Chimeric Bio (CBC) and Genomic Enterprise join forces to tackle big data

January 21st, 2020

This blog post will focus on the announcement of the two life sciences’ companies that will start to work together to provide new, user-friendly, and revolutionary mobile access of big data to biotechnology, pharmaceutical, life sciences and technology companies. There is a new trend highlighting the need for new technologies to address the big data challenges and associated opportunities at pharma right now in drug development, biomedicine and healthcare. This is mainly because there is a need to develop and use big data analytics tools’ from hundreds of thousands to millions of patients and samples throughout the drug development process. That is what the CEO of Novartis (NYSE: NVS), Vas Narasimhan, started to implement at the company when he became CEO. This new trend is important to accelerate drug discovery, the development of new treatments and target identification applied to complex and rare diseases. However, there are several challenges associated to the current tools’ in the market and changes in the pharma “culture” to adopt new technologies as discussed by him and others at the JP Morgan Conference in San Francisco in January of 2020 (check the article at Forbes “Novartis CEO Who Wanted To Bring Tech Into Pharma Now Explains Why It’s So Hard”). Thus, Chimeric Bio (CBC) and Genomic Enterprise will start a “joint venture” called MobileBiOS to provide the best in class scientific solutions, services and support to customers identifying specific medicines and treatments to various diseases combing it to the development of solutions for mobile devices. “I think this will be a very nice opportunity for clients that are looking for state of the art consulting services in biomedicine and healthcare. Tristan’s expertise and my experience after founding and co-founding four companies and being the Head of one of the Apple Labs Program in Brazil, South America will definitely make a difference that the market needs right now” said Fabricio Costa about joining forces with CBC. “ModbileBiOS will allow biopharma professionals to have immediate access to new big data analytics tools’ and the corresponding actionable results culminating from these analysis. CBC already offers a number of powerful big data solutions across various types of data including curated scientific and biomedical content, patient data, single cell data, clinical trials’ information, biomedical conferences, publications, regulatory information and patents.” remarks Tristan Gill about the new Joint Venture.

Genomic Enterprise’s mission is to provide consultancy services in several different fields involving STEM (Science, Technology, Engineering and Math) for the government, academic centers, universities, start ups, mid-size companies and big corporations globally. In addition, Genomic Enterprise creates awareness on the new scientific discoveries and technologies that will impact society worldwide or even locally. Fabricio Costa is the founder and CEO of Genomic Enterprise and he has more than 20 years of experience in top academic labs and companies such as The Ludwig Institute for Cancer Research, Harvard University, Northwestern University, University of Chicago, Start Up Health Academy, Singularity University, 1871 Chicago, MATTER, Shire Pharmaceuticals (acquired by Takeda – NYSE: TAK), Google (NASDAQ: GOOGL) (Ventures, Higher Education and Start ups), Apple (NASDAQ: AAPL) and others. Fabricio is also a serial entrepreneur (co-founded and founded four startups; two were successfully sold).

Chimeric Bio (CBC)’s main mission is to support a complimentary set of innovative life science offerings and deliver first class consulting services to customers. CBC accomplishes this by understanding the pharmaceutical customer’s challenges and offering solutions to help customers’ achieve these critical needs. CBC can define unique solutions that best meet the scientific and business needs for customers.  Chimeric Bio’s founder and CEO Tristan Gill is an accomplished life sciences professional. Prior to founding CBC Tristan has 25 years of experience in top labs and companies including Cold Spring Harbor Laboratory, California Institute of Technology, Paracel (a subsidiary of Celera Genomics acquired by Quest Diagnostics – NYSE: DGX), Rossetta BioSoftware (a subsidiary of Merck & Co.- NASDAQ: MRK), Ingenuity (acquired by of Qiagen – NASDAQ: QGEN), Bejing Genomics Institute (BGI), and Thomson Reuters (NYSE: TRI).     

Big Tech Digital Health Revolution?

