Jackfruit seeds could substitute cocoa beans to make chocolate

London, Jul 2: The World Health Organisation says smoking is linked to a higher risk of severe illness and death from the coronavirus in hospitalised patients, although it was unable to specify exactly how much greater those risks might be.

In a scientific brief published this week, the U.N. health agency reviewed 34 published studies on the association between smoking and Covid-19, including the probability of infection, hospitalisation, severity of disease and death.

WHO noted that smokers represent up to 18% of hospitalised coronavirus patients and that there appeared to be a significant link between whether or not patients smoked and the severity of disease they suffered, the type of hospital interventions required and patients' risk of dying.

In April, French researchers released a small study suggesting smokers were at less risk of catching Covid-19 and planned to test nicotine patches on patients and health workers — but their findings were questioned by many scientists at the time who cited the lack of definitive data.

WHO says "the available evidence suggests that smoking is associated with increased severity of disease and death in hospitalized Covid-19 patients. It recommends that smokers quit.

Researchers have found that patients with peripheral artery disease or stroke were less likely to receive recommended treatments to prevent heart attack than those with coronary artery disease. All three are types of atherosclerotic cardiovascular disease.

Depending on the location of the blockage, atherosclerosis increases the risk for three serious conditions: coronary artery disease, stroke and peripheral artery disease.

"Our study highlights the need for public health campaigns to direct equal attention to all three major forms of atherosclerotic cardiovascular disease," said senior study author Erin Michos from the Johns Hopkins University in the US.

"We need to generate awareness among both clinicians and patients that all of these diseases should be treated with aggressive secondary preventive medications, including aspirin and statins, regardless of whether people have heart disease or not," Michos added.

Since atherosclerosis can affect arteries in more than one part of the body, medical guidelines are to treat coronary artery disease, stroke and peripheral artery disease similarly with lifestyle changes and medication, including statins to lower cholesterol levels and aspirin to prevent blood clots.

Lifestyle changes include eating a healthy diet, being physically active, quitting smoking, controlling high cholesterol, controlling high blood pressure, treating high blood sugar and losing weight.

What was unclear was if people with stroke and peripheral artery disease received the same treatments prescribed for those with coronary artery disease.

This study compared more than 14,000 US adults enrolled in the 2006-2015 Medical Expenditure Panel Survey, a national survey of patient-reported health outcomes and conditions, and health care use and expenses.

Slightly more than half of the patients were men, the average age was 65, and all had either coronary artery disease, stroke or peripheral artery disease.

These individuals were the representative of nearly 16 million US adults living with one of the three forms of atherosclerotic cardiovascular disease.

Compared to participants with coronary artery disease, participants with peripheral artery disease were twice more likely to report no statin use and three times more likely to report no aspirin use.

Additionally, people with peripheral artery disease had the highest, annual, total out-of-pocket expenditures among the three atherosclerotic conditions.

The findings showed that participants with stroke were more than twice as likely to report no statin or aspirin use.

Moreover, those with stroke were more likely to report poor patient-provider communication, poor health care satisfaction and more emergency room visits.

"Our study highlights a missed opportunity for implementing life-saving preventive medications among these high-risk individuals," Michos said.

The study was presented in the virtual conference at the American Heart Association's Quality of Care & Outcomes Research Scientific Sessions 2020.

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.