Hematology


15 top science & tech leaders offer surprising predictions for 2018

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The past year has been a momentous one for science and technology. From the detection of gravitational waves (predicted almost a century ago by Einstein) to the rise of virtual currencies like Bitcoin to the creation of genetically modified human embryos, 2017 was marked by all sorts of remarkable discoveries and innovations. No one knows for sure. But as we did for 2017, we asked top scientists and thought leaders in innovation what they expect to see in the new year. Here, lightly edited, are their predictions.


Artificial Human Heart Muscle Created To Help Coronary Attack Victims

International Business Times

Researchers at the Duke University, Durham, North Carolina, claim they have made an artificial human heart muscle that's big enough to be used to solve damage seen in heart attack victims. The team said that this development takes us closer towards the aim of repairing dead heart muscles in patients. The study called "Cardiopatch Platform Enables Maturation and Scale-Up of Human Pluripotent Stem Cell-Derived Engineered Heart Tissues" published on Nov. 28, 2017, appeared on Nature Communications. "Right now, virtually all existing therapies are aimed at reducing the symptoms from the damage that's already been done to the heart, but no approaches have been able to replace the muscle that's lost, because once it's dead, it does not grow back on its own," said Ilya Shadrin -- the first author of the study who is also a biomedical engineering doctoral student at Duke University. "This is a way that we could replace lost muscle with tissue made outside the body."


machine-learning-delivers-insight-stroke-patients

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Researchers have used machine learning to identify how individual stroke patients might respond to different medications, based on the unique structure of their brain. An experiment by University College London (UCL) found that applying computer intelligence to data from from people who had suffered a stroke allowed researchers to see what effect drugs had on brains with varying patterns of damage. For the study, a machine learning algorithm was applied to CT and MRI scans of 1172 stroke patients and mapped the anatomical pattern of damage throughout the brain of each individual. The researchers then simulated the effects of certain hypothetical drugs, to see if any reactions that would have been missed by conventional methods could be identified. They found that the algorithm was particularly advantageous when looking at medication effects that reduced the size of lesions in patients' brains.


Machine learning delivers insight into drugs' effects on stroke patients (Digital Health)

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Machine learning delivers insight into drugs' effects on stroke patients Researchers have used machine learning to identify how individual stroke patients might respond to different medications, based on the unique structure of their brain. An experiment by University College London (UCL) found that applying computer intelligence to data from from people who had suffered a stroke allowed researchers to see what effect drugs had on brains with varying patterns of damage. For the study, a machine learning algorithm was applied to CT and MRI scans of 1172 stroke patients and mapped the anatomical pattern of damage throughout the brain of each individual. The researchers then simulated the effects of certain hypothetical drugs, to see if any reactions that would have been missed by conventional methods could be identified. They found that the algorithm was particularly advantageous when looking at medication effects that reduced the size of lesions in patients' brains.


New machine learning system can automatically identify shapes of red blood cells

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Using a computational approach known as deep learning, scientists have developed a new system to classify the shapes of red blood cells in a patient's blood. The findings, published in PLOS Computational Biology, could potentially help doctors monitor people with sickle cell disease. A person with sickle cell disease produces abnormally shaped, stiff red blood cells that can build up and block blood vessels, causing pain and sometimes death. The disease is named after sickle-shaped (crescent-like) red blood cells, but it also results in many other shapes, such as oval or elongated red blood cells. The particular shapes found in a given patient can hold clues to the severity of their disease, but it is difficult to manually classify these shapes.


How Artificial Intelligence is Revolutionizing Personalized Medicine Mellanox Technologies Blog

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Imagine becoming gravely ill and yet being able to receive an accurate diagnosis with a recommended treatment plan in just 10 minutes. This is actually happening now with the help of Artificial Intelligence (AI). The University of Tokyo recently reported that Watson, IBM's cognitive supercomputer, correctly diagnosed a rare form of leukemia in a 60-year-old woman. Doctors originally thought the woman had acute myeloid leukemia, but after examining 20 million cancer research papers in 10 minutes, Watson was able to correctly determine the actual disease and recommend a personalized treatment plan. AI – and its related applications, Machine Learning (ML) and Deep Learning (DL) – are changing healthcare as we know it.


Artificial Human Embryos Are Coming, and No One Knows How to Handle Them

MIT Technology Review

Two years ago, Shao, a mechanical engineer with a flair for biology, was working with embryonic stem cells, the kind derived from human embryos able to form any cell type. The work in Michigan is part of a larger boom in organoid research--scientists are using stem cells to create clumps of cells that increasingly resemble bits of brain, lungs, or intestine (see "10 Breakthrough Technologies: Brain Organoids"). Scientists have started seeking ways to coax stem cells to form more complicated, organized tissues, called organoids. Following guidelines promulgated last year by Kimmelman's international stem-cell society, Fu's team destroys the cells just five days after they're made.


Prospect of Synthetic Embryos Sparks New Bioethics Debate

MIT Technology Review

Two years ago, Shao, a mechanical engineer with a flair for biology, was working with embryonic stem cells, the kind derived from human embryos able to form any cell type. The work in Michigan is part of a larger boom in organoid research--scientists are using stem cells to create clumps of cells that increasingly resemble bits of brain, lungs, or intestine (see "10 Breakthrough Technologies: Brain Organoids"). Scientists have started seeking ways to coax stem cells to form more complicated, organized tissues, called organoids. Following guidelines promulgated last year by Kimmelman's international stem-cell society, Fu's team destroys the cells just five days after they're made.


Apple's Siri Saves Sick Girl From Hurricane Harvey Flood: Here's The Story

@machinelearnbot

Apple's Siri, which has faced increased competition in the digital personal assistant space with the Google Assistant and Amazon's Alexa, helped the girl contact the Coast Guard to pick them up and fly them to safety. Tyler Frank, a 14-year-old-girl suffering from sickle cell anemia, was running a fever and in great pain at the height of Hurricane Harvey's onslaught. Tyler and her family went through great effort to call for help, as the sick girl started to experience a sickle cell crisis as she was exposed to the elements. Tyler then had the idea of asking Siri for help, and Apple's personal digital assistant delivered.


From bone marrow transplant to winning medals

BBC News

Innovation in this area is being helped by the UK National Health Service's (NHS) Electronic Prescription Service (EPS), which has been rolled out over the last few years. It enables doctors to send prescriptions direct to pharmacies electronically without any need for paper. Such efficiencies have saved the NHS £137m; doctors' practices £328m; pharmacies £59m; and patients £75m, between 2013 and 2016, NHS Digital says. So his company spent three-and-a-half years building a platform, PharmacyOS, to handle every aspect of the repeat prescription process: prescribing, dispensing, delivering, billing, handling insurance claims, as well as pill-taking monitoring.