December 6th, 2019

A series of breakthroughs in biomedical science and digital technologies are triggering a convergence between the tech industry and the life sciences industries that will quickly lead to more intimate and interactive relations among patients, their doctors and biopharmaceutical companies. The ability to share information and data has changed completely in the past 10 years with the emergence of both search engines, social media, and now tools for Artificial Intelligence (AI), especially with companies such as Microsoft, Google, Facebook and now Amazon getting into healthcare. In life sciences, novel web technologies have been used to track diseases in more efficient ways. Today, millions of people around the world that have access to the Internet will engage in Internet-enabled self-diagnosis for diseases by using search engines and other web tools. However, self-diagnosis is dangerous if the sources are not trustful enough. Another concern regarding these new technologies applied to healthcare and life sciences is patient privacy, data transfer and data storage (something “cloud” services now has solved with some specific concerns). Therefore, the question now is ‘‘can these tools be applied to healthcare and biomedicine in order to improve scientific and medical research?’’ I’ve been publishing articles about this digital revolution since 2012 (for some articles I’ve wrote on this subject check “Social Networks, Web-based Tools and Diseases: implications for Biomedical Research” and “Big Data in Biomedicine”). Well, this week the cover of the British magazine Newsweek and its headline puts Jeff Bezos, CEO of Amazon, in the forefront of solving the healthcare and life sciences problems now and in the future; not just in the United States but in the world (for more information check the Newsweek article here). Well, I am a little skeptical and concerned but excited at the same time on what is coming from Amazon (even Apple, Google and other tech companies are developing their own solutions). The truth is that the web has impacted our society, but over the next few years, the web and Apps will entirely re-invent the way scientists and physicians interact, especially with patients. There is indeed a conundrum faced by life sciences because both pharmaceutical and biotechnology industries need information from people (mostly patients), patients need information from these companies and the life sciences industry also need to collaborate outside their walls building connections with other industries (such as big tech and other institutions). Science and the biomedical field need to be more open and scientists/physicians have to be able to share data to speed up the process of transition between basic to translational research. As regulatory clarity emerges with better algorithms, AI and data encryption to ease privacy issues, more companies will adopt digital tools and these technologies will have the potential to revolutionize healthcare, clinical trials and research the same way that they are impacting our society. Developing solutions to allow better ways to share information will add value to the life sciences and biomedical communities. In fact, science and medicine are undergoing one of the most exciting changes in history. The full adoption of online platforms and telemedicine by physicians will transform medicine. Apps that can diagnose patients in seconds are already in the market. Alexa, the speech recognition device from Amazon could be (and will be) able to diagnose diseases based on what people tell it daily. A whole new world in healthcare could be build and the main players will be big tech companies. Healthcare professionals will start to interact and communicate in ways that will have an enormous impact on disease diagnosis, scientific progress, drug discovery and in the development of new treatments for complex, rare and unknown diseases. I bet Amazon has several “secrets” that it will be unveiling in the coming years applied to healthcare. Did anybody think of Amazon, an online Book selling website in the 1990s, as of what it has become? Let’s see what they will disrupt next in healthcare and life sciences’ fields.

Healthcare in Emerging Economies – a Sad Story

April 2nd, 2019
Image Source: Harvard Business Review

Healthcare is a very complex subject anywhere in the world. Some countries have an established structure in which the government subsidizes healthcare for the population and others trust on the private sector, also known as health insurance companies. There are also examples of hybrid systems: the public and the private sectors working concurrently. Independent on the system, the poor piece of the population always suffers. The subject of this blog post is going to be healthcare in emerging economies since I am doing an online Course at Harvard focused in entrepreneurship in emerging markets. Healthcare and Education are given as examples in this course of areas that technology barely touched in this course. The explanation for that, especially in emerging economies, is mainly due to restrictions of the government and lots of corruption in the system. Credibility, trust and transparency are not the best adjectives in emerging economies. Corruption is everywhere and the poor are the ones who will suffer. I will exemplify one of these issues, focused in healthcare (and also education at some degree since the solution we have built was with students), since I participated as Head of one of the most interesting Projects that the Apple International Program developed in Brazil, my home country. Just as a spoiler alert, this is not a story with happy ending, but worth reading about. Let’s start explaining the basis of the problem we were trying to address in my home country: most of the healthcare system is subsidized by the government in a Program named SUS (“Sistema Unico de Saude” or “Unique Health System” in English). This Program is running for decades and has very successful histories such as giving AIDS medication for free to HIV positive patients (this Health Program got many awards worldwide and was used by other countries). Our main goal was to evaluate the SUS Program as a whole since the government had a database called DataSUS with all the information on public hospitals, facilities and healthcare professionals in the country. To do that, a group from the Apple International Program decided to build a citizen centric App named “Heath Map of Brazil” and aggregate all the DataSUS information with geolocation in the country: for example, if somebody had a health issue, let’s say a heart attack in the streets of Sao Paulo or Rio de Janeiro, a citizen close to the person would do a first response and try finding the closest public hospital using the geolocation feature of the App. Also, citizens from Brazil would be able to give feedback using the App. The main challenge here was to evaluate the efficiency and efficacy of the SUS healthcare system in Brazil. The App was built after we had long talks to the DataSUS people to be able to have a direct connection to their database. The beta of the system was released in the capital of Brazil, Brasilia, where I have lived for a while. The idea was simple: make a geolocation map, such as Google Maps, of all public institutions from DataSUS and the people working in all of them, from nurses to doctors. Very simple task. Since the beta test in the capital worked nicely so we have decided to aggregate data from the whole country and let the citizens interact with it for one year. After this time, we have collected all the inputs from people using it and something shocked us all: lots of citizens in the whole country were trying to find public hospitals that never existed in their town (which I call “ghost places”), trying to find doctors that never worked in a specific place, have already retired or even died amongst several other infrastructure problems. With all this info in hand we started crunching numbers to see how much money was being poored in all of these “ghosts” that the citizens found using the App solution. Our jaws dropped; the numbers were in the billions of the Brazilian currency, even billions of Dollars! All of this money was going somewhere, but not to the right place. So, we decided to present this to the Finance Department of the Country at that time. People listened to us, of course. We had the numbers, the places that never existed but not where the money was going. Interestingly, after this presentation, the government cut our direct connection to the database DataSUS with all the healthcare data and the App started “dying”. There is no need to explain why. Corruption, lack of transparency and lack of trust. This money meant that people were getting rich with these “schemes” all over the nation. This is a very successful idea to help a government that unfortunately is very corrupt (and I am not saying and disclosing any political parties or names from the government involved). In the end, who is suffering with a terrible healthcare infrastructure, unprepared healthcare professionals and long waits to be accepted to do an exam in the public system: the poor people in my home country. Sad but true story. My take on this story is what I’ve learned along the way of building a nice solution that could help lots of poor people. But that doesn’t matter, does it?

