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.
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.
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.
Smartphone app to detect atrial fibrillation or irregular heartbeat. Credit: Hannah Oksanen, University of Turku.
Researchers conducted a study on three hundred patients with heart problems, 50% had atrial fibrillation. The researchers managed to identify the patients with atrial fibrillation from the other group with a smartphone with around 96% accuracy. According to Chief Physician and Professor of Cardiology Juhani Airaksinen from Turku University Hospital, this is the first time that ordinary consumer electronics have achieved such reliable results.
The technology behind the application involves using small accelerometers that are present in most smartphones. Researchers call this technique mechanochardiography, as it uses mechanical stimulus to generate heart trace.
The researchers want to make this app available for all as quickly as possible. According to Mr. Koivisto, the commercialization of the method is advancing quickly.
Reference: Jaakkola, Jussi, Samuli Jaakkola, Olli Lahdenoja, Tero Hurnanen, Tero Koivisto, Mikko Pänkäälä, Timo Knuutila, Tuomas O. Kiviniemi, Tuija Vasankari, and K.e. Juhani Airaksinen. “Mobile Phone Detection of Atrial Fibrillation With Mechanocardiography: The MODE-AF Study (Mobile Phone Detection of Atrial Fibrillation).” Circulation, 2018. doi:10.1161/circulationaha.117.032804.
Adapted from press release by the University of Turku.