Health Life

Researchers from the Moscow Institute of Physics and Technology, Kazan Federal University, and George Washington University have proposed an algorithm for producing patient-specific mathematical models describing the electrical excitation of human heart cells. Published in PLOS One, the study looks at two possible approaches—one using experimental records of electrical activity and the other based on gene expression profiles.

Each heart contraction is caused by a preceding electrical excitation, the so-called action potential. The latter results from electrical currents through ion channels. The number of such channels forming ion currents varies with both pathological conditions and the individual properties of heart tissue in healthy patients. When the balance between various types of ion currents gets disrupted, this may lead to dangerous arrhythmias and death.

Since many factors are involved in excitation propagation, the studies investigating the basic principles underlying arrhythmia have relied on mathematical models over the past 50 years. Despite the effort behind developing these models, they are so far rarely used in the clinical practice, mainly because they describe a hypothesized average patient. The research reported in this story addresses the challenging task of applying such models to real individual patients.

The first approach discussed in the paper relies on experimental recordings of action potential and subsequent model optimization using dedicated computer algorithms. They employ evolutionary principles to find the parameters that make the model reproduce the experiment. Randomly generated models are subjected to selection, crossover, and mutation. Prior research by a number of scientific groups has identified the key challenge faced by this approach. Namely, it is hard to find the unique solution, because of the numerous distinct combinations of parameters that result in the same action potential waveform.

Study co-author Andrey Pikunov from the MIPT Laboratory of Human Physiology commented: “We have

A University of California, Riverside, study that sought to determine barriers to health care among Spanish-speaking Latino farmworkers in rural communities has devised an innovative health care service delivery model that addresses many challenges these communities face.

The researchers, led by Ann Cheney, a medical anthropologist and assistant professor in the Department of Social Medicine, Population, and Public Health in the School of Medicine, advocate the use of mobile health clinics, or MHCs, that bring health care services to patients in their community spaces. Cheney was assisted in the research by Dr. Monica Tulimiero, who graduated from the UCR School of Medicine earlier this year and is now a resident in family medicine at Ventura County Medical Center.

MHCs, the researchers argue, offer health care services at times outside of business hours, which suits farmworkers. The researchers also urge providers to immerse themselves and practice in patient communities to better understand their health care needs.

The study, published in The Journal of Rural Health, was conducted in inland Southern California’s eastern Coachella Valley, an agricultural region home to many undocumented and underinsured Latino immigrants. It also included focus group discussions and one-on-one interviews with patients.

In partnership with Health to Hope, a federally qualified health center, Cheney and her team implemented three MHCs in 2019 in locations close to patients’ homes and community spaces, making sure the clinics accommodated patients’ time constraints. The MHC included two exam rooms.

According to the researchers, traditional models of care—the kind that expect patients to access health care services at brick-and-mortar structures within defined clinic hours—work for patients with resources such as paid sick leave and job stability but are not practical for Latino farmworkers in rural communities.

Many studies published by major spinal surgery journals do not include full disclosure of researchers’ financial conflicts of interest (COIs), reports a study in Spine.

“[A] large number of physician-industry interactions…are underreported by authors publishing within the surgical spine literature,” according to the study by Jeremy D. Shaw, MD, and colleagues of University of Pittsburgh Medical Center. They propose steps to encourage more accurate disclosure of potential COIs—especially in spinal surgery, where relationships between surgeons and the biomedical device industry play a crucial role in research and innovation.

High rate of incomplete financial disclosures in spinal surgery studies

The researchers reviewed financial COIs by authors who published studies in Spine and two other major spinal surgery journals between 2014 and 2017. The analysis included nearly 40,000 authors contributing to about 6,800 research articles.

The COI disclosures for each author were compared to those in the Center for Medicare and Medicaid Services’ publicly available Open Payments Database (OPD). Established under the Affordable Care Act, the OPD requires pharmaceutical, medical device, and biological products manufacturers to report all payments to physicians of more than $10. Overall, 15.8 percent of spinal surgery authors had payments reported in the OPD.

Dr. Shaw and colleagues then compared payments reported to the OPD with the authors’ financial disclosures in the published studies. Most payments reported to the OPD were reflected in the COI statements. Of the total $1.90 billion received by authors, approximately $1.48 billion—78 percent of the total—was accurately disclosed.

“Undisclosed payments included $180 million in researcher funding and $188 million in royalties,” Dr. Shaw and colleagues write. Charitable contributions and payments from royalties/licenses were most likely to be accurately reported, while funds for research and entertainment were least likely to be disclosed.

About 77 percent of authors of

Global data networks that connect people through their devices have made it possible to create accurate short-term forecasts of new COVID-19 cases, using a method pioneered by two researchers at Sandia National Laboratories.

Jaideep Ray and Cosmin Safta used a model developed by Ray more than a decade ago to track plague epidemics using statistics. For COVID-19 they also drew upon the advice of their Sandia co-workers with expertise in modeling, mathematics and software engineering.

“I first started using this method in 2008-09. Cosmin and I adapted it in 2010 to track influenza-like illnesses,” Ray said. “When COVID-19 began to spread so rapidly, we knew we could use the same method to help forecast the outbreak.”

Ray and Safta use publicly available data from the Centers for Disease Control and Prevention, The New York Times Data Repository, Johns Hopkins University and various state departments of health. Within minutes, and without the need for high-performance computing resources, the researchers can forecast new cases in a region or nationally for the next seven to 10 days. Since April, the number of new cases have roughly followed the trends predicted by Ray and Safta.

“This method is a relatively easy and inexpensive way to get short-term forecasts about new coronavirus cases that decision-makers can use to allocate health care resources and response,” Safta explained. “This method is much easier and cheaper to do than methods that require more robust computers and manpower.”

The range of accuracy for the predictions