Disclaimer: The opinions and views in this article are my own. In this article I have no political or party association with any governmental entity in Brazil.

Humankind’s History, Science, Technology and Beyond

March 1st, 2019

Brain under layers of circuit boards

Today I will write a post about the intersection between technology, science and human nature based on the last three books I’ve read. Since I am a passionate person that reads a lot and try to be on the verge of what goes on in the fields of science and technology (amongst other things), I’ve recently read very enthusiastically the famous “trilogy” of books from the author Yuval Noah Harari (Twitter: @harari_yuval). He writes brilliantly about our species story, how we are evolving and the challenges posed by fields such as science and technology to keep humankind “on track”. The way he portrayed our history as a species together with the challenges we have already faced (and are facing now) and our cultural aspects are just amazing. On his first book “Sapiens”, as a historian, Yuval gives the reader several interesting perspectives on how we became a society with cultural values and how money “was born” as a way to exchange goods creating empires such as the Persians, Romans, England and now the United States. In his second book, “Homo Deus”, he was able to show how humans are using scientific breakthroughs and technology to improve several sectors important to us such as health, finances, logistics, etc. In a world were mobile phones are in everybody’s hands and big data is the new “oil”, we face a lot of challenges with our own privacy and how to keep it private. Science and Technology always bring the good and the bad in humanity. The balance between both will be our “salvation” I guess. Yuval used several examples on how computer science, data science and artificial intelligence (AI) are changing our present and he also shows several scenarios on how it will impact our future. He also discusses the breakthroughs in biotechnology. Interestingly, he makes a very nice comparison between algorithms and how living cells work. In the end, we have biochemical reactions and several organic compounds in our cells and bodies that “use” codes that he calls biological algorithms to bring life to earth. An algorithm is a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer. In this case, Yuval discusses how human bodies and cells remind us of computer algorithms. Since we are now building algorithms using AI technologies that are already taking jobs from people and could be as smart as humans in the near future this makes complete sense. Some of these technologies have been already used. For example, genetic engineering is using algorithms to edit cells and babies based on the CRISPR technology in life sciences causing a lot of discussion in the scientific community. In his last book of the “trilogy”, “21 lessons for the 21st century”, Yuval brings up interesting philosophical discussions using very concrete arguments. He discusses how political models such as democracy and socialism in countries have failed us and how we need to rethink ways of improving our political views as individuals. Also, he discusses religion and how people have been reacting to it since the beginning of humankind. His point is that we as humans need to believe in something bigger than us. However, what about if a “God” as we imagine does not exist and we are just made of algorithms in a very random way? Again in his third book he touches the future of humankind and how AI and data science are and will impact our lives in a daily basis. I myself believe that scientific and technological breakthroughs are very important, but we need to think very carefully about their social impacts and privacy issues. Are they increasing or decreasing the gap between the rich and the poor people in the world? That is a very good question that is still open for discussion. I really suggest everybody to read all his three books. They complete each other and opened my mind to humankind’s history, our present and several nuances on our future mainly focused in science, technology and beyond. I can disclose here that I am not getting a dime to promote Yuval’s books, just felt like every one of us need to read, even with some criticism, his amazing book “trilogy”. I really enjoyed reading them myself.

