Someday, a smart bandage could heal chronic wounds or battlefield injuries.
Researchers from the University of Nebraska-Lincoln, Harvard Medical School and MIT have designed a smart bandage that could eventually heal chronic wounds or battlefield injuries with every fibre of its being.
The bandage consists of electrically conductive fibres coated in a gel that can be individually loaded with infection-fighting antibiotics, tissue-regenerating growth factors, painkillers or other medications.
A microcontroller no larger than a postage stamp, which could be triggered by a smartphone or other wireless device, sends small amounts of voltage through a chosen fibre. That voltage heats the fibre and its hydrogel, releasing whatever cargo it contains.
A single bandage could accommodate multiple medications tailored to a specific type of wound, the researchers said, while offering the ability to precisely control the dose and delivery schedule of those medications. That combination of customization and control could substantially improve or accelerate the healing process, said Ali Tamayol, assistant professor of mechanical and materials engineering at Nebraska.
“This is the first bandage that is capable of dose-dependent drug release,” Tamayol said. “You can release multiple drugs with different release profiles. That’s a big advantage in comparison with other systems. What we did here was come up with a strategy for building a bandage from the bottom up.
“This is a platform that can be applied to many different areas of biomedical engineering and medicine.”
The team envisions its smart bandage being used initially to treat chronic skin wounds that stem from diabetes. More than 25 million Americans – and more than 25 percent of U.S. adults 65 and older – could suffer from such wounds. The Centers for Disease Control and Prevention has estimated that diabetes cases will double or triple by the year 2050.
Those wounded in combat might also benefit from the bandage’s versatility and customizability, Tamayol said, whether to stimulate faster healing of bullet and shrapnel wounds or prevent the onset of infection in remote environments.
“Soldiers on the battlefield may be suffering from a number of different injuries or infections,” he said. “They might be dealing with a number of different pathogens. Imagine that you have a variable patch that has antidotes or drugs targeted toward specific hazards in the environment.”
Existing bandages range from basic dry patches to more advanced designs that can passively release an embedded medication over time. To evaluate the potential advantages of their smart bandage, Tamayol and his colleagues at Harvard ran a series of experiments.
In one, the researchers applied a smart bandage loaded with growth factor to wounded mice. When compared with a dry bandage, the team’s version regrew three times as much of the blood-rich tissue critical to the healing process.
Another experiment showed that an antibiotic-loaded version of the bandage could eradicate infection-causing bacteria. Collectively, Tamayol said, the experiments also demonstrated that the heat needed to release the medications did not affect their potency.
Though the researchers have patented their design, it will need to undergo further animal and then human testing before going to market. That could take several years, though the fact that most of the design’s components are already approved by the Food and Drug Administration should streamline the process, Tamayol said.

University of Nebraska-Lincoln
news.unl.edu/newsrooms/today/article/smart-bandage-could-promote-better-faster-healing/

The heart is capable of terminating arrhythmias itself after local gene therapy, potentially avoiding the need for patients to undergo painful electric shocks, according to a proof-of-concept study.
Atrial fibrillation is the most common heart rhythm disorder (arrhythmia). Treatment aims to restore the heart’s normal rhythm and includes drugs, which are not effective in all patients, ablation, for which efficiency remains suboptimal in the long-term, and electric shocks, which are effective but painful and require hospitalization. This leaves a large and growing group of patients without optimal treatment options.
That is why study author Dr Emile Nyns, a physician and PhD candidate in the laboratory of Daniël Pijnappels at the Leiden University Medical Centre, Leiden, the Netherlands, took a completely different approach. He said: “As the heart itself is already electrically active, we tested whether and how it could generate the electrical current needed for arrhythmia termination.”
The researchers used a technique called optogenetics, which uses light to control functioning of cells that have been genetically modified to express light-sensitive ion channels.
First they genetically modified the right atrium in eight adult rats using a process called gene painting, which involves a small thoracic incision and actually painting the atrium with vectors coding for these ion channels.
The researchers waited four to six weeks for the light-sensitive ion channels to be expressed, then made a small incision in the thorax of each rat and induced atrial fibrillation. Next they shone a light on the atrium for one second. This terminated 94% of atrial fibrillation.
Dr Nyns said: “Shining light on the atrium opened the light-sensitive ion channels. This led to depolarization of the atrium, which terminated atrial fibrillation and restored the heart’s normal rhythm. We only needed a single light pulse of one second to terminate nearly all arrhythmias.
“The heart itself generated the electrical current needed to stop the arrhythmias,” he continued. “It is completely pain free, unlike electric shocks.”
He said: “Our study provides proof-of-concept that the heart can be enabled to terminate atrial fibrillation by itself after optogenetic gene therapy.”
In future Dr Nyns envisages that the technique could be used in atrial fibrillation patients together with an implantable light-emitting diode (LED) device. “The result would be continuous, ambulatory and pain-free maintenance of the heart’s normal rhythm, something that cannot be achieved today,” he said. “The quality of life and prognosis of AF patients could be significantly increased, especially for patients with frequent episodes of drug refractory, symptomatic atrial fibrillation, despite ablation therapy.”
The researchers did not observe adverse effects from the method, but Dr Nyns said: “Further research is certainly needed before this technique can be used in patients. However, the results are promising and we believe that the time has come to develop the next generation of therapy for cardiac arrhythmias, which do not rely on pills or electronics, but on biology instead.”

