Researchers from the University of Warwick showed that virtual reality (VR) combined with 3D Motion capture could allow movements to be translated onto an avatar that the patient can follow. They accomplished this using consumer VR technologies currently available. Research showed that these consumer virtual reality technologies could be used for both providing guidance to physiotherapy exercises, but also to make the exercises more interesting and encourage people to complete the course. This research published in the Journal PLOS ONE has focused on whether people can accurately follow the movements of a virtual avatar.
Currently prescribed physiotherapy often requires patients to complete regular exercises at home. Outside of the clinic, patients rarely receive any guidance other than a leaflet of sketches or static photographs to instruct them on how to complete their exercises. This leads to poor adherence, with patients becoming anxious about not getting the exercise right, or simply getting bored by the repetitiveness of the movements. Virtual reality could help physiotherapy patients complete their exercises at home successfully.
The advent of consumer virtual reality technology combined with 3D motion capture allows real movements to be accurately translated onto an avatar that can be viewed in a virtual environment. Researchers at the Institute of Digital Healthcare, WMG, University of Warwick are investigating whether this technology can be used to provide guidance to physiotherapy patients, by providing a virtual physiotherapist in the home to demonstrate the prescribed exercises. Researchers had to investigate whether people were able to accurately coordinate and follow the movements of an avatar in a virtual environment. They asked participants to step in time with an avatar viewed through a VR headset.
With increasing health care costs and also a shortage of healthcare professions there is a growing need for technologies that enable remote monitoring and treatment. It is also important that these technologies are effective and easy to use and this research study shows that.
Researchers recently published a study that shows proof of concept for how artificial intelligence can help doctors and brain tumor patients predicting survival and help make better treatment decisions. This study is published in Nature partner journal Digital Medicine. They also developed an open-source smartphone app meningioma.app to allow clinicians and other researchers to interactively explore the predictive algorithms described in the paper.
In this study, researchers from The Neuro (Montreal Neurological Institute-Hospital) and the Montreal Children’s Hospital of the McGill University Health Centre trained machine learning algorithms on data from more than 62,000 patients with a meningioma. Their goal was to find statistical associations between malignancy, survival, and a series of basic clinical variables including tumor size, tumor location, and surgical procedure. While the study demonstrated that the models could effectively predict outcomes in individual patients, the researchers emphasized the need for further refinements using larger sets that include brain imaging and molecular data.
Researchers from KU Leuven in Belgium have developed a new antibacterial strategy that weakens bacteria by preventing them from cooperating. The researchers showed that blocking slime (extracellular polymeric substance) production of salmonella bacteria weakens the bacterial community, making it easier to remove.
Traditional antibiotics kill or reduce the activity of individual bacteria. Some bacteria become resistant to these antibiotics, allowing them to grow further and take over from non-resistant ones. The use of antibiotics, therefore, causes more and more bacteria to become resistant to antibiotics. Bacteria, however, also exhibit group behavior: for example, they can make a protective slime layer or biofilm that envelops their entire bacterial community. The social behavior of bacteria is an interesting new target for antibacterial therapy. Their experiments also suggested a reduction of antibiotic resistance development.
Researchers note that there are several applications possible in agriculture, industry, and even households. To this end, the researchers collaborate with experts in various applications, and with producers of animal feeds and cleaning products and disinfectants. The researchers are also investigating whether they can reproduce the phenomenon in other forms of microbial collaboration, and with other bacteria.
The study published in Nature Medicine examined the diagnostic accuracy of brain tumor image classification through artificial intelligence tool, compared with the accuracy of pathologist interpretation of conventional histologic images. The results for both methods were comparable: the artificial intelligence based diagnosis was 94.6% accurate, compared with 93.9% for the traditional pathologist-based assessment and interpretation. This research was conducted at NYU Langone Health.
The imaging technique known as stimulated Raman histology (SRH), reveals tumor infiltration in human tissue by collecting scattered laser light, illuminating essential features not typically seen in standard histologic images. Using deep convolutional neural network (CNN) with more than 2.5 million samples from 415 patients researchers trained a machine-learning algorithm to classify tissue into 13 histologic categories that represent the most common brain tumors, including malignant glioma, lymphoma, metastatic tumors, and meningioma.
Researchers note advantages with this method include quicker and almost realtime results (two and half minutes) with this method compared to traditional pathological examination (twenty to thirty minutes). This can be very useful especially during intraoperative decision making. In addition, researchers found that diagnostic errors due to this method are different from traditional pathology and combining both methods would increase diagnostic accuracy to 100%.
A new web app eNTRyway speeds the discovery of drugs to kill Gram-negative bacteria by quickly evaluating potential drugs ability to accumulate in these bacterial cell. Entryway calculates physiochemical properties of molecules and compares to a training set of compounds. The tool also offers insights into discrete chemical changes that can convert drugs that kill other bacteria into drugs to fight Gram-negative infections.
The team proved the system works by modifying a Gram-positive drug and testing it against three different Gram-negative bacterial culprits in mouse sepsis. The drug was successful against each.
Researchers have so far identified more than 60 antibiotics that are effective only against Gram-positive bacteria but can be converted into drugs to fight Gram-negative infections. These compounds kill bacteria in a variety of different ways. The newly created drug, known as Debio-1452-NH3, interferes with fatty acid synthesis in bacterial – but not mammalian – cells.
Researchers from the University of California San Diego developed a machine-learning model by training a boosted decision tree algorithm on de-identified electronic health records data of 5,822 hospitalized or ambulatory patients with heart failure from the University of California San Diego.
