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Acute Changes in Serotonin Detected by AI-designed Serotonin Sensor

Reporter: Adina Hazan, PhD

 

With 14 receptors and expression in central and peripheral nervous system, serotonin is an essential modulatory molecule. It plays critical roles in a multitude of functions, including addiction, appetite, blood pressure, digestion, and sleep. The clinical implications of this is clearly seen during the treatment of depression or anxiety, where reuptake inhibition of serotonin may improve mood but can unpredictably affect appetite and weight. Thus, development of precise tools to study serotonin, the receptor, or overall cell response is necessary.

Elizabeth Unger from the Tian group at UC Davis, Jacob Keller from the Looger lab from HHMI, Michael Altermatt from the Gradinaru group at California Institute of Technology, and colleagues did just this, by redesigned the binding pocket of periplasmic binding proteins (PBPs) using artificial intelligence, such that it became a fluorescent sensor specific for serotonin. Not only this, the group showed that it could express and use this molecule to detect serotonin on the cell, tissue, and whole animal level.

By starting with a microbial PBP and early version of an acetyl choline sensor (iAChSnFR), the scientists used machine learning and modeling to redesign the binding site to exhibit a higher affinity and specificity to serotonin. After three repeats of mutagenesis, modeling, and library readouts, they produced iSeroSnFR. This version harbors 19 mutations compared to iAChSnFR0.6 and a Kd of 310 µM. This results in an increase in fluorescence in HEK293T cells expressing the serotonin receptor of 800%. Of over 40 neurotransmitters, amino acids, and small molecules screened, only two endogenous molecules evoked some fluorescence, but at significantly higher concentrations.

To acutely test the ability of the sensor to detect rapid changes of serotonin in the environment, the researchers used caged serotonin, a technique in which the serotonin is rapidly released into the environment with light pulses, and showed that iSeroSnFR accurately and robustly produced a signal with each flash of light. With this tool, it was then possible to move to ex-vivo mouse brain slices and detect endogenous serotonin release patterns across the brain. Three weeks after targeted injection of iSeroSnFR to specifically deliver the receptor into the prefrontal cortex and dorsal striatum, strong fluorescent signal could be detected during perfusion of serotonin or electrical stimulation.

Most significantly, this molecule was also shown to be detected in freely moving mice, a tool which could offer critical insight into the acute role of serotonin regulation during important functions such as mood and alertness. Through optical fiber placements in the basolateral amygdala and prefrontal cortex, the team measured dynamic and real-time changes in serotonin release in fear-trained mice, social interactions, and sleep wake cycles. For example, while both areas of the brain have been established as relevant to the fear response, they reliably tracked that the PFC response was immediate, while the BSA displayed a delayed response. This additional temporal resolution of neuromodulation may have important implications in neurotransmitter pharmacology of the central nervous system.

This study provided the scientific community with several insights and tools. The serotonin sensor itself will be a critical tool in the study of the central nervous system and possibly beyond. Additionally, an AI approach to mutagenesis in order to redesign a binding pocket of a receptor opens new avenues to the development of pharmacological tools and may lead to many new designs in therapeutics and research.

SOURCE:

Unger, E. K., Keller, J. P., Altermatt, M., et al. “Directed evolution of a selective and sensitive serotonin sensor via machine learning,” December 23, 2020, Cell; DOI: 10.1016/j.cell.2020.11.040

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

 

https://pharmaceuticalintelligence.com/?s=Serotonin

 

Not Lower Levels of Serotonin, but Damaged Brain Synapses as the Origin for Mental Depression

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/12/09/not-lower-levels-of-serotonin-but-damaged-brain-synapses-as-the-origin-for-mental-depression/

 

Neuroscience impact of synaptic pruning discovery

Larry H. Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2016/05/03/neuroscience-impact-of-synaptic-pruning-discovery/

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Deep Medicine: How Artificial Intelligence Can Make Health Care Human Again

