A Robohand is a customized, fitted set of mechanical fingers that open and close to grasp things based on the motion of the wrist. When the wrist folds and contracts, the cables attaching the fingers to the base structure cause the fingers to curl. Nearly all the parts of a Robohand are 3D printed on MakerBot Replicator 2 Desktop 3D printers.
Harvard researchers are developing a device that could be used to rapidly remove pathogens from the blood of patients with sepsis. The dialysis-like machine acts as an artificial spleen, filtering the blood using injectable magnetic nanobeads engineered to stick to microorganisms and toxins. After the beads are injected, blood is removed and run through a device that uses a magnetic-field gradient to extract the nanobead-bound germs. The blood is then returned to the body. The team at the Wyss Institute for Biologically Inspired Engineering hopes that the device will be able to identify the specific microorganism causing the patient’s blood infection. This could help physicians more quickly determine the most effective antibiotic treatment.
Canadian and German neuroscientists have unveiled the most detailed 3-D image of the human brain to date. It reveals structures as tiny as 20 microns, 50 times smaller than those created using the best MRI technology. The image, created as part of a project called the BigBrain, is part of a larger effort to create a high-resolution computer model of the human brain that can serve as a reference point for future studies. Data from other studies can be combined with this model to allow scientists to link brain function to specific groups of nerve cells. The information can be used to test theories about brain activity and lead to treatments for diseases. Until now, brain scans used MRI and PET technology, which can only capture structures as small as a millimeter. This had not been enough to understand what happens when a person gets Alzheimer’s disease or epilepsy.
In a new paper, Stanford’s Andrew Ng describes how to use graphics microprocessors to build a $20,000 computerized brain that is similar to the cat-detector he developed with Google last year for $1M.
To test his hypothesis about GPU-driven Deep Learning, he also built a larger version of the platform for $100,000. It utilized 64 Nvidia GTX 680 GPUs on 16 computers. It was able to accomplish the same cat-spotting tasks as the Google system, which needed 1,000 computers to operate. That system, modeling the activities and processes of the human brain, was able to learn what a cat looks like, and then translate that knowledge into spotting different cats across multiple YouTube videos.
Vigi’Fall detects falls using multidimensional contextual analysis. It is a miniature accelerometric box attached to the chest with an adhesive patch. Motion sensors are placed in several rooms of the home and doubt-removal software is placed in a home box. The system is linked to a remote call center which contacts rescue teams in the event of a fall. The next phase of the product will incorporate heartbeat monitoring.
The New York Times reports on the growing trend of sensor based protection/early warning systems for athletes. The devices, packed with sensors and microprocessors, register a blow to a player’s skull and immediately signal the news by blinking brightly, or by sending a wireless alert. Algorithms evaluate the impact and determine severity.
Professor David Vaillancourt of the University of Florida believes that a diffusion tensor imaging technique could allow clinicians to assess movement disorders earlier, leading to improved treatment interventions and therapies.
Movement disorders such as Parkinson’s disease, essential tremor, multiple system atrophy and progressive supranuclear palsy exhibit similar symptoms in the early stages, which can make it challenging to assign a specific diagnosis. Often, the original diagnosis changes as the disease progresses.
Diffusion tensor imaging, known as DTI, is a non-invasive method that examines the diffusion of water molecules within the brain and can identify key areas that have been affected as a result of damage to gray matter and white matter in the brain. Vaillancourt and his team measured areas of the basal ganglia and cerebellum in individuals, and used a statistical approach to predict group classification. By asking different questions within the data and comparing different groups to one another, they were able to show distinct separation among disorders.
In an NIH funded clinical trial, researchers at Emory University have discovered that specific patterns of brain activity may indicate whether a depressed patient will or will not respond to treatment with medication or psychotherapy.
Professor Helen Mayberg, MD and colleagues used PET scans to measure brain glucose metabolism, an important index of brain functioning to test this hypothesis. Participants in the trial were randomly assigned to receive a 12-week course of either the SSRI medication escitalopram or cognitive behavior therapy after first undergoing a pretreatment PET scan. The team found that activity in one particular region of the brain, the anterior insula, could discriminate patients who recovered from those who were non-responders to the treatment assigned. Specifically, patients with low activity in the insula showed remission with CBT, but poor response to medication; patients with high activity in the insula did well with medication, and poorly with CBT.
A study from the Monell Center and the University of Pennsylvania suggests that non-invasive odor analysis may be a valuable technique in the detection and early diagnosis of human melanoma. The researchers used sophisticated sampling and analytical techniques to identify VOCs from melanoma cells at three stages of the disease, as well as from normal melanocytes. All the cells were grown in culture. They then examined VOCs from normal melanocytes and melanoma cells using a sensor constructed of nano-sized carbon tubes coated with strands of DNA and bioengineered to recognize a wide variety of targets, including specific odor molecules.
A team led by Professor A. T. Charlie Johnson of the University of Pennsylvania has developed a biosensor from carbon-nanotube transistors that can rapidly detect the antigens of Lyme disease. The device can detect the biomarkers at concentrations as low as 1 ng/ml.
The group’s work is a continuation of similar strategies to detect prostate cancer biomarkers using CNTs. The researchers hope to one day be able to detect any disease with such nanotube devices simply by coating them with the appropriate proteins.