two urinary measurements per developmental point may not accurately reflect usual exposure. The study population was limited to Latino children of low socioeconomic status living in a farmworker community and may not be widely generalizable. But, this study contributes to a growing literature that suggests that exposure to certain endocrine disrupting chemicals may impact timing of puberty in children.

 

References:

 

https://www.universityofcalifornia.edu/news/prenatal-exposure-chemicals-personal-care-products-may-speed-puberty-girls?utm_source=fiat-lux

 

https://www.ncbi.nlm.nih.gov/pubmed/30517665

 

https://www.ncbi.nlm.nih.gov/pubmed/24781428

 

https://www.ncbi.nlm.nih.gov/pubmed/30203993

 

https://www.ncbi.nlm.nih.gov/pubmed/25173057

 

Read Full Post »


Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

Curator: Stephen J. Williams, Ph.D.

Updated 12/18/2018

In the efforts to reduce healthcare costs, provide increased accessibility of service for patients, and drive biomedical innovations, many healthcare and biotechnology professionals have looked to advances in digital technology to determine the utility of IT to drive and extract greater value from healthcare industry.  Two areas of recent interest have focused how best to use blockchain and artificial intelligence technologies to drive greater efficiencies in our healthcare and biotechnology industries.

More importantly, with the substantial increase in ‘omic data generated both in research as well as in the clinical setting, it has become imperative to develop ways to securely store and disseminate the massive amounts of ‘omic data to various relevant parties (researchers or clinicians), in an efficient manner yet to protect personal privacy and adhere to international regulations.  This is where blockchain technologies may play an important role.

A recent Oncotarget paper by Mamoshina et al. (1) discussed the possibility that next-generation artificial intelligence and blockchain technologies could synergize to accelerate biomedical research and enable patients new tools to control and profit from their personal healthcare data, and assist patients with their healthcare monitoring needs. According to the abstract:

The authors introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship value of the data.  They also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare.  In this system, blockchain and deep learning technologies would provide the secure and transparent distribution of personal data in a healthcare marketplace, and would also be useful to resolve challenges faced by the regulators and return control over personal data including medical records to the individual.

The review discusses:

  1. Recent achievements in next-generation artificial intelligence
  2. Basic concepts of highly distributed storage systems (HDSS) as a preferred method for medical data storage
  3. Open source blockchain Exonium and its application for healthcare marketplace
  4. A blockchain-based platform allowing patients to have control of their data and manage access
  5. How advances in deep learning can improve data quality, especially in an era of big data

Advances in Artificial Intelligence

  • Integrative analysis of the vast amount of health-associated data from a multitude of large scale global projects has proven to be highly problematic (REF 27), as high quality biomedical data is highly complex and of a heterogeneous nature, which necessitates special preprocessing and analysis.
  • Increased computing processing power and algorithm advances have led to significant advances in machine learning, especially machine learning involving Deep Neural Networks (DNNs), which are able to capture high-level dependencies in healthcare data. Some examples of the uses of DNNs are:
  1. Prediction of drug properties(2, 3) and toxicities(4)
  2. Biomarker development (5)
  3. Cancer diagnosis (6)
  4. First FDA approved system based on deep learning Arterys Cardio DL
  • Other promising systems of deep learning include:
    • Generative Adversarial Networks (https://arxiv.org/abs/1406.2661): requires good datasets for extensive training but has been used to determine tumor growth inhibition capabilities of various molecules (7)
    • Recurrent neural Networks (RNN): Originally made for sequence analysis, RNN has proved useful in analyzing text and time-series data, and thus would be very useful for electronic record analysis. Has also been useful in predicting blood glucose levels of Type I diabetic patients using data obtained from continuous glucose monitoring devices (8)
    • Transfer Learning: focused on translating information learned on one domain or larger dataset to another, smaller domain. Meant to reduce the dependence on large training datasets that RNN, GAN, and DNN require.  Biomedical imaging datasets are an example of use of transfer learning.
    • One and Zero-Shot Learning: retains ability to work with restricted datasets like transfer learning. One shot learning aimed to recognize new data points based on a few examples from the training set while zero-shot learning aims to recognize new object without seeing the examples of those instances within the training set.

Highly Distributed Storage Systems (HDSS)

The explosion in data generation has necessitated the development of better systems for data storage and handling. HDSS systems need to be reliable, accessible, scalable, and affordable.  This involves storing data in different nodes and the data stored in these nodes are replicated which makes access rapid. However data consistency and affordability are big challenges.

Blockchain is a distributed database used to maintain a growing list of records, in which records are divided into blocks, locked together by a crytosecurity algorithm(s) to maintain consistency of data.  Each record in the block contains a timestamp and a link to the previous block in the chain.  Blockchain is a distributed ledger of blocks meaning it is owned and shared and accessible to everyone.  This allows a verifiable, secure, and consistent history of a record of events.

