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Medical schools are using augmented reality in their curricula to give students hands-on learning opportunities. Medical students can use augmented reality to see and apply theories throughout their training. Augmented reality (AR) and virtual reality (VR) in the healthcare sector are giving a new perspective to the world. Virtual reality (VR) and other innovative technologies such as augmented reality are already being used by many healthcare organizations (AR). Technologists employ virtual reality in various settings, including treating patients, medical teaching, and hospital management. Augmented Surgery Surgeons can utilize AR to learn more about their patients’ anatomy. They may feed their MRI and CT scan results into an AR headset. Then, just before surgery, place-specific patient physiology over their body. Doctors can examine muscles, bones, and essential organs with this technology. Previously, surgery had a significant mortality rate. Procedures can now be a lot safer because of AR. When physicians and experts try to save patients’ lives, AR can help by providing people with all the information they need. Physicians could be more aware of organ placement, vein meshes, and diagnostic reports as they operate since they are all there in front of their eyes. Augmented Diagnosis Some patients, you may have seen, have trouble accurately articulating actual symptoms to doctors. Because of AR, patients will finally be able to communicate their symptoms. As a result of this procedure, doctors can more readily examine their patients’ complaints and diagnose them more accurately. Using augmented reality, the nurse can now swiftly find veins. How? AccuVein, an AR firm that uses a portable scanner, allows nurses to determine the location of veins. Augmented Practice Medical schools use augmented reality in their curricula to give students hands-on learning opportunities. It would be simple to replicate patients and surgical interactions for students using AR in education. Medical students can use augmented reality to see and apply theories throughout their training. Virtual Collaboration Between Physicians With AR What if your surgeon is thousands of miles away? AR may be able to help you. If the surgeon is unavailable and a specialized on hand possesses AR tools, the specialist can assist and follow orders. Doctors may employ AR for collaborative operations and AR video conferencing for successful meetings on any health topic. With doctors from outside the clinic assisting, it might be a life-saving situation.
Immunohistochemical stains for individual markers revolutionized diagnostic pathology decades ago but cannot capture enough information to accurately predict response to immunotherapy. Newer multiplex immunofluorescent technologies provide the potential to visualize the expression patterns of many functionally relevant molecules but present numerous challenges in accurate image analysis and data handling, particularly over large tumor areas. Drawing from the field of astronomy, in which petabytes of imaging data are routinely analyzed across a wide spectral range, Berry et al. developed a platform for multispectral imaging of whole-tumor sections with high-fidelity single-cell resolution. The resultant AstroPath platform was used to develop a multiplex immunofluorescent assay highly predictive of responses and outcomes for melanoma patients receiving immunotherapy.
Work for everyone is hard right now. COVID-19 makes it a challenge to stay focused and motivated. How is the open source community responding to the global pandemic? Since the end of January 2020, the community has contributed to thousands of open source repositories that mention coronavirus or COVID-19. These repositories consist of datasets, models, visualizations, web and mobile applications, and more, and the majority are written in JavaScript and Python.
So far we’ve seen when you will die and how other people tend to die. Now let’s put the two together to see how and when you will die, given your sex, race, and age. The Underlying Cause of Death database is maintained by the Centers for Disease Control and Prevention. It provides data for the number of people who died in the United States between 1999 and 2014. The records are based on death certificates, which require an entry for a single cause of death. The CDC classifies the causes into 113 subcategories, which fit under the umbrella of 20 categories of disease and external causes. More specifically, the CDC uses the International Statistical Classification of Diseases and Related Health Problems (ICD), which is published by the World Health Organization. The simulation below covers the main categories, or chapters, as they’re referred to by the ICD. Enter your sex, race, and age. Each dot represents one of your simulated lives, and as each year passes, more of your simulated selves pass away. Color corresponds to cause of death, and the bars on the right keep track of the cumulative percentages. By the end, you’re left with the chances that you will die of each cause.
The AI program reliably interprets mammograms and translates patient data into diagnostic information 30 times faster than a human doctor, with 99 per cent accuracy. It was developed by researchers at Houston Methodist Research Institute in Texas. “This software intelligently reviews millions of records in a short amount of time, enabling us to determine breast cancer risk more efficiently using a patient's mammogram,” said Stephen T Wong, chair of the Department of Systems Medicine and Bioengineering at the institute. “This has the potential to decrease unnecessary biopsies," he added. The team used the AI software to evaluate mammograms and pathology reports of 500 breast cancer patients. The software scanned patient charts, collected diagnostic features and correlated mammogram findings with breast cancer subtype. Clinicians used results, such the expression of tumor proteins, to accurately predict each patient's probability of breast cancer diagnosis. In the US, 12.1 million mammograms are performed annually, but half yield false results, according to the American Cancer Society, resulting in one in two healthy women being told they have cancer. Patients who are told they are at particular risk of breast cancer are often recommended for biopsies – a necessary but invasive procedure that removes tissue or fluid from a suspicious area so cells can be analyzed. However, 20 per cent are performed unnecessarily. The researchers hope their software will help physicians better choose which patients need the procedure and decrease unnecessary breast biopsies. Manual review of 50 charts took two clinicians 50 to 70 hours, whereas the AI software reviewed 500 charts in a few hours, saving the human doctors 500 hours of their time.
