Remdesivir slows COVID-19 progression in macaques: Study

Agencies
June 10, 2020

Early treatment with the antiviral drug remdesivir has been found to reduce viral load and prevent lung disease in macaques infected with SARS-CoV-2 that causes COVID-19, according to a study.

The findings, published in the journal Nature on Tuesday, support the early use of remdesivir treatment in patients with COVID-19 to prevent progression to pneumonia.

Researchers from the National Institutes of Health in the US noted that remdesivir has broad antiviral activity and has been shown to be effective against infections with SARS-CoV and MERS-CoV in animal models.

The drug is being tested in human clinical trials for the treatment of COVID-19, they said.

Researcher Emmie de Wit and colleagues investigated the effects of remdesivir treatment in rhesus macaques, a recently established model of SARS-CoV-2 infection.

Two sets of six macaques were inoculated with SARS-CoV-2.

One group was treated with remdesivir 12 hours later -- close to the peak of virus reproduction in the lungs -- and these macaques received treatment every 24 hours until six days after inoculation.

In contrast to the control group, the researchers found that macaques that received remdesivir did not show signs of respiratory disease, and had reduced damage to the lungs.

Viral loads in the lower respiratory tract were also reduced in the treated animals; viral levels were around 100 times lower in the lower-respiratory tract of remdesivir-treated macaques 12 hours after the first dose, they said.

The researchers said that infectious virus could no longer be detected in the treatment group three days after initial infection, but was still detectable in four out of six control animals.

Despite this virus reduction in the lower respiratory tract, no reduction in virus shedding was observed, which indicates that clinical improvement may not equate to a lack of infectiousness, they said.

Dosing of remdesivir in the rhesus macaques is equivalent to that used in humans, the researchers noted.

They cautioned that it is difficult to directly translate the timing of treatment used in corresponding disease stages in humans, because rhesus macaques normally develop only mild disease.

However, researchers said the results indicate that remdesivir treatment of COVID-19 should be initiated as early as possible to achieve the maximum treatment effect.

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Agencies
January 4,2020

Washington D.C., Jan 3: A new study has found out that diet significantly affects the mental health and well being of an individual.

The study was published in the journal European Neuropsychopharmacology.

"We have found that there is increasing evidence of a link between a poor diet and the worsening of mood disorders, including anxiety and depression. However, many common beliefs about the health effects of certain foods are not supported by solid evidence," said the lead researcher, Professor Suzanne Dickson.

According to the researchers, the link between diet and mental health can be firmly established in certain cases like that of the ability of a ketogenic diet being helpful for children with epilepsy and the impact of vitamin B12 deficiency on poor memory, depression and fatigue.

"With individual conditions, we often found very mixed evidence. With ADHD for example, we can see an increase in the quantity of refined sugar in the diet seems to increase ADHD and hyperactivity, whereas eating more fresh fruit and vegetables seems to protect against these conditions," said Dickson

But there are comparatively few studies, and many of them don't last long enough to show long-term effects," added Dickson.

The study further concludes that some food items can be associated with treatment and the betterment of certain mental health conditions.

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Agencies
April 15,2020

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.

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News Network
February 26,2020

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.

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