Image Source: Time Magazine

AI and the Future of Biomedicine

November 23rd, 2018

81367868 - telemedicine concept with doctor wearing vr glasses

It took a while for me to write a new blog post (six months!), but I think it was worth it since I’ve been reading a lot about new technologies applied to biomedicine. Today my post will focus in Artificial Intelligence (AI) and how it could change biomedicine (and several other sectors) as we know today. Everything started back in the 50s, the fathers of the field Minsky and McCarthy, described AI as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, also, social intelligence and creativity. Well, tech giants such as Apple, Google, Amazon, Microsoft and others use AI daily to deal with the deluge of data they are acquiring every day. However, the biotechnology and pharma sectors are way behind in using these technologies. This scenario has changed in the last five years or so since pharma companies started looking in to what they call Real World Data (RWD) which is collecting patient data (in a clinical trial, for example) in real time. The way I personally see AI changing the biomedical sector is in five ways that I will describe here. First, I believe Real Time and Real World Data will be crucial to Clinical Trials and this will need a lot of AI technology. Imagine if pharma companies could enroll and collect real-time data on the patients of their trials. In addition, imagine a “situation room” in which the data points could be seen and accessed for each patient in a trial in real-time and the system could be improved by the AI Technologies (Machine Learning, for example). Novartis is already doing this and their new CEO think pharma companies have to become data companies. I totally agree with his statement. Second, we already know that clinical trials are hard on patients suffering from incurable diseases, mainly because the placebo group (they do not know which group is receiving the drug) will probably die or have more suffering. RWD can identify, using AI technology, small groups of patients that are responsive in the treatment group in clinical trials considered as a “failure” by the FDA standards. In addition, depending on the trial, if the treatment groups are having amazing responses, AI systems can be fast in detecting it. So, the trial can be stopped and both placebo and treatment groups will receive the drug that could save their lives. Third, speech recognition and Machine Learning (ML) are becoming usual in our daily lives. In this case, I imagine a future in which patients in trials could “talk” to Alexa from Amazon or other similar devices (such as Apple, Google and Facebook devices) and say how they feel daily (signs and symptoms, specific issues, etc). Data points collected from each patient daily will speed a lot the trials and drug approval will be faster. Fourth, AI is already being used by academic centers and several “start ups” in the biomedical field to do “in silico” drug design using several complex data generated from patients. Big data, in this case, will be able to test millions of complex interactions in seconds to evaluate drug efficacy and for new drug development. This will increase exponentially success in developing or finding drugs for specific complex diseases. Last, but not least, the fifth example is how the academic sector will be impacted. Several groups have already used AI to basically get the data generated in a scientific laboratory and write the scientific article. This is already a reality, meaning that the Ph.D. students and Post Docs will generate the data and input in an AI system to write the article(s) for them in a faster and more accurate way. This could also be applied to write patents. Image a future in which the academic sector is mostly “technical” and the “intellectual” part will be done by a computer using AI scanning all articles in a specific field to write a paper (or a patent…). This is already happening! Even though it is amazing what AI can do and how it could impact biomedicine, the main concern will be the privacy of the data from patients and samples used in trials and studies. Thus, solutions to better encrypt data will be necessary. Patient data privacy is an issue and it should be taken care carefully. The future of biomedicine using AI solutions looks very bright and full of challenges. I am glad to be helping shape this future using AI and several other technologies. That is a dream come true!

