Author: jack

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.