 

Reviewers: Aviva Lev-Ari, PhD, RN

 

5,310 views
Jun 24, 2019

 

123K subscribers

In his new book, Deep Medicine, Eric Topol – cardiologist, geneticist, digital medicine researcher – claims that artificial intelligence can put the humanity back into medicine. By freeing physicians from rote tasks, such as taking notes and performing medical scans, AI creates space for the real healing that occurs between a doctor who listens and a patient who needs to be heard. The counterintuitive recognition that technology can create space for compassion in the clinical setting could mean fewer burned-out doctors, more empowered patients, cost savings, and an entirely new way to approach medicine. Featuring: David Brooks, Eric Topol This conversation was recorded during Aspen Ideas: Health in Aspen, Colorado. Presented by the Aspen Institute, the three-day event opens the Aspen Ideas Festival and features more than 200 speakers engaging with urgent health care challenges and exploring cutting-edge innovations in medicine and science. Learn more at https://www.aspenideas.org

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Artificial pancreas effectively controls type 1 diabetes in children age 6 and up

Reporter: Irina Robu, PhD

A new trial funded by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institute of Health created a clinical trial at four pediatric diabetes centers in the US of a new artificial pancreas system, which monitors and regulates blood glucose levels automatically. The artificial pancreas technology, the Control-IQ system has an insulin pump programmed with advanced control algorithms based on a mathematical model using the person’s glucose monitoring information to automatically adjust the insulin dose, and it was originally developed at University of Virginia (UVA), Charlottesville with funding support from NIDDK.

The artificial pancreas closed-loop control is all in one diabetes management system which monitors and tracks blood glucose levels using a continuous glucose monitor and at the same time delivers the insulin when needed via an insulin pump. The system is not only useful in children age 6 and up, but it also replaces reliance on testing by fingerstick or delivering insulin via injection multiple times a day.

The study contains 101 children between ages of 6 and 13 and the children are assigned either to the control or experimental group. The control group uses a standard injection method and separate insulin pump and the experimental uses the artificial pancreas system. Data was conducted every week for four months, while the participants continue on daily lives.

The results of the study showed that using an artificial pancreas system has a 7% improvement in keeping blood glucose in range during the daytime, and a 26% improvement in nighttime control compared to the control group. However, night time control group is important in people with type 1 diabetes, since unchecked hypoglycemia can lead to seizure, coma or even death. The artificial pancreas system shows about 11 % improvement to the standard method and it shows that the improvement in blood glucose control is impressive and safer for kids. No severe case of hypoglycemia or diabetic ketoacidosis occurred during the study, only some minor issues with the equipment.

After the clinical trial and based on the data received, Tandem Diabetes Care has received clearance from the U.S. FDA for use of the Control-IQ system in children as young as age 6 years.

SOURCE
https://www.nih.gov/news-events/news-releases/artificial-pancreas-effectively-controls-type-1-diabetes-children-age-6

 

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Tweets and Retweets @ COVID-19 and AI: A Virtual Conference – Human-Centered Artificial Intelligence Institute, Stanford University, 4/1/2020, 9AM PST – 3:30PM PST @StanfordHAI  BY @pharma_BI and @AVIVA1950

COVID-19 and AI: A Virtual Conference – Human-Centered Artificial Intelligence Institute, Stanford University, 4/1/2020, 9AM PST – 3:30PM PST @StanfordHAI @pharma_BI @AVIVA1950

Real Time coverage: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/04/01/covid-19-and-ai-a-virtual-conference-human-centered-artificial-intelligence-stanford-university-4-1-2020-9am-pst-330pm-pst/

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@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po Fei-Fei Li AGE Fatality rate and infection rate of the aged Interaction between Acute Infection and Chronic Disease Safety of home – AI sensors at home Sensors data on secure systems clinically data recognized detection

