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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.

Boston, Feb 4: Practising yoga may increase levels of a messenger molecule involved in regulating brain activity, and completing one yoga class per week may maintain elevated levels of this chemical, according to a study which may lead to better ways of mitigating depressive symptoms.

The study, published in the Journal of Alternative and Complementary Medicine, assessed a group of 30 clinically depressed patients who were randomly divided into two groups.

According to the researchers, including those from Boston University in the US, both groups engaged in coherent breathing, and Iyengar yoga -- a form of hatha yoga, developed by B. K. S. Iyengar, emphasising on detail, precision, and alignment in the performance of yoga postures.

The only difference between the groups, the scientists said, was the number of 90 minute yoga sessions, and home sessions in which each group participated.

Over three months, they said, the high-dose group (HDG) was assigned three sessions per week, while the low-intensity group (LIG) engaged in two sessions per week.

The participants underwent magnetic resonance imaging (MRI) scans of their brain before the first and after the last yoga session, and also completed a clinical depression scale to monitor their symptoms, the study noted.

Results of the study revealed that both groups had improvement in depressive symptoms after three months.

Their MRI analysis showed that levels of the brain messenger molecule GABA were elevated after three months of yoga, as compared to the levels before starting yoga.

According to the study, this increase was found for approximately four days after the last yoga session, but the rise was no longer observed after about eight days.

"The study suggests that the associated increase in GABA levels after a yoga session are 'time-limited' similar to that of pharmacologic treatments such that completing one session of yoga per week may maintain elevated levels of GABA," explained study co-author Chris Streeter from Boston University.

Providing evidence-based data may help in getting more individuals to try yoga as a strategy for improving their health and well-being, the scientists said.

"A unique strength of this study is that pairing the yoga intervention with brain imaging provides important neurobiological insight as to the 'how' yoga may help to alleviate depression and anxiety," said study co-author Marisa Silveri from Harvard University.

In this study, we found that an important neurochemical, GABA, which is related to mood, anxiety, and sleep, is significantly increased in association with a yoga intervention," Silveri said.

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.