Researchers from the Universitat Oberta de Catalunya (UOC) and the technology center Eurecat have developed an innovative formal representation of rare disease data, including information unavailable in current models on rare disease patients’ biological, psychological and social profile. For their research, the researchers have obtained data on 25 patients from organizations such as Eurordis, the Spanish Rare Diseases Federation (FEDER) and the Rare Diseases Patients’ Association of Iran with the goal of including testimonials from different territories with different health systems.

The term used to refer to formal representations of knowledge that establish the different concepts of a specific field and the relationships between them is ontology. In such representations, it is important to use an open-source data format and international standards in order to ensure that this representation is accessible in all spheres. The ontology performed by the UOC uses an open source code and is based on standards defined by the World Health Organization (WHO).

A tool for understanding patients that goes beyond treatments

The research is described in the article “Biomedical Holistic Ontology for Patients With Rare Diseases”, published in the International Journal of Environmental Research and Public Health. Lead researcher Laia Subirats explained that its value lies in “the fact that a single ontology integrates not just medical information but also information about other aspects that affect patients’ lives, such as environmental, geographical and psychological factors, their social relations and their interests. It also includes information taken from Twitter, which gives us social data.

The end result is an improved understanding of the patient and access to new data about the patient’s interaction with the disease. Viewed in this light, we can say that it is a holistic ontology”. Subirats is a course instructor at the UOC’s Faculty of

To help slow the spread of COVID-19 and save lives, UCLA public health and urban planning experts have developed a predictive model that pinpoints which populations in which neighborhoods of Los Angeles County are most at risk of becoming infected.

The researchers hope the new model, which can be applied to other counties and jurisdictions as well, will assist decision makers, public health officials and scientists in effectively and equitably implementing vaccine distribution, testing, closures and reopenings, and other virus-mitigation measures.

The model maps Los Angeles County neighborhood by neighborhood, based on four important indicators known to significantly increase a person’s medical vulnerability to COVID-19 infection—preexisting medical conditions, barriers to accessing health care, built-environment characteristics and socioeconomic challenges.

The research data demonstrate that neighborhoods characterized by significant clustering of racial and ethnic minorities, low-income households and unmet medical needs are most vulnerable to COVID-19 infection, specifically areas in and around South Los Angeles and the eastern portion of the San Fernando Valley. Communities along the coast and in the northwestern part of the county, which are disproportionately white and higher-income, were found to be the least vulnerable.

“The model we have includes specific resource vulnerabilities that can guide public health officials and local leaders across the nation to harness already available local data to determine which groups in which neighborhoods are most vulnerable and how to prevent new infections to save lives,” said research author Vickie Mays, a professor of psychology in the UCLA College and of health policy and management at the UCLA Fielding School of Public Health.

Mays, who also directs the National Institutes of Health-funded

Congenital adrenal hyperplasia (CAH) is a disorder that affects the adrenal gland’s ability to release hormones that regulate the body’s response to stress and illness. CAH is treatable, but can be potentially life-threatening during illness or if not managed. The disorder is difficult to identify, and much still needs to be understood about the condition. But new research conducted at Children’s Hospital Los Angeles has shown that computers may be able to use subtle facial features to recognize CAH. This finding could lead to better identification of the disorder and better care of CAH patients.

“In endocrinology, CAH is one of the few emergency conditions we encounter,” says Mimi Kim, MD, MSc, co-Director of the CAH Comprehensive Care Clinic at Children’s Hospital Los Angeles. “It’s the leading cause of adrenal insufficiency in children, which means the body can’t produce aldosterone, adrenaline and cortisol.”

These hormones allow the body to manage blood pressure and respond to crises. In addition, CAH is marked by higher levels of the sex hormone testosterone. This can lead to changes in genitalia for female patients. But testosterone has another effect not directly linked to sex or gender—an effect that could be used to help identify CAH.

“It’s pretty well accepted that hormones like testosterone help to shape facial features,” says Dr. Kim. “Since CAH causes high testosterone during development, it stands to reason that differences, even subtle ones, could be present in CAH patients.” This, she says, led her to wonder whether facial morphology—a collection of physical traits—could assist clinicians in identifying patients with CAH.

“There was no established link yet between CAH and facial morphology,” says Dr. Kim. This could be because facial differences are subtle enough to be missed by most clinicians. “But advances in machine learning have come