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@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po Identifying COVID-19 Vaccine Candidates with ML Binbin Chen, MD and Ph.D. Student, Department of Genetics, Stanford University Immunogenic component of vaccine for COVID-19 spike protein bind epitome

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@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po Repurposing Existing Drugs to Fight COVID-19 Stefano Rensi #NLP Mine the literature for Proteins: Genomes genes proteins Biophysics #docking simulations for energy of 18 molecules as inhibitors  Selection of candidate

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@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po #ML can be helpful in critical care navigate complexity by automating processes vaccine mutations in the spike protein binding ACE2

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@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po Mining article on sample size domain ares expert add to the challenges vs CS expertise alone

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@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po #Virtual #informed #consent of #patient to accelerate ##clinical #trials

Aviva Lev-Ari
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pharmaceuticalintelligence.com/coronavirus-po Xavier Amatriain Lack accessibility to health care systems HC Accessibility and Scalability AI based HC IT System PDA – Personalized Diagnostics Assessment – for self reporting AI Automations + Physicians home testing

Coronavirus Portal
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pharmaceuticalintelligence.com/coronavirus-po Tina White, Ph.D. Candidate, Department of Mechanical Engineering, Stanford University China death toll >1000 China launched App to monitor quarantine early 1/2020 GPS based new App for contact tracing regulation on data

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Aviva Lev-Ari
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pharmaceuticalintelligence.com/coronavirus-po John Brownstein Late December 2019 collecting dat a HealthMap – public domain Baidu – has movement information connected with cases Temperature Data published Buoy data base customized to collect MA data on Temperature

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pharmaceuticalintelligence.com/coronavirus-po Jason Wang commend center in December 2019 All flight entering the country – Level 3 alert country: China Huhan, Hubei Quarantine all arriving from Level 3 alert country National STOKE PILES Activated x5 mask production

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Aviva Lev-Ari
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#AI

pharmaceuticalintelligence.com/coronavirus-po Jason Wang Since 2003 Taiwan is preparing for a Pangemic JAMA paper on the topic is beebn reported  Location of patient Taiwan National Epidemic Center 100 persons 24×7 in the Command Center Taiwan activated

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Aviva Lev-Ari
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pharmaceuticalintelligence.com/coronavirus-po Michele Barry,TRACE together – Bluetooth tool on distance among people CHINA – contact racing surveillence scanning temp strict social distancing Hong Kong – tracing bracelets for quarantine Street locations of infected

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Aviva Lev-Ari
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pharmaceuticalintelligence.com/coronavirus-po Michele Barry 5Million people travel out of Huhan Singapore – Free testing 1st country Temp testing stay at home, text phone from Authorities, show picture they are in quarantine for 5 days if negative  TRACE together

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Aviva Lev-Ari
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pharmaceuticalintelligence.com/coronavirus-po Seema Yasmin March 7, 2020 Italy news quarantine of 16 million lockdown large movement of people moving out of lockeddown areas, this movement based on information lead to spread of the viral spread

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pharmaceuticalintelligence.com/coronavirus-po Nigam Shah,  Operational Planning – Utilization – Resource planning Clinical – who to test Research Questions – ACE2 receptors

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pharmaceuticalintelligence.com/coronavirus-po Stanford Institute for Human-Centered Artificial Intelligence (HAI) Conference on COVID-19 and AI: A Virtual Conference on April 1, 2020 beginning at 9:00am (PDT). event covered in real time

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@StanfordHAI

Vaccines are one of the most powerful tools to curb a pandemic and prevent its recurrence,

says. He discusses how AI tools built upon immunology knowledge and data can increase the chances of finding an effective vaccine. stanford.io/3aBidgh

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Aviva Lev-Ari
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covering in real time Stanford HAI – COVID-19 and AI: A Virtual Conference youtu.be/z4105Exe23Q via