The Power of Persistence

April 30th, 2018

Persistence Figure

In this blog post I will explore my own experience with persistence in the Academic Sector. I was thinking about this history that went on for more than a decade and came up with some closure based on real facts. As a start, I will write about something that happened to me in the Academic arena that I’ve named “the power of persistence” and how it can affect young scientists. This history started almost 15 years ago, when I was doing my Ph.D. thesis studying cancer when I stumbled with some DNA regions that were transcribed even though there were no indications that these regions were translated into proteins. At that time, they called these regions “junk DNA” and they make up to 98% of our genome. I was excited but at the same time confused when discussing this with my Ph.D. advisor at that time. She told me that it was just something the method caught by error and to throw it away. She said: “keep working and looking for regions that are differentially expressed.” Well, even though I did what she said, I started digging the literature about regions of the DNA that are transcribed but not translated (now you can see how curious I am…). Some literature here and there but nothing very exciting. I’ve collected mostly everything published about these “junk DNA” regions that were transcribed and started seeing that several of them have some function and are more expressed in tumors or even less expressed when comparing with normal cells. However, lots of literature discussing that these regions could be artifacts like my advisor told me. Contrary to everything that was told me and that I’ve read, I had a gut feeling and thought: “There is something here. I am going to read everything about it and come up with a hypothesis”. Of course there were microRNAs and other “small” RNAs being studied and published but these guys were different. One year before finishing my Ph.D. thesis with what was “presentable” under the scientific “status quo” I came to my Ph.D. advisor and said: “ I have all this growing evidence about these RNAs that do no code for proteins and want to write a review about it. What do you think?”. Her response was blunt: “You won’t be able to because reviews are just for well-known and established researchers. You are a mere Ph.D. Student” (she did not want to tell me: you are a nobody; but that is how I felt). Hesitant, I started writing by myself a review article and started writing letters to Editors of Scientific Journals explaining the importance of this growing evidence. Of course, I staterd from top to bottom regarding the scientific journals. As I remind correctly, I’ve got at least twenty “Nos” (or more…). In the meantime, I’ve finished my Ph.D. thesis with something that my Ph.D. advisor was happy about and moved to the United States (I did my Ph.D. in Brazil) to do a Post-Doctoral Degree at Harvard University. With 90% of the review article already written, as soon as I’ve got to Harvard University in Boston, I showed the article to my new Advisor there. Guess what? Same answers: “this is nonsense and reviews are just for stars in Science” and “You are a nobody”. Interestingly, after sending numerous letters to the Editors, one of them, a japonese Editor at the Gene Journal replied and told me to send what I had until then. After a week that I’ve sent the piece, he replied: “I like it. Finish it and we will send it for revision”. This was 2005 and against all odds somebody at least gave me a chance. I was ecstactic! After 2-3 weeks more I got a positive response and the review article was accepted by the Gene jornal. Remember that I was told more than once that I was a nobody in Science and that review articles were for the “Stars”. More than a decade forward, the first review article about non-coding RNAs (ncRNAs) is one of the most cited articles from Gene (PMID:16111837) and after this one I wrote several more; two others for Gene – the “ncRNA Trilogy” as I classify it today (PMID:17113247; PMID:18226475) and other Reviews for different journals that include Drug Discovery Today (PMID:19429503), Bioessays (my review was in the Cover of Bioessays in 2010 – PMID:20544733). In addition, I edited and wrote a Book Chapter (for more information on the book click here) about this subject that was featured in several scientific journals and News outlets around the world. Today the Gene trilogy sum around 1,200 citations and all articles that I’ve wrote about non-coding RNAs approximatelly 1,600 citations. It might not sound like a lot; however, for journals that have a low to medium Impact Factor (IF), it is indeed a lot. In the meantime, there was an explosion of articles (experimental and reviews) discussing the importance of non-coding RNAs, especially the so-called long non-coding RNAs (lncRNAs) in several aspects of eukaryotic cells and the importance of those in complex and rare diseases. All of this story (I tried to make a long story very short for this post) is to show how powerfull persistence can be even if everybody around you tell you that you are wrong, or tell you not to do something. Even discourajing you saying that you are not capable or “famous” enough. Sorry for the word, but I think of it today as total “bullshit”. If you have an idea, identify or discover something interesting that has potential to become of importante not just in science but even to start a company go ahead. The sky is the limit. The take home message from this post is: believe in your “gut feeling” and go forward. Show them they are wrong! So wrong…

