Knowledge and practices of handwashing ‘abysmally poor’: study

New York, May 19: Cigarette smoke spurs the lungs to make more of the receptor protein which the novel coronavirus uses to enter human cells, according to a study which suggests that quitting smoking might reduce the risk of a severe coronavirus infection.

The findings, published in the journal Developmental Cell, may explain why smokers appear to be particularly vulnerable to severe COVID-19 disease.

"Our results provide a clue as to why smokers who develop COVID-19 tend to have poor clinical outcomes," said study senior author Jason Sheltzer, a cancer geneticist at Cold Spring Harbor Laboratory in the US.

"We found that smoking caused a significant increase in the expression of ACE2, the protein that SARS-CoV-2 uses to enter human cells," Sheltzer said.

According to the scientists, quitting smoking might reduce the risk of a severe coronavirus infection.

They said most individuals infected with the virus suffer only mild illness, if they experience any at all.

However, some require intensive care when the sometimes-fatal virus attacks, the researchers said.

In particular, they said three groups have been significantly more likely than others to develop severe illness -- men, the elderly, and smokers.

Turning to previously published data for possible explanations for these disparities, the scientists assessed if vulnerable groups share some key features related to the human proteins that the coronavirus relies on for infection.

First, they said, they focused on comparing gene activity in the lungs across different ages, between the sexes, and between smokers and nonsmokers.

The scientists said both mice that had been exposed to smoke in a laboratory, and humans who were current smokers had significant upregulation of ACE2.

According to Sheltzer, smokers produced 30-55 per cent more ACE2 than their non-smoking counterparts.

While the researchers found no evidence that age or sex impacts ACE2 levels in the lungs, they said the influence of smoke exposure was surprisingly strong.

However, they said, the change seemed to be temporary.

According to the data, the level of the receptors ACE2 in the lungs of people who had quit smoking was similar to that of non-smokers.

The study noted that the most prolific producers of ACE2 in the airways are mucus-producing cells called goblet cells.

Smoking is known to increase the prevalence of such cells, the scientists said.

"Goblet cells produce mucous to protect the respiratory tract from inhaled irritants. Thus, the increased expression of ACE2 in smokers' lungs could be a byproduct of smoking-induced secretory cell hyperplasia," Sheltzer explained.

However, Sheltzer said other studies on the effects of cigarette smoke have shown mixed results.

"Cigarette smoke contains hundreds of different chemicals. It's possible that certain ingredients like nicotine have a different effect than whole smoke does," he said.

The researchers cautioned that the actual ACE2 protein may be regulated in ways not addressed in the current study.

"One could imagine that having more cells that express ACE2 could make it easier for SARS-CoV-2 to spread in someone's lungs, but there is still a lot more we need to explore," Sheltzer said.

Washington D.C, Feb 10: Children's vulnerability towards depression, anxiety, impulsive behaviour, and poor cognitive performance could be determined by considering the hours of sleep they manage to get.

Sleep states are active processes that support the reorganisation of brain circuitry. This makes sleep especially important for children, whose brains are developing and reorganising rapidly.

In a study by researchers from the University of Warwick -- recently published in the journal Molecular Psychiatry -- cases of 11,000 children aged between 9 and 11 years from the Adolescent Brain Cognitive Development dataset were analyzed to find out the relationship between sleep duration and brain structure.

The study was carried out by researchers Professor Jianfeng Feng, Professor Edmund Rolls, Dr. Wei Cheng and colleagues from the University of Warwick's Department of Computer Science and Fudan University.

Measures of depression, anxiety, impulsive behaviour and poor cognitive performance in the children were associated with shorter sleep duration. Moreover, the depressive problems were associated with short sleep duration one year later.

The reduced brain volume of areas such as orbitofrontal cortex, prefrontal, and temporal cortex, precuneus, and supramarginal gyrus was found to be associated with the shorter sleep duration.

Professor Jianfeng Feng, from the University of Warwick's Department of Computer Science, comments: "The recommended amount of sleep for children 6 to 12 years of age is 9-12 hours. However, sleep disturbances are common among children and adolescents around the world due to the increasing demand on their time from school, increased screen time use, and sports and social activities."

A previous study showed that about 60 per cent of adolescents in the United States receive less than eight hours of sleep on school nights.

Professor Jianfeng Feng further added: "Our findings showed that the total score for behavior problems in children with less than 7 hours sleep was 53 per cent higher on average and the cognitive total score was 7.8 per cent lower on average than for children with 9-11 hours of sleep. It highlights the importance of enough sleep in both cognition and mental health in children."

Professor Edmund Rolls from the University of Warwick's Department of Computer Science also commented: "These are important associations that have been identified between sleep duration in children, brain structure, and cognitive and mental health measures, but further research is needed to discover the underlying reasons for these relationships."

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.