Researchers at the University of Calgary are using robotics technology to try to come up with more effective treatments for children who have had strokes. The robotic device measures a patient's position sense — what doctors call proprioception — the unconscious perception of where the body is while in motion or at rest. "Someone whose position sense has been affected might have difficulty knowing where their hand or arm is in space, adding to their difficulty in using their affected, weaker limb," said one of the study's senior researchers, Dr. Kirton of the Cumming School of Medicine's departments of pediatrics and clinical neurosciences. "We can try to make a hand stronger but, if your brain doesn't know where the hand is, this may not translate into meaningful function in daily life." PhD candidate Andrea Kuczynski is doing ongoing research using the KINARM (Kinesiological Instrument for Normal and Altered Reaching Movements) robotic device. During the test the children sit in the KINARM machine with their arms supported by its exoskeleton, which measured movement as they played video games and did other tasks. All the children also had MRIs, which gave researchers a detailed picture of their brain structures.
The virtual body is the work of Sergey Makarov at the Worcester Polytechnic Institute in Massachusetts and his colleagues. They used software to help them stitch the thousands of images together, and the final model was checked by five doctors, each with a different medical specialism. “It needs to be anatomically correct,” says Makarov, who presented the work at the IEEE Engineering in Medicine and Biology Society meeting in Milan, Italy, last month.
Their phantom is the most detailed digital reconstruction of a whole human body ever to be pieced together. She has 231 tissue parts, ranging from windpipe to eyeballs, but is missing nose cartilage and 14 other bits of the body.
Other teams have created phantoms from MRI and CT scans of living volunteers, but the resolution is nowhere near as good. Entire body scans take several hours and any slight movements blur the image. The scans also lack colour, which is important for understanding different tissues, says Makarov.
“Sectioned color images allow you to distinguish virtually all the anatomical structures we are made of,” says Silvia Farcito at the Foundation for Research on Information Technologies in Society, based in Zurich, Switzerland, although she says that blood vessels tend to collapse in cadavers.
“They have ten times as much information as you’d get from an MRI scan,” says Fernando Bello, who develops simulations for medical procedures at Imperial College London. “It means the team will have much more information about organs and their structuring.”
The high resolution of the model makes it ideal for virtual experiments. Each of the woman’s tissues has a well-defined set of parameters, such as density and thermal conductivity. This makes it possible to compute the impact that radiation, for example, and various imaging techniques are likely to have on living tissues.
“The phantom gives us a great opportunity to study human tissues without having to do human studies, which are lengthy and expensive,” says Ara Nazarian, an orthopedic surgeon at Harvard Medical School who is collaborating with Makarov.
There’s no shortage of experts monitoring influenza outbreaks around the globe. The Centers for Disease Control and Prevention tracks flu activity in the United States year-round and produces weekly flu activity reports between the peak months of October to May. Likewise, the World Health Organisation constantly gathers epidemiological surveillance data, and releases updates on outbreaks taking place anywhere, anytime.
Still, despite the monitoring and the annual push to administer flu vaccines, influenza sickens millions of people around the world each year, leading to as many as 500,000 deaths annually. Young children and the elderly in particular are at risk. But what if we were better at predicting – and preparing for – seasonal outbreaks.That’s the impetus driving a team of researchers trying to show that it is possible to predict the timing and intensity of flu outbreaks in subtropical climates, such as Hong Kong, where flu seasons occur at irregular intervals throughout the year. The group, which includes scientists from Columbia University and the University of Hong Kong, has created a computer model to run various simulations of an outbreak and predict its magnitude and peak, according to a study published in the journal PLOS Computational Biology.
As a test case, the researchers gathering flu data from dozens of outpatient clinics and lab reports in Hong Kong between 1998 and 2013, then explored whether their system could accurately predict how outbreaks played out during those years. They said the program did remarkably well at predicting the peak of an outbreak several weeks in advance.
That’s not to say it was perfect. Researchers said the accuracy of the predictions varied, depending on the strength of an outbreak and how far in advance they tried to make a prediction. In addition, forecasts for specific strains of influenza proved more reliable than those for overall epidemics, and it was easier to predict the peak and magnitude of an outbreak than exactly when it would begin or how long it might last.
The deal to produce clinical summaries under the Mayo Clinic name for Google searches symbolizes the medical priesthood's acceptance that information technology has reshaped the doctor-patient relationship. More disruptions are already on the way.
If information is power, digitized information is distributed power. While “patient-centered care” has been directed by professionals towards patients, collaborative health – what some call “participatory medicine” or “person-centric care” – shifts the perspective from the patient outwards.
