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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.

Probiotics that broaden the mix of helpful bacteria in the gut may help to ease depression, say researchers.

Foods that broaden the profile of helpful bacteria in the gut are collectively known as probiotics. These "good bacteria" can be taken as supplements, or found naturally in yoghurts or fermented foods.

For the findings, the research team from the University of Brighton in the UK searched for relevant studies published in English between 2003 and 2019, which looked at the potential therapeutic contribution of pre-and probiotics in adults with depression and/or anxiety disorders.

Out of an initial haul of 71 studies, just seven met all the criteria for inclusion. All 7 investigated at least one probiotic strain; four looked at the effect of combinations of multiple strains.In all, 12 probiotic strains featured in the selected studies, primarily Lactobacillus acidophilus, Lactobacillus casei, and Bifidobacterium bifidium.

One study looked at combined pre-probiotic treatment, while one looked at prebiotic therapy by itself. The studies varied considerably in their design, methods used, and clinical considerations, but all of them concluded that probiotic supplements either alone or in combination with prebiotics may be linked to measurable reductions in depression.

And every study showed a significant fall or improvement in anxiety symptoms and clinically relevant changes in biochemical measures of anxiety or depression with probiotic or combined pre-probiotic use.

Of the 12 different probiotics investigated, 11 were potentially useful, the findings showed.'Probiotics may help reduce the production of inflammatory chemicals, such as cytokines, as is the case in inflammatory bowel disease, the researchers suggested.

"They may help direct the action of tryptophan, a chemical thought to be important in the gut-brain axis in psychiatric disorders," they added.

In this way, with a better understanding of the mechanisms, probiotics may prove to be a useful tool across a wide range of conditions," the authors wrote.

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.