Tobacco use makes precancerous cells that fertilize cancer growth: Study

Toronto, Feb 4: People who text while walking face a higher risk of an accident than those listening to music or talking on the phone, a study has found.

The study, published in the journal Injury Prevention, found that smartphone texting is linked to compromised pedestrian safety, with higher rates of 'near misses', and failure to look left and right before crossing a road.

Researchers from the University of Calgary in Canada call for a more thorough approach to exploring the impact of distracted pedestrian behaviours on crash risk.

Worldwide, around 270,000 pedestrians die every year, accounting for around a fifth of all road traffic deaths, according to the researchers.

'Pedestrian distraction' has become a recognised safety issue as more and more people use their smartphones or hand held devices while walking on the pavement and crossing roads, they said.

The researchers looked for published evidence to gauge the potential impact on road safety of hand-held or hands-free device activities.

This included talking on the phone, text messaging, browsing and listening to music.

From among 33 relevant studies, they pooled the data from 14 -- involving 872 people -- and systematically reviewed the data from another eight.

The analysis showed that listening to music wasn't associated with any heightened risk of potentially harmful pedestrian behaviours.

Talking on the phone was associated with a small increase in the time taken to start crossing the road, and slightly more missed opportunities to cross the road safely.

The researchers found that text messaging emerged as the potentially most harmful behaviour.

It was associated with significantly lower rates of looking left and right before or while crossing the road, and with moderately increased rates of collisions, and close calls with other pedestrians or vehicles, they said.

Texting also affected the time taken to cross a road, and missed opportunities to cross safely, but to a lesser extent, according to the researchers.

The review of the eight observational studies revealed that the percentage of pedestrians who were distracted ranged from 12 to 45 per cent, they said.

It also found behaviours were influenced by several factors, including gender, time of day, solo or group crossing, and walking speed.

The researchers acknowledge "a variety of study quality issues" which limit the generalisability of the findings.

"Given the ubiquity of smartphones, social media, apps, digital video and streaming music, which has infiltrated most aspects of daily life, distracted walking and street cross will be a road safety issue for the foreseeable future," the researchers noted.

"And as signage and public awareness campaigns don't seem to alter pedestrian behaviour, establishing the relationship between distracted walking behaviour and crash risk is an essential research need," they said.

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.

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.