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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.

After admitting that the world may have a COVID-19 vaccine within one year or even a few months earlier, the World Health Organisation (WHO) on Friday said that UK-based AstraZeneca is leading the vaccine race while US-based pharmaceutical major Moderna is not far behind.

WHO Chief Scientist Soumya Swaminathan stated that the AstraZeneca's coronavirus vaccine candidate is the most advanced vaccine currently in terms of development.

"I think AstraZeneca certainly has a more global scope at the moment in terms of where they are doing and planning their vaccine trials," she told the media.

AstraZeneca's Covid-19 vaccine candidate developed by researchers from the Oxford University will likely provide protection against the disease for one year, the British drug maker's CEO told Belgian radio station Bel RTL this month.

The Oxford University last month announced the start of a Phase II/III UK trial of the vaccine, named AZD1222 (formerly known as ChAdOx1 nCoV-19), in about 10,000 adult volunteers. Other late-stage trials are due to begin in a number of countries.

Last week, Swaminathan had said that nearly 2 billion doses of the COVID-19 vaccine would be ready by the end of next year.

Addressing the media from Geneva, she said that "at the moment, we do not have a proven vaccine but if we are lucky, there will be one or two successful candidates before the end of this year" and 2 billion doses by the end of next year.

Scientists predict that the world may have a COVID-19 vaccine within one year or even a few months earlier, said the Director-General of the World Health Organization even as he underlined the importance of global cooperation to develop, manufacture and distribute the vaccines.

However, making the vaccine available and distributing it to all will be a challenge and will require political will, WHO chief Tedros Adhanom Ghebreyesus said on Thursday during a meeting with the European Parliament's Committee for Environment, Public Health and Food Safety.

One option would be to give the vaccine only to those who are most vulnerable to the virus.

There are currently over 100 COVID-19 vaccine candidates in various stages of development.

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.