Collaboration means sharing. At places like Mayo and Houston’s MD Anderson Cancer Center, the doctor’s detailed notes, long seen only by other clinicians, are available through a mobile app for patients to see when they choose and share how they wish. mHealth makes the process mundane, while the content makes it an utterly radical act.
About 5 million patients nationwide currently have electronic access to open notes. Boston’s Beth Israel Deaconess Medical Center and a few other institutions are planning to allow patients to make additions and corrections to what they call “OurNotes.” Not surprisingly, many doctors remain mortified by this medical sacrilege.
Even more threatening is an imminent deluge of patient-generated health data churned out by a growing list of products from major consumer companies. Sensors are being incorporated into wearables, watches, smartphones and (in a Ford prototype) even a “car that cares” with biometric sensors in the seat and steering wheel. Sitting in your suddenly becomes telemedicine.
To be sure, traditional information channels remain. For example, a doctor-prescribed, Food and Drug Administration-approved app uses sensors and personalized analytics to prevent severe asthma attacks. Increasingly common, though, is digitized data that doesn’t need a doctor at all. For example, a Microsoft fitness band not only provides constant heart rate monitoring, according to a New York Times review, but is part of a health “platform” employing algorithms to deliver “actionable information” and contextual analysis. By comparison, “Dr. Google” belongs in a Norman Rockwell painting.
There is a quiet hacking revolution taking place in the Type 1 diabetes community. You can identify its followers by the popular hashtag #WeAreNotWaiting on Twitter. There are currently thousands of individuals running an app known as Nightscout to upload real-time blood glucose readings from their Dexcom continuous glucose monitors to their own private servers. This allows hundreds of parents to keep close watch over the health of children with Type 1. While this kind of customized surveillance can be done with current diabetes technology, it has yet to be approved by the FDA. Many have decided that we are not waiting for that agency’s blessing.
Stephen Black reports: "I was diagnosed with Type 1 four months ago. At that time, I knew nothing about diabetes. I was in disbelief when I discovered that if I wanted to see my glucose levels in real time, I would need to carry around an extra, bulky device in my pocket. If I wanted to see that data anywhere else, I would need to plug it into a computer and upload it. If a loved one wanted to check in to see if I was doing alright, they would need to call me and hope I answered. This seemed anachronistic in the wireless age. I promptly got to work on a project I have dubbed DexDrip, a wireless bluetooth bridge that would allow real-time blood glucose readings from a sensor to be delivered straight to my phone. I started by researching the Nightscout project. After doing some digging, I soon discovered an underground community of individuals working on similar projects to mine. Skirting just outside the peripheral vision of the FDA, they are working on all sorts of projects, including their own closed-loop artificial pancreas systems. Some are working in groups, others are working alone, but all share the same goal of making lives for people with diabetes easier, better, and (although they are taking personal risks) safer. Finding these people was difficult because many want to remain anonymous, but I was amazed by the community; everyone was there to help me. Once I discovered how to intercept transmissions from my Dexcom transmitter, it was pretty straightforward to steer the signal to my smartphone. The math presents a bigger challenge. Every 12 hours, the Dexcom receiver asks the user to enter his or her current blood glucose value so it can recalibrate. I couldn’t find a way to work around this automatic recalibration request. If I was going to cut the receiver out of the equation, I would need to write my own calibration algorithm."
Read Stephen Black's full report here
The human head weighs about a dozen pounds. But as the neck bends forward and down, the weight on the cervical spine begins to increase. At a 15-degree angle, this weight is about 27 pounds, at 30 degrees it’s 40 pounds, at 45 degrees it’s 49 pounds, and at 60 degrees it’s 60 pounds.
That’s the burden that comes with staring at a smartphone — the way millions do for hours every day, according to research published by Kenneth Hansraj in the National Library of Medicine. The study will appear next month in Surgical Technology International. Over time, researchers say, this poor posture, sometimes called “text neck,” can lead to early wear-and-tear on the spine, degeneration and even surgery.
“It is an epidemic or, at least, it’s very common,” Hansraj, chief of spine surgery at New York Spine Surgery and Rehabilitation Medicine, told The Washington Post. “Just look around you, everyone has their heads down.”
Can’t grasp the significance of 60 pounds? Imagine carrying an 8-year-old around your neck several hours per day. Smartphone users spend an average of two to four hours per day hunched over, reading e-mails, sending texts or checking social media sites. That’s 700 to 1,400 hours per year people are putting stress on their spines, according to the research. And high-schoolers might be the worst. They could conceivably spend an additional 5,000 hours in this position, Hansraj said.
“The problem is really profound in young people,” he said. “With this excessive stress in the neck, we might start seeing young people needing spine care. I would really like to see parents showing more guidance.”
Medical physicists at UT Southwestern Medical Center are finding new ways to use the speed of video game processors to promote research that is aimed at improving patient care.