Data Privacy and Regulatory Issues

The establishment of the Health Insurance Portability and Accountability Act (HIPAA) in 1996 has provided much needed regulatory guidance and framework for clinicians and all concerned parties within the healthcare and health data chain.  The HIPAA act has already provided much needed guidance for the latest technologies impacting healthcare, most notably the use of social media and mobile communications (discussed in this article  Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.).  The advent of blockchain technology in healthcare offers its own unique challenges however HIPAA offers a basis for developing a regulatory framework in this regard.  The special standards regarding electronic data transfer are explained in HIPAA’s Privacy Rule, which regulates how certain entities (covered entities) use and disclose individual identifiable health information (Protected Health Information PHI), and protects the transfer of such information over any medium or electronic data format. However, some of the benefits of blockchain which may revolutionize the healthcare system may be in direct contradiction with HIPAA rules as outlined below:

Issues of Privacy Specific In Use of Blockchain to Distribute Health Data

  • Blockchain was designed as a distributed database, maintained by multiple independent parties, and decentralized
  • Linkage timestamping; although useful in time dependent data, proof that third parties have not been in the process would have to be established including accountability measures
  • Blockchain uses a consensus algorithm even though end users may have their own privacy key
  • Applied cryptography measures and routines are used to decentralize authentication (publicly available)
  • Blockchain users are divided into three main categories: 1) maintainers of blockchain infrastructure, 2) external auditors who store a replica of the blockchain 3) end users or clients and may have access to a relatively small portion of a blockchain but their software may use cryptographic proofs to verify authenticity of data.

 

YouTube video on How #Blockchain Will Transform Healthcare in 25 Years (please click below)

 

 

In Big Data for Better Outcomes, BigData@Heart, DO->IT, EHDN, the EU data Consortia, and yes, even concepts like pay for performance, Richard Bergström has had a hand in their creation. The former Director General of EFPIA, and now the head of health both at SICPA and their joint venture blockchain company Guardtime, Richard is always ahead of the curve. In fact, he’s usually the one who makes the curve in the first place.

 

 

 

Please click on the following link for a podcast on Big Data, Blockchain and Pharma/Healthcare by Richard Bergström:

References

  1. Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., Ogu, I. O., and Zhavoronkov, A. (2018) Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare, Oncotarget 9, 5665-5690.
  2. Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., and Zhavoronkov, A. (2016) Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data, Molecular pharmaceutics 13, 2524-2530.
  3. Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., and Lu, H. (2017) Deep-Learning-Based Drug-Target Interaction Prediction, Journal of proteome research 16, 1401-1409.
  4. Gao, M., Igata, H., Takeuchi, A., Sato, K., and Ikegaya, Y. (2017) Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds, Journal of pharmacological sciences 133, 70-78.
  5. Putin, E., Mamoshina, P., Aliper, A., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C., Vijg, J., and Zhavoronkov, A. (2016) Deep biomarkers of human aging: Application of deep neural networks to biomarker development, Aging 8, 1021-1033.
  6. Vandenberghe, M. E., Scott, M. L., Scorer, P. W., Soderberg, M., Balcerzak, D., and Barker, C. (2017) Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer, Scientific reports 7, 45938.
  7. Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., and Zhavoronkov, A. (2017) druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico, Molecular pharmaceutics 14, 3098-3104.
  8. Ordonez, F. J., and Roggen, D. (2016) Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors (Basel) 16.

Articles from clinicalinformaticsnews.com

Healthcare Organizations Form Synaptic Health Alliance, Explore Blockchain’s Impact On Data Quality

From http://www.clinicalinformaticsnews.com/2018/12/05/healthcare-organizations-form-synaptic-health-alliance-explore-blockchains-impact-on-data-quality.aspx

By Benjamin Ross

December 5, 2018 | The boom of blockchain and distributed ledger technologies have inspired healthcare organizations to test the capabilities of their data. Quest Diagnostics, in partnership with Humana, MultiPlan, and UnitedHealth Group’s Optum and UnitedHealthcare, have launched a pilot program that applies blockchain technology to improve data quality and reduce administrative costs associated with changes to healthcare provider demographic data.

The collective body, called Synaptic Health Alliance, explores how blockchain can keep only the most current healthcare provider information available in health plan provider directories. The alliance plans to share their progress in the first half of 2019.

Providing consumers looking for care with accurate information when they need it is essential to a high-functioning overall healthcare system, Jason O’Meara, Senior Director of Architecture at Quest Diagnostics, told Clinical Informatics News in an email interview.

