A glass of milk daily good for your heart

Washington D.C., Jan 25: A new study conducted by a team of researchers reveals why individuals who have a history of early life adversity (ELA) are disproportionately prone to opioid addiction.

The study conducted examined how early adversities interact with factors such as increased access to opioids to directly influence brain development and function, causing a higher potential for opioid addiction.

The study was lead by UCI researchers and was published in Molecular Psychiatry.

Tallie Z. Baram, MD, PhD, the Danette Shepard Chair in Neurological Sciences at the UCI School of Medicine and one of the senior researchers for the study, was on the take that the widely known factor genetics that plays major role in addiction vulnerability, cannot be solely held responsible for the recent rise in opioid abuse.

To further clarify, the researchers simulated ELA in rats by limiting bedding and nesting materials during a short, postnatal period of time.

In female rats, this led to striking opioid addiction-like characteristics including an increased relapse- behaviour, for example.

As observed in addicted humans, the rats were willing to work very hard (pay a very high price) to obtain the drug.

Baram said: "Ultimately, we found that conditions during sensitive developmental periods can lead to vulnerability to the addictive effects of opioid drugs, especially in females, which is consistent with the prevalence of ELA in heroin-addicted women."

These findings can be used to highlight the importance given to sex differences in future ELA-related studies on opioid addiction, and in future prevention or intervention strategies being developed to address the growing opioid crisis.

The study conducted examined how early adversities interact with factors such as increased access to opioids to directly influence brain development and function, causing a higher potential for opioid addiction.

The study was lead by UCI researchers and was published in Molecular Psychiatry.

The study found that unpredictable, fragmented early life environments may lead to abnormal maturation of certain brain circuits, which profoundly impacts brain function and persists into adolescence and adulthood.

Tallie Z. Baram, MD, PhD, the Danette Shepard Chair in Neurological Sciences at the UCI School of Medicine and one of the senior researchers for the study, was on the take that the widely known factor genetics that plays major role in addiction vulnerability, cannot be solely held responsible for the recent rise in opioid abuse.

To further clarify, the researchers implanted ELA in rats by limiting bedding and nesting materials during a short, postnatal period of time.

In female rats, this led to striking opioid addiction-like characteristics including an increased relapse- behaviour, for example.

As observed in addicted humans, the rats were willing to work very hard (pay a very high price) to obtain the drug.

Baram said: "Ultimately, we found that conditions during sensitive developmental periods can lead to vulnerability to the addictive effects of opioid drugs, especially in females, which is consistent with the prevalence of ELA in heroin-addicted women."

These findings can be used to highlight the importance given to sex differences in future ELA-related studies on opioid addiction, and in future prevention or intervention strategies being developed to address the growing opioid crisis.

Despite tremendous advances in treatment of congenital heart disease (CHD), a new global study shows that the chances for a child to survive a CHD diagnosis is significantly less in low-income countries.

The research revealed that nearly 12 million people are currently living with CHD globally, 18.7 per cent more than in 1990.

The findings, published in The Lancet, is drawn from the first comprehensive study of congenital heart disease across 195 countries, prepared using data from the Global Burden of Diseases, Injuries and Risk Factors Study 2017 (GBD).

"Previous congenital heart estimates came from few data sources, were geographically narrow and did not evaluate CHD throughout the life course," said the study authors from Children's National Hospital in the US.

This is the first time the GBD study data was used along with all available data sources and previous publications - making it the most comprehensive study on the congenital heart disease burden to date.

The study found a 34.5 per cent decline in deaths from congenital disease between 1990 to 2017. Nearly 70 per cent of deaths caused by CHD in 2017 (180,624) were in infants less than one year old.

Most CHD deaths occurred in countries within the low and low-middle socio-demographic index (SDI) quintiles.

Mortality rates get lower as a country's Socio-demographic Index (SDI) rises, the study said.

According to the researchers, birth prevalence of CHD was not related to a country's socio-demographic status, but overall prevalence was much lower in the poorest countries of the world.

This is because children in these countries do not have access to life saving surgical services, they added.

"In high income countries like the United States, we diagnose some heart conditions prenatally during the 20-week ultrasound," said Gerard Martin from Children's National Hospital who contributed to the study.

"For children born in middle- and low-income countries, these data draw stark attention to what we as cardiologists already knew from our own work in these countries -- the lack of diagnostic and treatment tools leads to lower survival rates for children born with CHD," said researcher Craig Sable.

"The UN has prioritised reduction of premature deaths from heart disease, but to meet the target of 'ending preventable deaths of newborns and children under 5 years of age,' health policy makers will need to develop specific accountability measures that address barriers and improve access to care and treatment," the authors wrote.

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.