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Scientists have designed a “catch and kill” air filter which they say can trap the novel coronavirus and neutralise it instantly, an invention that may reduce the spread of COVID-19 in closed spaces such as schools, hospitals and health care facilities, as well as public transit environments like airplanes.

According to the study, published in the journal Materials Today Physics, the device killed 99.8 per cent of the novel coronavirus, SARS-CoV-2, in a single pass through its filter. It said the device, made from commercially available nickel foam heated to 200 degrees Celsius, also killed 99.9 per cent of the spores of the deadly bacterium Bacillus anthracis which causes the anthrax disease.

“This filter could be useful in airports and in airplanes, in office buildings, schools, and cruise ships to stop the spread of COVID-19,” said Zhifeng Ren, a co-author of the study from the University of Houston (UH) in the US.

“Its ability to help control the spread of the virus could be very useful for society,” Ren added.

The researchers said they are also developing a desk-top model for the device which is capable of purifying the air in an office worker’s immediate surroundings. According to the scientists, since the virus can remain in the air for about three hours, a filter that could remove it quickly was a viable plan, and with businesses reopening across the world, they believe controlling the spread in air conditioned spaces was urgent.

The study noted that the novel coronavirus cannot survive temperatures above 70 degrees Celsius, so by making the filter temperature far hotter — about 200 degree Celsius, the researchers said they were able to kill the virus almost instantly.

Ren said the nickel foam met several key requirements. “It is porous, allowing the flow of air, and electrically conductive, which allowed it to be heated. It is also flexible,” the researchers noted in a statement.But they added that nickel foam also had low resistivity, making it difficult to raise the temperature high enough to quickly kill the virus.

The researchers said they solved this problem by folding the foam, connecting multiple compartments with electrical wires to increase the resistance high enough to raise the temperature as high as 250 degrees Celsius. By making the filter electrically heated, rather than heating it from an external source, they said the the amount of heat that escaped from the filter is minimised, allowing air conditioning to function with very low strain.

When the scientists built and tested a prototype for the relationship between voltage/current and temperature, they said it satisfies the requirements for conventional heating, ventilation, and air conditioning (HVAC) systems, and could kill the coronavirus.

“This novel biodefense indoor air protection technology offers the first-in-line prevention against environmentally mediated transmission of airborne SARS-CoV-2, and will be on the forefront of technologies available to combat the current pandemic and any future airborne biothreats in indoor environments,” said Faisal Cheema, another co-author of the study from UH.

The researchers have called for a phased roll-out of the device, “beginning with high-priority venues, where essential workers are at elevated risk of exposure.” They believe the novel device will both improve safety for frontline workers in essential industries and allow nonessential workers to return to public work spaces.

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.