Machine learning to predict the clinical utility of biomedical research

A machine learning model to predict which scientific advances are likely to eventually translate to the clinic has been developed by Ian Hutchins and colleagues in the Office of Portfolio Analysis (OPA), a team led by George Santangelo at the National Institutes of Health (NIH).

This work published in the journal PLOS Biology aims to decrease the interval between scientific discovery and clinical application. The model determines the likelihood that a research article will be cited by a future clinical trial or guideline, an early indicator of translational progress.

Researchers have quantified these predictions as a novel metric called “Approximate Potential to Translate” (APT). Approximate Potential to Translate values can be used by researchers and decision-makers to focus attention on areas of science that have strong signatures of translational potential. Although numbers alone should never be a substitute for evaluation by human experts, the Approximate Potential to Translate metric has the potential to accelerate biomedical progress as one component of data-driven decision-making.

The model that computes Approximate Potential to Translate values makes predictions based upon the content of research articles and citations. A long-standing barrier to research and development of metrics like Approximate Potential to Translate is that such citation data has remained hidden behind proprietary, restrictive, and often costly licensing agreements. To disrupt this impediment to the scientific community, to increase transparency, and to facilitate reproducibility, OPA has aggregated citation data from publicly available resources to create an open citation collection (NIH-OCC).

The open citation collection comprises over 420 million citation links at present and will be updated monthly. For publications since 2010, the open citation collection is already more comprehensive than leading proprietary sources of citation data. Citation data from the open citation collection are used to calculate both Approximate Potential to Translate values and Relative Citation Ratios (RCRs). The latter, a measure of scientific influence at the article level, normalized for the field of study and time since publication.

Approximate Potential to Translate values and the open citation collection are publicly available as components of the iCite webtool. This tool will continue as the primary source of Relative Citation Ratios data.

New microfluidic system using artificial membrane keep brain tissue viable for a longer duration

Researchers at the RIKEN Center for Biosystems Dynamics Research in Japan have developed a new system for keeping tissue viable for long-term study once transferred from an animal to a culture medium. The new system uses a microfluidic device made of polydimethylsiloxane (PDMS) with a porous membrane that can keep tissue from both drying out and from drowning in fluid. This study was published in the journal Analytical Sciences.

The team tested the device using tissue from the mouse suprachiasmatic nucleus, a complex part of the brain that governs circadian rhythms. By measuring the level of bioluminescence coming from the brain tissue, they were able to see that tissue kept alive by their system stayed active and functional for over 25 days with nice circadian activity. In contrast, neural activity in tissue kept in a conventional culture decreased by 6% after only 10 hours.

This new method will be useful in observing development and testing how tissues respond to drugs. Experiments with tissues are much more complex and provide important information such as cell to cell interaction, unlike seeded cells where such observation is difficult.

Researchers develop self sterilizing bacterial air filter using graphene

Rice University researchers have developed self-sterilizing bacterial air filters using graphene. Workings of this novel air filter are published in the American Chemical Society journal ACS Nano.

This devise captures bacteria, fungi, spores, prions, endotoxins and other biological contaminants carried by droplets, aerosols and particulate matter. The filter then prevents the microbes and other contaminants from proliferating by periodically heating up to 350 degrees Celsius (662 degrees Fahrenheit), enough to obliterate pathogens and their toxic byproducts.

The filter utilizes laser-induced graphene.  This is a conductive foam of pure, atomically thin carbon sheets synthesized through heating the surface of a common polyimide sheet with an industrial laser cutter. The process discovered by Tour’s lab in 2014 has led to a range of applications for electronics, triboelectric nanogenerators, electrocatalysis, water filtration and even art.

The lab tested LIG filters with a commercial vacuum filtration system, pulling air through at a rate of 10 liters per minute for 90 hours, and found that Joule heating successfully sanitized the filters of all pathogens and byproducts. Incubating used filters for an additional 130 hours revealed no subsequent bacterial growth on the heated units, unlike control LIG filters that had not been heated.

This filter provides use case scenarios especially in hospitals, schools, and passenger aircraft. Although similar air filtration systems are available currently, self sterilization of filter holds promise as it can reduce number of filters used and their replacement frequency there by providing a cost reduction. However further research is required before this product is fully implemented.

MNO

In Russia, lower alcohol consumption associated with increase in life expectancy

A study published in the Journal of Studies on Alcohol & Drugs shown that there is a correlation between lower alcohol consumption and decreased mortality rate. According to the research life expectancy for men and women is 6.1 and 4.7 years longer than it was in 1980, with alcohol consumption patterns playing a significant role.

Researchers utilized the Russian Fertility and Mortality Database to obtain data on death rates and cause of death, including deaths specifically due to alcohol consumption (e.g., alcohol poisoning, liver disease, and cardiomyopathy) as well as deaths frequently related to drinking (e.g., suicide and homicide). They obtained data on life expectancy broken down by sex and beer sales from the Russian Statistical Service. Alcohol consumption rates were estimated using a technique developed by the study’s first author, Alexander Nemtsov, of the Moscow Research Institute of Psychiatry.

Researchers observed three waves overtime in which drinking and mortality dropped together. The first was 1985 to 1987, a time that corresponded with Mikhail Gorbachev’s anti-alcohol campaign of the 1980s. Shortly after repeal of these measures in 1990, life expectancy again dropped while alcohol consumption, particularly of illegal vodka, increased.