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COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…
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Aviva Lev-Ari
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pharmaceuticalintelligence.com/coronavirus-po Stanford Institute for Human-Centered Artificial Intelligence (HAI) Conference on COVID-19 and AI: A Virtual Conference on April 1, 2020 beginning at 9:00am (PDT). event covered in real time

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The Future of Synthetic Biology

Reporter: Irina Robu, PhD

With an estimated global evaluation of around $14 billion US, synthetic biology is a rapidly accelerating market. Nonetheless while the growth of the market has been remarkable, the ttrue impact has not yet been seen. The era of AI will quickly increase the pace of discovery, and produce materials not seen in nature, through extrapolation and generative design. The extraordinary is now possible: producing spider silk without spiders, egg proteins without chickens and fragrances without flowers.

Synthetic biology companies are associating with fashion designers as well as forming ‘organism foundries. Rapidly, AI will utilize its learning of the natural world to make guided inferences which produce entirely new materials. From a technology perspective, we’re experiencing an explosion of capability that will be invasive in the next 3-5 years. Language models have come a long way, to the point where full models are being kept private so as not to endanger the public.

Already today, the average person has the ability to start their own commercial space venture for less than the cost of a juice franchise. PwC Australia’s Charmaine Green believes secret trends can hide among obvious ones. She outlines three trends leading to her hypothesis that Australia is well placed to become the global creative hub for video game development.

Economies like Australia are situated to capitalize on this trend, and video game development can become a permanent and substantial part of the economy. In Australia, Green argues, we have all the basic elements needed: high ingenuity, creative risk taking, and the freedom and flexibility that comes with the country’s small-to-mid studios.

SOURCE

Tech trends that will change the world by 2025

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Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine

Reporter: Aviva Lev-Ari, PhD, RN

 

This report is based on an article in Nature Medicine | VOL 25 | December 2019 | 1800–1809 | http://www.nature.com/naturemedicine

Looking forward 25 years: the future of medicine.

Nat Med 25, 1804–1807 (2019) doi:10.1038/s41591-019-0693-y

 

Aviv Regev, PhD

Core member and chair of the faculty, Broad Institute of MIT and Harvard; director, Klarman Cell Observatory, Broad Institute of MIT and Harvard; professor of biology, MIT; investigator, Howard Hughes Medical Institute; founding co-chair, Human Cell Atlas.

  • millions of genome variants, tens of thousands of disease-associated genes, thousands of cell types and an almost unimaginable number of ways they can combine, we had to approximate a best starting point—choose one target, guess the cell, simplify the experiment.
  • In 2020, advances in polygenic risk scores, in understanding the cell and modules of action of genes through genome-wide association studies (GWAS), and in predicting the impact of combinations of interventions.
  • we need algorithms to make better computational predictions of experiments we have never performed in the lab or in clinical trials.
  • Human Cell Atlas and the International Common Disease Alliance—and in new experimental platforms: data platforms and algorithms. But we also need a broader ecosystem of partnerships in medicine that engages interaction between clinical experts and mathematicians, computer scientists and engineers

Feng Zhang, PhD

investigator, Howard Hughes Medical Institute; core member, Broad Institute of MIT and Harvard; James and Patricia Poitras Professor of Neuroscience, McGovern Institute for Brain Research, MIT.

  • fundamental shift in medicine away from treating symptoms of disease and toward treating disease at its genetic roots.
  • Gene therapy with clinical feasibility, improved delivery methods and the development of robust molecular technologies for gene editing in human cells, affordable genome sequencing has accelerated our ability to identify the genetic causes of disease.
  • 1,000 clinical trials testing gene therapies are ongoing, and the pace of clinical development is likely to accelerate.
  • refine molecular technologies for gene editing, to push our understanding of gene function in health and disease forward, and to engage with all members of society

Elizabeth Jaffee, PhD

Dana and Albert “Cubby” Broccoli Professor of Oncology, Johns Hopkins School of Medicine; deputy director, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins.