“We were intentional about calling ourselves an alliance as it speaks to the shared interest in improving health care through better, collaborative use of an innovative technology,” O’Meara wrote. “Our large collective dataset and national footprints enable us to prove the value of data sharing across company lines, which has been limited in healthcare to date.”

O’Meara said Quest Diagnostics has been investing time and resources the past year or two in understanding blockchain, its ability to drive purpose within the healthcare industry, and how to leverage it for business value.

“Many health care and life science organizations have cast an eye toward blockchain’s potential to inform their digital strategies,” O’Meara said. “We recognize it takes time to learn how to leverage a new technology. We started exploring the technology in early 2017, but we quickly recognized the technology’s value is in its application to business to business use cases: to help transparently share information, automate mutually-beneficial processes and audit interactions.”

Quest began discussing the potential for an alliance with the four other companies a year ago, O’Meara said. Each company shared traits that would allow them to prove the value of data sharing across company lines.

“While we have different perspectives, each member has deep expertise in healthcare technology, a collaborative culture, and desire to continuously improve the patient/customer experience,” said O’Meara. “We also recognize the value of technology in driving efficiencies and quality.”

Following its initial launch in April, Synaptic Health Alliance is deploying a multi-company, multi-site, permissioned blockchain. According to a whitepaper published by Synaptic Health, the choice to use a permissioned blockchain rather than an anonymous one is crucial to the alliance’s success.

“This is a more effective approach, consistent with enterprise blockchains,” an alliance representative wrote. “Each Alliance member has the flexibility to deploy its nodes based on its enterprise requirements. Some members have elected to deploy their nodes within their own data centers, while others are using secured public cloud services such as AWS and Azure. This level of flexibility is key to growing the Alliance blockchain network.”

As the pilot moves forward, O’Meara says the Alliance plans to open ability to other organizations. Earlier this week Aetna and Ascension announced they joined the project.

“I am personally excited by the amount of cross-company collaboration facilitated by this project,” O’Meara says. “We have already learned so much from each other and are using that knowledge to really move the needle on improving healthcare.”

 

US Health And Human Services Looks To Blockchain To Manage Unstructured Data

http://www.clinicalinformaticsnews.com/2018/11/29/us-health-and-human-services-looks-to-blockchain-to-manage-unstructured-data.aspx

By Benjamin Ross

November 29, 2018 | The US Department of Health and Human Services (HHS) is making waves in the blockchain space. The agency’s Division of Acquisition (DA) has developed a new system, called Accelerate, which gives acquisition teams detailed information on pricing, terms, and conditions across HHS in real-time. The department’s Associate Deputy Assistant Secretary for Acquisition, Jose Arrieta, gave a presentation and live demo of the blockchain-enabled system at the Distributed: Health event earlier this month in Nashville, Tennessee.

Accelerate is still in the prototype phase, Arrieta said, with hopes that the new system will be deployed at the end of the fiscal year.

HHS spends around $25 billion a year in contracts, Arrieta said. That’s 100,000 contracts a year with over one million pages of unstructured data managed through 45 different systems. Arrieta and his team wanted to modernize the system.

“But if you’re going to change the way a workforce of 20,000 people do business, you have to think your way through how you’re going to do that,” said Arrieta. “We didn’t disrupt the existing systems: we cannibalized them.”

The cannibalization process resulted in Accelerate. According to Arrieta, the system functions by creating a record of data rather than storing it, leveraging machine learning, artificial intelligence (AI), and robotic process automation (RPA), all through blockchain data.

“We’re using that data record as a mechanism to redesign the way we deliver services through micro-services strategies,” Arrieta said. “Why is that important? Because if you have a single application or data use that interfaces with 55 other applications in your business network, it becomes very expensive to make changes to one of the 55 applications.”

Accelerate distributes the data to the workforce, making it available to them one business process at a time.

“We’re building those business processes without disrupting the existing systems,” said Arrieta, and that’s key. “We’re not shutting off those systems. We’re using human-centered design sessions to rebuild value exchange off of that data.”

The first application for the system, Arrieta said, can be compared to department stores price-matching their online competitors.

It takes the HHS close to a month to collect the amalgamation of data from existing system, whether that be terms and conditions that drive certain price points, or software licenses.

“The micro-service we built actually analyzes that data, and provides that information to you within one second,” said Arrieta. “This is distributed to the workforce, to the 5,000 people that do the contracting, to the 15,000 people that actually run the programs at [HHS].”

This simple micro-service is replicated on every node related to HHS’s internal workforce. If somebody wants to change the algorithm to fit their needs, they can do that in a distributed manner.

Arrieta hopes to use Accelerate to save researchers money at the point of purchase. The program uses blockchain to simplify the process of acquisition.

“How many of you work with the federal government?” Arrieta asked the audience. “Do you get sick of reentering the same information over and over again? Every single business opportunity you apply for, you have to resubmit your financial information. You constantly have to check for validation and verification, constantly have to resubmit capabilities.”