ScienceDailyhttps://tinyurl.com/yc4atkg8

The ability to quantify the extent of kidney damage and predict the life remaining in the kidney, using an image obtained at the time when a patient visits the hospital for a kidney biopsy, now is possible using a computer model based on artificial intelligence (AI).
The findings can help make predictions at the point-of-care and assist clinical decision-making.
Nephropathology is a specialization that analyses kidney biopsy images. While large clinical centres in the U.S. might greatly benefit from having ‘in-house’ nephropathologists, this is not the case in most parts of the country or around the world.
According to the researchers, the application of machine learning frameworks, such as convolutional neural networks (CNN) for object recognition tasks, is proving to be valuable for classification of diseases as well as reliable for the analysis of radiology images including malignancies.
To test the feasibility of applying this technology to the analysis of routinely-obtained kidney biopsies, the researchers performed a proof of principle study on kidney biopsy sections with various amounts of kidney fibrosis (also commonly known as scarring of tissue). The machine learning framework based on CNN relied on pixel density of digitized images, while the severity of disease was determined by several clinical laboratory measures and renal survival. CNN model performance then was compared with that of the models generated using the amount of fibrosis reported by a nephropathologist as the sole input and corresponding lab measures and renal survival as the outputs. For all scenarios, CNN models outperformed the other models.
“While the trained eyes of expert pathologists are able to gauge the severity of disease and detect nuances of kidney damage with remarkable accuracy, such expertise is not available in all locations, especially at a global level. Moreover, there is an urgent need to standardize the quantification of kidney disease severity such that the efficacy of therapies established in clinical trials can be applied to treat patients with equally severe disease in routine practice,” explained corresponding author Vijaya B. Kolachalama, PhD, assistant professor of medicine at Boston University School of Medicine. “When implemented in the clinical setting, our work will allow pathologists to see things early and obtain insights that were not previously available,” said Kolachalama.
The researchers believe their model has both diagnostic and prognostic applications and may lead to the development of a software application for diagnosing kidney disease and predicting kidney survival. “If healthcare providers around the world can have the ability to classify kidney biopsy images with the accuracy of a nephropathologist right at the point-of-care, then this can significantly impact renal practice. In essence, our model has the potential to act as a surrogate nephropathologist, especially in resource-limited settings,” said Kolachalama.
Boston University School of Medicinehttps://tinyurl.com/y7p83anb

NUST MISIS scientists jointly with their colleagues from the Ecole de Technologie Superiore (Montreal, Canada) have experienced a new combination of alloy processing that produces solid and durable implants that are fully compatible with the human body.
The authors sought to develop an industrial technology for the production of metal rod stocks which are used in the production of modern bone implants, and in particular, for treatment of spinal problems.
This new generation of alloys made on the basis of Ti-Zr-Nb (titanium-zirconium-niobium) which possesses a high functional complex and so-called “superelasticity” (able to restore the original shape against large and repeated deformation) are the working material.
According to scientists, these alloys are the most promising class of metallic biomaterials. This is due to the unique combination of their biochemical and biomechanical properties: Ti-Zr-Nb differs from the complete biocompatibility of composition and high corrosion resistance, while at the same time exhibiting hyperelastic behaviour — very similar to “normal” bone behaviour.
“Our method of combined thermomechanical processing of alloys — in particular, radial-displacement rolling and rotary forging — allows researchers to get the highest quality blanks for biocompatible implants by controlling their structure and properties. Such processing of blanks gives them an outstanding resistance to fatigue and overall functional stability”, said Vadim Sheremetyev, one of research authors, and a senior research associate at NUST MISIS.
According to him, the high-quality rod stocks have already found a potential customer. A large Russian manufacturer of medical products made of titanium is an industrial partner of NUST MISIS`s project. Together with them, scientists are now developing a technology to obtain beams for spinal transpedicular fixation, which should improve the therapy quality in severe cases of scoliosis.
Additionally, scientists are now aimed at developing the thermomechanical processing and optimizing technology modes to obtain materials of the necessary form and sizes with the best complexity of properties.
NUST MISISen.misis.ru/university/news/science/2018-03/5281/