The New Shared Economy and Scientific Research

March 20th, 2018


The outlook for research science isn’t pretty. During the current explosion of technology innovation, a near instinctual collaborative environment, and an unparalleled time of data housing and processing being a scientist and asking daring questions should be the norm. But it isn’t. Instead, being a research scientist is extremely hard. And according to a recently poll of 270 leading scientists, the chief problem facing research is funding (See The 7 biggest problems facing science). For the last 15 years, the funding for basic scientific research has decreased to ~22% (See NIH Research Funding Trends). During the same timespan, the cost for doing experiments has increased about 20% (See NIH BRDPI). So, if you’re a faculty member running a lab over the last decade, you have experienced a +40% resource gap. In fact, Dr. Francis Collins (NIH Director), recently stated: “Medical research right now is not limited by ideals. It’s not limited by research potential. It’s not limited by talent. It’s limited by resources”. Something has to change with how research is done and there are good models to think through what scientists can do to save research. The most apparent answer: build a shared economy. The central purpose of a share economy is to use technology to empower individuals, businesses, and government to think differently about resource sharing and reusing. If successful, shared economies provide new opportunities for growth and productivity while reducing composition and waste by increasing the repurposing and conversation of resources. Shared economies force all participants to look at resources differently and use them more efficiently in order to ensure their preservation. In fact, there has been published accounts of the need for an overarching shared economy in research (See “The sharing economy comes to scientific research“). But until we launched Rheaply, there wasn’t a marketplace or similar platform that created a shared economy for scientific research resources. Given this obvious need, we decided to build one. Our guiding principles for developing the first version of Rheaply’s marketplace were simple, make it: (1) easy to use, (2) accessible on every type of computing device, and (3) compatible with research organization’s IT infrastructure enabling easy on-boarding. By all accounts, we have accomplished these goals and more, and we are proud to be the only marketplace and shared economy for research that connects research organizations and scientists. Rheaply is bringing the shared economy to scientific research, one research organization at a time. Shared economies can be natural circular economies  - sustainability pays for itself. Rheaply’s efforts in creating a shared economy around scientific research resources was recently featured in Nature, in an article entitled, “How going green can raise cash for your lab”. This article highlights the power of sharing surplus scientific supplies and resources with your colleagues, and how doing so can bring extra cash into the lab. In fact, going green through use of Rheaply is both a cost efficient and sustainable practice. Recycling leftover chemicals and equipment slashes energy bills and boosts research budgets. We like to think that the shared economy we are building for research is actually a circular economy. According to the Ellen MacArthur Foundation (a leader in circular economy construction), a circular economy is one that goes beyond “take, make, and dispose” (a linear economy) and is restorative and regenerative by aiming to redefine products and services to design waste out while minimizing negative impacts on productivity (See What is a Circular Economy?). At Rheaply, we like to call our marriage of the shared economy + circular economy “circular discovery” where we connect scientists to better share resources enabling more breakthroughs and developments without the need of more funding. One key principle to shared economies, and is especially true as it pertains to scientific research, is participation. Every research institution, organization, and outlet face the same resource gap realities as well as have the same sustainability goals. In fact, the US Federal Government has now mandated a system like Rheaply at every organization that’s takes >$1 of Federal grant money, (See the link here). Now is the right time that world-class research organizations lead the movement to a more shared and circular economy around research resources, and we are poised to help them do so. Words are nice and promote solidarity on this issue, but action is required. If not, the structural problems facing scientific research  – larger resource gaps, greater talent/skill churn, and uncertainty  –  will grow. It’s time to do something to save scientific research for the next generation, and our hope is that all scientists feel obligated to be a apart of that fight.

This Blog Post was adapted from the Medium Post by Rheaply’s CEO Garry Cooper (check the link here). I personally thank Dr Garry for sharing this amazing post (and Ideas) with our readers.

Figure Source: Nature Article cited in this blog post.

Rare Diseases: a parent’s journey

February 2nd, 2018

Journey is really the best word.  Thirty years of researching, detoxifying the home environment, creating an organic, colorful and nutrient rich diet, increasing the oxygen infusion in the blood and to the brain, employing creative hands-on learning techniques, and working through day long sessions of physical therapy and yet there was always that feeling that we were riding a roller coaster. The neurologist had told us to expect that wild ride when they attached the diagnosis of Lennox-Gastaut Syndrome (LGS) to my daughter. “There will be bad periods of seizures and then things might get better, just for a while. Keep in mind, LGS is just a description of a group of signs and symptoms often found together in patients, it is not the organic cause for her symptoms,” he explained. For us as parents, it was a holding pattern or a waiting room, until someday we might know the real cause. That cause was to remain a mystery until just after her 30th birthday. The wait came with a good deal of wondering.  Could we have…What if… Should I be doing this or that…? Most of these questions had to do with whether we were doing our best for our precious daughter.  She was ten when a sense of peace came over me, not based on my understanding of the problem but based on faith.  This was not about whether I could solve the medical mystery; that was not going to be my discovery to claim.  Instead it was about being faithful to the care and love for my daughter.  And so we kept current on the science and dove even more deeply into the care and relied on physicians and therapists who stood beside us steadfastly. And the doctors were right about the roller coaster. There were real victories for her: she went from an ataxic awkward walk to a 12 minute mile run, single words changed to complete sentences, unable to get free from gravity to 500 jumps in a row, holding her breath in the pool to a water worm, and not recognizing letters and numbers to reading and understanding at the 5th grade level! But there were low points too, countless medication changes, daily shots for growth and allergies, loss of her front teeth as her cerebellar atrophy progressed, and most significantly, the ruptured appendix and unrelenting seizures that necessitated four successive hospitals over three months, and an induced coma resulting in paralysis. She is wheelchair bound now but still wakes every morning with joy on her face ready to take on her job, a full day of therapy, she is determined to  make more progress.  Her indomitable optimism, faith and humor have enriched all of our lives. There have been sacrifices.  Her dad has been her fearless hero with his tickles and his nicknames, her sisters have fed, slept with, and helped her with therapy when they could have been with their friends, family members have cared for those precious sisters in our absence when she was hospitalized, a law career was shelved, ball games were missed and vacations cancelled. But she has been as inspiration to all of us and has had an adhesive affect that has joined our extended family at the hip. She is our hero. We felt that someday we would know the cause of her problem and that day finally came on August 16, 2017 in the form of a genetic test and report showing a de novo mutation in the KCNA2 gene, a gain of function mutation that interferes with her brain. As far as I can tell there are seven other people in the world with the R297Q mutation.  Because of privacy rules, I have not found them, yet, but while I search, I am researching tirelessly. And when I find them (and will!) I will be armed with information to share as we march to finding a treatment together.  We are not going to ride the roller coaster anymore, we are going to put on our running shoes, find our team mates and jump the hurdles until we find hope help for my beloved daughter and for the young children whose full and happy lives will be ahead of them.