In recent years, video game processors, known as graphic processing units, or GPUs, have become massively powerful as game makers support increasingly elaborate video graphics. Medical experts took note of the GPU’s rapid-fire processing. Among the pioneers seeking ways to apply the processing speed of GPUs to medical use is Dr. Steve Jiang, UT Southwestern’s new Director of the Division of Medical Physics and Engineering, and Professor and Vice Chairman of Radiation Oncology. One practical application is reducing the time required to calculate the radiation dose delivered to a tumor during proton radiotherapy, he said. The faster video processors can reduce the time of the most complex calculation method from 70 hours to just 10 seconds.
“That’s an astonishing improvement in processing speed,” Dr. Jiang said. “We should really thank video gamers. The popularity of video games has resulted in a tool that is very beneficial for scientific computing in medicine. The quicker results mean increased convenience for patients and physicians, and translate in a significant way to better patient care,” he said.
Radiotherapy is often delivered in many treatments that can span weeks, during which time the patient’s anatomy or the tumor itself can change. Dr. Jiang’s highly efficient calculation allows for more accurate treatment plans based on daily calculations that are adapted to changes in the patient’s daily geometry (such as weight, size and shape of the tumor), as well as the healthy tissue around the tumor. With the faster processor, doctors can make calculations before each treatment, instead of re-using older data, and new calculations can make the treatments more exact, sparing surrounding healthy tissue.
“The main idea is to change the way we treat patients,” Dr. Jiang said. “If someone has a cancer, you want to treat the disease immediately and precisely. The current slower calculations require patients to wait for about a week to receive the first radiation treatment after consulting with doctors.”
Although video games may seem to offer little beyond entertainment, the consumer demand was so intense that game developers created better, faster, and cheaper processors for video games than for any other applications.
“Market forces are strong and act much quicker than federal or state research funding mechanisms,” Dr. Jiang said.
Have you ever Googled for an online diagnosis before visiting a doctor? If so, you may have helped provide early warning of an infectious disease epidemic.
In a new study published in Lancet Infectious Diseases, Internet-based surveillance has been found to detect infectious diseases such as Dengue Fever and Influenza up to two weeks earlier than traditional surveillance methods, according to Queensland University of Technology (QUT) research fellow and senior author of the paper Wenbiao Hu.
Hu, based at the Institute for Health and Biomedical Innovation, said there was often a lag time of two weeks before traditional surveillance methods could detect an emerging infectious disease.
“This is because traditional surveillance relies on the patient recognizing the symptoms and seeking treatment before diagnosis, along with the time taken for health professionals to alert authorities through their health networks. In contrast, digital surveillance can provide real-time detection of epidemics.”
Hu said the study used search engine algorithms such as Google Trends and Google Insights. It found that detecting the 2005–06 avian influenza outbreak “Bird Flu” would have been possible between one and two weeks earlier than official surveillance reports.
“In another example, a digital data collection network was found to be able to detect the SARS outbreak more than two months before the first publications by the World Health Organization (WHO),” Hu said.
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Scientists in Barcelona have today launched GENIGMA, a videogame that enlists players to solve puzzles while generating real-world scientific data that can detect alterations in genomic sequences and ultimately advance breast cancer research. The game, out today on iOS and Android and available in English, Spanish, Catalan and Italian, is the result of a two-and-a-half-year long citizen science project developed by a team of researchers at the Centre for Genomic Regulation (CRG), the Centro Nacional de Análisis Genómico (CNAG-CRG) and game professionals. The game was created to boost worldwide research efforts that depend on cancer cell lines, a critical resource used by scientists to study cancer and test new drugs to treat the disease. One of the limitations of cancer cell lines are a lack of high-resolution genome reference maps, which are necessary to help researchers interpret their scientific results, for example pinpointing the location of genes of therapeutic interest or potential mutation sites. "Cell lines are responsible for the discovery of vaccines, chemotherapies for cancer or IVF for infertility. This makes them a pillar of modern biology," explains ICREA Research Professor Marc A. Marti-Renom, with dual affiliation at the CRG and CNAG-CRG and whose research underpins GENIGMA. "However, the lack of genome reference maps limits current scientific progress. It's like asking people to navigate modern cities using maps from the past. With the help of other people, we can update these maps, which will allow us to make fast progress in breast cancer research." Professor Marti-Renom's research group has developed methods to create genomic reference maps by visualising the genome in three-dimensional space. However, this requires significant time and resources to train artificial intelligence, as well as vast computational power. The researchers launched GENIGMA because they believe that data generated by players could be a more effective method of updating the reference maps compared to using AI alone. The 'herd intelligence' of players can also provide creative solutions in ways that AI might not be able to. To play GENIGMA, players have to solve a puzzle involving a string of blocks of different colours and shapes. Each string represents a genetic sequence in the cancer cell line, and how players organise the blocks is a potential solution to the location of genes. Players have to reorganise the blocks so that they attain the highest-score possible. The higher the number of players and high scores, the higher likelihood that researchers have found the correct sequence for this particular location in the reference map. "Anyone with a smartphone from anywhere in the world can download GENIGMA for free and make a direct contribution to research, lending their logic and dexterity to the service of science," says Elisabetta Broglio, citizen science facilitator at the CRG. "GENIGMA will analyze the solutions provided by the players as a collective and not as individuals, and will take advantage of creative solutions impossible to find with deterministic algorithms."