Before performing radiation therapy, radiation oncologists first carefully review medical images of the patient to identify the gross tumour volume, the observable portion of the disease. They then design patient-specific clinical target volumes that include surrounding tissues, since these regions can hide cancerous cells and provide pathways for metastasis.
Known as contouring, this process establishes how much radiation a patient will receive and how it will be delivered. In the case of head and neck cancer, this is a particularly sensitive task due to the presence of vulnerable tissues in the vicinity.
Though it may sound straightforward, contouring clinical target volumes is quite subjective. A recent study from Utrecht University found wide variability in how trained physicians contoured the same patient’s computed tomography (CT) scan, leading some doctors to suggest high-risk clinical target volumes eight times larger than their colleagues.
This inter-physician variability is a problem for patients, who may be over- or under-dosed based on the doctor they work with. It is also a problem for determining best practices, so standards of care can emerge.
Recently, Carlos Cardenas, a graduate research assistant and PhD candidate at The University of Texas MD Anderson Cancer Center in Houston, Texas, and a team of researchers at MD Anderson, working under the supervision of Laurence Court with support from the National Institutes of Health, developed a new method for automating the contouring of high-risk clinical target volumes using artificial intelligence and deep neural networks.
Cardenas’ work focuses on translating a physician’s decision-making process into a computer program. "We have a lot of clinical data and radiation therapy treatment plan data at MD Anderson," he said. "If we think about the problem in a smart way, we can replicate the patterns that our physicians are using to treat specific types of tumours."
In their study, they analysed data from 52 oropharyngeal cancer patients who had been treated at MD Anderson between January 2006 to August 2010, and had previously had their gross tumour volumes and clinical tumour volumes contoured for their radiation therapy treatment.
Cardenas spent a lot of time observing the radiation oncology team at MD Anderson, which has one of the few teams of head and neck subspecialist oncologists in the world, trying to determine how they define the targets.
"For high-risk target volumes, a lot of times radiation oncologists use the existing gross tumour disease and apply a non-uniform distance margin based on the shape of the tumour and its adjacent tissues," Cardenas said. "We started by investigating this first, using simple distance vectors."
Cardenas began the project in 2015 and had quickly accumulated an unwieldy amount of data to analyse. He turned to deep learning as a way of mining that data and uncovering the unwritten rules guiding the experts’ decisions.
The deep learning algorithm he developed uses auto-encoders — a form of neural networks that can learn how to represent datasets — to identify and recreate physician contouring patterns.
The model uses the gross tumour volume and distance map information from surrounding anatomic structures as its inputs. It then classifies the data to identify voxels — three-dimensional pixels — that are part of the high-risk clinical target volumes. In oropharyngeal cancer cases, the head and neck are usually treated with different volumes for high, low and intermediate risk. The paper described automating the target for the high-risk areas. Additional forthcoming papers will describe the low and intermediate predictions.
In addition to potentially reducing inter-physician variability and allowing comparisons of outcomes in clinical trials, a tertiary advantage of the method is the speed and efficiency it offers. It takes a radiation oncologist two to four hours to determine clinical target volumes. At MD Anderson, this result is then peer reviewed by additional physicians to minimize the risk of missing the disease.
Using the Maverick supercomputer at the Texas Advanced Computing Center (TACC), they were able to produce clinical target volumes in under a minute. Training the system took the longest amount of time, but for that step too, TACC resources helped speed up the research significantly.
"If we were to do it on our local GPU [graphics processing unit], it would have taken two months," Cardenas said. "But we were able to parallelize the process and do the optimization on each patient by sending those paths to TACC and that’s where we found a lot of advantages by using the TACC system."

Texas Advanced Computing Centerwww.tacc.utexas.edu/-/an-ai-oncologist-to-help-cancer-patients-worldwide