Wouldn’t it be better to have historical notes available for each transaction? said Arrieta. This would allow clinical researchers to be able to focus on “the things they’re really good at,” instead of red tape.

“If we had the top cancer researcher in the world, would you really want her spending her time learning about federal regulations as to how to spend money, or do you want her trying to solve cancer?” Arrieta said. “What we’re doing is providing that data to the individual in a distributed manner so they can read the information of historical purchases that support activity, and they can focus on the objectives and risks they see as it relates to their programming and their objectives.”

Blockchain also creates transparency among researchers, Arrieta said, which says creates an “uncomfortable reality” in the fact that they have to make a decision regarding data, fundamentally changing value exchange.

“The beauty of our business model is internal investment,” Arrieta said. For instance, the HHS could take all the sepsis data that exists in their system, put it into a distributed ledger, and share it with an external source.

“Maybe that could fuel partnership,” Arrieta said. “I can make data available to researchers in the field in real-time so they can actually test their hypothesis, test their intuition, and test their imagination as it relates to solving real-world problems.”

 

Shivom is creating a genomic data hub to elongate human life with AI

From VentureBeat.com
Blockchain-based genomic data hub platform Shivom recently reached its $35 million hard cap within 15 seconds of opening its main token sale. Shivom received funding from a number of crypto VC funds, including Collinstar, Lateral, and Ironside.

The goal is to create the world’s largest store of genomic data while offering an open web marketplace for patients, data donors, and providers — such as pharmaceutical companies, research organizations, governments, patient-support groups, and insurance companies.

“Disrupting the whole of the health care system as we know it has to be the most exciting use of such large DNA datasets,” Shivom CEO Henry Ines told me. “We’ll be able to stratify patients for better clinical trials, which will help to advance research in precision medicine. This means we will have the ability to make a specific drug for a specific patient based on their DNA markers. And what with the cost of DNA sequencing getting cheaper by the minute, we’ll also be able to sequence individuals sooner, so young children or even newborn babies could be sequenced from birth and treated right away.”

While there are many solutions examining DNA data to explain heritage, intellectual capabilities, health, and fitness, the potential of genomic data has largely yet to be unlocked. A few companies hold the monopoly on genomic data and make sizeable profits from selling it to third parties, usually without sharing the earnings with the data donor. Donors are also not informed if and when their information is shared, nor do they have any guarantee that their data is secure from hackers.

Shivom wants to change that by creating a decentralized platform that will break these monopolies, democratizing the processes of sharing and utilizing the data.

“Overall, large DNA datasets will have the potential to aid in the understanding, prevention, diagnosis, and treatment of every disease known to mankind, and could create a future where no diseases exist, or those that do can be cured very easily and quickly,” Ines said. “Imagine that, a world where people do not get sick or are already aware of what future diseases they could fall prey to and so can easily prevent them.”

Shivom’s use of blockchain technology and smart contracts ensures that all genomic data shared on the platform will remain anonymous and secure, while its OmiX token incentivizes users to share their data for monetary gain.

Rise in Population Genomics: Local Government in India Will Use Blockchain to Secure Genetic Data

Blockchain will secure the DNA database for 50 million citizens in the eighth-largest state in India. The government of Andhra Pradesh signed a Memorandum of Understanding with a German genomics and precision medicine start-up, Shivom, which announced to start the pilot project soon. The move falls in line with a trend for governments turning to population genomics, and at the same time securing the sensitive data through blockchain.

Andhra Pradesh, DNA, and blockchain

Storing sensitive genetic information safely and securely is a big challenge. Shivom builds a genomic data-hub powered by blockchain technology. It aims to connect researchers with DNA data donors thus facilitating medical research and the healthcare industry.

With regards to Andhra Pradesh, the start-up will first launch a trial to determine the viability of their technology for moving from a proactive to a preventive approach in medicine, and towards precision health. “Our partnership with Shivom explores the possibilities of providing an efficient way of diagnostic services to patients of Andhra Pradesh by maintaining the privacy of the individual data through blockchain technologies,” said J A Chowdary, IT Advisor to Chief Minister, Government of Andhra Pradesh.

Other Articles in this Open Access Journal on Digital Health include:

Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

Medical Applications and FDA regulation of Sensor-enabled Mobile Devices: Apple and the Digital Health Devices Market

 

How Social Media, Mobile Are Playing a Bigger Part in Healthcare

 

E-Medical Records Get A Mobile, Open-Sourced Overhaul By White House Health Design Challenge Winners

 

Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI

 

Digital Health Breakthrough Business Models, June 5, 2018 @BIOConvention, Boston, BCEC

 

 

 

 

 

 

Read Full Post »

Older Posts »