  • a single blood test could inform individuals of the diseases they are at risk of (diabetes, cancer, heart disease, etc.) and that safe interventions will be available.
  • developing cancer vaccines. Vaccines targeting the causative agents of cervical and hepatocellular cancers have already proven to be effective. With these technologies and the wealth of data that will become available as precision medicine becomes more routine, new discoveries identifying the earliest genetic and inflammatory changes occurring within a cell as it transitions into a pre-cancer can be expected. With these discoveries, the opportunities to develop vaccine approaches preventing cancers development will grow.

Jeremy Farrar, OBE FRCP FRS FMedSci

Director, Wellcome Trust.

  • shape how the culture of research will develop over the next 25 years, a culture that cares more about what is achieved than how it is achieved.
  • building a creative, inclusive and open research culture will unleash greater discoveries with greater impact.

John Nkengasong, PhD

Director, Africa Centres for Disease Control and Prevention.

  • To meet its health challenges by 2050, the continent will have to be innovative in order to leapfrog toward solutions in public health.
  • Precision medicine will need to take center stage in a new public health order— whereby a more precise and targeted approach to screening, diagnosis, treatment and, potentially, cure is based on each patient’s unique genetic and biologic make-up.

Eric Topol, MD

Executive vice-president, Scripps Research Institute; founder and director, Scripps Research Translational Institute.

  • In 2045, a planetary health infrastructure based on deep, longitudinal, multimodal human data, ideally collected from and accessible to as many as possible of the 9+ billion people projected to then inhabit the Earth.
  • enhanced capabilities to perform functions that are not feasible now.
  • AI machines’ ability to ingest and process biomedical text at scale—such as the corpus of the up-to-date medical literature—will be used routinely by physicians and patients.
  • the concept of a learning health system will be redefined by AI.

Linda Partridge, PhD

Professor, Max Planck Institute for Biology of Ageing.

  • Geroprotective drugs, which target the underlying molecular mechanisms of ageing, are coming over the scientific and clinical horizons, and may help to prevent the most intractable age-related disease, dementia.

Trevor Mundel, MD

President of Global Health, Bill & Melinda Gates Foundation.

  • finding new ways to share clinical data that are as open as possible and as closed as necessary.
  • moving beyond drug donations toward a new era of corporate social responsibility that encourages biotechnology and pharmaceutical companies to offer their best minds and their most promising platforms.
  • working with governments and multilateral organizations much earlier in the product life cycle to finance the introduction of new interventions and to ensure the sustainable development of the health systems that will deliver them.
  • deliver on the promise of global health equity.

Josep Tabernero, MD, PhD

Vall d’Hebron Institute of Oncology (VHIO); president, European Society for Medical Oncology (2018–2019).

  • genomic-driven analysis will continue to broaden the impact of personalized medicine in healthcare globally.
  • Precision medicine will continue to deliver its new paradigm in cancer care and reach more patients.
  • Immunotherapy will deliver on its promise to dismantle cancer’s armory across tumor types.
  • AI will help guide the development of individually matched
  • genetic patient screenings
  • the promise of liquid biopsy policing of disease?

Pardis Sabeti, PhD

Professor, Harvard University & Harvard T.H. Chan School of Public Health and Broad Institute of MIT and Harvard; investigator, Howard Hughes Medical Institute.

  • the development and integration of tools into an early-warning system embedded into healthcare systems around the world could revolutionize infectious disease detection and response.
  • But this will only happen with a commitment from the global community.

Els Toreele, PhD

Executive director, Médecins Sans Frontières Access Campaign

  • we need a paradigm shift such that medicines are no longer lucrative market commodities but are global public health goods—available to all those who need them.
  • This will require members of the scientific community to go beyond their role as researchers and actively engage in R&D policy reform mandating health research in the public interest and ensuring that the results of their work benefit many more people.
  • The global research community can lead the way toward public-interest driven health innovation, by undertaking collaborative open science and piloting not-for-profit R&D strategies that positively impact people’s lives globally.