Japanese researchers have turned to the world's fastest supercomputer to help test the effectiveness of different COVID-19 mitigation tactics. The Tokyo-based scientific research institute Riken devised a series of complex simulations to observe how SARS-CoV-2 might spread indoors, and created a list of suggestions to limit transmission based on those simulations. The researchers enlisted Fugaku, the $1billion supercomputer that was crowned the world's fastest supercomputer in June, to conduct the simulations. Fugaku features 158,976 custom ARM processors, each of which have 48 CPU cores, and which can collectively process 415.5 petaflops a second, almost three times as much as the previous fastest computer in the world. The team used Fugaku to simulate how air particles that an infected person might cough out would travel through the air in an enclosed space, according to a report from The Asahi Shimbun.Using a base assumption that every cough releases around 50,000 particles that range in size between one micrometer and several hundred micrometers, they simulated the likelihood of another person inhaling one of those particles in a variety of different circumstances. The team, led by Makoto Tsuobokura, concluded that one of the effective solutions was also one of the cheapest and involved simply opening windows. According to a simulation of commuter train car, just opening the windows increased ventilation by between two and three times and substantially lowered the amount of microbes in the air. The simulation also showed that limiting the number of passengers in each car would lower transmission even further. For hospitals, the simulation suggested the optimal solution would be floor to ceiling curtains that completely enclose potentially infected patients.
Using a free computer game called Foldit, researchers are enlisting the help of citizen scientists to design drugs that could stop the novel coronavirus from infecting human cells. HHMI scientists are joining many of their colleagues worldwide in working to combat the new coronavirus. They’re developing diagnostic testing, understanding the virus’s basic biology, modeling the epidemiology, and developing potential therapies or vaccines. Over the next several weeks, we will be sharing stories of some of this work. The Foldit team is running calculations on coronavirus-blocking protein designs (one shown) submitted by players, to get a better sense for how the proteins might work out in real life. The best thing most of us can do to fight COVID-19 is stay home, to curb the respiratory disease’s spread. But anyone with a computer can pitch in more actively too: by helping scientists design drugs to combat the virus now responsible for hundreds of thousands of infections worldwide. Howard Hughes Medical Institute Investigator David Baker creates new proteins tailor-made for specific tasks. Now, his team at the University of Washington is hunting for proteins that might stop the novel coronavirus (called SARS-CoV-2) from infecting human cells – and they’re enlisting citizen scientists to help via a free computer game called Foldit. Foldit players around the world compete to solve protein puzzles digitally, designing molecules with certain specs. Foldit players have already come up with more than 20,000 different designs for potential COVID-19 antiviral proteins. Scientists plan to test 99 of the most promising designs (shown here) in the lab.Scientists are now challenging players to design proteins that can glom onto the new coronavirus and block its entry into cells. Such proteins could potentially be developed into antiviral drugs, to lessen the severity of patients’ symptoms. “We find that the creativity of crowdsourcing is really, really useful — if you ask 100 people to do something, they'll do it in 100 different ways. That’s really valuable for us in protein design problems,” says Brian Koepnick, a research scientist in Baker’s lab who helps run Foldit.
Clinicians use the Watson computer to mine cancer patients' records and provide treatment advice in Thailand, India, and soon, China. Watson for Oncology analyzes patient medical records, along with millions of pages of articles and textbooks, to make treatment recommendations. In 2011, a supercomputer won $1 million on Jeopardy! In 2016, that same supercomputer is tackling a challenge quantified not in millions of dollars but in millions of cancer patients. The goal is to use Watson’s natural language processing to mine the medical literature and a patient’s records to provide treatment advice. And this month the Watson computer system is drastically expanding its reach — from one hospital in Thailand to six in India and a planned 21 more in China. This instantiation of Watson, dubbed Watson for Oncology, is an artificial intelligence system that has access to millions of pages of medical textbooks and journal articles. Oncologists at Memorial Sloan Kettering Cancer Center trained the system to provide appropriate treatment recommendations by giving it descriptions of patients and then telling Watson the correct treatment.
