Machine Learning model developed to identify bat species that can spread Nipah

Agencies
June 29, 2019

Researchers have developed a model using Machine Learning (ML) to identify bat species with the potential to host the Nipah virus, with a focus on India. Four new bat species were flagged as surveillance priorities.

"While there is a growing understanding that bats play a role in the transmission of Nipah virus in Southeast Asia, less is known about which species pose the most risk. 

"Our goal was to help pinpoint additional species with a high likelihood of carrying Nipah, to target surveillance and protect public health," said Barbara Han from Cary Institute of Ecosystem Studies in the US. 

India is home to an estimated 113 bat species. Just 31 of these species have been sampled for the Nipah virus, and 11 have been found to have antibodies that signal host potential, according to the study published in the journal PLOS Neglected Tropical Diseases.

The Nipah virus is a highly lethal, emerging henipavirus that can be transmitted to people from the body fluids of infected bats. Eating fruit or drinking date palm sap that has been contaminated by bats has been flagged as a transmission pathway. Domestic pigs are also bridging hosts that can infect people.

Once infected, people can spread the virus directly to other people, sparking an outbreak. There is no vaccine and the virus has a high mortality rate.

For the study, Machine Learning, a form of Artificial Intelligence, was used to flag bat species with the potential to harbour Nipah. 

"By looking at the traits of bat species known to carry Nipah globally, our model was able to make predictions about additional bat species residing in India with the potential to carry the virus and transmit it to people. These bats are currently not on the public health radar and are worthy of additional study," Han said.

For the study, the research team compiled published data on bat species known to carry Nipah and other henipaviruses globally. 

Data included 48 traits of 523 bat species, including information on foraging methods, diet, migration behaviours, geographic ranges and reproduction. 

During the study, their algorithm identified known Nipah-positive bat species with 83 per cent accuracy. 

It also identified six bat species that occur in Asia, Australia and Oceania that have traits that could make them competent hosts and should be prioritised for surveillance. Four of these species occur in India, two of which are found in Kerala.

"We set out to make trait-based predictions of likely henipavirus reservoirs near Kerala. Our focus was narrow, but the model was successful in identifying Nipah hosts, demonstrating that this method could serve as a powerful tool in guiding surveillance for Nipah and other disease systems," said Raina K. Plowright from Montana State University in the US.

"Identifying which species harbour disease is an important first step in surveillance planning. We also need to prioritise research on which virus strains pose the greatest risk to people. Ultimately, the goal is to extinguish risk, not fight fires," Han concluded.

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Agencies
January 25,2020

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.

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Agencies
June 21,2020

Lower neighbourhood socioeconomic status and greater household crowding increase the risk of becoming infected with SARS-CoV-2, the virus that causes COVID-19, warn researchers.

"Our study shows that neighbourhood socioeconomic status and household crowding are strongly associated with risk of infection," said study lead author Alexander Melamed from Columbia University in the US.

"This may explain why Black and Hispanic people living in these neighbourhoods are disproportionately at risk for contracting the virus," Melamed added.

For the findings, published in the journal JAMA, the researchers examined the relationships between COVID-19 infection and neighbourhood characteristics in 396 women who gave birth during the peak of the Covid-19 outbreak in New York City. Since March 22, all women admitted to the hospitals for delivery have been tested for the virus, which gave the researchers the opportunity to detect all infections -- including infections with no symptoms -- in a defined population

The strongest predictor of COVID-19 infection among these women was residence in a neighbourhood where households with many people are common.The findings showed that women who lived in a neighbourhood with high household membership were three times more likely to be infected with the virus. Neighbourhood poverty also appeared to be a factor, the researchers said.Women were twice as likely to get COVID-19 if they lived in neighbourhoods with a high poverty rate, although that relationship was not statistically significant due to the small sample size.

The study revealed that there was no association between infection and population density.

"New York City has the highest population density of any city in the US, but our study found that the risks are related more to density in people's domestic environments rather than density in the city or within neighbourhoods," says co-author Cynthia Gyamfi-Bannerman."

The knowledge that SARS-CoV-2 infection rates are higher in disadvantaged neighbourhoods and among people who live in crowded households could help public health officials target preventive measures," the authors wrote.

Recently, another study published in the Journal of the American Planning Association, showed that dense areas were associated with lower COVID-19 death rates.

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