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

 

One of the most contagious diseases known to humankind, measles killed an average of 2.6 million people each year before a vaccine was developed, according to the World Health Organization. Widespread vaccination has slashed the death toll. However, lack of access to vaccination and refusal to get vaccinated means measles still infects more than 7 million people and kills more than 100,000 each year worldwide as reported by WHO. The cases are on the rise, tripling in early 2019 and some experience well-known long-term consequences, including brain damage and vision and hearing loss. Previous epidemiological research into immune amnesia suggests that death rates attributed to measles could be even higher, accounting for as much as 50 percent of all childhood mortality.

 

Over the last decade, evidence has mounted that the measles vaccine protects in two ways. It prevents the well-known acute illness with spots and fever and also appears to protect from other infections over the long term by giving general boost to the immune system. The measles virus can impair the body’s immune memory, causing so-called immune amnesia. By protecting against measles infection, the vaccine prevents the body from losing or “forgetting” its immune memory and preserves its resistance to other infections. Researchers showed that the measles virus wipes out 11% to 73% of the different antibodies that protect against viral and bacterial strains a person was previously immune to like from influenza to herpes virus to bacteria that cause pneumonia and skin infections.

 

This study at Harvard Medical School and their collaborators is the first to measure the immune damage caused by the virus and underscores the value of preventing measles infection through vaccination. The discovery that measles depletes people’s antibody repertoires, partially obliterating immune memory to most previously encountered pathogens, supports the immune amnesia hypothesis. It was found that those who survive measles gradually regain their previous immunity to other viruses and bacteria as they get re-exposed to them. But because this process may take months to years, people remain vulnerable in the meantime to serious complications of those infections and thus booster shots of routine vaccines may be required.

 

VirScan detects antiviral and antibacterial antibodies in the blood that result from current or past encounters with viruses and bacteria, giving an overall snapshot of the immune system. Researchers gathered blood samples from unvaccinated children during a 2013 measles outbreak in the Netherlands and used VirScan to measure antibodies before and two months after infection in 77 children who’d contracted the disease. The researchers also compared the measurements to those of 115 uninfected children and adults. Researchers found a striking drop in antibodies from other pathogens in the measles-infected children that clearly suggested a direct effect on the immune system resembling measles-induced immune amnesia.

 

Further tests revealed that severe measles infection reduced people’s overall immunity more than mild infection. This could be particularly problematic for certain categories of children and adults, the researchers said. The present study observed the effects in previously healthy children only. But, measles is known to hit malnourished children much harder, the degree of immune amnesia and its effects could be even more severe in less healthy populations. Inoculation with the MMR (measles, mumps, rubella) vaccine did not impair children’s overall immunity. The results align with decades of research. Ensuring widespread vaccination against measles would not only help prevent the expected 120,000 deaths that will be directly attributed to measles this year alone, but could also avert potentially hundreds of thousands of additional deaths attributable to the lasting damage to the immune system.

 

References:

 

https://hms.harvard.edu/news/inside-immune-amnesia?utm_source=Silverpop

 

https://science.sciencemag.org/content/366/6465/599

 

www.who.int/immunization/newsroom/measles-data-2019/en/

 

https://www.ncbi.nlm.nih.gov/pubmed/20636817

 

https://www.ncbi.nlm.nih.gov/pubmed/27157064

 

https://www.ncbi.nlm.nih.gov/pubmed/30797735

 

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Cardiac MRI Imaging Breakthrough: The First AI-assisted Cardiac MRI Scan Solution, HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package

Reporter: Aviva Lev-Ari, PhD, RN

 

HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package, the First AI-assisted Cardiac MRI Scan Solution

The future of imaging is here—and FDA cleared.