Pharmaceutical companies typically develop new drugs with thousands of staff and budgets that run into the billions of dollars. One estimate puts the cost of bringing a new drug to market at $2.6 billion with others suggesting that it could be double that cost at $5 billion. One man, Professor Atul Butte director of the University of California Institute of Computational Health Sciences, believes that like other Silicon Valley startups, almost anyone can bring a drug to market from their garage with just a computer, the internet, and freely available data. In a talk given at the Science on the Swan conference held in Perth this week, Professor Butte outlined the process for an audience of local and international scientists and medics. The starting point is the genetic data from thousands of studies on humans, mice and other animals, that is now freely available on sites from the National Institute of Health and the European Molecular Biology Laboratory. The proliferation of genetic data from experiments has been driven by the ever decreasing cost of sequencing genetic information using gene chip technologies. Professor Butte, students, and research staff have found a range of different ways of using this data to look for new drugs. In one approach, they have constructed a map of how the genetic profiles of people with particular diseases are related to each other. In particular, to look for diseases with very similar genetic profiles. Having done that, they noticed that the genetic profile of people with heart conditions were very closely related to that of the much rarer condition of muscular dystrophy. What this potentially suggested was that drugs that work for one condition could potentially work in the other. This process of discovering other uses of drugs, called “drug repositioning”, is not new. Drugs like Viagra were originally used for treatment of cardiovascular conditions. The difference is that Viagra’s repositioned use resulted from the observation of side-effects in patients taking the drug for its original intended purpose. Professor Butte on the other hand is using “Big Data” and computers to show that given the close relationship in the genetic profile of two diseases, the potential cross-over effect of drugs working for one condition working in another. Still in the garage, the next step from discovering a potential drug is to test if it actually works in an experimental setting on animals. Here again, Professor Butte has turned to the internet and sites like Assay Depot. This is a site, structured like Amazon, from which a researcher can order an experiment to be carried out to test a drug on a range of animal models. It is literally a case of choosing the experiment type you want, adding it to a shopping cart, paying by credit card and getting the experimental results mailed back in a few weeks time. “Shoppers” are given the choice of laboratory they want to use, including a choice of which country the lab is based. Once a new use for a drug has been shown to work in an animal model, the next step would be to test the drug in humans, get approval for the use of the drug for that condition and then finally take the drug to market.
In by far the majority of cancer cases, the doctor can quickly identify the source of the disease, for example cancer of the liver, lungs, etc. However, in about one in 20 cases, the doctor can confirm that the patient has cancer -- but cannot find the source. These patients then face the prospect of a long wait with numerous diagnostic tests and attempts to locate the origin of the cancer before starting any treatment.
Now, researchers at DTU Systems Biology have combined genetics with computer science and created a new diagnostic technology based on advanced self-learning computer algorithms which -- on the basis of a biopsy from a metastasis -- can with 85 per cent certainty identify the source of the disease and thus target treatment and, ultimately, improve the prognosis for the patient.
Each year, about 35,000 people are diagnosed with cancer in Denmark, and many of them face the prospect of a long wait until the cancer has been diagnosed and its source located. However, even after very extensive tests, there will still be 2-3 per cent of patients where it has not been possible to find the origin of the cancer. In such cases, the patient will be treated with a cocktail of chemotherapy instead of a more appropriately targeted treatment, which could be more effective and gentler on the patient.
The newly developed method, which researchers are calling TumorTracer, are based on analyses of DNA mutations in cancer tissue samples from patients with metastasized cancer, i.e. cancer which has spread. The pattern of mutations is analyzed in a computer program which has been trained to find possible primary tumor localizations. The method has been tested on many thousands of samples where the primary tumour was already identified, and it has proven extremely precise. The next step will be to test the method on patients with unknown primary tumours. In recent years, researchers have discovered several ways of using genome sequencing of tumours to predict whether an individual cancer patient will benefit from a specific type of medicine.
This is a very effective method, and it is becoming increasingly common to conduct such sequencing for cancer patients. Associate Professor Aron Eklund from DTU Systems Biology explains: "We are very pleased that we can now use the same sequencing data together with our new algorithms to provide a much faster diagnosis for cancer cases that are difficult to diagnose, and to provide a useful diagnosis in cases which are currently impossible to diagnose. At the moment, it takes researchers two days to obtain a biopsy result, but we expect this time to be reduced as it becomes possible to do the sequencing increasingly faster. And it will be straightforward to integrate the method with the methods already being used by doctors."
HIV spreads throughout the body in a similar way to some computer worms, according to a new model, which also suggests that early treatment is key to finding a cure to the disease.
HIV specialists and network security experts at University College London (UCL) have found that HIV progresses both via the bloodstream and directly between cells – akin to computer worms spreading themselves through two routes to infect as many computers as possible.
Prof Benny Chain, from UCL’s infection and immunity division, the co-senior author of the research, said: “I was involved in a study looking in general at spreading of worms across the internet and then I realised the parallel. They have to consistently find another computer to infect outside. They can either look locally in their own networks, their own computers, or you could remotely transmit out a worm to every computer on the internet. HIV also uses two ways of spreading within the body.”
The model was inspired by similarities between HIV and computer worms such as the highly damaging “Conficker” worm, first detected in 2008, which has infected military and police networks across Europe and is still active today.