This Blog Post was written by a Mom and her journey with a daughter diagnosed with a rare genetic disorder. I will not disclose the name of both the mother and daughter for their privacy. If there are other parents, relatives and/or patients with this disease and this specific genetic mutation described here please “Contact Usat Genome For more information on rare disorders please check my last article entitled “Rare Genetic Diseases: Update on Diagnosis, Treatment and Online Resources”.

 Image Source: Global Genes – a Rare Disease Initiative

Is a Nobel Prize really worth it?

November 7th, 2017

Glamorous. Your name will never be forgotten. You are in the “Hall of Fame”. Plus Money (well, not really a lot; approximately U$ 350K when you divide with two others and U$ 1.1 Million when you get it solo). Yes, winning a Nobel Proze gives you all of that. Everybody working in areas that are contemplated with a Nobel Prize, especially Scientists have as a Career goal to get this Prize. That is the “Oscars” of Research. Even I, a mere mortal though about it when I started my Career. How pretentious! My question now is: is it worth it? Just a little bit of history: since 1901, the Nobel Prize has been honoring men and women from all the globe for outstanding achievements in physics, chemistry, physiology or medicine, literature, and for work in peace. The Foundations for the Prize were laid in 1895 when Alfred Nobel wrote his last Will, leaving much of his wealth to the establishment of the Nobel Prize. Alfred Nobel was himself a scientist that has developed a “safer” explosive: the dynamite. Nobel was the holder of 355 patents and used his vast fortune to establish all the Nobel Prizes we see today. Since the Invention of the Prize, it became the main goal of Scientists, Economists, Mathematicians and other specialists. This year’s (2017) Nobel Prize in Medicine and Physiology was awarded jointly to Jeffrey C. Hall, Michael Rosbash and Michael W. Young “for their discoveries of molecular mechanisms controlling the circadian rhythm”. Interestingly, I was reading about the winners and one of them got my attention: Jeffrey C. Hall. He is a retired Professor at Brandeis University (see that this is not Harvard, Stanford or any other top 10 Universities in the United States). Hall said in an interview from his home in rural Maine that he collaborated with Roshbash because they shared common interests in “sports, rock and roll, beautiful substances and other stuff.” (check the article entitled “A 2017 Nobel laureate says he left science because he ran out of money and was fed up with Academia”). About half of Hall’s professional career, starting in the 1980s, was spent trying to unravel the mysteries of the biological clock. Then he left Science 10 years ago in 2007 angry and in a very bad mood. In a lengthy 2008 interview with the journal “Current Biology” (see the complete Interview here), he brought up some serious issues with how research Funding is allocated and how biases creep into Scientific Publications and in the Publishing System. He complained that some of the “Stars” in Science “have not really earned their status” yet they continued to receive massive amounts of Funding. He also said that these Stars have boasted to him that they almost never send their articles to “anywhere but Nature, Cell, or Science“ – among the three most prestigious scientific journals. “And they are nearly always published in one of those magazines – where, when you see something you know about, you realize that it’s not always so great…” Everything he said in the Interview is true  (check my previous post “Science is Broken: how, why and when?”) and the publishing System is biased and wrong. He also worries about young Scientists as he quotes: “one component of my last-gasp disquiet stems from pompously worrying about biologists who are starting out or are in mid-career.” He should be worried, and all of us should be too! Everything he said is true and nothing seems to be changing. Young scientists have difficulty to get a Job, to start establishing his/her own Laboratory and getting grants since the “Stars”, as he quotes, get it all. There are exceptions, obviously, and politics matters too. The bottom line is that he got fed up with this Broken System and left Science. Ten years after that, he is awarded with a Nobel Prize. He was surprised and with his reasons. After all the frustration he described in his piece about his Career of 3 decades and how the system not giving him more money for his research expelled him from what he liked to do most. The lesson here is that we need disruptions and disruptors to change the Scientific System right now! I talk to a lot of Entrepreneurs and one of these days I met a very interesting Start Up with some Ideas to disrupt parts of this broken Scientific System. I cannot disclose here about the Idea but I think we need more people to disrupt this Scientific System that is outdated and slow. Winning a Nobel Prize is important, no doubt, but the issues raised by this Scientist (now a Nobel Prize winner) reflect a System needing a 360 degrees change. We hope that happens soon! And my question is open to discussion: is it Worth it to win a Nobel Prize after decades dedicated to work with lots of frustration and disappointment with the current System?