LOS ALTOS, Calif.–(BUSINESS WIRE)–HeartVista, a pioneer in AI-assisted MRI solutions, today announced that it received 510(k) clearance from the U.S. Food and Drug Administration to deliver its AI-assisted One Click™ MRI acquisition software for cardiac exams. Despite the many advantages of cardiac MRI, or cardiac magnetic resonance (CMR), its use has been largely limited due to a lack of trained technologists, high costs, longer scan time, and complexity of use. With HeartVista’s solution, cardiac MRI is now simple, time-efficient, affordable, and highly consistent.

“HeartVista’s Cardiac Package is a vital tool to enhance the consistency and productivity of cardiac magnetic resonance studies, across all levels of CMR expertise,” said Dr. Raymond Kwong, MPH, Director of Cardiac Magnetic Resonance Imaging at Brigham and Women’s Hospital and Associate Professor of Medicine at Harvard Medical School.

A recent multi-center, outcome-based study (MR-INFORM), published in the New England Journal of Medicine, demonstrated that non-invasive myocardial perfusion cardiovascular MRI was as good as invasive FFR, the previous gold standard method, to guide treatment for patients with stable chest pain, while leading to 20% fewer catheterizations.

“This recent NEJM study further reinforces the clinical literature that cardiac MRI is the gold standard for cardiac diagnosis, even when compared against invasive alternatives,” said Itamar Kandel, CEO of HeartVista. “Our One Click™ solution makes these kinds of cardiac MRI exams practical for widespread adoption. Patients across the country now have access to the only AI-guided cardiac MRI exam, which will deliver continuous imaging via an automated process, minimize errors, and simplify scan operation. Our AI solution generates definitive, accurate and actionable real-time data for cardiologists. We believe it will elevate the standard of care for cardiac imaging, enhance patient experience and access, and improve patient outcomes.”

HeartVista’s FDA-cleared Cardiac Package uses AI-assisted software to prescribe the standard cardiac views with just one click, and in as few as 10 seconds, while the patient breathes freely. A unique artifact detection neural network is incorporated in HeartVista’s protocol to identify when the image quality is below the acceptable threshold, prompting the operator to reacquire the questioned images if desired. Inversion time is optimized with further AI assistance prior to the myocardial delayed-enhancement acquisition. A 4D flow measurement application uses a non-Cartesian, volumetric parallel imaging acquisition to generate high quality images in a fraction of the time. The Cardiac Package also provides preliminary measures of left ventricular function, including ejection fraction, left ventricular volumes, and mass.

HeartVista is presenting its new One Click™ Cardiac Package features at the Radiological Society of North America (RSNA) annual meeting in Chicago, on Dec. 4, 2019, at 2 p.m., in the AI Showcase Theater. HeartVista will also be at Booth #11137 for the duration of the conference, from Dec. 1 through Dec. 5.

About HeartVista

HeartVista believes in leveraging artificial intelligence with the goal of improving access to MRI and improved patient care. The company’s One Click™ software platform enables real-time MRI for a variety of clinical and research applications. Its AI-driven, one-click cardiac localization method received first place honors at the International Society for Magnetic Resonance in Medicine’s Machine Learning Workshop in 2018. The company’s innovative technology originated at the Stanford Magnetic Resonance Systems Research Laboratory. HeartVista is funded by Khosla Ventures, and the National Institute of Health’s Small Business Innovation Research program.

For more information, visit www.heartvista.ai

SOURCE

Reply-To: Kimberly Ha <kimberly.ha@kkhadvisors.com>

Date: Tuesday, October 29, 2019 at 11:01 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: HeartVista Receives FDA Clearance for First AI-assisted Cardiac MRI Solution

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Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity

Curator: Stephen J. Williams, PhD

 

scTrio-seq identifies colon cancer lineages

Single-cell multiomics sequencing and analyses of human colorectal cancer. Shuhui Bian et al. Science  30 Nov 2018:Vol. 362, Issue 6418, pp. 1060-1063

To better design treatments for cancer, it is important to understand the heterogeneity in tumors and how this contributes to metastasis. To examine this process, Bian et al. used a single-cell triple omics sequencing (scTrio-seq) technique to examine the mutations, transcriptome, and methylome within colorectal cancer tumors and metastases from 10 individual patients. The analysis provided insights into tumor evolution, linked DNA methylation to genetic lineages, and showed that DNA methylation levels are consistent within lineages but can differ substantially among clones.