The researchers’ findings, published on Thursday, told them that just as computer worms spread most efficiently by a combination of two routes, so must HIV – enabling the researchers to create a model for this “hybrid spreading”, which accurately predicted patients’ progression from HIV to Aids.
Detailed sample data from 17 HIV patients from London were used to verify the model, suggesting that hybrid spreading provides the best explanation for HIV progression and highlighting the benefits of very prompt treatment.
Chain said the model provided strong evidence of cell-to-cell spread, which he said some HIV scientists remained sceptical about, as it is difficult to observe in human beingsbecause it occurs in tissue.
Scientists are using previously top-secret technology to zoom through the human body down to the level of a single cell. Scientists are also using cutting-edge microtome and MRI technology to examine how movement and weight bearing affects the movement of molecules within joints, exploring the relationship between blood, bone, lymphatics and muscle.
UNSW biomedical engineer Melissa Knothe Tate is using previously top-secret semiconductor technology to zoom through organs of the human body, down to the level of a single cell.
A world-first UNSW collaboration that uses previously top-secret technology to zoom through the human body down to the level of a single cell could be a game-changer for medicine, an international research conference in the United States has been told.
The imaging technology, developed by high-tech German optical and industrial measurement manufacturer Zeiss, was originally developed to scan silicon wafers for defects.
UNSW Professor Melissa Knothe Tate, the Paul Trainor Chair of Biomedical Engineering, is leading the project, which is using semiconductor technology to explore osteoporosis and osteoarthritis.
Using Google algorithms, Professor Knothe Tate -- an engineer and expert in cell biology and regenerative medicine -- is able to zoom in and out from the scale of the whole joint down to the cellular level "just as you would with Google Maps," reducing to "a matter of weeks analyses that once took 25 years to complete."
Her team is also using cutting-edge microtome and MRI technology to examine how movement and weight bearing affects the movement of molecules within joints, exploring the relationship between blood, bone, lymphatics and muscle. "For the first time we have the ability to go from the whole body down to how the cells are getting their nutrition and how this is all connected," said Professor Knothe Tate. "This could open the door to as yet unknown new therapies and preventions."
Professor Knothe Tate is the first to use the system in humans. She has forged a pioneering partnership with the US-based Cleveland Clinic, Brown and Stanford Universities, as well as Zeiss and Google to help crunch terabytes of data gathered from human hip studies. Similar research is underway at Harvard University and Heidelberg in Germany to map neural pathways and connections in the brains of mice.
The above story is based on materials provided by University of New South Wales.
Some of the world’s most brilliant minds are working as data scientists at places like Google, Facebook, and Twitter—analyzing the enormous troves of online information generated by these tech giants—and for hacker and entrepreneur Jeremy Howard, that’s a bit depressing. Howard, a data scientist himself, spent a few years as the president of the Kaggle, a kind of online data scientist community that sought to feed the growing thirst for information analysis. He came to realize that while many of Kaggle’s online data analysis competitions helped scientists make new breakthroughs, the potential of these new techniques wasn’t being fully realized. “Data science is a very sexy job at the moment,” he says. “But when I look at what a lot of data scientists are actually doing, the vast majority of work out there is on product recommendations and advertising technology and so forth.”
So, after leaving Kaggle last year, Howard decided he would find a better use for data science. Eventually, he settled on medicine. And he even did a kind of end run around the data scientists, leveraging not so much the power of the human brain but the rapidly evolving talents of artificial brains. His new company is called Enlitic, and it wants to use state-of-the-art machine learning algorithms—what’s known as “deep learning”—to diagnosis illness and disease.
Publicly revealed for the first time today, the project is only just getting off the ground—“the big opportunities are going to take years to develop,” Howard says—but it’s yet another step forward for deep learning, a form of artificial intelligence that more closely mimics the way our brains work. Facebook is exploring deep learning as a way of recognizing faces in photos. Google uses it for image tagging and voice recognition. Microsoft does real-time translation in Skype. And the list goes on.
But Howard hopes to use deep learning for something more meaningful. His basic idea is to create a system akin to the Star Trek Tricorder, though perhaps not as portable. Enlitic will gather data about a particular patient—from medical images to lab test results to doctors’ notes—and its deep learning algorithms will analyze this data in an effort to reach a diagnosis and suggest treatments. The point, Howard says, isn’t to replace doctors, but to give them the tools they need to work more effectively. With this in mind, the company will share its algorithms with clinics, hospitals, and other medical outfits, hoping they can help refine its techniques. Howard says that the health care industry has been slow to pick-up on the deep-learning trend because it was rather expensive to build the computing clusters needed to run deep learning algorithms. But that’s changing.
The real challenge, Howard says, isn’t writing algorithms but getting enough data to train those algorithms. He says Enlitic is working with a number of organizations that specialize in gathering anonymized medical data for this type of research, but he declines to reveal the names of the organizations he’s working with. And while he’s tight-lipped about the company’s technique now, he says that much of the work the company does will eventually be published in research papers.
