Health Life

Researchers at the Center for Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University have preliminarily validated an artificial intelligence (AI) tool to predict how likely the disease is to recur following surgical treatment for prostate cancer.

The tool, called RadClip, uses AI algorithms to examine a variety of data, from MRI scans to molecular information. The research team included Cleveland Clinic, University Hospitals and the Louis Stokes Cleveland Veterans Administration Medical Center.

“This tool can help urologists, oncologists and surgeons create better treatment plans so that their patients can have the most precise treatment,” said Lin Li, a doctoral student in Case Western Reserve’s Biomedical Engineering Department and a member of the CCIPD team that developed the tool. “RadClip allows physicians to evaluate the aggressiveness of the cancer and the response to treatment so they don’t overtreat or undertreat the patient.”

Li is first author on a study used to validate the tool, which appeared this month in The Lancet‘s EBioMedicine journal. While other studies on prostate cancer have examined data from single sites, the CCIPD study included MRI scans from Cleveland Clinic, The Mount Sinai Hospital, University Hospitals and the Hospital of the University of Pennsylvania.

The multi-institutional study applied RadClip AI tool to pre-operative scans from nearly 200 patients whose surgeons removed their prostate gland because of cancer, then compared its results of other predictive approaches—as well as the patients’ outcomes in succeeding years.

One of the critical questions in managing prostate cancer in men undergoing surgery is identifying which are at highest risk of recurrence and prostate cancer-specific mortality so they can be identified early for additional therapy.

While RadClip has been shown to be able to predict the risk of disease recurrence, clinical trials will be needed

Researchers from the University of Cambridge, the University of Milan and Google Research have used machine learning techniques to predict how proteins, particularly those implicated in neurological diseases, completely change their shapes in a matter of microseconds.

They found that when amyloid beta, a key protein implicated in Alzheimer’s disease, adopts a highly disordered shape, it actually becomes less likely to stick together and form the toxic clusters which lead to the death of brain cells.

The results, reported in the journal Nature Computational Science, could aid in the future development of treatments for diseases involving disordered proteins, such as Alzheimer’s disease and Parkinson’s disease.

“We are used to thinking of proteins as molecules that fold into well-defined structures: finding out how this process happens has been a major research focus over the last 50 years,” said Professor Michele Vendruscolo from Cambridge’s Centre for Misfolding Diseases, who led the research. “However, about a third of the proteins in our body do not fold, and instead remain in disordered shapes, sort of like noodles in a soup.”

We do not know much about the behavior of these disordered proteins, since traditional methods tend to address the problem of determining static structures, not structures in motion. The approach developed by the researchers harnesses the power of Google’s computer network to generate large numbers of short trajectories. The most common motions show up multiple times in these ‘movies’, making it possible to define the frequencies by which disordered proteins jumps between different shapes.

“By counting these motions, we can predict which states the protein occupies and how quickly it transitions between them,” said first author Thomas Löhr from Cambridge’s Yusuf Hamied Department of Chemistry.

The researchers focused their attention on the amyloid beta peptide, a protein

What happens when municipalities in the Copenhagen area experience a COVID-19 flare-up? Would closing the schools have any effect, or would a better choice be directing the parents to work from home? Due to the COVID-19 pandemic, authorities worldwide have several times implemented steps to keep the pandemic in check.

Now, researchers from the Department of Computer Science at Aalborg University have come forward with a new agent-based model that can be used as a tool for making even better, informed choices regarding which restrictions to implement.

The background of using agent-based modeling to analyze, predict and control the rapid spreading of COVID-19 is described in the paper Fluid Model-Checking in UPPAAL for COVID-19 published in the distinguished conference proceedings series Lecture Notes in Computer Science.

167 fewer new cases a day

In the new model, the researchers simulate interactions between specific agents or in other words, individuals. Based on data from Statistics Denmark, the Danish Building and Housing Register (BBR), the Central Business Register and the State Serum Institute of Denmark, the researchers have used the agent-based technic to construct a model of Northern Jutland, with more than 500,000 individuals.

The region was placed under lockdown in November due to the fear of spreading the cluster-5 variant of the coronavirus, circulating primarily in farmed minks.

In the model, each individual is assigned a state of health, which is combined with general data on addresses, places of employment, family sizes and commuting patterns to calculate realistic simulations of the mobility patterns of all Northern Jutland inhabitants.

Project head, Professor Kim Guldstrand Larsen, explains that the researchers have simulated the case numbers in the region over

More than half the clinicians surveyed at the Peter Munk Cardiac Centre reported burnout and high levels of distress according to a series of studies published today in the Canadian Medical Association Journal Open (CMAJ-OPEN). In these studies carried out before the COVID-19 pandemic, 78% of nurses, 73% of allied health staff and 65% of physicians described experiencing burnout.

“In my 35 years as a physician I have never seen a more serious issue for clinicians than burnout,” says lead author Dr. Barry Rubin, Chair and Medical Director, the Peter Munk Cardiac Centre, UHN.

Completed in 2019, the study used the Well-Being Index, a survey tool developed by the Mayo Clinic, a globally recognized academic medical centre. 414 physicians, nurses and allied health staff answered a series of questions about the level of stress they experienced in the previous month.

The index measured fatigue, depression, burnout, anxiety or stress, mental and physical quality of life, work-life integration, meaning in work and distress.

The study also evaluated the respondent’s perception of the adequacy of staffing levels, and of fair treatment in the workplace. The results were then compared to outcomes for corresponding healthcare professionals at academic health science centres in the United States.

• 78% of nurses, 73% of allied health staff and 65% of physicians described burnout in the month prior to when the survey was administered.
• 79% of nurses, 56% of allied health staff and 55% of physicians had high levels of distress.
• Lower levels of distress among all clinicians were associated with a perception of fair treatment at work and a perception of adequate staffing levels.

The impact of burnout on clinicians can include extreme fatigue, professional dissatisfaction, job turnover, decreased quality of