Study links children's mental health to sleep duration

Dear parents, if you want your children to have proper sleep, read this carefully. Joining a growing list of studies that tell parents to shun devices at bed-time, researchers say that children who use devices and decide what time they go to sleep, achieve less sleep and feel more sleepier the following day than their peers.

The study of children in this age-group (aged 11 to 13 years), published in the New Zealand Medical Journal, found most (72 per cent) of the 163 students interviewed by University of Otago researchers achieved recommended guidelines of an average 9 to 11 hours sleep nightly over one week.

"But that also means that almost one in four students did not achieve sleep within these guidelines, which highlights an area for improvement," said study researcher Kate Ford.

However, consistent with previous research in 15 to 17-year-old New Zealanders, the study results show less sleep on the nights where devices are used in the hour before bed.

According to the researchers, students who used devices before going to sleep were also more likely to report that they felt sleepy the following morning. Watching television before bed had no significant effect on sleep length.

There were also some interesting observations over the weekends where students went to bed later but woke later achieving similar sleep length to the school days, the researchers said.

A small group of students (six per cent) who reported less than seven hours of sleep, including a small number reporting not sleeping at all, according to the study,

Therefore, while the average across the week of 72 per cent of students reporting adequate sleep is reassuring, it is far from the goal of every child achieving sleep within the recommended guidelines," Ford said.

Dr Paul Kelly, head of the Sleep Health Service at Canterbury District Health Board, supervised the study and explained that the foundations for good health are based on proper nutrition, regular exercise and good sleep quality.

Sleep quality is often overlooked as a contributory factor to poor health.

"The study findings suggest the need for parental guidance around bedtimings and moderation of the use and availability of electronic devices before bed," Kelly said.

"Respect and protect your sleep, as good daytime functioning is reliant on adequate sleep," Kelly added.

A team of scientists has produced first open source all-atom models of full-length COVID-19 Spike protein that facilitates viral entry into host cells – a discovery that can facilitate a faster vaccine and antiviral drug development.

The group from Seoul National University in South Korea, University of Cambridge in the UK and Lehigh University in the US produced the first open-source all-atom models of a full-length S protein.

The researchers say this is of particular importance because the S protein plays a central role in viral entry into cells, making it a main target for vaccine and antiviral drug development.

"Our models are the first full-length SARS-CoV-2 spike (S) protein models that are available to other scientists," said Wonpil Im, a professor in Lehigh University.

"Our team spent days and nights to build these models very carefully from the known cryo-EM structure portions. Modeling was very challenging because there were many regions where simple modeling failed to provide high-quality models," he wrote in a paper published in The Journal of Physical Chemistry B.

Scientists can use the models to conduct innovative and novel simulation research for the prevention and treatment of Covid-19.

Though the coronavirus uses many different proteins to replicate and invade cells, the Spike protein is the major surface protein that it uses to bind to a receptor.

The total number of global COVID-19 cases was nearing 9 million, while the deaths have increased to over 467,000, according to the Johns Hopkins University.

With 2,279,306 cases and 119,967 deaths, the US continues with the world's highest number of COVID-19 infections and fatalities, according to the CSSE.

Brazil comes in the second place with 1,083,341 infections and 50,591 deaths.

New York, Feb 26:  A new wearable sensor that works in conjunction with artificial intelligence (AI) technology could help doctors remotely detect critical changes in heart failure patients days before a health crisis occurs, says a study.

The researchers said the system could eventually help avert up to one in three heart failure readmissions in the weeks following initial discharge from the hospital and help patients sustain a better quality of life.

"This study shows that we can accurately predict the likelihood of hospitalisation for heart failure deterioration well before doctors and patients know that something is wrong," says the study's lead author Josef Stehlik from University of Utah in the US.

"Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalisation and stave off worsening heart failure," Stehlik added.

According to the researchers, even if patients survive, they have poor functional capacity, poor exercise tolerance and low quality of life after hospitalisations.

"This patch, this new diagnostic tool, could potentially help us prevent hospitalizations and decline in patient status," Stehlik said.

For the findings, published in the journal Circulation: Heart Failure, the researchers followed 100 heart failure patients, average age 68, who were diagnosed and treated at four veterans administration (VA) hospitals in Utah, Texas, California, and Florida.

After discharge, participants wore an adhesive sensor patch on their chests 24 hours a day for up to three months.

The sensor monitored continuous electrocardiogram (ECG) and motion of each subject.

This information was transmitted from the sensor via Bluetooth to a smartphone and then passed on to an analytics platform, developed by PhysIQ, on a secure server, which derived heart rate, heart rhythm, respiratory rate, walking, sleep, body posture and other normal activities.

Using artificial intelligence, the analytics established a normal baseline for each patient. When the data deviated from normal, the platform generated an indication that the patient's heart failure was getting worse.

Overall, the system accurately predicted the impending need for hospitalization more than 80 per cent of the time.

On average, this prediction occurred 10.4 days before a readmission took place (median 6.5 days), the study said.