This machine learning model is based on eight readily available variables. These include, diastolic blood pressure, creatinine, blood urea nitrogen, haemoglobin, white blood cell count, platelets, albumin, and red blood cell distribution width. This model was able to predict life expectancy in 88% of the patients.
The tool was additionally tested using data from the University of California, San Francisco, and a database derived from 11 European medical centers.
With the advance of machine learning and artificial intelligence tools, large amounts of health data thanks to electronic health records, and computing power, we able to work on creating more and more accurate risk prediction tools. These tools will be commonplace in clinical practice to help with data-based decisions.
Researchers from King’s College London and the University of California San Diego School of Medicine conducted an animal study in mice and shown promise of bacteriophage therapy in treating alcohol-related liver disease.
The team discovered that patients with severe alcoholic hepatitis had high numbers of a destructive gut bacterium Enterococcus faecalis, which produced a toxin called cytolysin. This toxin is shown to injure liver cells. Enterococcus faecalis is normally found in low numbers in the healthy human gut.
To investigate the potential for phage therapy, the researchers isolated four different phages that specifically target cytolysin-producing Enterococcus faecalis. When they treated the mice with these, the bacteria were eradicated, and alcohol-induced liver disease was abolished. Control phages that target other bacteria or non-cytolytic E. faecalis had no effect.
With the rise of multidrug-resistant infections, people are looking at alternatives to antibiotics. Bacteriophages are viruses that kill bacteria. These bacteriophages are naturally occurring and offer a promising alternative to antibiotics. However, much research is needed to establish their safety and efficacy in clinical practice. The current study show promise of using phage therapy to alter the gut microbiome in cases with alcoholic liver disease.
A new risk calculator tool to predict the risk of chronic kidney disease is developed by Chronic Kidney Disease Prognosis Consortium, a large global collaboration led by researchers at the Johns Hopkins Bloomberg School of Public Health. It utilizes a mix of variables to predict accurately whether someone is likely to develop chronic kidney disease within five years.
The risk calculator published in the Journal of the American Medical Association (JAMA), is based on an analysis of clinical data from more than five million people around the world. The calculator is based on risk prediction model that utilizes age, sex, race/ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, body mass index, and albuminuria concentration. For participants with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction between those two.
Risk prediction tools can help identify high-risk patients that can be followed with interventions that can slow or stop disease progression. In the case of kidney disease, progression can be delayed or stopped with treatments that address kidney-harming disorders such as hypertension and diabetes, and by limiting the use of kidney-stressing substances such as certain antibiotics, NSAID painkillers, and imaging contrast agents.
According to a research study presented at the 45th Argentine Congress of Cardiology heart patients using a smartphone app reminder are more likely to take their medication than those who receive written instructions,
This study tested the impact of a smartphone application on medication compliance. A total of 90 heart attack patients admitted to hospital were randomly allocated to the app or detailed written information (standard care). Adherence to medical treatment was measured at 90 days using the Morisky Medical Adherence Scale (MMAS-8). For those assigned to the smartphone group, the prescribed medication schedule was uploaded to the digital application, and an alarm would ring each time a pill should be taken. After taking the pills, patients confirmed it in the application. Doctors could check daily adherence using a professional digital platform linked to the patient’s smartphone.
The average age of patients in the study was 63 years and 75% were men. At 90 days, significantly more patients in the digital application group were correctly taking their pills (65%) compared to those who received standard care (21%; p<0.001). A secondary objective of the study was to examine how many patients in each group were hospitalized for another heart attack or had an unplanned visit to the doctor or emergency department. No differences between groups were found.
Overall this study shown increasing use of digital tools to monitor the treatment delivery. However this study does not prove that taking medications on time and digital monitoring would improve patient outcomes. Further research is needed in that respect.
Scientists at the Center for Interdisciplinary Research in Biology have shown that delta waves emitted while we sleep are not generalized periods of silence during which the cortex rests, as has been described for decades in the scientific literature. Instead, they isolate assemblies of neurons that play an essential role in long-term memory formation. These results were published in journal Science.
While we sleep, the hippocampus reactivates itself spontaneously by generating activity similar to that while we are awake. It sends information to the cortex, which reacts in turn. This exchange is often followed by a period of silence called a ‘delta wave’ then by a rhythmic activity called a ‘sleep spindle’. This is when the cortical circuits reorganize to form stable memories.
However, the role of delta waves in the formation of new memories is still a puzzle: why does a period of silence interrupt the sequence of information exchanges between the hippocampus and the cortex, and the functional reorganization of the cortex?
The authors here looked more closely at what happens during delta waves themselves. They discovered, surprisingly, that the cortex is not entirely silent but that a few neurons remain active and form assemblies, i.e. small, coactive sets that code information. This unexpected observation suggests that the small number of neurons that activate when all the others stay quiet can carry out important calculations while protected from possible disturbances.
And the discoveries from this work go even further! Spontaneous reactivations of the hippocampus determine which cortical neurons remain active during the delta waves and reveal transmission of information between the two cerebral structures. In addition, the assemblies activated during the delta waves are formed of neurons that have participated in learning a spatial memory task during the day. Together these elements suggest that these processes are involved in memory consolidation.
To demonstrate it, in rats the scientists caused artificial delta waves to isolate either neurons associated with reactivations in the hippocampus or random neurons. Result: when the right neurons were isolated, the rats managed to stabilize their memories and succeed at the spatial test the next day.