Wednesday December 11, 2019

New York City’s Mandatory Measles Vaccination Order Stands Still

The health department's lawyers argued that quarantining was ineffective because people carrying the virus can be contagious before symptoms appear.

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Materials are seen left at demonstration by people opposed to childhood vaccination after officials in Rockland County, a New York City suburb, banned children not vaccinated against measles from public spaces. VOA

Brooklyn judge on Thursday ruled against a group of parents who challenged New York City’s recently imposed mandatory measles vaccination order, rejecting their arguments that the city’s public health authority exceeded its authority.

In a six-page decision rendered hours after a hearing on the matter, Judge Lawrence Knipel denied the parents’ petition seeking to lift the vaccination order, imposed last week to stem the worst measles outbreak to hit the city since 1991.

The judge sided with municipal health officials who defended the order as a rare but necessary step to contain a surge in the highly contagious disease that has infected at least 329 people so far, most of them children from Orthodox Jewish communities in the borough of Brooklyn.

Another 222 cases have been diagnosed elsewhere in New York state, mostly in a predominantly ultra-Orthodox Jewish neighborhood of Rockland County, northwest of Manhattan.

The New York outbreaks are part of a larger resurgence of measles across the country, with at least 555 cases confirmed in 20 states, according to the U.S. Centers for Disease Control and Prevention.

Health experts say the virus, which can cause severe complications and even death, has spread mostly among school-age children whose parents declined to get them vaccinated. Most profess philosophical or religious reasons, or cite concerns — debunked by medical science — that the three-way measles-mumps-rubella (MMR) vaccine may cause autism.

The judge rejected the parents’ contention that the vaccination order was excessive or coercive, noting it does not call for forcibly administering the vaccine to those who refuse it.

He also dismissed assertions in the petition disputing the “clear and present danger” of the outbreak. “Vaccination is known to extinguish the fire of contagion,” the judge said.

FILE PHOTO: A sign warning people of measles in the ultra-Orthodox Jewish community of Williamsburg in New York City, April 11, 2019.
A sign warning people of measles in the ultra-Orthodox Jewish community of Williamsburg in New York City, April 11, 2019. VOA

Secret identities

The vaccination order, which was extended this week, requires residents of certain affected Brooklyn neighborhoods to obtain the MMR vaccine unless they can otherwise demonstrate immunity to measles, or face a fine.

The court challenge was brought in Brooklyn’s Supreme Court by five people identified only as parents living in the affected neighborhoods. Their identities were kept confidential to protect their children’s’ privacy, their lawyers said.

In court on Thursday, they told Knipel the city had overstepped its authority and that quarantining the infected would be a preferable approach.

Robert Krakow, an attorney for the parents, estimated that just 0.0006 percent of the population of Brooklyn and Queens had measles. “That’s not an epidemic,” he said. “It’s not Ebola. It’s not smallpox.”

The health department’s lawyers argued that quarantining was ineffective because people carrying the virus can be contagious before symptoms appear.

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The vaccination order, which was extended this week, requires residents of certain affected Brooklyn neighborhoods to obtain the MMR vaccine unless they can otherwise demonstrate immunity to measles, or face a fine. Pixabay

The judge cited 39 cases diagnosed in Michigan that have been traced to an individual traveling from the Williamsburg community at the epicenter of Brooklyn’s outbreak.

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The surge in measles there originated with an unvaccinated child who became infected on a visit to Israel, where the highly contagious virus is also running rampant.

The number of measles cases worldwide nearly quadrupled in the first quarter of 2019 to 112,163 compared with the same period last year, the World Health Organization said this week. (VOA)

Next Story

AI Can Better Help Doctors to Identify Cancer Cells in Human Body

The process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone

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The AI algorithm helps pathologists obtain the most accurate Cancer cell analysis - in a much faster way. Pixabay

Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize Cancer cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes.

The spatial distribution of different types of cells can reveal a cancer’s growth pattern, its relationship with the surrounding microenvironment, and the body’s immune response.

But the process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone.

“To make a diagnosis, pathologists usually only examine several ‘representative’ regions in detail, rather than the whole slide. However, some important details could be missed by this approach,” said Dr. Guanghua “Andy” Xiao, corresponding author of a study published in EbioMedicine.

A major technical challenge in systematically studying the tumor microenvironment is how to automatically classify different types of cells and quantify their spatial distributions.

The AI algorithm that Dr Xiao and his team developed, called “ConvPath”, overcomes these obstacles by using AI to classify cell types from lung cancer pathology images.

Cancer
Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize Cancer Cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes. Pixabay

The ConvPath algorithm can “look” at cells and identify their types based on their appearance in the pathology images using an AI algorithm that learns from human pathologists.

The algorithm helps pathologists obtain the most accurate cancer cell analysis – in a much faster way.

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“It is time-consuming and difficult for pathologists to locate very small tumour regions in tissue images, so this could greatly reduce the time that pathologists need to spend on each image,” said Dr Xiao. (IANS)