Masks required for players on sidelines as league enhances COVID-19 protocols

FILE PHOTO: The NFL logo is pictured at an event in the Manhattan borough of New York City, New York, U.S., November 30, 2017. REUTERS/Carlo Allegri

November 24, 2020

(Reuters) – Players in the National Football League (NFL) must wear masks on the sidelines unless they have their helmet on and are preparing to enter the game, the league said on Monday as it unveiled an enhanced set of COVID-19 protocols.

In a memo distributed to teams, the NFL also outlined increased safety regulations for play-callers and said post-game interactions between players and staff would be limited.

Players that failed to comply would be subject to discipline, the league said.

“Clubs are required to enforce these rules. Violations by players and/or staff will result in accountability measures being imposed upon the club,” the NFL said.

The league added that the maximum number of players permitted to travel to road games would be reduced to 62 and access to club facilities would be limited for non-essential personnel.

From Week 13, all members of a team’s traveling party must wear N95 or KN95 masks on team planes and buses, it said.

The new guidance was issued on the same day multiple players, including Baltimore Ravens running backs Mark Ingram and J.K. Dobbins, and Minnesota Vikings wide receiver Adam Thielen, were placed on the Reserve/COVID-19 list.

(Reporting by Arvind Sriram in Bengaluru; Editing by Peter Rutherford)

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Machine Learning Enhances Mental Illness Diagnosis | Asian Scientist Magazine

AsianScientist (Oct. 2, 2020) – A machine learning algorithm has grasped how to identify mental health conditions such as autism and schizophrenia from magnetic resonance imaging (MRI) brain scans. These findings, by researchers at the University of Tokyo, Japan, have been published in Translational Psychiatry.

While most of modern medicine has physical tests or objective techniques to define much of what ails us, there is currently no blood or genetic test that can definitively diagnose a mental illness, and certainly none to distinguish between different psychiatric disorders with similar symptoms.

“Psychiatrists, including me, often talk about symptoms and behaviors with patients and their teachers, friends and parents. We only meet patients in the hospital or clinic, not out in their daily lives. We have to make medical conclusions using subjective, secondhand information,” explained Shinsuke Koike, an associate professor at the University of Tokyo and a senior author of the study. “Frankly, we need objective measures.”

Using the brain scans of 206 participants—including patients already diagnosed with autism spectrum disorder or schizophrenia, individuals considered high risk for schizophrenia and those who have experienced their first instance of psychosis—Koike and his team were able to train a machine learning algorithm to distinguish between the different conditions.

The algorithm learned to associate different psychiatric diagnoses with variations in the thickness, surface area or volume of areas of the brain in MRI images. It is not yet known why any physical difference in the brain is often linked to a specific mental health condition.

After the training period, the algorithm was tested with brain scans from 43 additional patients. The machine’s diagnosis matched the psychiatrists’ assessments with high reliability and up to 85 percent accuracy. Importantly, the algorithm could distinguish between non-patients, patients with autism spectrum disorder, and patients with either schizophrenia or schizophrenia risk factors.

“Autism spectrum disorder patients have a ten-times higher risk of schizophrenia than the general population. Social support is needed for autism, but generally the psychosis of schizophrenia requires medication, so distinguishing between the two conditions or knowing when they co-occur is very important,” said Koike.

The team found that the thickness of the cerebral cortex was the most useful feature for correctly distinguishing between individuals with autism spectrum disorder, schizophrenia and neurotypical individuals. This highlights the role of cortex thickness in distinguishing between different psychiatric disorders and may direct future studies on understanding the causes of mental illness.

Now that their machine learning algorithm has proven its value, the researchers plan to begin using larger datasets and hopefully coordinate multisite studies to train the program to work regardless of the MRI differences.
The article can be found at: Yassin et al. (2020) Machine-learning Classification Using Neuroimaging Data in Schizophrenia, Autism, Ultra-high Risk and First-episode Psychosis.


Source: University of Tokyo; Photo: Shinsuke Koike.
Disclaimer: This article does not necessarily reflect the views of AsianScientist or its staff.

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