COPD is an inflammatory lung disease, comprising several conditions, including chronic bronchitis and emphysema, and can be caused by genetics and irritants like smoke or pollution. The disease affects more than 30 million Americans and is the fourth leading cause of death worldwide.

Exacerbations, or flare-ups, can cause accelerated loss of lung function, a decrease in quality of life, and reduce a person’s physical function and activity. More than 50% of people with COPD experience at least one acute COPD exacerbation within four years of an initial COPD diagnosis. Increased respiratory rates and decreased lung volumes are early signs of an exacerbation.

This new study examines how digital inhalers using remote monitoring data can help predict and identify early or impending COPD exacerbations. The digital inhaler used in this study measured peak inspiratory flow, inhalation volume, inhalation duration, time to peak inhalation, and inhaler use. The study examined whether the data collected by the digital inhaler could identify a possible early-stage exacerbation.

“While this study examined a small group of participants, the remote monitoring data showed people experienced significant decreases in the amount of air they inhaled and how long that inhalation lasted in the approximately two weeks prior to experiencing an exacerbation,” said M. Bradley Drummond, M.D., MHS, professor of medicine in the Division of Pulmonary Diseases and Critical Care Medicine at the University of North Carolina, Chapel Hill School of Medicine and lead author of the study. “As these remote monitoring technologies get more advanced, we can help both patients and health care providers identify exacerbations earlier, which allows us to provide better exacerbation management and improve health outcomes.”

To access current and past issues of Chronic Obstructive Pulmonary Diseases: Journal of the COPD Foundation, visit journal.copdfoundation.org.

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Early detection of breast cancer, before symptoms are present, is key to survivability. According to the American Cancer Society, women between the ages of 45 and 54 should get mammograms every year. Women who are 55 years and older can switch to every other year or continue with annual mammograms. Skipping just one scheduled mammogram could result in a more advanced breast cancer diagnosis, significantly impacting a patient’s chance of survival.

“The purpose of mammography is to detect breast cancer during the few years it can be seen on a mammogram, but before symptoms are apparent,” said study author Robert A. Smith, Ph.D., senior vice president and director of the American Cancer Society Center for Cancer Screening in Atlanta, Georgia. “If a woman unknowingly has breast cancer and misses or postpones her mammogram during this time when she has no symptoms, but her breast cancer is growing and perhaps spreading, then the window for early detection will be lost.”

Even though regular mammograms are an important factor in early breast cancer detection, there are still many barriers that restrict women from receiving this preventative care, including access and work or family obligations.

“It is challenging to keep track of your schedule, and in the U.S., many women do not receive reminders. Further, for all of us, the obligations of work and family compete with our scheduled health care,” Dr. Smith said.

Dr. Smith and colleagues sought to identify the exact impact of missing even one mammogram.

The researchers obtained women’s screening history from oncology centers throughout Sweden for a period from 1992 to 2016. A total of 36,079 breast cancer patients were included in the study.

Using data from the Swedish Cause of Death Register, the researchers identified 4,564 breast cancer deaths among the patients included in the study.

The researchers then tracked all of the women’s participation in five or fewer most recent invitations for breast cancer screening prior to cancer diagnosis.

Women who attended all their invited screening mammograms had a survivability rate of over 80%. Women who didn’t participate in any screenings had a survival rate that ranged from 59.1% to 77.6%.

Women who attended all five screening mammograms saw a 72% reduction in the risk of dying from breast cancer compared to women who didn’t participate in any screening mammograms. Even after conservative adjustment for potential self-selection factors, there was a highly significant 66% reduction in the risk of breast cancer death.

“Women who attended all five previous mammography examinations prior to a diagnosis of breast cancer were nearly three times less likely to die from breast cancer compared with women who had not attended any examinations, and each additional examination attended among the five previous examinations conferred an additive protective effect against dying from breast cancer,” Dr. Smith said.

The researchers stressed that imaging facilities should prioritize getting patients in for screening at the earliest opportunity. This is especially important when women have to cancel their appointments. Facilities should reschedule these screening mammograms for the next earliest available appointment.

“These findings show that as much as possible, adherence to regular mammography screening is the very best insurance a woman has against being diagnosed with an advanced breast cancer that could be life-threatening,” Dr. Smith said.

Co-authors are Stephen W. Duffy, M.Sc., Amy Ming-Fang Yen, Ph.D., László Tabár, M.D., Abbie Ting-Yu, Ph.D., Sam Li-Sheng Chen, Ph.D., Chen-Yant Hsu, M.D., Peter B. Dean, M.D., and Tony Hsiu-His Chen, Ph.D.

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In this work, the research team led by Prof. Karl Leo, Dr. Hans Kleemann and Matteo Cucchi demonstrates an approach for real-time classification of healthy and diseased bio-signals based on a biocompatible AI chip. They used polymer-based fiber networks that structurally resemble the human brain and enable the neuromorphic AI principle of reservoir computing. The random arrangement of polymer fibers forms a so-called “recurrent network,” which allows it to process data, analogous to the human brain. The nonlinearity of these networks enables to amplify even the smallest signal changes, which — in the case of the heartbeat, for example — are often difficult for doctors to evaluate. However, the nonlinear transformation using the polymer network makes this possible without any problems.

In trials, the AI was able to differentiate between healthy heartbeats from three common arrhythmias with an 88% accuracy rate. In the process, the polymer network consumed less energy than a pacemaker. The potential applications for implantable AI systems are manifold: For example, they could be used to monitor cardiac arrhythmias or complications after surgery and report them to both doctors and patients via smartphone, allowing for swift medical assistance.

“The vision of combining modern electronics with biology has come a long way in recent years with the development of so-called organic mixed conductors,” explains Matteo Cucchi, PhD student and first author of the paper. “So far, however, successes have been limited to simple electronic components such as individual synapses or sensors. Solving complex tasks has not been possible so far. In our research, we have now taken a crucial step toward realizing this vision. By harnessing the power of neuromorphic computing, such as reservoir computing used here, we have succeeded in not only solving complex classification tasks in real time but we will also potentially be able to do this within the human body. This approach will make it possible to develop further intelligent systems in the future that can help save human lives.”

Journal Reference:

1. Matteo Cucchi, Christopher Gruener, Lautaro Petrauskas, Peter Steiner, Hsin Tseng, Axel Fischer, Bogdan Penkovsky, Christian Matthus, Peter Birkholz, Hans Kleemann, Karl Leo. Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification. Science Advances, 2021; 7 (34): eabh0693 DOI: 1126/sciadv.abh0693

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