Earlier this month, Medscape published the results of their recent survey (here) which asked 18,575 physicians across 25 specialties to rate their Electronic Health Record (EHR) system. For overall satisfaction, the #1 ranked EHR solution was the VA’s Computerized Patient Record System ‒ also known as VistA. It was built using open‒source software and is therefore license free.
There’s also a publicly available version of VistA called OpenVista and several companies leverage a services-only business model for larger OpenVista installations. For smaller installations, a YouTube video (here) suggests the free OpenVista software can be installed in about 10 minutes ‒ bring your own hardware.
Of course free software licensing doesn’t make the hardware, installation or maintenance free, but the lack of any software licensing fees at all does reduce the overall cost ‒ especially for large installations ‒ and that can typically save millions of dollars.
Open-source software also charts a much different course for design changes that are not dependent on the resources, budgets (or revenue requirements) of independent software vendors (ISV’s).
In many ways, VistA’s top rating is no surprise because it’s the only EHR installation in the U.S. with a truly national footprint. As a single software solution, VistA is designed to support almost 9 million vets through about 1,700 different care sites around the country.
Could software reveal whether Abraham Lincoln had Marfan syndrome? Doctors faced with the tricky task of spotting rare genetic diseases in children may soon be asking parents to email their family photos. A computer program can now learn to identify rare conditions by analysing a face from an ordinary digital photograph. It should even be able to identify unknown genetic disorders if groups of photos in its database share specific facial features.
Rare genetic disorders are thought to affect 6 per cent of people. Genetic tests exist for the more common conditions such as Down's syndrome, but many people with the rarer disorders never get a proper clinical diagnosis. Genetic tests aren't available for many conditions because the gene variants that cause them haven't been identified. This means doctors often have to rely on the pronounced facial features that occur in between 30 and 40 per cent of rare disorders to make a diagnosis, but few people are trained to recognise them.
"Clinicians skilled in the use of facial features to support diagnosis are few and far between," says Alastair Kent, director of the charity Genetic Alliance UK. "As a result, families frequently experience long delays – years rather than months – before they receive a diagnosis for their child."
The software developed by Christoffer Nellåker and Andrew Zisserman of the University of Oxford and their colleagues should help family doctors or general paediatricians make a preliminary diagnosis. "The idea is to offer it to health systems right across the world because all you need is a computer and a digital photo," says Nellåker.
To train the system, Nellåker's team fed a computer vision algorithm 1363 publicly available pictures of people with eight genetic disorders, including Down's syndrome, fragile X syndrome and progeria (fourth, fifth and sixth in the graphic below). The computer learned to identify each condition from a pattern of 36 facial features in each shot, such as the shapes of eyes, brows, lips and noses.
"It automatically analyses the picture and annotates key feature points, producing from that a description of the face which expands the features that are important for distinctiveness," Nellåker says. These features are then compared with those from pictures of patients with confirmed disorders, allowing the system to suggest and rank predictions for new patients.
To show that it works, the team analysed photos of people with known genetic disorders. The accuracy of the software increases with the number of photos of a specific disorder it learns from. For the eight training diseases, for example, each disorder was represented by between 100 and 283 images. On average, this resulted in 93 per cent of the predictions being correct.
A new jet-lag mobile app called Entrain released by University of Michigan mathematicians reveals previously unknown shortcuts that can help travelers entrain (synchronize) their circadian rhythms to new time zones as efficiently as possible.
Entrain is built around the premise that light, particularly from the sun and in wavelengths that appear to our eyes as the color blue, is the strongest signal to regulate circadian rhythms. These fluctuations in behaviors and bodily functions, tied to the planet’s 24-hour day, do more than guide us to eat and sleep. They govern processes in each one of our cells.
The study, published April 10, 2014, in Public Library of Science Computational Biology (open access journal), relies on two leading mathematical models that have been shown to accurately describe human circadian rhythms. The researchers used these equations and a technique called optimal control theory to calculate ideal adjustment schedules for more than 1,000 possible trips.
The app gives users access to these schedules. Start by entering your typical hours of light and darkness in your current time zone, then choose the time zone you’re traveling to and when, as well as the brightest light you expect to spend the most time in during your trip (indoor or outdoor.) The app offers a specialized plan and predicts how long it will you take to adjust.
The shortcuts the app offers are custom schedules of light and darkness depending on the itinerary. The schedules boil down to one block of time each day when you should seek the brightest light possible and another when you should put yourself in the dark, or at least in dim light. You don’t even have to be asleep.
If you must go outside, you can wear pink-tinted glasses to block blue wavelength light, the researchers say. And if the app prescribes “bright outdoor light” in the middle of the night, a therapeutic lightbox can do the job — yes, its shortcuts sometimes require odd hours.
The Entrain app is available now as a free app in the Apple store.
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