Science, this issue p. 1060

Abstract

Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single-cell resolution. Using optimized single-cell multiomics sequencing together with multiregional sampling of the primary tumor and lymphatic and distant metastases, we developed insights beyond intratumoral heterogeneity. Genome-wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 patients whose DNA we sequenced. The cancer cells’ DNA demethylation degrees clearly correlated with the densities of the heterochromatin-associated histone modification H3K9me3 of normal tissue and those of repetitive element long interspersed nuclear element 1. Our work demonstrates the feasibility of reconstructing genetic lineages and tracing their epigenomic and transcriptomic dynamics with single-cell multiomics sequencing.

Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples from the TCGA Project and patient CRC01’s cancer cells are shown.

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Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples

 

 

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Showcase: How Deep Learning could help radiologists spend their time more efficiently

Reporter and Curator: Dror Nir, PhD

 

The debate on the function AI could or should realize in modern radiology is buoyant presenting wide spectrum of positive expectations and also fears.

The article: A Deep Learning Model to Triage Screening Mammograms: A Simulation Study that was published this month shows the best, and very much feasible, utility for AI in radiology at the present time. It would be of great benefit for radiologists and patients if such applications will be incorporated (with all safety precautions taken) into routine practice as soon as possible.

In a simulation study, a deep learning model to triage mammograms as cancer free improves workflow efficiency and significantly improves specificity while maintaining a noninferior sensitivity.

Background

Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist specificity or efficiency.

Purpose

To develop a DL model to triage a portion of mammograms as cancer free, improving performance and workflow efficiency.

Materials and Methods

In this retrospective study, 223 109 consecutive screening mammograms performed in 66 661 women from January 2009 to December 2016 were collected with cancer outcomes obtained through linkage to a regional tumor registry. This cohort was split by patient into 212 272, 25 999, and 26 540 mammograms from 56 831, 7021, and 7176 patients for training, validation, and testing, respectively. A DL model was developed to triage mammograms as cancer free and evaluated on the test set. A DL-triage workflow was simulated in which radiologists skipped mammograms triaged as cancer free (interpreting them as negative for cancer) and read mammograms not triaged as cancer free by using the original interpreting radiologists’ assessments. Sensitivities, specificities, and percentage of mammograms read were calculated, with and without the DL-triage–simulated workflow. Statistics were computed across 5000 bootstrap samples to assess confidence intervals (CIs). Specificities were compared by using a two-tailed t test (P < .05) and sensitivities were compared by using a one-sided t test with a noninferiority margin of 5% (P < .05).

Results

The test set included 7176 women (mean age, 57.8 years ± 10.9 [standard deviation]). When reading all mammograms, radiologists obtained a sensitivity and specificity of 90.6% (173 of 191; 95% CI: 86.6%, 94.7%) and 93.5% (24 625 of 26 349; 95% CI: 93.3%, 93.9%). In the DL-simulated workflow, the radiologists obtained a sensitivity and specificity of 90.1% (172 of 191; 95% CI: 86.0%, 94.3%) and 94.2% (24 814 of 26 349; 95% CI: 94.0%, 94.6%) while reading 80.7% (21 420 of 26 540) of the mammograms. The simulated workflow improved specificity (P = .002) and obtained a noninferior sensitivity with a margin of 5% (P < .001).

Conclusion

This deep learning model has the potential to reduce radiologist workload and significantly improve specificity without harming sensitivity.

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