In the second wave from 1995 to 1998, life expectancy again grew as the economy faltered and alcohol consumption declined. But beginning in 1998, purchasing power increased in Russia, followed by increased drinking and decreased life expectancy.

The third wave, beginning around 2003 and continuing through the present, saw the enactment of a number of government policies aimed at alcohol consumption. These included greater restrictions on hours of sale and the locations in which alcohol can be sold, increases in minimum pricing and alcohol taxes, stricter licensing, and prohibitions on public drinking. At the same time, the authors note that Russian drinking patterns changed, shifting somewhat away from vodka and toward beer.

Although the study can’t prove that the decrease in alcohol consumption directly led to improved life expectancy, the link is strong, waxing and waning in tandem over time.

Increased mortality associated with short sleep duration

According to a new study published in the Journal of the American Heart Association middle-aged adults with high blood pressure, type 2 diabetes, heart disease or stroke could be at high risk for cancer and early death when sleeping less than six hours per day.

This study was done on a total of 1654 adults (aged 20–74 years) from the Penn State Adult Cohort. All adults in this cohort had cardiometabolic risk factors like stage 2 hypertension or type 2 diabetes. In addition, some had diagnosis and treatment for and or stroke. Participants were studied in the sleep laboratory (1991-1998) for one night and then researchers tracked their cause of death up to the end of 2016.

Statistical analysis showed that participants who slept less than 6 hours had higher all-cause mortality. They also had a higher incidence of cerebro and cardiovascular-related mortality. Another significant finding was increased cancer-related mortality in patients who had less than 6 hours of sleep.

Although the study was based on only one-night sleep assessment this is an important study that shows a relation between lack of sleep and mortality in patients with cardiometabolic risk factors. Further research is warranted in understanding this relationship and to promote adequate sleep duration as effective risk modifier.

Researchers identify role of protein TOM-1 in Alzheimer’s disease pathology

The scientists from the University of California Irvine discovered that reducing the amount of protein TOM-1 in Alzheimer’s rodent models increased pathology, which included increased inflammation, and exacerbated cognitive problems associated with the disease and restoring TOM-1 levels reversed those effects.

This research is significant as it explores the molecular pathways underlying Alzheimer’s disease. It also provides information about the TOM-1 signaling pathway and its role in interleukin-1β mediated inflammation in the brain. This provides a new therapeutic target to treat Alzheimer’s disease

This animal study is published in Proceedings of the National Academy of Sciences.

Optoceutics: controlling regenerative cells using visual light.

For the first time, the research group from Istituto Italiano di Tecnologia (IIT) in Milan proposed an innovative strategy to gain optical control of epithelial regenerative cells by using visible light together with photo-sensitive and bio-compatible conjugated polymers used as photo-actuators. This research is published in Science Advances.

In this study, the researchers managed to effectively promote the in vitro cellular growth and differentiation to new blood vessels by using photoactive materials as cellular substrates and by stimulating them with short pulses of visible light. They demonstrated that polymer-mediated optical excitation induces a robust enhancement of proliferation and lumen formation in vitro.

Implications of the study include the development of a novel method to control of regenerative cells using visible light. This method provides alternatives to currently researched and available electromechanical and chemical stimulation approaches.

New technology to monitor chemotherapy using magnetic nanoparticles

Researchers from Michigan State University propose a novel non-invasive magnetic particle imaging (MPI) to monitor chemotherapy release in vivo. This method employs superparamagnetic nanoparticles as the contrast agent to monitor drug release in the body.

In this study researchers designed iron oxide nanocomposite loaded with a chemotherapy drug doxorubicin which serves as a drug delivery system and magnetic particle imaging (MPI) quantification tracer. They showed that nanocomposite-induced MPI signal changes display a linear correlation with the release rate of doxorubicin over time.

Researchers performed this study in both in-vitro cell cultures and murine breast cancer model.

Implications: In vivo drug monitoring technologies are important as they monitor adequate drug release at the site of tumors. Being non-invasive as it is easier to perform and repeat.

This research is published Nano Letters (ACS publications)

Machine learning algorithms to speed up image biomarker analysis in heart MRI scans

According to research published in journal Circulation:Cardiaovascular imaging cardiac MRI analysis utilizing machine learning algorithms can be performed significantly faster and with similar precision compared to human experts.

In the study, researchers trained a neural network to read the cardiac MRI scans. Utilizing artificial intelligence, a scan can be analyzed in approximately four seconds compared to 13 minutes for the human reviewer. When the AI was tested for precision researchers found that there was no significant difference compared to humans.

Researchers made available the data utilized for this study at thevolumeresource.com. This resource also intends to test and validate new cardiac MRI post-processing technology and machine learning techniques.

Fat mass index better predictor of cardiovascular events in diabetics

According to research published in the Canadian Medical Association Journal, type 2 diabetics with higher fat mass index are prone to develop cardiovascular events. The study showed that fat mass index (FMI) is superior to lean body mass index (BMI) in predicting heart-related events.

Researchers conducted a post hoc analysis of data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. This study involved 10251 patients and had a mean follow up of 8.8 years. Of these, a major cardiovascular event occurred in 1801 patients (17.8%). In the study patients in the fourth quartile for fat mass index had a hazard ratio of 1.53 compared to patients in the first quartile.

This study is important as it provides better metric fat mass index (FMI) to predict cardiovascular events compared to BMI which is commonly used to measure obesity.