StartUp Incubators x Accelerators: What is the Difference?

September 11th, 2017

In my last Blog Post I wrote about “The Art of Learning by Doing” in Entrepreneurship. Of course, nobody does things alone. We need support. From people, connections, spaces, schools, mentoring, investors and so on. One thing that I’ve always hear when Mentoring or even Lecturing about Entrepreneurship is the question: should I apply for a StartUp Incubator or a StartUp Accelerator? What is the difference between both? Of course there are several different Models, but in my conception the truth is that nobody really knows their differences. But they are different. And the difference in Definition varies from Country to Country. An incubator in the United States is physically locating your business in one central workspace (generally a co-working space) with many other StartUp companies. In many cases, the StartUps in these incubators can all be Venture funded by the same investor group or early stage. You can stay in the space as long as you need to, or until your business has grown to the scale it needs to relocate to its own space. The mentorship is typically provided by proven serial entrepreneurial, investors, and by shared Knowledge of your StartUp CEO peers. For the Incubator Model, you or your Startup pays a Monthly or sometimes Yearly fee to use it. So, Incubators are a Real State Rental Business basically. A Startup Accelerator in the United States has some distinct differences. Your time in the space is typically limited to a 3-4 month period (when they have an Infrastructure that they run), basically intended to jump start your business and then kick you out of the nest. The cash investment into your business from the Accelerator itself is very minimal (e.g., U$20,000-50,000; with exceptions giving up to U$200K in exchange for a higher equity of the company), but your time in the Accelerator should largely improve your chances of raising Venture Capital from a third party entity on the back-end, after you graduate from their Program. Most Accelerators take Equity and become shareholders in your Startup with the percentage going from 4-12% depending on the Stage of the Company. Mentorship could be coming from Serial Entrepreneurs that are affiliated with the Accelerator (many of which are proven CEOs, or Investors looking for their next opportunity or simply helping the local StartUp community with a history of exits) – parts of this text were taken and adapted from “Is A Startup Incubator or Accelerator Right For You?” by George Deeb. There are also Accelerators that are Location Agnostic and you can be remotely linked to them. They will not have an Infrastructure for the Companies but they bring a strong network of Mentors and Investors. They also take Equity in the StartUp depending on the Stage of the company. These are more “Global” since StartUps from all over the World could participate. They generally do an Event (or even combine their Event with a Major one) every quarter or once every 2 months. They have a different Model. One interesting thing: both models deal with Quantity versus Quality. Of course there is a Selection but the more StartUps you have in the System or Accelerated the chances of getting an Unicorn (a StartUp that reaches a U$1 Billion Dollar Valuation) out of them are higher. Now, let’s talk about the Brazilian Model. Well, I think the “Copy-Cat” Model that Brazilians use for everything Americans do does not work for this. They have mixed up Incubators with Accelerators, offering terrible Infrastructure (with exceptions, of course) charging a Monthly fee and getting Equity at the same time of the StartUps. Most times they do not even offer a good Network of Entrepreneurs, Mentors and Investors. It is a horrible and confusing Model indeed. Another important thing: the Brazilian Model is “poor”, dealing with a few Ideas and StartUps and not quantity (No Unicorns on the Horizon…). So, what Model(s) and Country have more chances to succeed? I think the reader knows the answer already. And I did not even talk about the “Spin Offs” coming out from Universities in the US and in Brazil. Well, I think that is a subject for another Blog Post…